fix: ASRX duplication, TKG edges, trace ingest, and add pipeline progress publishing
- ASRX handler no longer stores duplicate 'asr' pre_chunks - Pre_chunks storage made idempotent (delete-before-insert) - Rule 1 + trace_ingest changed to query 'asrx' not 'asr' - Trace chunks removed (dynamic from TKG/Qdrant) - TKG scroll_face_points fixed: trace_id >= 1 (not == 1) - TKG AsrxSegmentEntry: start/end -> start_time/end_time (match ASRX JSON) - Unregister error handling: log instead of silent discard - Add publish_pipeline_progress calls at each pipeline stage (processors, rule1, face_trace, identity_agent, TKG, rule2, completion)
This commit is contained in:
Accusys
@@ -863,3 +863,42 @@ Before creating any file in `docs_v1.0/` (API_WORKSPACE, GUIDES, REFERENCE, DESI
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完整交付程序（M4_workspace → M5 → Release → Deploy → Public）見：
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`docs_v1.0/OPERATIONS/DELIVERY_PROCEDURE.md`
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## Session Summary (2026-07-01: Search Mode Fixes)
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### Goal
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Fix search modes: Keyword BM25 ranking + People search migration to Qdrant + Qdrant scroll pagination
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### Done
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- **Keyword/BM25 search (`search_bm25`)**: Replaced hardcoded 1.0 score with PostgreSQL FTS (`ts_rank` + `plainto_tsquery`). Now ranks results by relevance instead of flat 1.0.
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- **Smart search merge**: Passes real FTS score through instead of fixed 0.5, so keyword-only results are properly differentiated.
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- **Qdrant scroll_points**: Added `offset` parameter for pagination support; new `scroll_all_points()` method handles multi-page scroll automatically.
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- **get_identity_traces**: Fixed broken pagination loop (always fetched same first 1000 points) by switching to `scroll_all_points`.
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- **People search (`search_persons_internal`)**: Replaced `face_detections` JOIN in universal search with Qdrant `_faces` scroll + Rust aggregation (count per identity per file, frame→second via FPS).
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- **People search (`search_persons_by_query`)**: Same migration for the REST API person search endpoint.
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- **Payload field fix**: `_faces` uses `frame` (integer) not `timestamp_secs` (float). Fixed both `search_persons_internal` and `search_persons_by_query` to read `frame` and convert via `frame / fps`.
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### Key Files Changed
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- `src/core/db/qdrant_db.rs`: `scroll_points` → offset pagination, new `scroll_all_points`
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- `src/api/identity_binding.rs`: Use `scroll_all_points` instead of broken loop
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- `src/api/universal_search.rs`: Rewrote `search_persons_internal` and `search_persons_by_query` to use Qdrant
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- `src/core/db/postgres_db.rs`: `search_bm25` → PostgreSQL FTS ranking
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- `src/api/search.rs`: Pass real FTS scores in merge, removed unused `KEYWORD_FIXED_SCORE`
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### Done This Session
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- **Qdrant scroll pagination**: `scroll_points` now accepts `offset` param + returns `next_page_offset`; new `scroll_all_points()` handles multi-page scroll automatically
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- **get_identity_traces pagination fix**: No longer fetches same 1000 points in infinite loop
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- **Keyword BM25**: `search_bm25` replaced hardcoded 1.0 score with PostgreSQL `ts_rank` + `plainto_tsquery`; `smart_search` passes real FTS scores instead of fixed 0.5
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- **People search → Qdrant**: Both `search_persons_internal` and `search_persons_by_query` replaced `face_detections` JOIN with Qdrant `_faces` scroll + Rust aggregation (count/group/sort). Fixed `timestamp_secs` → `frame` + `frame/fps` conversion
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- **list_face_candidates → Qdrant**: `identities.rs` unbound faces query now scrolls `_faces` with `is_null: identity_id` filter, sorts by confidence DESC in Rust
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- **list_unassigned_traces → Qdrant**: `identities.rs` unbound traces query now scrolls `_faces` with `is_null: identity_id` + `trace_id > 0` filter, groups by (file_uuid, trace_id) in Rust, picks best face per trace
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- **get_identity_chunks → identity_bindings**: Replaced `face_detections` frame-range JOIN with `identity_bindings` + `chunk.metadata->>'trace_id'`
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- **postgres_db.rs 5 remaining READs → Qdrant**: `get_trace_count_by_file`, `get_trace_frame_count_distribution`, `get_identity_files`, `get_identity_faces`, `get_file_faces` all migrated to `_faces` scroll + Rust aggregation
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- **agent/tools.rs fully migrated**: `exec_find_file`, `exec_list_files`, `exec_tkg_query` (8 sub-queries), `exec_identity_text`, `exec_identities_search` — all face_detections JOINs replaced with Qdrant scroll or identity_bindings
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- **job_worker.rs + storage.rs**: Remaining face_detections READs migrated to Qdrant scroll
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### Remaining face_detections references (all inactive/safe)
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- Schema definition (CREATE TABLE/INDEX in `postgres_db.rs`)
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- `store_face_detections_batch` — already skipped (Phase 1)
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- `workspace_sqlite.rs` — local processing DB, separate from PG
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- `bin/release.rs` — standalone release utility
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@@ -0,0 +1,545 @@
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<!-- module: progress -->
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<!-- description: Real-time progress tracking for processing pipeline, TKG build, and identity agent -->
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<!-- depends: 01_auth, 03_register, 05_process -->
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# Progress Tracking — API Workspace Module
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## Overview
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The progress tracking system provides real-time visibility into all processing stages:
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| System | Redis Key | Coverage |
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|--------|-----------|----------|
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| **Processor Progress** | `{prefix}progress:{file_uuid}` | 7 main processors (cut, asr, asrx, ocr, face, pose, appearance) |
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| **TKG Progress** | `{prefix}progress:{file_uuid}:tkg` | 18 TKG build phases (9 node types + 8 edge types + face_tracing) |
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| **Agent Progress** | `{prefix}progress:{file_uuid}:agent` | 5 Identity Agent phases |
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---
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## `POST /api/v1/progress/:file_uuid`
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**Auth**: Required
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**Scope**: file-level
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Get real-time processing progress including processor status, TKG build phases, and identity agent phases.
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### Example
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```bash
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curl -s -X POST "$API/api/v1/progress/$FILE_UUID" \
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-H "X-API-Key: $KEY" | jq '.'
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```
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### Response (200)
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```json
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{
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"file_uuid": "3a6c1865...",
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"overall_progress": 71,
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"cpu_percent": 45.2,
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"gpu_percent": 30.1,
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"memory_percent": 62.4,
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"processors": [
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{"name": "asr", "status": "complete", "progress": 100, "current": 0, "total": 0, "message": "done"},
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{"name": "face", "status": "complete", "progress": 100, "current": 0, "total": 0, "message": "done"},
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{"name": "pose", "status": "complete", "progress": 100, "current": 0, "total": 0, "message": "done"}
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],
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"tkg_progress": {
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"file_uuid": "3a6c1865...",
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"phase": "mutual_gaze_edges",
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"phase_index": 13,
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"total_phases": 18,
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"phase_progress": 0.8,
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"overall_progress": 0.72,
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"stats": {
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"total_faces": 1250,
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"traced_faces": 1250,
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"total_traces": 45,
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"face_track_nodes": 45,
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"gaze_track_nodes": 45,
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"lip_track_nodes": 12,
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"text_region_nodes": 8,
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"appearance_nodes": 38,
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"accessory_nodes": 5,
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"object_nodes": 156,
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"hand_nodes": 22,
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"speaker_nodes": 14,
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"co_occurrence_edges": 890,
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"speaker_face_edges": 120,
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"face_face_edges": 234,
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"mutual_gaze_edges": 67,
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"total_nodes": 345,
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"total_edges": 1311
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},
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"message": "67 mutual gaze edges",
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"updated_at": "2026-07-02T10:30:00Z"
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},
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"agent_progress": {
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"file_uuid": "3a6c1865...",
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"phase": "completed",
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"phase_index": 5,
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"total_phases": 5,
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"phase_progress": 1.0,
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"overall_progress": 1.0,
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"stats": {
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"total_faces": 1250,
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"total_traces": 45,
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"clusters": 18,
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"identities_created": 18,
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"tmdb_matches": 5,
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"speaker_bindings": 12,
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"confirmations": 18
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},
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"message": "Identity Agent processing completed",
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"updated_at": "2026-07-02T10:28:00Z"
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}
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}
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```
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### Field Descriptions
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#### Top Level
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| Field | Type | Description |
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|-------|------|-------------|
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| `file_uuid` | string | 32-char hex UUID |
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| `overall_progress` | integer | Overall processor progress (0–100) |
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| `processors` | array | Per-processor status |
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| `tkg_progress` | object | TKG build progress (null if not started) |
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| `agent_progress` | object | Identity Agent progress (null if not started) |
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#### TKG Progress Fields
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| Field | Type | Description |
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|-------|------|-------------|
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| `phase` | string | Current phase name (see TKG Phases below) |
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| `phase_index` | integer | Current phase index (0–17) |
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| `total_phases` | integer | Total phases: 18 |
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| `phase_progress` | float | Progress within current phase (0.0–1.0) |
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| `overall_progress` | float | Overall TKG progress (0.0–1.0) |
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| `stats` | object | Counts for all node and edge types |
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| `message` | string | Human-readable status message |
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#### TKG Phases (18 total)
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| Index | Phase | Description |
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|-------|-------|-------------|
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| 0 | `face_tracing` | Populate trace_id from face.json |
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| 1 | `face_track_nodes` | Build face_track nodes |
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| 2 | `gaze_track_nodes` | Build gaze_track nodes |
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| 3 | `lip_track_nodes` | Build lip_track nodes |
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| 4 | `text_region_nodes` | Build text_region nodes |
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| 5 | `appearance_nodes` | Build appearance_trace nodes |
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| 6 | `accessory_nodes` | Build accessory nodes |
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| 7 | `object_nodes` | Build yolo_object nodes |
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| 8 | `hand_nodes` | Build hand nodes |
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| 9 | `speaker_nodes` | Build speaker nodes |
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| 10 | `co_occurrence_edges` | Build co_occurrence edges |
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| 11 | `speaker_face_edges` | Build speaker_face edges |
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| 12 | `face_face_edges` | Build face_face edges |
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| 13 | `mutual_gaze_edges` | Build mutual_gaze edges |
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| 14 | `lip_sync_edges` | Build lip_sync edges |
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| 15 | `has_appearance_edges` | Build has_appearance edges |
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| 16 | `wears_edges` | Build wears edges |
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| 17 | `hand_object_edges` | Build hand_object edges |
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#### TKG Stats Fields
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| Field | Type | Description |
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|-------|------|-------------|
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| `total_faces` | integer | Total face detections |
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| `traced_faces` | integer | Faces with trace_id assigned |
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| `total_traces` | integer | Unique trace count |
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| `face_track_nodes` | integer | Face track nodes created |
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| `gaze_track_nodes` | integer | Gaze track nodes created |
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| `lip_track_nodes` | integer | Lip track nodes created |
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| `text_region_nodes` | integer | Text region nodes created |
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| `appearance_nodes` | integer | Appearance trace nodes created |
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| `accessory_nodes` | integer | Accessory nodes created |
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| `object_nodes` | integer | YOLO object nodes created |
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| `hand_nodes` | integer | Hand nodes created |
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| `speaker_nodes` | integer | Speaker nodes created |
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| `co_occurrence_edges` | integer | Co-occurrence edges created |
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| `speaker_face_edges` | integer | Speaker-face edges created |
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| `face_face_edges` | integer | Face-face edges created |
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| `mutual_gaze_edges` | integer | Mutual gaze edges created |
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| `lip_sync_edges` | integer | Lip sync edges created |
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| `has_appearance_edges` | integer | Has-appearance edges created |
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| `wears_edges` | integer | Wears edges created |
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| `hand_object_edges` | integer | Hand-object edges created |
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| `total_nodes` | integer | Total nodes (sum of all node types) |
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| `total_edges` | integer | Total edges (sum of all edge types) |
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---
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## `GET /api/v1/stats/ingestion-status/:file_uuid`
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**Auth**: Required
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**Scope**: file-level
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Get detailed ingestion status showing completion of all 24 processing steps.
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### Example
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|
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```bash
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curl -s "$API/api/v1/stats/ingestion-status/$FILE_UUID" \
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-H "X-API-Key: $KEY" | jq '.steps[] | {name, status, detail}'
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```
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### Response (200)
|
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|
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```json
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{
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"file_uuid": "3a6c1865...",
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"steps": [
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{"name": "rule1_sentence", "status": "done", "detail": "156 sentence chunks"},
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{"name": "auto_vectorize", "status": "done", "detail": "156 embedded"},
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{"name": "face_track", "status": "done", "detail": "45 traces / 1250 detections"},
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{"name": "trace_chunks", "status": "done", "detail": "45 trace chunks"},
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{"name": "tkg_face_track", "status": "done", "detail": "45 nodes"},
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{"name": "tkg_gaze_track", "status": "done", "detail": "45 nodes"},
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{"name": "tkg_lip_track", "status": "done", "detail": "12 nodes"},
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{"name": "tkg_text_region", "status": "done", "detail": "8 nodes"},
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{"name": "tkg_appearance", "status": "done", "detail": "38 nodes"},
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{"name": "tkg_accessory", "status": "done", "detail": "5 nodes"},
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{"name": "tkg_object", "status": "done", "detail": "156 nodes"},
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{"name": "tkg_hand", "status": "done", "detail": "22 nodes"},
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{"name": "tkg_speaker", "status": "done", "detail": "14 nodes"},
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{"name": "tkg_co_occurrence", "status": "done", "detail": "890 edges"},
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{"name": "tkg_speaker_face", "status": "done", "detail": "120 edges"},
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{"name": "tkg_face_face", "status": "done", "detail": "234 edges"},
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{"name": "tkg_mutual_gaze", "status": "done", "detail": "67 edges"},
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{"name": "tkg_lip_sync", "status": "done", "detail": "12 edges"},
|
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{"name": "tkg_has_appearance", "status": "done", "detail": "38 edges"},
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{"name": "tkg_wears", "status": "done", "detail": "22 edges"},
|
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{"name": "tkg_hand_object", "status": "done", "detail": "18 edges"},
|
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{"name": "rule2_relationship", "status": "done", "detail": "1331 relationship chunks"},
|
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{"name": "identity_match", "status": "done", "detail": "18 identities matched"},
|
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{"name": "scene_metadata", "status": "done", "detail": null}
|
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],
|
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"related_identities": [
|
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{"uuid": "a9a901056d6b46ff92da0c3c1a57dff4", "name": "John Smith"}
|
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],
|
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"strangers": 3
|
||||
}
|
||||
```
|
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|
||||
### Step Descriptions
|
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|
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| Step | Status When Done |
|
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|------|-----------------|
|
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| `rule1_sentence` | sentence_count > 0 |
|
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| `auto_vectorize` | sentence_embedded > 0 |
|
||||
| `face_track` | trace_count > 0 |
|
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| `trace_chunks` | trace_chunks > 0 |
|
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| `tkg_face_track` → `tkg_speaker` | Node count > 0 (9 steps) |
|
||||
| `tkg_co_occurrence` → `tkg_hand_object` | Edge count > 0 (8 steps) |
|
||||
| `rule2_relationship` | relationship_chunks > 0 |
|
||||
| `identity_match` | identity_count > 0 |
|
||||
| `scene_metadata` | scene_meta.json exists |
|
||||
|
||||
---
|
||||
|
||||
## `POST /api/v1/file/:file_uuid/tkg/rebuild`
|
||||
|
||||
**Auth**: Required
|
||||
**Scope**: file-level
|
||||
|
||||
Manually trigger TKG rebuild. Automatically triggers Rule 2 ingestion after TKG completes.
|
||||
|
||||
### Example
|
||||
|
||||
```bash
|
||||
curl -s -X POST "$API/api/v1/file/$FILE_UUID/tkg/rebuild" \
|
||||
-H "X-API-Key: $KEY" \
|
||||
-H "Content-Type: application/json" -d '{}'
|
||||
```
|
||||
|
||||
### Response (200)
|
||||
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"message": "TKG rebuild started",
|
||||
"nodes": 345,
|
||||
"edges": 1311
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## `POST /api/v1/file/:file_uuid/rule2`
|
||||
|
||||
**Auth**: Required
|
||||
**Scope**: file-level
|
||||
|
||||
Manually trigger Rule 2 ingestion (TKG edges → relationship chunks).
|
||||
|
||||
### Example
|
||||
|
||||
```bash
|
||||
curl -s -X POST "$API/api/v1/file/$FILE_UUID/rule2" \
|
||||
-H "X-API-Key: $KEY" \
|
||||
-H "Content-Type: application/json" -d '{}'
|
||||
```
|
||||
|
||||
### Response (200)
|
||||
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"message": "Rule 2 ingestion: 1331 relationship chunks created",
|
||||
"rule2_count": 1331
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Processing Pipeline Flow
|
||||
|
||||
```
|
||||
1. Processors (concurrent)
|
||||
├── cut, asr, ocr, face, pose, appearance → complete
|
||||
└── asrx → after cut+asr
|
||||
|
||||
2. Post-Processor Triggers (automatic)
|
||||
├── Rule 1 Ingestion (ASR+OCR → sentence chunks)
|
||||
├── Face Trace + DB Store (face_traced.json → Qdrant trace_id)
|
||||
├── TMDb Face Matching (if enabled)
|
||||
├── Heuristic Scene Metadata
|
||||
├── Identity Agent (face + ASRX)
|
||||
└── TKG Build (automatic after processors complete)
|
||||
└── Rule 2 Ingestion (automatic after TKG)
|
||||
└── Relationship chunks vectorized
|
||||
|
||||
3. Completion
|
||||
└── Job marked completed when all ingestion steps done
|
||||
```
|
||||
|
||||
## Error Codes
|
||||
|
||||
| Code | HTTP | When |
|
||||
|------|------|------|
|
||||
| E001 | 400 | Invalid file_uuid format |
|
||||
| E002 | 404 | File not found |
|
||||
| E003 | 404 | No TKG data available |
|
||||
| E010 | 500 | Qdrant connection failed |
|
||||
| E011 | 500 | Database connection failed |
|
||||
|
||||
---
|
||||
|
||||
## `GET /api/v1/stats/pipeline/:file_uuid`
|
||||
|
||||
**Auth**: Required
|
||||
**Scope**: file-level
|
||||
|
||||
Get segmented pipeline progress with weighted stage breakdown. Shows overall progress as weighted sum of all pipeline stages.
|
||||
|
||||
### Pipeline Stages and Weights
|
||||
|
||||
| Stage | Weight | Description |
|
||||
|-------|--------|-------------|
|
||||
| `processors` | 30% | 7 concurrent processors (cut, asr, asrx, ocr, face, pose, appearance) |
|
||||
| `rule1_ingestion` | 5% | ASR+OCR → sentence chunks |
|
||||
| `face_tracing` | 5% | Face trace_id assignment |
|
||||
| `identity_agent` | 10% | Identity creation, TMDb matching, speaker binding |
|
||||
| `tkg_nodes` | 20% | TKG node building (9 node types) |
|
||||
| `tkg_edges` | 15% | TKG edge building (8 edge types) |
|
||||
| `rule2_ingestion` | 15% | TKG edges → relationship chunks |
|
||||
|
||||
### Example
|
||||
|
||||
```bash
|
||||
curl -s "$API/api/v1/stats/pipeline/$FILE_UUID" \
|
||||
-H "X-API-Key: $KEY" | jq '.'
|
||||
```
|
||||
|
||||
### Response (200)
|
||||
|
||||
```json
|
||||
{
|
||||
"file_uuid": "3a6c1865...",
|
||||
"overall_progress": 0.65,
|
||||
"stages": [
|
||||
{"name": "processors", "weight": 0.30, "progress": 1.0, "status": "completed", "detail": "7/7 complete"},
|
||||
{"name": "rule1_ingestion", "weight": 0.05, "progress": 1.0, "status": "completed", "detail": "156 chunks"},
|
||||
{"name": "face_tracing", "weight": 0.05, "progress": 1.0, "status": "completed", "detail": "45 traces"},
|
||||
{"name": "identity_agent", "weight": 0.10, "progress": 1.0, "status": "completed", "detail": "18 identities"},
|
||||
{"name": "tkg_nodes", "weight": 0.20, "progress": 1.0, "status": "completed", "detail": "345 nodes"},
|
||||
{"name": "tkg_edges", "weight": 0.15, "progress": 0.5, "status": "running", "detail": "mutual_gaze_edges: 67/8 expected"},
|
||||
{"name": "rule2_ingestion", "weight": 0.15, "progress": 0.0, "status": "pending", "detail": null}
|
||||
],
|
||||
"updated_at": "2026-07-02T10:30:00Z"
|
||||
}
|
||||
```
|
||||
|
||||
### Field Descriptions
|
||||
|
||||
| Field | Type | Description |
|
||||
|-------|------|-------------|
|
||||
| `file_uuid` | string | 32-char hex UUID |
|
||||
| `overall_progress` | float | Weighted sum of all stage progress (0.0–1.0) |
|
||||
| `stages` | array | Per-stage progress breakdown |
|
||||
| `stages[].name` | string | Stage name |
|
||||
| `stages[].weight` | float | Stage weight in overall progress |
|
||||
| `stages[].progress` | float | Stage completion (0.0–1.0) |
|
||||
| `stages[].status` | string | `"pending"`, `"running"`, `"completed"`, `"failed"` |
|
||||
| `stages[].detail` | string | Human-readable detail (optional) |
|
||||
| `updated_at` | string | ISO 8601 timestamp |
|
||||
|
||||
### Overall Progress Calculation
|
||||
|
||||
```
|
||||
overall_progress = Σ(stage.weight × stage.progress) for all stages
|
||||
```
|
||||
|
||||
Example calculation:
|
||||
- processors: 0.30 × 1.0 = 0.30
|
||||
- rule1_ingestion: 0.05 × 1.0 = 0.05
|
||||
- face_tracing: 0.05 × 1.0 = 0.05
|
||||
- identity_agent: 0.10 × 1.0 = 0.10
|
||||
- tkg_nodes: 0.20 × 1.0 = 0.20
|
||||
- tkg_edges: 0.15 × 0.5 = 0.075
|
||||
- rule2_ingestion: 0.15 × 0.0 = 0.0
|
||||
- **Total: 0.775 (77.5%)**
|
||||
|
||||
---
|
||||
|
||||
## `GET /api/v1/stats/file/:file_uuid`
|
||||
|
||||
**Auth**: Required
|
||||
**Scope**: file-level
|
||||
|
||||
Get comprehensive file statistics from all data sources: JSON processing status, PostgreSQL counts, Qdrant collections, TKG nodes/edges, and Identity Agent stats.
|
||||
|
||||
### Example
|
||||
|
||||
```bash
|
||||
curl -s "$API/api/v1/stats/file/$FILE_UUID" \
|
||||
-H "X-API-Key: $KEY" | jq '.'
|
||||
```
|
||||
|
||||
### Response (200)
|
||||
|
||||
```json
|
||||
{
|
||||
"file_uuid": "3a6c1865...",
|
||||
"file_name": "video.mp4",
|
||||
"status": "processing",
|
||||
"processors": [
|
||||
{"name": "asr", "status": "complete", "progress": 100, "message": "done"},
|
||||
{"name": "face", "status": "complete", "progress": 100, "message": "done"}
|
||||
],
|
||||
"postgres": {
|
||||
"sentence_chunks": 156,
|
||||
"trace_chunks": 45,
|
||||
"relationship_chunks": 1331,
|
||||
"identities": 18,
|
||||
"file_identities": 18
|
||||
},
|
||||
"qdrant": {
|
||||
"faces": 1250,
|
||||
"face_traces": 45,
|
||||
"face_identities": 18,
|
||||
"text_chunks": 4562,
|
||||
"speakers": 434
|
||||
},
|
||||
"tkg": {
|
||||
"total_nodes": 345,
|
||||
"total_edges": 1311,
|
||||
"face_track_nodes": 45,
|
||||
"gaze_track_nodes": 45,
|
||||
"lip_track_nodes": 12,
|
||||
"text_region_nodes": 8,
|
||||
"appearance_nodes": 38,
|
||||
"accessory_nodes": 5,
|
||||
"object_nodes": 156,
|
||||
"hand_nodes": 22,
|
||||
"speaker_nodes": 14,
|
||||
"co_occurrence_edges": 890,
|
||||
"speaker_face_edges": 120,
|
||||
"face_face_edges": 234,
|
||||
"mutual_gaze_edges": 67,
|
||||
"lip_sync_edges": 12,
|
||||
"has_appearance_edges": 38,
|
||||
"wears_edges": 22,
|
||||
"hand_object_edges": 18
|
||||
},
|
||||
"identity_agent": {
|
||||
"clusters": 18,
|
||||
"identities_created": 18,
|
||||
"tmdb_matches": 5,
|
||||
"speaker_bindings": 12,
|
||||
"confirmations": 18
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Field Descriptions
|
||||
|
||||
#### Top Level
|
||||
|
||||
| Field | Type | Description |
|
||||
|-------|------|-------------|
|
||||
| `file_uuid` | string | 32-char hex UUID |
|
||||
| `file_name` | string | Original filename |
|
||||
| `status` | string | File status: `registered`, `processing`, `completed`, `failed` |
|
||||
| `processors` | array | Per-processor status from processing_status JSONB |
|
||||
| `postgres` | object | PostgreSQL table counts |
|
||||
| `qdrant` | object | Qdrant collection point counts |
|
||||
| `tkg` | object | TKG node and edge counts by type |
|
||||
| `identity_agent` | object | Identity Agent statistics |
|
||||
|
||||
#### PostgreSQL Stats
|
||||
|
||||
| Field | Type | Description |
|
||||
|-------|------|-------------|
|
||||
| `sentence_chunks` | integer | Rule 1 sentence chunks count |
|
||||
| `trace_chunks` | integer | Face trace chunks count |
|
||||
| `relationship_chunks` | integer | Rule 2 relationship chunks count |
|
||||
| `identities` | integer | Unique identities bound to this file |
|
||||
| `file_identities` | integer | File-identity mapping records |
|
||||
|
||||
#### Qdrant Stats
|
||||
|
||||
| Field | Type | Description |
|
||||
|-------|------|-------------|
|
||||
| `faces` | integer | Total face points in `_faces` collection |
|
||||
| `face_traces` | integer | Unique trace IDs in `_faces` |
|
||||
| `face_identities` | integer | Unique identity IDs bound in `_faces` |
|
||||
| `text_chunks` | integer | Text chunk vectors in `momentry_*_rule1_v2` |
|
||||
| `speakers` | integer | Speaker segments in `momentry_*_speaker` |
|
||||
|
||||
#### TKG Stats
|
||||
|
||||
| Field | Type | Description |
|
||||
|-------|------|-------------|
|
||||
| `total_nodes` | integer | Sum of all node types |
|
||||
| `total_edges` | integer | Sum of all edge types |
|
||||
| `face_track_nodes` | integer | Face track nodes |
|
||||
| `gaze_track_nodes` | integer | Gaze track nodes |
|
||||
| `lip_track_nodes` | integer | Lip track nodes |
|
||||
| `text_region_nodes` | integer | Text region nodes |
|
||||
| `appearance_nodes` | integer | Appearance trace nodes |
|
||||
| `accessory_nodes` | integer | Accessory nodes |
|
||||
| `object_nodes` | integer | YOLO object nodes |
|
||||
| `hand_nodes` | integer | Hand nodes |
|
||||
| `speaker_nodes` | integer | Speaker nodes |
|
||||
| `co_occurrence_edges` | integer | Co-occurrence edges |
|
||||
| `speaker_face_edges` | integer | Speaker-face edges |
|
||||
| `face_face_edges` | integer | Face-face edges |
|
||||
| `mutual_gaze_edges` | integer | Mutual gaze edges |
|
||||
| `lip_sync_edges` | integer | Lip sync edges |
|
||||
| `has_appearance_edges` | integer | Has-appearance edges |
|
||||
| `wears_edges` | integer | Wears edges |
|
||||
| `hand_object_edges` | integer | Hand-object edges |
|
||||
|
||||
#### Identity Agent Stats
|
||||
|
||||
| Field | Type | Description |
|
||||
|-------|------|-------------|
|
||||
| `clusters` | integer | Face clusters from face_clustered.json |
|
||||
| `identities_created` | integer | Identities created from clusters |
|
||||
| `tmdb_matches` | integer | TMDb identity matches |
|
||||
| `speaker_bindings` | integer | Speaker-to-identity bindings |
|
||||
| `confirmations` | integer | Confirmed identity bindings |
|
||||
@@ -0,0 +1,81 @@
|
||||
---
|
||||
title: Charade Identity Processing Fix Report
|
||||
date: 2026-06-29
|
||||
author: OpenCode
|
||||
status: completed
|
||||
---
|
||||
|
||||
## Summary
|
||||
|
||||
**Problem**: Charade file (UUID: c36f35685177c981aa139b66bbbccc5b) identity processing failed because of data corruption and missing TKG nodes.
|
||||
|
||||
**Root Cause**: Circular dependency chain broken:
|
||||
- face_detections had 3x duplicate records (12726 instead of 4242)
|
||||
- All trace_id = NULL (UPDATE failed)
|
||||
- TKG Phase 2.5 couldn't create face_track nodes (needs trace_id)
|
||||
- Identity Agent couldn't mark suggestions (needs TKG nodes)
|
||||
|
||||
## Fix Steps
|
||||
|
||||
### Step 1: Clean Duplicate Data ✅
|
||||
- Deleted 8484 duplicate records
|
||||
- 12726 → 4242 unique face_detections
|
||||
|
||||
### Step 2: Write trace_id ✅
|
||||
- store_traced_faces.py successfully updated DB
|
||||
- 4242 faces with trace_id (100% populated)
|
||||
- 426 unique traces
|
||||
|
||||
### Step 3: Create TKG Nodes ✅
|
||||
- Created 426 face_track nodes via SQL
|
||||
- Fixed external_id format: "face_track_*" (matches Rust code)
|
||||
|
||||
### Step 4: Run Identity Agent ✅
|
||||
- Identity matching: 2 traces matched to Audrey Hepburn
|
||||
- TKG marking: 2/2 nodes marked as "suggested"
|
||||
|
||||
## Final Results
|
||||
|
||||
| Metric | Before | After |
|
||||
|--------|--------|-------|
|
||||
| face_detections | 12726 (3x duplicates) | 4242 (unique) |
|
||||
| trace_id populated | 0 | 4242 (100%) |
|
||||
| TKG face_track nodes | 0 | 426 |
|
||||
| Identity suggestions | 0 | 2 (Audrey Hepburn) |
|
||||
|
||||
**Identity Matches**:
|
||||
- Trace 202: Audrey Hepburn (score=0.6002)
|
||||
- Trace 311: Audrey Hepburn (score=0.6724)
|
||||
|
||||
## Technical Details
|
||||
|
||||
### Data Sources
|
||||
- face.json: 3176 frames, 4242 faces
|
||||
- face_traced.json: 426 traces (IoU tracking)
|
||||
- Qdrant _faces: 374 traces with embeddings
|
||||
- Qdrant _seeds: 2 TMDb seeds
|
||||
|
||||
### Tools Used
|
||||
- PostgreSQL: face_detections, tkg_nodes tables
|
||||
- Python: store_traced_faces.py, identity_matcher.py
|
||||
- Qdrant: _faces, _seeds collections
|
||||
|
||||
## Next Steps
|
||||
|
||||
1. User confirmation: Check suggested identities via Portal UI
|
||||
2. Manual confirmation: Confirm Audrey Hepburn matches
|
||||
3. Propagation: Run Round 2 matching (propagate confirmed identities)
|
||||
4. Stranger clustering: Cluster unmatched traces (TH=0.40)
|
||||
|
||||
## Files Modified
|
||||
|
||||
- PostgreSQL: public.face_detections (deleted 8484 duplicates)
|
||||
- PostgreSQL: public.tkg_nodes (created 426 face_track nodes)
|
||||
- Qdrant: _faces collection (updated 3176 trace_ids)
|
||||
|
||||
## Related Documents
|
||||
|
||||
- docs/PROCESSING_PIPELINE.md
|
||||
- src/core/processor/tkg.rs:550-683 (build_face_track_nodes)
|
||||
- scripts/store_traced_faces.py (trace_id storage)
|
||||
- scripts/identity_matcher.py (TMDb matching)
|
||||
@@ -0,0 +1,116 @@
|
||||
---
|
||||
title: Cut Scene Detection Escape Fix
|
||||
date: 2026-06-30
|
||||
author: OpenCode
|
||||
status: completed
|
||||
---
|
||||
|
||||
## Summary
|
||||
|
||||
**Problem**: Cut scene detection returned only 1 scene (fallback) instead of 833 scenes for Charade video.
|
||||
|
||||
**Root Cause**: Python script `cut_processor.py` line 68 used `\\\\` (4 backslashes) → ffprobe received `\\` → scene detection failed → 0 scene times → fallback to single scene.
|
||||
|
||||
## Fix
|
||||
|
||||
### Code Changes
|
||||
|
||||
1. **scripts/cut_processor.py** line 68:
|
||||
- Before: `f"movie={video_path},select='gt(scene\\\\,0.3)',showinfo"`
|
||||
- After: `f"movie={video_path},select='gt(scene\\,0.3)',showinfo"`
|
||||
|
||||
2. **src/core/processor/cut.rs** line 127:
|
||||
- Already correct: `&format!("movie={},select='gt(scene\\,0.3)',showinfo", video_path)`
|
||||
- No changes needed
|
||||
|
||||
### Escape Analysis
|
||||
|
||||
| Escape Level | Python String | ffprobe receives | Result |
|
||||
|--------------|---------------|------------------|--------|
|
||||
| `\\\\` | `"\\"` | `\\` | ❌ 0 scenes |
|
||||
| `\\` | `"\\"` | `\` | ✅ 832 scenes |
|
||||
| `\` (raw) | `r"\ "` | `\` | ✅ 832 scenes |
|
||||
|
||||
### Testing
|
||||
|
||||
```bash
|
||||
# Before fix
|
||||
python3 scripts/cut_processor.py video.mp4 output.json
|
||||
# Result: 1 scene (fallback)
|
||||
|
||||
# After fix
|
||||
python3 scripts/cut_processor.py video.mp4 output.json
|
||||
# Result: 833 scenes
|
||||
```
|
||||
|
||||
## Verification
|
||||
|
||||
### File: 3dfc20618fb522e795240b5f0e5ff6f0 (Charade)
|
||||
|
||||
| Metric | Before | After |
|
||||
|--------|--------|-------|
|
||||
| cut.json scenes | 1 | 833 |
|
||||
| workspace.sqlite pre_chunks (cut) | 12 | 833 |
|
||||
| Scene 1 end_frame | 162695 (whole video) | 932 |
|
||||
|
||||
### Workspace.sqlite Status
|
||||
|
||||
```bash
|
||||
sqlite3 output/3dfc20618fb522e795240b5f0e5ff6f0.workspace.sqlite \
|
||||
"SELECT processor_type, COUNT(*) FROM pre_chunks GROUP BY processor_type;"
|
||||
|
||||
cut|833
|
||||
ocr|942
|
||||
```
|
||||
|
||||
## Technical Details
|
||||
|
||||
### ffprobe Command
|
||||
|
||||
Correct format:
|
||||
```bash
|
||||
ffprobe -v quiet -show_entries frame=pts_time -of default=nk=0 \
|
||||
-f lavfi "movie=/path/to/video.mp4,select='gt(scene\\,0.3)',showinfo" \
|
||||
-show_frames
|
||||
```
|
||||
|
||||
- `scene\\,0.3` in shell → ffprobe receives `scene\,0.3`
|
||||
- The `\` escapes the comma in ffmpeg filter syntax
|
||||
|
||||
### Python subprocess Behavior
|
||||
|
||||
- Without `shell=True`: arguments passed directly to executable
|
||||
- Python string `"\\\\"` → subprocess receives `"\\"`
|
||||
- Python string `"\\"` → subprocess receives `"\"`
|
||||
- Raw string `r"\ "` → subprocess receives `"\"`
|
||||
|
||||
## Impact
|
||||
|
||||
### Affected Videos
|
||||
|
||||
- Charade (UUID: 3dfc20618fb522e795240b5f0e5ff6f0)
|
||||
- Other videos registered before this fix may have incorrect scene counts
|
||||
|
||||
### Remediation
|
||||
|
||||
1. Re-run cut detection for affected videos
|
||||
2. Update workspace.sqlite pre_chunks
|
||||
3. If in PostgreSQL: update public.pre_chunks table
|
||||
|
||||
## Next Steps
|
||||
|
||||
1. Verify fix in production by registering new video
|
||||
2. Check if other videos need remediation
|
||||
3. Consider adding unit test for cut escape handling
|
||||
|
||||
## Related Files
|
||||
|
||||
- scripts/cut_processor.py
|
||||
- src/core/processor/cut.rs
|
||||
- src/api/files.rs (register API uses Python script)
|
||||
|
||||
## Version History
|
||||
|
||||
| Version | Date | Author | Changes |
|
||||
|---------|------|--------|---------|
|
||||
| 1.0 | 2026-06-30 | OpenCode | Initial report |
|
||||
@@ -0,0 +1,117 @@
|
||||
# Face Detections 表清理計劃
|
||||
|
||||
## 問題
|
||||
所有使用 `face_detections` 表的代碼都是錯誤的，需要改為使用 Qdrant workspace traces。
|
||||
|
||||
## 正確架構
|
||||
|
||||
### PostgreSQL
|
||||
```
|
||||
identities (全局人物主表)
|
||||
├── id
|
||||
├── uuid
|
||||
├── name
|
||||
├── status
|
||||
└── metadata
|
||||
```
|
||||
|
||||
### Qdrant Payload
|
||||
```
|
||||
{prefix}_workspace_traces (512d vectors)
|
||||
├── file_uuid
|
||||
├── trace_id
|
||||
├── frame_number
|
||||
├── identity_id ← 绑定存储在这里
|
||||
├── bbox
|
||||
├── confidence
|
||||
└── embedding
|
||||
```
|
||||
|
||||
## 錯誤代碼位置 (197 處)
|
||||
|
||||
### 1. Processor 層 (寫入錯誤)
|
||||
- `src/core/processor/processor.rs` - line 744, 1311
|
||||
- `src/core/processor/job_worker.rs` - line 647
|
||||
- `src/core/db/workspace_sqlite.rs` - line 257-263 (函數定義)
|
||||
- `src/core/db/postgres_db.rs` - line 2712 (函數定義)
|
||||
|
||||
### 2. TKG 處理器 (大量使用)
|
||||
- `src/core/processor/tkg.rs` - ~50 處使用 `face_detections` 表
|
||||
|
||||
### 3. Chunk Ingest
|
||||
- `src/core/chunk/trace_ingest.rs` - line 10
|
||||
- `src/core/chunk/rule2_ingest.rs` - line 26
|
||||
|
||||
### 4. API 層 (查詢/更新錯誤)
|
||||
- `src/api/identity_api.rs` - 22 處
|
||||
- `src/api/identity_binding.rs` - 12 處
|
||||
- `src/api/identities.rs` - 2 處
|
||||
- `src/api/identity_agent_api.rs` - 7 處
|
||||
- `src/api/files.rs` - 4 處
|
||||
- `src/api/media_api.rs` - 3 處
|
||||
|
||||
### 5. Identity 層
|
||||
- `src/core/identity/storage.rs` - 3 處
|
||||
|
||||
## 修改計劃
|
||||
|
||||
### Phase 1: 分析現有代碼
|
||||
1. 理解當前 face_detections 表的使用方式
|
||||
2. 理解 Qdrant workspace traces 的結構
|
||||
3. 確定需要修改的函數列表
|
||||
|
||||
### Phase 2: 創建 Qdrant 查詢輔助函數
|
||||
1. 創建 `QdrantWorkspace` 查詢方法
|
||||
2. 創建 trace 到 identity 的綁定查詢
|
||||
3. 創建 face 匹配查詢
|
||||
|
||||
### Phase 3: 修改 Processor 層
|
||||
1. 修改 `processor.rs` - 移除 face_detections 寫入
|
||||
2. 修改 `job_worker.rs` - 移除 face_detections 查詢
|
||||
3. 修改 `workspace_sqlite.rs` - 移除 face_detections 相關函數
|
||||
4. 修改 `postgres_db.rs` - 移除 face_detections 相關函數
|
||||
|
||||
### Phase 4: 修改 TKG 處理器
|
||||
1. 重構 `tkg.rs` - 使用 Qdrant workspace traces 代替 face_detections
|
||||
2. 移除 `populate_face_detections_from_face_json` 函數
|
||||
3. 修改 face 匹配邏輯
|
||||
|
||||
### Phase 5: 修改 API 層
|
||||
1. 修改 `identity_api.rs` - 使用 Qdrant 查詢
|
||||
2. 修改 `identity_binding.rs` - 使用 Qdrant 綁定
|
||||
3. 修改 `identities.rs` - 使用 Qdrant 查詢
|
||||
4. 修改 `identity_agent_api.rs` - 使用 Qdrant 匹配
|
||||
5. 修改 `files.rs` - 移除 face_detections 查詢
|
||||
6. 修改 `media_api.rs` - 移除 face_detections 查詢
|
||||
|
||||
### Phase 6: 修改 Chunk Ingest
|
||||
1. 修改 `trace_ingest.rs` - 使用 Qdrant traces
|
||||
2. 修改 `rule2_ingest.rs` - 使用 Qdrant traces
|
||||
|
||||
### Phase 7: 測試
|
||||
1. 測試 face 追蹤
|
||||
2. 測試 identity 綁定
|
||||
3. 測試 TKG 構建
|
||||
4. 測試 API 端點
|
||||
|
||||
### Phase 8: 清理
|
||||
1. 移除 face_detections 表（可選）
|
||||
2. 更新文檔
|
||||
3. 更新測試
|
||||
|
||||
## 風險評估
|
||||
- **高風險**: TKG 處理器有大量 face_detections 使用
|
||||
- **中風險**: API 層需要重構查詢邏輯
|
||||
- **低風險**: Processor 層修改相對簡單
|
||||
|
||||
## 預估時間
|
||||
- Phase 1-2: 2-3 小時
|
||||
- Phase 3-4: 4-6 小時
|
||||
- Phase 5-6: 3-4 小時
|
||||
- Phase 7-8: 2-3 小時
|
||||
- **總計**: 11-16 小時
|
||||
|
||||
## 依賴關係
|
||||
- 需要 Qdrant workspace traces 正確填充
|
||||
- 需要 face.json 格式正確
|
||||
- 需要 SwiftFacePose 正常工作
|
||||
@@ -0,0 +1,49 @@
|
||||
-- ================================================================
|
||||
-- Migration 036: ASR/ASRX and Face Detailed Status
|
||||
-- Version: 036
|
||||
-- Date: 2026-06-26
|
||||
-- Description: Add asr_status and face_status columns for detailed result status
|
||||
-- to support unified output SOP
|
||||
-- ================================================================
|
||||
|
||||
-- 36.1: Add asr_status column to processor_results
|
||||
ALTER TABLE processor_results ADD COLUMN IF NOT EXISTS asr_status VARCHAR(20);
|
||||
|
||||
COMMENT ON COLUMN processor_results.asr_status IS
|
||||
'ASR-specific status: no_audio_track, silent_audio, has_transcript, processing';
|
||||
|
||||
-- 36.2: Add check constraint for asr_status
|
||||
ALTER TABLE processor_results DROP CONSTRAINT IF EXISTS chk_processor_results_asr_status;
|
||||
ALTER TABLE processor_results ADD CONSTRAINT chk_processor_results_asr_status
|
||||
CHECK (asr_status IS NULL OR asr_status IN ('no_audio_track', 'silent_audio', 'has_transcript', 'processing'));
|
||||
|
||||
-- 36.3: Add segment_count column for quick reference
|
||||
ALTER TABLE processor_results ADD COLUMN IF NOT EXISTS segment_count INTEGER DEFAULT 0;
|
||||
|
||||
COMMENT ON COLUMN processor_results.segment_count IS
|
||||
'Number of transcript segments (ASR) or speaker segments (ASRX)';
|
||||
|
||||
-- 36.4: Create index for asr_status queries
|
||||
CREATE INDEX IF NOT EXISTS idx_processor_results_asr_status ON processor_results(asr_status)
|
||||
WHERE asr_status IS NOT NULL;
|
||||
|
||||
-- 36.5: Add face_status column to processor_results
|
||||
ALTER TABLE processor_results ADD COLUMN IF NOT EXISTS face_status VARCHAR(20);
|
||||
|
||||
COMMENT ON COLUMN processor_results.face_status IS
|
||||
'Face detection status: no_faces, has_faces, processing';
|
||||
|
||||
-- 36.6: Add check constraint for face_status
|
||||
ALTER TABLE processor_results DROP CONSTRAINT IF EXISTS chk_processor_results_face_status;
|
||||
ALTER TABLE processor_results ADD CONSTRAINT chk_processor_results_face_status
|
||||
CHECK (face_status IS NULL OR face_status IN ('no_faces', 'has_faces', 'processing'));
|
||||
|
||||
-- 36.7: Add total_faces column for quick reference
|
||||
ALTER TABLE processor_results ADD COLUMN IF NOT EXISTS total_faces INTEGER DEFAULT 0;
|
||||
|
||||
COMMENT ON COLUMN processor_results.total_faces IS
|
||||
'Total number of faces detected across all frames';
|
||||
|
||||
-- 36.8: Create index for face_status queries
|
||||
CREATE INDEX IF NOT EXISTS idx_processor_results_face_status ON processor_results(face_status)
|
||||
WHERE face_status IS NOT NULL;
|
||||
+237
-74
@@ -1,15 +1,17 @@
|
||||
#!/opt/homebrew/bin/python3.11
|
||||
"""
|
||||
Appearance Processor - HSV color feature extraction for person tracking
|
||||
Appearance Processor - Body part color extraction using pose keypoints
|
||||
|
||||
Input:
|
||||
- video_path: source video
|
||||
- pose_json: pose.json with frame bboxes
|
||||
- pose_json: pose.json with keypoints and bbox
|
||||
- output_path: output JSON
|
||||
|
||||
Output: appearance.json with HSV histogram per person per frame
|
||||
Output: appearance.json with per-person per-frame body part colors
|
||||
|
||||
Depends on pose.json (bbox). Same 0-based frame numbering as face/pose/mediapipe.
|
||||
Regions: head, neck, front_upper_body, front_lower_body,
|
||||
back_upper_body, back_lower_body, left_hand, right_hand,
|
||||
left_foot, right_foot
|
||||
"""
|
||||
|
||||
import sys
|
||||
@@ -20,82 +22,223 @@ import cv2
|
||||
import numpy as np
|
||||
|
||||
|
||||
def extract_appearance(frame, bbox):
|
||||
x, y, w, h = bbox["x"], bbox["y"], bbox["width"], bbox["height"]
|
||||
if w <= 0 or h <= 0:
|
||||
return None
|
||||
def get_kp(keypoints, name):
|
||||
for kp in keypoints:
|
||||
if kp.get("name") == name:
|
||||
return (kp["x"], kp["y"], kp.get("confidence", 1.0))
|
||||
return None
|
||||
|
||||
x1, y1 = max(0, x), max(0, y)
|
||||
x2 = min(frame.shape[1], x + w)
|
||||
y2 = min(frame.shape[0], y + h)
|
||||
if x2 <= x1 or y2 <= y1:
|
||||
return None
|
||||
|
||||
person_roi = frame[y1:y2, x1:x2]
|
||||
hsv = cv2.cvtColor(person_roi, cv2.COLOR_BGR2HSV)
|
||||
def determine_facing(keypoints):
|
||||
nose = get_kp(keypoints, "nose")
|
||||
left_shoulder = get_kp(keypoints, "left_shoulder")
|
||||
right_shoulder = get_kp(keypoints, "right_shoulder")
|
||||
|
||||
if nose and nose[2] > 0.5:
|
||||
return "front"
|
||||
|
||||
sh_vis = sum(1 for s in [left_shoulder, right_shoulder] if s and s[2] > 0.5)
|
||||
if sh_vis >= 2 and (not nose or nose[2] < 0.2):
|
||||
return "back"
|
||||
|
||||
if sh_vis >= 1:
|
||||
return "profile"
|
||||
|
||||
return "unknown"
|
||||
|
||||
|
||||
def extract_color(roi_bgr):
|
||||
"""Extract HSV histogram and dominant colors from an ROI"""
|
||||
if roi_bgr is None or roi_bgr.size == 0:
|
||||
return None
|
||||
if roi_bgr.shape[0] < 2 or roi_bgr.shape[1] < 2:
|
||||
return None
|
||||
hsv = cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV)
|
||||
pixels = hsv.reshape(-1, 3).astype(np.float32)
|
||||
|
||||
# HSV histograms
|
||||
h_hist = cv2.calcHist([hsv], [0], None, [30], [0, 180]).flatten()
|
||||
s_hist = cv2.calcHist([hsv], [1], None, [32], [0, 256]).flatten()
|
||||
v_hist = cv2.calcHist([hsv], [2], None, [32], [0, 256]).flatten()
|
||||
h_sum = h_hist.sum() or 1
|
||||
s_sum = s_hist.sum() or 1
|
||||
v_sum = v_hist.sum() or 1
|
||||
hs = h_hist.sum() or 1
|
||||
ss = s_hist.sum() or 1
|
||||
vs = v_hist.sum() or 1
|
||||
|
||||
# Dominant colors via k-means
|
||||
dominant = []
|
||||
if len(pixels) >= 5:
|
||||
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
|
||||
_, labels, centers = cv2.kmeans(
|
||||
pixels, 5, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS
|
||||
)
|
||||
_, labels, centers = cv2.kmeans(pixels, 5, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)
|
||||
counts = np.bincount(labels.flatten())
|
||||
dominant = centers[np.argsort(-counts)[:5]].tolist()
|
||||
elif len(pixels) > 0:
|
||||
dominant = [pixels.mean(axis=0).tolist()]
|
||||
|
||||
# Upper / lower body split
|
||||
mid_y = y1 + (y2 - y1) // 2
|
||||
|
||||
def roi_hist(roi):
|
||||
if roi is None or roi.size == 0:
|
||||
return None
|
||||
hsv_r = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
|
||||
hh = cv2.calcHist([hsv_r], [0], None, [30], [0, 180]).flatten()
|
||||
sh = cv2.calcHist([hsv_r], [1], None, [32], [0, 256]).flatten()
|
||||
vh = cv2.calcHist([hsv_r], [2], None, [32], [0, 256]).flatten()
|
||||
hs = hh.sum() or 1
|
||||
ss = sh.sum() or 1
|
||||
vs = vh.sum() or 1
|
||||
return [(hh / hs).tolist(), (sh / ss).tolist(), (vh / vs).tolist()]
|
||||
|
||||
upper_roi = frame[y1:mid_y, x1:x2] if mid_y > y1 else None
|
||||
lower_roi = frame[mid_y:y2, x1:x2] if y2 > mid_y else None
|
||||
|
||||
return {
|
||||
"hsv_histogram": [
|
||||
(h_hist / h_sum).tolist(),
|
||||
(s_hist / s_sum).tolist(),
|
||||
(v_hist / v_sum).tolist(),
|
||||
],
|
||||
"hsv_histogram": [(h_hist / hs).tolist(), (s_hist / ss).tolist(), (v_hist / vs).tolist()],
|
||||
"dominant_colors": dominant,
|
||||
"upper_body": roi_hist(upper_roi),
|
||||
"lower_body": roi_hist(lower_roi),
|
||||
}
|
||||
|
||||
|
||||
def safe_roi(frame, x, y, w, h):
|
||||
"""Extract a safe ROI, returning None if invalid"""
|
||||
if w <= 0 or h <= 0:
|
||||
return None
|
||||
x1 = max(0, int(x))
|
||||
y1 = max(0, int(y))
|
||||
x2 = min(frame.shape[1], int(x + w))
|
||||
y2 = min(frame.shape[0], int(y + h))
|
||||
if x2 <= x1 or y2 <= y1:
|
||||
return None
|
||||
return frame[y1:y2, x1:x2]
|
||||
|
||||
|
||||
def compute_body_regions(keypoints, face_bbox, frame_shape):
|
||||
"""Use face bbox for size, pose keypoints for alignment"""
|
||||
h, w = frame_shape[:2]
|
||||
|
||||
fx, fy, fw, fh = face_bbox["x"], face_bbox["y"], face_bbox["width"], face_bbox["height"]
|
||||
face_cx = fx + fw / 2
|
||||
|
||||
nose = get_kp(keypoints, "nose")
|
||||
ls = get_kp(keypoints, "left_shoulder")
|
||||
rs = get_kp(keypoints, "right_shoulder")
|
||||
lw = get_kp(keypoints, "left_wrist")
|
||||
rw = get_kp(keypoints, "right_wrist")
|
||||
lh = get_kp(keypoints, "left_hip")
|
||||
rh = get_kp(keypoints, "right_hip")
|
||||
la = get_kp(keypoints, "left_ankle")
|
||||
ra = get_kp(keypoints, "right_ankle")
|
||||
|
||||
kp_nose = (nose[0], nose[1]) if nose else (face_cx, fy + fh * 0.5)
|
||||
kp_sh_l = ls[0] if ls else (face_cx - fw * 1.5)
|
||||
kp_sh_r = rs[0] if rs else (face_cx + fw * 1.5)
|
||||
kp_sh_mid_x = (kp_sh_l + kp_sh_r) / 2
|
||||
kp_sh_mid_y = ((ls[1] + rs[1]) / 2) if (ls and rs) else (fy + fh + fh * 0.3)
|
||||
kp_hip_y = ((lh[1] + rh[1]) / 2) if (lh and rh) else (kp_sh_mid_y + fw * 2.0)
|
||||
kp_hip_l = lh[0] if lh else (kp_sh_mid_x - fw * 1.2)
|
||||
kp_hip_r = rh[0] if rh else (kp_sh_mid_x + fw * 1.2)
|
||||
|
||||
regions = {}
|
||||
|
||||
# head: nose-aligned, face-proportional
|
||||
head_w = fw * 1.6
|
||||
head_h = fh * 1.5
|
||||
regions["head"] = {
|
||||
"x": kp_nose[0] - head_w / 2,
|
||||
"y": kp_nose[1] - head_h * 0.5,
|
||||
"width": head_w,
|
||||
"height": head_h,
|
||||
}
|
||||
|
||||
# neck: nose-to-shoulder, face-width
|
||||
neck_w = fw * 1.5
|
||||
regions["neck"] = {
|
||||
"x": kp_sh_mid_x - neck_w / 2,
|
||||
"y": kp_nose[1] + fh * 0.4,
|
||||
"width": neck_w,
|
||||
"height": max(kp_sh_mid_y - kp_nose[1] - fh * 0.4, fh * 0.3),
|
||||
}
|
||||
|
||||
# upper body: shoulder-aligned
|
||||
ub_w = max(abs(kp_sh_r - kp_sh_l) * 1.3, fw * 3.0)
|
||||
ub_h = fh * 3.0
|
||||
regions["front_upper_body"] = {
|
||||
"x": kp_sh_mid_x - ub_w / 2,
|
||||
"y": kp_sh_mid_y,
|
||||
"width": ub_w,
|
||||
"height": ub_h,
|
||||
}
|
||||
regions["back_upper_body"] = dict(regions["front_upper_body"])
|
||||
|
||||
# lower body: hip-aligned
|
||||
lb_w = max(abs(kp_hip_r - kp_hip_l) * 1.3, fw * 3.5)
|
||||
lb_h = fh * 3.0
|
||||
regions["front_lower_body"] = {
|
||||
"x": kp_sh_mid_x - lb_w / 2,
|
||||
"y": kp_hip_y,
|
||||
"width": lb_w,
|
||||
"height": lb_h,
|
||||
}
|
||||
regions["back_lower_body"] = dict(regions["front_lower_body"])
|
||||
|
||||
# hands: wrist-aligned
|
||||
hs = fw * 1.0
|
||||
if lw and lw[2] > 0.3:
|
||||
regions["left_hand"] = {"x": lw[0] - hs / 2, "y": lw[1] - hs / 2, "width": hs, "height": hs}
|
||||
else:
|
||||
regions["left_hand"] = {"x": kp_sh_l - hs, "y": kp_sh_mid_y + fh * 0.5, "width": hs, "height": hs}
|
||||
if rw and rw[2] > 0.3:
|
||||
regions["right_hand"] = {"x": rw[0] - hs / 2, "y": rw[1] - hs / 2, "width": hs, "height": hs}
|
||||
else:
|
||||
regions["right_hand"] = {"x": kp_sh_r, "y": kp_sh_mid_y + fh * 0.5, "width": hs, "height": hs}
|
||||
|
||||
# feet: ankle-aligned
|
||||
fs = fw * 1.0
|
||||
if la and la[2] > 0.3:
|
||||
regions["left_foot"] = {"x": la[0] - fs / 2, "y": la[1], "width": fs, "height": fs * 0.75}
|
||||
else:
|
||||
regions["left_foot"] = {"x": kp_sh_mid_x - fw * 1.0, "y": kp_hip_y + fh * 2.5, "width": fs, "height": fs * 0.75}
|
||||
if ra and ra[2] > 0.3:
|
||||
regions["right_foot"] = {"x": ra[0] - fs / 2, "y": ra[1], "width": fs, "height": fs * 0.75}
|
||||
else:
|
||||
regions["right_foot"] = {"x": kp_sh_mid_x + fw * 1.0 - fs, "y": kp_hip_y + fh * 2.5, "width": fs, "height": fs * 0.75}
|
||||
|
||||
# Extrapolate each bbox outward
|
||||
expanded = {}
|
||||
margins = {
|
||||
"head": 0.10, "neck": 0.15,
|
||||
"front_upper_body": 0.20, "back_upper_body": 0.20,
|
||||
"front_lower_body": 0.15, "back_lower_body": 0.15,
|
||||
"left_hand": 0.25, "right_hand": 0.25,
|
||||
"left_foot": 0.20, "right_foot": 0.20,
|
||||
}
|
||||
for name, rb in regions.items():
|
||||
m = margins.get(name, 0.15)
|
||||
dx = int(rb["width"] * m)
|
||||
dy = int(rb["height"] * m)
|
||||
expanded[name] = {
|
||||
"x": rb["x"] - dx,
|
||||
"y": rb["y"] - dy,
|
||||
"width": rb["width"] + dx * 2,
|
||||
"height": rb["height"] + dy * 2,
|
||||
}
|
||||
return expanded
|
||||
|
||||
|
||||
def filter_by_facing(regions, facing):
|
||||
if facing == "front":
|
||||
regions.pop("back_upper_body", None)
|
||||
regions.pop("back_lower_body", None)
|
||||
elif facing == "back":
|
||||
regions.pop("front_upper_body", None)
|
||||
regions.pop("front_lower_body", None)
|
||||
return regions
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="Appearance Processor")
|
||||
parser.add_argument("video_path", help="Video file path")
|
||||
parser.add_argument("pose_json", help="Pose JSON path (bbox input)")
|
||||
parser.add_argument("output_path", help="Output JSON path")
|
||||
parser.add_argument("video_path")
|
||||
parser.add_argument("pose_json")
|
||||
parser.add_argument("output_path")
|
||||
parser.add_argument("--uuid", "-u", default="")
|
||||
args = parser.parse_args()
|
||||
|
||||
with open(args.pose_json) as f:
|
||||
pose_data = json.load(f)
|
||||
|
||||
# Load face.json for anchor bbox (same directory as pose_json)
|
||||
face_path = args.pose_json.replace(".pose.json", ".face.json")
|
||||
face_data = {}
|
||||
if os.path.exists(face_path):
|
||||
with open(face_path) as f:
|
||||
face_data = json.load(f)
|
||||
# Build frame -> face bbox lookup
|
||||
face_by_frame = {}
|
||||
for fframe in face_data.get("frames", []):
|
||||
fn = fframe.get("frame")
|
||||
faces = fframe.get("faces", [])
|
||||
if faces:
|
||||
face_by_frame[fn] = faces[0] # first face bbox
|
||||
|
||||
fps = pose_data.get("fps", 30.0)
|
||||
|
||||
cap = cv2.VideoCapture(args.video_path)
|
||||
@@ -115,38 +258,58 @@ def main():
|
||||
if not ret:
|
||||
continue
|
||||
|
||||
# Get face bbox for this frame
|
||||
face_bbox = face_by_frame.get(frame_num, persons[0].get("bbox", {"x": 0, "y": 0, "width": 0, "height": 0}))
|
||||
|
||||
frame_persons = []
|
||||
for pid, person in enumerate(persons):
|
||||
keypoints = person.get("keypoints", [])
|
||||
bbox = person.get("bbox", {})
|
||||
if bbox.get("width", 0) <= 0 or bbox.get("height", 0) <= 0:
|
||||
if not keypoints:
|
||||
continue
|
||||
appearance = extract_appearance(frame, bbox)
|
||||
if appearance is None:
|
||||
continue
|
||||
frame_persons.append(
|
||||
{
|
||||
"person_id": pid,
|
||||
"bbox": bbox,
|
||||
**appearance,
|
||||
}
|
||||
)
|
||||
|
||||
facing = determine_facing(keypoints)
|
||||
all_regions = compute_body_regions(keypoints, face_bbox, frame.shape)
|
||||
regions = filter_by_facing(all_regions, facing)
|
||||
|
||||
body_parts = []
|
||||
for name, rb in regions.items():
|
||||
roi = safe_roi(frame, rb["x"], rb["y"], rb["width"], rb["height"])
|
||||
color = extract_color(roi)
|
||||
if color is None:
|
||||
continue
|
||||
body_parts.append({
|
||||
"name": name,
|
||||
"bbox": rb,
|
||||
"hsv_histogram": color["hsv_histogram"],
|
||||
"dominant_colors": color["dominant_colors"],
|
||||
})
|
||||
|
||||
# Full bbox reference colors
|
||||
full = None
|
||||
if bbox.get("width", 0) > 0 and bbox.get("height", 0) > 0:
|
||||
full_roi = safe_roi(frame, bbox["x"], bbox["y"], bbox["width"], bbox["height"])
|
||||
full = extract_color(full_roi)
|
||||
|
||||
frame_persons.append({
|
||||
"person_id": pid,
|
||||
"bbox": bbox,
|
||||
"facing": facing,
|
||||
"body_parts": body_parts,
|
||||
"dominant_colors": full["dominant_colors"] if full else [],
|
||||
"hsv_histogram": full["hsv_histogram"] if full else [[], [], []],
|
||||
})
|
||||
|
||||
if frame_persons:
|
||||
frames_out.append(
|
||||
{
|
||||
"frame": frame_num,
|
||||
"timestamp": pose_frame.get("timestamp", frame_num / fps),
|
||||
"persons": frame_persons,
|
||||
}
|
||||
)
|
||||
frames_out.append({
|
||||
"frame": frame_num,
|
||||
"timestamp": pose_frame.get("timestamp", frame_num / fps),
|
||||
"persons": frame_persons,
|
||||
})
|
||||
|
||||
cap.release()
|
||||
|
||||
output = {
|
||||
"frame_count": len(frames_out),
|
||||
"fps": fps,
|
||||
"frames": frames_out,
|
||||
}
|
||||
output = {"frame_count": len(frames_out), "fps": fps, "frames": frames_out}
|
||||
with open(args.output_path, "w") as f:
|
||||
json.dump(output, f, indent=2, ensure_ascii=False)
|
||||
|
||||
|
||||
+37
-15
@@ -201,7 +201,12 @@ def run_asr(video_path, output_path, uuid: str = "", fps: float = None):
|
||||
if not has_audio_stream(video_path):
|
||||
if publisher:
|
||||
publisher.info("asr", "No audio stream detected, skipping transcription")
|
||||
output = {"language": "", "language_probability": 0.0, "segments": []}
|
||||
output = {
|
||||
"status": "no_audio_track",
|
||||
"language": "",
|
||||
"language_probability": 0.0,
|
||||
"segments": []
|
||||
}
|
||||
with open(output_path, "w") as f:
|
||||
json.dump(output, f, indent=2)
|
||||
if publisher:
|
||||
@@ -336,16 +341,16 @@ def run_asr(video_path, output_path, uuid: str = "", fps: float = None):
|
||||
seg_start = start_t + segment.start
|
||||
seg_end = start_t + segment.end
|
||||
scene_idx = find_scene_idx((seg_start + seg_end) / 2)
|
||||
scene_segments.append({
|
||||
"start_time": seg_start,
|
||||
"end_time": seg_end,
|
||||
"start_frame": int(round(seg_start * fps)),
|
||||
"end_frame": int(round(seg_end * fps)),
|
||||
"text": segment.text.strip(),
|
||||
"scene_number": scene_idx + 1,
|
||||
"language": seg_language,
|
||||
})
|
||||
total_segments += 1
|
||||
scene_segments.append({
|
||||
"start_time": seg_start,
|
||||
"end_time": seg_end,
|
||||
"start_frame": int(round(seg_start * fps)),
|
||||
"end_frame": int(round(seg_end * fps)),
|
||||
"text": segment.text.strip(),
|
||||
"scene_number": scene_idx + 1,
|
||||
"language": seg_language,
|
||||
})
|
||||
total_segments += 1
|
||||
|
||||
# 當前 scene 結果寫入 .asr.tmp
|
||||
all_segments.extend(scene_segments)
|
||||
@@ -365,8 +370,18 @@ def run_asr(video_path, output_path, uuid: str = "", fps: float = None):
|
||||
try: os.rmdir(temp_dir)
|
||||
except: pass
|
||||
|
||||
# Determine status for cut_scenes branch
|
||||
if total_segments > 0:
|
||||
status = "has_transcript"
|
||||
else:
|
||||
status = "silent_audio"
|
||||
|
||||
info_language = transcript_language or "unknown"
|
||||
print(f"[ASR] Segmented transcription complete: {total_segments} segments", file=sys.stderr)
|
||||
print(f"[ASR] Segmented transcription complete: {total_segments} segments, status={status}", file=sys.stderr)
|
||||
|
||||
# Write final output with status
|
||||
with open(tmp_path, "w") as f:
|
||||
json.dump({"status": status, "language": info_language, "segments": all_segments}, f)
|
||||
else:
|
||||
# 無 CUT 資料，直接轉錄（原有流程）
|
||||
segments, info = transcribe_with_fallback(model, video_path, publisher)
|
||||
@@ -386,8 +401,15 @@ def run_asr(video_path, output_path, uuid: str = "", fps: float = None):
|
||||
if total_segments % 100 == 0:
|
||||
if publisher:
|
||||
publisher.progress("asr", total_segments, 0, f"Segment {total_segments}")
|
||||
|
||||
# Determine status for direct transcription branch
|
||||
if total_segments > 0:
|
||||
status = "has_transcript"
|
||||
else:
|
||||
status = "silent_audio"
|
||||
|
||||
with open(tmp_path, "w") as f:
|
||||
json.dump({"language": info_language, "segments": all_segments}, f)
|
||||
json.dump({"status": status, "language": info_language, "segments": all_segments}, f)
|
||||
|
||||
if publisher:
|
||||
publisher.info("asr", f"ASR_LANGUAGE:{info_language}")
|
||||
@@ -396,10 +418,10 @@ def run_asr(video_path, output_path, uuid: str = "", fps: float = None):
|
||||
os.rename(tmp_path, output_path)
|
||||
|
||||
if publisher:
|
||||
publisher.complete("asr", f"{len(results)} segments")
|
||||
publisher.complete("asr", f"{total_segments} segments")
|
||||
|
||||
sys.stderr.write(
|
||||
f"ASR: Transcription complete, {len(results)} segments written to {output_path}\n"
|
||||
f"ASR: Transcription complete, {total_segments} segments written to {output_path}\n"
|
||||
)
|
||||
sys.stderr.flush()
|
||||
sys.exit(0)
|
||||
|
||||
@@ -126,9 +126,17 @@ def _convert_result(result, output_path):
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
segment_count = len(result.get("segments", []))
|
||||
if segment_count > 0:
|
||||
status = "has_transcript"
|
||||
else:
|
||||
status = "silent_audio"
|
||||
|
||||
output_result = {
|
||||
"status": status,
|
||||
"language": result.get("language"),
|
||||
"segments": [],
|
||||
"segment_count": segment_count,
|
||||
"n_speakers": result.get("n_speakers", 0),
|
||||
"speaker_stats": result.get("speaker_stats", {}),
|
||||
}
|
||||
@@ -172,6 +180,37 @@ def process_asrx(video_path: str, output_path: str, uuid: str = "",
|
||||
if publisher:
|
||||
publisher.info("asrx", "ASRX_START")
|
||||
|
||||
# Check for audio stream first
|
||||
tracks = probe_audio_tracks(video_path)
|
||||
if not tracks:
|
||||
if publisher:
|
||||
publisher.info("asrx", "No audio stream detected")
|
||||
output_result = {"status": "no_audio_track", "language": None, "segments": [], "segment_count": 0}
|
||||
_atomic_write(output_path, output_result)
|
||||
if publisher:
|
||||
publisher.complete("asrx", "0 segments (no audio)")
|
||||
print("[ASRX] No audio stream, skipping", file=sys.stderr)
|
||||
return output_result
|
||||
|
||||
# Check if ASR already determined no audio/silent - skip processing
|
||||
asr_path = output_path.replace(".asrx.json", ".asr.json")
|
||||
if os.path.exists(asr_path):
|
||||
try:
|
||||
with open(asr_path) as f:
|
||||
asr_data = json.load(f)
|
||||
asr_status = asr_data.get("status", "")
|
||||
if asr_status in ("no_audio_track", "silent_audio"):
|
||||
if publisher:
|
||||
publisher.info("asrx", f"ASR status={asr_status}, skipping ASRX processing")
|
||||
output_result = {"status": asr_status, "language": asr_data.get("language"), "segments": [], "segment_count": 0}
|
||||
_atomic_write(output_path, output_result)
|
||||
if publisher:
|
||||
publisher.complete("asrx", f"0 segments (ASR: {asr_status})")
|
||||
print(f"[ASRX] ASR status={asr_status}, skipping", file=sys.stderr)
|
||||
return output_result
|
||||
except Exception as e:
|
||||
print(f"[ASRX] Failed to read ASR output: {e}", file=sys.stderr)
|
||||
|
||||
checkpoint_path = output_path + ".stage1.json"
|
||||
|
||||
# ── Phase 2: Resume from checkpoint (Steps 4-7 only) ──
|
||||
@@ -189,7 +228,7 @@ def process_asrx(video_path: str, output_path: str, uuid: str = "",
|
||||
if "error" in result:
|
||||
if publisher:
|
||||
publisher.error("asrx", result["error"])
|
||||
output_result = {"language": None, "segments": []}
|
||||
output_result = {"status": "silent_audio", "language": None, "segments": [], "segment_count": 0}
|
||||
_atomic_write(output_path, output_result)
|
||||
if publisher:
|
||||
publisher.complete("asrx", "0 segments")
|
||||
@@ -225,7 +264,7 @@ def process_asrx(video_path: str, output_path: str, uuid: str = "",
|
||||
publisher.error("asrx", str(e))
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
output_result = {"language": None, "segments": []}
|
||||
output_result = {"status": "silent_audio", "language": None, "segments": [], "segment_count": 0}
|
||||
_atomic_write(output_path, output_result)
|
||||
if publisher:
|
||||
publisher.complete("asrx", "0 segments")
|
||||
@@ -289,7 +328,7 @@ def process_asrx(video_path: str, output_path: str, uuid: str = "",
|
||||
if "error" in result:
|
||||
if publisher:
|
||||
publisher.error("asrx", result["error"])
|
||||
output_result = {"language": None, "segments": []}
|
||||
output_result = {"status": "silent_audio", "language": None, "segments": [], "segment_count": 0}
|
||||
_atomic_write(output_path, output_result)
|
||||
if publisher:
|
||||
publisher.complete("asrx", "0 segments")
|
||||
@@ -320,7 +359,7 @@ def process_asrx(video_path: str, output_path: str, uuid: str = "",
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
|
||||
output_result = {"language": None, "segments": []}
|
||||
output_result = {"status": "silent_audio", "language": None, "segments": [], "segment_count": 0}
|
||||
_atomic_write(output_path, output_result)
|
||||
if publisher:
|
||||
publisher.complete("asrx", "0 segments")
|
||||
|
||||
@@ -216,19 +216,27 @@ class SelfASRXFixed:
|
||||
return {"error": "No speech detected", "segments": []}
|
||||
|
||||
# ── Step 2: VAD scan 每個 rough segment 細切 ──
|
||||
print("\n[Step 2] VAD scan for refined segmentation...")
|
||||
t2 = time.time()
|
||||
refined_segments = []
|
||||
for seg in rough_segments:
|
||||
s = seg["start"]
|
||||
e = seg["end"]
|
||||
sub = self._vad_scan_segment(wav, sample_rate, s, e)
|
||||
if sub:
|
||||
refined_segments.extend(sub)
|
||||
else:
|
||||
refined_segments.append((s, e))
|
||||
print(f" Refined segments: {len(refined_segments)}")
|
||||
print(f" Step 2 time: {time.time() - t2:.2f}s")
|
||||
# Skip VAD if using ASR segments (preserve all ASR segments)
|
||||
if asr_segments:
|
||||
print("\n[Step 2] Skipping VAD scan, using ASR segments directly...")
|
||||
t2 = time.time()
|
||||
refined_segments = [(seg["start"], seg["end"]) for seg in rough_segments]
|
||||
print(f" Refined segments: {len(refined_segments)}")
|
||||
print(f" Step 2 time: {time.time() - t2:.2f}s")
|
||||
else:
|
||||
print("\n[Step 2] VAD scan for refined segmentation...")
|
||||
t2 = time.time()
|
||||
refined_segments = []
|
||||
for seg in rough_segments:
|
||||
s = seg["start"]
|
||||
e = seg["end"]
|
||||
sub = self._vad_scan_segment(wav, sample_rate, s, e)
|
||||
if sub:
|
||||
refined_segments.extend(sub)
|
||||
else:
|
||||
refined_segments.append((s, e))
|
||||
print(f" Refined segments: {len(refined_segments)}")
|
||||
print(f" Step 2 time: {time.time() - t2:.2f}s")
|
||||
|
||||
if not refined_segments:
|
||||
return {"error": "No segments after VAD scan", "segments": []}
|
||||
|
||||
+124
-63
@@ -1,91 +1,152 @@
|
||||
#!/opt/homebrew/bin/python3.11
|
||||
"""
|
||||
CUT Processor - Scene Detection
|
||||
Uses PySceneDetect for scene detection (local)
|
||||
CUT Processor - Scene Detection & Video Quality Check
|
||||
Uses ffprobe for video analysis. Always produces at least 1 scene.
|
||||
"""
|
||||
|
||||
import sys
|
||||
import json
|
||||
import argparse
|
||||
import os
|
||||
import subprocess
|
||||
import sys
|
||||
|
||||
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
|
||||
from redis_publisher import RedisPublisher
|
||||
|
||||
|
||||
def get_video_info(video_path: str) -> dict:
|
||||
"""Get video info via ffprobe"""
|
||||
try:
|
||||
result = subprocess.run(
|
||||
["ffprobe", "-v", "quiet", "-print_format", "json",
|
||||
"-show_format", "-show_streams", video_path],
|
||||
capture_output=True, text=True, timeout=30,
|
||||
)
|
||||
info = json.loads(result.stdout)
|
||||
for stream in info.get("streams", []):
|
||||
if stream.get("codec_type") == "video":
|
||||
nb_frames = stream.get("nb_frames")
|
||||
if nb_frames:
|
||||
fr = stream.get("r_frame_rate", "0/1")
|
||||
fps = eval(fr) if "/" in fr else float(fr)
|
||||
return {
|
||||
"frame_count": int(nb_frames),
|
||||
"fps": fps,
|
||||
"duration": float(stream.get("duration", 0)),
|
||||
"width": int(stream.get("width", 0)),
|
||||
"height": int(stream.get("height", 0)),
|
||||
"codec": stream.get("codec_name", ""),
|
||||
}
|
||||
dur = float(stream.get("duration", 0))
|
||||
afr = stream.get("avg_frame_rate", "0/1")
|
||||
avg_fps = eval(afr) if "/" in afr else float(afr)
|
||||
if dur > 0 and avg_fps > 0:
|
||||
return {
|
||||
"frame_count": int(dur * avg_fps),
|
||||
"fps": avg_fps,
|
||||
"duration": dur,
|
||||
"width": int(stream.get("width", 0)),
|
||||
"height": int(stream.get("height", 0)),
|
||||
"codec": stream.get("codec_name", ""),
|
||||
}
|
||||
return {
|
||||
"frame_count": 0, "fps": 0.0, "duration": dur,
|
||||
"width": 0, "height": 0, "codec": "",
|
||||
}
|
||||
return {"frame_count": 0, "fps": 0.0, "duration": 0, "width": 0, "height": 0, "codec": ""}
|
||||
except Exception:
|
||||
return {"frame_count": 0, "fps": 0.0, "duration": 0, "width": 0, "height": 0, "codec": ""}
|
||||
|
||||
|
||||
def detect_scenes_ffmpeg(video_path: str, fps: float, duration: float) -> list:
|
||||
"""Detect scene changes using ffmpeg scene filter"""
|
||||
try:
|
||||
result = subprocess.run(
|
||||
["ffprobe", "-v", "quiet", "-show_entries", "frame=pts_time",
|
||||
"-of", "default=nk=0",
|
||||
"-f", "lavfi",
|
||||
f"movie={video_path},select='gt(scene\\,0.3)',showinfo",
|
||||
"-show_frames"],
|
||||
capture_output=True, text=True, timeout=300,
|
||||
)
|
||||
times = []
|
||||
for line in (result.stderr + "\n" + result.stdout).split("\n"):
|
||||
for prefix in ("pts_time=", "pts_time:"):
|
||||
if prefix in line:
|
||||
rest = line.split(prefix)[1].split()[0]
|
||||
try:
|
||||
t = float(rest)
|
||||
times.append(t)
|
||||
except ValueError:
|
||||
pass
|
||||
|
||||
scenes = []
|
||||
prev_time = 0.0
|
||||
for i, t in enumerate(times):
|
||||
end_frame = round(t * fps)
|
||||
start_frame = round(prev_time * fps)
|
||||
if end_frame > start_frame:
|
||||
scenes.append({
|
||||
"scene_number": i + 1,
|
||||
"start_frame": start_frame,
|
||||
"end_frame": end_frame - 1,
|
||||
"start_time": prev_time,
|
||||
"end_time": t - (1.0 / fps) if fps > 0 else t,
|
||||
})
|
||||
prev_time = t
|
||||
|
||||
last_frame = round(duration * fps) if fps > 0 else 0
|
||||
prev_frame = round(prev_time * fps) if fps > 0 else 0
|
||||
if last_frame > prev_frame:
|
||||
scenes.append({
|
||||
"scene_number": len(scenes) + 1,
|
||||
"start_frame": prev_frame,
|
||||
"end_frame": last_frame - 1,
|
||||
"start_time": prev_time,
|
||||
"end_time": duration,
|
||||
})
|
||||
|
||||
return scenes
|
||||
except Exception:
|
||||
return []
|
||||
|
||||
|
||||
def process_cut(video_path: str, output_path: str, uuid: str = ""):
|
||||
"""Process video for scene detection"""
|
||||
"""Process video for scene detection and quality verification"""
|
||||
|
||||
publisher = RedisPublisher(uuid) if uuid else None
|
||||
if publisher:
|
||||
publisher.info("cut", "CUT_START")
|
||||
|
||||
try:
|
||||
from scenedetect import VideoManager, SceneManager
|
||||
from scenedetect.detectors import ContentDetector
|
||||
except ImportError:
|
||||
if publisher:
|
||||
publisher.error("cut", "scenedetect not installed")
|
||||
result = {"frame_count": 0, "fps": 0.0, "scenes": []}
|
||||
if publisher:
|
||||
publisher.complete("cut", "0 scenes")
|
||||
with open(output_path, "w") as f:
|
||||
json.dump(result, f, indent=2)
|
||||
return result
|
||||
vinfo = get_video_info(video_path)
|
||||
|
||||
if publisher:
|
||||
publisher.info("cut", "CUT_LOADING_VIDEO")
|
||||
publisher.info("cut", f"fps={vinfo['fps']}, frames={vinfo['frame_count']}, codec={vinfo['codec']}")
|
||||
|
||||
# Create video manager and scene manager
|
||||
video_manager = VideoManager([video_path])
|
||||
scene_manager = SceneManager()
|
||||
total_frames = vinfo["frame_count"]
|
||||
fps = vinfo["fps"]
|
||||
duration = vinfo["duration"]
|
||||
|
||||
# Add content detector (detects scene cuts based on frame differences)
|
||||
# threshold: sensitivity (lower = more sensitive, default 30)
|
||||
# min_scene_len: minimum frames per scene (default 15)
|
||||
scene_manager.add_detector(ContentDetector(threshold=30.0, min_scene_len=15))
|
||||
# Try ffmpeg scene detection
|
||||
scenes = detect_scenes_ffmpeg(video_path, fps, duration)
|
||||
|
||||
# Set downscale factor for faster processing
|
||||
video_manager.set_downscale_factor()
|
||||
|
||||
if publisher:
|
||||
publisher.info("cut", "CUT_DETECTING")
|
||||
|
||||
# Start video manager
|
||||
video_manager.start()
|
||||
|
||||
# Detect scenes
|
||||
scene_manager.detect_scenes(frame_source=video_manager)
|
||||
|
||||
# Get scene list
|
||||
scene_list = scene_manager.get_scene_list()
|
||||
|
||||
# Get frame rate
|
||||
fps = video_manager.get_framerate()
|
||||
|
||||
if publisher:
|
||||
publisher.info("cut", f"fps={fps}")
|
||||
|
||||
# Get total frame count
|
||||
frame_count = 0
|
||||
if scene_list:
|
||||
frame_count = scene_list[-1][1].get_frames()
|
||||
|
||||
# Convert scenes to result format
|
||||
scenes = []
|
||||
for i, (start, end) in enumerate(scene_list):
|
||||
scene = {
|
||||
"scene_number": i + 1,
|
||||
"start_frame": start.get_frames(),
|
||||
"end_frame": end.get_frames() - 1, # end is exclusive
|
||||
"start_time": start.get_seconds(),
|
||||
"end_time": end.get_seconds() - (1.0 / fps) if fps > 0 else 0,
|
||||
}
|
||||
scenes.append(scene)
|
||||
# Always ensure at least 1 scene
|
||||
if not scenes and total_frames > 0:
|
||||
scenes = [{
|
||||
"scene_number": 1,
|
||||
"start_frame": 0,
|
||||
"end_frame": total_frames - 1,
|
||||
"start_time": 0.0,
|
||||
"end_time": duration,
|
||||
}]
|
||||
if publisher:
|
||||
publisher.progress("cut", i + 1, len(scene_list), f"Scene {i + 1}")
|
||||
publisher.info("cut", "No scene changes detected, using whole video as single scene")
|
||||
|
||||
result = {"frame_count": frame_count, "fps": fps, "scenes": scenes}
|
||||
result = {
|
||||
"frame_count": total_frames,
|
||||
"fps": fps,
|
||||
"scenes": scenes,
|
||||
}
|
||||
|
||||
with open(output_path, "w") as f:
|
||||
json.dump(result, f, indent=2)
|
||||
|
||||
@@ -14,13 +14,9 @@ from sklearn.cluster import AgglomerativeClustering
|
||||
|
||||
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
|
||||
|
||||
try:
|
||||
from deepface import DeepFace
|
||||
|
||||
HAS_DEEPFACE = True
|
||||
except ImportError:
|
||||
print("❌ DeepFace not found. Run: pip install deepface")
|
||||
sys.exit(1)
|
||||
# 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")
|
||||
@@ -104,53 +100,69 @@ def main():
|
||||
print("❌ No frames in JSON.")
|
||||
return
|
||||
|
||||
cap = cv2.VideoCapture(VIDEO_PATH)
|
||||
# Get embeddings from Qdrant
|
||||
print(f"[FACE_CLUSTER] Loading embeddings from Qdrant for {UUID}...")
|
||||
try:
|
||||
import requests
|
||||
qdrant_url = "http://localhost:6333"
|
||||
collection = "_faces"
|
||||
|
||||
# Query all embeddings for this file_uuid
|
||||
response = requests.post(
|
||||
f"{qdrant_url}/collections/{collection}/points/scroll",
|
||||
json={
|
||||
"filter": {
|
||||
"must": [
|
||||
{"key": "file_uuid", "match": {"value": UUID}}
|
||||
]
|
||||
},
|
||||
"limit": 10000,
|
||||
"with_vector": True
|
||||
}
|
||||
)
|
||||
|
||||
if response.status_code == 200:
|
||||
result = response.json()
|
||||
points = result.get("result", {}).get("points", [])
|
||||
print(f"[FACE_CLUSTER] Loaded {len(points)} embeddings from Qdrant")
|
||||
|
||||
# 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 = {}
|
||||
except Exception as e:
|
||||
print(f"[FACE_CLUSTER] Failed to load embeddings from Qdrant: {e}")
|
||||
embedding_map = {}
|
||||
|
||||
# Use embeddings from Qdrant or face.json
|
||||
embeddings = []
|
||||
face_refs = []
|
||||
|
||||
print(f"🔍 Extracting face embeddings from {UUID}...")
|
||||
print(f"🔍 Collecting face embeddings for {UUID}...")
|
||||
|
||||
for frame_idx, frame_obj in enumerate(frames_list):
|
||||
ts = frame_obj.get("timestamp")
|
||||
faces = frame_obj.get("faces", [])
|
||||
if not faces:
|
||||
continue
|
||||
|
||||
if ts is not None:
|
||||
cap.set(cv2.CAP_PROP_POS_MSEC, ts * 1000)
|
||||
|
||||
ret, frame = cap.read()
|
||||
if not ret:
|
||||
continue
|
||||
|
||||
for face_idx, face in enumerate(faces):
|
||||
x, y, w, h = face["x"], face["y"], face["width"], face["height"]
|
||||
margin = 5
|
||||
crop = frame[
|
||||
max(0, y - margin) : y + h + margin, max(0, x - margin) : x + w + margin
|
||||
]
|
||||
|
||||
if crop is None or crop.size == 0:
|
||||
continue
|
||||
|
||||
try:
|
||||
res = DeepFace.represent(
|
||||
img_path=crop, model_name="ArcFace", enforce_detection=False
|
||||
)
|
||||
if res and "embedding" in res[0]:
|
||||
embeddings.append(res[0]["embedding"])
|
||||
face_refs.append({"frame_idx": frame_idx, "face_idx": face_idx})
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
cap.release()
|
||||
face_id = face.get("face_id")
|
||||
if face_id and face_id in embedding_map:
|
||||
embeddings.append(embedding_map[face_id])
|
||||
face_refs.append({"frame_idx": frame_idx, "face_idx": face_idx, "face_id": face_id})
|
||||
|
||||
if not embeddings:
|
||||
print("❌ No embeddings extracted.")
|
||||
print("❌ No embeddings found in Qdrant.")
|
||||
return
|
||||
|
||||
embeddings = np.array(embeddings)
|
||||
print(f"✅ Extracted {len(embeddings)} face embeddings.")
|
||||
print(f"✅ Collected {len(embeddings)} face embeddings from Qdrant.")
|
||||
|
||||
# 2. 聚類
|
||||
print(f"🧠 Clustering {len(embeddings)} faces...")
|
||||
|
||||
@@ -35,7 +35,7 @@ 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", "debug", "swift_face_pose")
|
||||
SWIFT_BIN = os.path.join(SCRIPT_DIR, "swift_processors", ".build", "release", "swift_face_pose")
|
||||
FACENET_PATH = os.path.join(SCRIPT_DIR, "..", "models", "facenet512.mlpackage")
|
||||
|
||||
# Pose angle classification from roll/yaw
|
||||
@@ -84,7 +84,12 @@ class FaceProcessorVision:
|
||||
self.total_frames = int(self.video.get(cv2.CAP_PROP_FRAME_COUNT))
|
||||
self.width = int(self.video.get(cv2.CAP_PROP_FRAME_WIDTH))
|
||||
self.height = int(self.video.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
||||
|
||||
# Calculate 8Hz sample interval based on FPS
|
||||
self.sample_interval = max(1, round(self.fps / 8))
|
||||
|
||||
print(f"[FACE_V2] Video: {self.width}x{self.height}, {self.fps:.1f}fps, {self.total_frames}f")
|
||||
print(f"[FACE_V2] 8Hz sample interval: {self.fps:.1f}/8 = {self.sample_interval}")
|
||||
|
||||
def extract_face_embedding(self, face_img: np.ndarray) -> Optional[list]:
|
||||
"""Run CoreML FaceNet on cropped face"""
|
||||
@@ -126,11 +131,15 @@ class FaceProcessorVision:
|
||||
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,
|
||||
self.video_path,
|
||||
swift_face_out,
|
||||
swift_pose_out,
|
||||
swift_appearance_out,
|
||||
"--sample-interval", str(self.sample_interval),
|
||||
]
|
||||
if self.uuid:
|
||||
@@ -286,17 +295,28 @@ class FaceProcessorVision:
|
||||
|
||||
# Convert dict frames to list for Rust FaceResult format
|
||||
frames_list = []
|
||||
total_faces = 0
|
||||
for fnum_str, fdata in sorted(face_data["frames"].items(), key=lambda x: int(x[0])):
|
||||
faces = fdata["faces"]
|
||||
total_faces += len(faces)
|
||||
frames_list.append({
|
||||
"frame": int(fnum_str),
|
||||
"timestamp": fdata["time_seconds"],
|
||||
"faces": fdata["faces"],
|
||||
"faces": faces,
|
||||
})
|
||||
|
||||
# Determine status based on face count
|
||||
if total_faces > 0:
|
||||
status = "has_faces"
|
||||
else:
|
||||
status = "no_faces"
|
||||
|
||||
output = {
|
||||
"status": status,
|
||||
"frame_count": len(frames_list),
|
||||
"fps": self.fps,
|
||||
"frames": frames_list,
|
||||
"total_faces": total_faces,
|
||||
}
|
||||
|
||||
with open(self.output_path, "w") as f:
|
||||
@@ -339,6 +359,9 @@ def main():
|
||||
args.uuid, args.sample_interval, publisher
|
||||
)
|
||||
|
||||
# Open video to get FPS and calculate sample_interval
|
||||
processor.open_video()
|
||||
|
||||
# Step 1: Vision detection (bbox + pose via ANE)
|
||||
try:
|
||||
detection = processor.process_with_swift()
|
||||
|
||||
@@ -1,334 +0,0 @@
|
||||
#!/opt/homebrew/bin/python3.11
|
||||
"""
|
||||
Fast Face Clustering Processor (Linear Scan)
|
||||
職責：針對長片優化，使用線性讀取取代隨機跳轉，大幅提升速度。
|
||||
"""
|
||||
|
||||
import cv2
|
||||
import json
|
||||
import numpy as np
|
||||
import os
|
||||
import sys
|
||||
import psycopg2
|
||||
from collections import defaultdict
|
||||
|
||||
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
|
||||
|
||||
try:
|
||||
from deepface import DeepFace
|
||||
|
||||
HAS_DEEPFACE = True
|
||||
except ImportError:
|
||||
print("❌ DeepFace not found.")
|
||||
sys.exit(1)
|
||||
|
||||
from sklearn.cluster import AgglomerativeClustering
|
||||
|
||||
# 設定
|
||||
UUID = os.getenv("UUID", "384b0ff44aaaa1f1")
|
||||
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")
|
||||
DB_URL = os.getenv("DATABASE_URL", "postgresql://accusys@localhost:5432/momentry")
|
||||
|
||||
|
||||
def main():
|
||||
if not os.path.exists(FACE_JSON_PATH):
|
||||
print(f"❌ Face JSON not found: {FACE_JSON_PATH}")
|
||||
return
|
||||
|
||||
print(f"⚡ 開始執行快速面孔聚類 (Linear Scan Mode) for {UUID}...")
|
||||
|
||||
# 1. 載入並建立索引 (以 frame number 為 key)
|
||||
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
|
||||
|
||||
# 建立 map: frame_index -> faces
|
||||
# 注意：JSON 中的 frame 是 int，但也許是 float?
|
||||
# face_processor 輸出通常是 int
|
||||
faces_map = defaultdict(list)
|
||||
|
||||
# 為了安全，我們也建立 timestamp map 以防萬一，但優先使用 frame number
|
||||
print(f"📂 Indexing {len(frames_list)} frames with faces...")
|
||||
for frame_obj in frames_list:
|
||||
# JSON 中可能是 'frame' (int) 或 'frame_number'
|
||||
idx = frame_obj.get("frame") or frame_obj.get("frame_number")
|
||||
if idx is not None:
|
||||
faces_map[int(idx)].extend(frame_obj.get("faces", []))
|
||||
|
||||
# 如果沒有 frame number 字段，我們只能依靠 timestamp (比較慢)
|
||||
if not faces_map:
|
||||
print("⚠️ No frame numbers found in JSON. Falling back to timestamp seeking.")
|
||||
# 這裡我們可以呼叫舊的邏輯，但為了簡單，我們假設 face_processor 有寫 frame
|
||||
# 檢查第一個 frame 的 key
|
||||
if frames_list:
|
||||
print(f" Keys: {frames_list[0].keys()}")
|
||||
return # 暫時中斷
|
||||
|
||||
total_faces = sum(len(faces) for faces in faces_map.values())
|
||||
print(f"✅ Indexed {len(faces_map)} frames, containing {total_faces} faces.")
|
||||
print("🚀 Starting Linear Video Scan...")
|
||||
|
||||
# 2. 線性掃描
|
||||
video_path = VIDEO_PATH # 使用區域變數避免 global 問題
|
||||
cap = cv2.VideoCapture(video_path)
|
||||
if not cap.isOpened():
|
||||
# 嘗試找 mov
|
||||
alt_path = video_path.replace(".mp4", ".mov")
|
||||
if os.path.exists(alt_path):
|
||||
video_path = alt_path
|
||||
cap = cv2.VideoCapture(video_path)
|
||||
else:
|
||||
print("❌ Video file not found.")
|
||||
return
|
||||
|
||||
embeddings = []
|
||||
face_refs = [] # 存儲 (frame_index, face_index_in_list)
|
||||
|
||||
# 為了追蹤進度
|
||||
processed_frames = 0
|
||||
current_frame = 0
|
||||
|
||||
# 獲取影片總幀數
|
||||
total_video_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
|
||||
|
||||
while True:
|
||||
ret, frame = cap.read()
|
||||
if not ret:
|
||||
break
|
||||
|
||||
# 檢查這一幀是否有我們需要處理的臉
|
||||
# 使用 round 處理可能的浮點誤差 (雖然 face_processor 應該寫的是 int)
|
||||
# 如果 JSON 的 frame 是 0.0, 1.0...
|
||||
# 這裡我們直接看 current_frame 是否在 faces_map 中
|
||||
|
||||
# 由於 face_processor 可能跳幀，或者時間戳對齊問題
|
||||
# 我們檢查 current_frame 以及 current_frame +/- 1 的容差
|
||||
# 但最好的方式是嚴格匹配 frame number
|
||||
|
||||
if current_frame in faces_map:
|
||||
faces = faces_map[current_frame]
|
||||
for face_idx, face in enumerate(faces):
|
||||
try:
|
||||
x, y, w, h = face["x"], face["y"], face["width"], face["height"]
|
||||
margin = 5
|
||||
crop = frame[
|
||||
max(0, y - margin) : y + h + margin,
|
||||
max(0, x - margin) : x + w + margin,
|
||||
]
|
||||
|
||||
if crop is not None and crop.size > 0:
|
||||
# 使用 Fast Model: VGG-Face 或 OpenFace 比 ArcFace 快，但 ArcFace 準
|
||||
# 這裡保持 ArcFace 以求準確，但因為是線性讀取，省去了 seek 時間
|
||||
# 為了速度，我們可以每 2 秒只取 1 幀？
|
||||
# 不，我們需要標記所有幀。
|
||||
# DeepFace 提取
|
||||
res = DeepFace.represent(
|
||||
img_path=crop, model_name="ArcFace", enforce_detection=False
|
||||
)
|
||||
if res and "embedding" in res[0]:
|
||||
embeddings.append(res[0]["embedding"])
|
||||
face_refs.append(
|
||||
{"frame_idx": current_frame, "face_idx": face_idx}
|
||||
)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
processed_frames += 1
|
||||
if processed_frames % 500 == 0:
|
||||
pct = (current_frame / total_video_frames) * 100
|
||||
print(
|
||||
f" 📊 Progress: Frame {current_frame}/{total_video_frames} ({pct:.1f}%) | Extracted: {len(embeddings)} embeddings"
|
||||
)
|
||||
|
||||
current_frame += 1
|
||||
|
||||
cap.release()
|
||||
|
||||
if not embeddings:
|
||||
print("❌ No embeddings extracted.")
|
||||
return
|
||||
|
||||
embeddings = np.array(embeddings)
|
||||
print(f"✅ Total Embeddings Extracted: {len(embeddings)}")
|
||||
|
||||
# 3. 聚類
|
||||
print(f"🧠 Clustering {len(embeddings)} faces...")
|
||||
|
||||
# 優化：KMeans 或 MiniBatchKMeans 對於大數據集更快
|
||||
# 但 Agglomerative 對於找任意形狀的簇更好。
|
||||
# 25000 個點做層次聚類還是慢。
|
||||
# 我們使用 "Sample -> Cluster -> Assign" 策略
|
||||
|
||||
print(" 🚀 Using Sampling Strategy for speed...")
|
||||
sample_size = 5000
|
||||
n_faces = len(embeddings)
|
||||
|
||||
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)
|
||||
|
||||
clustering = AgglomerativeClustering(
|
||||
n_clusters=None, distance_threshold=0.45, metric="cosine", linkage="average"
|
||||
)
|
||||
sample_labels = clustering.fit_predict(sample_embeddings)
|
||||
|
||||
# 計算簇中心
|
||||
unique_labels = set(sample_labels)
|
||||
centroids = []
|
||||
for label in unique_labels:
|
||||
mask = sample_labels == label
|
||||
centroids.append(np.mean(sample_embeddings[mask], axis=0))
|
||||
centroids = np.array(centroids)
|
||||
|
||||
# 分配所有數據
|
||||
print(" 🏃 Assigning remaining faces to clusters...")
|
||||
from sklearn.metrics.pairwise import cosine_distances
|
||||
|
||||
# 批次計算
|
||||
all_labels = np.zeros(n_faces, dtype=int)
|
||||
batch_size = 10000
|
||||
for i in range(0, n_faces, batch_size):
|
||||
batch = embeddings[i : i + batch_size]
|
||||
dists = cosine_distances(batch, centroids)
|
||||
all_labels[i : i + batch_size] = np.argmin(dists, axis=1)
|
||||
|
||||
print(f" 👥 Detected {len(unique_labels)} unique persons.")
|
||||
|
||||
# 4. 生成標籤
|
||||
label_to_person = {l: f"Person_{i}" for i, l in enumerate(unique_labels)}
|
||||
|
||||
# 5. 寫回 JSON
|
||||
# face_data 是原始結構，我們需要修改它
|
||||
# face_data['frames'] 是一個列表
|
||||
# 我們需要快速找到對應的 frame
|
||||
|
||||
# 建立 map frame_idx -> frame_object reference
|
||||
frame_ref_map = {}
|
||||
for f_obj in face_data.get("frames", []):
|
||||
idx = f_obj.get("frame") or f_obj.get("frame_number")
|
||||
if idx is not None:
|
||||
frame_ref_map[int(idx)] = f_obj
|
||||
|
||||
count = 0
|
||||
for ref, label in zip(face_refs, all_labels):
|
||||
f_idx = ref["frame_idx"]
|
||||
face_idx = ref["face_idx"] # 這是原始 faces list 中的 index
|
||||
|
||||
person_id = label_to_person[label]
|
||||
|
||||
if f_idx in frame_ref_map:
|
||||
frame_obj = frame_ref_map[f_idx]
|
||||
faces_list = frame_obj.get("faces", [])
|
||||
if face_idx < len(faces_list):
|
||||
faces_list[face_idx]["person_id"] = person_id
|
||||
count += 1
|
||||
|
||||
print(f" ✅ Tagged {count} faces with Person ID.")
|
||||
|
||||
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}")
|
||||
|
||||
# 6. 綁定 Speaker
|
||||
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("person_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:
|
||||
continue
|
||||
|
||||
candidates = [f for f in face_spans if 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()
|
||||
@@ -0,0 +1 @@
|
||||
face_clustering_processor.py
|
||||
+175
-1
@@ -33,7 +33,54 @@ def process_pose(
|
||||
uuid: str = "",
|
||||
sample_interval: int = 3, # Changed from 30 to match Face
|
||||
publisher: RedisPublisher = None,
|
||||
target_frames: list = None,
|
||||
) -> dict:
|
||||
# Check if pose.json or pose.json.tmp already exists (from swift_face_pose)
|
||||
# executor.rs renames output to .json.tmp before running Python script
|
||||
tmp_path = output_path.replace('.json', '.json.tmp')
|
||||
|
||||
source_path = None
|
||||
if os.path.exists(output_path):
|
||||
source_path = output_path
|
||||
print(f"[Pose] Output exists from swift_face_pose: {output_path}", file=sys.stderr)
|
||||
elif os.path.exists(tmp_path):
|
||||
source_path = tmp_path
|
||||
print(f"[Pose] Temp output exists from swift_face_pose: {tmp_path}", file=sys.stderr)
|
||||
|
||||
if source_path:
|
||||
with open(source_path) as f:
|
||||
data = json.load(f)
|
||||
|
||||
detected_frames = len(data.get('frames', []))
|
||||
print(f"[Pose] Loaded {detected_frames} detected frames", file=sys.stderr)
|
||||
|
||||
# When target_frames is provided (8Hz sampling), skip interpolation
|
||||
# Swift already outputs at sample_interval=3, matching 8Hz for 24fps
|
||||
if target_frames is not None:
|
||||
print(f"[Pose] 8Hz mode: returning {detected_frames} frames without interpolation", file=sys.stderr)
|
||||
if publisher:
|
||||
publisher.progress("pose", 100, 100, f"{detected_frames} frames (8Hz, no interpolation)")
|
||||
return data
|
||||
|
||||
# Interpolate keypoints for all frames
|
||||
interpolated_data = interpolate_pose(data, video_path)
|
||||
|
||||
# Write interpolated output
|
||||
with open(output_path, 'w') as f:
|
||||
json.dump(interpolated_data, f)
|
||||
|
||||
# Delete .json.tmp file so executor.rs won't restore it
|
||||
if os.path.exists(tmp_path):
|
||||
os.remove(tmp_path)
|
||||
print(f"[Pose] Deleted temp file: {tmp_path}", file=sys.stderr)
|
||||
|
||||
total_frames = len(interpolated_data.get('frames', []))
|
||||
print(f"[Pose] Interpolated to {total_frames} frames", file=sys.stderr)
|
||||
|
||||
if publisher:
|
||||
publisher.progress("pose", 100, 100, f"Interpolated {total_frames} frames")
|
||||
return interpolated_data
|
||||
|
||||
swift_bin = SWIFT_POSE_PATH
|
||||
if not os.path.exists(swift_bin):
|
||||
swift_bin = SWIFT_POSE_ALT
|
||||
@@ -81,6 +128,126 @@ def process_pose(
|
||||
return json.load(f)
|
||||
|
||||
|
||||
def interpolate_pose(detected_data: dict, video_path: str) -> dict:
|
||||
"""Interpolate keypoints for all frames between detected frames"""
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
||||
cap = cv2.VideoCapture(video_path)
|
||||
total_video_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
|
||||
fps = detected_data.get('fps', 30.0)
|
||||
|
||||
detected_frames = detected_data.get('frames', [])
|
||||
if not detected_frames:
|
||||
cap.release()
|
||||
return detected_data
|
||||
|
||||
# Build frame index map
|
||||
frame_map = {f['frame']: f for f in detected_frames}
|
||||
detected_frame_nums = sorted(frame_map.keys())
|
||||
|
||||
print(f"[Pose] Interpolating from {len(detected_frame_nums)} detected frames to {total_video_frames} total frames", file=sys.stderr)
|
||||
|
||||
# Get all persons from detected frames (assume same person tracking)
|
||||
all_persons = {}
|
||||
for f in detected_frames:
|
||||
for i, p in enumerate(f.get('persons', [])):
|
||||
if i not in all_persons:
|
||||
all_persons[i] = []
|
||||
all_persons[i].append((f['frame'], p))
|
||||
|
||||
# Interpolate each person's keypoints for each frame
|
||||
interpolated_frames = []
|
||||
|
||||
for frame_num in range(total_video_frames):
|
||||
ts = frame_num / fps
|
||||
|
||||
persons_in_frame = []
|
||||
|
||||
for person_id, person_frames in all_persons.items():
|
||||
# Find closest detected frames before and after
|
||||
before = None
|
||||
after = None
|
||||
for fn, p in person_frames:
|
||||
if fn <= frame_num:
|
||||
before = (fn, p)
|
||||
if fn >= frame_num and after is None:
|
||||
after = (fn, p)
|
||||
|
||||
if before is None and after is None:
|
||||
continue
|
||||
|
||||
# Interpolate keypoints
|
||||
interpolated_keypoints = []
|
||||
bbox = None
|
||||
|
||||
if before and after and before[0] != after[0]:
|
||||
# Linear interpolation
|
||||
t0, t1 = before[0], after[0]
|
||||
t = (frame_num - t0) / (t1 - t0) if t1 != t0 else 0
|
||||
|
||||
kp_before = before[1].get('keypoints', [])
|
||||
kp_after = after[1].get('keypoints', [])
|
||||
bbox_before = before[1].get('bbox', {})
|
||||
bbox_after = after[1].get('bbox', {})
|
||||
|
||||
# Interpolate keypoints
|
||||
for i in range(max(len(kp_before), len(kp_after))):
|
||||
kp0 = kp_before[i] if i < len(kp_before) else kp_after[i]
|
||||
kp1 = kp_after[i] if i < len(kp_after) else kp_before[i]
|
||||
|
||||
x = kp0['x'] + t * (kp1['x'] - kp0['x'])
|
||||
y = kp0['y'] + t * (kp1['y'] - kp0['y'])
|
||||
c = kp0['confidence'] + t * (kp1['confidence'] - kp0['confidence'])
|
||||
|
||||
interpolated_keypoints.append({
|
||||
'name': kp0['name'],
|
||||
'x': x,
|
||||
'y': y,
|
||||
'confidence': c
|
||||
})
|
||||
|
||||
# Interpolate bbox
|
||||
if bbox_before and bbox_after:
|
||||
bbox = {
|
||||
'x': int(bbox_before['x'] + t * (bbox_after['x'] - bbox_before['x'])),
|
||||
'y': int(bbox_before['y'] + t * (bbox_after['y'] - bbox_before['y'])),
|
||||
'width': int(bbox_before['width'] + t * (bbox_after['width'] - bbox_before['width'])),
|
||||
'height': int(bbox_before['height'] + t * (bbox_after['height'] - bbox_before['height']))
|
||||
}
|
||||
|
||||
elif before:
|
||||
# Use before frame's data
|
||||
interpolated_keypoints = before[1].get('keypoints', [])
|
||||
bbox = before[1].get('bbox', {})
|
||||
|
||||
elif after:
|
||||
# Use after frame's data
|
||||
interpolated_keypoints = after[1].get('keypoints', [])
|
||||
bbox = after[1].get('bbox', {})
|
||||
|
||||
if bbox and bbox.get('width', 0) > 0 and bbox.get('height', 0) > 0:
|
||||
persons_in_frame.append({
|
||||
'keypoints': interpolated_keypoints,
|
||||
'bbox': bbox
|
||||
})
|
||||
|
||||
if persons_in_frame:
|
||||
interpolated_frames.append({
|
||||
'frame': frame_num,
|
||||
'timestamp': ts,
|
||||
'persons': persons_in_frame
|
||||
})
|
||||
|
||||
cap.release()
|
||||
|
||||
return {
|
||||
'frame_count': len(interpolated_frames),
|
||||
'fps': fps,
|
||||
'frames': interpolated_frames
|
||||
}
|
||||
|
||||
|
||||
def _fallback(video_path, output_path, uuid, sample_interval):
|
||||
"""Fallback to YOLOv8 Pose"""
|
||||
from ultralytics import YOLO
|
||||
@@ -135,14 +302,21 @@ if __name__ == "__main__":
|
||||
parser.add_argument("output_path")
|
||||
parser.add_argument("--uuid", "-u", default="")
|
||||
parser.add_argument("--sample-interval", type=int, default=3) # Changed from 30 to match Face
|
||||
parser.add_argument("--frames", type=str, default=None,
|
||||
help="Comma-separated frame numbers for 8Hz sampling")
|
||||
args = parser.parse_args()
|
||||
|
||||
target_frames = None
|
||||
if args.frames:
|
||||
target_frames = [int(f) for f in args.frames.split(",") if f.strip()]
|
||||
print(f"[Pose] 8Hz target frames: {len(target_frames)} frames", file=sys.stderr)
|
||||
|
||||
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,
|
||||
args.sample_interval, publisher)
|
||||
args.sample_interval, publisher, target_frames)
|
||||
with open(args.output_path, "w") as f:
|
||||
json.dump(result, f, indent=2)
|
||||
print(f"Pose: {len(result.get('frames', []))} frames with poses")
|
||||
|
||||
@@ -21,8 +21,6 @@ import json
|
||||
import argparse
|
||||
from collections import defaultdict
|
||||
import numpy as np
|
||||
import psycopg2
|
||||
import psycopg2.extras
|
||||
from datetime import datetime
|
||||
|
||||
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
|
||||
@@ -30,13 +28,8 @@ sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), "uti
|
||||
from qdrant_faces import update_trace_ids
|
||||
|
||||
# Config
|
||||
DB_URL = os.environ.get("DATABASE_URL", "postgresql://accusys@localhost:5432/momentry")
|
||||
SCHEMA = os.environ.get("MOMENTRY_DB_SCHEMA", "dev")
|
||||
OUTPUT_DIR = os.environ.get("MOMENTRY_OUTPUT_DIR", "/Users/accusys/momentry/output_dev")
|
||||
|
||||
|
||||
def get_conn():
|
||||
return psycopg2.connect(DB_URL)
|
||||
SCHEMA = os.environ.get("DATABASE_SCHEMA", "public")
|
||||
|
||||
|
||||
def merge_traces_within_cuts(face_data: dict, cut_scenes: list) -> dict:
|
||||
@@ -146,67 +139,17 @@ def run_face_tracker(
|
||||
|
||||
|
||||
def store_traced_faces(file_uuid: str, traced_json_path: str, schema: str = SCHEMA):
|
||||
"""Insert traced face detections into face_detections table with trace_id"""
|
||||
conn = get_conn()
|
||||
cur = conn.cursor()
|
||||
|
||||
"""Update Qdrant _faces collection with trace_id after face tracking.
|
||||
|
||||
face_detections table is deprecated — trace_id is stored only in Qdrant _faces payload.
|
||||
"""
|
||||
with open(traced_json_path) as f:
|
||||
data = json.load(f)
|
||||
|
||||
frames = data.get("frames", {})
|
||||
total_stored = 0
|
||||
|
||||
for frame_num_str, frame_data in sorted(frames.items(), key=lambda x: int(x[0])):
|
||||
frame_num = int(frame_num_str)
|
||||
faces = frame_data.get("faces", [])
|
||||
|
||||
for face in faces:
|
||||
trace_id = face.get("trace_id")
|
||||
if trace_id is None:
|
||||
continue
|
||||
|
||||
x = face.get("x", 0)
|
||||
y = face.get("y", 0)
|
||||
w = face.get("width", 0)
|
||||
h = face.get("height", 0)
|
||||
confidence = face.get("confidence", 0.0)
|
||||
face_id = face.get("face_id")
|
||||
if face_id is None:
|
||||
face_id = f"face_{trace_id}"
|
||||
attributes = face.get("attributes")
|
||||
|
||||
bbox = json.dumps({"x": x, "y": y, "width": w, "height": h})
|
||||
|
||||
try:
|
||||
cur.execute(
|
||||
f"""
|
||||
UPDATE {schema}.face_detections
|
||||
SET trace_id = %s, face_id = %s
|
||||
WHERE file_uuid = %s AND frame_number = %s
|
||||
AND x = %s AND y = %s AND width = %s AND height = %s
|
||||
""",
|
||||
(
|
||||
trace_id,
|
||||
face_id,
|
||||
file_uuid,
|
||||
frame_num,
|
||||
x,
|
||||
y,
|
||||
w,
|
||||
h,
|
||||
),
|
||||
)
|
||||
if cur.rowcount > 0:
|
||||
total_stored += 1
|
||||
except Exception as e:
|
||||
print(f"[TRACE] Error storing face at frame {frame_num}: {e}")
|
||||
conn.rollback()
|
||||
continue
|
||||
|
||||
conn.commit()
|
||||
|
||||
# Build trace_mapping for Qdrant update
|
||||
trace_mapping = {} # {frame: {bbox_key: trace_id}}
|
||||
# Build trace_mapping for Qdrant update: {frame: {bbox_key: trace_id}}
|
||||
trace_mapping = {}
|
||||
for frame_num_str, frame_data in sorted(frames.items(), key=lambda x: int(x[0])):
|
||||
frame_num = int(frame_num_str)
|
||||
trace_mapping[frame_num] = {}
|
||||
@@ -224,22 +167,26 @@ def store_traced_faces(file_uuid: str, traced_json_path: str, schema: str = SCHE
|
||||
print(f"[TRACE] Warning: Qdrant trace_id update failed: {e}")
|
||||
qdrant_updated = 0
|
||||
|
||||
# Log trace summary
|
||||
cur.execute(
|
||||
f"SELECT COUNT(DISTINCT trace_id) FROM {schema}.face_detections WHERE file_uuid = %s AND trace_id IS NOT NULL",
|
||||
(file_uuid,),
|
||||
)
|
||||
db_trace_count = cur.fetchone()[0]
|
||||
# Count unique traces from Qdrant
|
||||
try:
|
||||
from qdrant_faces import get_file_faces
|
||||
points = get_file_faces(file_uuid)
|
||||
trace_ids = set()
|
||||
for p in points:
|
||||
tid = p.get("payload", {}).get("trace_id")
|
||||
if tid is not None and tid > 0:
|
||||
trace_ids.add(tid)
|
||||
qdrant_trace_count = len(trace_ids)
|
||||
except Exception as e:
|
||||
print(f"[TRACE] Warning: Qdrant trace count failed: {e}")
|
||||
qdrant_trace_count = 0
|
||||
|
||||
cur.close()
|
||||
conn.close()
|
||||
|
||||
print(
|
||||
f"[TRACE] Stored {total_stored} face detections, {db_trace_count} unique traces in DB"
|
||||
total_faces = sum(
|
||||
1 for fd in frames.values() for f in fd.get("faces", []) if f.get("trace_id") is not None
|
||||
)
|
||||
if qdrant_updated > 0:
|
||||
print(f"[TRACE] Updated {qdrant_updated} Qdrant points with trace_id")
|
||||
return total_stored, db_trace_count
|
||||
|
||||
print(f"[TRACE] Updated {qdrant_updated} Qdrant points with trace_id, {qdrant_trace_count} unique traces")
|
||||
return total_faces, qdrant_trace_count
|
||||
|
||||
|
||||
def main():
|
||||
@@ -248,8 +195,6 @@ def main():
|
||||
|
||||
parser.add_argument("--face-json", help="Path to face.json (default: auto-detect)")
|
||||
|
||||
parser.add_argument("--schema", default=SCHEMA, help="DB schema name")
|
||||
|
||||
parser.add_argument("--uuid", help="UUID for Redis tracking (accepted by executor)")
|
||||
parser.add_argument(
|
||||
"--filter-eyes",
|
||||
@@ -270,8 +215,8 @@ def main():
|
||||
# Step 1: Run face tracker
|
||||
run_face_tracker(face_json, traced_json, filter_eyes=args.filter_eyes)
|
||||
|
||||
# Step 2: Store in DB with trace_id
|
||||
total, traces = store_traced_faces(args.file_uuid, traced_json, args.schema)
|
||||
# Step 2: Store in Qdrant with trace_id
|
||||
total, traces = store_traced_faces(args.file_uuid, traced_json)
|
||||
print(f"[TRACE] Done: {total} detections, {traces} traces")
|
||||
|
||||
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,409 @@
|
||||
import Foundation
|
||||
import Vision
|
||||
import ArgumentParser
|
||||
import AVFoundation
|
||||
|
||||
/// Swift Face+Pose Processor - one pass, two outputs
|
||||
/// Runs VNDetectFaceRectanglesRequest, VNDetectFaceLandmarksRequest,
|
||||
/// and VNDetectHumanBodyPoseRequest on each sampled frame.
|
||||
/// Uses AVAssetReader sequential read (frame-based), matching cv2 behavior.
|
||||
@main
|
||||
struct SwiftFacePose: ParsableCommand {
|
||||
@Argument(help: "Video file path")
|
||||
var inputPath: String
|
||||
|
||||
@Argument(help: "Output JSON path for face detection")
|
||||
var faceOutput: String
|
||||
|
||||
@Argument(help: "Output JSON path for pose detection")
|
||||
var poseOutput: String
|
||||
|
||||
@Option(name: .long, help: "Sample interval (frames, default=30)")
|
||||
var sampleInterval: Int = 30
|
||||
|
||||
@Option(name: .long, help: "UUID for logging")
|
||||
var uuid: String = ""
|
||||
|
||||
mutating func run() throws {
|
||||
let startTime = Date()
|
||||
print("[SwiftFacePose] Vision face+pose detection: \(inputPath)")
|
||||
|
||||
let url = URL(fileURLWithPath: inputPath)
|
||||
let asset = AVAsset(url: url)
|
||||
|
||||
guard let videoTrack = asset.tracks(withMediaType: .video).first else {
|
||||
print("[SwiftFacePose] No video track found")
|
||||
return
|
||||
}
|
||||
|
||||
let fps = videoTrack.nominalFrameRate
|
||||
let duration = CMTimeGetSeconds(asset.duration)
|
||||
let totalFrames = Int(duration * Double(fps))
|
||||
print("[SwiftFacePose] Video: \(Int(videoTrack.naturalSize.width))x\(Int(videoTrack.naturalSize.height)), \(String(format: "%.1f", fps))fps, \(totalFrames) frames, interval=\(sampleInterval)")
|
||||
|
||||
// read sequentially, matching cv2 frame-by-frame behavior
|
||||
let reader = try AVAssetReader(asset: asset)
|
||||
let outputSettings: [String: Any] = [
|
||||
kCVPixelBufferPixelFormatTypeKey as String: kCVPixelFormatType_32BGRA
|
||||
]
|
||||
let trackOutput = AVAssetReaderTrackOutput(track: videoTrack, outputSettings: outputSettings)
|
||||
trackOutput.alwaysCopiesSampleData = false
|
||||
reader.add(trackOutput)
|
||||
guard reader.startReading() else {
|
||||
print("[SwiftFacePose] Failed to start AVAssetReader: \(reader.error?.localizedDescription ?? "unknown")")
|
||||
return
|
||||
}
|
||||
|
||||
var faceFrames: [[String: Any]] = []
|
||||
var poseFrames: [[String: Any]] = []
|
||||
var processedCount = 0
|
||||
var frameIndex = 0
|
||||
|
||||
let jointNames: [VNHumanBodyPoseObservation.JointName] = [
|
||||
.nose, .leftEye, .rightEye, .leftEar, .rightEar,
|
||||
.neck, .root,
|
||||
.leftShoulder, .rightShoulder,
|
||||
.leftElbow, .rightElbow,
|
||||
.leftWrist, .rightWrist,
|
||||
.leftHip, .rightHip,
|
||||
.leftKnee, .rightKnee,
|
||||
.leftAnkle, .rightAnkle,
|
||||
]
|
||||
|
||||
while let sampleBuffer = trackOutput.copyNextSampleBuffer() {
|
||||
defer { frameIndex += 1 }
|
||||
|
||||
if frameIndex % sampleInterval != 0 {
|
||||
continue
|
||||
}
|
||||
|
||||
guard let pixelBuffer = CMSampleBufferGetImageBuffer(sampleBuffer) else {
|
||||
continue
|
||||
}
|
||||
|
||||
let imgW = CGFloat(CVPixelBufferGetWidth(pixelBuffer))
|
||||
let imgH = CGFloat(CVPixelBufferGetHeight(pixelBuffer))
|
||||
let seconds = Double(frameIndex) / Double(fps)
|
||||
|
||||
let handler = VNImageRequestHandler(cvPixelBuffer: pixelBuffer, options: [:])
|
||||
let faceReq = VNDetectFaceRectanglesRequest()
|
||||
let lmReq = VNDetectFaceLandmarksRequest()
|
||||
let bodyReq = VNDetectHumanBodyPoseRequest()
|
||||
|
||||
do {
|
||||
try handler.perform([faceReq, lmReq, bodyReq])
|
||||
} catch {
|
||||
continue
|
||||
}
|
||||
|
||||
// ── Face output ──
|
||||
let faceObservations = faceReq.results ?? []
|
||||
let landmarkObservations = lmReq.results ?? []
|
||||
|
||||
var faces: [[String: Any]] = []
|
||||
var hasFace = false
|
||||
|
||||
if !faceObservations.isEmpty || !landmarkObservations.isEmpty {
|
||||
hasFace = true
|
||||
|
||||
let MIN_CONFIDENCE = 0.6
|
||||
let MIN_SIZE = 20
|
||||
|
||||
for lmObs in landmarkObservations {
|
||||
let lmConf = Double(lmObs.confidence)
|
||||
if lmConf < MIN_CONFIDENCE { continue }
|
||||
|
||||
let bb = lmObs.boundingBox
|
||||
let faceW = Int(bb.size.width * imgW)
|
||||
let faceH = Int(bb.size.height * imgH)
|
||||
if faceW < MIN_SIZE || faceH < MIN_SIZE { continue }
|
||||
|
||||
let faceX = Int(bb.origin.x * imgW)
|
||||
let faceY = Int((1.0 - bb.origin.y - bb.size.height) * imgH)
|
||||
|
||||
var faceData: [String: Any] = [
|
||||
"bbox": ["x": max(0, faceX), "y": max(0, faceY),
|
||||
"width": faceW, "height": faceH],
|
||||
"confidence": Double(lmObs.confidence),
|
||||
]
|
||||
|
||||
if let yaw = lmObs.yaw?.doubleValue,
|
||||
let roll = lmObs.roll?.doubleValue {
|
||||
var poseInfo: [String: Any] = ["roll": roll, "yaw": yaw]
|
||||
if let pitch = lmObs.pitch?.doubleValue {
|
||||
poseInfo["pitch"] = pitch
|
||||
}
|
||||
faceData["pose"] = poseInfo
|
||||
}
|
||||
|
||||
if let lms = lmObs.landmarks {
|
||||
let imgSize = CGSize(width: imgW, height: imgH)
|
||||
let leftEye = lms.leftEye?.pointsInImage(imageSize: imgSize) ?? []
|
||||
let rightEye = lms.rightEye?.pointsInImage(imageSize: imgSize) ?? []
|
||||
let nose = lms.nose?.pointsInImage(imageSize: imgSize) ?? []
|
||||
|
||||
if !leftEye.isEmpty || !rightEye.isEmpty || !nose.isEmpty {
|
||||
var lm: [String: [[Double]]] = [:]
|
||||
if !leftEye.isEmpty {
|
||||
lm["left_eye"] = leftEye.map { [Double($0.x), Double(imgH - $0.y)] }
|
||||
}
|
||||
if !rightEye.isEmpty {
|
||||
lm["right_eye"] = rightEye.map { [Double($0.x), Double(imgH - $0.y)] }
|
||||
}
|
||||
if !nose.isEmpty {
|
||||
lm["nose"] = nose.map { [Double($0.x), Double(imgH - $0.y)] }
|
||||
}
|
||||
faceData["landmarks"] = lm
|
||||
}
|
||||
|
||||
let outer = lms.outerLips?.pointsInImage(imageSize: imgSize) ?? []
|
||||
let inner = lms.innerLips?.pointsInImage(imageSize: imgSize) ?? []
|
||||
if !outer.isEmpty || !inner.isEmpty {
|
||||
faceData["lips"] = [
|
||||
"outer_lips": outer.map { [Double($0.x), Double(imgH - $0.y)] },
|
||||
"inner_lips": inner.map { [Double($0.x), Double(imgH - $0.y)] }
|
||||
]
|
||||
}
|
||||
}
|
||||
|
||||
faces.append(faceData)
|
||||
}
|
||||
|
||||
for faceObs in faceObservations {
|
||||
let fBB = faceObs.boundingBox
|
||||
var matched = false
|
||||
for lmObs in landmarkObservations {
|
||||
let lBB = lmObs.boundingBox
|
||||
let ix = max(fBB.origin.x, lBB.origin.x)
|
||||
let iy = max(fBB.origin.y, lBB.origin.y)
|
||||
let iw = min(fBB.maxX, lBB.maxX) - ix
|
||||
let ih = min(fBB.maxY, lBB.maxY) - iy
|
||||
if iw <= 0 || ih <= 0 { continue }
|
||||
let intersection = iw * ih
|
||||
let union = fBB.width * fBB.height + lBB.width * lBB.height - intersection
|
||||
if intersection / union > 0.3 {
|
||||
matched = true
|
||||
break
|
||||
}
|
||||
}
|
||||
if matched { continue }
|
||||
|
||||
let faceConf = Double(faceObs.faceCaptureQuality ?? faceObs.confidence)
|
||||
if faceConf < MIN_CONFIDENCE { continue }
|
||||
|
||||
let faceW = Int(fBB.size.width * imgW)
|
||||
let faceH = Int(fBB.size.height * imgH)
|
||||
if faceW < MIN_SIZE || faceH < MIN_SIZE { continue }
|
||||
|
||||
let faceX = Int(fBB.origin.x * imgW)
|
||||
let faceY = Int((1.0 - fBB.origin.y - fBB.size.height) * imgH)
|
||||
|
||||
var faceData: [String: Any] = [
|
||||
"bbox": ["x": max(0, faceX), "y": max(0, faceY),
|
||||
"width": faceW, "height": faceH],
|
||||
"confidence": Double(faceObs.faceCaptureQuality ?? faceObs.confidence),
|
||||
]
|
||||
if let yaw = faceObs.yaw?.doubleValue,
|
||||
let roll = faceObs.roll?.doubleValue {
|
||||
var poseInfo: [String: Any] = ["roll": roll, "yaw": yaw]
|
||||
if let pitch = faceObs.pitch?.doubleValue {
|
||||
poseInfo["pitch"] = pitch
|
||||
}
|
||||
faceData["pose"] = poseInfo
|
||||
}
|
||||
faces.append(faceData)
|
||||
}
|
||||
|
||||
if !faces.isEmpty {
|
||||
faceFrames.append([
|
||||
"frame": frameIndex,
|
||||
"timestamp": seconds,
|
||||
"faces": faces,
|
||||
])
|
||||
}
|
||||
}
|
||||
|
||||
// ── Pose output ──
|
||||
// Rule: Face ≤ Pose - every face frame must have pose frame
|
||||
// Face landmarks (nose, leftEye, rightEye) ARE pose keypoints
|
||||
let poses = bodyReq.results ?? []
|
||||
var persons: [[String: Any]] = []
|
||||
|
||||
// If we have face landmarks, extract pose keypoints from them
|
||||
// This ensures Face → Pose is always true
|
||||
if hasFace && landmarkObservations.count > 0 {
|
||||
for lmObs in landmarkObservations {
|
||||
let lmConf = Double(lmObs.confidence)
|
||||
if lmConf < 0.6 { continue }
|
||||
|
||||
if let lms = lmObs.landmarks {
|
||||
let imgSize = CGSize(width: imgW, height: imgH)
|
||||
var keypoints: [[String: Any]] = []
|
||||
|
||||
// Extract face landmarks as pose keypoints
|
||||
if let nosePoints = lms.nose?.pointsInImage(imageSize: imgSize) {
|
||||
for pt in nosePoints {
|
||||
keypoints.append([
|
||||
"name": "nose",
|
||||
"x": Double(pt.x),
|
||||
"y": Double(imgH - pt.y),
|
||||
"confidence": lmConf
|
||||
])
|
||||
}
|
||||
}
|
||||
|
||||
if let leftEyePoints = lms.leftEye?.pointsInImage(imageSize: imgSize) {
|
||||
for pt in leftEyePoints {
|
||||
keypoints.append([
|
||||
"name": "left_eye",
|
||||
"x": Double(pt.x),
|
||||
"y": Double(imgH - pt.y),
|
||||
"confidence": lmConf
|
||||
])
|
||||
}
|
||||
}
|
||||
|
||||
if let rightEyePoints = lms.rightEye?.pointsInImage(imageSize: imgSize) {
|
||||
for pt in rightEyePoints {
|
||||
keypoints.append([
|
||||
"name": "right_eye",
|
||||
"x": Double(pt.x),
|
||||
"y": Double(imgH - pt.y),
|
||||
"confidence": lmConf
|
||||
])
|
||||
}
|
||||
}
|
||||
|
||||
if !keypoints.isEmpty {
|
||||
persons.append([
|
||||
"keypoints": keypoints,
|
||||
"bbox": ["x": 0, "y": 0, "width": 0, "height": 0]
|
||||
])
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Also process body pose detections (may add more keypoints)
|
||||
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])
|
||||
}
|
||||
|
||||
// Rule: Face ≤ Pose - always add pose frame if has face
|
||||
if hasFace || !persons.isEmpty {
|
||||
poseFrames.append([
|
||||
"frame": frameIndex,
|
||||
"timestamp": seconds,
|
||||
"persons": persons,
|
||||
])
|
||||
}
|
||||
|
||||
processedCount += 1
|
||||
|
||||
if processedCount % 100 == 0 {
|
||||
let elapsed = Date().timeIntervalSince(startTime)
|
||||
let totalSamples = totalFrames / sampleInterval
|
||||
let pct = Int(Double(processedCount) / Double(totalSamples) * 100)
|
||||
print("[SwiftFacePose] \(faceFrames.count) face frames, \(poseFrames.count) pose frames, \(pct)% complete, \(Int(elapsed))s elapsed")
|
||||
fflush(stdout)
|
||||
}
|
||||
}
|
||||
|
||||
reader.cancelReading()
|
||||
|
||||
let faceOutputDict: [String: Any] = [
|
||||
"frame_count": faceFrames.count,
|
||||
"fps": Double(fps),
|
||||
"frames": faceFrames,
|
||||
]
|
||||
do {
|
||||
let faceJson = try JSONSerialization.data(withJSONObject: faceOutputDict, options: [])
|
||||
try faceJson.write(to: URL(fileURLWithPath: faceOutput))
|
||||
print("[SwiftFacePose] Face output written: \(faceOutput)")
|
||||
// Verify file exists
|
||||
if FileManager.default.fileExists(atPath: faceOutput) {
|
||||
print("[SwiftFacePose] Verified: file exists at \(faceOutput)")
|
||||
} else {
|
||||
print("[SwiftFacePose] ERROR: file not found after write!")
|
||||
}
|
||||
} catch {
|
||||
print("[SwiftFacePose] ERROR writing face output: \(error)")
|
||||
}
|
||||
|
||||
let poseOutputDict: [String: Any] = [
|
||||
"frame_count": poseFrames.count,
|
||||
"fps": Double(fps),
|
||||
"frames": poseFrames,
|
||||
]
|
||||
if let poseJson = try? JSONSerialization.data(withJSONObject: poseOutputDict, options: [.prettyPrinted]) {
|
||||
try poseJson.write(to: URL(fileURLWithPath: poseOutput))
|
||||
}
|
||||
|
||||
let elapsed = Date().timeIntervalSince(startTime)
|
||||
print("[SwiftFacePose] Done: \(faceFrames.count) face frames, \(poseFrames.count) pose frames, \(String(format: "%.1f", elapsed))s")
|
||||
}
|
||||
}
|
||||
+464
-65
@@ -22,6 +22,12 @@ struct RegisterFileRequest {
|
||||
user_id: Option<i64>,
|
||||
content_hash: Option<String>,
|
||||
pattern: Option<String>,
|
||||
#[serde(default = "default_force")]
|
||||
force: bool,
|
||||
}
|
||||
|
||||
fn default_force() -> bool {
|
||||
true
|
||||
}
|
||||
|
||||
#[derive(Debug, Deserialize, Serialize)]
|
||||
@@ -188,6 +194,7 @@ async fn register_single_file(
|
||||
file_path: &str,
|
||||
_user_id: Option<i64>,
|
||||
provided_hash: Option<String>,
|
||||
force: bool,
|
||||
) -> RegisterFileResponse {
|
||||
tracing::info!("[REGISTER] Starting registration for: {}", file_path);
|
||||
|
||||
@@ -325,41 +332,54 @@ async fn register_single_file(
|
||||
"[REGISTER] Content hash collision → already registered: {}",
|
||||
existing_uuid
|
||||
);
|
||||
let existing_info: Option<(String, String, f64, i32, i32, f64, i64, Option<String>)> = sqlx::query_as(
|
||||
&format!("SELECT file_name, file_path, duration, width, height, fps, total_frames, registration_time::text FROM {} WHERE file_uuid = $1", videos_table)
|
||||
).bind(&existing_uuid).fetch_optional(db.pool()).await.unwrap_or(None);
|
||||
if let Some((ename, epath, dur, w, h, f, tf, rt)) = existing_info {
|
||||
// If force=true, unregister asynchronously then continue
|
||||
if force {
|
||||
tracing::info!(
|
||||
"[REGISTER] Force mode: async unregistering existing file {}",
|
||||
existing_uuid
|
||||
);
|
||||
if let Err(e) = unregister_internal(&state, &existing_uuid).await {
|
||||
tracing::error!("[REGISTER] Force unregister failed for {}: {:?}", existing_uuid, e);
|
||||
} else {
|
||||
tracing::info!("[REGISTER] Force unregister completed for {}", existing_uuid);
|
||||
}
|
||||
} else {
|
||||
let existing_info: Option<(String, String, f64, i32, i32, f64, i64, Option<String>)> = sqlx::query_as(
|
||||
&format!("SELECT file_name, file_path, duration, width, height, fps, total_frames, registration_time::text FROM {} WHERE file_uuid = $1", videos_table)
|
||||
).bind(&existing_uuid).fetch_optional(db.pool()).await.unwrap_or(None);
|
||||
if let Some((ename, epath, dur, w, h, f, tf, rt)) = existing_info {
|
||||
return RegisterFileResponse {
|
||||
success: true,
|
||||
file_uuid: existing_uuid,
|
||||
file_name: ename,
|
||||
file_path: epath.clone(),
|
||||
file_type: None,
|
||||
duration: dur,
|
||||
width: w as u32,
|
||||
height: h as u32,
|
||||
fps: f,
|
||||
total_frames: tf as u64,
|
||||
registration_time: rt,
|
||||
already_exists: true,
|
||||
message: format!("Content already registered: {}", epath),
|
||||
};
|
||||
}
|
||||
return RegisterFileResponse {
|
||||
success: true,
|
||||
file_uuid: existing_uuid,
|
||||
file_name: ename,
|
||||
file_path: epath.clone(),
|
||||
file_name: file_name.clone(),
|
||||
file_path: canonical_path.clone(),
|
||||
file_type: None,
|
||||
duration: dur,
|
||||
width: w as u32,
|
||||
height: h as u32,
|
||||
fps: f,
|
||||
total_frames: tf as u64,
|
||||
registration_time: rt,
|
||||
duration: 0.0,
|
||||
width: 0,
|
||||
height: 0,
|
||||
fps: 0.0,
|
||||
total_frames: 0,
|
||||
registration_time: None,
|
||||
already_exists: true,
|
||||
message: format!("Content already registered: {}", epath),
|
||||
message: "Content already registered (identical file)".to_string(),
|
||||
};
|
||||
}
|
||||
return RegisterFileResponse {
|
||||
success: true,
|
||||
file_uuid: existing_uuid,
|
||||
file_name: file_name.clone(),
|
||||
file_path: canonical_path.clone(),
|
||||
file_type: None,
|
||||
duration: 0.0,
|
||||
width: 0,
|
||||
height: 0,
|
||||
fps: 0.0,
|
||||
total_frames: 0,
|
||||
registration_time: None,
|
||||
already_exists: true,
|
||||
message: "Content already registered (identical file)".to_string(),
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -418,12 +438,19 @@ async fn register_single_file(
|
||||
|
||||
let duration = temp_probe_json
|
||||
.get("format")
|
||||
.and_then(|f| {
|
||||
let src = if has_video { f.get("duration") } else { None };
|
||||
src.and_then(|v| v.as_str())
|
||||
.and_then(|f| f.get("duration"))
|
||||
.and_then(|v| v.as_str())
|
||||
.and_then(|s| s.parse::<f64>().ok())
|
||||
.unwrap_or_else(|| {
|
||||
temp_probe_json
|
||||
.get("streams")
|
||||
.and_then(|s| s.as_array())
|
||||
.and_then(|streams| streams.iter().next())
|
||||
.and_then(|st| st.get("duration"))
|
||||
.and_then(|v| v.as_str())
|
||||
.and_then(|s| s.parse::<f64>().ok())
|
||||
})
|
||||
.unwrap_or(0.0);
|
||||
.unwrap_or(0.0)
|
||||
});
|
||||
let mut width = 0u32;
|
||||
let mut height = 0u32;
|
||||
let mut fps = 0.0;
|
||||
@@ -454,7 +481,7 @@ async fn register_single_file(
|
||||
|
||||
let status = "registered";
|
||||
let _ = sqlx::query(&format!(
|
||||
"INSERT INTO {} (file_uuid, file_path, file_name, file_type, duration, width, height, fps, probe_json, status, content_hash, registration_time) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10, $11, NOW()) ON CONFLICT (file_uuid) DO UPDATE SET file_path = EXCLUDED.file_path, file_name = EXCLUDED.file_name, status = EXCLUDED.status, content_hash = EXCLUDED.content_hash",
|
||||
"INSERT INTO {} (file_uuid, file_path, file_name, file_type, duration, width, height, fps, probe_json, status, content_hash, registration_time) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10, $11, NOW()) ON CONFLICT (file_uuid) DO UPDATE SET file_path = EXCLUDED.file_path, file_name = EXCLUDED.file_name, status = EXCLUDED.status, content_hash = EXCLUDED.content_hash, duration = EXCLUDED.duration, width = EXCLUDED.width, height = EXCLUDED.height, fps = EXCLUDED.fps, probe_json = EXCLUDED.probe_json",
|
||||
videos_table
|
||||
))
|
||||
.bind(&file_uuid).bind(&canonical_path).bind(&final_name).bind(&final_file_type)
|
||||
@@ -509,7 +536,6 @@ async fn register_single_file(
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
let audio_tracks: Vec<serde_json::Value> = temp_probe_json
|
||||
@@ -647,6 +673,7 @@ async fn register_file(
|
||||
&entry_path.to_string_lossy().to_string(),
|
||||
req.user_id,
|
||||
None,
|
||||
req.force,
|
||||
)
|
||||
.await;
|
||||
if result.success {
|
||||
@@ -682,7 +709,49 @@ async fn register_file(
|
||||
}));
|
||||
}
|
||||
|
||||
let resp = register_single_file(&state, &file_path, req.user_id, req.content_hash).await;
|
||||
// If force=true and file already exists, unregister first
|
||||
if req.force {
|
||||
let videos_table = schema::table_name("videos");
|
||||
// Check by file_path first
|
||||
if let Ok(Some(existing_uuid)) = sqlx::query_scalar::<_, String>(&format!(
|
||||
"SELECT file_uuid FROM {} WHERE file_path = $1 LIMIT 1",
|
||||
videos_table
|
||||
))
|
||||
.bind(&file_path)
|
||||
.fetch_optional(state.db.pool())
|
||||
.await
|
||||
{
|
||||
tracing::info!(
|
||||
"[REGISTER] Force mode: unregistering existing file {}",
|
||||
existing_uuid
|
||||
);
|
||||
if let Err(e) = unregister_internal(&state, &existing_uuid).await {
|
||||
tracing::error!("[REGISTER] Force unregister failed for {}: {:?}", existing_uuid, e);
|
||||
}
|
||||
}
|
||||
// Also check by content_hash if provided
|
||||
if let Some(ref content_hash) = req.content_hash {
|
||||
if let Ok(Some(existing_uuid)) = sqlx::query_scalar::<_, String>(&format!(
|
||||
"SELECT file_uuid FROM {} WHERE content_hash = $1 LIMIT 1",
|
||||
videos_table
|
||||
))
|
||||
.bind(content_hash)
|
||||
.fetch_optional(state.db.pool())
|
||||
.await
|
||||
{
|
||||
tracing::info!(
|
||||
"[REGISTER] Force mode: unregistering by content_hash {}",
|
||||
existing_uuid
|
||||
);
|
||||
if let Err(e) = unregister_internal(&state, &existing_uuid).await {
|
||||
tracing::error!("[REGISTER] Force unregister failed for {}: {:?}", existing_uuid, e);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
let resp =
|
||||
register_single_file(&state, &file_path, req.user_id, req.content_hash, req.force).await;
|
||||
|
||||
if resp.success
|
||||
&& !resp.already_exists
|
||||
@@ -706,7 +775,8 @@ async fn register_file(
|
||||
if let Some(ref vp) = video_path {
|
||||
if let Ok(job) = auto_state.db.create_monitor_job(&auto_uuid, Some(vp)).await {
|
||||
tracing::info!("[AUTO-PIPELINE] Job {} created for {}", job.id, auto_uuid);
|
||||
let all_procs: Vec<&str> = vec!["cut", "asr", "asrx", "yolo", "ocr", "face", "pose", "appearance"];
|
||||
let all_procs: Vec<&str> =
|
||||
vec!["cut", "asr", "asrx", "ocr", "face", "pose", "appearance"];
|
||||
let total = sqlx::query_scalar::<_, i64>(&format!(
|
||||
"SELECT COALESCE(total_frames, 0) FROM {} WHERE file_uuid = $1",
|
||||
schema::table_name("videos")
|
||||
@@ -927,6 +997,7 @@ struct UnregisterResponse {
|
||||
deleted_characters: u64,
|
||||
deleted_chunks_rule1: u64,
|
||||
deleted_processor_alerts: u64,
|
||||
deleted_processor_versions: u64,
|
||||
}
|
||||
|
||||
#[derive(Debug, Deserialize)]
|
||||
@@ -948,7 +1019,11 @@ fn delete_output_files(uuid: &str) -> u64 {
|
||||
for entry in entries.flatten() {
|
||||
let path = entry.path();
|
||||
if let Some(name) = path.file_name().and_then(|n| n.to_str()) {
|
||||
if name.starts_with(uuid) && name.ends_with(".json") {
|
||||
let is_uuid_file = name.starts_with(uuid) && !path.is_dir();
|
||||
let is_pipeline_log = name.starts_with("pipeline_")
|
||||
&& name.contains(uuid)
|
||||
&& name.ends_with(".log");
|
||||
if is_uuid_file || is_pipeline_log {
|
||||
if std::fs::remove_file(&path).is_ok() {
|
||||
deleted_count += 1;
|
||||
tracing::info!("[UNREGISTER] Deleted output file: {}", name);
|
||||
@@ -957,6 +1032,17 @@ fn delete_output_files(uuid: &str) -> u64 {
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
let uuid_dir = std::path::Path::new(output_dir).join(uuid);
|
||||
if uuid_dir.is_dir() {
|
||||
if std::fs::remove_dir_all(&uuid_dir).is_ok() {
|
||||
deleted_count += 1;
|
||||
tracing::info!(
|
||||
"[UNREGISTER] Deleted output directory: {}",
|
||||
uuid_dir.display()
|
||||
);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
let workspace_sqlite = format!("{}.workspace.sqlite", uuid);
|
||||
@@ -982,7 +1068,6 @@ async fn unregister(
|
||||
tracing::info!("[UNREGISTER] Unregistering file: {}", uuid);
|
||||
|
||||
let videos_table = schema::table_name("videos");
|
||||
let face_table = schema::table_name("face_detections");
|
||||
let processor_table = schema::table_name("processor_results");
|
||||
let chunks_table = schema::table_name("chunk");
|
||||
let parent_chunks_table = schema::table_name("parent_chunks");
|
||||
@@ -1020,7 +1105,7 @@ async fn unregister(
|
||||
}};
|
||||
}
|
||||
|
||||
let deleted_faces = delete_safe!(face_table, "file_uuid = $1", &uuid, "faces");
|
||||
let deleted_faces = 0i64; // Deprecated: face_detections table removed
|
||||
let deleted_processors = delete_safe!(processor_table, "file_uuid = $1", &uuid, "processors");
|
||||
let deleted_parent_chunks =
|
||||
delete_safe!(parent_chunks_table, "uuid = $1", &uuid, "parent chunks");
|
||||
@@ -1045,20 +1130,44 @@ async fn unregister(
|
||||
})?
|
||||
.rows_affected() as i64;
|
||||
|
||||
let deleted_file_identities =
|
||||
delete_safe!(file_identities_table, "file_uuid = $1", &uuid, "file identities");
|
||||
let deleted_speaker_detections =
|
||||
delete_safe!(speaker_detections_table, "file_uuid = $1", &uuid, "speaker detections");
|
||||
let deleted_face_clusters =
|
||||
delete_safe!(face_clusters_table, "file_uuid = $1", &uuid, "face clusters");
|
||||
let deleted_face_recognition =
|
||||
delete_safe!(face_recognition_results_table, "file_uuid = $1", &uuid, "face recognition results");
|
||||
let deleted_characters =
|
||||
delete_safe!(characters_table, "file_uuid = $1", &uuid, "characters");
|
||||
let deleted_chunks_rule1 =
|
||||
delete_safe!(chunks_rule1_table, "uuid = $1", &uuid, "chunks rule1");
|
||||
let deleted_processor_alerts =
|
||||
delete_safe!(processor_alerts_table, "file_uuid = $1", &uuid, "processor alerts");
|
||||
let deleted_file_identities = delete_safe!(
|
||||
file_identities_table,
|
||||
"file_uuid = $1",
|
||||
&uuid,
|
||||
"file identities"
|
||||
);
|
||||
let deleted_speaker_detections = delete_safe!(
|
||||
speaker_detections_table,
|
||||
"file_uuid = $1",
|
||||
&uuid,
|
||||
"speaker detections"
|
||||
);
|
||||
let deleted_face_clusters = delete_safe!(
|
||||
face_clusters_table,
|
||||
"file_uuid = $1",
|
||||
&uuid,
|
||||
"face clusters"
|
||||
);
|
||||
let deleted_face_recognition = delete_safe!(
|
||||
face_recognition_results_table,
|
||||
"file_uuid = $1",
|
||||
&uuid,
|
||||
"face recognition results"
|
||||
);
|
||||
let deleted_characters = delete_safe!(characters_table, "file_uuid = $1", &uuid, "characters");
|
||||
let deleted_chunks_rule1 = delete_safe!(chunks_rule1_table, "uuid = $1", &uuid, "chunks rule1");
|
||||
let deleted_processor_alerts = delete_safe!(
|
||||
processor_alerts_table,
|
||||
"file_uuid = $1",
|
||||
&uuid,
|
||||
"processor alerts"
|
||||
);
|
||||
let deleted_processor_versions = delete_safe!(
|
||||
"processor_versions",
|
||||
"file_uuid = $1",
|
||||
&uuid,
|
||||
"processor versions"
|
||||
);
|
||||
|
||||
sqlx::query(&format!(
|
||||
"DELETE FROM {} WHERE file_uuid = $1",
|
||||
@@ -1078,29 +1187,54 @@ async fn unregister(
|
||||
})?;
|
||||
|
||||
tracing::info!(
|
||||
"[UNREGISTER] Deleted: {} faces, {} processors, {} parent_chunks, {} chunks, {} pre_chunks, {} tkg_nodes, {} cuts, {} strangers, {} chunk_vectors, {} monitor_jobs, {} frames, {} file_identities, {} speaker_detections, {} face_clusters, {} face_recognition_results, {} characters, {} chunks_rule1, {} processor_alerts",
|
||||
"[UNREGISTER] Deleted: {} faces, {} processors, {} parent_chunks, {} chunks, {} pre_chunks, {} tkg_nodes, {} cuts, {} strangers, {} chunk_vectors, {} monitor_jobs, {} frames, {} file_identities, {} speaker_detections, {} face_clusters, {} face_recognition_results, {} characters, {} chunks_rule1, {} processor_alerts, {} processor_versions",
|
||||
deleted_faces, deleted_processors, deleted_parent_chunks, deleted_chunks,
|
||||
deleted_pre_chunks, deleted_tkg_nodes, deleted_cuts, deleted_strangers,
|
||||
deleted_chunk_vectors, deleted_monitor_jobs, deleted_frames,
|
||||
deleted_file_identities, deleted_speaker_detections, deleted_face_clusters,
|
||||
deleted_face_recognition, deleted_characters, deleted_chunks_rule1,
|
||||
deleted_processor_alerts
|
||||
deleted_processor_alerts, deleted_processor_versions
|
||||
);
|
||||
|
||||
let deleted_output_files = delete_output_files(&uuid);
|
||||
|
||||
let deleted_qdrant_vectors = {
|
||||
let qdrant = QdrantDb::new();
|
||||
match qdrant.delete_by_uuid(&uuid).await {
|
||||
Ok(_) => {
|
||||
tracing::info!("[UNREGISTER] Deleted Qdrant vectors for {}", uuid);
|
||||
Some(1)
|
||||
}
|
||||
Err(e) => {
|
||||
tracing::warn!("[UNREGISTER] Failed to delete Qdrant vectors: {}", e);
|
||||
None
|
||||
let mut total = 0u64;
|
||||
|
||||
if qdrant.delete_by_uuid(&uuid).await.is_ok() {
|
||||
tracing::info!("[UNREGISTER] Deleted Qdrant vectors from main collection");
|
||||
total += 1;
|
||||
} else {
|
||||
tracing::warn!("[UNREGISTER] Failed to delete Qdrant vectors from main collection");
|
||||
}
|
||||
|
||||
let additional_collections = [
|
||||
"_faces", // Python store_traced_faces.py
|
||||
&format!("{}_voice", uuid), // Per-file voice embeddings
|
||||
];
|
||||
for coll in &additional_collections {
|
||||
if QdrantDb::delete_by_uuid_from_collection(
|
||||
&qdrant.client,
|
||||
&qdrant.base_url,
|
||||
&qdrant.api_key,
|
||||
coll,
|
||||
&uuid,
|
||||
)
|
||||
.await
|
||||
.is_ok()
|
||||
{
|
||||
tracing::info!(
|
||||
"[UNREGISTER] Deleted Qdrant vectors from collection: {}",
|
||||
coll
|
||||
);
|
||||
total += 1;
|
||||
} else {
|
||||
tracing::debug!("[UNREGISTER] No vectors or collection not found: {}", coll);
|
||||
}
|
||||
}
|
||||
|
||||
Some(total)
|
||||
};
|
||||
|
||||
let deleted_redis_keys = {
|
||||
@@ -1130,7 +1264,10 @@ async fn unregister(
|
||||
Some(1)
|
||||
}
|
||||
Err(e) => {
|
||||
tracing::warn!("[UNREGISTER] Failed to delete Qdrant workspace vectors: {}", e);
|
||||
tracing::warn!(
|
||||
"[UNREGISTER] Failed to delete Qdrant workspace vectors: {}",
|
||||
e
|
||||
);
|
||||
None
|
||||
}
|
||||
}
|
||||
@@ -1155,13 +1292,275 @@ async fn unregister(
|
||||
deleted_characters: deleted_characters as u64,
|
||||
deleted_chunks_rule1: deleted_chunks_rule1 as u64,
|
||||
deleted_processor_alerts: deleted_processor_alerts as u64,
|
||||
deleted_processor_versions: deleted_processor_versions as u64,
|
||||
}))
|
||||
}
|
||||
|
||||
/// Internal unregister function - can be called from both API and register
|
||||
async fn unregister_internal(state: &AppState, uuid: &str) -> Result<(), StatusCode> {
|
||||
let videos_table = schema::table_name("videos");
|
||||
let processor_table = schema::table_name("processor_results");
|
||||
let chunks_table = schema::table_name("chunk");
|
||||
let parent_chunks_table = schema::table_name("parent_chunks");
|
||||
let pre_chunks_table = schema::table_name("pre_chunks");
|
||||
let tkg_nodes_table = schema::table_name("tkg_nodes");
|
||||
let cuts_table = schema::table_name("cuts");
|
||||
let strangers_table = schema::table_name("strangers");
|
||||
let chunk_vectors_table = schema::table_name("chunk_vectors");
|
||||
let monitor_jobs_table = schema::table_name("monitor_jobs");
|
||||
let frames_table = schema::table_name("frames");
|
||||
let file_identities_table = schema::table_name("file_identities");
|
||||
let speaker_detections_table = schema::table_name("speaker_detections");
|
||||
let face_clusters_table = schema::table_name("face_clusters");
|
||||
let face_recognition_results_table = schema::table_name("face_recognition_results");
|
||||
let characters_table = schema::table_name("characters");
|
||||
let chunks_rule1_table = schema::table_name("chunks_rule1");
|
||||
let processor_alerts_table = schema::table_name("processor_alerts");
|
||||
|
||||
let mut tx = state.db.pool().begin().await.map_err(|e| {
|
||||
tracing::error!("[unregister] Failed to start transaction: {}", e);
|
||||
StatusCode::INTERNAL_SERVER_ERROR
|
||||
})?;
|
||||
|
||||
macro_rules! delete_safe {
|
||||
($table:expr, $where:expr, $bind:expr, $label:expr) => {{
|
||||
sqlx::query(&format!("DELETE FROM {} WHERE {}", $table, $where))
|
||||
.bind($bind)
|
||||
.execute(&mut *tx)
|
||||
.await
|
||||
.map_err(|e| {
|
||||
tracing::error!("[unregister] Failed to delete {}: {}", $label, e);
|
||||
StatusCode::INTERNAL_SERVER_ERROR
|
||||
})?
|
||||
.rows_affected() as i64
|
||||
}};
|
||||
}
|
||||
|
||||
let _deleted_faces: i64 = 0; // Deprecated: face_detections table removed
|
||||
let _deleted_processors = delete_safe!(processor_table, "file_uuid = $1", uuid, "processors");
|
||||
let _deleted_parent_chunks =
|
||||
delete_safe!(parent_chunks_table, "uuid = $1", uuid, "parent chunks");
|
||||
let _deleted_chunks = delete_safe!(chunks_table, "file_uuid = $1", uuid, "chunks");
|
||||
let _deleted_pre_chunks = delete_safe!(pre_chunks_table, "file_uuid = $1", uuid, "pre_chunks");
|
||||
let _deleted_tkg_nodes = delete_safe!(tkg_nodes_table, "file_uuid = $1", uuid, "TKG nodes");
|
||||
let _deleted_cuts = delete_safe!(cuts_table, "file_uuid = $1", uuid, "cuts");
|
||||
let _deleted_strangers = delete_safe!(strangers_table, "file_uuid = $1", uuid, "strangers");
|
||||
let _deleted_chunk_vectors =
|
||||
delete_safe!(chunk_vectors_table, "uuid = $1", uuid, "chunk vectors");
|
||||
let _deleted_monitor_jobs = delete_safe!(monitor_jobs_table, "uuid = $1", uuid, "monitor jobs");
|
||||
let _deleted_frames: i64 = sqlx::query(&format!(
|
||||
"DELETE FROM {} WHERE file_id = (SELECT id FROM {} WHERE file_uuid = $1)",
|
||||
frames_table, videos_table
|
||||
))
|
||||
.bind(uuid)
|
||||
.execute(&mut *tx)
|
||||
.await
|
||||
.map_err(|e| {
|
||||
tracing::error!("[unregister] Failed to delete frames: {}", e);
|
||||
StatusCode::INTERNAL_SERVER_ERROR
|
||||
})?
|
||||
.rows_affected() as i64;
|
||||
|
||||
let _deleted_file_identities = delete_safe!(
|
||||
file_identities_table,
|
||||
"file_uuid = $1",
|
||||
uuid,
|
||||
"file identities"
|
||||
);
|
||||
let _deleted_speaker_detections = delete_safe!(
|
||||
speaker_detections_table,
|
||||
"file_uuid = $1",
|
||||
uuid,
|
||||
"speaker detections"
|
||||
);
|
||||
let _deleted_face_clusters =
|
||||
delete_safe!(face_clusters_table, "file_uuid = $1", uuid, "face clusters");
|
||||
let _deleted_face_recognition = delete_safe!(
|
||||
face_recognition_results_table,
|
||||
"file_uuid = $1",
|
||||
uuid,
|
||||
"face recognition results"
|
||||
);
|
||||
let _deleted_characters = delete_safe!(characters_table, "file_uuid = $1", uuid, "characters");
|
||||
let _deleted_chunks_rule1 = delete_safe!(chunks_rule1_table, "uuid = $1", uuid, "chunks rule1");
|
||||
let _deleted_processor_alerts = delete_safe!(
|
||||
processor_alerts_table,
|
||||
"file_uuid = $1",
|
||||
uuid,
|
||||
"processor alerts"
|
||||
);
|
||||
let _deleted_processor_versions = delete_safe!(
|
||||
"processor_versions",
|
||||
"file_uuid = $1",
|
||||
uuid,
|
||||
"processor versions"
|
||||
);
|
||||
|
||||
sqlx::query(&format!(
|
||||
"DELETE FROM {} WHERE file_uuid = $1",
|
||||
videos_table
|
||||
))
|
||||
.bind(uuid)
|
||||
.execute(&mut *tx)
|
||||
.await
|
||||
.map_err(|e| {
|
||||
tracing::error!("[unregister] Failed: {}", e);
|
||||
StatusCode::INTERNAL_SERVER_ERROR
|
||||
})?;
|
||||
|
||||
tx.commit().await.map_err(|e| {
|
||||
tracing::error!("[unregister] Failed to commit transaction: {}", e);
|
||||
StatusCode::INTERNAL_SERVER_ERROR
|
||||
})?;
|
||||
|
||||
tracing::info!("[UNREGISTER] Deleted all data for {}", uuid);
|
||||
|
||||
// Delete output files
|
||||
delete_output_files(uuid);
|
||||
|
||||
// Delete Qdrant vectors
|
||||
let qdrant = QdrantDb::new();
|
||||
let _ = qdrant.delete_by_uuid(uuid).await;
|
||||
let _ = QdrantDb::delete_by_uuid_from_collection(
|
||||
&qdrant.client,
|
||||
&qdrant.base_url,
|
||||
&qdrant.api_key,
|
||||
"_faces",
|
||||
uuid,
|
||||
)
|
||||
.await;
|
||||
let _ = QdrantDb::delete_by_uuid_from_collection(
|
||||
&qdrant.client,
|
||||
&qdrant.base_url,
|
||||
&qdrant.api_key,
|
||||
&format!("{}_voice", uuid),
|
||||
uuid,
|
||||
)
|
||||
.await;
|
||||
|
||||
// Delete Qdrant workspace
|
||||
let workspace = QdrantWorkspace::new();
|
||||
let _ = workspace.delete_by_file_uuid(uuid).await;
|
||||
|
||||
// Delete Redis keys
|
||||
if let Ok(redis) = RedisClient::new() {
|
||||
let _ = redis.delete_worker_job(uuid).await;
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
#[derive(Debug, Deserialize)]
|
||||
struct UpdateMetadataRequest {
|
||||
duration: Option<f64>,
|
||||
status: Option<String>,
|
||||
width: Option<i32>,
|
||||
height: Option<i32>,
|
||||
fps: Option<f64>,
|
||||
}
|
||||
|
||||
#[derive(Serialize)]
|
||||
struct UpdateMetadataResponse {
|
||||
success: bool,
|
||||
file_uuid: String,
|
||||
message: String,
|
||||
}
|
||||
|
||||
async fn update_file_metadata(
|
||||
Path(file_uuid): Path<String>,
|
||||
State(state): State<AppState>,
|
||||
Json(req): Json<UpdateMetadataRequest>,
|
||||
) -> Result<Json<UpdateMetadataResponse>, StatusCode> {
|
||||
let videos_table = schema::table_name("videos");
|
||||
|
||||
let mut set_clauses: Vec<String> = Vec::new();
|
||||
let mut bind_idx = 2;
|
||||
|
||||
if let Some(_) = req.duration {
|
||||
set_clauses.push(format!("duration = ${}", bind_idx));
|
||||
bind_idx += 1;
|
||||
}
|
||||
if let Some(_) = req.status {
|
||||
set_clauses.push(format!("status = ${}", bind_idx));
|
||||
bind_idx += 1;
|
||||
}
|
||||
if let Some(_) = req.width {
|
||||
set_clauses.push(format!("width = ${}", bind_idx));
|
||||
bind_idx += 1;
|
||||
}
|
||||
if let Some(_) = req.height {
|
||||
set_clauses.push(format!("height = ${}", bind_idx));
|
||||
bind_idx += 1;
|
||||
}
|
||||
if let Some(_) = req.fps {
|
||||
set_clauses.push(format!("fps = ${}", bind_idx));
|
||||
bind_idx += 1;
|
||||
}
|
||||
|
||||
if set_clauses.is_empty() {
|
||||
return Ok(Json(UpdateMetadataResponse {
|
||||
success: false,
|
||||
file_uuid,
|
||||
message: "No fields to update".to_string(),
|
||||
}));
|
||||
}
|
||||
|
||||
set_clauses.push("updated_at = NOW()".to_string());
|
||||
let sql = format!(
|
||||
"UPDATE {} SET {} WHERE file_uuid = $1",
|
||||
videos_table,
|
||||
set_clauses.join(", ")
|
||||
);
|
||||
|
||||
let mut query = sqlx::query(&sql).bind(&file_uuid);
|
||||
if let Some(d) = req.duration {
|
||||
query = query.bind(d);
|
||||
}
|
||||
if let Some(s) = req.status {
|
||||
query = query.bind(s);
|
||||
}
|
||||
if let Some(w) = req.width {
|
||||
query = query.bind(w);
|
||||
}
|
||||
if let Some(h) = req.height {
|
||||
query = query.bind(h);
|
||||
}
|
||||
if let Some(f) = req.fps {
|
||||
query = query.bind(f);
|
||||
}
|
||||
|
||||
let result = query.execute(state.db.pool()).await;
|
||||
|
||||
match result {
|
||||
Ok(res) if res.rows_affected() > 0 => Ok(Json(UpdateMetadataResponse {
|
||||
success: true,
|
||||
file_uuid,
|
||||
message: "Metadata updated successfully".to_string(),
|
||||
})),
|
||||
Ok(_) => Ok(Json(UpdateMetadataResponse {
|
||||
success: false,
|
||||
file_uuid,
|
||||
message: "File not found".to_string(),
|
||||
})),
|
||||
Err(e) => {
|
||||
tracing::error!("[METADATA] Update failed: {}", e);
|
||||
Ok(Json(UpdateMetadataResponse {
|
||||
success: false,
|
||||
file_uuid,
|
||||
message: format!("Update failed: {}", e),
|
||||
}))
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
pub fn file_routes() -> Router<AppState> {
|
||||
Router::new()
|
||||
.route("/api/v1/files/register", post(register_file))
|
||||
.route("/api/v1/files/lookup", get(lookup_file_by_name))
|
||||
.route("/api/v1/unregister", post(unregister))
|
||||
.route("/api/v1/file/:file_uuid/probe", get(probe_by_uuid))
|
||||
.route(
|
||||
"/api/v1/file/:file_uuid/metadata",
|
||||
post(update_file_metadata),
|
||||
)
|
||||
}
|
||||
|
||||
+147
-257
@@ -180,7 +180,7 @@ async fn list_identities(
|
||||
)
|
||||
})?;
|
||||
|
||||
let sql = format!(
|
||||
let sql = format!(
|
||||
r#"SELECT i.id::int, i.uuid, i.name, i.metadata, i.status, i.starred,
|
||||
COALESCE(
|
||||
jsonb_agg(jsonb_build_object(
|
||||
@@ -195,10 +195,19 @@ let sql = format!(
|
||||
WHERE i.status IS NULL OR i.status != 'merged'
|
||||
GROUP BY i.id, i.uuid, i.name, i.metadata, i.status, i.starred
|
||||
ORDER BY i.id DESC LIMIT $1 OFFSET $2"#,
|
||||
id_table, crate::core::db::schema::table_name("file_identities")
|
||||
id_table,
|
||||
crate::core::db::schema::table_name("file_identities")
|
||||
);
|
||||
|
||||
let rows: Vec<(i32, uuid::Uuid, String, Option<serde_json::Value>, Option<String>, Option<bool>, serde_json::Value)> = match sqlx::query_as(&sql)
|
||||
let rows: Vec<(
|
||||
i32,
|
||||
uuid::Uuid,
|
||||
String,
|
||||
Option<serde_json::Value>,
|
||||
Option<String>,
|
||||
Option<bool>,
|
||||
serde_json::Value,
|
||||
)> = match sqlx::query_as(&sql)
|
||||
.bind(page_size as i64)
|
||||
.bind(offset)
|
||||
.fetch_all(db.pool())
|
||||
@@ -216,10 +225,18 @@ let sql = format!(
|
||||
let identities: Vec<IdentityResponse> = rows
|
||||
.into_iter()
|
||||
.map(|r| {
|
||||
let file_bindings: Vec<FileBinding> = r.6.as_array()
|
||||
.map(|arr| arr.iter().filter_map(|v| serde_json::from_value(v.clone()).ok()).collect())
|
||||
.unwrap_or_default();
|
||||
let file_uuids: Vec<String> = file_bindings.iter().map(|fb| fb.file_uuid.clone()).collect();
|
||||
let file_bindings: Vec<FileBinding> =
|
||||
r.6.as_array()
|
||||
.map(|arr| {
|
||||
arr.iter()
|
||||
.filter_map(|v| serde_json::from_value(v.clone()).ok())
|
||||
.collect()
|
||||
})
|
||||
.unwrap_or_default();
|
||||
let file_uuids: Vec<String> = file_bindings
|
||||
.iter()
|
||||
.map(|fb| fb.file_uuid.clone())
|
||||
.collect();
|
||||
IdentityResponse {
|
||||
id: r.0,
|
||||
identity_uuid: r.1.to_string().replace('-', ""),
|
||||
@@ -332,149 +349,57 @@ pub struct IdentityListResponse {
|
||||
async fn list_face_candidates(
|
||||
Query(query): Query<FaceCandidatesQuery>,
|
||||
) -> Result<Json<FaceCandidatesResponse>, (StatusCode, String)> {
|
||||
let db = match PostgresDb::init().await {
|
||||
Ok(db) => db,
|
||||
Err(e) => {
|
||||
return Err((
|
||||
StatusCode::INTERNAL_SERVER_ERROR,
|
||||
format!("Failed to connect to database: {}", e),
|
||||
))
|
||||
}
|
||||
};
|
||||
|
||||
let page = query.page.unwrap_or(1);
|
||||
let page_size = std::cmp::min(query.page_size.unwrap_or(15), 100);
|
||||
let offset = (page - 1) * page_size;
|
||||
let min_confidence = query.min_confidence.unwrap_or(0.5);
|
||||
|
||||
let table = crate::core::db::schema::table_name("face_detections");
|
||||
// Query Qdrant _faces for unbound faces (identity_id IS NULL)
|
||||
let qdrant = crate::core::db::qdrant_db::QdrantDb::new();
|
||||
let mut filter_must = vec![
|
||||
serde_json::json!({"is_null": {"key": "identity_id"}}),
|
||||
serde_json::json!({"key": "confidence", "range": {"gte": min_confidence}}),
|
||||
];
|
||||
if let Some(ref file_uuid) = query.file_uuid {
|
||||
filter_must.push(serde_json::json!({"key": "file_uuid", "match": {"value": file_uuid}}));
|
||||
}
|
||||
let scroll_filter = serde_json::json!({"must": filter_must});
|
||||
|
||||
let total: i64 = if let Some(file_uuid) = &query.file_uuid {
|
||||
let count_sql = format!(
|
||||
"SELECT COUNT(*) FROM {} WHERE identity_id IS NULL AND confidence >= $1 AND file_uuid = $2",
|
||||
table
|
||||
);
|
||||
match sqlx::query_scalar(&count_sql)
|
||||
.bind(min_confidence)
|
||||
.bind(file_uuid)
|
||||
.fetch_one(db.pool())
|
||||
.await
|
||||
{
|
||||
Ok(count) => count,
|
||||
Err(e) => {
|
||||
return Err((
|
||||
StatusCode::INTERNAL_SERVER_ERROR,
|
||||
format!("Count error: {}", e),
|
||||
))
|
||||
}
|
||||
}
|
||||
} else {
|
||||
let count_sql = format!(
|
||||
"SELECT COUNT(*) FROM {} WHERE identity_id IS NULL AND confidence >= $1",
|
||||
table
|
||||
);
|
||||
match sqlx::query_scalar(&count_sql)
|
||||
.bind(min_confidence)
|
||||
.fetch_one(db.pool())
|
||||
.await
|
||||
{
|
||||
Ok(count) => count,
|
||||
Err(e) => {
|
||||
return Err((
|
||||
StatusCode::INTERNAL_SERVER_ERROR,
|
||||
format!("Count error: {}", e),
|
||||
))
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
let rows = if let Some(file_uuid) = &query.file_uuid {
|
||||
let sql = format!(
|
||||
"SELECT id, face_id, file_uuid, frame_number::bigint, confidence::float4,
|
||||
jsonb_build_object('x', x, 'y', y, 'width', width, 'height', height) as bbox,
|
||||
NULL::jsonb as attributes
|
||||
FROM {}
|
||||
WHERE identity_id IS NULL AND confidence >= $1 AND file_uuid = $2
|
||||
ORDER BY confidence DESC
|
||||
LIMIT $3 OFFSET $4",
|
||||
table
|
||||
);
|
||||
match sqlx::query_as::<
|
||||
_,
|
||||
(
|
||||
i32,
|
||||
Option<String>,
|
||||
String,
|
||||
i64,
|
||||
f32,
|
||||
Option<serde_json::Value>,
|
||||
Option<serde_json::Value>,
|
||||
),
|
||||
>(&sql)
|
||||
.bind(min_confidence)
|
||||
.bind(file_uuid)
|
||||
.bind(page_size as i64)
|
||||
.bind(offset as i64)
|
||||
.fetch_all(db.pool())
|
||||
let all_points = qdrant
|
||||
.scroll_all_points("_faces", scroll_filter, 1000)
|
||||
.await
|
||||
{
|
||||
Ok(rows) => rows,
|
||||
Err(e) => {
|
||||
return Err((
|
||||
StatusCode::INTERNAL_SERVER_ERROR,
|
||||
format!("Query error: {}", e),
|
||||
))
|
||||
}
|
||||
}
|
||||
} else {
|
||||
let sql = format!(
|
||||
"SELECT id, face_id, file_uuid, frame_number::bigint, confidence::float4,
|
||||
jsonb_build_object('x', x, 'y', y, 'width', width, 'height', height) as bbox,
|
||||
NULL::jsonb as attributes
|
||||
FROM {}
|
||||
WHERE identity_id IS NULL AND confidence >= $1
|
||||
ORDER BY confidence DESC
|
||||
LIMIT $2 OFFSET $3",
|
||||
table
|
||||
);
|
||||
match sqlx::query_as::<
|
||||
_,
|
||||
.map_err(|e| {
|
||||
(
|
||||
i32,
|
||||
Option<String>,
|
||||
String,
|
||||
i64,
|
||||
f32,
|
||||
Option<serde_json::Value>,
|
||||
Option<serde_json::Value>,
|
||||
),
|
||||
>(&sql)
|
||||
.bind(min_confidence)
|
||||
.bind(page_size as i64)
|
||||
.bind(offset as i64)
|
||||
.fetch_all(db.pool())
|
||||
.await
|
||||
{
|
||||
Ok(rows) => rows,
|
||||
Err(e) => {
|
||||
return Err((
|
||||
StatusCode::INTERNAL_SERVER_ERROR,
|
||||
format!("Query error: {}", e),
|
||||
))
|
||||
}
|
||||
}
|
||||
};
|
||||
StatusCode::INTERNAL_SERVER_ERROR,
|
||||
format!("Qdrant scroll failed: {}", e),
|
||||
)
|
||||
})?;
|
||||
|
||||
let candidates: Vec<FaceCandidate> = rows
|
||||
let total = all_points.len() as i64;
|
||||
|
||||
// Sort by confidence DESC then paginate
|
||||
let mut sorted: Vec<&serde_json::Value> = all_points.iter().collect();
|
||||
sorted.sort_by(|a, b| {
|
||||
let ca = a["payload"]["confidence"].as_f64().unwrap_or(0.0);
|
||||
let cb = b["payload"]["confidence"].as_f64().unwrap_or(0.0);
|
||||
cb.partial_cmp(&ca).unwrap_or(std::cmp::Ordering::Equal)
|
||||
});
|
||||
let paginated: Vec<&&serde_json::Value> = sorted.iter().skip(offset).take(page_size).collect();
|
||||
|
||||
let candidates: Vec<FaceCandidate> = paginated
|
||||
.into_iter()
|
||||
.map(|r| FaceCandidate {
|
||||
id: r.0,
|
||||
face_id: r.1,
|
||||
file_uuid: r.2,
|
||||
frame_number: r.3,
|
||||
confidence: r.4,
|
||||
bbox: r.5,
|
||||
attributes: r.6,
|
||||
.map(|p| {
|
||||
let payload = &p["payload"];
|
||||
let point_id = p["id"].as_u64().unwrap_or(0);
|
||||
FaceCandidate {
|
||||
id: point_id as i32,
|
||||
face_id: Some(format!("{:x}", point_id)),
|
||||
file_uuid: payload["file_uuid"].as_str().unwrap_or("").to_string(),
|
||||
frame_number: payload["frame"].as_i64().unwrap_or(0),
|
||||
confidence: payload["confidence"].as_f64().unwrap_or(0.0) as f32,
|
||||
bbox: payload.get("bbox").cloned(),
|
||||
attributes: None,
|
||||
}
|
||||
})
|
||||
.collect();
|
||||
|
||||
@@ -518,133 +443,98 @@ pub struct UnassignedTracesResponse {
|
||||
async fn list_unassigned_traces(
|
||||
Query(query): Query<UnassignedTracesQuery>,
|
||||
) -> Result<Json<UnassignedTracesResponse>, (StatusCode, String)> {
|
||||
let db = match PostgresDb::init().await {
|
||||
Ok(db) => db,
|
||||
Err(e) => {
|
||||
return Err((
|
||||
StatusCode::INTERNAL_SERVER_ERROR,
|
||||
format!("Failed to connect to database: {}", e),
|
||||
))
|
||||
}
|
||||
};
|
||||
|
||||
let page = query.page.unwrap_or(1);
|
||||
let page_size = std::cmp::min(query.page_size.unwrap_or(20), 100);
|
||||
let offset = (page - 1) * page_size;
|
||||
|
||||
let table = crate::core::db::schema::table_name("face_detections");
|
||||
// Query Qdrant _faces for unbound traces (identity_id IS NULL, trace_id > 0)
|
||||
let qdrant = crate::core::db::qdrant_db::QdrantDb::new();
|
||||
let mut filter_must: Vec<serde_json::Value> = vec![
|
||||
serde_json::json!({"is_null": {"key": "identity_id"}}),
|
||||
serde_json::json!({"key": "trace_id", "range": {"gt": 0}}),
|
||||
];
|
||||
if let Some(ref file_uuid) = query.file_uuid {
|
||||
filter_must.push(serde_json::json!({"key": "file_uuid", "match": {"value": file_uuid}}));
|
||||
}
|
||||
let scroll_filter = serde_json::json!({"must": filter_must});
|
||||
|
||||
let total: i64 = if let Some(file_uuid) = &query.file_uuid {
|
||||
let count_sql = format!(
|
||||
"SELECT COUNT(DISTINCT trace_id) FROM {} WHERE identity_id IS NULL AND trace_id IS NOT NULL AND file_uuid = $1",
|
||||
table
|
||||
);
|
||||
sqlx::query_scalar(&count_sql)
|
||||
.bind(file_uuid)
|
||||
.fetch_one(db.pool())
|
||||
.await
|
||||
.map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, format!("Count error: {}", e)))?
|
||||
} else {
|
||||
let count_sql = format!(
|
||||
"SELECT COUNT(DISTINCT trace_id) FROM {} WHERE identity_id IS NULL AND trace_id IS NOT NULL",
|
||||
table
|
||||
);
|
||||
sqlx::query_scalar(&count_sql)
|
||||
.fetch_one(db.pool())
|
||||
.await
|
||||
.map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, format!("Count error: {}", e)))?
|
||||
};
|
||||
|
||||
let sql = if let Some(file_uuid) = &query.file_uuid {
|
||||
format!(
|
||||
"WITH trace_agg AS (
|
||||
SELECT trace_id, file_uuid,
|
||||
COUNT(*) as frame_count,
|
||||
MIN(frame_number) as start_frame,
|
||||
MAX(frame_number) as end_frame
|
||||
FROM {}
|
||||
WHERE identity_id IS NULL AND trace_id IS NOT NULL AND file_uuid = $1
|
||||
GROUP BY trace_id, file_uuid
|
||||
),
|
||||
best_face AS (
|
||||
SELECT DISTINCT ON (fd.trace_id, fd.file_uuid)
|
||||
fd.trace_id, fd.file_uuid, fd.id as best_face_id,
|
||||
fd.frame_number as best_face_frame,
|
||||
fd.confidence as best_face_confidence,
|
||||
jsonb_build_object('x', fd.x, 'y', fd.y, 'width', fd.width, 'height', fd.height) as best_face_bbox
|
||||
FROM {} fd
|
||||
WHERE fd.identity_id IS NULL AND fd.trace_id IS NOT NULL AND fd.file_uuid = $1
|
||||
ORDER BY fd.trace_id, fd.file_uuid, fd.confidence DESC
|
||||
let all_points = qdrant
|
||||
.scroll_all_points("_faces", scroll_filter, 1000)
|
||||
.await
|
||||
.map_err(|e| {
|
||||
(
|
||||
StatusCode::INTERNAL_SERVER_ERROR,
|
||||
format!("Qdrant scroll failed: {}", e),
|
||||
)
|
||||
SELECT ta.trace_id, ta.file_uuid, ta.frame_count, ta.start_frame, ta.end_frame,
|
||||
bf.best_face_id, bf.best_face_frame, bf.best_face_confidence, bf.best_face_bbox
|
||||
FROM trace_agg ta
|
||||
JOIN best_face bf ON ta.trace_id = bf.trace_id AND ta.file_uuid = bf.file_uuid
|
||||
ORDER BY ta.frame_count DESC
|
||||
LIMIT $2 OFFSET $3",
|
||||
table, table
|
||||
)
|
||||
} else {
|
||||
format!(
|
||||
"WITH trace_agg AS (
|
||||
SELECT trace_id, file_uuid,
|
||||
COUNT(*) as frame_count,
|
||||
MIN(frame_number) as start_frame,
|
||||
MAX(frame_number) as end_frame
|
||||
FROM {}
|
||||
WHERE identity_id IS NULL AND trace_id IS NOT NULL
|
||||
GROUP BY trace_id, file_uuid
|
||||
),
|
||||
best_face AS (
|
||||
SELECT DISTINCT ON (fd.trace_id, fd.file_uuid)
|
||||
fd.trace_id, fd.file_uuid, fd.id as best_face_id,
|
||||
fd.frame_number as best_face_frame,
|
||||
fd.confidence as best_face_confidence,
|
||||
jsonb_build_object('x', fd.x, 'y', fd.y, 'width', fd.width, 'height', fd.height) as best_face_bbox
|
||||
FROM {} fd
|
||||
WHERE fd.identity_id IS NULL AND fd.trace_id IS NOT NULL
|
||||
ORDER BY fd.trace_id, fd.file_uuid, fd.confidence DESC
|
||||
)
|
||||
SELECT ta.trace_id, ta.file_uuid, ta.frame_count, ta.start_frame, ta.end_frame,
|
||||
bf.best_face_id, bf.best_face_frame, bf.best_face_confidence, bf.best_face_bbox
|
||||
FROM trace_agg ta
|
||||
JOIN best_face bf ON ta.trace_id = bf.trace_id AND ta.file_uuid = bf.file_uuid
|
||||
ORDER BY ta.frame_count DESC
|
||||
LIMIT $1 OFFSET $2",
|
||||
table, table
|
||||
)
|
||||
};
|
||||
})?;
|
||||
|
||||
let rows: Vec<(i32, String, i64, i64, i64, i32, i64, f64, Option<serde_json::Value>)> =
|
||||
if let Some(file_uuid) = &query.file_uuid {
|
||||
sqlx::query_as(&sql)
|
||||
.bind(file_uuid)
|
||||
.bind(page_size as i64)
|
||||
.bind(offset as i64)
|
||||
.fetch_all(db.pool())
|
||||
.await
|
||||
.map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, format!("Query error: {}", e)))?
|
||||
} else {
|
||||
sqlx::query_as(&sql)
|
||||
.bind(page_size as i64)
|
||||
.bind(offset as i64)
|
||||
.fetch_all(db.pool())
|
||||
.await
|
||||
.map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, format!("Query error: {}", e)))?
|
||||
// Group by (file_uuid, trace_id) and aggregate
|
||||
use std::collections::BTreeMap;
|
||||
#[derive(Default)]
|
||||
struct TraceAgg {
|
||||
frame_count: i64,
|
||||
start_frame: i64,
|
||||
end_frame: i64,
|
||||
best_confidence: f64,
|
||||
best_point_id: i64,
|
||||
best_frame: i64,
|
||||
best_bbox: Option<serde_json::Value>,
|
||||
}
|
||||
|
||||
let mut trace_map: BTreeMap<(String, i32), TraceAgg> = BTreeMap::new();
|
||||
for point in &all_points {
|
||||
let payload = &point["payload"];
|
||||
let file_uuid = match payload["file_uuid"].as_str() {
|
||||
Some(f) => f.to_string(),
|
||||
None => continue,
|
||||
};
|
||||
let trace_id = payload["trace_id"].as_i64().unwrap_or(0) as i32;
|
||||
if trace_id <= 0 {
|
||||
continue;
|
||||
}
|
||||
let frame = payload["frame"].as_i64().unwrap_or(0);
|
||||
let confidence = payload["confidence"].as_f64().unwrap_or(0.0);
|
||||
let point_id = point["id"].as_i64().unwrap_or(0);
|
||||
|
||||
let traces: Vec<UnassignedTrace> = rows
|
||||
let entry = trace_map.entry((file_uuid, trace_id)).or_default();
|
||||
entry.frame_count += 1;
|
||||
if frame < entry.start_frame || entry.start_frame == 0 {
|
||||
entry.start_frame = frame;
|
||||
}
|
||||
if frame > entry.end_frame {
|
||||
entry.end_frame = frame;
|
||||
}
|
||||
if confidence > entry.best_confidence {
|
||||
entry.best_confidence = confidence;
|
||||
entry.best_point_id = point_id;
|
||||
entry.best_frame = frame;
|
||||
entry.best_bbox = payload.get("bbox").cloned();
|
||||
}
|
||||
}
|
||||
|
||||
let total = trace_map.len() as i64;
|
||||
|
||||
// Sort by frame_count DESC, paginate
|
||||
let mut sorted_traces: Vec<((String, i32), TraceAgg)> = trace_map.into_iter().collect();
|
||||
sorted_traces.sort_by(|a, b| b.1.frame_count.cmp(&a.1.frame_count));
|
||||
let paginated: Vec<_> = sorted_traces
|
||||
.into_iter()
|
||||
.map(|r| UnassignedTrace {
|
||||
trace_id: r.0,
|
||||
file_uuid: r.1,
|
||||
frame_count: r.2,
|
||||
start_frame: r.3,
|
||||
end_frame: r.4,
|
||||
best_face_id: r.5,
|
||||
best_face_frame: r.6,
|
||||
best_face_confidence: r.7,
|
||||
best_face_bbox: r.8,
|
||||
.skip(offset)
|
||||
.take(page_size)
|
||||
.collect();
|
||||
|
||||
let traces: Vec<UnassignedTrace> = paginated
|
||||
.into_iter()
|
||||
.map(|((file_uuid, trace_id), agg)| UnassignedTrace {
|
||||
trace_id,
|
||||
file_uuid,
|
||||
frame_count: agg.frame_count,
|
||||
start_frame: agg.start_frame,
|
||||
end_frame: agg.end_frame,
|
||||
best_face_id: agg.best_point_id as i32,
|
||||
best_face_frame: agg.best_frame,
|
||||
best_face_confidence: agg.best_confidence,
|
||||
best_face_bbox: agg.best_bbox,
|
||||
})
|
||||
.collect();
|
||||
|
||||
|
||||
+351
-222
@@ -8,10 +8,14 @@ use axum::{
|
||||
use serde::{Deserialize, Serialize};
|
||||
use sqlx::Row;
|
||||
use std::path::PathBuf;
|
||||
use std::sync::Arc;
|
||||
|
||||
use crate::api::types::AppState;
|
||||
use crate::core::db::schema;
|
||||
use crate::core::db::PostgresDb;
|
||||
use crate::core::db::QdrantDb;
|
||||
use crate::core::progress::{AgentPhase, AgentProgress, AgentStats, publish_agent_progress};
|
||||
use crate::core::db::redis_client::RedisClient;
|
||||
|
||||
pub fn identity_agent_routes() -> Router<AppState> {
|
||||
Router::new()
|
||||
@@ -27,10 +31,7 @@ pub fn identity_agent_routes() -> Router<AppState> {
|
||||
"/api/v1/agents/identity/generate-seeds",
|
||||
post(generate_seeds_handler),
|
||||
)
|
||||
.route(
|
||||
"/api/v1/agents/identity/run",
|
||||
post(run_identity_handler),
|
||||
)
|
||||
.route("/api/v1/agents/identity/run", post(run_identity_handler))
|
||||
.route(
|
||||
"/api/v1/agents/identity/confirm",
|
||||
post(confirm_identity_handler),
|
||||
@@ -209,39 +210,42 @@ async fn match_from_photo(
|
||||
}
|
||||
};
|
||||
|
||||
// 4. Find best matching trace (highest similarity, no threshold)
|
||||
let fd_table = schema::table_name("face_detections");
|
||||
let best_match: Option<(i32, i32, f64)> = sqlx::query_as(&format!(
|
||||
r#"SELECT id, trace_id,
|
||||
1 - (embedding::vector <=> $1::vector) as similarity
|
||||
FROM {}
|
||||
WHERE file_uuid = $2 AND embedding IS NOT NULL
|
||||
ORDER BY embedding::vector <=> $1::vector
|
||||
LIMIT 1"#,
|
||||
fd_table
|
||||
))
|
||||
.bind(&embedding_f32)
|
||||
.bind(&file_uuid)
|
||||
.fetch_optional(state.db.pool())
|
||||
.await
|
||||
.map_err(|e| {
|
||||
(
|
||||
StatusCode::INTERNAL_SERVER_ERROR,
|
||||
Json(serde_json::json!({"message": format!("Search failed: {}", e)})),
|
||||
)
|
||||
})?;
|
||||
// 4. Find best matching trace via Qdrant _faces search
|
||||
let qdrant = QdrantDb::new();
|
||||
|
||||
// 5. Update best match face_detection
|
||||
let best_match: Option<(i32, f64)> = match qdrant.search_face_collection(
|
||||
"_faces",
|
||||
&embedding_f32,
|
||||
1,
|
||||
"file_uuid",
|
||||
"",
|
||||
Some(&file_uuid),
|
||||
).await {
|
||||
Ok(hits) if !hits.is_empty() => {
|
||||
let (score, payload) = &hits[0];
|
||||
let trace_id = payload.get("trace_id").and_then(|v| v.as_i64()).unwrap_or(0) as i32;
|
||||
Some((trace_id, *score))
|
||||
}
|
||||
_ => None,
|
||||
};
|
||||
|
||||
// 5. Update best match in Qdrant _faces (trace-scoped)
|
||||
let mut traces_matched: Vec<i32> = Vec::new();
|
||||
if let Some((fb_id, fb_trace, fb_sim)) = best_match {
|
||||
let _ = sqlx::query(&format!(
|
||||
"UPDATE {} SET identity_id = $1 WHERE id = $2",
|
||||
fd_table
|
||||
))
|
||||
.bind(identity_id)
|
||||
.bind(fb_id)
|
||||
.execute(state.db.pool())
|
||||
.await;
|
||||
if let Some((fb_trace, fb_sim)) = best_match {
|
||||
let qdrant = QdrantDb::new();
|
||||
let filter = serde_json::json!({
|
||||
"must": [
|
||||
{"key": "file_uuid", "match": {"value": file_uuid}},
|
||||
{"key": "trace_id", "match": {"value": fb_trace}}
|
||||
]
|
||||
});
|
||||
let payload = serde_json::json!({"identity_id": identity_id});
|
||||
if let Err(e) = qdrant
|
||||
.update_payload_by_filter("_faces", filter, payload)
|
||||
.await
|
||||
{
|
||||
tracing::warn!("[match_from_photo] Qdrant update failed: {}", e);
|
||||
}
|
||||
traces_matched.push(fb_trace);
|
||||
|
||||
// 6. Save identity file
|
||||
@@ -283,25 +287,26 @@ async fn match_from_trace(
|
||||
) -> Result<Json<MatchFromPhotoResponse>, (StatusCode, Json<serde_json::Value>)> {
|
||||
let uuid_clean = req.identity_uuid.replace('-', "");
|
||||
|
||||
// 1. Get 3 best face embeddings from this trace at different angles
|
||||
// Divide trace frame range into 3 segments, pick best face from each
|
||||
let fd_table = schema::table_name("face_detections");
|
||||
let all_faces: Vec<(Vec<f32>, i64)> = sqlx::query_as::<_, (Vec<f32>, i64)>(&format!(
|
||||
"SELECT embedding, frame_number FROM {} \
|
||||
WHERE file_uuid = $1 AND trace_id = $2 AND embedding IS NOT NULL \
|
||||
ORDER BY frame_number ASC",
|
||||
fd_table
|
||||
))
|
||||
.bind(&req.file_uuid)
|
||||
.bind(req.trace_id)
|
||||
.fetch_all(state.db.pool())
|
||||
.await
|
||||
.map_err(|e| {
|
||||
(
|
||||
StatusCode::INTERNAL_SERVER_ERROR,
|
||||
Json(serde_json::json!({"message": format!("DB error: {}", e)})),
|
||||
)
|
||||
})?;
|
||||
// 1. Get face embeddings from Qdrant _faces for this trace
|
||||
let qdrant = QdrantDb::new();
|
||||
let trace_filter = serde_json::json!({
|
||||
"must": [
|
||||
{"key": "file_uuid", "match": {"value": req.file_uuid}},
|
||||
{"key": "trace_id", "match": {"value": req.trace_id}}
|
||||
]
|
||||
});
|
||||
let points = qdrant.scroll_all_points("_faces", trace_filter, 500).await.unwrap_or_default();
|
||||
|
||||
let all_faces: Vec<(Vec<f32>, i64)> = points.iter().filter_map(|p| {
|
||||
let vector = p.get("vector").and_then(|v| v.as_array())?;
|
||||
let embedding: Vec<f32> = vector.iter().filter_map(|v| v.as_f64().map(|f| f as f32)).collect();
|
||||
let frame = p["payload"]["frame"].as_i64()?;
|
||||
if embedding.len() == 512 {
|
||||
Some((embedding, frame))
|
||||
} else {
|
||||
None
|
||||
}
|
||||
}).collect();
|
||||
|
||||
if all_faces.is_empty() {
|
||||
return Err((
|
||||
@@ -322,18 +327,14 @@ async fn match_from_trace(
|
||||
|
||||
let mut query_embeddings: Vec<Vec<f32>> = Vec::new();
|
||||
|
||||
// Get width*height info if available (not all pipelines store it)
|
||||
let face_sizes: Vec<(i64, i32)> = sqlx::query_as::<_, (i64, i32)>(&format!(
|
||||
"SELECT frame_number, COALESCE(width, 0) * COALESCE(height, 0) AS area \
|
||||
FROM {} WHERE file_uuid = $1 AND trace_id = $2 AND embedding IS NOT NULL \
|
||||
ORDER BY frame_number ASC",
|
||||
fd_table
|
||||
))
|
||||
.bind(&req.file_uuid)
|
||||
.bind(req.trace_id)
|
||||
.fetch_all(state.db.pool())
|
||||
.await
|
||||
.unwrap_or_default();
|
||||
// Get bbox size info from Qdrant payload
|
||||
let face_sizes: Vec<(i64, i32)> = points.iter().filter_map(|p| {
|
||||
let frame = p["payload"]["frame"].as_i64()?;
|
||||
let bbox = &p["payload"]["bbox"];
|
||||
let w = bbox["width"].as_f64().unwrap_or(0.0) as i32;
|
||||
let h = bbox["height"].as_f64().unwrap_or(0.0) as i32;
|
||||
Some((frame, w * h))
|
||||
}).collect();
|
||||
|
||||
let face_sizes_map: std::collections::HashMap<i64, i32> = face_sizes.into_iter().collect();
|
||||
|
||||
@@ -358,37 +359,39 @@ async fn match_from_trace(
|
||||
query_embeddings.push(all_faces[total / 2].0.clone());
|
||||
}
|
||||
|
||||
// 2. Three angles each find their best match; union all results
|
||||
// 2. Three angles each find their best match via Qdrant; union all results
|
||||
let mut validated: Vec<(i32, i32, f64)> = Vec::new();
|
||||
let mut seen_trace_ids = std::collections::HashSet::new();
|
||||
|
||||
for qemb in &query_embeddings {
|
||||
let top = sqlx::query_as::<_, (i32, i32, f64)>(&format!(
|
||||
r#"SELECT id, trace_id,
|
||||
1 - (embedding::vector <=> $1::vector) as similarity
|
||||
FROM {}
|
||||
WHERE file_uuid = $2
|
||||
AND trace_id != $3
|
||||
AND embedding IS NOT NULL
|
||||
ORDER BY embedding::vector <=> $1::vector
|
||||
LIMIT 1"#,
|
||||
fd_table
|
||||
))
|
||||
.bind(qemb)
|
||||
.bind(&req.file_uuid)
|
||||
.bind(req.trace_id)
|
||||
.fetch_optional(state.db.pool())
|
||||
.await
|
||||
.map_err(|e| {
|
||||
(
|
||||
StatusCode::INTERNAL_SERVER_ERROR,
|
||||
Json(serde_json::json!({"message": format!("Search failed: {}", e)})),
|
||||
)
|
||||
})?;
|
||||
let filter = serde_json::json!({
|
||||
"must": [
|
||||
{"key": "file_uuid", "match": {"value": req.file_uuid}}
|
||||
],
|
||||
"must_not": [
|
||||
{"key": "trace_id", "match": {"value": req.trace_id}}
|
||||
]
|
||||
});
|
||||
|
||||
if let Some((cface_id, c_trace_id, c_sim)) = top {
|
||||
if seen_trace_ids.insert(c_trace_id) {
|
||||
validated.push((cface_id, c_trace_id, c_sim));
|
||||
let hits = match qdrant.search_face_collection(
|
||||
"_faces",
|
||||
qemb,
|
||||
1,
|
||||
"trace_id",
|
||||
&req.trace_id.to_string(),
|
||||
Some(&req.file_uuid),
|
||||
).await {
|
||||
Ok(h) => h,
|
||||
Err(e) => {
|
||||
tracing::warn!("[match_from_trace] Qdrant search failed: {}", e);
|
||||
continue;
|
||||
}
|
||||
};
|
||||
|
||||
if let Some((score, payload)) = hits.first() {
|
||||
let trace_id = payload.get("trace_id").and_then(|v| v.as_i64()).unwrap_or(0) as i32;
|
||||
if seen_trace_ids.insert(trace_id) {
|
||||
validated.push((0, trace_id, *score));
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -421,41 +424,49 @@ async fn match_from_trace(
|
||||
}
|
||||
};
|
||||
|
||||
// 4. Update matched face_detections
|
||||
// 4. Update matched traces in Qdrant _faces
|
||||
let qdrant = QdrantDb::new();
|
||||
let mut traces_matched: Vec<i32> = Vec::new();
|
||||
for (id, trace_id, _similarity) in &validated {
|
||||
if let Err(e) = sqlx::query(&format!(
|
||||
"UPDATE {} SET identity_id = $1 WHERE id = $2",
|
||||
fd_table
|
||||
))
|
||||
.bind(identity_id)
|
||||
.bind(id)
|
||||
.execute(state.db.pool())
|
||||
.await
|
||||
for (_id, trace_id, _similarity) in &validated {
|
||||
let filter = serde_json::json!({
|
||||
"must": [
|
||||
{"key": "file_uuid", "match": {"value": req.file_uuid}},
|
||||
{"key": "trace_id", "match": {"value": trace_id}}
|
||||
]
|
||||
});
|
||||
let payload = serde_json::json!({"identity_id": identity_id});
|
||||
if let Err(e) = qdrant
|
||||
.update_payload_by_filter("_faces", filter, payload)
|
||||
.await
|
||||
{
|
||||
tracing::warn!(
|
||||
"[match-from-trace] Failed to update face_detection {}: {}",
|
||||
id,
|
||||
"[match-from-trace] Qdrant update failed for trace {}: {}",
|
||||
trace_id,
|
||||
e
|
||||
);
|
||||
} else {
|
||||
if !traces_matched.contains(trace_id) {
|
||||
traces_matched.push(*trace_id);
|
||||
}
|
||||
} else if !traces_matched.contains(trace_id) {
|
||||
traces_matched.push(*trace_id);
|
||||
}
|
||||
}
|
||||
|
||||
// 5. Also bind the source trace itself
|
||||
let _ = sqlx::query(&format!(
|
||||
"UPDATE {} SET identity_id = $1 WHERE file_uuid = $2 AND trace_id = $3",
|
||||
fd_table
|
||||
))
|
||||
.bind(identity_id)
|
||||
.bind(&req.file_uuid)
|
||||
.bind(req.trace_id)
|
||||
.execute(state.db.pool())
|
||||
.await;
|
||||
|
||||
let filter = serde_json::json!({
|
||||
"must": [
|
||||
{"key": "file_uuid", "match": {"value": req.file_uuid}},
|
||||
{"key": "trace_id", "match": {"value": req.trace_id}}
|
||||
]
|
||||
});
|
||||
let payload = serde_json::json!({"identity_id": identity_id});
|
||||
if let Err(e) = qdrant
|
||||
.update_payload_by_filter("_faces", filter, payload)
|
||||
.await
|
||||
{
|
||||
tracing::warn!(
|
||||
"[match-from-trace] Qdrant update failed for source trace {}: {}",
|
||||
req.trace_id,
|
||||
e
|
||||
);
|
||||
}
|
||||
if !traces_matched.contains(&req.trace_id) {
|
||||
traces_matched.push(req.trace_id);
|
||||
}
|
||||
@@ -667,33 +678,34 @@ fn average_embeddings<'a>(embeddings: impl Iterator<Item = &'a Vec<f32>>) -> Vec
|
||||
async fn match_faces_iterative(pool: &sqlx::PgPool, file_uuid: &str) -> anyhow::Result<usize> {
|
||||
use crate::core::processor::executor::PythonExecutor;
|
||||
use std::time::Duration;
|
||||
|
||||
|
||||
let executor = PythonExecutor::new()?;
|
||||
|
||||
|
||||
let output_dir = std::env::var("MOMENTRY_OUTPUT_DIR")
|
||||
.unwrap_or_else(|_| "/Users/accusys/momentry/output".to_string());
|
||||
|
||||
|
||||
let output_path = std::path::PathBuf::from(&output_dir)
|
||||
.join(file_uuid)
|
||||
.join(format!("{}.identity_match_round1.json", file_uuid));
|
||||
|
||||
|
||||
std::fs::create_dir_all(output_path.parent().unwrap()).ok();
|
||||
|
||||
|
||||
let scripts_dir = executor.script_dir();
|
||||
let python_path = executor.python_path();
|
||||
let script_path = scripts_dir.join("identity_matcher.py");
|
||||
|
||||
let qdrant_url = std::env::var("QDRANT_URL")
|
||||
.unwrap_or_else(|_| "http://localhost:6333".to_string());
|
||||
let qdrant_api_key = std::env::var("QDRANT_API_KEY")
|
||||
.unwrap_or_else(|_| "Test3200Test3200Test3200".to_string());
|
||||
|
||||
let qdrant_url =
|
||||
std::env::var("QDRANT_URL").unwrap_or_else(|_| "http://localhost:6333".to_string());
|
||||
let qdrant_api_key =
|
||||
std::env::var("QDRANT_API_KEY").unwrap_or_else(|_| "Test3200Test3200Test3200".to_string());
|
||||
let db_url = std::env::var("DATABASE_URL")
|
||||
.unwrap_or_else(|_| "postgresql://accusys@localhost:5432/momentry".to_string());
|
||||
|
||||
|
||||
let db_schema = std::env::var("DATABASE_SCHEMA").unwrap_or_else(|_| "public".to_string());
|
||||
let mut cmd = tokio::process::Command::new(python_path);
|
||||
cmd.env("MOMENTRY_OUTPUT_DIR", &output_dir);
|
||||
cmd.env("DATABASE_SCHEMA", "public");
|
||||
cmd.env("MOMENTRY_DB_SCHEMA", "public");
|
||||
cmd.env("DATABASE_SCHEMA", &db_schema);
|
||||
cmd.env("MOMENTRY_DB_SCHEMA", &db_schema);
|
||||
cmd.env("DATABASE_URL", &db_url);
|
||||
cmd.env("QDRANT_URL", &qdrant_url);
|
||||
cmd.env("QDRANT_API_KEY", &qdrant_api_key);
|
||||
@@ -702,42 +714,50 @@ async fn match_faces_iterative(pool: &sqlx::PgPool, file_uuid: &str) -> anyhow::
|
||||
cmd.arg("--round").arg("1");
|
||||
cmd.arg("--mark-tkg");
|
||||
cmd.arg("--output").arg(&output_path);
|
||||
|
||||
|
||||
cmd.stdout(std::process::Stdio::piped());
|
||||
cmd.stderr(std::process::Stdio::piped());
|
||||
|
||||
|
||||
tracing::info!("[FaceMatch] Starting identity_matcher for {}", file_uuid);
|
||||
|
||||
|
||||
let output = cmd.output().await?;
|
||||
|
||||
|
||||
let stdout = String::from_utf8_lossy(&output.stdout);
|
||||
let stderr = String::from_utf8_lossy(&output.stderr);
|
||||
|
||||
|
||||
if !output.status.success() {
|
||||
tracing::error!("[FaceMatch] identity_matcher failed with exit code: {:?}", output.status.code());
|
||||
tracing::error!(
|
||||
"[FaceMatch] identity_matcher failed with exit code: {:?}",
|
||||
output.status.code()
|
||||
);
|
||||
tracing::error!("[FaceMatch] stderr: {}", stderr);
|
||||
tracing::error!("[FaceMatch] stdout: {}", stdout);
|
||||
return Ok(0);
|
||||
}
|
||||
|
||||
|
||||
tracing::info!("[FaceMatch] stdout: {}", stdout);
|
||||
|
||||
|
||||
if !output_path.exists() {
|
||||
tracing::info!("[FaceMatch] No matches found for {}", file_uuid);
|
||||
return Ok(0);
|
||||
}
|
||||
|
||||
|
||||
let content = std::fs::read_to_string(&output_path)?;
|
||||
let result: serde_json::Value = serde_json::from_str(&content)?;
|
||||
|
||||
|
||||
let matched = result.get("matched").and_then(|v| v.as_i64()).unwrap_or(0) as usize;
|
||||
let tkg_updated = result.get("tkg_nodes_updated").and_then(|v| v.as_i64()).unwrap_or(0) as usize;
|
||||
|
||||
let tkg_updated = result
|
||||
.get("tkg_nodes_updated")
|
||||
.and_then(|v| v.as_i64())
|
||||
.unwrap_or(0) as usize;
|
||||
|
||||
tracing::info!(
|
||||
"[FaceMatch] Round 1 for {}: {} matches, {} TKG nodes updated",
|
||||
file_uuid, matched, tkg_updated
|
||||
file_uuid,
|
||||
matched,
|
||||
tkg_updated
|
||||
);
|
||||
|
||||
|
||||
Ok(matched)
|
||||
}
|
||||
|
||||
@@ -755,17 +775,33 @@ async fn match_faces_iterative_pg(pool: &sqlx::PgPool, file_uuid: &str) -> anyho
|
||||
/// segments (speaker_id, start_time, end_time), computes overlap,
|
||||
/// and stores bindings in identity_bindings table.
|
||||
pub async fn bind_speakers(pool: &sqlx::PgPool, file_uuid: &str) -> anyhow::Result<usize> {
|
||||
// Load face traces with identity_id and frame numbers
|
||||
let fd_table = schema::table_name("face_detections");
|
||||
let traces = sqlx::query_as::<_, (i32, Vec<i32>)>(&format!(
|
||||
"SELECT trace_id, array_agg(frame_number ORDER BY frame_number) \
|
||||
FROM {} WHERE file_uuid=$1 AND trace_id IS NOT NULL AND identity_id IS NOT NULL \
|
||||
GROUP BY trace_id",
|
||||
fd_table
|
||||
))
|
||||
.bind(file_uuid)
|
||||
.fetch_all(pool)
|
||||
.await?;
|
||||
use crate::core::db::qdrant_db::QdrantDb;
|
||||
use serde_json::json;
|
||||
|
||||
// Load face traces with identity_id from Qdrant _faces
|
||||
let qdrant = QdrantDb::new();
|
||||
let trace_filter = json!({
|
||||
"must": [
|
||||
{"key": "file_uuid", "match": {"value": file_uuid}},
|
||||
{"key": "identity_id", "exists": true},
|
||||
{"key": "trace_id", "match": {"value": 1}}
|
||||
]
|
||||
});
|
||||
let points = qdrant.scroll_all_points("_faces", trace_filter, 500).await.unwrap_or_default();
|
||||
|
||||
// Group by trace_id, collect frames
|
||||
let mut traces: HashMap<i32, Vec<i64>> = HashMap::new();
|
||||
for point in &points {
|
||||
let payload = &point["payload"];
|
||||
let trace_id = payload["trace_id"].as_i64().unwrap_or(0) as i32;
|
||||
let frame = payload["frame"].as_i64().unwrap_or(0);
|
||||
traces.entry(trace_id).or_default().push(frame);
|
||||
}
|
||||
|
||||
// Sort frames per trace
|
||||
for frames in traces.values_mut() {
|
||||
frames.sort();
|
||||
}
|
||||
|
||||
if traces.is_empty() {
|
||||
tracing::info!("[SpeakerBind] No face traces with identities");
|
||||
@@ -818,8 +854,23 @@ pub async fn bind_speakers(pool: &sqlx::PgPool, file_uuid: &str) -> anyhow::Resu
|
||||
return Ok(0);
|
||||
}
|
||||
|
||||
// Get fps for frame-to-time conversion
|
||||
let fps: f64 = 25.0; // default, could also read from DB
|
||||
// Compute fps from video table
|
||||
let fps: f64 = sqlx::query_scalar::<_, f64>(
|
||||
"SELECT COALESCE(fps, 25.0) FROM videos WHERE file_uuid=$1"
|
||||
)
|
||||
.bind(file_uuid)
|
||||
.fetch_optional(pool)
|
||||
.await
|
||||
.ok()
|
||||
.flatten()
|
||||
.unwrap_or(25.0);
|
||||
|
||||
tracing::info!(
|
||||
"[SpeakerBind] Using fps={:.3} for {} ({} traces)",
|
||||
fps,
|
||||
file_uuid,
|
||||
traces.len()
|
||||
);
|
||||
|
||||
// For each trace, compute overlap with each speaker
|
||||
let mut bindings = 0usize;
|
||||
@@ -828,13 +879,15 @@ pub async fn bind_speakers(pool: &sqlx::PgPool, file_uuid: &str) -> anyhow::Resu
|
||||
continue;
|
||||
}
|
||||
|
||||
// Get identity_id for this trace
|
||||
let fd_table = schema::table_name("face_detections");
|
||||
let identity_id: Option<i32> = sqlx::query_scalar(
|
||||
&format!("SELECT identity_id FROM {} WHERE file_uuid=$1 AND trace_id=$2 AND identity_id IS NOT NULL LIMIT 1", fd_table)
|
||||
)
|
||||
.bind(file_uuid).bind(trace_id)
|
||||
.fetch_optional(pool).await?.flatten();
|
||||
// Get identity_id for this trace from Qdrant payload
|
||||
let identity_id: Option<i32> = points.iter()
|
||||
.find(|p| {
|
||||
p["payload"]["trace_id"].as_i64() == Some(*trace_id as i64)
|
||||
&& p["payload"]["identity_id"].as_i64().is_some()
|
||||
&& p["payload"]["identity_id"].as_i64().unwrap() > 0
|
||||
})
|
||||
.and_then(|p| p["payload"]["identity_id"].as_i64())
|
||||
.map(|id| id as i32);
|
||||
|
||||
if identity_id.is_none() {
|
||||
continue;
|
||||
@@ -873,18 +926,20 @@ pub async fn bind_speakers(pool: &sqlx::PgPool, file_uuid: &str) -> anyhow::Resu
|
||||
});
|
||||
|
||||
let ib_table = schema::table_name("identity_bindings");
|
||||
let _ = sqlx::query(
|
||||
&format!("INSERT INTO {} (identity_id, identity_type, identity_value, file_uuid, confidence, metadata) \
|
||||
VALUES ($1, 'speaker', $2, $3, $4, $5::jsonb) \
|
||||
ON CONFLICT (identity_id, identity_type, identity_value, file_uuid) \
|
||||
if let Err(e) = sqlx::query(
|
||||
&format!("INSERT INTO {} (identity_id, identity_type, identity_value, confidence, metadata) \
|
||||
VALUES ($1, 'speaker', $2, $3, $4::jsonb) \
|
||||
ON CONFLICT (identity_id, identity_type, identity_value) \
|
||||
DO UPDATE SET confidence = EXCLUDED.confidence, metadata = EXCLUDED.metadata", ib_table)
|
||||
)
|
||||
.bind(identity_id)
|
||||
.bind(&best_speaker)
|
||||
.bind(file_uuid)
|
||||
.bind(overlap_ratio)
|
||||
.bind(&metadata)
|
||||
.execute(pool).await;
|
||||
.execute(pool).await
|
||||
{
|
||||
tracing::error!("[SpeakerBind] INSERT failed for trace_id={}, identity_id={}, speaker={}: {}", trace_id, identity_id, best_speaker, e);
|
||||
}
|
||||
|
||||
// Also update speaker_detections with the identity_id
|
||||
let sd_table = schema::table_name("speaker_detections");
|
||||
@@ -915,16 +970,40 @@ pub async fn bind_speakers(pool: &sqlx::PgPool, file_uuid: &str) -> anyhow::Resu
|
||||
/// Pipeline-triggered entry point: runs the full identity agent for a file.
|
||||
/// Reads face_clustered.json + asrx.json, extracts persons/speakers, creates identities,
|
||||
/// runs iterative face matching, and binds speakers.
|
||||
pub async fn run_identity_agent(db: &PostgresDb, file_uuid: &str) -> anyhow::Result<()> {
|
||||
pub async fn run_identity_agent(
|
||||
db: &PostgresDb,
|
||||
file_uuid: &str,
|
||||
redis: Option<std::sync::Arc<RedisClient>>,
|
||||
) -> anyhow::Result<()> {
|
||||
let output_dir = std::env::var("MOMENTRY_OUTPUT_DIR")
|
||||
.unwrap_or_else(|_| "/Users/accusys/momentry/output".to_string());
|
||||
|
||||
let pool = db.pool();
|
||||
|
||||
// Step 1: 先跑 face matching（不需 face_clustered.json）
|
||||
let mut progress = AgentProgress::new(file_uuid);
|
||||
if let Some(r) = redis.as_ref() {
|
||||
publish_agent_progress(&r, file_uuid, &progress).await;
|
||||
}
|
||||
|
||||
// Step 1: Face matching (iterative TMDb matching)
|
||||
progress.update_phase(AgentPhase::TmdbMatching, 0.3, "Running face matching...");
|
||||
if let Some(r) = redis.as_ref() {
|
||||
publish_agent_progress(&r, file_uuid, &progress).await;
|
||||
}
|
||||
|
||||
let matched = match_faces_iterative(pool, file_uuid).await.unwrap_or(0);
|
||||
progress.stats.tmdb_matches = matched as i64;
|
||||
progress.update_phase(AgentPhase::TmdbMatching, 1.0, &format!("Face matching: {} matches", matched));
|
||||
if let Some(r) = redis.as_ref() {
|
||||
publish_agent_progress(&r, file_uuid, &progress).await;
|
||||
}
|
||||
|
||||
// Step 2: Load face_clustered.json and create identities
|
||||
progress.update_phase(AgentPhase::FaceClustering, 0.5, "Loading face clusters...");
|
||||
if let Some(r) = redis.as_ref() {
|
||||
publish_agent_progress(&r, file_uuid, &progress).await;
|
||||
}
|
||||
|
||||
// Step 2: 試著載入 face_clustered.json 建立新 identities
|
||||
let video_dir = PathBuf::from(&output_dir).join(file_uuid);
|
||||
let face_clustered_path = video_dir.join(format!("{}.face_clustered.json", file_uuid));
|
||||
let face_clustered_path = if face_clustered_path.exists() {
|
||||
@@ -947,6 +1026,8 @@ pub async fn run_identity_agent(db: &PostgresDb, file_uuid: &str) -> anyhow::Res
|
||||
let speakers = extract_speakers_from_asrx_data(&asrx_data);
|
||||
let identities = analyze_person_speaker_overlap(&persons, &speakers);
|
||||
|
||||
progress.stats.clusters = identities.len() as i64;
|
||||
|
||||
let _ = identities.len();
|
||||
if !identities.is_empty() {
|
||||
let metadata = serde_json::json!({
|
||||
@@ -969,6 +1050,13 @@ pub async fn run_identity_agent(db: &PostgresDb, file_uuid: &str) -> anyhow::Res
|
||||
.execute(pool)
|
||||
.await;
|
||||
}
|
||||
progress.stats.identities_created = identities.len() as i64;
|
||||
progress.update_phase(AgentPhase::IdentityCreation, 1.0, &format!(
|
||||
"Created {} identities from clusters", identities.len()
|
||||
));
|
||||
if let Some(r) = redis.as_ref() {
|
||||
publish_agent_progress(&r, file_uuid, &progress).await;
|
||||
}
|
||||
tracing::info!(
|
||||
"[IdentityAgent] Analyzed {} face clusters from face_clustered for {}",
|
||||
identities.len(),
|
||||
@@ -979,9 +1067,29 @@ pub async fn run_identity_agent(db: &PostgresDb, file_uuid: &str) -> anyhow::Res
|
||||
"[IdentityAgent] face_clustered.json not found for {}, skipping identity creation",
|
||||
file_uuid
|
||||
);
|
||||
progress.update_phase(AgentPhase::IdentityCreation, 0.0, "No face_clustered.json");
|
||||
if let Some(r) = redis.as_ref() {
|
||||
publish_agent_progress(&r, file_uuid, &progress).await;
|
||||
}
|
||||
}
|
||||
|
||||
// Step 3: Speaker binding
|
||||
progress.update_phase(AgentPhase::SpeakerBinding, 0.5, "Binding speakers...");
|
||||
if let Some(r) = redis.as_ref() {
|
||||
publish_agent_progress(&r, file_uuid, &progress).await;
|
||||
}
|
||||
|
||||
let bound = bind_speakers(pool, file_uuid).await.unwrap_or(0);
|
||||
progress.stats.speaker_bindings = bound as i64;
|
||||
progress.update_phase(AgentPhase::SpeakerBinding, 1.0, &format!("Speaker binding: {} bound", bound));
|
||||
if let Some(r) = redis.as_ref() {
|
||||
publish_agent_progress(&r, file_uuid, &progress).await;
|
||||
}
|
||||
|
||||
progress.mark_completed();
|
||||
if let Some(r) = redis.as_ref() {
|
||||
publish_agent_progress(&r, file_uuid, &progress).await;
|
||||
}
|
||||
|
||||
tracing::info!(
|
||||
"[IdentityAgent] Done for {}: {} face matches, {} speaker bindings",
|
||||
@@ -999,14 +1107,12 @@ async fn generate_seeds_handler(
|
||||
let db = &state.db;
|
||||
let pool = db.pool();
|
||||
|
||||
let count = generate_seed_embeddings(db)
|
||||
.await
|
||||
.map_err(|e| {
|
||||
(
|
||||
StatusCode::INTERNAL_SERVER_ERROR,
|
||||
Json(serde_json::json!({"success": false, "message": format!("{}", e)})),
|
||||
)
|
||||
})?;
|
||||
let count = generate_seed_embeddings(db).await.map_err(|e| {
|
||||
(
|
||||
StatusCode::INTERNAL_SERVER_ERROR,
|
||||
Json(serde_json::json!({"success": false, "message": format!("{}", e)})),
|
||||
)
|
||||
})?;
|
||||
|
||||
// Auto-trigger identity agent for all ready files
|
||||
if count > 0 {
|
||||
@@ -1019,13 +1125,13 @@ async fn generate_seeds_handler(
|
||||
);
|
||||
for file_uuid in &ready_files {
|
||||
let db = state.db.clone();
|
||||
let redis = crate::core::db::RedisClient::new().ok().map(Arc::new);
|
||||
let fid = file_uuid.clone();
|
||||
tokio::spawn(async move {
|
||||
match run_identity_agent(&db, &fid).await {
|
||||
Ok(_) => tracing::info!(
|
||||
"[GenerateSeeds] Identity agent completed for {}",
|
||||
fid
|
||||
),
|
||||
match run_identity_agent(&db, &fid, redis).await {
|
||||
Ok(_) => {
|
||||
tracing::info!("[GenerateSeeds] Identity agent completed for {}", fid)
|
||||
}
|
||||
Err(e) => tracing::warn!(
|
||||
"[GenerateSeeds] Identity agent failed for {}: {}",
|
||||
fid,
|
||||
@@ -1044,16 +1150,28 @@ async fn generate_seeds_handler(
|
||||
})))
|
||||
}
|
||||
|
||||
/// Find videos that are ready for identity processing (have face embeddings).
|
||||
/// Find videos that are ready for identity processing (have face embeddings in Qdrant).
|
||||
async fn find_ready_files(pool: &sqlx::PgPool) -> anyhow::Result<Vec<String>> {
|
||||
let fd_table = crate::core::db::schema::table_name("face_detections");
|
||||
let rows: Vec<(String,)> = sqlx::query_as(&format!(
|
||||
"SELECT DISTINCT file_uuid FROM {} WHERE embedding IS NOT NULL AND identity_id IS NULL",
|
||||
fd_table
|
||||
))
|
||||
.fetch_all(pool)
|
||||
.await?;
|
||||
Ok(rows.into_iter().map(|r| r.0).collect())
|
||||
use crate::core::db::qdrant_db::QdrantDb;
|
||||
use serde_json::json;
|
||||
|
||||
let qdrant = QdrantDb::new();
|
||||
// Find files with faces that don't have identity_id set
|
||||
let filter = json!({
|
||||
"must": [
|
||||
{"key": "identity_id", "match": {"value": null}}
|
||||
]
|
||||
});
|
||||
let points = qdrant.scroll_all_points("_faces", filter, 1000).await.unwrap_or_default();
|
||||
|
||||
let mut file_uuids: std::collections::HashSet<String> = std::collections::HashSet::new();
|
||||
for point in &points {
|
||||
if let Some(fu) = point["payload"]["file_uuid"].as_str() {
|
||||
file_uuids.insert(fu.to_string());
|
||||
}
|
||||
}
|
||||
|
||||
Ok(file_uuids.into_iter().collect())
|
||||
}
|
||||
|
||||
/// API handler: POST /api/v1/agents/identity/run
|
||||
@@ -1071,7 +1189,8 @@ async fn run_identity_handler(
|
||||
)
|
||||
})?;
|
||||
|
||||
match run_identity_agent(&state.db, file_uuid).await {
|
||||
let redis = crate::core::db::RedisClient::new().ok().map(Arc::new);
|
||||
match run_identity_agent(&state.db, file_uuid, redis).await {
|
||||
Ok(()) => Ok(Json(serde_json::json!({
|
||||
"success": true,
|
||||
"message": format!("Identity agent completed for {}", file_uuid),
|
||||
@@ -1109,29 +1228,28 @@ async fn confirm_identity_handler(
|
||||
Json(req): Json<ConfirmIdentityRequest>,
|
||||
) -> Result<Json<ConfirmIdentityResponse>, (StatusCode, Json<serde_json::Value>)> {
|
||||
use crate::core::processor::executor::PythonExecutor;
|
||||
|
||||
|
||||
let executor = PythonExecutor::new().map_err(|e| {
|
||||
(
|
||||
StatusCode::INTERNAL_SERVER_ERROR,
|
||||
Json(serde_json::json!({"success": false, "message": format!("PythonExecutor error: {}", e)})),
|
||||
)
|
||||
})?;
|
||||
|
||||
|
||||
let scripts_dir = executor.script_dir();
|
||||
let python_path = executor.python_path();
|
||||
let script_path = scripts_dir.join("confirm_identity.py");
|
||||
|
||||
let qdrant_url = std::env::var("QDRANT_URL")
|
||||
.unwrap_or_else(|_| "http://localhost:6333".to_string());
|
||||
let qdrant_api_key = std::env::var("QDRANT_API_KEY")
|
||||
.unwrap_or_else(|_| "Test3200Test3200Test3200".to_string());
|
||||
|
||||
let qdrant_url =
|
||||
std::env::var("QDRANT_URL").unwrap_or_else(|_| "http://localhost:6333".to_string());
|
||||
let qdrant_api_key =
|
||||
std::env::var("QDRANT_API_KEY").unwrap_or_else(|_| "Test3200Test3200Test3200".to_string());
|
||||
let db_url = std::env::var("DATABASE_URL")
|
||||
.unwrap_or_else(|_| "postgresql://accusys@localhost:5432/momentry".to_string());
|
||||
let db_schema = std::env::var("DATABASE_SCHEMA")
|
||||
.unwrap_or_else(|_| "dev".to_string());
|
||||
|
||||
let db_schema = std::env::var("DATABASE_SCHEMA").unwrap_or_else(|_| "dev".to_string());
|
||||
|
||||
let propagate = req.propagate.unwrap_or(true);
|
||||
|
||||
|
||||
let mut cmd = tokio::process::Command::new(python_path);
|
||||
cmd.env("DATABASE_URL", &db_url);
|
||||
cmd.env("DATABASE_SCHEMA", &db_schema);
|
||||
@@ -1144,31 +1262,39 @@ async fn confirm_identity_handler(
|
||||
cmd.arg("--identity-id").arg(req.identity_id.to_string());
|
||||
cmd.arg("--identity-uuid").arg(&req.identity_uuid);
|
||||
cmd.arg("--name").arg(&req.name);
|
||||
|
||||
|
||||
if !propagate {
|
||||
cmd.arg("--no-propagate");
|
||||
}
|
||||
|
||||
|
||||
cmd.stdout(std::process::Stdio::piped());
|
||||
cmd.stderr(std::process::Stdio::piped());
|
||||
|
||||
|
||||
tracing::info!(
|
||||
"[ConfirmIdentity] Starting for {} trace {} -> {} ({})",
|
||||
req.file_uuid, req.trace_id, req.identity_uuid, req.name
|
||||
req.file_uuid,
|
||||
req.trace_id,
|
||||
req.identity_uuid,
|
||||
req.name
|
||||
);
|
||||
|
||||
|
||||
let output = cmd.output().await.map_err(|e| {
|
||||
(
|
||||
StatusCode::INTERNAL_SERVER_ERROR,
|
||||
Json(serde_json::json!({"success": false, "message": format!("Command failed: {}", e)})),
|
||||
Json(
|
||||
serde_json::json!({"success": false, "message": format!("Command failed: {}", e)}),
|
||||
),
|
||||
)
|
||||
})?;
|
||||
|
||||
|
||||
let stdout = String::from_utf8_lossy(&output.stdout);
|
||||
let stderr = String::from_utf8_lossy(&output.stderr);
|
||||
|
||||
|
||||
if !output.status.success() {
|
||||
tracing::error!("[ConfirmIdentity] Script failed with exit code: {:?}", output.status.code());
|
||||
tracing::error!(
|
||||
"[ConfirmIdentity] Script failed with exit code: {:?}",
|
||||
output.status.code()
|
||||
);
|
||||
tracing::error!("[ConfirmIdentity] stderr: {}", stderr);
|
||||
tracing::error!("[ConfirmIdentity] stdout: {}", stdout);
|
||||
return Err((
|
||||
@@ -1180,9 +1306,9 @@ async fn confirm_identity_handler(
|
||||
})),
|
||||
));
|
||||
}
|
||||
|
||||
|
||||
tracing::info!("[ConfirmIdentity] stdout: {}", stdout);
|
||||
|
||||
|
||||
let json_start = stdout.find('{');
|
||||
if json_start.is_none() {
|
||||
return Err((
|
||||
@@ -1195,7 +1321,7 @@ async fn confirm_identity_handler(
|
||||
));
|
||||
}
|
||||
let json_str = &stdout[json_start.unwrap()..];
|
||||
|
||||
|
||||
let result: serde_json::Value = serde_json::from_str(json_str).map_err(|e| {
|
||||
(
|
||||
StatusCode::INTERNAL_SERVER_ERROR,
|
||||
@@ -1207,14 +1333,17 @@ async fn confirm_identity_handler(
|
||||
})),
|
||||
)
|
||||
})?;
|
||||
|
||||
|
||||
Ok(Json(ConfirmIdentityResponse {
|
||||
success: result.get("status").and_then(|v| v.as_str()) == Some("success"),
|
||||
file_uuid: req.file_uuid,
|
||||
trace_id: req.trace_id,
|
||||
identity_uuid: req.identity_uuid,
|
||||
name: req.name,
|
||||
steps: result.get("steps").cloned().unwrap_or(serde_json::json!({})),
|
||||
steps: result
|
||||
.get("steps")
|
||||
.cloned()
|
||||
.unwrap_or(serde_json::json!({})),
|
||||
propagation: result.get("propagation").cloned(),
|
||||
}))
|
||||
}
|
||||
|
||||
+214
-253
@@ -9,7 +9,7 @@ use serde::{Deserialize, Serialize};
|
||||
use sqlx::Row;
|
||||
use std::process::Command;
|
||||
|
||||
use crate::core::db::ResourceRecord;
|
||||
use crate::core::db::{QdrantDb, ResourceRecord};
|
||||
|
||||
pub fn identity_routes() -> Router<crate::api::types::AppState> {
|
||||
Router::new()
|
||||
@@ -269,12 +269,7 @@ async fn get_file_identities(
|
||||
let fi_table = crate::core::db::schema::table_name("file_identities");
|
||||
let total = match sqlx::query_scalar::<_, i64>(
|
||||
&format!(
|
||||
r#"SELECT COUNT(DISTINCT identity_id) FROM (
|
||||
SELECT identity_id FROM {} WHERE file_uuid = $1 AND identity_id IS NOT NULL
|
||||
UNION
|
||||
SELECT identity_id FROM {} WHERE file_uuid = $1
|
||||
) combined"#,
|
||||
crate::core::db::schema::table_name("face_detections"),
|
||||
r#"SELECT COUNT(DISTINCT identity_id) FROM {} WHERE file_uuid = $1 AND identity_id IS NOT NULL"#,
|
||||
fi_table
|
||||
)
|
||||
)
|
||||
@@ -419,7 +414,6 @@ async fn delete_identity(
|
||||
Extension(auth): Extension<crate::api::middleware::UserAuth>,
|
||||
Path(identity_uuid): Path<String>,
|
||||
) -> Result<StatusCode, StatusCode> {
|
||||
let table = crate::core::db::schema::table_name("face_detections");
|
||||
let id_table = crate::core::db::schema::table_name("identities");
|
||||
let history_table = crate::core::db::schema::table_name("identity_history");
|
||||
|
||||
@@ -440,15 +434,27 @@ async fn delete_identity(
|
||||
// Delete identity file from disk
|
||||
let _ = crate::core::identity::storage::delete_identity_file(&uuid_clean);
|
||||
|
||||
// Capture unbound faces before unbinding
|
||||
let unbound_faces: Vec<(String, Option<String>, Option<i32>)> = sqlx::query_as(&format!(
|
||||
"SELECT file_uuid, face_id, trace_id FROM {} WHERE identity_id = $1",
|
||||
table
|
||||
))
|
||||
.bind(identity_id)
|
||||
.fetch_all(state.db.pool())
|
||||
.await
|
||||
.map_err(|_| StatusCode::INTERNAL_SERVER_ERROR)?;
|
||||
// Capture unbound faces from Qdrant _faces before unbinding
|
||||
use crate::core::db::qdrant_db::QdrantDb;
|
||||
use serde_json::json;
|
||||
|
||||
let qdrant = QdrantDb::new();
|
||||
let face_filter = json!({
|
||||
"must": [
|
||||
{"key": "identity_id", "match": {"value": identity_id}}
|
||||
]
|
||||
});
|
||||
let points = qdrant.scroll_all_points("_faces", face_filter, 1000).await.unwrap_or_default();
|
||||
|
||||
let unbound_faces: Vec<(String, Option<String>, Option<i32>)> = points.iter()
|
||||
.filter_map(|p| {
|
||||
let payload = &p["payload"];
|
||||
let file_uuid = payload["file_uuid"].as_str()?.to_string();
|
||||
let face_id = payload.get("face_id").and_then(|v| v.as_str()).map(|s| s.to_string());
|
||||
let trace_id = payload["trace_id"].as_i64().map(|t| t as i32);
|
||||
Some((file_uuid, face_id, trace_id))
|
||||
})
|
||||
.collect();
|
||||
|
||||
let face_list: Vec<serde_json::Value> = unbound_faces
|
||||
.into_iter()
|
||||
@@ -494,15 +500,17 @@ async fn delete_identity(
|
||||
.execute(state.db.pool())
|
||||
.await;
|
||||
|
||||
// Unbind all faces
|
||||
sqlx::query(&format!(
|
||||
"UPDATE {} SET identity_id = NULL WHERE identity_id = $1",
|
||||
table
|
||||
))
|
||||
.bind(identity_id)
|
||||
.execute(state.db.pool())
|
||||
.await
|
||||
.map_err(|_| StatusCode::INTERNAL_SERVER_ERROR)?;
|
||||
// Unbind all faces in Qdrant _faces
|
||||
let qdrant = QdrantDb::new();
|
||||
let filter = serde_json::json!({
|
||||
"must": [
|
||||
{"key": "identity_id", "match": {"value": identity_id}}
|
||||
]
|
||||
});
|
||||
let payload = serde_json::json!({"identity_id": serde_json::Value::Null});
|
||||
let _ = qdrant
|
||||
.update_payload_by_filter("_faces", filter, payload)
|
||||
.await;
|
||||
|
||||
// Delete identity
|
||||
sqlx::query(&format!("DELETE FROM {} WHERE id = $1", id_table))
|
||||
@@ -572,17 +580,21 @@ async fn get_identity_files(
|
||||
})
|
||||
.collect();
|
||||
|
||||
let total = match sqlx::query_scalar::<_, i64>(&format!(
|
||||
"SELECT COUNT(DISTINCT fd.file_uuid) FROM {} fd WHERE fd.identity_id = $1",
|
||||
crate::core::db::schema::table_name("face_detections"),
|
||||
))
|
||||
.bind(identity_id)
|
||||
.fetch_one(state.db.pool())
|
||||
.await
|
||||
{
|
||||
Ok(c) => c,
|
||||
Err(_) => data.len() as i64,
|
||||
};
|
||||
// Get total from Qdrant _faces
|
||||
use crate::core::db::qdrant_db::QdrantDb;
|
||||
use serde_json::json;
|
||||
|
||||
let qdrant = QdrantDb::new();
|
||||
let face_filter = json!({
|
||||
"must": [
|
||||
{"key": "identity_id", "match": {"value": identity_id}}
|
||||
]
|
||||
});
|
||||
let points = qdrant.scroll_all_points("_faces", face_filter, 1000).await.unwrap_or_default();
|
||||
let unique_files: std::collections::HashSet<String> = points.iter()
|
||||
.filter_map(|p| p["payload"]["file_uuid"].as_str().map(|s| s.to_string()))
|
||||
.collect();
|
||||
let total = unique_files.len() as i64;
|
||||
|
||||
Ok(Json(IdentityFilesResponse {
|
||||
success: true,
|
||||
@@ -673,17 +685,14 @@ async fn get_identity_faces(
|
||||
})
|
||||
.collect();
|
||||
|
||||
let total = match sqlx::query_scalar::<_, i64>(&format!(
|
||||
"SELECT COUNT(*) FROM {} fd WHERE fd.identity_id = $1",
|
||||
crate::core::db::schema::table_name("face_detections"),
|
||||
))
|
||||
.bind(identity_id)
|
||||
.fetch_one(state.db.pool())
|
||||
.await
|
||||
{
|
||||
Ok(c) => c,
|
||||
Err(_) => data.len() as i64,
|
||||
};
|
||||
let qdrant2 = QdrantDb::new();
|
||||
let face_filter2 = serde_json::json!({
|
||||
"must": [
|
||||
{"key": "identity_id", "match": {"value": identity_id}}
|
||||
]
|
||||
});
|
||||
let points2 = qdrant2.scroll_all_points("_faces", face_filter2, 2000).await.unwrap_or_default();
|
||||
let total = points2.len() as i64;
|
||||
|
||||
Ok(Json(IdentityFacesResponse {
|
||||
success: true,
|
||||
@@ -759,151 +768,114 @@ async fn get_file_faces(
|
||||
let page_size = params.page_size.unwrap_or(50);
|
||||
let offset = ((page - 1) as i64) * (page_size as i64);
|
||||
|
||||
let fd_table = crate::core::db::schema::table_name("face_detections");
|
||||
let id_table = crate::core::db::schema::table_name("identities");
|
||||
let st_table = crate::core::db::schema::table_name("strangers");
|
||||
let video_table = crate::core::db::schema::table_name("videos");
|
||||
|
||||
// Build WHERE clauses
|
||||
let mut where_clauses = vec![format!(
|
||||
"fd.file_uuid = '{}'",
|
||||
file_uuid.replace('\'', "''")
|
||||
)];
|
||||
// Get fps
|
||||
let fps: f64 = sqlx::query_scalar(&format!(
|
||||
"SELECT COALESCE(fps, 25.0) FROM {} WHERE file_uuid = $1",
|
||||
video_table
|
||||
))
|
||||
.bind(&file_uuid)
|
||||
.fetch_optional(state.db.pool())
|
||||
.await
|
||||
.map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, e.to_string()))?
|
||||
.unwrap_or(25.0);
|
||||
|
||||
// Get face points from Qdrant _faces
|
||||
use crate::core::db::qdrant_db::QdrantDb;
|
||||
use serde_json::json;
|
||||
|
||||
let qdrant = QdrantDb::new();
|
||||
let mut filter_conditions = vec![
|
||||
json!({"key": "file_uuid", "match": {"value": file_uuid}})
|
||||
];
|
||||
|
||||
if let Some(ref binding) = params.binding {
|
||||
match binding.as_str() {
|
||||
"identity" => {
|
||||
where_clauses.push(format!("fd.identity_id IN (SELECT id FROM {})", id_table));
|
||||
filter_conditions.push(json!({"key": "identity_id", "exists": true}));
|
||||
}
|
||||
"stranger" => {
|
||||
where_clauses.push("fd.stranger_id IS NOT NULL".to_string());
|
||||
}
|
||||
"dangling" => {
|
||||
where_clauses.push(format!(
|
||||
"fd.identity_id IS NOT NULL AND NOT EXISTS (SELECT 1 FROM {} WHERE id = fd.identity_id)",
|
||||
id_table
|
||||
));
|
||||
filter_conditions.push(json!({"key": "stranger_id", "exists": true}));
|
||||
}
|
||||
"unbound" => {
|
||||
where_clauses.push("fd.identity_id IS NULL AND fd.stranger_id IS NULL".to_string());
|
||||
filter_conditions.push(json!({"key": "identity_id", "match": {"value": null}}));
|
||||
}
|
||||
_ => {}
|
||||
}
|
||||
}
|
||||
|
||||
if let Some(tid) = params.trace_id {
|
||||
where_clauses.push(format!("fd.trace_id = {}", tid));
|
||||
}
|
||||
if let Some(mc) = params.min_confidence {
|
||||
where_clauses.push(format!("fd.confidence >= {}", mc));
|
||||
}
|
||||
if let Some(sf) = params.start_frame {
|
||||
where_clauses.push(format!("fd.frame_number >= {}", sf));
|
||||
}
|
||||
if let Some(ef) = params.end_frame {
|
||||
where_clauses.push(format!("fd.frame_number <= {}", ef));
|
||||
filter_conditions.push(json!({"key": "trace_id", "match": {"value": tid}}));
|
||||
}
|
||||
|
||||
let where_sql = where_clauses.join(" AND ");
|
||||
let face_filter = json!({"must": filter_conditions});
|
||||
let points = qdrant.scroll_all_points("_faces", face_filter, 2000).await.unwrap_or_default();
|
||||
|
||||
let select_sql = format!(
|
||||
"SELECT fd.id::bigint as id, fd.file_uuid, \
|
||||
fd.frame_number::bigint as frame_number, \
|
||||
(fd.frame_number::float8 / NULLIF(v.fps, 0)) as timestamp_secs, \
|
||||
fd.face_id, fd.trace_id, \
|
||||
fd.x::float8 as x, fd.y::float8 as y, \
|
||||
fd.width::float8 as width, fd.height::float8 as height, \
|
||||
fd.confidence::float8 as confidence, \
|
||||
fd.identity_id, fd.stranger_id, \
|
||||
i.uuid::text as identity_uuid, i.name as identity_name, \
|
||||
s.metadata as stranger_metadata \
|
||||
FROM {} fd \
|
||||
JOIN {} v ON v.file_uuid = fd.file_uuid \
|
||||
LEFT JOIN {} i ON i.id = fd.identity_id \
|
||||
LEFT JOIN {} s ON s.id = fd.stranger_id \
|
||||
WHERE {} \
|
||||
ORDER BY fd.frame_number, fd.trace_id \
|
||||
LIMIT {} OFFSET {}",
|
||||
fd_table, video_table, id_table, st_table, where_sql, page_size as i64, offset
|
||||
);
|
||||
// Apply additional filters in Rust
|
||||
let filtered: Vec<_> = points.into_iter().filter(|p| {
|
||||
let payload = &p["payload"];
|
||||
let confidence = payload["confidence"].as_f64().unwrap_or(0.0);
|
||||
let frame = payload["frame"].as_i64().unwrap_or(0);
|
||||
|
||||
let count_sql = format!(
|
||||
"SELECT COUNT(*) FROM {} fd \
|
||||
WHERE {}",
|
||||
fd_table, where_sql
|
||||
);
|
||||
if let Some(mc) = params.min_confidence {
|
||||
if confidence < mc { return false; }
|
||||
}
|
||||
if let Some(sf) = params.start_frame {
|
||||
if frame < sf { return false; }
|
||||
}
|
||||
if let Some(ef) = params.end_frame {
|
||||
if frame > ef { return false; }
|
||||
}
|
||||
true
|
||||
}).collect();
|
||||
|
||||
use sqlx::Row;
|
||||
let rows = sqlx::query(&select_sql)
|
||||
.fetch_all(state.db.pool())
|
||||
.await
|
||||
.map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, e.to_string()))?;
|
||||
let total = filtered.len() as i64;
|
||||
|
||||
let total: i64 = sqlx::query_scalar(&count_sql)
|
||||
.fetch_one(state.db.pool())
|
||||
.await
|
||||
.map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, e.to_string()))?;
|
||||
// Apply pagination
|
||||
let paged: Vec<_> = filtered.into_iter().skip(offset as usize).take(page_size as usize).collect();
|
||||
|
||||
let data: Vec<FileFaceItem> = rows
|
||||
.into_iter()
|
||||
.map(|r| {
|
||||
let identity_id: Option<i32> = r.get("identity_id");
|
||||
let identity_uuid: Option<String> = r.get("identity_uuid");
|
||||
let identity_name: Option<String> = r.get("identity_name");
|
||||
let stranger_id: Option<i32> = r.get("stranger_id");
|
||||
// Build response items
|
||||
let mut data = Vec::new();
|
||||
for point in &paged {
|
||||
let payload = &point["payload"];
|
||||
let bbox = &payload["bbox"];
|
||||
let frame = payload["frame"].as_i64().unwrap_or(0);
|
||||
let confidence = payload["confidence"].as_f64().unwrap_or(0.0);
|
||||
|
||||
let binding = if let (Some(iid), Some(iuuid), Some(iname)) =
|
||||
(identity_id, identity_uuid, identity_name)
|
||||
{
|
||||
FaceBinding::Identity {
|
||||
identity_id: iid,
|
||||
identity_uuid: iuuid,
|
||||
identity_name: iname,
|
||||
}
|
||||
} else if let Some(sid) = stranger_id {
|
||||
FaceBinding::Stranger {
|
||||
stranger_id: sid,
|
||||
metadata: r
|
||||
.get::<Option<serde_json::Value>, _>("stranger_metadata")
|
||||
.unwrap_or(serde_json::Value::Null),
|
||||
}
|
||||
} else if let Some(iid) = identity_id {
|
||||
FaceBinding::Dangling {
|
||||
old_identity_id: iid,
|
||||
}
|
||||
} else {
|
||||
FaceBinding::Unbound
|
||||
};
|
||||
|
||||
FileFaceItem {
|
||||
id: r.get("id"),
|
||||
file_uuid: r.get("file_uuid"),
|
||||
frame_number: r.get("frame_number"),
|
||||
timestamp_secs: r.get("timestamp_secs"),
|
||||
face_id: r.get("face_id"),
|
||||
trace_id: r.get("trace_id"),
|
||||
bbox: BBox {
|
||||
x: r.get("x"),
|
||||
y: r.get("y"),
|
||||
width: r.get("width"),
|
||||
height: r.get("height"),
|
||||
},
|
||||
confidence: r.get("confidence"),
|
||||
binding,
|
||||
}
|
||||
})
|
||||
.collect();
|
||||
let item = FileFaceItem {
|
||||
id: 0,
|
||||
file_uuid: file_uuid.clone(),
|
||||
frame_number: frame,
|
||||
timestamp_secs: Some(frame as f64 / fps),
|
||||
face_id: payload.get("face_id").and_then(|v| v.as_str()).map(|s| s.to_string()),
|
||||
trace_id: payload["trace_id"].as_i64().map(|t| t as i32),
|
||||
bbox: BBox {
|
||||
x: bbox["x"].as_f64().unwrap_or(0.0),
|
||||
y: bbox["y"].as_f64().unwrap_or(0.0),
|
||||
width: bbox["width"].as_f64().unwrap_or(0.0),
|
||||
height: bbox["height"].as_f64().unwrap_or(0.0),
|
||||
},
|
||||
confidence,
|
||||
binding: FaceBinding::Unbound,
|
||||
};
|
||||
data.push(item);
|
||||
}
|
||||
|
||||
Ok(Json(FileFacesResponse {
|
||||
success: true,
|
||||
file_uuid,
|
||||
total,
|
||||
page,
|
||||
page_size,
|
||||
page: page as usize,
|
||||
page_size: page_size as usize,
|
||||
data,
|
||||
}))
|
||||
}
|
||||
|
||||
// --- List Face Candidates ---
|
||||
|
||||
#[derive(Debug, Serialize)]
|
||||
pub struct IdentityChunksResponse {
|
||||
pub success: bool,
|
||||
@@ -1305,76 +1277,62 @@ async fn set_profile_from_face(
|
||||
Json(req): Json<SetProfileFromFaceRequest>,
|
||||
) -> Result<Json<ProfileImageResponse>, (StatusCode, Json<serde_json::Value>)> {
|
||||
use crate::core::db::schema;
|
||||
let fd_table = schema::table_name("face_detections");
|
||||
use crate::core::db::qdrant_db::QdrantDb;
|
||||
use serde_json::json;
|
||||
let videos_table = schema::table_name("videos");
|
||||
|
||||
let uuid_clean = identity_uuid.replace('-', "");
|
||||
|
||||
let (face_identifier, use_trace, use_frame) = match (&req.face_id, req.id, req.trace_id) {
|
||||
(Some(fid), _, _) => (fid.clone(), false, None),
|
||||
(None, Some(id), _) => (id.to_string(), false, None),
|
||||
(None, None, Some(trace_id)) => (trace_id.to_string(), true, req.frame_number),
|
||||
(Some(fid), _, _) => (fid.clone(), None, None),
|
||||
(None, Some(id), _) => (id.to_string(), None, None),
|
||||
(None, None, Some(trace_id)) => (trace_id.to_string(), Some(trace_id), req.frame_number),
|
||||
(None, None, None) => {
|
||||
return Err((
|
||||
StatusCode::BAD_REQUEST,
|
||||
Json(serde_json::json!({"success": false, "message": "Either face_id, id, or trace_id is required"})),
|
||||
Json(
|
||||
serde_json::json!({"success": false, "message": "Either face_id, id, or trace_id is required"}),
|
||||
),
|
||||
));
|
||||
}
|
||||
};
|
||||
|
||||
let row: Option<(i64, i32, i32, i32, i32, f64)> = if use_trace {
|
||||
// Get face data from Qdrant _faces
|
||||
let qdrant = QdrantDb::new();
|
||||
let row: Option<(i64, i32, i32, i32, i32, f64)> = if let Some(trace_id) = use_trace {
|
||||
let mut filter_conds = vec![
|
||||
json!({"key": "file_uuid", "match": {"value": req.file_uuid}}),
|
||||
json!({"key": "trace_id", "match": {"value": trace_id}})
|
||||
];
|
||||
if let Some(frame) = use_frame {
|
||||
sqlx::query_as(&format!(
|
||||
"SELECT frame_number, x, y, width, height, confidence FROM {} WHERE file_uuid = $1 AND trace_id = $2 AND frame_number = $3 LIMIT 1",
|
||||
fd_table
|
||||
))
|
||||
.bind(&req.file_uuid)
|
||||
.bind(use_trace)
|
||||
.bind(frame as i32)
|
||||
.fetch_optional(state.db.pool())
|
||||
.await
|
||||
} else {
|
||||
sqlx::query_as(&format!(
|
||||
"SELECT frame_number, x, y, width, height, confidence FROM {} WHERE file_uuid = $1 AND trace_id = $2 ORDER BY confidence DESC LIMIT 1",
|
||||
fd_table
|
||||
))
|
||||
.bind(&req.file_uuid)
|
||||
.bind(use_trace)
|
||||
.fetch_optional(state.db.pool())
|
||||
.await
|
||||
filter_conds.push(json!({"key": "frame", "match": {"value": frame}}));
|
||||
}
|
||||
let face_filter = json!({"must": filter_conds});
|
||||
let points = qdrant.scroll_all_points("_faces", face_filter, 10).await.unwrap_or_default();
|
||||
points.first().map(|p| {
|
||||
let payload = &p["payload"];
|
||||
let bbox = &payload["bbox"];
|
||||
(
|
||||
payload["frame"].as_i64().unwrap_or(0),
|
||||
bbox["x"].as_f64().unwrap_or(0.0) as i32,
|
||||
bbox["y"].as_f64().unwrap_or(0.0) as i32,
|
||||
bbox["width"].as_f64().unwrap_or(0.0) as i32,
|
||||
bbox["height"].as_f64().unwrap_or(0.0) as i32,
|
||||
payload["confidence"].as_f64().unwrap_or(0.0),
|
||||
)
|
||||
})
|
||||
} else if req.id.is_some() {
|
||||
sqlx::query_as(&format!(
|
||||
"SELECT frame_number, x, y, width, height, confidence FROM {} WHERE file_uuid = $1 AND id = $2",
|
||||
fd_table
|
||||
))
|
||||
.bind(&req.file_uuid)
|
||||
.bind(req.id.unwrap())
|
||||
.fetch_optional(state.db.pool())
|
||||
.await
|
||||
// id lookup not supported in Qdrant - skip
|
||||
None
|
||||
} else {
|
||||
sqlx::query_as(&format!(
|
||||
"SELECT frame_number, x, y, width, height, confidence FROM {} WHERE file_uuid = $1 AND face_id = $2",
|
||||
fd_table
|
||||
))
|
||||
.bind(&req.file_uuid)
|
||||
.bind(&face_identifier)
|
||||
.fetch_optional(state.db.pool())
|
||||
.await
|
||||
}
|
||||
.map_err(|e| {
|
||||
(
|
||||
StatusCode::INTERNAL_SERVER_ERROR,
|
||||
Json(serde_json::json!({"success": false, "message": format!("DB error: {}", e)})),
|
||||
)
|
||||
})?;
|
||||
// face_id lookup not supported in Qdrant - skip
|
||||
None
|
||||
};
|
||||
|
||||
let (frame_number, x, y, width, height, confidence) = row.ok_or_else(|| {
|
||||
(
|
||||
StatusCode::NOT_FOUND,
|
||||
Json(serde_json::json!({"success": false, "message": "Face not found"})),
|
||||
)
|
||||
})?;
|
||||
let (frame_number, x, y, w, h, confidence) = row.ok_or((
|
||||
StatusCode::NOT_FOUND,
|
||||
Json(serde_json::json!({"success": false, "message": "Face not found"})),
|
||||
))?;
|
||||
|
||||
let video_row: Option<(String, Option<i32>, Option<i32>)> = sqlx::query_as(&format!(
|
||||
"SELECT file_path, width, height FROM {} WHERE file_uuid = $1",
|
||||
@@ -1400,7 +1358,7 @@ async fn set_profile_from_face(
|
||||
let vw = video_width.unwrap_or(1920);
|
||||
let vh = video_height.unwrap_or(1080);
|
||||
|
||||
crate::core::thumbnail::validator::validate_crop(x, y, width, height, vw, vh).map_err(|e| {
|
||||
crate::core::thumbnail::validator::validate_crop(x, y, w, h, vw, vh).map_err(|e| {
|
||||
(
|
||||
StatusCode::BAD_REQUEST,
|
||||
Json(serde_json::json!({"success": false, "message": format!("Crop validation failed: {}", e)})),
|
||||
@@ -1408,7 +1366,7 @@ async fn set_profile_from_face(
|
||||
})?;
|
||||
|
||||
let select = format!("select=eq(n\\,{})", frame_number);
|
||||
let vf = format!("{},crop={}:{}:{}:{}", select, width, height, x, y);
|
||||
let vf = format!("{},crop={}:{}:{}:{}", select, w, h, x, y);
|
||||
|
||||
let output = Command::new("ffmpeg")
|
||||
.args([
|
||||
@@ -1465,7 +1423,10 @@ async fn set_profile_from_face(
|
||||
success: true,
|
||||
identity_uuid: uuid_clean,
|
||||
path: file_path.to_string_lossy().to_string(),
|
||||
message: format!("Profile image set from face {} (frame {}, confidence {:.2})", face_identifier, frame_number, confidence),
|
||||
message: format!(
|
||||
"Profile image set from face {} (frame {}, confidence {:.2})",
|
||||
face_identifier, frame_number, confidence
|
||||
),
|
||||
}))
|
||||
}
|
||||
|
||||
@@ -1567,21 +1528,20 @@ async fn search_identity_text(
|
||||
) -> Result<Json<IdentityTextResponse>, StatusCode> {
|
||||
use crate::core::db::schema;
|
||||
let chunk_table = schema::table_name("chunk");
|
||||
let fd_table = schema::table_name("face_detections");
|
||||
let id_table = schema::table_name("identities");
|
||||
let ib_table = schema::table_name("identity_bindings");
|
||||
let like_q = format!("%{}%", params.q.replace('%', "%%"));
|
||||
let limit = params.limit.unwrap_or(50).min(100);
|
||||
|
||||
let sd_table = schema::table_name("speaker_detections");
|
||||
let query = format!(
|
||||
r#"SELECT c.file_uuid, c.chunk_id, c.start_time, c.end_time, c.text_content,
|
||||
fd.identity_id, i.name AS identity_name, i.source AS identity_source,
|
||||
fd.trace_id
|
||||
i.id AS identity_id, i.name AS identity_name, i.source AS identity_source,
|
||||
(c.metadata->>'trace_id')::int AS trace_id
|
||||
FROM {} c
|
||||
LEFT JOIN {} fd ON fd.file_uuid = c.file_uuid
|
||||
AND fd.frame_number BETWEEN c.start_frame AND c.end_frame
|
||||
AND fd.identity_id IS NOT NULL
|
||||
LEFT JOIN {} i ON i.id = fd.identity_id
|
||||
LEFT JOIN {} ib ON ib.identity_value = c.metadata->>'trace_id'
|
||||
AND ib.identity_type = 'trace'
|
||||
LEFT JOIN {} i ON i.id = ib.identity_id
|
||||
WHERE ($1::text IS NULL OR c.file_uuid = $1) AND (LOWER(c.text_content) LIKE LOWER($2) OR LOWER(c.content::text) LIKE LOWER($2))
|
||||
|
||||
UNION ALL
|
||||
@@ -1597,7 +1557,7 @@ async fn search_identity_text(
|
||||
|
||||
ORDER BY 3
|
||||
LIMIT $3"#,
|
||||
chunk_table, fd_table, id_table, sd_table, id_table, chunk_table
|
||||
chunk_table, ib_table, id_table, sd_table, id_table, chunk_table
|
||||
);
|
||||
|
||||
let rows = sqlx::query_as::<
|
||||
@@ -1696,7 +1656,6 @@ async fn search_identities_by_text(
|
||||
) -> Result<Json<IdentitySearchResponse>, StatusCode> {
|
||||
use crate::core::db::schema;
|
||||
let id_table = schema::table_name("identities");
|
||||
let fd_table = schema::table_name("face_detections");
|
||||
let chunk_table = schema::table_name("chunk");
|
||||
let like_q = format!("%{}%", params.q.replace('%', "%%"));
|
||||
let page = params.page.unwrap_or(1).max(1);
|
||||
@@ -1710,26 +1669,26 @@ async fn search_identities_by_text(
|
||||
|
||||
let sd_table = schema::table_name("speaker_detections");
|
||||
let ib_table = schema::table_name("identity_bindings");
|
||||
let fi_table = schema::table_name("file_identities");
|
||||
let query = format!(
|
||||
r#"WITH matched AS (
|
||||
SELECT i.id::int, i.name, i.source, i.tmdb_id,
|
||||
fd.file_uuid, fd.trace_id,
|
||||
c.file_uuid, (c.metadata->>'trace_id')::int AS trace_id,
|
||||
c.chunk_id, c.start_frame, c.end_frame, c.fps,
|
||||
c.start_time, c.end_time, c.text_content
|
||||
FROM {} i
|
||||
JOIN {} fi ON fi.identity_id = i.id
|
||||
JOIN {} ib ON ib.identity_id = i.id AND ib.identity_type = 'trace'
|
||||
JOIN {} fd ON fd.trace_id = ib.identity_value::int
|
||||
JOIN {} c ON c.file_uuid = fd.file_uuid
|
||||
AND c.start_time <= fd.frame_number / COALESCE(c.fps, 25.0)
|
||||
AND c.end_time >= fd.frame_number / COALESCE(c.fps, 25.0)
|
||||
JOIN {} c ON c.file_uuid = fi.file_uuid
|
||||
AND c.metadata->>'trace_id' = ib.identity_value
|
||||
WHERE (i.name ILIKE $1
|
||||
OR EXISTS (
|
||||
SELECT 1 FROM jsonb_array_elements(i.metadata->'aliases') AS a
|
||||
WHERE a->>'name' ILIKE $1
|
||||
))
|
||||
AND ($2::text IS NULL OR fd.file_uuid = $2)
|
||||
AND ($2::text IS NULL OR c.file_uuid = $2)
|
||||
|
||||
UNION ALL
|
||||
UNION ALL
|
||||
|
||||
SELECT i.id::int, i.name, i.source, i.tmdb_id,
|
||||
sd.file_uuid, NULL::int AS trace_id,
|
||||
@@ -1755,7 +1714,7 @@ SELECT *, COUNT(*) OVER() AS total_count
|
||||
FROM deduped
|
||||
ORDER BY name, start_time
|
||||
LIMIT $3 OFFSET $4"#,
|
||||
id_table, ib_table, fd_table, chunk_table, id_table, sd_table, chunk_table
|
||||
id_table, fi_table, ib_table, chunk_table, id_table, sd_table, chunk_table
|
||||
);
|
||||
|
||||
let rows = sqlx::query(&query)
|
||||
@@ -2093,7 +2052,6 @@ async fn undo_identity(
|
||||
|
||||
let table = crate::core::db::schema::table_name("identities");
|
||||
let history_table = crate::core::db::schema::table_name("identity_history");
|
||||
let face_table = crate::core::db::schema::table_name("face_detections");
|
||||
|
||||
// Try normal identity lookup
|
||||
let identity_row: Option<(i32,)> = sqlx::query_as(&format!(
|
||||
@@ -2174,22 +2132,23 @@ async fn undo_identity(
|
||||
)
|
||||
})?;
|
||||
|
||||
// Re-bind faces
|
||||
// Re-bind faces via Qdrant _faces
|
||||
if let Some(faces) = snapshot.get("unbound_faces").and_then(|v| v.as_array()) {
|
||||
let qdrant = QdrantDb::new();
|
||||
for face in faces {
|
||||
let file_uuid = face.get("file_uuid").and_then(|v| v.as_str());
|
||||
let face_id = face.get("face_id").and_then(|v| v.as_str());
|
||||
let trace_id = face.get("trace_id").and_then(|v| v.as_i64());
|
||||
if let (Some(fu), Some(fid)) = (file_uuid, face_id) {
|
||||
let _ = sqlx::query(&format!(
|
||||
"UPDATE {} SET identity_id = $1 WHERE file_uuid = $2 AND face_id = $3",
|
||||
face_table
|
||||
))
|
||||
.bind(new_id)
|
||||
.bind(fu)
|
||||
.bind(fid)
|
||||
.execute(state.db.pool())
|
||||
.await;
|
||||
if let (Some(fu), Some(tid)) = (file_uuid, trace_id) {
|
||||
let filter = serde_json::json!({
|
||||
"must": [
|
||||
{"key": "file_uuid", "match": {"value": fu}},
|
||||
{"key": "trace_id", "match": {"value": tid}}
|
||||
]
|
||||
});
|
||||
let payload = serde_json::json!({"identity_id": new_id});
|
||||
let _ = qdrant
|
||||
.update_payload_by_filter("_faces", filter, payload)
|
||||
.await;
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -2377,7 +2336,6 @@ async fn redo_identity(
|
||||
|
||||
let table = crate::core::db::schema::table_name("identities");
|
||||
let history_table = crate::core::db::schema::table_name("identity_history");
|
||||
let face_table = crate::core::db::schema::table_name("face_detections");
|
||||
|
||||
// Get identity_id
|
||||
let identity_id: i32 = sqlx::query_scalar(&format!(
|
||||
@@ -2417,14 +2375,17 @@ async fn redo_identity(
|
||||
// ── Delete redo: re-delete the identity ──
|
||||
let _ = crate::core::identity::storage::delete_identity_file(&uuid_clean);
|
||||
|
||||
// Unbind all faces
|
||||
let _ = sqlx::query(&format!(
|
||||
"UPDATE {} SET identity_id = NULL WHERE identity_id = $1",
|
||||
face_table
|
||||
))
|
||||
.bind(identity_id)
|
||||
.execute(state.db.pool())
|
||||
.await;
|
||||
// Unbind all faces in Qdrant _faces
|
||||
let qdrant = QdrantDb::new();
|
||||
let filter = serde_json::json!({
|
||||
"must": [
|
||||
{"key": "identity_id", "match": {"value": identity_id}}
|
||||
]
|
||||
});
|
||||
let payload = serde_json::json!({"identity_id": serde_json::Value::Null});
|
||||
let _ = qdrant
|
||||
.update_payload_by_filter("_faces", filter, payload)
|
||||
.await;
|
||||
|
||||
// Delete identity
|
||||
sqlx::query(&format!("DELETE FROM {} WHERE id = $1", table))
|
||||
|
||||
+356
-479
File diff suppressed because it is too large
Load Diff
+114
-47
@@ -7,9 +7,11 @@ use axum::{
|
||||
Router,
|
||||
};
|
||||
use once_cell::sync::Lazy;
|
||||
use serde_json::json;
|
||||
use std::collections::HashMap;
|
||||
use uuid::Uuid;
|
||||
|
||||
use crate::core::db::qdrant_db::QdrantDb;
|
||||
use crate::core::db::{schema, PostgresDb};
|
||||
|
||||
/// Shared video query params: mode=normal|debug, audio=on|off
|
||||
@@ -217,15 +219,32 @@ async fn bbox_overlay_video(
|
||||
|
||||
let start_sec = start_f as f64 / fps;
|
||||
|
||||
// Get face bboxes
|
||||
// frame_number is BIGINT (i64) in database
|
||||
let face_table = schema::table_name("face_detections");
|
||||
let rows: Vec<(i64, i32, i32, i32, i32, Option<i32>, Option<String>)> = sqlx::query_as(
|
||||
&format!("SELECT frame_number, x, y, width, height, trace_id, face_id FROM {} WHERE file_uuid = $1 AND frame_number BETWEEN $2 AND $3 ORDER BY frame_number", face_table)
|
||||
)
|
||||
.bind(face_fuid).bind(start_f).bind(end_f)
|
||||
.fetch_all(state.db.pool()).await
|
||||
.unwrap_or_else(|e| { tracing::error!("bbox query error: {}", e); vec![] });
|
||||
// Get face bboxes from Qdrant _faces
|
||||
use crate::core::db::qdrant_db::QdrantDb;
|
||||
use serde_json::json;
|
||||
|
||||
let qdrant = QdrantDb::new();
|
||||
let face_filter = json!({
|
||||
"must": [
|
||||
{"key": "file_uuid", "match": {"value": face_fuid}},
|
||||
{"key": "frame", "range": {"gte": start_f, "lte": end_f}},
|
||||
{"key": "trace_id", "match": {"value": 1}}
|
||||
]
|
||||
});
|
||||
let points = qdrant.scroll_all_points("_faces", face_filter, 500).await.unwrap_or_default();
|
||||
|
||||
let rows: Vec<(i64, i32, i32, i32, i32, Option<i32>, Option<String>)> = points.iter().filter_map(|p| {
|
||||
let payload = &p["payload"];
|
||||
let frame = payload["frame"].as_i64()?;
|
||||
let bbox = &payload["bbox"];
|
||||
let x = bbox["x"].as_f64()? as i32;
|
||||
let y = bbox["y"].as_f64()? as i32;
|
||||
let w = bbox["width"].as_f64()? as i32;
|
||||
let h = bbox["height"].as_f64()? as i32;
|
||||
let trace_id = payload["trace_id"].as_i64().map(|t| t as i32);
|
||||
let face_id = payload.get("face_id").and_then(|v| v.as_str()).map(|s| s.to_string());
|
||||
Some((frame, x, y, w, h, trace_id, face_id))
|
||||
}).collect();
|
||||
|
||||
// Build filters — each bbox enabled only on its frame
|
||||
let mut parts: Vec<String> = Vec::new();
|
||||
@@ -334,16 +353,26 @@ async fn trace_video_inner(
|
||||
.map_err(|_| StatusCode::INTERNAL_SERVER_ERROR)?;
|
||||
let (video_path, fps, _width, _height) = row.ok_or(StatusCode::NOT_FOUND)?;
|
||||
|
||||
// Query face detections to find frame range for target trace
|
||||
// frame_number is BIGINT (i64) in database
|
||||
let face_table = schema::table_name("face_detections");
|
||||
let rows: Vec<(i64, i32, i32, i32, i32)> = sqlx::query_as(&format!(
|
||||
"SELECT frame_number, x, y, width, height FROM {} WHERE file_uuid = $1 AND trace_id = $2 ORDER BY frame_number",
|
||||
face_table
|
||||
))
|
||||
.bind(&file_uuid).bind(trace_id)
|
||||
.fetch_all(state.db.pool()).await
|
||||
.unwrap_or_else(|e| { tracing::error!("trace query error: {}", e); vec![] });
|
||||
// Query face detections from Qdrant to find frame range for target trace
|
||||
let qdrant = QdrantDb::new();
|
||||
let trace_filter = json!({
|
||||
"must": [
|
||||
{"key": "file_uuid", "match": {"value": file_uuid}},
|
||||
{"key": "trace_id", "match": {"value": trace_id}}
|
||||
]
|
||||
});
|
||||
let points = qdrant.scroll_all_points("_faces", trace_filter, 500).await.unwrap_or_default();
|
||||
|
||||
let rows: Vec<(i64, i32, i32, i32, i32)> = points.iter().filter_map(|p| {
|
||||
let payload = &p["payload"];
|
||||
let frame = payload["frame"].as_i64()?;
|
||||
let bbox = &payload["bbox"];
|
||||
let x = bbox["x"].as_f64()? as i32;
|
||||
let y = bbox["y"].as_f64()? as i32;
|
||||
let w = bbox["width"].as_f64()? as i32;
|
||||
let h = bbox["height"].as_f64()? as i32;
|
||||
Some((frame, x, y, w, h))
|
||||
}).collect();
|
||||
|
||||
if rows.is_empty() {
|
||||
return Err(StatusCode::NOT_FOUND);
|
||||
@@ -393,22 +422,50 @@ async fn trace_video_inner(
|
||||
let end_fn = ((start_sec + duration) * fps) as i64;
|
||||
|
||||
// Query all traces with identity names and bbox positions in the visible frame range
|
||||
// frame_number is BIGINT (i64) in database
|
||||
let identities_table = schema::table_name("identities");
|
||||
let all_rows: Vec<(i32, i64, i32, i32, i32, i32, Option<String>)> = sqlx::query_as(&format!(
|
||||
"SELECT fd.trace_id, fd.frame_number, fd.x, fd.y, fd.width, fd.height, i.name \
|
||||
FROM {} fd \
|
||||
LEFT JOIN {} i ON fd.identity_id = i.id \
|
||||
WHERE fd.file_uuid = $1 AND fd.frame_number BETWEEN $2 AND $3 AND fd.trace_id IS NOT NULL \
|
||||
ORDER BY fd.trace_id, fd.frame_number",
|
||||
face_table, identities_table
|
||||
))
|
||||
.bind(&file_uuid)
|
||||
.bind(start_fn)
|
||||
.bind(end_fn)
|
||||
.fetch_all(state.db.pool())
|
||||
.await
|
||||
.unwrap_or_default();
|
||||
let all_points = qdrant.scroll_all_points("_faces", json!({
|
||||
"must": [
|
||||
{"key": "file_uuid", "match": {"value": file_uuid}},
|
||||
{"key": "frame", "range": {"gte": start_fn, "lte": end_fn}},
|
||||
{"key": "trace_id", "match": {"value": 1}}
|
||||
]
|
||||
}), 1000).await.unwrap_or_default();
|
||||
|
||||
// Get identity names for traces that have identity_id
|
||||
let mut identity_names: HashMap<i32, String> = HashMap::new();
|
||||
for point in &all_points {
|
||||
let payload = &point["payload"];
|
||||
if let Some(iid) = payload["identity_id"].as_i64() {
|
||||
let trace_id = payload["trace_id"].as_i64().unwrap_or(0) as i32;
|
||||
if iid > 0 && !identity_names.contains_key(&trace_id) {
|
||||
if let Some(name) = sqlx::query_scalar::<_, String>(&format!(
|
||||
"SELECT name FROM {} WHERE id = $1",
|
||||
identities_table
|
||||
))
|
||||
.bind(iid as i32)
|
||||
.fetch_optional(state.db.pool())
|
||||
.await
|
||||
.ok()
|
||||
.flatten()
|
||||
{
|
||||
identity_names.insert(trace_id, name);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
let all_rows: Vec<(i32, i64, i32, i32, i32, i32, Option<String>)> = all_points.iter().filter_map(|p| {
|
||||
let payload = &p["payload"];
|
||||
let trace_id = payload["trace_id"].as_i64()? as i32;
|
||||
let frame = payload["frame"].as_i64()?;
|
||||
let bbox = &payload["bbox"];
|
||||
let x = bbox["x"].as_f64()? as i32;
|
||||
let y = bbox["y"].as_f64()? as i32;
|
||||
let w = bbox["width"].as_f64()? as i32;
|
||||
let h = bbox["height"].as_f64()? as i32;
|
||||
let name = identity_names.get(&trace_id).cloned();
|
||||
Some((trace_id, frame, x, y, w, h, name))
|
||||
}).collect();
|
||||
|
||||
// Group frames by trace_id, compute start_frame per trace; collect bbox per frame
|
||||
// frame_number is i64 (BIGINT), so HashMaps need i64 for frame values
|
||||
@@ -1082,21 +1139,31 @@ async fn stranger_video_inner(
|
||||
fps
|
||||
);
|
||||
|
||||
// Query face detections by stranger_id directly
|
||||
let face_table = schema::table_name("face_detections");
|
||||
tracing::debug!("[stranger_video] face_table: {}", face_table);
|
||||
// Query face detections by stranger_id from Qdrant _faces
|
||||
use crate::core::db::qdrant_db::QdrantDb;
|
||||
use serde_json::json;
|
||||
|
||||
// frame_number is BIGINT (i64) in database
|
||||
let rows: Vec<(i64, i32, i32, i32, i32)> = sqlx::query_as(&format!(
|
||||
"SELECT frame_number, x, y, width, height FROM {} WHERE file_uuid = $1 AND stranger_id = $2 ORDER BY frame_number",
|
||||
face_table
|
||||
))
|
||||
.bind(&file_uuid).bind(stranger_id)
|
||||
.fetch_all(state.db.pool()).await
|
||||
.unwrap_or_else(|e| {
|
||||
tracing::error!("[stranger_video] Face query error: {}", e);
|
||||
vec![]
|
||||
let qdrant = QdrantDb::new();
|
||||
let face_filter = json!({
|
||||
"must": [
|
||||
{"key": "file_uuid", "match": {"value": file_uuid}},
|
||||
{"key": "stranger_id", "match": {"value": stranger_id}}
|
||||
]
|
||||
});
|
||||
let points = qdrant.scroll_all_points("_faces", face_filter, 1000).await.unwrap_or_default();
|
||||
|
||||
let rows: Vec<(i64, i32, i32, i32, i32)> = points.iter()
|
||||
.filter_map(|p| {
|
||||
let payload = &p["payload"];
|
||||
let frame = payload["frame"].as_i64()?;
|
||||
let bbox = &payload["bbox"];
|
||||
let x = bbox["x"].as_f64()? as i32;
|
||||
let y = bbox["y"].as_f64()? as i32;
|
||||
let w = bbox["width"].as_f64()? as i32;
|
||||
let h = bbox["height"].as_f64()? as i32;
|
||||
Some((frame, x, y, w, h))
|
||||
})
|
||||
.collect();
|
||||
|
||||
tracing::info!("[stranger_video] Found {} faces", rows.len());
|
||||
|
||||
|
||||
+71
-46
@@ -305,14 +305,21 @@ async fn trigger_processing(
|
||||
tracing::error!("[TRIGGER] Failed to update monitor job for {}: {}", file_uuid, e);
|
||||
StatusCode::INTERNAL_SERVER_ERROR
|
||||
})?;
|
||||
|
||||
|
||||
// Update videos.processing_status to PROCESSING immediately
|
||||
let processor_names_upper: Vec<String> = processors_to_run.iter().map(|p| p.to_uppercase()).collect();
|
||||
let progress: serde_json::Map<String, serde_json::Value> = processors_to_run.iter().map(|p| {
|
||||
(p.to_uppercase(), serde_json::json!({
|
||||
"current_frame": 0, "total_frames": 0, "percentage": 0, "status": "pending"
|
||||
}))
|
||||
}).collect();
|
||||
let processor_names_upper: Vec<String> =
|
||||
processors_to_run.iter().map(|p| p.to_uppercase()).collect();
|
||||
let progress: serde_json::Map<String, serde_json::Value> = processors_to_run
|
||||
.iter()
|
||||
.map(|p| {
|
||||
(
|
||||
p.to_uppercase(),
|
||||
serde_json::json!({
|
||||
"current_frame": 0, "total_frames": 0, "percentage": 0, "status": "pending"
|
||||
}),
|
||||
)
|
||||
})
|
||||
.collect();
|
||||
let status = serde_json::json!({
|
||||
"phase": "PROCESSING",
|
||||
"active_processors": processor_names_upper,
|
||||
@@ -320,7 +327,7 @@ async fn trigger_processing(
|
||||
"progress": progress
|
||||
});
|
||||
sqlx::query(&format!(
|
||||
"UPDATE {videos_table} SET status = 'queued', processing_status = $1, updated_at = CURRENT_TIMESTAMP WHERE file_uuid = $2"
|
||||
"UPDATE {videos_table} SET status = 'processing', processing_status = $1, updated_at = CURRENT_TIMESTAMP WHERE file_uuid = $2"
|
||||
))
|
||||
.bind(&status)
|
||||
.bind(&file_uuid)
|
||||
@@ -396,7 +403,7 @@ async fn get_chunk_by_path(
|
||||
row.map(Json).ok_or(StatusCode::NOT_FOUND)
|
||||
}
|
||||
|
||||
async fn get_progress(file_uuid: Path<String>) -> Result<Json<ProgressResponse>, StatusCode> {
|
||||
async fn get_progress(file_uuid: Path<String>) -> Result<Json<serde_json::Value>, StatusCode> {
|
||||
let file_uuid = file_uuid.0;
|
||||
let redis = RedisClient::new().map_err(|_| StatusCode::INTERNAL_SERVER_ERROR)?;
|
||||
let mut conn = redis
|
||||
@@ -459,6 +466,24 @@ async fn get_progress(file_uuid: Path<String>) -> Result<Json<ProgressResponse>,
|
||||
})
|
||||
.collect();
|
||||
|
||||
// Fetch TKG and Agent progress from Redis
|
||||
let tkg_key = format!("{}progress:{}:tkg", REDIS_KEY_PREFIX.as_str(), file_uuid);
|
||||
let agent_key = format!("{}progress:{}:agent", REDIS_KEY_PREFIX.as_str(), file_uuid);
|
||||
|
||||
let tkg_progress: Option<serde_json::Value> = if let Ok(mut c) = redis.get_conn().await {
|
||||
let val: Option<String> = redis::cmd("GET").arg(&tkg_key).query_async(&mut c).await.ok();
|
||||
val.and_then(|s| serde_json::from_str(&s).ok())
|
||||
} else {
|
||||
None
|
||||
};
|
||||
|
||||
let agent_progress: Option<serde_json::Value> = if let Ok(mut c) = redis.get_conn().await {
|
||||
let val: Option<String> = redis::cmd("GET").arg(&agent_key).query_async(&mut c).await.ok();
|
||||
val.and_then(|s| serde_json::from_str(&s).ok())
|
||||
} else {
|
||||
None
|
||||
};
|
||||
|
||||
let overall = if processors.is_empty() {
|
||||
0
|
||||
} else {
|
||||
@@ -466,20 +491,20 @@ async fn get_progress(file_uuid: Path<String>) -> Result<Json<ProgressResponse>,
|
||||
(sum / processors.len() as u64) as u32
|
||||
};
|
||||
|
||||
Ok(Json(ProgressResponse {
|
||||
file_uuid,
|
||||
user: None,
|
||||
group: None,
|
||||
file_name: video.as_ref().map(|v| v.file_name.clone()),
|
||||
duration: video.as_ref().map(|v| v.duration),
|
||||
overall_progress: overall,
|
||||
cpu_percent: cpu,
|
||||
gpu_percent: gpu,
|
||||
memory_percent: mem_pct,
|
||||
memory_mb: mem_mb,
|
||||
system: Some(sys),
|
||||
processors,
|
||||
}))
|
||||
Ok(Json(serde_json::json!({
|
||||
"file_uuid": file_uuid,
|
||||
"file_name": video.as_ref().map(|v| &v.file_name),
|
||||
"duration": video.as_ref().map(|v| v.duration),
|
||||
"overall_progress": overall,
|
||||
"cpu_percent": cpu,
|
||||
"gpu_percent": gpu,
|
||||
"memory_percent": mem_pct,
|
||||
"memory_mb": mem_mb,
|
||||
"system": sys,
|
||||
"processors": processors,
|
||||
"tkg_progress": tkg_progress,
|
||||
"agent_progress": agent_progress,
|
||||
})))
|
||||
}
|
||||
|
||||
async fn list_jobs(Json(params): Json<JobsQuery>) -> Result<Json<JobListResponse>, StatusCode> {
|
||||
@@ -575,7 +600,7 @@ async fn get_job(Path(uuid): Path<String>) -> Result<Json<JobDetailResponse>, St
|
||||
started_at,
|
||||
updated_at,
|
||||
) = job.ok_or(StatusCode::NOT_FOUND)?;
|
||||
|
||||
|
||||
// Calculate queue position (pending or queued jobs ahead of this one)
|
||||
let queue_position = if status == "pending" || status == "queued" {
|
||||
sqlx::query_scalar::<_, i64>(&format!(
|
||||
@@ -714,7 +739,7 @@ async fn get_processor_counts(
|
||||
}
|
||||
}
|
||||
|
||||
if let Ok(content) = std::fs::read_to_string(&json_path) {
|
||||
if let Ok(content) = std::fs::read_to_string(&json_path) {
|
||||
if let Ok(json) = serde_json::from_str::<serde_json::Value>(&content) {
|
||||
// CUT: prioritize scenes count over frame_count
|
||||
if proc_name == "cut" {
|
||||
@@ -737,27 +762,27 @@ if let Ok(content) = std::fs::read_to_string(&json_path) {
|
||||
.map(|v| v as u32);
|
||||
}
|
||||
|
||||
segment_count = json
|
||||
.get("segments")
|
||||
.and_then(|v| v.as_array())
|
||||
.map(|arr| arr.len() as u32);
|
||||
chunk_count = json
|
||||
.get("child_chunks")
|
||||
.and_then(|v| v.as_array())
|
||||
.map(|arr| arr.len() as u32)
|
||||
.or_else(|| {
|
||||
json.get("parent_chunks")
|
||||
.and_then(|v| v.as_array())
|
||||
.map(|arr| arr.len() as u32)
|
||||
});
|
||||
if chunk_count.is_none() {
|
||||
chunk_count = json
|
||||
.get("chunks")
|
||||
.and_then(|v| v.as_array())
|
||||
.map(|arr| arr.len() as u32);
|
||||
}
|
||||
}
|
||||
}
|
||||
segment_count = json
|
||||
.get("segments")
|
||||
.and_then(|v| v.as_array())
|
||||
.map(|arr| arr.len() as u32);
|
||||
chunk_count = json
|
||||
.get("child_chunks")
|
||||
.and_then(|v| v.as_array())
|
||||
.map(|arr| arr.len() as u32)
|
||||
.or_else(|| {
|
||||
json.get("parent_chunks")
|
||||
.and_then(|v| v.as_array())
|
||||
.map(|arr| arr.len() as u32)
|
||||
});
|
||||
if chunk_count.is_none() {
|
||||
chunk_count = json
|
||||
.get("chunks")
|
||||
.and_then(|v| v.as_array())
|
||||
.map(|arr| arr.len() as u32);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
results.push(ProcessorCountInfo {
|
||||
|
||||
+447
-22
@@ -10,6 +10,83 @@ use serde::{Deserialize, Serialize};
|
||||
use super::types::AppState;
|
||||
use crate::core::db::schema;
|
||||
|
||||
/// Comprehensive file stats endpoint — provides all data sources for frontend transparency
|
||||
/// Combines: JSON file status + PostgreSQL counts + Qdrant collections + TKG stats + Identity Agent stats
|
||||
#[derive(Debug, Serialize)]
|
||||
struct FileStatsResponse {
|
||||
file_uuid: String,
|
||||
file_name: Option<String>,
|
||||
status: Option<String>,
|
||||
// Processor status
|
||||
processors: Vec<ProcessorStatus>,
|
||||
// PostgreSQL counts
|
||||
postgres: PostgresStats,
|
||||
// Qdrant collection counts
|
||||
qdrant: QdrantStats,
|
||||
// TKG stats
|
||||
tkg: TkgFileStats,
|
||||
// Identity Agent stats
|
||||
identity_agent: IdentityAgentStats,
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize)]
|
||||
struct ProcessorStatus {
|
||||
name: String,
|
||||
status: String,
|
||||
progress: u32,
|
||||
message: Option<String>,
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize, Default)]
|
||||
struct PostgresStats {
|
||||
sentence_chunks: i64,
|
||||
trace_chunks: i64,
|
||||
relationship_chunks: i64,
|
||||
identities: i64,
|
||||
file_identities: i64,
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize)]
|
||||
struct QdrantStats {
|
||||
faces: i64,
|
||||
face_traces: i64,
|
||||
face_identities: i64,
|
||||
text_chunks: i64,
|
||||
speakers: i64,
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize, Default)]
|
||||
struct TkgFileStats {
|
||||
total_nodes: i64,
|
||||
total_edges: i64,
|
||||
face_track_nodes: i64,
|
||||
gaze_track_nodes: i64,
|
||||
lip_track_nodes: i64,
|
||||
text_region_nodes: i64,
|
||||
appearance_nodes: i64,
|
||||
accessory_nodes: i64,
|
||||
object_nodes: i64,
|
||||
hand_nodes: i64,
|
||||
speaker_nodes: i64,
|
||||
co_occurrence_edges: i64,
|
||||
speaker_face_edges: i64,
|
||||
face_face_edges: i64,
|
||||
mutual_gaze_edges: i64,
|
||||
lip_sync_edges: i64,
|
||||
has_appearance_edges: i64,
|
||||
wears_edges: i64,
|
||||
hand_object_edges: i64,
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize, Default)]
|
||||
struct IdentityAgentStats {
|
||||
clusters: i64,
|
||||
identities_created: i64,
|
||||
tmdb_matches: i64,
|
||||
speaker_bindings: i64,
|
||||
confirmations: i64,
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize, Deserialize)]
|
||||
struct ScannedFileInfo {
|
||||
file_name: String,
|
||||
@@ -372,9 +449,46 @@ async fn get_ingestion_status(
|
||||
) -> Result<Json<IngestionStatusResponse>, StatusCode> {
|
||||
let pool = state.db.pool();
|
||||
let chunk = schema::table_name("chunk");
|
||||
let fd = schema::table_name("face_detections");
|
||||
let identities = schema::table_name("identities");
|
||||
|
||||
// Get face counts from Qdrant _faces
|
||||
use crate::core::db::qdrant_db::QdrantDb;
|
||||
use serde_json::json;
|
||||
|
||||
let qdrant = QdrantDb::new();
|
||||
let face_filter = json!({
|
||||
"must": [
|
||||
{"key": "file_uuid", "match": {"value": file_uuid}}
|
||||
]
|
||||
});
|
||||
let points = qdrant.scroll_all_points("_faces", face_filter, 1000).await.unwrap_or_default();
|
||||
|
||||
let face_total = points.len() as i64;
|
||||
let mut trace_ids: std::collections::HashSet<i64> = std::collections::HashSet::new();
|
||||
let mut identity_ids: std::collections::HashSet<i64> = std::collections::HashSet::new();
|
||||
let mut stranger_traces: std::collections::HashSet<i64> = std::collections::HashSet::new();
|
||||
|
||||
for point in &points {
|
||||
let payload = &point["payload"];
|
||||
if let Some(tid) = payload["trace_id"].as_i64() {
|
||||
if tid > 0 {
|
||||
trace_ids.insert(tid);
|
||||
if payload["identity_id"].is_null() {
|
||||
stranger_traces.insert(tid);
|
||||
}
|
||||
}
|
||||
}
|
||||
if let Some(iid) = payload["identity_id"].as_i64() {
|
||||
if iid > 0 {
|
||||
identity_ids.insert(iid);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
let trace_count = trace_ids.len() as i64;
|
||||
let identity_count = identity_ids.len() as i64;
|
||||
let strangers = stranger_traces.len() as i64;
|
||||
|
||||
let scene_meta_path = format!(
|
||||
"{}/{}.scene_meta.json",
|
||||
crate::core::config::OUTPUT_DIR.as_str(),
|
||||
@@ -398,14 +512,12 @@ async fn get_ingestion_status(
|
||||
let scene_count = count_sql!(&format!(
|
||||
"SELECT COUNT(*) FROM {chunk} WHERE file_uuid = '{file_uuid}' AND chunk_type = 'cut'"
|
||||
));
|
||||
let face_total = count_sql!(&format!(
|
||||
"SELECT COUNT(*) FROM {fd} WHERE file_uuid = '{file_uuid}'"
|
||||
));
|
||||
let trace_count = count_sql!(&format!("SELECT COUNT(DISTINCT trace_id) FROM {fd} WHERE file_uuid = '{file_uuid}' AND trace_id IS NOT NULL"));
|
||||
let face_total = face_total;
|
||||
let trace_count = trace_count;
|
||||
let trace_chunks = count_sql!(&format!(
|
||||
"SELECT COUNT(*) FROM {chunk} WHERE file_uuid = '{file_uuid}' AND chunk_type = 'trace'"
|
||||
));
|
||||
let identity_count = count_sql!(&format!("SELECT COUNT(DISTINCT identity_id) FROM {fd} WHERE file_uuid = '{file_uuid}' AND identity_id IS NOT NULL"));
|
||||
let identity_count = identity_count;
|
||||
let tkg_nodes = count_sql!(&format!(
|
||||
"SELECT COUNT(*) FROM {} WHERE file_uuid = '{file_uuid}'",
|
||||
schema::table_name("tkg_nodes")
|
||||
@@ -414,12 +526,41 @@ async fn get_ingestion_status(
|
||||
"SELECT COUNT(*) FROM {} WHERE file_uuid = '{file_uuid}'",
|
||||
schema::table_name("tkg_edges")
|
||||
));
|
||||
let related_identities: Vec<IdentityRef> =
|
||||
|
||||
// Get individual node counts by type
|
||||
let tkg_nodes_table = schema::table_name("tkg_nodes");
|
||||
let face_track_nodes: i64 = count_sql!(&format!("SELECT COUNT(*) FROM {tkg_nodes_table} WHERE file_uuid = '{file_uuid}' AND node_type = 'face_track'"));
|
||||
let gaze_track_nodes: i64 = count_sql!(&format!("SELECT COUNT(*) FROM {tkg_nodes_table} WHERE file_uuid = '{file_uuid}' AND node_type = 'gaze_track'"));
|
||||
let lip_track_nodes: i64 = count_sql!(&format!("SELECT COUNT(*) FROM {tkg_nodes_table} WHERE file_uuid = '{file_uuid}' AND node_type = 'lip_track'"));
|
||||
let text_region_nodes: i64 = count_sql!(&format!("SELECT COUNT(*) FROM {tkg_nodes_table} WHERE file_uuid = '{file_uuid}' AND node_type = 'text_region'"));
|
||||
let appearance_nodes: i64 = count_sql!(&format!("SELECT COUNT(*) FROM {tkg_nodes_table} WHERE file_uuid = '{file_uuid}' AND node_type = 'appearance_trace'"));
|
||||
let accessory_nodes: i64 = count_sql!(&format!("SELECT COUNT(*) FROM {tkg_nodes_table} WHERE file_uuid = '{file_uuid}' AND node_type = 'accessory'"));
|
||||
let object_nodes: i64 = count_sql!(&format!("SELECT COUNT(*) FROM {tkg_nodes_table} WHERE file_uuid = '{file_uuid}' AND node_type = 'yolo_object'"));
|
||||
let hand_nodes: i64 = count_sql!(&format!("SELECT COUNT(*) FROM {tkg_nodes_table} WHERE file_uuid = '{file_uuid}' AND node_type = 'hand'"));
|
||||
let speaker_nodes: i64 = count_sql!(&format!("SELECT COUNT(*) FROM {tkg_nodes_table} WHERE file_uuid = '{file_uuid}' AND node_type = 'speaker'"));
|
||||
|
||||
// Get individual edge counts by type
|
||||
let tkg_edges_table = schema::table_name("tkg_edges");
|
||||
let co_occurrence_edges: i64 = count_sql!(&format!("SELECT COUNT(*) FROM {tkg_edges_table} WHERE file_uuid = '{file_uuid}' AND edge_type = 'CO_OCCURS_WITH'"));
|
||||
let speaker_face_edges: i64 = count_sql!(&format!("SELECT COUNT(*) FROM {tkg_edges_table} WHERE file_uuid = '{file_uuid}' AND edge_type = 'SPEAKS_AS'"));
|
||||
let face_face_edges: i64 = count_sql!(&format!("SELECT COUNT(*) FROM {tkg_edges_table} WHERE file_uuid = '{file_uuid}' AND edge_type = 'FACE_TO_FACE'"));
|
||||
let mutual_gaze_edges: i64 = count_sql!(&format!("SELECT COUNT(*) FROM {tkg_edges_table} WHERE file_uuid = '{file_uuid}' AND edge_type = 'MUTUAL_GAZE'"));
|
||||
let lip_sync_edges: i64 = count_sql!(&format!("SELECT COUNT(*) FROM {tkg_edges_table} WHERE file_uuid = '{file_uuid}' AND edge_type = 'LIP_SYNC'"));
|
||||
let has_appearance_edges: i64 = count_sql!(&format!("SELECT COUNT(*) FROM {tkg_edges_table} WHERE file_uuid = '{file_uuid}' AND edge_type = 'HAS_APPEARANCE'"));
|
||||
let wears_edges: i64 = count_sql!(&format!("SELECT COUNT(*) FROM {tkg_edges_table} WHERE file_uuid = '{file_uuid}' AND edge_type = 'WEARS'"));
|
||||
let hand_object_edges: i64 = count_sql!(&format!("SELECT COUNT(*) FROM {tkg_edges_table} WHERE file_uuid = '{file_uuid}' AND edge_type = 'HAND_OBJECT'"));
|
||||
|
||||
// Rule 2 relationship chunks
|
||||
let rule2_chunks = count_sql!(&format!(
|
||||
"SELECT COUNT(*) FROM {chunk} WHERE file_uuid = '{file_uuid}' AND chunk_type = 'relationship'"
|
||||
));
|
||||
// Get related identities from Qdrant _faces
|
||||
let related_identity_ids: Vec<i64> = identity_ids.into_iter().collect();
|
||||
let related_identities: Vec<IdentityRef> = if !related_identity_ids.is_empty() {
|
||||
let id_list: String = related_identity_ids.iter().map(|id| id.to_string()).collect::<Vec<_>>().join(",");
|
||||
match sqlx::query_as::<_, (String, String)>(&format!(
|
||||
"SELECT DISTINCT i.uuid::text, i.name FROM {identities} i \
|
||||
JOIN {fd} fd ON fd.identity_id = i.id \
|
||||
WHERE fd.file_uuid = '{file_uuid}' AND fd.identity_id IS NOT NULL \
|
||||
ORDER BY i.name"
|
||||
"SELECT DISTINCT uuid::text, name FROM {identities} \
|
||||
WHERE id IN ({id_list}) ORDER BY name"
|
||||
))
|
||||
.fetch_all(pool)
|
||||
.await
|
||||
@@ -435,12 +576,12 @@ async fn get_ingestion_status(
|
||||
tracing::error!("related_identities query failed: {}", e);
|
||||
vec![]
|
||||
}
|
||||
};
|
||||
}
|
||||
} else {
|
||||
vec![]
|
||||
};
|
||||
|
||||
let strangers = count_sql!(&format!(
|
||||
"SELECT COUNT(DISTINCT trace_id) FROM {fd} \
|
||||
WHERE file_uuid = '{file_uuid}' AND trace_id IS NOT NULL AND identity_id IS NULL"
|
||||
));
|
||||
let strangers = strangers;
|
||||
|
||||
macro_rules! step {
|
||||
($name:expr, $done:expr, $detail:expr) => {
|
||||
@@ -462,9 +603,9 @@ async fn get_ingestion_status(
|
||||
"auto_vectorize",
|
||||
sentence_embedded > 0,
|
||||
Some(format!("{sentence_embedded} embedded"))
|
||||
),
|
||||
step!(
|
||||
"face_track",
|
||||
),
|
||||
step!(
|
||||
"face_track",
|
||||
trace_count > 0,
|
||||
Some(format!("{trace_count} traces / {face_total} detections"))
|
||||
),
|
||||
@@ -473,11 +614,32 @@ step!(
|
||||
trace_chunks > 0,
|
||||
Some(format!("{trace_chunks} trace chunks"))
|
||||
),
|
||||
// TKG Nodes
|
||||
step!("tkg_face_track", face_track_nodes > 0, Some(format!("{face_track_nodes} nodes"))),
|
||||
step!("tkg_gaze_track", gaze_track_nodes > 0, Some(format!("{gaze_track_nodes} nodes"))),
|
||||
step!("tkg_lip_track", lip_track_nodes > 0, Some(format!("{lip_track_nodes} nodes"))),
|
||||
step!("tkg_text_region", text_region_nodes > 0, Some(format!("{text_region_nodes} nodes"))),
|
||||
step!("tkg_appearance", appearance_nodes > 0, Some(format!("{appearance_nodes} nodes"))),
|
||||
step!("tkg_accessory", accessory_nodes > 0, Some(format!("{accessory_nodes} nodes"))),
|
||||
step!("tkg_object", object_nodes > 0, Some(format!("{object_nodes} nodes"))),
|
||||
step!("tkg_hand", hand_nodes > 0, Some(format!("{hand_nodes} nodes"))),
|
||||
step!("tkg_speaker", speaker_nodes > 0, Some(format!("{speaker_nodes} nodes"))),
|
||||
// TKG Edges
|
||||
step!("tkg_co_occurrence", co_occurrence_edges > 0, Some(format!("{co_occurrence_edges} edges"))),
|
||||
step!("tkg_speaker_face", speaker_face_edges > 0, Some(format!("{speaker_face_edges} edges"))),
|
||||
step!("tkg_face_face", face_face_edges > 0, Some(format!("{face_face_edges} edges"))),
|
||||
step!("tkg_mutual_gaze", mutual_gaze_edges > 0, Some(format!("{mutual_gaze_edges} edges"))),
|
||||
step!("tkg_lip_sync", lip_sync_edges > 0, Some(format!("{lip_sync_edges} edges"))),
|
||||
step!("tkg_has_appearance", has_appearance_edges > 0, Some(format!("{has_appearance_edges} edges"))),
|
||||
step!("tkg_wears", wears_edges > 0, Some(format!("{wears_edges} edges"))),
|
||||
step!("tkg_hand_object", hand_object_edges > 0, Some(format!("{hand_object_edges} edges"))),
|
||||
// Rule 2
|
||||
step!(
|
||||
"tkg",
|
||||
tkg_nodes > 0 || tkg_edges > 0,
|
||||
Some(format!("{tkg_nodes} nodes, {tkg_edges} edges"))
|
||||
"rule2_relationship",
|
||||
rule2_chunks > 0,
|
||||
Some(format!("{rule2_chunks} relationship chunks"))
|
||||
),
|
||||
// Identity & Scene
|
||||
step!(
|
||||
"identity_match",
|
||||
identity_count > 0,
|
||||
@@ -494,6 +656,248 @@ step!(
|
||||
}))
|
||||
}
|
||||
|
||||
/// Comprehensive file stats endpoint — combines all data sources for frontend transparency
|
||||
async fn get_file_stats(
|
||||
State(state): State<AppState>,
|
||||
Path(file_uuid): Path<String>,
|
||||
) -> Result<Json<FileStatsResponse>, StatusCode> {
|
||||
let pool = state.db.pool();
|
||||
|
||||
// 1. Get file info from PostgreSQL
|
||||
let videos_table = schema::table_name("videos");
|
||||
let file_info: Option<(String, String, String)> = sqlx::query_as(&format!(
|
||||
"SELECT file_uuid, file_name, status FROM {} WHERE file_uuid = $1",
|
||||
videos_table
|
||||
))
|
||||
.bind(&file_uuid)
|
||||
.fetch_optional(pool)
|
||||
.await
|
||||
.map_err(|_| StatusCode::INTERNAL_SERVER_ERROR)?;
|
||||
|
||||
let (file_uuid_str, file_name, status) = file_info
|
||||
.map(|(uuid, name, s)| (uuid, Some(name), Some(s)))
|
||||
.unwrap_or_else(|| (file_uuid.clone(), None, None));
|
||||
|
||||
// 2. Get processor status from processing_status JSONB
|
||||
let processing_status: serde_json::Value =
|
||||
sqlx::query_scalar(&format!(
|
||||
"SELECT processing_status FROM {} WHERE file_uuid = $1",
|
||||
videos_table
|
||||
))
|
||||
.bind(&file_uuid)
|
||||
.fetch_optional(pool)
|
||||
.await
|
||||
.map_err(|_| StatusCode::INTERNAL_SERVER_ERROR)?
|
||||
.unwrap_or(serde_json::json!({}));
|
||||
|
||||
let processors: Vec<ProcessorStatus> = processing_status
|
||||
.get("progress")
|
||||
.and_then(|p| p.as_object())
|
||||
.map(|progress| {
|
||||
progress
|
||||
.iter()
|
||||
.filter_map(|(name, info)| {
|
||||
info.as_object().map(|obj| {
|
||||
let status = obj
|
||||
.get("status")
|
||||
.and_then(|s| s.as_str())
|
||||
.unwrap_or("pending")
|
||||
.to_string();
|
||||
let progress_val = obj
|
||||
.get("percentage")
|
||||
.and_then(|p| p.as_u64())
|
||||
.unwrap_or(0) as u32;
|
||||
let message = obj
|
||||
.get("message")
|
||||
.and_then(|m| m.as_str())
|
||||
.map(|s| s.to_string());
|
||||
ProcessorStatus {
|
||||
name: name.clone(),
|
||||
status,
|
||||
progress: progress_val,
|
||||
message,
|
||||
}
|
||||
})
|
||||
})
|
||||
.collect()
|
||||
})
|
||||
.unwrap_or_default();
|
||||
|
||||
// 3. Get PostgreSQL counts
|
||||
let chunk_table = schema::table_name("chunk");
|
||||
let identities_table = schema::table_name("identities");
|
||||
let file_identities_table = schema::table_name("file_identities");
|
||||
|
||||
let postgres = PostgresStats {
|
||||
sentence_chunks: sqlx::query_scalar::<_, i64>(&format!(
|
||||
"SELECT COUNT(*) FROM {chunk_table} WHERE file_uuid = $1 AND chunk_type = 'sentence'"
|
||||
))
|
||||
.bind(&file_uuid)
|
||||
.fetch_one(pool)
|
||||
.await
|
||||
.unwrap_or(0),
|
||||
trace_chunks: sqlx::query_scalar::<_, i64>(&format!(
|
||||
"SELECT COUNT(*) FROM {chunk_table} WHERE file_uuid = $1 AND chunk_type = 'trace'"
|
||||
))
|
||||
.bind(&file_uuid)
|
||||
.fetch_one(pool)
|
||||
.await
|
||||
.unwrap_or(0),
|
||||
relationship_chunks: sqlx::query_scalar::<_, i64>(&format!(
|
||||
"SELECT COUNT(*) FROM {chunk_table} WHERE file_uuid = $1 AND chunk_type = 'relationship'"
|
||||
))
|
||||
.bind(&file_uuid)
|
||||
.fetch_one(pool)
|
||||
.await
|
||||
.unwrap_or(0),
|
||||
identities: sqlx::query_scalar::<_, i64>(&format!(
|
||||
"SELECT COUNT(DISTINCT i.id) FROM {identities_table} i \
|
||||
JOIN {file_identities_table} fi ON fi.identity_id = i.id \
|
||||
WHERE fi.file_uuid = $1"
|
||||
))
|
||||
.bind(&file_uuid)
|
||||
.fetch_one(pool)
|
||||
.await
|
||||
.unwrap_or(0),
|
||||
file_identities: sqlx::query_scalar::<_, i64>(&format!(
|
||||
"SELECT COUNT(*) FROM {file_identities_table} WHERE file_uuid = $1"
|
||||
))
|
||||
.bind(&file_uuid)
|
||||
.fetch_one(pool)
|
||||
.await
|
||||
.unwrap_or(0),
|
||||
};
|
||||
|
||||
// 4. Get Qdrant stats
|
||||
use crate::core::db::qdrant_db::QdrantDb;
|
||||
use serde_json::json;
|
||||
|
||||
let qdrant_db = QdrantDb::new();
|
||||
|
||||
// Face stats
|
||||
let face_filter = json!({
|
||||
"must": [{"key": "file_uuid", "match": {"value": file_uuid}}]
|
||||
});
|
||||
let face_points = qdrant_db
|
||||
.scroll_all_points("_faces", face_filter.clone(), 500)
|
||||
.await
|
||||
.unwrap_or_default();
|
||||
|
||||
let mut face_traces = std::collections::HashSet::new();
|
||||
let mut face_identities = std::collections::HashSet::new();
|
||||
for point in &face_points {
|
||||
let payload = &point["payload"];
|
||||
if let Some(tid) = payload["trace_id"].as_i64() {
|
||||
if tid > 0 {
|
||||
face_traces.insert(tid);
|
||||
}
|
||||
}
|
||||
if let Some(iid) = payload["identity_id"].as_i64() {
|
||||
if iid > 0 {
|
||||
face_identities.insert(iid);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Text chunk stats (rule1 collection)
|
||||
let schema = std::env::var("DATABASE_SCHEMA").unwrap_or_else(|_| "dev".to_string());
|
||||
let rule1_collection = format!("momentry_{}_rule1_v2", schema);
|
||||
let text_filter = json!({
|
||||
"must": [{"key": "file_uuid", "match": {"value": file_uuid}}]
|
||||
});
|
||||
let text_points = qdrant_db
|
||||
.scroll_all_points(&rule1_collection, text_filter, 500)
|
||||
.await
|
||||
.unwrap_or_default();
|
||||
|
||||
// Speaker stats
|
||||
let speaker_collection = format!("momentry_{}_speaker", schema);
|
||||
let speaker_filter = json!({
|
||||
"must": [{"key": "file_uuid", "match": {"value": file_uuid}}]
|
||||
});
|
||||
let speaker_points = qdrant_db
|
||||
.scroll_all_points(&speaker_collection, speaker_filter, 500)
|
||||
.await
|
||||
.unwrap_or_default();
|
||||
|
||||
let qdrant_stats = QdrantStats {
|
||||
faces: face_points.len() as i64,
|
||||
face_traces: face_traces.len() as i64,
|
||||
face_identities: face_identities.len() as i64,
|
||||
text_chunks: text_points.len() as i64,
|
||||
speakers: speaker_points.len() as i64,
|
||||
};
|
||||
|
||||
// 5. Get TKG stats from PostgreSQL
|
||||
let tkg_nodes_table = schema::table_name("tkg_nodes");
|
||||
let tkg_edges_table = schema::table_name("tkg_edges");
|
||||
|
||||
let tkg = TkgFileStats {
|
||||
face_track_nodes: count_by_type(pool, &tkg_nodes_table, &file_uuid, "face_track").await,
|
||||
gaze_track_nodes: count_by_type(pool, &tkg_nodes_table, &file_uuid, "gaze_track").await,
|
||||
lip_track_nodes: count_by_type(pool, &tkg_nodes_table, &file_uuid, "lip_track").await,
|
||||
text_region_nodes: count_by_type(pool, &tkg_nodes_table, &file_uuid, "text_region").await,
|
||||
appearance_nodes: count_by_type(pool, &tkg_nodes_table, &file_uuid, "appearance_trace").await,
|
||||
accessory_nodes: count_by_type(pool, &tkg_nodes_table, &file_uuid, "accessory").await,
|
||||
object_nodes: count_by_type(pool, &tkg_nodes_table, &file_uuid, "yolo_object").await,
|
||||
hand_nodes: count_by_type(pool, &tkg_nodes_table, &file_uuid, "hand").await,
|
||||
speaker_nodes: count_by_type(pool, &tkg_nodes_table, &file_uuid, "speaker").await,
|
||||
co_occurrence_edges: count_by_type(pool, &tkg_edges_table, &file_uuid, "CO_OCCURS_WITH").await,
|
||||
speaker_face_edges: count_by_type(pool, &tkg_edges_table, &file_uuid, "SPEAKS_AS").await,
|
||||
face_face_edges: count_by_type(pool, &tkg_edges_table, &file_uuid, "FACE_TO_FACE").await,
|
||||
mutual_gaze_edges: count_by_type(pool, &tkg_edges_table, &file_uuid, "MUTUAL_GAZE").await,
|
||||
lip_sync_edges: count_by_type(pool, &tkg_edges_table, &file_uuid, "LIP_SYNC").await,
|
||||
has_appearance_edges: count_by_type(pool, &tkg_edges_table, &file_uuid, "HAS_APPEARANCE").await,
|
||||
wears_edges: count_by_type(pool, &tkg_edges_table, &file_uuid, "WEARS").await,
|
||||
hand_object_edges: count_by_type(pool, &tkg_edges_table, &file_uuid, "HAND_OBJECT").await,
|
||||
..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();
|
||||
|
||||
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
|
||||
};
|
||||
|
||||
Ok(Json(FileStatsResponse {
|
||||
file_uuid: file_uuid_str,
|
||||
file_name,
|
||||
status,
|
||||
processors,
|
||||
postgres,
|
||||
qdrant: qdrant_stats,
|
||||
tkg,
|
||||
identity_agent,
|
||||
}))
|
||||
}
|
||||
|
||||
async fn count_by_type(pool: &sqlx::PgPool, table: &str, file_uuid: &str, type_val: &str) -> i64 {
|
||||
sqlx::query_scalar::<_, i64>(&format!(
|
||||
"SELECT COUNT(*) FROM {} WHERE file_uuid = $1 AND (node_type = $2 OR edge_type = $2)",
|
||||
table
|
||||
))
|
||||
.bind(file_uuid)
|
||||
.bind(type_val)
|
||||
.fetch_one(pool)
|
||||
.await
|
||||
.unwrap_or(0)
|
||||
}
|
||||
|
||||
pub fn scan_routes() -> Router<AppState> {
|
||||
Router::new()
|
||||
.route("/api/v1/files/scan", get(scan_files))
|
||||
@@ -502,4 +906,25 @@ pub fn scan_routes() -> Router<AppState> {
|
||||
"/api/v1/stats/ingestion-status/:file_uuid",
|
||||
get(get_ingestion_status),
|
||||
)
|
||||
.route(
|
||||
"/api/v1/stats/file/:file_uuid",
|
||||
get(get_file_stats),
|
||||
)
|
||||
.route(
|
||||
"/api/v1/stats/pipeline/:file_uuid",
|
||||
get(get_pipeline_progress_handler),
|
||||
)
|
||||
}
|
||||
|
||||
/// Get segmented pipeline progress with weighted stages
|
||||
async fn get_pipeline_progress_handler(
|
||||
State(state): State<AppState>,
|
||||
Path(file_uuid): Path<String>,
|
||||
) -> Result<Json<crate::core::progress::PipelineProgress>, StatusCode> {
|
||||
let redis_lock = state.redis_cache.get_client().await;
|
||||
let redis_guard = redis_lock.read().await;
|
||||
let pipeline = crate::core::progress::get_pipeline_progress(&*redis_guard, &file_uuid)
|
||||
.await
|
||||
.unwrap_or_else(|| crate::core::progress::PipelineProgress::new(&file_uuid));
|
||||
Ok(Json(pipeline))
|
||||
}
|
||||
|
||||
+7
-8
@@ -149,7 +149,6 @@ pub async fn smart_search(
|
||||
},
|
||||
)?;
|
||||
|
||||
const KEYWORD_FIXED_SCORE: f64 = 0.5;
|
||||
const IDENTITY_FIXED_SCORE: f64 = 0.85;
|
||||
|
||||
let fetch_limit = limit * 3;
|
||||
@@ -302,23 +301,23 @@ pub async fn smart_search(
|
||||
});
|
||||
}
|
||||
|
||||
// Add keyword results (fixed score 0.5)
|
||||
let keyword_fixed = KEYWORD_FIXED_SCORE;
|
||||
for (file_uuid, chunk_id, _) in keyword_results.iter() {
|
||||
// Add keyword results (score from FTS rank, capped at 1.0)
|
||||
for (file_uuid, chunk_id, actual_score) in keyword_results.iter() {
|
||||
let key = (file_uuid.clone(), chunk_id.clone());
|
||||
let capped = actual_score.min(1.0).max(0.1);
|
||||
merged
|
||||
.entry(key)
|
||||
.and_modify(|e| {
|
||||
e.score = e.score.max(keyword_fixed);
|
||||
e.keyword_score = Some(keyword_fixed);
|
||||
e.score = e.score.max(capped);
|
||||
e.keyword_score = Some(capped);
|
||||
e.source = format!("{}_keyword", e.source);
|
||||
})
|
||||
.or_insert(MergedResult {
|
||||
file_uuid: file_uuid.clone(),
|
||||
chunk_id: chunk_id.clone(),
|
||||
score: keyword_fixed,
|
||||
score: capped,
|
||||
semantic_score: None,
|
||||
keyword_score: Some(keyword_fixed),
|
||||
keyword_score: Some(capped),
|
||||
identity_score: None,
|
||||
source: "keyword".to_string(),
|
||||
});
|
||||
|
||||
+9
-3
@@ -16,7 +16,7 @@ use super::checkin_api;
|
||||
use super::docs;
|
||||
use super::files;
|
||||
use super::health;
|
||||
use super::health::{health, health_detailed, health_consistency};
|
||||
use super::health::{health, health_consistency, health_detailed};
|
||||
use super::identities;
|
||||
use super::identity_agent_api;
|
||||
use super::identity_api;
|
||||
@@ -138,8 +138,14 @@ pub async fn start_server(host: &str, port: u16) -> anyhow::Result<()> {
|
||||
|
||||
let public_health_routes = Router::new()
|
||||
.route("/api/v1/health", axum::routing::get(health))
|
||||
.route("/api/v1/health/detailed", axum::routing::get(health_detailed))
|
||||
.route("/api/v1/health/consistency", axum::routing::get(health_consistency));
|
||||
.route(
|
||||
"/api/v1/health/detailed",
|
||||
axum::routing::get(health_detailed),
|
||||
)
|
||||
.route(
|
||||
"/api/v1/health/consistency",
|
||||
axum::routing::get(health_consistency),
|
||||
);
|
||||
|
||||
let app = Router::new()
|
||||
.merge(auth::auth_routes())
|
||||
|
||||
+2
-1
@@ -619,6 +619,7 @@ async fn tmdb_match_handler(
|
||||
file_uuid,
|
||||
bindings_created: 0,
|
||||
tmdb_identities_available: 0,
|
||||
message: "TMDb matching disabled - needs reimplementation with _faces collection".to_string(),
|
||||
message: "TMDb matching disabled - needs reimplementation with _faces collection"
|
||||
.to_string(),
|
||||
}))
|
||||
}
|
||||
|
||||
+244
-228
@@ -7,6 +7,7 @@ use axum::{
|
||||
Router,
|
||||
};
|
||||
use serde::{Deserialize, Serialize};
|
||||
use std::sync::Arc;
|
||||
|
||||
use crate::core::db::PostgresDb;
|
||||
|
||||
@@ -73,6 +74,7 @@ struct TraceInfo {
|
||||
duration_sec: f64,
|
||||
avg_confidence: f64,
|
||||
sample_face_id: Option<String>,
|
||||
thumbnail_url: String,
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize)]
|
||||
@@ -118,46 +120,76 @@ async fn list_traces_sorted(
|
||||
.map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, e.to_string()))?
|
||||
.unwrap_or(24.0);
|
||||
|
||||
let query = format!(
|
||||
"SELECT tt.*, fd.id AS sample_face_id FROM (
|
||||
SELECT trace_id::int AS trace_id,
|
||||
COUNT(*) AS face_count,
|
||||
MIN(frame_number)::bigint AS start_frame,
|
||||
MAX(frame_number)::bigint AS end_frame,
|
||||
(MAX(frame_number) - MIN(frame_number))::float8 AS duration_sec,
|
||||
AVG(confidence)::float8 AS avg_confidence
|
||||
FROM {}
|
||||
WHERE file_uuid = $1 AND trace_id IS NOT NULL
|
||||
AND confidence >= $5 AND confidence <= $6
|
||||
GROUP BY trace_id
|
||||
HAVING COUNT(*) >= $2
|
||||
ORDER BY {}
|
||||
LIMIT $3 OFFSET $4
|
||||
) tt
|
||||
LEFT JOIN LATERAL (
|
||||
SELECT id FROM {}
|
||||
WHERE trace_id = tt.trace_id AND file_uuid = $1
|
||||
ORDER BY confidence DESC LIMIT 1
|
||||
) fd ON true",
|
||||
crate::core::db::schema::table_name("face_detections"),
|
||||
order_clause,
|
||||
crate::core::db::schema::table_name("face_detections"),
|
||||
);
|
||||
// Get face points from Qdrant _faces
|
||||
use crate::core::db::qdrant_db::QdrantDb;
|
||||
use serde_json::json;
|
||||
use std::collections::HashMap;
|
||||
|
||||
let rows: Vec<(i32, i64, i64, i64, f64, f64, Option<i32>)> = sqlx::query_as(&query)
|
||||
.bind(&file_uuid)
|
||||
.bind(min_faces)
|
||||
.bind(effective_limit)
|
||||
.bind(db_offset)
|
||||
.bind(min_confidence)
|
||||
.bind(max_confidence)
|
||||
.fetch_all(state.db.pool())
|
||||
.await
|
||||
.map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, e.to_string()))?;
|
||||
let qdrant = QdrantDb::new();
|
||||
let face_filter = json!({
|
||||
"must": [
|
||||
{"key": "file_uuid", "match": {"value": file_uuid}}
|
||||
]
|
||||
});
|
||||
let points = qdrant.scroll_all_points("_faces", face_filter, 2000).await.unwrap_or_default();
|
||||
|
||||
let traces: Vec<TraceInfo> = rows
|
||||
// Aggregate by trace_id
|
||||
struct TraceAgg {
|
||||
face_count: i64,
|
||||
start_frame: i64,
|
||||
end_frame: i64,
|
||||
avg_confidence: f64,
|
||||
sum_confidence: f64,
|
||||
}
|
||||
|
||||
let mut trace_data: HashMap<i32, TraceAgg> = HashMap::new();
|
||||
for point in &points {
|
||||
let payload = &point["payload"];
|
||||
let trace_id = payload["trace_id"].as_i64().unwrap_or(0) as i32;
|
||||
let frame = payload["frame"].as_i64().unwrap_or(0);
|
||||
let confidence = payload["confidence"].as_f64().unwrap_or(0.5);
|
||||
|
||||
if confidence < min_confidence || confidence > max_confidence {
|
||||
continue;
|
||||
}
|
||||
|
||||
let entry = trace_data.entry(trace_id).or_insert(TraceAgg {
|
||||
face_count: 0,
|
||||
start_frame: i64::MAX,
|
||||
end_frame: i64::MIN,
|
||||
avg_confidence: 0.0,
|
||||
sum_confidence: 0.0,
|
||||
});
|
||||
entry.face_count += 1;
|
||||
entry.start_frame = entry.start_frame.min(frame);
|
||||
entry.end_frame = entry.end_frame.max(frame);
|
||||
entry.sum_confidence += confidence;
|
||||
}
|
||||
|
||||
// Filter by min_faces and sort
|
||||
let mut traces_vec: Vec<(i32, i64, i64, i64, f64, f64)> = trace_data.into_iter()
|
||||
.filter(|(_, agg)| agg.face_count >= min_faces)
|
||||
.map(|(tid, agg)| {
|
||||
let duration = (agg.end_frame - agg.start_frame) as f64;
|
||||
let avg_conf = if agg.face_count > 0 { agg.sum_confidence / agg.face_count as f64 } else { 0.0 };
|
||||
(tid, agg.face_count, agg.start_frame, agg.end_frame, duration, avg_conf)
|
||||
})
|
||||
.collect();
|
||||
|
||||
match order_clause {
|
||||
"face_count DESC" => traces_vec.sort_by(|a, b| b.1.cmp(&a.1)),
|
||||
"duration_sec DESC" => traces_vec.sort_by(|a, b| b.4.partial_cmp(&a.4).unwrap_or(std::cmp::Ordering::Equal)),
|
||||
_ => traces_vec.sort_by(|a, b| a.2.cmp(&b.2)),
|
||||
}
|
||||
|
||||
// Apply pagination
|
||||
let total_traces = traces_vec.len() as i64;
|
||||
let total_faces: i64 = points.len() as i64;
|
||||
let traces_vec: Vec<_> = traces_vec.into_iter().skip(db_offset as usize).take(effective_limit as usize).collect();
|
||||
|
||||
let traces: Vec<TraceInfo> = traces_vec
|
||||
.into_iter()
|
||||
.map(|(tid, fc, sf, ef, dur, conf, fid)| TraceInfo {
|
||||
.map(|(tid, fc, sf, ef, dur, conf)| TraceInfo {
|
||||
trace_id: tid,
|
||||
face_count: fc,
|
||||
start_frame: sf,
|
||||
@@ -166,19 +198,11 @@ async fn list_traces_sorted(
|
||||
end_time: ef as f64 / fps,
|
||||
duration_sec: dur / fps,
|
||||
avg_confidence: conf,
|
||||
sample_face_id: fid.map(|v| v.to_string()),
|
||||
sample_face_id: None,
|
||||
thumbnail_url: format!("/api/v1/file/{}/trace/{}/thumbnail", file_uuid, tid),
|
||||
})
|
||||
.collect();
|
||||
|
||||
let (total_traces, total_faces): (i64, i64) = sqlx::query_as(
|
||||
&format!("SELECT COUNT(DISTINCT trace_id), COUNT(*) FROM {} WHERE file_uuid = $1 AND trace_id IS NOT NULL",
|
||||
crate::core::db::schema::table_name("face_detections"))
|
||||
)
|
||||
.bind(&file_uuid)
|
||||
.fetch_one(state.db.pool())
|
||||
.await
|
||||
.map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, e.to_string()))?;
|
||||
|
||||
Ok(Json(TracesResponse {
|
||||
success: true,
|
||||
file_uuid,
|
||||
@@ -260,55 +284,57 @@ async fn list_trace_faces(
|
||||
.map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, e.to_string()))?
|
||||
.unwrap_or(24.0);
|
||||
|
||||
let total_detected: i64 = sqlx::query_scalar(&format!(
|
||||
"SELECT COUNT(*) FROM {} WHERE file_uuid = $1 AND trace_id = $2",
|
||||
crate::core::db::schema::table_name("face_detections")
|
||||
))
|
||||
.bind(&file_uuid)
|
||||
.bind(trace_id)
|
||||
.fetch_one(state.db.pool())
|
||||
.await
|
||||
.map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, e.to_string()))?;
|
||||
// Get face points from Qdrant _faces for this trace
|
||||
use crate::core::db::qdrant_db::QdrantDb;
|
||||
use serde_json::json;
|
||||
|
||||
let rows: Vec<(
|
||||
i32,
|
||||
i64,
|
||||
Option<i32>,
|
||||
Option<i32>,
|
||||
Option<i32>,
|
||||
Option<i32>,
|
||||
f32,
|
||||
)> = sqlx::query_as(&format!(
|
||||
"SELECT id, frame_number, x, y, width, height, confidence::float4 \
|
||||
FROM {} WHERE file_uuid = $1 AND trace_id = $2 \
|
||||
ORDER BY frame_number ASC LIMIT $3 OFFSET $4",
|
||||
crate::core::db::schema::table_name("face_detections")
|
||||
))
|
||||
.bind(&file_uuid)
|
||||
.bind(trace_id)
|
||||
.bind(limit)
|
||||
.bind(offset)
|
||||
.fetch_all(state.db.pool())
|
||||
.await
|
||||
.map_err(|e| (StatusCode::INTERNAL_SERVER_ERROR, e.to_string()))?;
|
||||
let qdrant = QdrantDb::new();
|
||||
let trace_filter = json!({
|
||||
"must": [
|
||||
{"key": "file_uuid", "match": {"value": file_uuid}},
|
||||
{"key": "trace_id", "match": {"value": trace_id}}
|
||||
]
|
||||
});
|
||||
let points = qdrant.scroll_all_points("_faces", trace_filter, 1000).await.unwrap_or_default();
|
||||
|
||||
let total_detected: i64 = points.len() as i64;
|
||||
|
||||
// Apply pagination
|
||||
let paged: Vec<_> = points.into_iter().skip(offset as usize).take(limit as usize).collect();
|
||||
|
||||
let mut faces: Vec<TraceFaceItem> = Vec::new();
|
||||
|
||||
for (i, (id, frame, x, y, w, h, conf)) in rows.iter().enumerate() {
|
||||
for (i, point) in paged.iter().enumerate() {
|
||||
let payload = &point["payload"];
|
||||
let frame = payload["frame"].as_i64().unwrap_or(0);
|
||||
let bbox = &payload["bbox"];
|
||||
let x = bbox["x"].as_f64().unwrap_or(0.0) as i32;
|
||||
let y = bbox["y"].as_f64().unwrap_or(0.0) as i32;
|
||||
let w = bbox["width"].as_f64().unwrap_or(0.0) as i32;
|
||||
let h = bbox["height"].as_f64().unwrap_or(0.0) as i32;
|
||||
let conf = payload["confidence"].as_f64().unwrap_or(0.5) as f32;
|
||||
let id = i as i32;
|
||||
|
||||
let cur = (x, y, w, h);
|
||||
|
||||
// Add interpolated frames between previous and current detection
|
||||
if interpolate && i > 0 {
|
||||
let prev = &rows[i - 1];
|
||||
let prev_frame = prev.1;
|
||||
let prev_point = &paged[i - 1];
|
||||
let prev_payload = &prev_point["payload"];
|
||||
let prev_bbox = &prev_payload["bbox"];
|
||||
let prev_frame = prev_payload["frame"].as_i64().unwrap_or(0);
|
||||
let prev_x = prev_bbox["x"].as_f64().unwrap_or(0.0) as i32;
|
||||
let prev_y = prev_bbox["y"].as_f64().unwrap_or(0.0) as i32;
|
||||
let prev_w = prev_bbox["width"].as_f64().unwrap_or(0.0) as i32;
|
||||
let prev_h = prev_bbox["height"].as_f64().unwrap_or(0.0) as i32;
|
||||
let gap = frame - prev_frame;
|
||||
if gap > 1 {
|
||||
for mid in 1..gap {
|
||||
let t = mid as f64 / gap as f64;
|
||||
let mid_x = lerp_i32(prev.2, *x, t);
|
||||
let mid_y = lerp_i32(prev.3, *y, t);
|
||||
let mid_w = lerp_i32(prev.4, *w, t);
|
||||
let mid_h = lerp_i32(prev.5, *h, t);
|
||||
let mid_x = lerp_i32(Some(prev_x), Some(x), t).unwrap_or(0);
|
||||
let mid_y = lerp_i32(Some(prev_y), Some(y), t).unwrap_or(0);
|
||||
let mid_w = lerp_i32(Some(prev_w), Some(w), t).unwrap_or(0);
|
||||
let mid_h = lerp_i32(Some(prev_h), Some(h), t).unwrap_or(0);
|
||||
let mid_frame = prev_frame + mid;
|
||||
let mt = (mid_frame as f64 / fps * 10.0).round() / 10.0;
|
||||
faces.push(TraceFaceItem {
|
||||
@@ -317,10 +343,10 @@ async fn list_trace_faces(
|
||||
end_frame: mid_frame,
|
||||
start_time: mt,
|
||||
end_time: mt,
|
||||
x: mid_x,
|
||||
y: mid_y,
|
||||
width: mid_w,
|
||||
height: mid_h,
|
||||
x: Some(mid_x),
|
||||
y: Some(mid_y),
|
||||
width: Some(mid_w),
|
||||
height: Some(mid_h),
|
||||
confidence: 0.0,
|
||||
interpolated: true,
|
||||
});
|
||||
@@ -329,19 +355,19 @@ async fn list_trace_faces(
|
||||
}
|
||||
|
||||
// Add the real detection
|
||||
let frame_val = *frame;
|
||||
let frame_val = frame;
|
||||
let ft = (frame_val as f64 / fps * 10.0).round() / 10.0;
|
||||
faces.push(TraceFaceItem {
|
||||
id: *id,
|
||||
id,
|
||||
start_frame: frame_val,
|
||||
end_frame: frame_val,
|
||||
start_time: ft,
|
||||
end_time: ft,
|
||||
x: *x,
|
||||
y: *y,
|
||||
width: *w,
|
||||
height: *h,
|
||||
confidence: *conf as f64,
|
||||
x: Some(x),
|
||||
y: Some(y),
|
||||
width: Some(w),
|
||||
height: Some(h),
|
||||
confidence: conf as f64,
|
||||
interpolated: false,
|
||||
});
|
||||
}
|
||||
@@ -413,7 +439,8 @@ where
|
||||
F: Fn(anyhow::Error) -> T,
|
||||
{
|
||||
use crate::core::db::schema;
|
||||
let fd_table = schema::table_name("face_detections");
|
||||
use crate::core::db::qdrant_db::QdrantDb;
|
||||
use serde_json::json;
|
||||
let video_table = schema::table_name("videos");
|
||||
|
||||
let fps: f64 = sqlx::query_scalar(&format!(
|
||||
@@ -426,15 +453,16 @@ where
|
||||
.map_err(|e| err_fn(anyhow::anyhow!("{}", e)))?
|
||||
.unwrap_or(25.0);
|
||||
|
||||
let face_count: (i64,) = sqlx::query_as(&format!(
|
||||
"SELECT COUNT(*) FROM {} WHERE file_uuid = $1 AND trace_id = $2",
|
||||
fd_table
|
||||
))
|
||||
.bind(file_uuid)
|
||||
.bind(trace_id)
|
||||
.fetch_one(pool)
|
||||
.await
|
||||
.map_err(|e| err_fn(anyhow::anyhow!("{}", e)))?;
|
||||
// Get face count from Qdrant
|
||||
let qdrant = QdrantDb::new();
|
||||
let trace_filter = json!({
|
||||
"must": [
|
||||
{"key": "file_uuid", "match": {"value": file_uuid}},
|
||||
{"key": "trace_id", "match": {"value": trace_id}}
|
||||
]
|
||||
});
|
||||
let points = qdrant.scroll_all_points("_faces", trace_filter, 1000).await.unwrap_or_default();
|
||||
let face_count: (i64,) = (points.len() as i64,);
|
||||
|
||||
struct Candidate {
|
||||
frame: i64,
|
||||
@@ -446,38 +474,35 @@ where
|
||||
score: f64,
|
||||
}
|
||||
|
||||
let rows = sqlx::query_as::<_, (i64, i32, i32, i32, i32, f64)>(&format!(
|
||||
"SELECT frame_number::bigint, x, y, width, height, confidence::float8 \
|
||||
FROM {} WHERE file_uuid = $1 AND trace_id = $2 AND confidence > 0.7 \
|
||||
AND ((metadata->>'qc_ok')::boolean IS NULL OR (metadata->>'qc_ok')::boolean = true) \
|
||||
ORDER BY (width::float8 * height::float8) * confidence::float8 DESC LIMIT 10",
|
||||
fd_table
|
||||
))
|
||||
.bind(file_uuid)
|
||||
.bind(trace_id)
|
||||
.fetch_all(pool)
|
||||
.await
|
||||
.map_err(|e| err_fn(anyhow::anyhow!("{}", e)))?;
|
||||
// Get top faces by quality from Qdrant
|
||||
let mut candidates: Vec<Candidate> = points.iter()
|
||||
.filter_map(|p| {
|
||||
let payload = &p["payload"];
|
||||
let bbox = &payload["bbox"];
|
||||
let w = bbox["width"].as_f64()? as i32;
|
||||
let h = bbox["height"].as_f64()? as i32;
|
||||
let conf = payload["confidence"].as_f64()?;
|
||||
if conf <= 0.7 { return None; }
|
||||
let score = (w as f64 * h as f64) * conf;
|
||||
Some(Candidate {
|
||||
frame: payload["frame"].as_i64().unwrap_or(0),
|
||||
x: bbox["x"].as_f64()? as i32,
|
||||
y: bbox["y"].as_f64()? as i32,
|
||||
w,
|
||||
h,
|
||||
conf,
|
||||
score,
|
||||
})
|
||||
})
|
||||
.collect();
|
||||
candidates.sort_by(|a, b| b.score.partial_cmp(&a.score).unwrap_or(std::cmp::Ordering::Equal));
|
||||
let rows: Vec<_> = candidates.into_iter().take(10).collect();
|
||||
|
||||
if rows.is_empty() {
|
||||
return Err(err_fn(anyhow::anyhow!("No suitable face found")));
|
||||
}
|
||||
|
||||
let candidates: Vec<Candidate> = rows
|
||||
.into_iter()
|
||||
.map(|(frame, x, y, w, h, conf)| {
|
||||
let score = (w as f64 * h as f64) * conf;
|
||||
Candidate {
|
||||
frame,
|
||||
x,
|
||||
y,
|
||||
w,
|
||||
h,
|
||||
conf,
|
||||
score,
|
||||
}
|
||||
})
|
||||
.collect();
|
||||
let candidates: Vec<Candidate> = rows;
|
||||
|
||||
let video_path: String = sqlx::query_scalar(&format!(
|
||||
"SELECT file_path FROM {} WHERE file_uuid = $1",
|
||||
@@ -759,8 +784,9 @@ async fn get_cooccurrence(
|
||||
Path((file_uuid, identity_uuid_a, identity_uuid_b)): Path<(String, String, String)>,
|
||||
) -> Result<Json<CoOccurResponse>, (StatusCode, Json<serde_json::Value>)> {
|
||||
use crate::core::db::schema;
|
||||
use crate::core::db::qdrant_db::QdrantDb;
|
||||
use serde_json::json;
|
||||
let id_table = schema::table_name("identities");
|
||||
let fd_table = schema::table_name("face_detections");
|
||||
|
||||
// Stage 1: Get identity names and IDs
|
||||
let id_a = sqlx::query_as::<_, (i32, String)>(&format!(
|
||||
@@ -803,27 +829,33 @@ async fn get_cooccurrence(
|
||||
)
|
||||
})?;
|
||||
|
||||
// Stage 2: Find first frame where both identity_ids appear
|
||||
let cooccur: Option<(i64,)> = sqlx::query_as(&format!(
|
||||
"SELECT MIN(fd.frame_number)::bigint FROM {} fd \
|
||||
WHERE fd.file_uuid = $1 AND fd.identity_id = $2 \
|
||||
AND fd.frame_number IN ( \
|
||||
SELECT frame_number FROM {} \
|
||||
WHERE file_uuid = $1 AND identity_id = $3 \
|
||||
)",
|
||||
fd_table, fd_table
|
||||
))
|
||||
.bind(&file_uuid)
|
||||
.bind(id_a.0)
|
||||
.bind(id_b.0)
|
||||
.fetch_optional(state.db.pool())
|
||||
.await
|
||||
.map_err(|e| {
|
||||
(
|
||||
StatusCode::INTERNAL_SERVER_ERROR,
|
||||
Json(serde_json::json!({"error": e.to_string()})),
|
||||
)
|
||||
})?;
|
||||
// Stage 2: Find first frame where both identity_ids appear (from Qdrant _faces)
|
||||
let qdrant = QdrantDb::new();
|
||||
|
||||
// Get frames for identity A
|
||||
let filter_a = json!({
|
||||
"must": [
|
||||
{"key": "file_uuid", "match": {"value": file_uuid}},
|
||||
{"key": "identity_id", "match": {"value": id_a.0}}
|
||||
]
|
||||
});
|
||||
let points_a = qdrant.scroll_all_points("_faces", filter_a, 1000).await.unwrap_or_default();
|
||||
let frames_a: std::collections::HashSet<i64> = points_a.iter()
|
||||
.filter_map(|p| p["payload"]["frame"].as_i64())
|
||||
.collect();
|
||||
|
||||
// Get frames for identity B and find first co-occurrence
|
||||
let filter_b = json!({
|
||||
"must": [
|
||||
{"key": "file_uuid", "match": {"value": file_uuid}},
|
||||
{"key": "identity_id", "match": {"value": id_b.0}}
|
||||
]
|
||||
});
|
||||
let points_b = qdrant.scroll_all_points("_faces", filter_b, 1000).await.unwrap_or_default();
|
||||
let cooccur: Option<(i64,)> = points_b.iter()
|
||||
.filter_map(|p| p["payload"]["frame"].as_i64())
|
||||
.find(|f| frames_a.contains(f))
|
||||
.map(|f| (f,));
|
||||
|
||||
let (first_frame,) = cooccur.ok_or_else(|| {
|
||||
(StatusCode::NOT_FOUND, Json(serde_json::json!({"error": "These two identities never appear together in this file"})))
|
||||
@@ -846,24 +878,16 @@ async fn get_cooccurrence(
|
||||
})?
|
||||
.unwrap_or(25.0);
|
||||
|
||||
// Stage 3: Get trace_ids for both at this frame
|
||||
let trace_a: Option<(i32,)> = sqlx::query_as(
|
||||
&format!("SELECT trace_id FROM {} WHERE file_uuid = $1 AND frame_number = $2 AND identity_id = $3 AND trace_id IS NOT NULL LIMIT 1", fd_table)
|
||||
)
|
||||
.bind(&file_uuid).bind(first_frame).bind(id_a.0)
|
||||
.fetch_optional(state.db.pool()).await
|
||||
.map_err(|e| {
|
||||
(StatusCode::INTERNAL_SERVER_ERROR, Json(serde_json::json!({"error": e.to_string()})))
|
||||
})?;
|
||||
// Stage 3: Get trace_ids for both at this frame (from Qdrant _faces)
|
||||
let trace_a: Option<(i32,)> = points_a.iter()
|
||||
.find(|p| p["payload"]["frame"].as_i64() == Some(first_frame))
|
||||
.and_then(|p| p["payload"]["trace_id"].as_i64())
|
||||
.map(|t| (t as i32,));
|
||||
|
||||
let trace_b: Option<(i32,)> = sqlx::query_as(
|
||||
&format!("SELECT trace_id FROM {} WHERE file_uuid = $1 AND frame_number = $2 AND identity_id = $3 AND trace_id IS NOT NULL LIMIT 1", fd_table)
|
||||
)
|
||||
.bind(&file_uuid).bind(first_frame).bind(id_b.0)
|
||||
.fetch_optional(state.db.pool()).await
|
||||
.map_err(|e| {
|
||||
(StatusCode::INTERNAL_SERVER_ERROR, Json(serde_json::json!({"error": e.to_string()})))
|
||||
})?;
|
||||
let trace_b: Option<(i32,)> = points_b.iter()
|
||||
.find(|p| p["payload"]["frame"].as_i64() == Some(first_frame))
|
||||
.and_then(|p| p["payload"]["trace_id"].as_i64())
|
||||
.map(|t| (t as i32,));
|
||||
|
||||
// Stage 4: Get representative faces for both traces (reusing select_rep_face)
|
||||
let rep_a = if let Some((tid,)) = trace_a {
|
||||
@@ -914,22 +938,14 @@ async fn get_cooccurrence(
|
||||
None
|
||||
};
|
||||
|
||||
// Total co-occurrence frames (from TKG if available, otherwise from face_detections)
|
||||
let total_cooccurrence_frames: i64 = sqlx::query_scalar(&format!(
|
||||
"SELECT COUNT(DISTINCT fd.frame_number)::bigint FROM {} fd \
|
||||
WHERE fd.file_uuid = $1 AND fd.identity_id = $2 \
|
||||
AND fd.frame_number IN ( \
|
||||
SELECT frame_number FROM {} \
|
||||
WHERE file_uuid = $1 AND identity_id = $3 \
|
||||
)",
|
||||
fd_table, fd_table
|
||||
))
|
||||
.bind(&file_uuid)
|
||||
.bind(id_a.0)
|
||||
.bind(id_b.0)
|
||||
.fetch_one(state.db.pool())
|
||||
.await
|
||||
.unwrap_or(0);
|
||||
// Total co-occurrence frames (from Qdrant _faces)
|
||||
let frames_b: std::collections::HashSet<i64> = points_b.iter()
|
||||
.filter_map(|p| p["payload"]["frame"].as_i64())
|
||||
.collect();
|
||||
let total_cooccurrence_frames: i64 = points_a.iter()
|
||||
.filter_map(|p| p["payload"]["frame"].as_i64())
|
||||
.filter(|f| frames_b.contains(f))
|
||||
.count() as i64;
|
||||
|
||||
Ok(Json(CoOccurResponse {
|
||||
success: true,
|
||||
@@ -971,7 +987,8 @@ async fn rebuild_tkg(
|
||||
use crate::core::chunk::rule2_ingest::ingest_rule2;
|
||||
use tracing::info;
|
||||
|
||||
let result = crate::core::processor::tkg::build_tkg(&state.db, &file_uuid, &OUTPUT_DIR).await;
|
||||
let redis = crate::core::db::RedisClient::new().ok();
|
||||
let result = crate::core::processor::tkg::build_tkg(&state.db, &file_uuid, &OUTPUT_DIR, redis.map(Arc::new)).await;
|
||||
|
||||
match result {
|
||||
Ok(r) => {
|
||||
@@ -987,7 +1004,7 @@ async fn rebuild_tkg(
|
||||
"[TKG] {} relationship edges found, triggering Rule 2 ingestion...",
|
||||
total_edges
|
||||
);
|
||||
match ingest_rule2(state.db.pool(), &file_uuid).await {
|
||||
match ingest_rule2(state.db.pool(), &file_uuid, None, None).await {
|
||||
Ok(count) => info!("[TKG] Rule 2 created {} relationship chunks", count),
|
||||
Err(e) => info!("[TKG] Rule 2 ingestion failed: {}", e),
|
||||
}
|
||||
@@ -1087,26 +1104,26 @@ async fn get_stranger_representative_face(
|
||||
State(state): State<crate::api::types::AppState>,
|
||||
Path((file_uuid, stranger_id)): Path<(String, i32)>,
|
||||
) -> Result<Json<RepFaceResponse>, (StatusCode, Json<serde_json::Value>)> {
|
||||
let faces_table = crate::core::db::schema::table_name("face_detections");
|
||||
// Get trace_id from Qdrant _faces by stranger_id
|
||||
use crate::core::db::qdrant_db::QdrantDb;
|
||||
use serde_json::json;
|
||||
|
||||
let trace_id: i32 = sqlx::query_scalar(&format!(
|
||||
"SELECT trace_id FROM {} WHERE file_uuid = $1 AND stranger_id = $2 LIMIT 1",
|
||||
faces_table
|
||||
))
|
||||
.bind(&file_uuid)
|
||||
.bind(stranger_id)
|
||||
.fetch_optional(state.db.pool())
|
||||
.await
|
||||
.map_err(|e| {
|
||||
(
|
||||
StatusCode::INTERNAL_SERVER_ERROR,
|
||||
Json(serde_json::json!({"error": e.to_string()})),
|
||||
)
|
||||
})?
|
||||
.ok_or((
|
||||
StatusCode::NOT_FOUND,
|
||||
Json(serde_json::json!({"error": "Stranger not found"})),
|
||||
))?;
|
||||
let qdrant = QdrantDb::new();
|
||||
let filter = json!({
|
||||
"must": [
|
||||
{"key": "file_uuid", "match": {"value": file_uuid}},
|
||||
{"key": "stranger_id", "match": {"value": stranger_id}}
|
||||
]
|
||||
});
|
||||
let points = qdrant.scroll_all_points("_faces", filter, 1).await.unwrap_or_default();
|
||||
|
||||
let trace_id: i32 = points.first()
|
||||
.and_then(|p| p["payload"]["trace_id"].as_i64())
|
||||
.map(|t| t as i32)
|
||||
.ok_or((
|
||||
StatusCode::NOT_FOUND,
|
||||
Json(serde_json::json!({"error": "Stranger not found"})),
|
||||
))?;
|
||||
|
||||
get_representative_face_inner(&state, &file_uuid, trace_id).await
|
||||
}
|
||||
@@ -1115,26 +1132,25 @@ async fn get_stranger_thumbnail(
|
||||
State(state): State<crate::api::types::AppState>,
|
||||
Path((file_uuid, stranger_id)): Path<(String, i32)>,
|
||||
) -> Result<Response, (StatusCode, Json<serde_json::Value>)> {
|
||||
let faces_table = crate::core::db::schema::table_name("face_detections");
|
||||
use crate::core::db::qdrant_db::QdrantDb;
|
||||
use serde_json::json;
|
||||
|
||||
let trace_id: i32 = sqlx::query_scalar(&format!(
|
||||
"SELECT trace_id FROM {} WHERE file_uuid = $1 AND stranger_id = $2 LIMIT 1",
|
||||
faces_table
|
||||
))
|
||||
.bind(&file_uuid)
|
||||
.bind(stranger_id)
|
||||
.fetch_optional(state.db.pool())
|
||||
.await
|
||||
.map_err(|e| {
|
||||
(
|
||||
StatusCode::INTERNAL_SERVER_ERROR,
|
||||
Json(serde_json::json!({"error": e.to_string()})),
|
||||
)
|
||||
})?
|
||||
.ok_or((
|
||||
StatusCode::NOT_FOUND,
|
||||
Json(serde_json::json!({"error": "Stranger not found"})),
|
||||
))?;
|
||||
let qdrant = QdrantDb::new();
|
||||
let filter = json!({
|
||||
"must": [
|
||||
{"key": "file_uuid", "match": {"value": file_uuid}},
|
||||
{"key": "stranger_id", "match": {"value": stranger_id}}
|
||||
]
|
||||
});
|
||||
let points = qdrant.scroll_all_points("_faces", filter, 1).await.unwrap_or_default();
|
||||
|
||||
let trace_id: i32 = points.first()
|
||||
.and_then(|p| p["payload"]["trace_id"].as_i64())
|
||||
.map(|t| t as i32)
|
||||
.ok_or((
|
||||
StatusCode::NOT_FOUND,
|
||||
Json(serde_json::json!({"error": "Stranger not found"})),
|
||||
))?;
|
||||
|
||||
get_trace_thumbnail_inner(&state, &file_uuid, trace_id).await
|
||||
}
|
||||
@@ -1526,7 +1542,7 @@ async fn ingest_rule2(
|
||||
use crate::core::embedding::Embedder;
|
||||
use tracing::info;
|
||||
|
||||
let result = ingest_rule2(state.db.pool(), &file_uuid).await;
|
||||
let result = ingest_rule2(state.db.pool(), &file_uuid, None, None).await;
|
||||
|
||||
match result {
|
||||
Ok(rule2_chunks) => {
|
||||
|
||||
+237
-94
@@ -10,6 +10,7 @@ use axum::{
|
||||
};
|
||||
use serde::{Deserialize, Serialize};
|
||||
|
||||
use crate::core::db::qdrant_db::QdrantDb;
|
||||
use crate::core::db::{schema, Database, PostgresDb};
|
||||
|
||||
#[derive(Debug, Deserialize)]
|
||||
@@ -590,76 +591,162 @@ async fn search_persons_internal(
|
||||
req: &UniversalSearchRequest,
|
||||
) -> Result<Vec<SearchResult>, anyhow::Error> {
|
||||
let id_table = schema::table_name("identities");
|
||||
let fd_table = schema::table_name("face_detections");
|
||||
let mut sql = format!(
|
||||
"SELECT i.id, i.uuid::text, i.name, COUNT(fd.id) AS appearance_count, \
|
||||
MIN(fd.timestamp_secs) AS first_time, MAX(fd.timestamp_secs) AS last_time, \
|
||||
fd.file_uuid \
|
||||
FROM {} i JOIN {} fd ON fd.identity_id = i.id WHERE 1=1",
|
||||
id_table, fd_table
|
||||
|
||||
// Query matching identities from PostgreSQL
|
||||
let mut id_sql = format!(
|
||||
"SELECT id, uuid::text, name FROM {} WHERE name IS NOT NULL",
|
||||
id_table
|
||||
);
|
||||
|
||||
if let Some(uuid) = &req.file_uuid {
|
||||
sql.push_str(&format!(
|
||||
" AND fd.file_uuid = '{}'",
|
||||
uuid.replace('\'', "''")
|
||||
));
|
||||
}
|
||||
|
||||
if !req.query.is_empty() {
|
||||
let q = req.query.replace('\'', "''");
|
||||
sql.push_str(&format!(" AND i.name ILIKE '%{}%'", q));
|
||||
id_sql.push_str(&format!(" AND name ILIKE '%{}%'", 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());
|
||||
}
|
||||
|
||||
sql.push_str(" GROUP BY i.id, i.uuid, i.name, fd.file_uuid");
|
||||
sql.push_str(" ORDER BY appearance_count DESC");
|
||||
sql.push_str(&format!(" 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<(
|
||||
i32,
|
||||
String,
|
||||
Option<String>,
|
||||
i64,
|
||||
Option<f64>,
|
||||
Option<f64>,
|
||||
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)
|
||||
|
||||
let results: Vec<SearchResult> = rows
|
||||
.into_iter()
|
||||
.map(
|
||||
|(
|
||||
identity_id,
|
||||
identity_uuid,
|
||||
name,
|
||||
appearance_count,
|
||||
first_time,
|
||||
last_time,
|
||||
file_uuid,
|
||||
)| {
|
||||
let score = if !req.query.is_empty()
|
||||
&& name.as_ref().map_or(false, |n| {
|
||||
n.to_lowercase().contains(&req.query.to_lowercase())
|
||||
}) {
|
||||
0.95
|
||||
} else {
|
||||
0.5
|
||||
};
|
||||
for (id, _uuid, _name) in &identities {
|
||||
let scroll_filter = serde_json::json!({
|
||||
"must": [
|
||||
{"key": "identity_id", "match": {"value": id}}
|
||||
]
|
||||
});
|
||||
|
||||
SearchResult::Person {
|
||||
file_uuid: Some(file_uuid),
|
||||
identity_id,
|
||||
identity_uuid,
|
||||
name,
|
||||
appearance_count: appearance_count as i32,
|
||||
score,
|
||||
first_appearance_time: first_time,
|
||||
last_appearance_time: last_time,
|
||||
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();
|
||||
|
||||
// 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);
|
||||
|
||||
Ok(results)
|
||||
}
|
||||
|
||||
@@ -752,49 +839,105 @@ async fn search_persons_by_query(
|
||||
limit: usize,
|
||||
) -> Result<Vec<PersonResult>, anyhow::Error> {
|
||||
let id_table = schema::table_name("identities");
|
||||
let fd_table = schema::table_name("face_detections");
|
||||
let mut sql = format!(
|
||||
"SELECT i.id, i.uuid::text, i.name, COUNT(fd.id) AS appearance_count, \
|
||||
MIN(fd.timestamp_secs) AS first_time, MAX(fd.timestamp_secs) AS last_time \
|
||||
FROM {} i JOIN {} fd ON fd.identity_id = i.id \
|
||||
WHERE fd.file_uuid = '{}'",
|
||||
id_table,
|
||||
fd_table,
|
||||
file_uuid.replace('\'', "''")
|
||||
);
|
||||
|
||||
// Query matching identities from PostgreSQL
|
||||
let mut id_sql = format!(
|
||||
"SELECT id, uuid::text, name FROM {} WHERE name IS NOT NULL",
|
||||
id_table
|
||||
);
|
||||
if let Some(q) = query {
|
||||
let safe = q.replace('\'', "''");
|
||||
sql.push_str(&format!(" AND i.name ILIKE '%{}%'", safe));
|
||||
id_sql.push_str(&format!(" AND name ILIKE '%{}%'", 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());
|
||||
}
|
||||
|
||||
sql.push_str(" GROUP BY i.id, i.uuid, i.name");
|
||||
// For each identity, scroll _faces points from Qdrant and aggregate
|
||||
let qdrant = QdrantDb::new();
|
||||
let mut results: Vec<PersonResult> = Vec::new();
|
||||
|
||||
if let Some(min) = min_appearances {
|
||||
sql.push_str(&format!(" HAVING COUNT(fd.id) >= {}", min));
|
||||
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,
|
||||
});
|
||||
}
|
||||
|
||||
sql.push_str(" ORDER BY appearance_count DESC");
|
||||
sql.push_str(&format!(" LIMIT {}", limit));
|
||||
|
||||
let rows: Vec<(i32, String, Option<String>, i64, Option<f64>, Option<f64>)> =
|
||||
sqlx::query_as(&sql).fetch_all(db.pool()).await?;
|
||||
|
||||
let results: Vec<PersonResult> = rows
|
||||
.into_iter()
|
||||
.map(
|
||||
|(identity_id, identity_uuid, name, appearance_count, first_time, last_time)| {
|
||||
PersonResult {
|
||||
identity_id,
|
||||
identity_uuid,
|
||||
name,
|
||||
appearance_count: appearance_count as i32,
|
||||
first_appearance_time: first_time,
|
||||
last_appearance_time: last_time,
|
||||
}
|
||||
},
|
||||
)
|
||||
.collect();
|
||||
// Sort by appearance_count descending, then limit
|
||||
results.sort_by(|a, b| b.appearance_count.cmp(&a.appearance_count));
|
||||
results.truncate(limit);
|
||||
|
||||
Ok(results)
|
||||
}
|
||||
|
||||
+373
-145
@@ -1,6 +1,7 @@
|
||||
use base64::{engine::general_purpose::STANDARD as BASE64, Engine};
|
||||
use serde_json;
|
||||
|
||||
use crate::core::db::qdrant_db::QdrantDb;
|
||||
use crate::core::db::schema;
|
||||
use crate::core::llm::function_calling::call_llm_vision;
|
||||
use crate::core::processor::tkg::query_auto_representative_frame;
|
||||
@@ -14,20 +15,32 @@ fn t(name: &str) -> String {
|
||||
}
|
||||
}
|
||||
|
||||
/// Check if a file has faces in Qdrant _faces (replaces face_detections has_data check)
|
||||
async fn has_faces_in_qdrant(file_uuid: &str) -> bool {
|
||||
let qdrant = QdrantDb::new();
|
||||
let filter = serde_json::json!({
|
||||
"must": [
|
||||
{"key": "file_uuid", "match": {"value": file_uuid}}
|
||||
]
|
||||
});
|
||||
match qdrant.scroll_points("_faces", filter, 1, None).await {
|
||||
Ok((points, _)) => !points.is_empty(),
|
||||
Err(_) => false,
|
||||
}
|
||||
}
|
||||
|
||||
pub async fn exec_find_file(
|
||||
pool: &sqlx::PgPool,
|
||||
args: &serde_json::Value,
|
||||
) -> Result<String, String> {
|
||||
let query = args.get("query").and_then(|v| v.as_str()).unwrap_or("");
|
||||
let videos = schema::table_name("videos");
|
||||
let fd_table = schema::table_name("face_detections");
|
||||
let like = format!("%{}%", query);
|
||||
let rows: Vec<(String, String, bool)> = sqlx::query_as(&format!(
|
||||
"SELECT v.file_uuid::text, v.file_name, \
|
||||
(SELECT COUNT(*) FROM {} fd WHERE fd.file_uuid = v.file_uuid) > 0 AS has_data \
|
||||
let rows: Vec<(String, String)> = sqlx::query_as(&format!(
|
||||
"SELECT v.file_uuid::text, v.file_name \
|
||||
FROM {} v WHERE v.file_name ILIKE $1 \
|
||||
ORDER BY v.created_at DESC LIMIT 10",
|
||||
fd_table, videos
|
||||
videos
|
||||
))
|
||||
.bind(&like)
|
||||
.fetch_all(pool)
|
||||
@@ -37,10 +50,11 @@ pub async fn exec_find_file(
|
||||
if rows.is_empty() {
|
||||
return Ok(serde_json::json!({"found": false, "message": "No files match the query. Try different keywords."}).to_string());
|
||||
}
|
||||
let files: Vec<serde_json::Value> = rows
|
||||
.into_iter()
|
||||
.map(|(u, n, hd)| serde_json::json!({"file_uuid": u, "file_name": n, "has_data": hd}))
|
||||
.collect();
|
||||
let mut files = Vec::new();
|
||||
for (u, n) in rows {
|
||||
let has_data = has_faces_in_qdrant(&u).await;
|
||||
files.push(serde_json::json!({"file_uuid": u, "file_name": n, "has_data": has_data}));
|
||||
}
|
||||
Ok(serde_json::json!({"found": true, "files": files}).to_string())
|
||||
}
|
||||
|
||||
@@ -50,22 +64,21 @@ pub async fn exec_list_files(
|
||||
) -> Result<String, String> {
|
||||
let limit = args.get("limit").and_then(|v| v.as_i64()).unwrap_or(10);
|
||||
let videos = schema::table_name("videos");
|
||||
let fd_table = schema::table_name("face_detections");
|
||||
let rows: Vec<(String, String, bool)> = sqlx::query_as(&format!(
|
||||
"SELECT v.file_uuid::text, v.file_name, \
|
||||
(SELECT COUNT(*) FROM {} fd WHERE fd.file_uuid = v.file_uuid) > 0 AS has_data \
|
||||
let rows: Vec<(String, String)> = sqlx::query_as(&format!(
|
||||
"SELECT v.file_uuid::text, v.file_name \
|
||||
FROM {} v ORDER BY v.created_at DESC LIMIT $1",
|
||||
fd_table, videos
|
||||
videos
|
||||
))
|
||||
.bind(limit)
|
||||
.fetch_all(pool)
|
||||
.await
|
||||
.map_err(|e| e.to_string())?;
|
||||
|
||||
let files: Vec<serde_json::Value> = rows
|
||||
.into_iter()
|
||||
.map(|(u, n, hd)| serde_json::json!({"file_uuid": u, "file_name": n, "has_data": hd}))
|
||||
.collect();
|
||||
let mut files = Vec::new();
|
||||
for (u, n) in rows {
|
||||
let has_data = has_faces_in_qdrant(&u).await;
|
||||
files.push(serde_json::json!({"file_uuid": u, "file_name": n, "has_data": has_data}));
|
||||
}
|
||||
Ok(serde_json::json!({"files": files}).to_string())
|
||||
}
|
||||
|
||||
@@ -74,6 +87,9 @@ pub async fn exec_tkg_query(
|
||||
args: &serde_json::Value,
|
||||
) -> Result<String, String> {
|
||||
let file_uuid = args.get("file_uuid").and_then(|v| v.as_str()).unwrap_or("");
|
||||
if file_uuid.is_empty() {
|
||||
return Err("file_uuid is required".to_string());
|
||||
}
|
||||
let query_type = args
|
||||
.get("query_type")
|
||||
.and_then(|v| v.as_str())
|
||||
@@ -82,117 +98,324 @@ pub async fn exec_tkg_query(
|
||||
let identity_b = args.get("identity_b").and_then(|v| v.as_str());
|
||||
let limit = args.get("limit").and_then(|v| v.as_i64()).unwrap_or(5);
|
||||
|
||||
// Pre-load _faces data from Qdrant
|
||||
let qdrant = QdrantDb::new();
|
||||
let face_filter = serde_json::json!({
|
||||
"must": [
|
||||
{"key": "file_uuid", "match": {"value": file_uuid}}
|
||||
]
|
||||
});
|
||||
let face_points = qdrant
|
||||
.scroll_all_points("_faces", face_filter, 1000)
|
||||
.await
|
||||
.map_err(|e| e.to_string())?;
|
||||
|
||||
// Build lookup maps from _faces payload
|
||||
use std::collections::{HashMap, HashSet};
|
||||
struct FacePoint {
|
||||
frame: i64,
|
||||
trace_id: i32,
|
||||
identity_id: Option<i32>,
|
||||
}
|
||||
let mut points_by_frame: HashMap<i64, Vec<i32>> = HashMap::new(); // frame → identity_ids
|
||||
let mut identity_face_count: HashMap<i32, i64> = HashMap::new();
|
||||
let mut trace_identity: HashMap<i32, i32> = HashMap::new(); // trace_id → identity_id
|
||||
let mut trace_frames: HashMap<i32, Vec<i64>> = HashMap::new(); // trace_id → frames
|
||||
let mut faces_in_file: Vec<FacePoint> = Vec::new();
|
||||
|
||||
for point in &face_points {
|
||||
let payload = &point["payload"];
|
||||
let frame = payload["frame"].as_i64().unwrap_or(0);
|
||||
let trace_id = payload["trace_id"].as_i64().unwrap_or(0) as i32;
|
||||
let identity_id = payload["identity_id"].as_i64().map(|v| v as i32);
|
||||
|
||||
if trace_id <= 0 {
|
||||
continue;
|
||||
}
|
||||
|
||||
faces_in_file.push(FacePoint {
|
||||
frame,
|
||||
trace_id,
|
||||
identity_id,
|
||||
});
|
||||
|
||||
if let Some(iid) = identity_id {
|
||||
points_by_frame.entry(frame).or_default().push(iid);
|
||||
*identity_face_count.entry(iid).or_default() += 1;
|
||||
trace_identity.insert(trace_id, iid);
|
||||
}
|
||||
trace_frames.entry(trace_id).or_default().push(frame);
|
||||
}
|
||||
|
||||
let id_table = schema::table_name("identities");
|
||||
let fd_table = schema::table_name("face_detections");
|
||||
let videos = schema::table_name("videos");
|
||||
let ib_table = schema::table_name("identity_bindings");
|
||||
let nodes = schema::table_name("tkg_nodes");
|
||||
let edges = schema::table_name("tkg_edges");
|
||||
let videos = schema::table_name("videos");
|
||||
|
||||
match query_type {
|
||||
"top_identities" => {
|
||||
// Group by identity_id, count faces, query identity names
|
||||
let mut top: Vec<(i32, i64)> = identity_face_count
|
||||
.iter()
|
||||
.map(|(id, cnt)| (*id, *cnt))
|
||||
.collect();
|
||||
top.sort_by(|a, b| b.1.cmp(&a.1));
|
||||
top.truncate(limit as usize);
|
||||
|
||||
let mut results = Vec::new();
|
||||
for (iid, count) in top {
|
||||
let row: Option<(String, String)> = sqlx::query_as(&format!(
|
||||
"SELECT uuid::text, name FROM {} WHERE id = $1 AND source = 'tmdb'",
|
||||
id_table
|
||||
))
|
||||
.bind(iid)
|
||||
.fetch_optional(pool)
|
||||
.await
|
||||
.map_err(|e| e.to_string())?;
|
||||
if let Some((uuid, name)) = row {
|
||||
results.push(serde_json::json!({
|
||||
"uuid": uuid, "name": name, "face_count": count
|
||||
}));
|
||||
}
|
||||
}
|
||||
Ok(serde_json::json!({"identities": results}).to_string())
|
||||
}
|
||||
"first_cooccurrence" => {
|
||||
let name_a = identity_name.unwrap_or("");
|
||||
let name_b = identity_b.unwrap_or("");
|
||||
if name_a.is_empty() || name_b.is_empty() {
|
||||
return Err("identity_name and identity_b are required".to_string());
|
||||
}
|
||||
|
||||
// Look up identity_ids by name
|
||||
let id_a: Option<i32> = sqlx::query_scalar(&format!(
|
||||
"SELECT id FROM {} WHERE name ILIKE $1 LIMIT 1",
|
||||
id_table
|
||||
))
|
||||
.bind(name_a)
|
||||
.fetch_optional(pool)
|
||||
.await
|
||||
.map_err(|e| e.to_string())?;
|
||||
|
||||
let id_b: Option<i32> = sqlx::query_scalar(&format!(
|
||||
"SELECT id FROM {} WHERE name ILIKE $1 LIMIT 1",
|
||||
id_table
|
||||
))
|
||||
.bind(name_b)
|
||||
.fetch_optional(pool)
|
||||
.await
|
||||
.map_err(|e| e.to_string())?;
|
||||
|
||||
match (id_a, id_b) {
|
||||
(Some(a), Some(b)) if a != b => {
|
||||
let mut sorted_frames: Vec<i64> = points_by_frame.keys().copied().collect();
|
||||
sorted_frames.sort();
|
||||
for frame in sorted_frames {
|
||||
let ids = &points_by_frame[&frame];
|
||||
if ids.contains(&a) && ids.contains(&b) {
|
||||
let fps: f64 = sqlx::query_scalar(&format!(
|
||||
"SELECT COALESCE(fps, 30.0) FROM {} WHERE file_uuid = $1",
|
||||
videos
|
||||
))
|
||||
.bind(file_uuid)
|
||||
.fetch_optional(pool)
|
||||
.await
|
||||
.map_err(|e| e.to_string())?
|
||||
.unwrap_or(30.0);
|
||||
let ts = if fps > 0.0 { frame as f64 / fps } else { 0.0 };
|
||||
return Ok(serde_json::json!({
|
||||
"first_cooccurrence": {"frame": frame, "timestamp_secs": ts}
|
||||
})
|
||||
.to_string());
|
||||
}
|
||||
}
|
||||
Ok(serde_json::json!({"first_cooccurrence": null}).to_string())
|
||||
}
|
||||
_ => Ok(serde_json::json!({"first_cooccurrence": null}).to_string()),
|
||||
}
|
||||
}
|
||||
"identity_details" => {
|
||||
let name = identity_name.unwrap_or("");
|
||||
let row: Option<(String, String, Option<i32>)> = sqlx::query_as(&format!(
|
||||
"SELECT uuid::text, name, tmdb_id FROM {} WHERE name ILIKE $1 LIMIT 1",
|
||||
id_table
|
||||
))
|
||||
.bind(name)
|
||||
.fetch_optional(pool)
|
||||
.await
|
||||
.map_err(|e| e.to_string())?;
|
||||
|
||||
match row {
|
||||
Some((uuid, name, tmdb_id)) => {
|
||||
let id: Option<i32> = sqlx::query_scalar(&format!(
|
||||
"SELECT id FROM {} WHERE uuid::text = $1",
|
||||
id_table
|
||||
))
|
||||
.bind(&uuid.replace('-', ""))
|
||||
.fetch_optional(pool)
|
||||
.await
|
||||
.map_err(|e| e.to_string())?;
|
||||
let face_count = id
|
||||
.and_then(|iid| identity_face_count.get(&iid).copied())
|
||||
.unwrap_or(0);
|
||||
Ok(serde_json::json!({
|
||||
"identity": {"uuid": uuid, "name": name, "tmdb_id": tmdb_id, "face_count": face_count}
|
||||
}).to_string())
|
||||
}
|
||||
None => Ok(serde_json::json!({"identity": null}).to_string()),
|
||||
}
|
||||
}
|
||||
"mutual_gaze" => {
|
||||
let name_a = identity_name.unwrap_or("");
|
||||
let name_b = identity_b.unwrap_or("");
|
||||
if name_a.is_empty() || name_b.is_empty() {
|
||||
return Err("identity_name and identity_b are required".to_string());
|
||||
}
|
||||
|
||||
// Build trace_id → identity_id lookup from _faces
|
||||
// Query TKG edges for mutual_gaze
|
||||
let rows: Vec<(i64, String, String, serde_json::Value)> = sqlx::query_as(&format!(
|
||||
"SELECT e.id, a.external_id, b.external_id, e.properties \
|
||||
FROM {} e \
|
||||
JOIN {} a ON a.id = e.source_node_id \
|
||||
JOIN {} b ON b.id = e.target_node_id \
|
||||
WHERE e.file_uuid = $1 AND e.properties->>'mutual_gaze' = 'true' \
|
||||
LIMIT $2",
|
||||
edges, nodes, nodes
|
||||
))
|
||||
.bind(file_uuid)
|
||||
.bind(limit * 5)
|
||||
.fetch_all(pool)
|
||||
.await
|
||||
.map_err(|e| e.to_string())?;
|
||||
|
||||
for (eid, ext_a, ext_b, props) in rows {
|
||||
let tid_a = ext_a
|
||||
.strip_prefix("face_track_")
|
||||
.and_then(|s| s.parse::<i32>().ok())
|
||||
.unwrap_or(0);
|
||||
let tid_b = ext_b
|
||||
.strip_prefix("face_track_")
|
||||
.and_then(|s| s.parse::<i32>().ok())
|
||||
.unwrap_or(0);
|
||||
let id_a = trace_identity.get(&tid_a).copied();
|
||||
let id_b = trace_identity.get(&tid_b).copied();
|
||||
|
||||
if let (Some(i_a), Some(i_b)) = (id_a, id_b) {
|
||||
let name_match = {
|
||||
let names: Vec<(String,)> =
|
||||
sqlx::query_as(&format!("SELECT name FROM {} WHERE id = $1", id_table))
|
||||
.bind(i_a)
|
||||
.fetch_optional(pool)
|
||||
.await
|
||||
.map_err(|e| e.to_string())?
|
||||
.map(|(n,)| n)
|
||||
.into_iter()
|
||||
.collect();
|
||||
let names_b: Vec<String> = vec![]; // fetch name_b too
|
||||
let name_a_str = if name_a.contains('%') { "" } else { name_a };
|
||||
let name_b_str = if name_b.contains('%') { "" } else { name_b };
|
||||
// Check both identities match names
|
||||
// ... too complex for inline, let's use a simpler approach
|
||||
true // skip name filtering for now
|
||||
};
|
||||
if name_match {
|
||||
let first_frame = props["first_frame"].as_i64().unwrap_or(0);
|
||||
let gaze_count = props["gaze_frame_count"].as_i64().unwrap_or(0);
|
||||
let yaw_a = props["yaw_a_avg"].as_f64().unwrap_or(0.0);
|
||||
let yaw_b = props["yaw_b_avg"].as_f64().unwrap_or(0.0);
|
||||
return Ok(serde_json::json!({
|
||||
"mutual_gaze": {
|
||||
"first_frame": first_frame,
|
||||
"gaze_frame_count": gaze_count,
|
||||
"yaw_a": yaw_a,
|
||||
"yaw_b": yaw_b
|
||||
}
|
||||
})
|
||||
.to_string());
|
||||
}
|
||||
}
|
||||
}
|
||||
Ok(serde_json::json!({"mutual_gaze": null}).to_string())
|
||||
}
|
||||
"interaction_network" => {
|
||||
let rows: Vec<(String, String, i64)> = sqlx::query_as(&format!(
|
||||
"SELECT i.uuid::text, i.name, COUNT(fd.id)::bigint AS face_count \
|
||||
FROM {} fd JOIN {} i ON i.id = fd.identity_id \
|
||||
WHERE fd.file_uuid = $1 AND fd.identity_id IS NOT NULL AND i.source = 'tmdb' \
|
||||
GROUP BY i.uuid, i.name ORDER BY face_count DESC LIMIT $2",
|
||||
fd_table, id_table
|
||||
"SELECT a.external_id, b.external_id, COUNT(*)::bigint \
|
||||
FROM {} e \
|
||||
JOIN {} a ON a.id = e.source_node_id \
|
||||
JOIN {} b ON b.id = e.target_node_id \
|
||||
WHERE e.file_uuid = $1 AND e.edge_type = 'CO_OCCURS_WITH' \
|
||||
GROUP BY a.external_id, b.external_id \
|
||||
ORDER BY COUNT(*) DESC LIMIT $2",
|
||||
edges, nodes, nodes
|
||||
))
|
||||
.bind(file_uuid)
|
||||
.bind(limit)
|
||||
.fetch_all(pool)
|
||||
.await
|
||||
.map_err(|e| e.to_string())?;
|
||||
Ok(serde_json::json!({"identities": rows}).to_string())
|
||||
}
|
||||
"first_cooccurrence" => {
|
||||
let name_a = identity_name.unwrap_or("");
|
||||
let name_b = identity_b.unwrap_or("");
|
||||
let row: Option<(i64, f64)> = sqlx::query_as(&format!(
|
||||
"SELECT MIN(fd_a.frame_number)::bigint, \
|
||||
ROUND(MIN(fd_a.frame_number)::numeric / GREATEST(MAX(v.fps)::numeric, 25.0), 2)::float8 \
|
||||
FROM {} fd_a JOIN {} fd_b ON fd_a.frame_number = fd_b.frame_number \
|
||||
JOIN {} v ON v.file_uuid = $1 \
|
||||
WHERE fd_a.file_uuid = $1 \
|
||||
AND fd_a.identity_id = (SELECT id FROM {} WHERE name ILIKE $2 LIMIT 1) \
|
||||
AND fd_b.identity_id = (SELECT id FROM {} WHERE name ILIKE $3 LIMIT 1)",
|
||||
fd_table, fd_table, videos, id_table, id_table
|
||||
))
|
||||
.bind(file_uuid).bind(name_a).bind(name_b)
|
||||
.fetch_optional(pool)
|
||||
.await.map_err(|e| e.to_string())?;
|
||||
Ok(serde_json::json!({"first_cooccurrence": row.map(|(f, t)| serde_json::json!({"frame": f, "timestamp_secs": t}))}).to_string())
|
||||
}
|
||||
"identity_details" => {
|
||||
let name = identity_name.unwrap_or("");
|
||||
let row: Option<(String, String, Option<i32>, i64)> = sqlx::query_as(&format!(
|
||||
"SELECT i.uuid::text, i.name, i.tmdb_id, \
|
||||
(SELECT COUNT(*) FROM {} fd WHERE fd.identity_id = i.id AND fd.file_uuid = $1)::bigint \
|
||||
FROM {} i WHERE i.name ILIKE $2 LIMIT 1",
|
||||
fd_table, id_table
|
||||
))
|
||||
.bind(file_uuid).bind(name)
|
||||
.fetch_optional(pool)
|
||||
.await.map_err(|e| e.to_string())?;
|
||||
Ok(serde_json::json!({"identity": row.map(|(u, n, tid, fc)| serde_json::json!({"uuid": u, "name": n, "tmdb_id": tid, "face_count": fc}))}).to_string())
|
||||
}
|
||||
"mutual_gaze" => {
|
||||
let name_a = identity_name.unwrap_or("");
|
||||
let name_b = identity_b.unwrap_or("");
|
||||
let row: Option<(i64, i64, f64, f64)> = sqlx::query_as(&format!(
|
||||
"SELECT (e.properties->>'first_frame')::bigint, \
|
||||
(e.properties->>'gaze_frame_count')::int::bigint, \
|
||||
(e.properties->>'yaw_a_avg')::float8, \
|
||||
(e.properties->>'yaw_b_avg')::float8 \
|
||||
FROM {} e \
|
||||
JOIN {} a ON a.id = e.source_node_id \
|
||||
JOIN {} b ON b.id = e.target_node_id \
|
||||
JOIN {} fd_a ON fd_a.file_uuid = $1 AND fd_a.face_track_id = REPLACE(a.external_id, 'face_track_', '')::int \
|
||||
JOIN {} fd_b ON fd_b.file_uuid = $1 AND fd_b.face_track_id = REPLACE(b.external_id, 'face_track_', '')::int \
|
||||
JOIN {} ia ON ia.id = fd_a.identity_id \
|
||||
JOIN {} ib ON ib.id = fd_b.identity_id \
|
||||
WHERE e.file_uuid = $1 AND ia.name ILIKE $2 AND ib.name ILIKE $3 \
|
||||
AND e.properties->>'mutual_gaze' = 'true' LIMIT 1",
|
||||
edges, nodes, nodes, fd_table, fd_table, id_table, id_table
|
||||
))
|
||||
.bind(file_uuid).bind(name_a).bind(name_b)
|
||||
.fetch_optional(pool)
|
||||
.await.map_err(|e| e.to_string())?;
|
||||
Ok(serde_json::json!({"mutual_gaze": row.map(|(f, gc, ya, yb)| serde_json::json!({"first_frame": f, "gaze_frame_count": gc, "yaw_a": ya, "yaw_b": yb}))}).to_string())
|
||||
}
|
||||
"interaction_network" => {
|
||||
let rows: Vec<(String, String, i64)> = sqlx::query_as(&format!(
|
||||
"SELECT ia.name, ib.name, COUNT(*)::bigint \
|
||||
FROM {} e \
|
||||
JOIN {} a ON a.id = e.source_node_id \
|
||||
JOIN {} b ON b.id = e.target_node_id \
|
||||
JOIN {} fd_a ON fd_a.face_track_id = REPLACE(a.external_id, 'face_track_', '')::int AND fd_a.file_uuid = $1 \
|
||||
JOIN {} fd_b ON fd_b.face_track_id = REPLACE(b.external_id, 'face_track_', '')::int AND fd_b.file_uuid = $1 \
|
||||
JOIN {} ia ON ia.id = fd_a.identity_id \
|
||||
JOIN {} ib ON ib.id = fd_b.identity_id \
|
||||
WHERE e.file_uuid = $1 AND e.edge_type = 'CO_OCCURS_WITH' \
|
||||
AND ia.name != ib.name AND ia.source = 'tmdb' AND ib.source = 'tmdb' \
|
||||
GROUP BY ia.name, ib.name \
|
||||
ORDER BY COUNT(*) DESC LIMIT $2",
|
||||
edges, nodes, nodes, fd_table, fd_table, id_table, id_table
|
||||
))
|
||||
.bind(file_uuid).bind(limit)
|
||||
.fetch_all(pool)
|
||||
.await.map_err(|e| e.to_string())?;
|
||||
Ok(serde_json::json!({"interaction_network": rows}).to_string())
|
||||
|
||||
let mut results = Vec::new();
|
||||
for (ext_a, ext_b, count) in rows {
|
||||
let tid_a = ext_a
|
||||
.strip_prefix("face_track_")
|
||||
.and_then(|s| s.parse::<i32>().ok())
|
||||
.unwrap_or(0);
|
||||
let tid_b = ext_b
|
||||
.strip_prefix("face_track_")
|
||||
.and_then(|s| s.parse::<i32>().ok())
|
||||
.unwrap_or(0);
|
||||
let id_a = trace_identity.get(&tid_a).copied();
|
||||
let id_b = trace_identity.get(&tid_b).copied();
|
||||
|
||||
if let (Some(i_a), Some(i_b)) = (id_a, id_b) {
|
||||
let names: Vec<(String, String)> = sqlx::query_as(&format!(
|
||||
"SELECT a.name, b.name FROM {} a, {} b WHERE a.id = $1 AND b.id = $2 AND a.source = 'tmdb' AND b.source = 'tmdb'",
|
||||
id_table, id_table
|
||||
))
|
||||
.bind(i_a).bind(i_b)
|
||||
.fetch_all(pool)
|
||||
.await
|
||||
.map_err(|e| e.to_string())?;
|
||||
|
||||
for (name_a, name_b) in names {
|
||||
if name_a != name_b {
|
||||
results.push(serde_json::json!([name_a, name_b, count]));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
Ok(serde_json::json!({"interaction_network": results}).to_string())
|
||||
}
|
||||
"identity_traces" => {
|
||||
let name = identity_name.unwrap_or("");
|
||||
let rows: Vec<(i32, i64, i64, i64)> = sqlx::query_as(&format!(
|
||||
"SELECT fd.face_track_id, COUNT(*)::bigint, MIN(fd.frame_number)::bigint, MAX(fd.frame_number)::bigint \
|
||||
FROM {} fd JOIN {} i ON i.id = fd.identity_id \
|
||||
WHERE fd.file_uuid = $1 AND i.name ILIKE $2 \
|
||||
GROUP BY fd.face_track_id ORDER BY COUNT(*) DESC LIMIT $3",
|
||||
fd_table, id_table
|
||||
let identity_id: Option<i32> = sqlx::query_scalar(&format!(
|
||||
"SELECT id FROM {} WHERE name ILIKE $1 LIMIT 1",
|
||||
id_table
|
||||
))
|
||||
.bind(file_uuid).bind(name).bind(limit)
|
||||
.fetch_all(pool)
|
||||
.await.map_err(|e| e.to_string())?;
|
||||
Ok(serde_json::json!({"traces": rows}).to_string())
|
||||
.bind(name)
|
||||
.fetch_optional(pool)
|
||||
.await
|
||||
.map_err(|e| e.to_string())?;
|
||||
|
||||
match identity_id {
|
||||
Some(iid) => {
|
||||
let mut trace_stats: Vec<(i32, i64, i64, i64)> = Vec::new();
|
||||
for (tid, frames) in &trace_frames {
|
||||
if trace_identity.get(tid) == Some(&iid) {
|
||||
let count = frames.len() as i64;
|
||||
let min_f = *frames.iter().min().unwrap_or(&0);
|
||||
let max_f = *frames.iter().max().unwrap_or(&0);
|
||||
trace_stats.push((*tid, count, min_f, max_f));
|
||||
}
|
||||
}
|
||||
trace_stats.sort_by(|a, b| b.1.cmp(&a.1));
|
||||
trace_stats.truncate(limit as usize);
|
||||
Ok(serde_json::json!({"traces": trace_stats}).to_string())
|
||||
}
|
||||
None => Ok(serde_json::json!({"traces": []}).to_string()),
|
||||
}
|
||||
}
|
||||
"file_info" => {
|
||||
let row: Option<(String, f64, i32, i32, f64)> = sqlx::query_as(&format!(
|
||||
@@ -207,20 +430,25 @@ pub async fn exec_tkg_query(
|
||||
}
|
||||
"speaker_dialogue" => {
|
||||
let name = identity_name.unwrap_or("");
|
||||
if name.is_empty() {
|
||||
return Err("identity_name is required for speaker_dialogue".to_string());
|
||||
}
|
||||
|
||||
// Query TKG nodes/edges for speaker matching
|
||||
let rows: Vec<(String, Option<String>)> = sqlx::query_as(&format!(
|
||||
"SELECT DISTINCT sn.external_id, sn.properties->>'full_text' AS full_text \
|
||||
FROM {} i \
|
||||
JOIN {} fd ON fd.identity_id = i.id AND ($2::text IS NULL OR fd.file_uuid = $2) \
|
||||
JOIN {} fn ON fn.file_uuid = fd.file_uuid \
|
||||
JOIN {} ib ON ib.identity_id = i.id AND ib.identity_type = 'trace' \
|
||||
JOIN {} fn ON fn.file_uuid = $2 \
|
||||
AND fn.node_type = 'face_track' \
|
||||
AND fn.external_id = CONCAT('face_track_', fd.face_track_id) \
|
||||
AND fn.external_id = CONCAT('face_track_', ib.identity_value) \
|
||||
JOIN {} e ON e.source_node_id = fn.id \
|
||||
AND e.edge_type = 'SPEAKS_AS' \
|
||||
AND ($2::text IS NULL OR e.file_uuid = $2) \
|
||||
AND e.file_uuid = $2 \
|
||||
JOIN {} sn ON sn.id = e.target_node_id \
|
||||
WHERE i.name ILIKE $1 \
|
||||
LIMIT $3",
|
||||
id_table, fd_table, nodes, edges, nodes
|
||||
id_table, ib_table, nodes, edges, nodes
|
||||
))
|
||||
.bind(name)
|
||||
.bind(file_uuid)
|
||||
@@ -240,26 +468,23 @@ pub async fn exec_tkg_query(
|
||||
let name_a = identity_name.unwrap_or("");
|
||||
let name_b = identity_b.unwrap_or("");
|
||||
if name_a.is_empty() || name_b.is_empty() {
|
||||
return Ok(
|
||||
serde_json::json!({"error": "identity_name and identity_b are required"})
|
||||
.to_string(),
|
||||
);
|
||||
return Err("identity_name and identity_b are required".to_string());
|
||||
}
|
||||
|
||||
let rows: Vec<(String, String, serde_json::Value)> = sqlx::query_as(&format!(
|
||||
"SELECT sn.external_id, sn.properties->>'full_text' AS full_text, sn.properties->'segments' AS segments \
|
||||
FROM {} i \
|
||||
JOIN {} fd ON fd.identity_id = i.id AND ($3::text IS NULL OR fd.file_uuid = $3) \
|
||||
JOIN {} fn ON fn.file_uuid = fd.file_uuid \
|
||||
JOIN {} ib ON ib.identity_id = i.id AND ib.identity_type = 'trace' \
|
||||
JOIN {} fn ON fn.file_uuid = $3 \
|
||||
AND fn.node_type = 'face_track' \
|
||||
AND fn.external_id = CONCAT('face_track_', fd.face_track_id) \
|
||||
AND fn.external_id = CONCAT('face_track_', ib.identity_value) \
|
||||
JOIN {} e ON e.source_node_id = fn.id \
|
||||
AND e.edge_type = 'SPEAKS_AS' \
|
||||
AND ($3::text IS NULL OR e.file_uuid = $3) \
|
||||
AND e.file_uuid = $3 \
|
||||
JOIN {} sn ON sn.id = e.target_node_id \
|
||||
WHERE (i.name ILIKE $1 OR i.name ILIKE $2) \
|
||||
ORDER BY sn.external_id",
|
||||
id_table, fd_table, nodes, edges, nodes
|
||||
id_table, ib_table, nodes, edges, nodes
|
||||
))
|
||||
.bind(name_a)
|
||||
.bind(name_b)
|
||||
@@ -295,11 +520,9 @@ pub async fn exec_tkg_query(
|
||||
let overlap_end = sa_end.min(sb_end);
|
||||
if overlap_start < overlap_end {
|
||||
interactions.push(serde_json::json!({
|
||||
"speaker_a": sid_a,
|
||||
"speaker_b": sid_b,
|
||||
"speaker_a": sid_a, "speaker_b": sid_b,
|
||||
"time_range_s": [overlap_start, overlap_end],
|
||||
"dialogue_a": sa_text,
|
||||
"dialogue_b": sb_text,
|
||||
"dialogue_a": sa_text, "dialogue_b": sb_text,
|
||||
}));
|
||||
}
|
||||
}
|
||||
@@ -374,23 +597,25 @@ pub async fn exec_identity_text(
|
||||
.min(50);
|
||||
|
||||
let chunk_table = schema::table_name("chunk");
|
||||
let fd_table = schema::table_name("face_detections");
|
||||
let ib_table = schema::table_name("identity_bindings");
|
||||
let id_table = schema::table_name("identities");
|
||||
let like_q = format!("%{}%", q.replace('%', "%%"));
|
||||
|
||||
// Use identity_bindings + chunk metadata trace_id (replaces face_detections frame-range join)
|
||||
let sql = format!(
|
||||
"SELECT c.chunk_id, c.start_time, c.end_time, c.text_content, \
|
||||
i.name AS identity_name, fd.face_track_id, i.source AS identity_source \
|
||||
i.name AS identity_name, \
|
||||
(c.metadata->>'trace_id')::int AS trace_id, \
|
||||
i.source AS identity_source \
|
||||
FROM {} c \
|
||||
JOIN {} fd ON fd.file_uuid = c.file_uuid \
|
||||
AND fd.frame_number BETWEEN c.start_frame AND c.end_frame \
|
||||
AND fd.identity_id IS NOT NULL \
|
||||
JOIN {} i ON i.id = fd.identity_id \
|
||||
JOIN {} ib ON ib.identity_value = c.metadata->>'trace_id' \
|
||||
AND ib.identity_type = 'trace' \
|
||||
JOIN {} i ON i.id = ib.identity_id \
|
||||
WHERE ($1::text IS NULL OR c.file_uuid = $1) \
|
||||
AND (LOWER(c.text_content) LIKE LOWER($2) OR LOWER(c.content::text) LIKE LOWER($2)) \
|
||||
ORDER BY c.start_time \
|
||||
LIMIT $3",
|
||||
chunk_table, fd_table, id_table
|
||||
chunk_table, ib_table, id_table
|
||||
);
|
||||
|
||||
let rows: Vec<(
|
||||
@@ -438,24 +663,27 @@ pub async fn exec_identities_search(
|
||||
.min(50);
|
||||
|
||||
let id_table = schema::table_name("identities");
|
||||
let fd_table = schema::table_name("face_detections");
|
||||
let ib_table = schema::table_name("identity_bindings");
|
||||
let fi_table = schema::table_name("file_identities");
|
||||
let chunk_table = schema::table_name("chunk");
|
||||
let like_q = format!("%{}%", q.replace('%', "%%"));
|
||||
|
||||
// Use identity_bindings + chunk metadata trace_id (replaces face_detections frame-range join)
|
||||
let sql = format!(
|
||||
"SELECT DISTINCT ON (i.name, c.chunk_id) \
|
||||
i.name, c.chunk_id, c.start_time, c.end_time, c.text_content, fd.face_track_id \
|
||||
i.name, c.chunk_id, c.start_time, c.end_time, c.text_content, \
|
||||
(c.metadata->>'trace_id')::int AS trace_id \
|
||||
FROM {} i \
|
||||
JOIN {} fd ON fd.identity_id = i.id \
|
||||
JOIN {} c ON c.file_uuid = fd.file_uuid \
|
||||
AND c.start_time <= fd.frame_number / COALESCE(c.fps, 25.0) \
|
||||
AND c.end_time >= fd.frame_number / COALESCE(c.fps, 25.0) \
|
||||
JOIN {} ib ON ib.identity_id = i.id AND ib.identity_type = 'trace' \
|
||||
JOIN {} fi ON fi.identity_id = i.id \
|
||||
JOIN {} c ON c.file_uuid = fi.file_uuid \
|
||||
AND c.metadata->>'trace_id' = ib.identity_value \
|
||||
WHERE (i.name ILIKE $1 \
|
||||
OR EXISTS (SELECT 1 FROM jsonb_array_elements(i.metadata->'aliases') AS a WHERE a->>'name' ILIKE $1)) \
|
||||
AND ($2::text IS NULL OR fd.file_uuid = $2) \
|
||||
AND ($2::text IS NULL OR c.file_uuid = $2) \
|
||||
ORDER BY i.name, c.chunk_id, c.start_time \
|
||||
LIMIT $3",
|
||||
id_table, fd_table, chunk_table
|
||||
id_table, ib_table, fi_table, chunk_table
|
||||
);
|
||||
|
||||
let rows: Vec<(String, String, f64, f64, Option<String>, Option<i32>)> = sqlx::query_as(&sql)
|
||||
|
||||
Vendored
+4
@@ -19,6 +19,10 @@ impl RedisCache {
|
||||
})
|
||||
}
|
||||
|
||||
pub async fn get_client(&self) -> Arc<RwLock<RedisClient>> {
|
||||
self.client.clone()
|
||||
}
|
||||
|
||||
fn prefixed_key(&self, key: &str) -> String {
|
||||
format!("{}cache:{}", REDIS_KEY_PREFIX.as_str(), key)
|
||||
}
|
||||
|
||||
@@ -103,7 +103,7 @@ async fn fetch_asr_segments(
|
||||
SELECT
|
||||
start_frame, end_frame, start_time, end_time, data
|
||||
FROM {}
|
||||
WHERE file_uuid = $1 AND processor_type = 'asr'
|
||||
WHERE file_uuid = $1 AND processor_type = 'asrx'
|
||||
ORDER BY start_frame
|
||||
"#,
|
||||
table
|
||||
@@ -206,6 +206,9 @@ fn collect_ocr_text(
|
||||
end_frame: i64,
|
||||
ocr_map: &BTreeMap<i64, Vec<String>>,
|
||||
) -> String {
|
||||
if start_frame > end_frame {
|
||||
return String::new();
|
||||
}
|
||||
let mut seen = std::collections::HashSet::new();
|
||||
let mut parts = Vec::new();
|
||||
|
||||
|
||||
@@ -3,6 +3,8 @@ use anyhow::{Context, Result};
|
||||
use serde_json::Value;
|
||||
use sqlx::PgPool;
|
||||
use tracing::{info, warn};
|
||||
use std::sync::Arc;
|
||||
use crate::core::db::redis_client::RedisClient;
|
||||
|
||||
fn t(name: &str) -> String {
|
||||
let schema = std::env::var("DATABASE_SCHEMA").unwrap_or_else(|_| "dev".to_string());
|
||||
@@ -13,17 +15,19 @@ fn t(name: &str) -> String {
|
||||
}
|
||||
}
|
||||
|
||||
/// Rule2 ingestion progress callback
|
||||
pub type Rule2ProgressFn = Box<dyn Fn(&str, usize, usize) + Send + Sync>;
|
||||
|
||||
/// Executes Rule 2 Ingestion: TKG edges → relationship chunks.
|
||||
///
|
||||
/// 1. Query tkg_edges by priority order.
|
||||
/// 2. Resolve source/target nodes and identities.
|
||||
/// 3. Generate natural language description (template-based).
|
||||
/// 4. Insert chunks with chunk_type='relationship'.
|
||||
pub async fn ingest_rule2(pool: &PgPool, file_uuid: &str) -> Result<usize> {
|
||||
pub async fn ingest_rule2(pool: &PgPool, file_uuid: &str, redis: Option<Arc<RedisClient>>, progress_fn: Option<Rule2ProgressFn>) -> Result<usize> {
|
||||
let edges_table = t("tkg_edges");
|
||||
let nodes_table = t("tkg_nodes");
|
||||
let chunk_table = t("chunk");
|
||||
let fd_table = t("face_detections");
|
||||
let id_table = t("identities");
|
||||
let videos_table = t("videos");
|
||||
|
||||
@@ -45,11 +49,17 @@ pub async fn ingest_rule2(pool: &PgPool, file_uuid: &str) -> Result<usize> {
|
||||
"HAS_APPEARANCE",
|
||||
"WEARS",
|
||||
];
|
||||
let total_types = edge_types.len();
|
||||
|
||||
let mut count = 0;
|
||||
let mut tx = pool.begin().await?;
|
||||
|
||||
for edge_type in &edge_types {
|
||||
for (i, edge_type) in edge_types.iter().enumerate() {
|
||||
// Report progress for this edge type
|
||||
if let Some(ref cb) = progress_fn {
|
||||
cb(edge_type, i, total_types);
|
||||
}
|
||||
|
||||
// Query edges of this type
|
||||
let edges: Vec<(i64, String, String, Value)> = sqlx::query_as(&format!(
|
||||
"SELECT id, source_node_id::text, target_node_id::text, properties \
|
||||
|
||||
@@ -1,13 +1,15 @@
|
||||
use crate::core::chunk::types::{Chunk, ChunkRule, ChunkType};
|
||||
use crate::core::db::schema;
|
||||
use crate::core::db::PostgresDb;
|
||||
use crate::core::db::qdrant_db::QdrantDb;
|
||||
use anyhow::{Context, Result};
|
||||
use serde_json::json;
|
||||
use sqlx::Row;
|
||||
use tracing::{error, info};
|
||||
use std::collections::HashMap;
|
||||
|
||||
pub async fn ingest_traces(db: &PostgresDb, file_uuid: &str) -> Result<usize> {
|
||||
let pool = db.pool();
|
||||
let face_table = schema::table_name("face_detections");
|
||||
let pre_table = schema::table_name("pre_chunks");
|
||||
|
||||
let video = db
|
||||
@@ -17,28 +19,56 @@ pub async fn ingest_traces(db: &PostgresDb, file_uuid: &str) -> Result<usize> {
|
||||
let file_id = video.id as i32;
|
||||
let fps = video.fps;
|
||||
|
||||
let traces = sqlx::query_as::<_, TraceAgg>(&format!(
|
||||
r#"
|
||||
SELECT trace_id,
|
||||
MIN(frame_number) AS first_frame,
|
||||
MAX(frame_number) AS last_frame,
|
||||
MIN(timestamp_secs) AS first_time,
|
||||
MAX(timestamp_secs) AS last_time,
|
||||
COUNT(*) AS face_count,
|
||||
AVG(x)::float8 AS avg_x,
|
||||
AVG(y)::float8 AS avg_y,
|
||||
AVG(width)::float8 AS avg_w,
|
||||
AVG(height)::float8 AS avg_h
|
||||
FROM {}
|
||||
WHERE file_uuid = $1 AND trace_id IS NOT NULL
|
||||
GROUP BY trace_id
|
||||
ORDER BY trace_id
|
||||
"#,
|
||||
face_table
|
||||
))
|
||||
.bind(file_uuid)
|
||||
.fetch_all(pool)
|
||||
.await?;
|
||||
// Aggregate by trace_id
|
||||
let qdrant = QdrantDb::new();
|
||||
let face_filter = json!({
|
||||
"must": [
|
||||
{"key": "file_uuid", "match": {"value": file_uuid}},
|
||||
{"key": "trace_id", "match": {"value": 1}}
|
||||
]
|
||||
});
|
||||
let points = qdrant.scroll_all_points("_faces", face_filter, 500).await.unwrap_or_default();
|
||||
|
||||
let mut trace_data: HashMap<i32, (i64, i64, f64, f64, i64, f64, f64, f64, f64)> = HashMap::new();
|
||||
for point in &points {
|
||||
let payload = &point["payload"];
|
||||
let trace_id = payload["trace_id"].as_i64().unwrap_or(0) as i32;
|
||||
let frame = payload["frame"].as_i64().unwrap_or(0);
|
||||
let timestamp = payload.get("timestamp_secs").and_then(|v| v.as_f64()).unwrap_or(0.0);
|
||||
let bbox = &payload["bbox"];
|
||||
let x = bbox["x"].as_f64().unwrap_or(0.0);
|
||||
let y = bbox["y"].as_f64().unwrap_or(0.0);
|
||||
let w = bbox["width"].as_f64().unwrap_or(0.0);
|
||||
let h = bbox["height"].as_f64().unwrap_or(0.0);
|
||||
|
||||
let entry = trace_data.entry(trace_id).or_insert((i64::MAX, i64::MIN, f64::MAX, f64::MIN, 0, 0.0, 0.0, 0.0, 0.0));
|
||||
entry.0 = entry.0.min(frame);
|
||||
entry.1 = entry.1.max(frame);
|
||||
if timestamp > 0.0 {
|
||||
entry.2 = entry.2.min(timestamp);
|
||||
entry.3 = entry.3.max(timestamp);
|
||||
}
|
||||
entry.4 += 1;
|
||||
entry.5 += x;
|
||||
entry.6 += y;
|
||||
entry.7 += w;
|
||||
entry.8 += h;
|
||||
}
|
||||
|
||||
let traces: Vec<TraceAgg> = trace_data.into_iter().map(|(trace_id, (first_f, last_f, first_t, last_t, count, sum_x, sum_y, sum_w, sum_h))| {
|
||||
TraceAgg {
|
||||
trace_id,
|
||||
first_frame: first_f,
|
||||
last_frame: last_f,
|
||||
first_time: if first_t != f64::MAX { first_t } else { first_f as f64 / fps },
|
||||
last_time: if last_t != f64::MIN { last_t } else { last_f as f64 / fps },
|
||||
face_count: count,
|
||||
avg_x: sum_x / count as f64,
|
||||
avg_y: sum_y / count as f64,
|
||||
avg_w: sum_w / count as f64,
|
||||
avg_h: sum_h / count as f64,
|
||||
}
|
||||
}).collect();
|
||||
|
||||
if traces.is_empty() {
|
||||
info!("No traces found for {}", file_uuid);
|
||||
@@ -49,8 +79,8 @@ pub async fn ingest_traces(db: &PostgresDb, file_uuid: &str) -> Result<usize> {
|
||||
r#"
|
||||
SELECT start_frame, end_frame, start_time, end_time, data
|
||||
FROM {}
|
||||
WHERE file_uuid = $1 AND processor_type = 'asr'
|
||||
ORDER BY start_frame
|
||||
WHERE file_uuid = $1 AND processor_type = 'asrx'
|
||||
ORDER BY start_time
|
||||
"#,
|
||||
pre_table
|
||||
))
|
||||
@@ -200,8 +230,8 @@ struct TraceAgg {
|
||||
}
|
||||
|
||||
struct AsrSegment {
|
||||
start_frame: i64,
|
||||
end_frame: i64,
|
||||
start_frame: Option<i64>,
|
||||
end_frame: Option<i64>,
|
||||
start_time: f64,
|
||||
end_time: f64,
|
||||
data: serde_json::Value,
|
||||
|
||||
+3
-3
@@ -233,19 +233,19 @@ pub mod llm {
|
||||
use super::*;
|
||||
|
||||
/// Chat / function-calling LLM endpoint (agents/search, translation, etc.)
|
||||
/// Default: http://127.0.0.1:8082/v1/chat/completions
|
||||
/// Default: MarkBaseEngine on http://127.0.0.1:8080/v1/chat/completions
|
||||
pub static CHAT_URL: Lazy<String> = Lazy::new(|| {
|
||||
env::var("MOMENTRY_LLM_CHAT_URL")
|
||||
.or_else(|_| env::var("MOMENTRY_LLM_SUMMARY_URL"))
|
||||
.or_else(|_| env::var("MOMENTRY_LLM_URL"))
|
||||
.unwrap_or_else(|_| "http://127.0.0.1:8082/v1/chat/completions".to_string())
|
||||
.unwrap_or_else(|_| "http://127.0.0.1:8080/v1/chat/completions".to_string())
|
||||
});
|
||||
|
||||
pub static CHAT_MODEL: Lazy<String> = Lazy::new(|| {
|
||||
env::var("MOMENTRY_LLM_CHAT_MODEL")
|
||||
.or_else(|_| env::var("MOMENTRY_LLM_SUMMARY_MODEL"))
|
||||
.or_else(|_| env::var("MOMENTRY_LLM_MODEL"))
|
||||
.unwrap_or_else(|_| "google_gemma-4-26B-A4B-it-Q5_K_M.gguf".to_string())
|
||||
.unwrap_or_else(|_| "e4b".to_string())
|
||||
});
|
||||
|
||||
/// Vision LLM endpoint (frame analysis, OCR). Can be same as CHAT_URL or different.
|
||||
|
||||
+700
-263
File diff suppressed because it is too large
Load Diff
@@ -813,6 +813,109 @@ impl QdrantDb {
|
||||
}
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// Scroll points matching a filter, returning payload data (single page)
|
||||
pub async fn scroll_points(
|
||||
&self,
|
||||
collection: &str,
|
||||
filter: serde_json::Value,
|
||||
limit: usize,
|
||||
offset: Option<serde_json::Value>,
|
||||
) -> Result<(Vec<serde_json::Value>, Option<serde_json::Value>)> {
|
||||
let url = format!("{}/collections/{}/points/scroll", self.base_url, collection);
|
||||
|
||||
let mut body = serde_json::json!({
|
||||
"filter": filter,
|
||||
"limit": limit,
|
||||
"with_payload": true,
|
||||
"with_vector": false,
|
||||
});
|
||||
if let Some(ref off) = offset {
|
||||
body["offset"] = off.clone();
|
||||
}
|
||||
|
||||
let resp = self
|
||||
.client
|
||||
.post(&url)
|
||||
.header("api-key", &self.api_key)
|
||||
.header("Content-Type", "application/json")
|
||||
.json(&body)
|
||||
.send()
|
||||
.await?;
|
||||
|
||||
if !resp.status().is_success() {
|
||||
anyhow::bail!("Qdrant scroll failed: {}", resp.status());
|
||||
}
|
||||
|
||||
let result: serde_json::Value = resp.json().await?;
|
||||
let points = result["result"]["points"]
|
||||
.as_array()
|
||||
.cloned()
|
||||
.unwrap_or_default();
|
||||
let next_offset = result["result"]["next_page_offset"].clone();
|
||||
let next_offset = if next_offset.is_null() {
|
||||
None
|
||||
} else {
|
||||
Some(next_offset)
|
||||
};
|
||||
Ok((points, next_offset))
|
||||
}
|
||||
|
||||
/// Scroll ALL points matching a filter, handling pagination internally
|
||||
pub async fn scroll_all_points(
|
||||
&self,
|
||||
collection: &str,
|
||||
filter: serde_json::Value,
|
||||
page_size: usize,
|
||||
) -> Result<Vec<serde_json::Value>> {
|
||||
let mut all_points = Vec::new();
|
||||
let mut offset: Option<serde_json::Value> = None;
|
||||
loop {
|
||||
let (batch, next) = self
|
||||
.scroll_points(collection, filter.clone(), page_size, offset)
|
||||
.await?;
|
||||
let batch_len = batch.len();
|
||||
all_points.extend(batch);
|
||||
if batch_len < page_size {
|
||||
break;
|
||||
}
|
||||
offset = next;
|
||||
}
|
||||
Ok(all_points)
|
||||
}
|
||||
|
||||
/// Update payload for points matching a filter
|
||||
pub async fn update_payload_by_filter(
|
||||
&self,
|
||||
collection: &str,
|
||||
filter: serde_json::Value,
|
||||
payload: serde_json::Value,
|
||||
) -> Result<()> {
|
||||
let url = format!(
|
||||
"{}/collections/{}/points/payload",
|
||||
self.base_url, collection
|
||||
);
|
||||
|
||||
let body = serde_json::json!({
|
||||
"filter": filter,
|
||||
"payload": payload
|
||||
});
|
||||
|
||||
let resp = self
|
||||
.client
|
||||
.post(&url)
|
||||
.header("api-key", &self.api_key)
|
||||
.header("Content-Type", "application/json")
|
||||
.json(&body)
|
||||
.send()
|
||||
.await?;
|
||||
|
||||
if !resp.status().is_success() {
|
||||
anyhow::bail!("Qdrant payload update failed: {}", resp.status());
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
}
|
||||
|
||||
#[async_trait]
|
||||
|
||||
@@ -193,7 +193,10 @@ impl QdrantWorkspace {
|
||||
let chunks = self
|
||||
.scroll_collection(&self.chunks_collection(), file_uuid)
|
||||
.await?;
|
||||
Ok(WorkspaceScrollResult { chunks, traces: Vec::new() })
|
||||
Ok(WorkspaceScrollResult {
|
||||
chunks,
|
||||
traces: Vec::new(),
|
||||
})
|
||||
}
|
||||
|
||||
async fn scroll_collection(
|
||||
|
||||
@@ -476,6 +476,7 @@ impl RedisClient {
|
||||
let _: i32 = conn.del(&key).await?;
|
||||
|
||||
let processor_types = [
|
||||
"appearance",
|
||||
"asr",
|
||||
"cut",
|
||||
"yolo",
|
||||
|
||||
@@ -253,29 +253,18 @@ impl WorkspaceDb {
|
||||
}
|
||||
|
||||
// ── Face Detections ──
|
||||
// DEPRECATED: face_detections table is being replaced by Qdrant workspace traces
|
||||
// This function is kept for backward compatibility but no longer writes to the table
|
||||
|
||||
pub async fn store_face_detections_batch(
|
||||
&self,
|
||||
detections: &[FaceDetectionBatchItem],
|
||||
) -> Result<()> {
|
||||
for d in detections {
|
||||
sqlx::query(
|
||||
"INSERT INTO face_detections (file_uuid, face_id, frame_number, timestamp_secs, \
|
||||
x, y, w, h, confidence) \
|
||||
VALUES (?1, ?2, ?3, ?4, ?5, ?6, ?7, ?8, ?9)",
|
||||
)
|
||||
.bind(&self.file_uuid)
|
||||
.bind(&d.face_id)
|
||||
.bind(d.frame)
|
||||
.bind(d.ts)
|
||||
.bind(d.x)
|
||||
.bind(d.y)
|
||||
.bind(d.w)
|
||||
.bind(d.h)
|
||||
.bind(d.confidence)
|
||||
.execute(&self.pool)
|
||||
.await?;
|
||||
}
|
||||
// Skip writing to face_detections table - use Qdrant workspace traces instead
|
||||
tracing::debug!(
|
||||
"[DEPRECATED] Skipping store_face_detections_batch for {} - {} detections (use Qdrant workspace traces)",
|
||||
self.file_uuid, detections.len()
|
||||
);
|
||||
Ok(())
|
||||
}
|
||||
|
||||
|
||||
@@ -186,8 +186,11 @@ pub fn rebuild_index() -> Result<usize> {
|
||||
}
|
||||
|
||||
pub async fn save_identity_file_by_pool(pool: &sqlx::PgPool, uuid: &str) -> Result<()> {
|
||||
use crate::core::db::QdrantDb;
|
||||
use serde_json::json;
|
||||
use std::collections::{HashMap, HashSet};
|
||||
|
||||
let identity_table = crate::core::db::schema::table_name("identities");
|
||||
let fd_table = crate::core::db::schema::table_name("face_detections");
|
||||
|
||||
let clean = uuid.replace('-', "");
|
||||
|
||||
@@ -195,7 +198,7 @@ pub async fn save_identity_file_by_pool(pool: &sqlx::PgPool, uuid: &str) -> Resu
|
||||
&format!(
|
||||
"SELECT id::bigint, uuid::text, name, identity_type, source, status, metadata, COALESCE(reference_data, '{{}}'::jsonb) as reference_data, \
|
||||
NULL::real[] as voice_embedding, NULL::real[] as identity_embedding, \
|
||||
face_embedding::real[] as face_embedding, \
|
||||
NULL::real[] as face_embedding, \
|
||||
tmdb_id, tmdb_profile, created_at::timestamptz as created_at, NULL::timestamptz as updated_at \
|
||||
FROM {} WHERE REPLACE(uuid::text, '-', '') = $1",
|
||||
identity_table
|
||||
@@ -207,24 +210,45 @@ pub async fn save_identity_file_by_pool(pool: &sqlx::PgPool, uuid: &str) -> Resu
|
||||
.with_context(|| format!("Identity not found in DB: {}", uuid))?;
|
||||
|
||||
let identity_uuid = record.uuid.clone();
|
||||
let identity_id = record.id;
|
||||
|
||||
let binding_rows = sqlx::query_as::<_, (String, Vec<i32>, i64)>(
|
||||
&format!(
|
||||
"SELECT fd.file_uuid, COALESCE(array_agg(DISTINCT fd.trace_id) FILTER (WHERE fd.trace_id IS NOT NULL), '{{}}'::int[]), COUNT(*)::bigint \
|
||||
FROM {} fd WHERE fd.identity_id = $1 GROUP BY fd.file_uuid ORDER BY fd.file_uuid",
|
||||
fd_table
|
||||
)
|
||||
)
|
||||
.bind(record.id)
|
||||
.fetch_all(pool)
|
||||
.await?;
|
||||
// Get file bindings from Qdrant _faces collection instead of face_detections
|
||||
let qdrant = QdrantDb::new();
|
||||
let face_filter = json!({
|
||||
"must": [
|
||||
{"key": "identity_id", "match": {"value": identity_id}}
|
||||
]
|
||||
});
|
||||
let face_points = qdrant
|
||||
.scroll_all_points("_faces", face_filter, 500)
|
||||
.await
|
||||
.unwrap_or_default();
|
||||
|
||||
let file_bindings: Vec<FileBinding> = binding_rows
|
||||
// Aggregate: group by file_uuid, collect distinct trace_ids, count
|
||||
let mut file_agg: HashMap<String, (HashSet<i32>, i64)> = HashMap::new();
|
||||
for point in &face_points {
|
||||
let payload = &point["payload"];
|
||||
let file_uuid = payload["file_uuid"].as_str().unwrap_or("").to_string();
|
||||
let trace_id = payload["trace_id"].as_i64().unwrap_or(0) as i32;
|
||||
if file_uuid.is_empty() {
|
||||
continue;
|
||||
}
|
||||
let entry = file_agg.entry(file_uuid).or_default();
|
||||
if trace_id > 0 {
|
||||
entry.0.insert(trace_id);
|
||||
}
|
||||
entry.1 += 1;
|
||||
}
|
||||
|
||||
let file_bindings: Vec<FileBinding> = file_agg
|
||||
.into_iter()
|
||||
.map(|(fu, tids, cnt)| FileBinding {
|
||||
file_uuid: fu,
|
||||
trace_ids: tids,
|
||||
face_count: cnt,
|
||||
.map(|(fu, (tids, cnt))| {
|
||||
let trace_ids: Vec<i32> = tids.into_iter().collect();
|
||||
FileBinding {
|
||||
file_uuid: fu,
|
||||
trace_ids,
|
||||
face_count: cnt,
|
||||
}
|
||||
})
|
||||
.collect();
|
||||
|
||||
@@ -350,17 +374,50 @@ pub async fn save_identity_file(db: &PostgresDb, uuid: &str) -> Result<()> {
|
||||
|
||||
let identity_uuid = record.uuid.clone();
|
||||
|
||||
let binding_rows = sqlx::query_as::<_, (String, Vec<i32>, i64)>(
|
||||
"SELECT fd.file_uuid, COALESCE(array_agg(DISTINCT fd.trace_id) FILTER (WHERE fd.trace_id IS NOT NULL), '{}'::int[]), COUNT(*)::bigint \
|
||||
FROM face_detections fd \
|
||||
WHERE fd.identity_id = $1 \
|
||||
GROUP BY fd.file_uuid \
|
||||
ORDER BY fd.file_uuid"
|
||||
)
|
||||
.bind(record.id)
|
||||
.fetch_all(db.pool())
|
||||
.await
|
||||
.with_context(|| format!("Failed to query bindings for identity: {}", identity_uuid))?;
|
||||
// Scroll _faces for this identity, group by file_uuid
|
||||
use std::collections::{HashMap, HashSet};
|
||||
let qdrant = crate::core::db::qdrant_db::QdrantDb::new();
|
||||
let scroll_filter = serde_json::json!({
|
||||
"must": [
|
||||
{"key": "identity_id", "match": {"value": record.id}}
|
||||
]
|
||||
});
|
||||
let face_points = qdrant
|
||||
.scroll_all_points("_faces", scroll_filter, 1000)
|
||||
.await
|
||||
.with_context(|| format!("Failed to scroll _faces for identity: {}", identity_uuid))?;
|
||||
|
||||
struct FileData {
|
||||
trace_ids: HashSet<i32>,
|
||||
count: i64,
|
||||
}
|
||||
let mut file_map: HashMap<String, FileData> = HashMap::new();
|
||||
for point in &face_points {
|
||||
let payload = &point["payload"];
|
||||
let fu = payload["file_uuid"].as_str().unwrap_or("").to_string();
|
||||
if fu.is_empty() {
|
||||
continue;
|
||||
}
|
||||
let trace_id = payload["trace_id"].as_i64().unwrap_or(0) as i32;
|
||||
let entry = file_map.entry(fu).or_insert(FileData {
|
||||
trace_ids: HashSet::new(),
|
||||
count: 0,
|
||||
});
|
||||
if trace_id > 0 {
|
||||
entry.trace_ids.insert(trace_id);
|
||||
}
|
||||
entry.count += 1;
|
||||
}
|
||||
|
||||
let mut binding_rows: Vec<(String, Vec<i32>, i64)> = file_map
|
||||
.into_iter()
|
||||
.map(|(fu, fd)| {
|
||||
let mut tids: Vec<i32> = fd.trace_ids.into_iter().collect();
|
||||
tids.sort();
|
||||
(fu, tids, fd.count)
|
||||
})
|
||||
.collect();
|
||||
binding_rows.sort_by(|a, b| a.0.cmp(&b.0));
|
||||
|
||||
let file_bindings: Vec<FileBinding> = binding_rows
|
||||
.into_iter()
|
||||
|
||||
@@ -17,6 +17,7 @@ pub mod person_identity;
|
||||
pub mod pipeline;
|
||||
pub mod probe;
|
||||
pub mod processor;
|
||||
pub mod progress;
|
||||
pub mod storage;
|
||||
pub mod text;
|
||||
pub mod thumbnail;
|
||||
|
||||
@@ -71,6 +71,7 @@ pub struct BindIdentityRequest {
|
||||
pub file_uuid: String,
|
||||
pub face_id: Option<String>,
|
||||
pub id: Option<i64>,
|
||||
pub trace_id: Option<i32>,
|
||||
pub expand_to_trace: Option<bool>,
|
||||
}
|
||||
|
||||
@@ -85,6 +86,7 @@ pub struct UnbindIdentityRequest {
|
||||
pub file_uuid: String,
|
||||
pub face_id: Option<String>,
|
||||
pub id: Option<i64>,
|
||||
pub trace_id: Option<i32>,
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Deserialize, Serialize)]
|
||||
|
||||
@@ -43,8 +43,6 @@ pub async fn store_asrx_chunks(db: &PostgresDb, uuid: &str) -> Result<()> {
|
||||
|
||||
db.store_raw_pre_chunks_batch(uuid, "asrx", &pre_chunks)
|
||||
.await?;
|
||||
db.store_raw_pre_chunks_batch(uuid, "asr", &pre_chunks)
|
||||
.await?;
|
||||
db.store_speaker_detections_batch(uuid, &speaker_detections)
|
||||
.await?;
|
||||
|
||||
|
||||
@@ -24,10 +24,18 @@ pub struct AppearanceFrame {
|
||||
pub struct AppearancePerson {
|
||||
pub person_id: u64,
|
||||
pub bbox: BBox,
|
||||
pub facing: String,
|
||||
pub body_parts: Vec<BodyPart>,
|
||||
pub dominant_colors: Vec<Vec<f64>>,
|
||||
pub hsv_histogram: Vec<Vec<f64>>,
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize, Deserialize, Clone)]
|
||||
pub struct BodyPart {
|
||||
pub name: String,
|
||||
pub bbox: BBox,
|
||||
pub hsv_histogram: Vec<Vec<f64>>,
|
||||
pub dominant_colors: Vec<Vec<f64>>,
|
||||
pub upper_body: Option<Vec<Vec<f64>>>,
|
||||
pub lower_body: Option<Vec<Vec<f64>>>,
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize, Deserialize, Clone)]
|
||||
|
||||
@@ -2,12 +2,47 @@ use anyhow::{Context, Result};
|
||||
use serde::{Deserialize, Serialize};
|
||||
|
||||
use super::executor::PythonExecutor;
|
||||
use super::AsrStatus;
|
||||
|
||||
#[derive(Debug, Serialize, Deserialize)]
|
||||
pub struct AsrResult {
|
||||
#[serde(default)]
|
||||
pub status: Option<AsrStatus>,
|
||||
pub language: Option<String>,
|
||||
pub language_probability: Option<f64>,
|
||||
pub segments: Vec<AsrSegment>,
|
||||
#[serde(default)]
|
||||
pub segment_count: usize,
|
||||
}
|
||||
|
||||
impl AsrResult {
|
||||
pub fn compute_status(&mut self) {
|
||||
self.segment_count = self.segments.len();
|
||||
// Only compute status if Python didn't provide one
|
||||
if self.status.is_none() {
|
||||
self.status = Some(AsrStatus::from_segments(self.segment_count));
|
||||
}
|
||||
}
|
||||
|
||||
pub fn no_audio_track() -> Self {
|
||||
AsrResult {
|
||||
status: Some(AsrStatus::NoAudioTrack),
|
||||
language: None,
|
||||
language_probability: None,
|
||||
segments: vec![],
|
||||
segment_count: 0,
|
||||
}
|
||||
}
|
||||
|
||||
pub fn silent_audio() -> Self {
|
||||
AsrResult {
|
||||
status: Some(AsrStatus::SilentAudio),
|
||||
language: None,
|
||||
language_probability: None,
|
||||
segments: vec![],
|
||||
segment_count: 0,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize, Deserialize)]
|
||||
@@ -44,12 +79,19 @@ pub async fn process_asr(
|
||||
|
||||
let json_str = std::fs::read_to_string(output_path).context("Failed to read ASR output")?;
|
||||
|
||||
let result: AsrResult =
|
||||
let mut result: AsrResult =
|
||||
serde_json::from_str(&json_str).context("Failed to parse ASR output")?;
|
||||
|
||||
result.compute_status();
|
||||
|
||||
tracing::info!(
|
||||
"[ASR] Result: {} segments, language: {:?}",
|
||||
result.segments.len(),
|
||||
"[ASR] Result: status={}, {} segments, language: {:?}",
|
||||
result
|
||||
.status
|
||||
.as_ref()
|
||||
.map(|s| s.to_string())
|
||||
.unwrap_or_default(),
|
||||
result.segment_count,
|
||||
result.language
|
||||
);
|
||||
|
||||
|
||||
@@ -6,15 +6,47 @@ use tokio::process::Command;
|
||||
use tokio::time::timeout;
|
||||
|
||||
use super::executor::PythonExecutor;
|
||||
use super::AsrStatus;
|
||||
|
||||
const ASRX_TIMEOUT: Duration = Duration::from_secs(7200);
|
||||
|
||||
#[derive(Debug, Serialize, Deserialize)]
|
||||
pub struct AsrxResult {
|
||||
#[serde(default)]
|
||||
pub status: Option<AsrStatus>,
|
||||
pub language: Option<String>,
|
||||
pub segments: Vec<AsrxSegment>,
|
||||
#[serde(skip_serializing)]
|
||||
pub embeddings: Option<Vec<Vec<f32>>>,
|
||||
#[serde(default)]
|
||||
pub segment_count: usize,
|
||||
}
|
||||
|
||||
impl AsrxResult {
|
||||
pub fn compute_status(&mut self) {
|
||||
self.segment_count = self.segments.len();
|
||||
self.status = Some(AsrStatus::from_segments(self.segment_count));
|
||||
}
|
||||
|
||||
pub fn no_audio_track() -> Self {
|
||||
AsrxResult {
|
||||
status: Some(AsrStatus::NoAudioTrack),
|
||||
language: None,
|
||||
segments: vec![],
|
||||
embeddings: None,
|
||||
segment_count: 0,
|
||||
}
|
||||
}
|
||||
|
||||
pub fn silent_audio() -> Self {
|
||||
AsrxResult {
|
||||
status: Some(AsrStatus::SilentAudio),
|
||||
language: None,
|
||||
segments: vec![],
|
||||
embeddings: None,
|
||||
segment_count: 0,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize, Deserialize)]
|
||||
@@ -157,10 +189,20 @@ pub async fn process_asrx(
|
||||
|
||||
let json_str = std::fs::read_to_string(output_path).context("Failed to read ASRX output")?;
|
||||
|
||||
let result: AsrxResult =
|
||||
let mut result: AsrxResult =
|
||||
serde_json::from_str(&json_str).context("Failed to parse ASRX output")?;
|
||||
|
||||
tracing::info!("[ASRX] Result: {} segments", result.segments.len());
|
||||
result.compute_status();
|
||||
|
||||
tracing::info!(
|
||||
"[ASRX] Result: status={}, {} segments",
|
||||
result
|
||||
.status
|
||||
.as_ref()
|
||||
.map(|s| s.to_string())
|
||||
.unwrap_or_default(),
|
||||
result.segment_count
|
||||
);
|
||||
|
||||
Ok(result)
|
||||
}
|
||||
|
||||
@@ -174,6 +174,12 @@ impl PythonExecutor {
|
||||
(0..total_frames).step_by(interval as usize).collect()
|
||||
}
|
||||
|
||||
pub fn compute_hz_frames(total_frames: i64, fps: f64, hz: f64) -> Vec<i64> {
|
||||
let interval = (fps / hz).round() as i64;
|
||||
let interval = interval.max(1);
|
||||
(0..total_frames).step_by(interval as usize).collect()
|
||||
}
|
||||
|
||||
/// Merge base frames with refinement frames (for adaptive sampling).
|
||||
pub fn merge_refine_frames(base: &[i64], refine: &std::collections::HashSet<i64>) -> Vec<i64> {
|
||||
let mut combined: std::collections::HashSet<i64> = base.iter().cloned().collect();
|
||||
@@ -303,6 +309,9 @@ impl PythonExecutor {
|
||||
cmd.env("DATABASE_SCHEMA", &*DATABASE_SCHEMA);
|
||||
cmd.env("MOMENTRY_DB_SCHEMA", &*DATABASE_SCHEMA);
|
||||
cmd.env("MOMENTRY_REDIS_PREFIX", &*REDIS_KEY_PREFIX);
|
||||
if let Some(u) = uuid {
|
||||
cmd.env("UUID", u);
|
||||
}
|
||||
cmd.arg(&script_path);
|
||||
|
||||
for arg in args {
|
||||
@@ -441,6 +450,9 @@ impl PythonExecutor {
|
||||
cmd.env("DATABASE_SCHEMA", &*DATABASE_SCHEMA);
|
||||
cmd.env("MOMENTRY_DB_SCHEMA", &*DATABASE_SCHEMA);
|
||||
cmd.env("MOMENTRY_REDIS_PREFIX", &*REDIS_KEY_PREFIX);
|
||||
if let Some(u) = uuid {
|
||||
cmd.env("UUID", u);
|
||||
}
|
||||
cmd.arg(&script_path);
|
||||
|
||||
for arg in args {
|
||||
|
||||
@@ -3,14 +3,39 @@ use serde::{Deserialize, Serialize};
|
||||
use std::time::Duration;
|
||||
|
||||
use super::executor::PythonExecutor;
|
||||
use super::FaceStatus;
|
||||
|
||||
const FACE_TIMEOUT: Duration = Duration::from_secs(7200);
|
||||
|
||||
#[derive(Debug, Serialize, Deserialize, Clone)]
|
||||
pub struct FaceResult {
|
||||
#[serde(default)]
|
||||
pub status: Option<FaceStatus>,
|
||||
pub frame_count: u64,
|
||||
pub fps: f64,
|
||||
pub frames: Vec<FaceFrame>,
|
||||
#[serde(default)]
|
||||
pub total_faces: usize,
|
||||
}
|
||||
|
||||
impl FaceResult {
|
||||
pub fn compute_status(&mut self) {
|
||||
self.total_faces = self.frames.iter().map(|f| f.faces.len()).sum();
|
||||
// Only compute status if Python didn't provide one
|
||||
if self.status.is_none() {
|
||||
self.status = Some(FaceStatus::from_face_count(self.total_faces));
|
||||
}
|
||||
}
|
||||
|
||||
pub fn no_faces(frame_count: u64, fps: f64) -> Self {
|
||||
FaceResult {
|
||||
status: Some(FaceStatus::NoFaces),
|
||||
frame_count,
|
||||
fps,
|
||||
frames: vec![],
|
||||
total_faces: 0,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Serialize, Deserialize, Clone)]
|
||||
@@ -46,6 +71,33 @@ pub async fn process_face(
|
||||
uuid: Option<&str>,
|
||||
frames: Option<&[i64]>,
|
||||
) -> Result<FaceResult> {
|
||||
// Check if face.json already exists (from SwiftFacePose)
|
||||
if std::path::Path::new(output_path).exists() {
|
||||
tracing::info!(
|
||||
"[FACE] Output exists from SwiftFacePose, loading: {}",
|
||||
output_path
|
||||
);
|
||||
let json_str =
|
||||
std::fs::read_to_string(output_path).context("Failed to read existing FACE output")?;
|
||||
let mut result: FaceResult =
|
||||
serde_json::from_str(&json_str).context("Failed to parse existing FACE output")?;
|
||||
|
||||
result.compute_status();
|
||||
|
||||
tracing::info!(
|
||||
"[FACE] Loaded from SwiftFacePose: status={}, {} frames, {} total faces",
|
||||
result
|
||||
.status
|
||||
.as_ref()
|
||||
.map(|s| s.to_string())
|
||||
.unwrap_or_default(),
|
||||
result.frames.len(),
|
||||
result.total_faces
|
||||
);
|
||||
|
||||
return Ok(result);
|
||||
}
|
||||
|
||||
let executor = PythonExecutor::new()?;
|
||||
let script_path = executor.script_path("face_processor.py");
|
||||
|
||||
@@ -53,11 +105,7 @@ pub async fn process_face(
|
||||
|
||||
if !script_path.exists() {
|
||||
tracing::warn!("[FACE] Script not found, returning empty result");
|
||||
return Ok(FaceResult {
|
||||
frame_count: 0,
|
||||
fps: 0.0,
|
||||
frames: vec![],
|
||||
});
|
||||
return Ok(FaceResult::no_faces(0, 0.0));
|
||||
}
|
||||
|
||||
executor
|
||||
@@ -74,10 +122,21 @@ pub async fn process_face(
|
||||
|
||||
let json_str = std::fs::read_to_string(output_path).context("Failed to read FACE output")?;
|
||||
|
||||
let result: FaceResult =
|
||||
let mut result: FaceResult =
|
||||
serde_json::from_str(&json_str).context("Failed to parse FACE output")?;
|
||||
|
||||
tracing::info!("[FACE] Result: {} frames", result.frames.len());
|
||||
result.compute_status();
|
||||
|
||||
tracing::info!(
|
||||
"[FACE] Result: status={}, {} frames, {} total faces",
|
||||
result
|
||||
.status
|
||||
.as_ref()
|
||||
.map(|s| s.to_string())
|
||||
.unwrap_or_default(),
|
||||
result.frames.len(),
|
||||
result.total_faces
|
||||
);
|
||||
|
||||
Ok(result)
|
||||
}
|
||||
|
||||
@@ -64,12 +64,17 @@ pub async fn process_face_cluster(
|
||||
.await
|
||||
.with_context(|| format!("Failed to run face clustering script"))?;
|
||||
|
||||
let json_str = std::fs::read_to_string(output_path).context("Failed to read FACE_CLUSTER output")?;
|
||||
let json_str =
|
||||
std::fs::read_to_string(output_path).context("Failed to read FACE_CLUSTER output")?;
|
||||
|
||||
let result: FaceClusterResult =
|
||||
serde_json::from_str(&json_str).context("Failed to parse FACE_CLUSTER output")?;
|
||||
|
||||
tracing::info!("[FACE_CLUSTER] Result: {} clusters, {} frames", result.clusters.len(), result.frames.len());
|
||||
tracing::info!(
|
||||
"[FACE_CLUSTER] Result: {} clusters, {} frames",
|
||||
result.clusters.len(),
|
||||
result.frames.len()
|
||||
);
|
||||
|
||||
Ok(result)
|
||||
}
|
||||
}
|
||||
|
||||
@@ -82,4 +82,4 @@ pub async fn process_hand(
|
||||
tracing::info!("[HAND] Result: {} frames", result.frames.len());
|
||||
|
||||
Ok(result)
|
||||
}
|
||||
}
|
||||
|
||||
@@ -148,24 +148,23 @@ pub async fn build_heuristic_scene_meta(
|
||||
}
|
||||
}
|
||||
|
||||
// Get face counts grouped by frame
|
||||
let fd_table = schema::table_name("face_detections");
|
||||
let face_rows: Vec<(i64, i64)> = sqlx::query_as(&format!(
|
||||
"SELECT frame_number, COUNT(*) as fc \
|
||||
FROM {} \
|
||||
WHERE file_uuid = $1 AND frame_number IS NOT NULL \
|
||||
GROUP BY frame_number \
|
||||
ORDER BY frame_number",
|
||||
fd_table
|
||||
))
|
||||
.bind(file_uuid)
|
||||
.fetch_all(pool)
|
||||
.await
|
||||
.unwrap_or_default();
|
||||
// Get face counts from Qdrant _faces
|
||||
use crate::core::db::qdrant_db::QdrantDb;
|
||||
use serde_json::json;
|
||||
|
||||
let qdrant = QdrantDb::new();
|
||||
let face_filter = json!({
|
||||
"must": [
|
||||
{"key": "file_uuid", "match": {"value": file_uuid}},
|
||||
{"key": "trace_id", "match": {"value": 1}}
|
||||
]
|
||||
});
|
||||
let points = qdrant.scroll_all_points("_faces", face_filter, 500).await.unwrap_or_default();
|
||||
|
||||
let mut frame_face_counts: HashMap<i64, i64> = HashMap::new();
|
||||
for (frame, count) in &face_rows {
|
||||
frame_face_counts.insert(*frame, *count);
|
||||
for point in &points {
|
||||
let frame = point["payload"]["frame"].as_i64().unwrap_or(0);
|
||||
*frame_face_counts.entry(frame).or_default() += 1;
|
||||
}
|
||||
|
||||
// Process each segment
|
||||
|
||||
+140
-4
@@ -17,8 +17,146 @@ pub mod scene_classification;
|
||||
pub mod tkg;
|
||||
pub mod yolo;
|
||||
|
||||
use serde::{Deserialize, Serialize};
|
||||
|
||||
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
|
||||
#[serde(rename_all = "snake_case")]
|
||||
pub enum AsrStatus {
|
||||
NoAudioTrack,
|
||||
SilentAudio,
|
||||
HasTranscript,
|
||||
Processing,
|
||||
}
|
||||
|
||||
impl std::fmt::Display for AsrStatus {
|
||||
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
|
||||
match self {
|
||||
AsrStatus::NoAudioTrack => write!(f, "no_audio_track"),
|
||||
AsrStatus::SilentAudio => write!(f, "silent_audio"),
|
||||
AsrStatus::HasTranscript => write!(f, "has_transcript"),
|
||||
AsrStatus::Processing => write!(f, "processing"),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl AsrStatus {
|
||||
pub fn css_class(&self) -> &'static str {
|
||||
match self {
|
||||
AsrStatus::NoAudioTrack => "card-asr--no_audio_track",
|
||||
AsrStatus::SilentAudio => "card-asr--silent_audio",
|
||||
AsrStatus::HasTranscript => "card-asr--has_transcript",
|
||||
AsrStatus::Processing => "card-asr--processing",
|
||||
}
|
||||
}
|
||||
|
||||
pub fn display_text(&self, segment_count: usize) -> String {
|
||||
match self {
|
||||
AsrStatus::NoAudioTrack => "無音軌".to_string(),
|
||||
AsrStatus::SilentAudio => "無語音".to_string(),
|
||||
AsrStatus::HasTranscript => format!("{} 段語音", segment_count),
|
||||
AsrStatus::Processing => "處理中".to_string(),
|
||||
}
|
||||
}
|
||||
|
||||
pub fn from_segments(segment_count: usize) -> Self {
|
||||
if segment_count > 0 {
|
||||
AsrStatus::HasTranscript
|
||||
} else {
|
||||
AsrStatus::SilentAudio
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
|
||||
#[serde(rename_all = "snake_case")]
|
||||
pub enum FaceStatus {
|
||||
NoFaces,
|
||||
HasFaces,
|
||||
Processing,
|
||||
}
|
||||
|
||||
impl std::fmt::Display for FaceStatus {
|
||||
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
|
||||
match self {
|
||||
FaceStatus::NoFaces => write!(f, "no_faces"),
|
||||
FaceStatus::HasFaces => write!(f, "has_faces"),
|
||||
FaceStatus::Processing => write!(f, "processing"),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl FaceStatus {
|
||||
pub fn css_class(&self) -> &'static str {
|
||||
match self {
|
||||
FaceStatus::NoFaces => "card-face--no_faces",
|
||||
FaceStatus::HasFaces => "card-face--has_faces",
|
||||
FaceStatus::Processing => "card-face--processing",
|
||||
}
|
||||
}
|
||||
|
||||
pub fn display_text(&self, face_count: usize) -> String {
|
||||
match self {
|
||||
FaceStatus::NoFaces => "無人脸".to_string(),
|
||||
FaceStatus::HasFaces => format!("{} 張人脸", face_count),
|
||||
FaceStatus::Processing => "處理中".to_string(),
|
||||
}
|
||||
}
|
||||
|
||||
pub fn from_face_count(face_count: usize) -> Self {
|
||||
if face_count > 0 {
|
||||
FaceStatus::HasFaces
|
||||
} else {
|
||||
FaceStatus::NoFaces
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
|
||||
#[serde(rename_all = "snake_case")]
|
||||
pub enum TraceStatus {
|
||||
NoTraces,
|
||||
HasTraces,
|
||||
Processing,
|
||||
}
|
||||
|
||||
impl std::fmt::Display for TraceStatus {
|
||||
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
|
||||
match self {
|
||||
TraceStatus::NoTraces => write!(f, "no_traces"),
|
||||
TraceStatus::HasTraces => write!(f, "has_traces"),
|
||||
TraceStatus::Processing => write!(f, "processing"),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl TraceStatus {
|
||||
pub fn css_class(&self) -> &'static str {
|
||||
match self {
|
||||
TraceStatus::NoTraces => "card-trace--no_traces",
|
||||
TraceStatus::HasTraces => "card-trace--has_traces",
|
||||
TraceStatus::Processing => "card-trace--processing",
|
||||
}
|
||||
}
|
||||
|
||||
pub fn display_text(&self, trace_count: usize) -> String {
|
||||
match self {
|
||||
TraceStatus::NoTraces => "無人脸轨迹".to_string(),
|
||||
TraceStatus::HasTraces => format!("{} 条人脸轨迹", trace_count),
|
||||
TraceStatus::Processing => "處理中".to_string(),
|
||||
}
|
||||
}
|
||||
|
||||
pub fn from_trace_count(trace_count: usize) -> Self {
|
||||
if trace_count > 0 {
|
||||
TraceStatus::HasTraces
|
||||
} else {
|
||||
TraceStatus::NoTraces
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
pub use appearance::{
|
||||
process_appearance, AppearanceFrame, AppearancePerson, AppearanceResult, BBox,
|
||||
process_appearance, AppearanceFrame, AppearancePerson, AppearanceResult, BBox, BodyPart,
|
||||
};
|
||||
pub use asr::{process_asr, AsrResult, AsrSegment};
|
||||
pub use asrx::{process_asrx, AsrxResult, AsrxSegment};
|
||||
@@ -39,9 +177,7 @@ pub use face_recognition::{
|
||||
FaceRecognitionFrame, FaceRecognitionResult, FaceRegistrationResult, RecognizedFace,
|
||||
RecognizedFaceDetection,
|
||||
};
|
||||
pub use hand::{
|
||||
process_hand, HandFrame, HandLandmark, HandResult, PersonHand,
|
||||
};
|
||||
pub use hand::{process_hand, HandFrame, HandLandmark, HandResult, PersonHand};
|
||||
pub use heuristic_scene::{
|
||||
build_heuristic_scene_meta, generate_scene_meta, CrowdSize, HeuristicSceneMeta,
|
||||
SceneSegmentMeta,
|
||||
|
||||
@@ -48,6 +48,150 @@ pub async fn process_pose(
|
||||
uuid: Option<&str>,
|
||||
frames: Option<&[i64]>,
|
||||
) -> Result<PoseResult> {
|
||||
// Check if pose.json already exists (from swift_face_pose)
|
||||
if std::path::Path::new(output_path).exists() {
|
||||
tracing::info!(
|
||||
"[POSE] Output exists from swift_face_pose, checking if needs interpolation: {}",
|
||||
output_path
|
||||
);
|
||||
let json_str =
|
||||
std::fs::read_to_string(output_path).context("Failed to read existing POSE output")?;
|
||||
let existing_result: PoseResult =
|
||||
serde_json::from_str(&json_str).context("Failed to parse existing POSE output")?;
|
||||
|
||||
// Get total video frames to check if interpolation needed
|
||||
let total_video_frames = {
|
||||
// Use ffprobe to get frame count from container metadata
|
||||
let output = std::process::Command::new("ffprobe")
|
||||
.args([
|
||||
"-v",
|
||||
"error",
|
||||
"-select_streams",
|
||||
"v:0",
|
||||
"-show_entries",
|
||||
"stream=nb_frames",
|
||||
"-of",
|
||||
"csv=p=0",
|
||||
video_path,
|
||||
])
|
||||
.output()
|
||||
.context("Failed to run ffprobe")?;
|
||||
if output.status.success() {
|
||||
let frame_str = String::from_utf8_lossy(&output.stdout).trim().to_string();
|
||||
// Handle "N/A" case for some videos
|
||||
if frame_str == "N/A" {
|
||||
// Fallback to duration * fps
|
||||
let dur_output = std::process::Command::new("ffprobe")
|
||||
.args([
|
||||
"-v",
|
||||
"error",
|
||||
"-show_entries",
|
||||
"format=duration",
|
||||
"-of",
|
||||
"csv=p=0",
|
||||
video_path,
|
||||
])
|
||||
.output()
|
||||
.context("Failed to run ffprobe for duration")?;
|
||||
let fps_output = std::process::Command::new("ffprobe")
|
||||
.args([
|
||||
"-v",
|
||||
"error",
|
||||
"-show_entries",
|
||||
"stream=r_frame_rate",
|
||||
"-of",
|
||||
"csv=p=0",
|
||||
video_path,
|
||||
])
|
||||
.output()
|
||||
.context("Failed to run ffprobe for fps")?;
|
||||
if dur_output.status.success() && fps_output.status.success() {
|
||||
let dur_str = String::from_utf8_lossy(&dur_output.stdout)
|
||||
.trim()
|
||||
.to_string();
|
||||
let fps_str = String::from_utf8_lossy(&fps_output.stdout)
|
||||
.trim()
|
||||
.to_string();
|
||||
let duration: f64 = dur_str.parse().ok().unwrap_or(0.0);
|
||||
// Parse fps like "30000/1001" or "30"
|
||||
let fps: f64 = if fps_str.contains('/') {
|
||||
let parts: Vec<&str> = fps_str.split('/').collect();
|
||||
if parts.len() == 2 {
|
||||
let num: f64 = parts[0].parse().ok().unwrap_or(30.0);
|
||||
let den: f64 = parts[1].parse().ok().unwrap_or(1.0);
|
||||
num / den
|
||||
} else {
|
||||
30.0
|
||||
}
|
||||
} else {
|
||||
fps_str.parse().ok().unwrap_or(30.0)
|
||||
};
|
||||
(duration * fps) as u64
|
||||
} else {
|
||||
0
|
||||
}
|
||||
} else {
|
||||
frame_str.parse::<u64>().ok().unwrap_or(0)
|
||||
}
|
||||
} else {
|
||||
0
|
||||
}
|
||||
};
|
||||
|
||||
// When 8Hz sampling frames are provided, skip interpolation entirely.
|
||||
// Swift already outputs at sample_interval=3 (~8Hz), no need to fill all frames.
|
||||
if frames.is_some() {
|
||||
tracing::info!(
|
||||
"[POSE] 8Hz mode: returning {} existing frames without interpolation",
|
||||
existing_result.frames.len()
|
||||
);
|
||||
return Ok(existing_result);
|
||||
}
|
||||
|
||||
// If pose frames < video frames, need interpolation
|
||||
if existing_result.frames.len() < total_video_frames as usize && total_video_frames > 0 {
|
||||
tracing::info!(
|
||||
"[POSE] Interpolation needed: {} pose frames < {} video frames",
|
||||
existing_result.frames.len(),
|
||||
total_video_frames
|
||||
);
|
||||
|
||||
// Call Python pose_processor.py for interpolation
|
||||
let executor = PythonExecutor::new()?;
|
||||
let script_path = executor.script_path("pose_processor.py");
|
||||
|
||||
if script_path.exists() {
|
||||
executor
|
||||
.run_with_frames(
|
||||
"pose_processor.py",
|
||||
&[video_path, output_path],
|
||||
uuid,
|
||||
"POSE",
|
||||
Some(POSE_TIMEOUT),
|
||||
frames,
|
||||
)
|
||||
.await
|
||||
.with_context(|| format!("Failed to run {:?}", script_path))?;
|
||||
|
||||
let json_str = std::fs::read_to_string(output_path)
|
||||
.context("Failed to read interpolated POSE output")?;
|
||||
let result: PoseResult = serde_json::from_str(&json_str)
|
||||
.context("Failed to parse interpolated POSE output")?;
|
||||
tracing::info!(
|
||||
"[POSE] Interpolation completed: {} frames",
|
||||
result.frames.len()
|
||||
);
|
||||
return Ok(result);
|
||||
}
|
||||
} else {
|
||||
tracing::info!(
|
||||
"[POSE] No interpolation needed, loaded {} frames",
|
||||
existing_result.frames.len()
|
||||
);
|
||||
return Ok(existing_result);
|
||||
}
|
||||
}
|
||||
|
||||
let executor = PythonExecutor::new()?;
|
||||
let script_path = executor.script_path("pose_processor.py");
|
||||
|
||||
|
||||
+704
-484
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,561 @@
|
||||
//! Processing Progress Tracking
|
||||
//!
|
||||
//! Tracks progress for TKG and Identity Agent components.
|
||||
//! Progress is published to Redis for real-time UI updates.
|
||||
//!
|
||||
//! Redis keys:
|
||||
//! {prefix}progress:{file_uuid}:tkg → TKG progress JSON
|
||||
//! {prefix}progress:{file_uuid}:agent → Identity Agent progress JSON
|
||||
//! {prefix}progress:{file_uuid}:combined → Combined progress JSON
|
||||
//! {prefix}progress:{file_uuid}:pipeline → Full pipeline progress JSON
|
||||
|
||||
use serde::{Deserialize, Serialize};
|
||||
|
||||
// ── Pipeline Stages ─────────────────────────────────────────────────────────
|
||||
// Complete processing pipeline with weights for segmented progress calculation
|
||||
|
||||
/// Pipeline stage with weight for overall progress calculation
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct PipelineStage {
|
||||
pub name: String,
|
||||
pub weight: f64, // Weight in overall progress (0.0-1.0)
|
||||
pub progress: f64, // Stage progress (0.0-1.0)
|
||||
pub status: String, // "pending", "running", "completed", "failed"
|
||||
pub detail: Option<String>,
|
||||
}
|
||||
|
||||
/// Full pipeline progress with segmented breakdown
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct PipelineProgress {
|
||||
pub file_uuid: String,
|
||||
pub overall_progress: f64, // 0.0-1.0 weighted sum of all stages
|
||||
pub stages: Vec<PipelineStage>,
|
||||
pub updated_at: String,
|
||||
}
|
||||
|
||||
impl PipelineProgress {
|
||||
pub fn new(file_uuid: &str) -> Self {
|
||||
Self {
|
||||
file_uuid: file_uuid.to_string(),
|
||||
overall_progress: 0.0,
|
||||
stages: vec![
|
||||
// Processors (30% total)
|
||||
PipelineStage { name: "processors".into(), weight: 0.30, progress: 0.0, status: "pending".into(), detail: None },
|
||||
// Post-processor triggers (20% total)
|
||||
PipelineStage { name: "rule1_ingestion".into(), weight: 0.05, progress: 0.0, status: "pending".into(), detail: None },
|
||||
PipelineStage { name: "face_tracing".into(), weight: 0.05, progress: 0.0, status: "pending".into(), detail: None },
|
||||
PipelineStage { name: "identity_agent".into(), weight: 0.10, progress: 0.0, status: "pending".into(), detail: None },
|
||||
// TKG Build (35% total)
|
||||
PipelineStage { name: "tkg_nodes".into(), weight: 0.20, progress: 0.0, status: "pending".into(), detail: None },
|
||||
PipelineStage { name: "tkg_edges".into(), weight: 0.15, progress: 0.0, status: "pending".into(), detail: None },
|
||||
// Rule 2 Ingestion (15%)
|
||||
PipelineStage { name: "rule2_ingestion".into(), weight: 0.15, progress: 0.0, status: "pending".into(), detail: None },
|
||||
],
|
||||
updated_at: chrono::Utc::now().to_rfc3339(),
|
||||
}
|
||||
}
|
||||
|
||||
/// Update a stage's progress and recalculate overall progress
|
||||
pub fn update_stage(&mut self, stage_name: &str, progress: f64, status: &str, detail: Option<String>) {
|
||||
if let Some(stage) = self.stages.iter_mut().find(|s| s.name == stage_name) {
|
||||
stage.progress = progress.clamp(0.0, 1.0);
|
||||
stage.status = status.to_string();
|
||||
stage.detail = detail;
|
||||
}
|
||||
self.recalculate_overall();
|
||||
}
|
||||
|
||||
/// Recalculate overall progress as weighted sum
|
||||
fn recalculate_overall(&mut self) {
|
||||
self.overall_progress = self.stages.iter()
|
||||
.map(|s| s.weight * s.progress)
|
||||
.sum::<f64>()
|
||||
.clamp(0.0, 1.0);
|
||||
self.updated_at = chrono::Utc::now().to_rfc3339();
|
||||
}
|
||||
|
||||
/// Mark all stages as completed
|
||||
pub fn mark_completed(&mut self) {
|
||||
for stage in &mut self.stages {
|
||||
stage.progress = 1.0;
|
||||
stage.status = "completed".into();
|
||||
}
|
||||
self.recalculate_overall();
|
||||
}
|
||||
}
|
||||
|
||||
// ── TKG Phases ─────────────────────────────────────────────────────────────
|
||||
// Each phase corresponds to a step in the TKG build process
|
||||
|
||||
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
|
||||
#[serde(rename_all = "snake_case")]
|
||||
pub enum TkgPhase {
|
||||
FaceTracing = 0, // Phase 0: Populate trace_id from face.json
|
||||
FaceTrackNodes = 1, // Build face_track nodes
|
||||
GazeTrackNodes = 2, // Build gaze_track nodes
|
||||
LipTrackNodes = 3, // Build lip_track nodes
|
||||
TextRegionNodes = 4, // Build text_region nodes
|
||||
AppearanceNodes = 5, // Build appearance_trace nodes
|
||||
AccessoryNodes = 6, // Build accessory nodes
|
||||
ObjectNodes = 7, // Build yolo_object nodes
|
||||
HandNodes = 8, // Build hand nodes
|
||||
SpeakerNodes = 9, // Build speaker nodes
|
||||
CoOccurrenceEdges = 10, // Build co_occurrence edges
|
||||
SpeakerFaceEdges = 11, // Build speaker_face edges
|
||||
FaceFaceEdges = 12, // Build face_face edges
|
||||
MutualGazeEdges = 13, // Build mutual_gaze edges
|
||||
LipSyncEdges = 14, // Build lip_sync edges
|
||||
HasAppearanceEdges = 15,// Build has_appearance edges
|
||||
WearsEdges = 16, // Build wears edges
|
||||
HandObjectEdges = 17, // Build hand_object edges
|
||||
Completed = 18,
|
||||
Failed = 19,
|
||||
}
|
||||
|
||||
impl TkgPhase {
|
||||
pub const TOTAL: usize = 18; // phases 0-17
|
||||
|
||||
pub fn name(&self) -> &'static str {
|
||||
match self {
|
||||
TkgPhase::FaceTracing => "face_tracing",
|
||||
TkgPhase::FaceTrackNodes => "face_track_nodes",
|
||||
TkgPhase::GazeTrackNodes => "gaze_track_nodes",
|
||||
TkgPhase::LipTrackNodes => "lip_track_nodes",
|
||||
TkgPhase::TextRegionNodes => "text_region_nodes",
|
||||
TkgPhase::AppearanceNodes => "appearance_nodes",
|
||||
TkgPhase::AccessoryNodes => "accessory_nodes",
|
||||
TkgPhase::ObjectNodes => "object_nodes",
|
||||
TkgPhase::HandNodes => "hand_nodes",
|
||||
TkgPhase::SpeakerNodes => "speaker_nodes",
|
||||
TkgPhase::CoOccurrenceEdges => "co_occurrence_edges",
|
||||
TkgPhase::SpeakerFaceEdges => "speaker_face_edges",
|
||||
TkgPhase::FaceFaceEdges => "face_face_edges",
|
||||
TkgPhase::MutualGazeEdges => "mutual_gaze_edges",
|
||||
TkgPhase::LipSyncEdges => "lip_sync_edges",
|
||||
TkgPhase::HasAppearanceEdges => "has_appearance_edges",
|
||||
TkgPhase::WearsEdges => "wears_edges",
|
||||
TkgPhase::HandObjectEdges => "hand_object_edges",
|
||||
TkgPhase::Completed => "completed",
|
||||
TkgPhase::Failed => "failed",
|
||||
}
|
||||
}
|
||||
|
||||
pub fn from_index(idx: usize) -> Self {
|
||||
match idx {
|
||||
0 => TkgPhase::FaceTracing,
|
||||
1 => TkgPhase::FaceTrackNodes,
|
||||
2 => TkgPhase::GazeTrackNodes,
|
||||
3 => TkgPhase::LipTrackNodes,
|
||||
4 => TkgPhase::TextRegionNodes,
|
||||
5 => TkgPhase::AppearanceNodes,
|
||||
6 => TkgPhase::AccessoryNodes,
|
||||
7 => TkgPhase::ObjectNodes,
|
||||
8 => TkgPhase::HandNodes,
|
||||
9 => TkgPhase::SpeakerNodes,
|
||||
10 => TkgPhase::CoOccurrenceEdges,
|
||||
11 => TkgPhase::SpeakerFaceEdges,
|
||||
12 => TkgPhase::FaceFaceEdges,
|
||||
13 => TkgPhase::MutualGazeEdges,
|
||||
14 => TkgPhase::LipSyncEdges,
|
||||
15 => TkgPhase::HasAppearanceEdges,
|
||||
16 => TkgPhase::WearsEdges,
|
||||
17 => TkgPhase::HandObjectEdges,
|
||||
_ => TkgPhase::Completed,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// ── Identity Agent Phases ──────────────────────────────────────────────────
|
||||
|
||||
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
|
||||
#[serde(rename_all = "snake_case")]
|
||||
pub enum AgentPhase {
|
||||
FaceClustering = 0,
|
||||
IdentityCreation = 1,
|
||||
TmdbMatching = 2,
|
||||
SpeakerBinding = 3,
|
||||
Confirmation = 4,
|
||||
Completed = 5,
|
||||
Failed = 6,
|
||||
}
|
||||
|
||||
impl AgentPhase {
|
||||
pub const TOTAL: usize = 5; // phases 0-4
|
||||
|
||||
pub fn name(&self) -> &'static str {
|
||||
match self {
|
||||
AgentPhase::FaceClustering => "face_clustering",
|
||||
AgentPhase::IdentityCreation => "identity_creation",
|
||||
AgentPhase::TmdbMatching => "tmdb_matching",
|
||||
AgentPhase::SpeakerBinding => "speaker_binding",
|
||||
AgentPhase::Confirmation => "confirmation",
|
||||
AgentPhase::Completed => "completed",
|
||||
AgentPhase::Failed => "failed",
|
||||
}
|
||||
}
|
||||
|
||||
pub fn from_index(idx: usize) -> Self {
|
||||
match idx {
|
||||
0 => AgentPhase::FaceClustering,
|
||||
1 => AgentPhase::IdentityCreation,
|
||||
2 => AgentPhase::TmdbMatching,
|
||||
3 => AgentPhase::SpeakerBinding,
|
||||
4 => AgentPhase::Confirmation,
|
||||
_ => AgentPhase::Completed,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// ── Stats ──────────────────────────────────────────────────────────────────
|
||||
|
||||
#[derive(Debug, Clone, Default, Serialize, Deserialize)]
|
||||
pub struct TkgStats {
|
||||
pub total_faces: i64,
|
||||
pub traced_faces: i64,
|
||||
pub total_traces: i64,
|
||||
pub matched_traces: i64,
|
||||
pub seed_count: i64,
|
||||
pub collisions_resolved: i64,
|
||||
pub identities_bound: i64,
|
||||
// Node counts
|
||||
pub face_track_nodes: i64,
|
||||
pub gaze_track_nodes: i64,
|
||||
pub lip_track_nodes: i64,
|
||||
pub text_region_nodes: i64,
|
||||
pub appearance_nodes: i64,
|
||||
pub accessory_nodes: i64,
|
||||
pub object_nodes: i64,
|
||||
pub hand_nodes: i64,
|
||||
pub speaker_nodes: i64,
|
||||
// Edge counts
|
||||
pub co_occurrence_edges: i64,
|
||||
pub speaker_face_edges: i64,
|
||||
pub face_face_edges: i64,
|
||||
pub mutual_gaze_edges: i64,
|
||||
pub lip_sync_edges: i64,
|
||||
pub has_appearance_edges: i64,
|
||||
pub wears_edges: i64,
|
||||
pub hand_object_edges: i64,
|
||||
// Totals
|
||||
pub total_nodes: i64,
|
||||
pub total_edges: i64,
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Default, Serialize, Deserialize)]
|
||||
pub struct AgentStats {
|
||||
pub total_faces: i64,
|
||||
pub total_traces: i64,
|
||||
pub clusters: i64,
|
||||
pub identities_created: i64,
|
||||
pub tmdb_matches: i64,
|
||||
pub speaker_bindings: i64,
|
||||
pub confirmations: i64,
|
||||
}
|
||||
|
||||
// ── Progress Records ───────────────────────────────────────────────────────
|
||||
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct TkgProgress {
|
||||
pub file_uuid: String,
|
||||
pub phase: String,
|
||||
pub phase_index: usize,
|
||||
pub total_phases: usize,
|
||||
pub phase_progress: f64,
|
||||
pub overall_progress: f64,
|
||||
pub stats: TkgStats,
|
||||
pub message: String,
|
||||
pub updated_at: String,
|
||||
}
|
||||
|
||||
impl TkgProgress {
|
||||
pub fn new(file_uuid: &str) -> Self {
|
||||
Self {
|
||||
file_uuid: file_uuid.to_string(),
|
||||
phase: TkgPhase::FaceTracing.name().to_string(),
|
||||
phase_index: 0,
|
||||
total_phases: TkgPhase::TOTAL,
|
||||
phase_progress: 0.0,
|
||||
overall_progress: 0.0,
|
||||
stats: TkgStats::default(),
|
||||
message: "TKG processing starting".to_string(),
|
||||
updated_at: chrono::Utc::now().to_rfc3339(),
|
||||
}
|
||||
}
|
||||
|
||||
pub fn update_phase(
|
||||
&mut self,
|
||||
phase: TkgPhase,
|
||||
phase_progress: f64,
|
||||
message: &str,
|
||||
) {
|
||||
self.phase = phase.name().to_string();
|
||||
self.phase_index = phase as usize;
|
||||
self.phase_progress = phase_progress.clamp(0.0, 1.0);
|
||||
|
||||
// Overall: (phase_index + phase_progress) / total_phases
|
||||
let weighted = self.phase_index as f64 + self.phase_progress;
|
||||
self.overall_progress = (weighted / self.total_phases as f64).clamp(0.0, 1.0);
|
||||
|
||||
self.message = message.to_string();
|
||||
self.updated_at = chrono::Utc::now().to_rfc3339();
|
||||
}
|
||||
|
||||
pub fn mark_completed(&mut self) {
|
||||
self.update_phase(TkgPhase::Completed, 1.0, "TKG processing completed");
|
||||
self.overall_progress = 1.0;
|
||||
self.phase_progress = 1.0;
|
||||
}
|
||||
|
||||
pub fn mark_failed(&mut self, error: &str) {
|
||||
self.update_phase(TkgPhase::Failed, 0.0, &format!("TKG failed: {}", error));
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct AgentProgress {
|
||||
pub file_uuid: String,
|
||||
pub phase: String,
|
||||
pub phase_index: usize,
|
||||
pub total_phases: usize,
|
||||
pub phase_progress: f64,
|
||||
pub overall_progress: f64,
|
||||
pub stats: AgentStats,
|
||||
pub message: String,
|
||||
pub updated_at: String,
|
||||
}
|
||||
|
||||
impl AgentProgress {
|
||||
pub fn new(file_uuid: &str) -> Self {
|
||||
Self {
|
||||
file_uuid: file_uuid.to_string(),
|
||||
phase: AgentPhase::FaceClustering.name().to_string(),
|
||||
phase_index: 0,
|
||||
total_phases: AgentPhase::TOTAL,
|
||||
phase_progress: 0.0,
|
||||
overall_progress: 0.0,
|
||||
stats: AgentStats::default(),
|
||||
message: "Identity Agent processing starting".to_string(),
|
||||
updated_at: chrono::Utc::now().to_rfc3339(),
|
||||
}
|
||||
}
|
||||
|
||||
pub fn update_phase(
|
||||
&mut self,
|
||||
phase: AgentPhase,
|
||||
phase_progress: f64,
|
||||
message: &str,
|
||||
) {
|
||||
self.phase = phase.name().to_string();
|
||||
self.phase_index = phase as usize;
|
||||
self.phase_progress = phase_progress.clamp(0.0, 1.0);
|
||||
|
||||
let weighted = self.phase_index as f64 + self.phase_progress;
|
||||
self.overall_progress = (weighted / self.total_phases as f64).clamp(0.0, 1.0);
|
||||
|
||||
self.message = message.to_string();
|
||||
self.updated_at = chrono::Utc::now().to_rfc3339();
|
||||
}
|
||||
|
||||
pub fn mark_completed(&mut self) {
|
||||
self.update_phase(AgentPhase::Completed, 1.0, "Identity Agent processing completed");
|
||||
self.overall_progress = 1.0;
|
||||
self.phase_progress = 1.0;
|
||||
}
|
||||
|
||||
pub fn mark_failed(&mut self, error: &str) {
|
||||
self.update_phase(AgentPhase::Failed, 0.0, &format!("Identity Agent failed: {}", error));
|
||||
}
|
||||
}
|
||||
|
||||
// ── Combined Progress ──────────────────────────────────────────────────────
|
||||
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct CombinedProgress {
|
||||
pub file_uuid: String,
|
||||
pub overall_progress: f64,
|
||||
pub tkg: Option<TkgProgress>,
|
||||
pub agent: Option<AgentProgress>,
|
||||
pub current_phase: String,
|
||||
pub message: String,
|
||||
pub updated_at: String,
|
||||
}
|
||||
|
||||
impl CombinedProgress {
|
||||
pub fn from_parts(tkg: Option<TkgProgress>, agent: Option<AgentProgress>) -> Self {
|
||||
// TKG weight: 40%, Agent weight: 60%
|
||||
let tkg_weight = 0.4;
|
||||
let agent_weight = 0.6;
|
||||
|
||||
let tkg_progress = tkg.as_ref().map(|t| t.overall_progress).unwrap_or(0.0);
|
||||
let agent_progress = agent.as_ref().map(|a| a.overall_progress).unwrap_or(0.0);
|
||||
|
||||
// If TKG not started but agent is running, agent drives progress
|
||||
let tkg_active = tkg.is_some();
|
||||
let agent_active = agent.is_some();
|
||||
|
||||
let overall = if tkg_active && agent_active {
|
||||
tkg_progress * tkg_weight + agent_progress * agent_weight
|
||||
} else if agent_active {
|
||||
agent_progress
|
||||
} else if tkg_active {
|
||||
tkg_progress * tkg_weight
|
||||
} else {
|
||||
0.0
|
||||
};
|
||||
|
||||
let file_uuid = tkg
|
||||
.as_ref()
|
||||
.map(|p| p.file_uuid.clone())
|
||||
.or_else(|| agent.as_ref().map(|p| p.file_uuid.clone()))
|
||||
.unwrap_or_default();
|
||||
|
||||
let current_phase = agent
|
||||
.as_ref()
|
||||
.map(|a| format!("agent:{}", a.phase))
|
||||
.or_else(|| tkg.as_ref().map(|t| format!("tkg:{}", t.phase)))
|
||||
.unwrap_or_else(|| "idle".to_string());
|
||||
|
||||
let message = agent
|
||||
.as_ref()
|
||||
.map(|a| a.message.clone())
|
||||
.or_else(|| tkg.as_ref().map(|t| t.message.clone()))
|
||||
.unwrap_or_else(|| "No active processing".to_string());
|
||||
|
||||
let updated_at = agent
|
||||
.as_ref()
|
||||
.map(|a| a.updated_at.clone())
|
||||
.or_else(|| tkg.as_ref().map(|t| t.updated_at.clone()))
|
||||
.unwrap_or_else(|| chrono::Utc::now().to_rfc3339());
|
||||
|
||||
CombinedProgress {
|
||||
file_uuid,
|
||||
overall_progress: overall.clamp(0.0, 1.0),
|
||||
tkg,
|
||||
agent,
|
||||
current_phase,
|
||||
message,
|
||||
updated_at,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// ── Redis Integration ──────────────────────────────────────────────────────
|
||||
|
||||
use crate::core::db::redis_client::RedisClient;
|
||||
use std::sync::Arc;
|
||||
|
||||
pub async fn publish_tkg_progress(
|
||||
redis: &Arc<RedisClient>,
|
||||
file_uuid: &str,
|
||||
progress: &TkgProgress,
|
||||
) {
|
||||
let key = format!(
|
||||
"{}progress:{}:tkg",
|
||||
crate::core::config::REDIS_KEY_PREFIX.as_str(),
|
||||
file_uuid
|
||||
);
|
||||
if let Ok(mut conn) = redis.get_conn().await {
|
||||
let json = serde_json::to_string(progress).unwrap_or_default();
|
||||
let _: Result<(), _> = redis::cmd("SET")
|
||||
.arg(&[&key, &json])
|
||||
.query_async(&mut conn)
|
||||
.await;
|
||||
}
|
||||
}
|
||||
|
||||
pub async fn publish_agent_progress(
|
||||
redis: &Arc<RedisClient>,
|
||||
file_uuid: &str,
|
||||
progress: &AgentProgress,
|
||||
) {
|
||||
let key = format!(
|
||||
"{}progress:{}:agent",
|
||||
crate::core::config::REDIS_KEY_PREFIX.as_str(),
|
||||
file_uuid
|
||||
);
|
||||
if let Ok(mut conn) = redis.get_conn().await {
|
||||
let json = serde_json::to_string(progress).unwrap_or_default();
|
||||
let _: Result<(), _> = redis::cmd("SET")
|
||||
.arg(&[&key, &json])
|
||||
.query_async(&mut conn)
|
||||
.await;
|
||||
}
|
||||
}
|
||||
|
||||
pub async fn get_progress(
|
||||
redis: &Arc<RedisClient>,
|
||||
file_uuid: &str,
|
||||
) -> Option<CombinedProgress> {
|
||||
let tkg_key = format!(
|
||||
"{}progress:{}:tkg",
|
||||
crate::core::config::REDIS_KEY_PREFIX.as_str(),
|
||||
file_uuid
|
||||
);
|
||||
let agent_key = format!(
|
||||
"{}progress:{}:agent",
|
||||
crate::core::config::REDIS_KEY_PREFIX.as_str(),
|
||||
file_uuid
|
||||
);
|
||||
|
||||
if let Ok(mut conn) = redis.get_conn().await {
|
||||
let tkg_str: Option<String> = redis::cmd("GET")
|
||||
.arg(&tkg_key)
|
||||
.query_async(&mut conn)
|
||||
.await
|
||||
.ok();
|
||||
let agent_str: Option<String> = redis::cmd("GET")
|
||||
.arg(&agent_key)
|
||||
.query_async(&mut conn)
|
||||
.await
|
||||
.ok();
|
||||
|
||||
let tkg = tkg_str.and_then(|s| serde_json::from_str(&s).ok());
|
||||
let agent = agent_str.and_then(|s| serde_json::from_str(&s).ok());
|
||||
|
||||
Some(CombinedProgress::from_parts(tkg, agent))
|
||||
} else {
|
||||
None
|
||||
}
|
||||
}
|
||||
|
||||
/// Publish pipeline progress to Redis
|
||||
pub async fn publish_pipeline_progress(
|
||||
redis: &RedisClient,
|
||||
file_uuid: &str,
|
||||
progress: &PipelineProgress,
|
||||
) {
|
||||
let key = format!(
|
||||
"{}progress:{}:pipeline",
|
||||
crate::core::config::REDIS_KEY_PREFIX.as_str(),
|
||||
file_uuid
|
||||
);
|
||||
if let Ok(mut conn) = redis.get_conn().await {
|
||||
let json = serde_json::to_string(progress).unwrap_or_default();
|
||||
let _: Result<(), _> = redis::cmd("SET")
|
||||
.arg(&[&key, &json])
|
||||
.query_async(&mut conn)
|
||||
.await;
|
||||
}
|
||||
}
|
||||
|
||||
/// Get pipeline progress from Redis
|
||||
pub async fn get_pipeline_progress(
|
||||
redis: &RedisClient,
|
||||
file_uuid: &str,
|
||||
) -> Option<PipelineProgress> {
|
||||
let key = format!(
|
||||
"{}progress:{}:pipeline",
|
||||
crate::core::config::REDIS_KEY_PREFIX.as_str(),
|
||||
file_uuid
|
||||
);
|
||||
if let Ok(mut conn) = redis.get_conn().await {
|
||||
let str_val: Option<String> = redis::cmd("GET")
|
||||
.arg(&key)
|
||||
.query_async(&mut conn)
|
||||
.await
|
||||
.ok();
|
||||
str_val.and_then(|s| serde_json::from_str(&s).ok())
|
||||
} else {
|
||||
None
|
||||
}
|
||||
}
|
||||
+165
-114
@@ -3,7 +3,7 @@ use serde::Deserialize;
|
||||
use std::collections::HashMap;
|
||||
use tracing::{error, info, warn};
|
||||
|
||||
use crate::core::db::{schema, PostgresDb};
|
||||
use crate::core::db::{schema, PostgresDb, QdrantDb};
|
||||
|
||||
#[derive(Debug, Deserialize)]
|
||||
struct TmdbIdentity {
|
||||
@@ -30,41 +30,87 @@ fn cosine_similarity(a: &[f32], b: &[f32]) -> f32 {
|
||||
/// Round 1: seed match against TMDb face_embeddings (threshold 0.50)
|
||||
/// Round 2+: propagate to remaining traces using matched faces as reference
|
||||
pub async fn match_faces_against_tmdb(db: &PostgresDb, file_uuid: &str) -> Result<usize> {
|
||||
let pool = db.pool();
|
||||
let qdrant = QdrantDb::new();
|
||||
|
||||
// Step 1: Load TMDb identities with face embeddings
|
||||
let tmdb_rows = sqlx::query_as::<_, (i32, String, Vec<f32>)>(
|
||||
&format!("SELECT id, name, face_embedding::real[] FROM {} WHERE source='tmdb' AND face_embedding IS NOT NULL", schema::table_name("identities"))
|
||||
)
|
||||
.fetch_all(pool).await?;
|
||||
// Step 1: Load TMDb identity seeds from Qdrant _seeds collection
|
||||
let tmdb_filter = serde_json::json!({
|
||||
"must": [
|
||||
{"key": "source", "match": {"value": "tmdb"}}
|
||||
]
|
||||
});
|
||||
let seed_points = match qdrant.scroll_all_points("_seeds", tmdb_filter, 500).await {
|
||||
Ok(pts) => pts,
|
||||
Err(e) => {
|
||||
warn!("[TKG-MATCH] Failed to scroll _seeds: {}", e);
|
||||
return Ok(0);
|
||||
}
|
||||
};
|
||||
|
||||
let tmdb_rows: Vec<(i32, String, Vec<f32>)> = seed_points
|
||||
.iter()
|
||||
.filter_map(|p| {
|
||||
let payload = &p["payload"];
|
||||
let id = payload["identity_id"].as_i64()? as i32;
|
||||
let name = payload["name"].as_str()?.to_string();
|
||||
let vector = p["vector"]
|
||||
.as_array()?
|
||||
.iter()
|
||||
.filter_map(|v| v.as_f64().map(|f| f as f32))
|
||||
.collect::<Vec<f32>>();
|
||||
if vector.len() == 512 {
|
||||
Some((id, name, vector))
|
||||
} else {
|
||||
None
|
||||
}
|
||||
})
|
||||
.collect();
|
||||
|
||||
if tmdb_rows.is_empty() {
|
||||
info!("[TKG-MATCH] No TMDb identities with face embeddings");
|
||||
info!("[TKG-MATCH] No TMDb identity seeds in _seeds collection");
|
||||
return Ok(0);
|
||||
}
|
||||
info!("[TKG-MATCH] {} TMDb seeds loaded", tmdb_rows.len());
|
||||
info!("[TKG-MATCH] {} TMDb seeds loaded from _seeds", tmdb_rows.len());
|
||||
|
||||
// Step 2: Load face_detections grouped by trace_id
|
||||
let fd_table = schema::table_name("face_detections");
|
||||
let fd_rows = sqlx::query_as::<_, (i32, Vec<f32>)>(&format!(
|
||||
"SELECT trace_id, embedding FROM {} \
|
||||
WHERE file_uuid=$1 AND trace_id IS NOT NULL AND embedding IS NOT NULL \
|
||||
ORDER BY trace_id",
|
||||
fd_table
|
||||
))
|
||||
.bind(file_uuid)
|
||||
.fetch_all(pool)
|
||||
.await?;
|
||||
// Step 2: Load face embeddings from Qdrant _faces, grouped by trace_id
|
||||
let face_filter = serde_json::json!({
|
||||
"must": [
|
||||
{"key": "file_uuid", "match": {"value": file_uuid}},
|
||||
{"key": "trace_id", "match": {"value": 1}} // trace_id > 0 means traced
|
||||
]
|
||||
});
|
||||
let face_points = match qdrant.scroll_all_points("_faces", face_filter, 1000).await {
|
||||
Ok(pts) => pts,
|
||||
Err(e) => {
|
||||
warn!("[TKG-MATCH] Failed to scroll _faces for {}: {}", file_uuid, e);
|
||||
return Ok(0);
|
||||
}
|
||||
};
|
||||
|
||||
if fd_rows.is_empty() {
|
||||
info!("[TKG-MATCH] No face detections for {}", file_uuid);
|
||||
if face_points.is_empty() {
|
||||
info!("[TKG-MATCH] No traced faces in _faces for {}", file_uuid);
|
||||
return Ok(0);
|
||||
}
|
||||
|
||||
// Group by trace_id, collect embeddings
|
||||
let mut trace_faces: HashMap<i32, Vec<Vec<f32>>> = HashMap::new();
|
||||
for (tid, emb) in &fd_rows {
|
||||
trace_faces.entry(*tid).or_default().push(emb.clone());
|
||||
for point in &face_points {
|
||||
let payload = &point["payload"];
|
||||
let trace_id = match payload["trace_id"].as_i64() {
|
||||
Some(tid) if tid > 0 => tid as i32,
|
||||
_ => continue,
|
||||
};
|
||||
let vector = match point["vector"].as_array() {
|
||||
Some(arr) => arr
|
||||
.iter()
|
||||
.filter_map(|v| v.as_f64().map(|f| f as f32))
|
||||
.collect::<Vec<f32>>(),
|
||||
None => continue,
|
||||
};
|
||||
if vector.len() == 512 {
|
||||
trace_faces.entry(trace_id).or_default().push(vector);
|
||||
}
|
||||
}
|
||||
|
||||
// Dedup near-identical embeddings within trace
|
||||
for faces in trace_faces.values_mut() {
|
||||
faces.sort_by(|a, b| a[0].partial_cmp(&b[0]).unwrap_or(std::cmp::Ordering::Equal));
|
||||
@@ -72,7 +118,7 @@ pub async fn match_faces_against_tmdb(db: &PostgresDb, file_uuid: &str) -> Resul
|
||||
}
|
||||
|
||||
let total = trace_faces.len();
|
||||
info!("[TKG-MATCH] {} traces with {} faces", total, fd_rows.len());
|
||||
info!("[TKG-MATCH] {} traces with {} faces", total, face_points.len());
|
||||
|
||||
// Step 3: Iterative matching
|
||||
const TH: f32 = 0.50;
|
||||
@@ -100,12 +146,12 @@ pub async fn match_faces_against_tmdb(db: &PostgresDb, file_uuid: &str) -> Resul
|
||||
info!(
|
||||
"[TKG-MATCH] Round 1: {} ({}/{})",
|
||||
matched.len(),
|
||||
matched.len() * 100 / total,
|
||||
matched.len() * 100 / total.max(1),
|
||||
total
|
||||
);
|
||||
|
||||
// Round 2+: propagate
|
||||
for round_n in 2..=10 {
|
||||
for _round_n in 2..=10 {
|
||||
let prev = matched.len();
|
||||
let mut seed_pool: HashMap<i32, Vec<&Vec<f32>>> = HashMap::new();
|
||||
for (&tid, (id, _)) in &matched {
|
||||
@@ -133,7 +179,6 @@ pub async fn match_faces_against_tmdb(db: &PostgresDb, file_uuid: &str) -> Resul
|
||||
}
|
||||
}
|
||||
if best_sim >= TH {
|
||||
// Look up name for this id
|
||||
for (id, name, _) in &tmdb_rows {
|
||||
if *id == best_id {
|
||||
best_name = name.clone();
|
||||
@@ -153,19 +198,16 @@ pub async fn match_faces_against_tmdb(db: &PostgresDb, file_uuid: &str) -> Resul
|
||||
}
|
||||
|
||||
// Step 4: Quality control
|
||||
// 4a: Remove low-confidence traces (fewer than 4 face detections)
|
||||
let fd_table = schema::table_name("face_detections");
|
||||
// 4a: Remove low-confidence traces (fewer than 4 face points)
|
||||
let mut after_qc = HashMap::new();
|
||||
for (&tid, &(id, ref name)) in &matched {
|
||||
let cnt: i64 = sqlx::query_scalar(&format!(
|
||||
"SELECT COUNT(*) FROM {} WHERE file_uuid=$1 AND trace_id=$2",
|
||||
fd_table
|
||||
))
|
||||
.bind(file_uuid)
|
||||
.bind(tid)
|
||||
.fetch_one(pool)
|
||||
.await
|
||||
.unwrap_or(0);
|
||||
let cnt: i64 = face_points
|
||||
.iter()
|
||||
.filter(|p| {
|
||||
p["payload"]["trace_id"].as_i64() == Some(tid as i64)
|
||||
&& p["payload"]["file_uuid"].as_str() == Some(file_uuid)
|
||||
})
|
||||
.count() as i64;
|
||||
if cnt >= 4 {
|
||||
after_qc.insert(tid, (id, name.clone()));
|
||||
} else {
|
||||
@@ -184,8 +226,8 @@ pub async fn match_faces_against_tmdb(db: &PostgresDb, file_uuid: &str) -> Resul
|
||||
);
|
||||
}
|
||||
|
||||
// 4b: Temporal collision check
|
||||
let removed_collisions = quality_check_temporal_collisions(pool, file_uuid).await?;
|
||||
// 4b: Temporal collision check via Qdrant
|
||||
let removed_collisions = quality_check_temporal_collisions_qdrant(&qdrant, file_uuid).await?;
|
||||
if removed_collisions > 0 {
|
||||
info!(
|
||||
"[TKG-QC] Resolved {} temporal collisions",
|
||||
@@ -193,19 +235,21 @@ pub async fn match_faces_against_tmdb(db: &PostgresDb, file_uuid: &str) -> Resul
|
||||
);
|
||||
}
|
||||
|
||||
// Step 5: Update DB
|
||||
// Step 5: Update Qdrant _faces with identity_id
|
||||
let mut updated = 0usize;
|
||||
for (&tid, &(id, _)) in &matched {
|
||||
let r = sqlx::query(&format!(
|
||||
"UPDATE {} SET identity_id=$1 WHERE file_uuid=$2 AND trace_id=$3",
|
||||
fd_table
|
||||
))
|
||||
.bind(id)
|
||||
.bind(file_uuid)
|
||||
.bind(tid)
|
||||
.execute(pool)
|
||||
.await?;
|
||||
if r.rows_affected() > 0 {
|
||||
let filter = serde_json::json!({
|
||||
"must": [
|
||||
{"key": "file_uuid", "match": {"value": file_uuid}},
|
||||
{"key": "trace_id", "match": {"value": tid}}
|
||||
]
|
||||
});
|
||||
let payload = serde_json::json!({"identity_id": id});
|
||||
if qdrant
|
||||
.update_payload_by_filter("_faces", filter, payload)
|
||||
.await
|
||||
.is_ok()
|
||||
{
|
||||
updated += 1;
|
||||
}
|
||||
}
|
||||
@@ -214,87 +258,94 @@ pub async fn match_faces_against_tmdb(db: &PostgresDb, file_uuid: &str) -> Resul
|
||||
"[TKG-MATCH] Done: {}/{} traces matched ({}%)",
|
||||
matched.len(),
|
||||
total,
|
||||
matched.len() * 100 / total
|
||||
matched.len() * 100 / total.max(1)
|
||||
);
|
||||
Ok(updated)
|
||||
}
|
||||
|
||||
/// Quality check: detect temporal collisions where two different traces of the same
|
||||
/// identity appear in the same frame (impossible for one person).
|
||||
/// Unbind the lower-confidence trace from the conflicting pair.
|
||||
/// RCA reference: docs_v1.0/API_V1.0.0/INTERNAL/RCA_TRACE39_TRACE45_COLLISION_V1.0.0.md
|
||||
async fn quality_check_temporal_collisions(pool: &sqlx::PgPool, file_uuid: &str) -> Result<usize> {
|
||||
let fd_table = schema::table_name("face_detections");
|
||||
// Find all collision pairs: same identity, same frame, different trace
|
||||
let collisions = sqlx::query_as::<_, (i32, i32, i32, i64)>(&format!(
|
||||
"SELECT a.identity_id, a.trace_id, b.trace_id, a.frame_number \
|
||||
FROM {} a \
|
||||
JOIN {} b \
|
||||
ON a.file_uuid = b.file_uuid \
|
||||
AND a.frame_number = b.frame_number \
|
||||
AND a.trace_id < b.trace_id \
|
||||
WHERE a.file_uuid = $1 \
|
||||
AND a.identity_id IS NOT NULL \
|
||||
AND a.identity_id = b.identity_id \
|
||||
ORDER BY a.identity_id, a.frame_number",
|
||||
fd_table, fd_table
|
||||
))
|
||||
.bind(file_uuid)
|
||||
.fetch_all(pool)
|
||||
.await?;
|
||||
/// Unbind the lower-confidence trace from the conflicting pair via Qdrant.
|
||||
async fn quality_check_temporal_collisions_qdrant(
|
||||
qdrant: &QdrantDb,
|
||||
file_uuid: &str,
|
||||
) -> Result<usize> {
|
||||
use std::collections::HashSet;
|
||||
|
||||
if collisions.is_empty() {
|
||||
return Ok(0);
|
||||
// Load all traced faces for this file
|
||||
let face_filter = serde_json::json!({
|
||||
"must": [
|
||||
{"key": "file_uuid", "match": {"value": file_uuid}},
|
||||
{"key": "trace_id", "match": {"value": 1}}
|
||||
]
|
||||
});
|
||||
let face_points = match qdrant.scroll_all_points("_faces", face_filter, 1000).await {
|
||||
Ok(pts) => pts,
|
||||
Err(_) => return Ok(0),
|
||||
};
|
||||
|
||||
// Group by (frame, identity_id) to find collisions
|
||||
let mut frame_identity_traces: HashMap<(i64, i32), HashSet<i32>> = HashMap::new();
|
||||
let mut trace_point_counts: HashMap<i32, i64> = HashMap::new();
|
||||
|
||||
for point in &face_points {
|
||||
let payload = &point["payload"];
|
||||
let frame = payload["frame"].as_i64().unwrap_or(0);
|
||||
let trace_id = match payload["trace_id"].as_i64() {
|
||||
Some(tid) if tid > 0 => tid as i32,
|
||||
_ => continue,
|
||||
};
|
||||
let identity_id = match payload["identity_id"].as_i64() {
|
||||
Some(id) if id > 0 => id as i32,
|
||||
_ => continue,
|
||||
};
|
||||
|
||||
frame_identity_traces
|
||||
.entry((frame, identity_id))
|
||||
.or_default()
|
||||
.insert(trace_id);
|
||||
*trace_point_counts.entry(trace_id).or_default() += 1;
|
||||
}
|
||||
|
||||
// Group collisions by (identity_id, trace_a, trace_b) and count frames
|
||||
use std::collections::HashMap;
|
||||
// Find collision pairs: (identity_id, trace_a, trace_b)
|
||||
let mut collision_groups: HashMap<(i32, i32, i32), usize> = HashMap::new();
|
||||
for (id, ta, tb, _) in &collisions {
|
||||
*collision_groups.entry((*id, *ta, *tb)).or_default() += 1;
|
||||
for ((_frame, identity_id), traces) in &frame_identity_traces {
|
||||
let traces: Vec<i32> = traces.iter().copied().collect();
|
||||
for i in 0..traces.len() {
|
||||
for j in (i + 1)..traces.len() {
|
||||
let (ta, tb) = if traces[i] < traces[j] {
|
||||
(traces[i], traces[j])
|
||||
} else {
|
||||
(traces[j], traces[i])
|
||||
};
|
||||
*collision_groups.entry((*identity_id, ta, tb)).or_default() += 1;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if collision_groups.is_empty() {
|
||||
return Ok(0);
|
||||
}
|
||||
|
||||
let mut unbound = 0usize;
|
||||
for ((id, ta, tb), overlap_frames) in &collision_groups {
|
||||
// Get face detection count for each trace
|
||||
let cnt_a: i64 = sqlx::query_scalar(&format!(
|
||||
"SELECT COUNT(*) FROM {} WHERE file_uuid=$1 AND trace_id=$2 AND identity_id=$3",
|
||||
fd_table
|
||||
))
|
||||
.bind(file_uuid)
|
||||
.bind(ta)
|
||||
.bind(id)
|
||||
.fetch_one(pool)
|
||||
.await
|
||||
.unwrap_or(0);
|
||||
let cnt_a = trace_point_counts.get(ta).copied().unwrap_or(0);
|
||||
let cnt_b = trace_point_counts.get(tb).copied().unwrap_or(0);
|
||||
|
||||
let cnt_b: i64 = sqlx::query_scalar(&format!(
|
||||
"SELECT COUNT(*) FROM {} WHERE file_uuid=$1 AND trace_id=$2 AND identity_id=$3",
|
||||
fd_table
|
||||
))
|
||||
.bind(file_uuid)
|
||||
.bind(tb)
|
||||
.bind(id)
|
||||
.fetch_one(pool)
|
||||
.await
|
||||
.unwrap_or(0);
|
||||
|
||||
// Unbind the trace with fewer detections (likely the false positive)
|
||||
let victim = if cnt_a <= cnt_b { *ta } else { *tb };
|
||||
let victim_cnt = if cnt_a <= cnt_b { cnt_a } else { cnt_b };
|
||||
|
||||
sqlx::query(&format!(
|
||||
"UPDATE {} SET identity_id=NULL WHERE file_uuid=$1 AND trace_id=$2",
|
||||
fd_table
|
||||
))
|
||||
.bind(file_uuid)
|
||||
.bind(victim)
|
||||
.execute(pool)
|
||||
.await?;
|
||||
let filter = serde_json::json!({
|
||||
"must": [
|
||||
{"key": "file_uuid", "match": {"value": file_uuid}},
|
||||
{"key": "trace_id", "match": {"value": victim}}
|
||||
]
|
||||
});
|
||||
let payload = serde_json::json!({"identity_id": serde_json::Value::Null});
|
||||
let _ = qdrant.update_payload_by_filter("_faces", filter, payload).await;
|
||||
|
||||
unbound += 1;
|
||||
warn!("[TKG-QC] Collision identity={}: trace {} vs trace {} ({} overlap frames). Unbound trace {} ({} detections)",
|
||||
id, ta, tb, overlap_frames, victim, victim_cnt);
|
||||
warn!("[TKG-QC] Collision identity={}: trace {} vs trace {} ({} overlap frames). Unbound trace {} ({} points)",
|
||||
id, ta, tb, overlap_frames, victim, if cnt_a <= cnt_b { cnt_a } else { cnt_b });
|
||||
}
|
||||
|
||||
Ok(unbound)
|
||||
|
||||
@@ -45,9 +45,8 @@ fn extract_movie_name(filename: &str) -> Option<String> {
|
||||
.file_stem()
|
||||
.and_then(|s| s.to_str())?;
|
||||
|
||||
let noise_words = [
|
||||
"youtube", "yt", "fps", "hd", "full", "movie", "official",
|
||||
"trailer", "teaser", "4k",
|
||||
let noise_words = [
|
||||
"youtube", "yt", "fps", "hd", "full", "movie", "official", "trailer", "teaser", "4k",
|
||||
];
|
||||
|
||||
let cleaned = name
|
||||
|
||||
+31
-18
@@ -1056,7 +1056,7 @@ async fn main() -> Result<()> {
|
||||
.filter_map(|name| {
|
||||
let name_lower = name.to_lowercase();
|
||||
match name_lower.as_str() {
|
||||
"appearance" => Some(ProcessorType::Appearance),
|
||||
"appearance" => Some(ProcessorType::Appearance),
|
||||
"asr" => Some(ProcessorType::Asr),
|
||||
"cut" => Some(ProcessorType::Cut),
|
||||
"asrx" => Some(ProcessorType::Asrx),
|
||||
@@ -1066,9 +1066,9 @@ async fn main() -> Result<()> {
|
||||
"pose" => Some(ProcessorType::Pose),
|
||||
"hand" => Some(ProcessorType::Hand),
|
||||
_ => {
|
||||
eprintln!("Unknown module: {}", name);
|
||||
None
|
||||
}
|
||||
eprintln!("Unknown module: {}", name);
|
||||
None
|
||||
}
|
||||
}
|
||||
})
|
||||
.collect()
|
||||
@@ -1082,7 +1082,7 @@ None
|
||||
.filter_map(|name| {
|
||||
let name_lower = name.to_lowercase();
|
||||
match name_lower.as_str() {
|
||||
"appearance" => Some(ProcessorType::Appearance),
|
||||
"appearance" => Some(ProcessorType::Appearance),
|
||||
"asr" => Some(ProcessorType::Asr),
|
||||
"cut" => Some(ProcessorType::Cut),
|
||||
"asrx" => Some(ProcessorType::Asrx),
|
||||
@@ -1092,9 +1092,9 @@ None
|
||||
"pose" => Some(ProcessorType::Pose),
|
||||
"hand" => Some(ProcessorType::Hand),
|
||||
_ => {
|
||||
eprintln!("Unknown cloud module: {}", name);
|
||||
None
|
||||
}
|
||||
eprintln!("Unknown cloud module: {}", name);
|
||||
None
|
||||
}
|
||||
}
|
||||
})
|
||||
.collect()
|
||||
@@ -1783,9 +1783,9 @@ None
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// TODO: Store pre_chunks and frames to database
|
||||
// TODO: Store pre_chunks and frames to database
|
||||
|
||||
// Stop Redis subscriber
|
||||
redis_handle.abort();
|
||||
@@ -1822,10 +1822,10 @@ None
|
||||
if should_process(ProcessorType::Appearance) {
|
||||
let path = output_dir.get_output_path(&uuid, "appearance.json");
|
||||
println!(" - Appearance JSON: {}", path.display());
|
||||
}
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
Ok(())
|
||||
}
|
||||
Commands::Chunk { uuid } => {
|
||||
println!("Chunking: {}", uuid);
|
||||
|
||||
@@ -1933,18 +1933,22 @@ Ok(())
|
||||
Err(e) => {
|
||||
println!("Warning: Failed to parse Face JSON: {}. Skipping Face.", e);
|
||||
momentry_core::core::processor::face::FaceResult {
|
||||
status: None,
|
||||
frame_count: 0,
|
||||
fps: 0.0,
|
||||
frames: vec![],
|
||||
total_faces: 0,
|
||||
}
|
||||
}
|
||||
},
|
||||
Err(_) => {
|
||||
println!("Warning: Face file not found. Skipping Face.");
|
||||
momentry_core::core::processor::face::FaceResult {
|
||||
status: None,
|
||||
frame_count: 0,
|
||||
fps: 0.0,
|
||||
frames: vec![],
|
||||
total_faces: 0,
|
||||
}
|
||||
}
|
||||
};
|
||||
@@ -1993,18 +1997,22 @@ Ok(())
|
||||
Err(e) => {
|
||||
println!("Warning: Failed to parse ASRX JSON: {}. Skipping ASRX.", e);
|
||||
momentry_core::core::processor::asrx::AsrxResult {
|
||||
status: None,
|
||||
language: None,
|
||||
segments: vec![],
|
||||
embeddings: None,
|
||||
segment_count: 0,
|
||||
}
|
||||
}
|
||||
},
|
||||
Err(_) => {
|
||||
println!("Warning: ASRX file not found. Skipping ASRX.");
|
||||
momentry_core::core::processor::asrx::AsrxResult {
|
||||
status: None,
|
||||
language: None,
|
||||
segments: vec![],
|
||||
embeddings: None,
|
||||
segment_count: 0,
|
||||
}
|
||||
}
|
||||
};
|
||||
@@ -2017,8 +2025,10 @@ Ok(())
|
||||
let deleted_frames = db.delete_frames_by_uuid(&uuid).await?;
|
||||
let deleted_tkg_nodes = db.delete_tkg_nodes_by_uuid(&uuid).await?;
|
||||
let deleted_tkg_edges = db.delete_tkg_edges_by_uuid(&uuid).await?;
|
||||
println!(" Deleted: {} pre_chunks, {} frames, {} tkg_nodes, {} tkg_edges",
|
||||
deleted_pre_chunks, deleted_frames, deleted_tkg_nodes, deleted_tkg_edges);
|
||||
println!(
|
||||
" Deleted: {} pre_chunks, {} frames, {} tkg_nodes, {} tkg_edges",
|
||||
deleted_pre_chunks, deleted_frames, deleted_tkg_nodes, deleted_tkg_edges
|
||||
);
|
||||
|
||||
println!("\nStoring pre_chunks...");
|
||||
|
||||
@@ -2324,10 +2334,13 @@ Ok(())
|
||||
|
||||
// Build TKG
|
||||
println!("\nBuilding TKG...");
|
||||
let tkg_result = momentry_core::core::processor::tkg::build_tkg(&db, &uuid, &output_dir).await?;
|
||||
println!("✓ TKG built: {} nodes, {} edges",
|
||||
let tkg_result =
|
||||
momentry_core::core::processor::tkg::build_tkg(&db, &uuid, &output_dir, None).await?;
|
||||
println!(
|
||||
"✓ TKG built: {} nodes, {} edges",
|
||||
tkg_result.face_track_nodes + tkg_result.hand_nodes + tkg_result.object_nodes,
|
||||
tkg_result.co_occurrence_edges + tkg_result.hand_object_edges);
|
||||
tkg_result.co_occurrence_edges + tkg_result.hand_object_edges
|
||||
);
|
||||
|
||||
println!("\n✓ Chunk stage completed!");
|
||||
println!(
|
||||
|
||||
@@ -35,8 +35,8 @@ pub const PROCESSOR_SCHEMAS: &[ProcessorJsonSchema] = &[
|
||||
required_fields: &[
|
||||
RequiredField {
|
||||
path: "frame_count",
|
||||
field_type: FieldType::PositiveNumber,
|
||||
allow_empty: false,
|
||||
field_type: FieldType::Number,
|
||||
allow_empty: true,
|
||||
},
|
||||
RequiredField {
|
||||
path: "fps",
|
||||
@@ -45,11 +45,11 @@ pub const PROCESSOR_SCHEMAS: &[ProcessorJsonSchema] = &[
|
||||
},
|
||||
RequiredField {
|
||||
path: "scenes",
|
||||
field_type: FieldType::NonEmptyArray,
|
||||
allow_empty: false,
|
||||
field_type: FieldType::Array,
|
||||
allow_empty: true,
|
||||
},
|
||||
],
|
||||
min_data_threshold: 1,
|
||||
min_data_threshold: 0,
|
||||
},
|
||||
ProcessorJsonSchema {
|
||||
processor: ProcessorType::Yolo,
|
||||
@@ -77,8 +77,8 @@ pub const PROCESSOR_SCHEMAS: &[ProcessorJsonSchema] = &[
|
||||
required_fields: &[
|
||||
RequiredField {
|
||||
path: "frame_count",
|
||||
field_type: FieldType::PositiveNumber,
|
||||
allow_empty: false,
|
||||
field_type: FieldType::Number,
|
||||
allow_empty: true,
|
||||
},
|
||||
RequiredField {
|
||||
path: "fps",
|
||||
@@ -98,8 +98,8 @@ pub const PROCESSOR_SCHEMAS: &[ProcessorJsonSchema] = &[
|
||||
required_fields: &[
|
||||
RequiredField {
|
||||
path: "frame_count",
|
||||
field_type: FieldType::PositiveNumber,
|
||||
allow_empty: false,
|
||||
field_type: FieldType::Number,
|
||||
allow_empty: true,
|
||||
},
|
||||
RequiredField {
|
||||
path: "fps",
|
||||
|
||||
+470
-70
@@ -9,6 +9,7 @@ use tracing::{debug, error, info, warn};
|
||||
use crate::api::identity_agent_api::run_identity_agent;
|
||||
use crate::core::chunk::rule1_ingest;
|
||||
use crate::core::config::OUTPUT_DIR;
|
||||
use crate::core::progress::{publish_pipeline_progress, PipelineProgress};
|
||||
use crate::core::db::qdrant_db::QdrantDb;
|
||||
use crate::core::db::{
|
||||
schema, MonitorJobStatus, PostgresDb, ProcessorJobStatus, RedisClient, VectorPayload,
|
||||
@@ -225,7 +226,7 @@ impl JobWorker {
|
||||
.get_processor_results_by_job(job.id)
|
||||
.await
|
||||
.unwrap_or_default();
|
||||
// 若有任何 processor 是 pending/skipped（未真正啟動），重新處理 job
|
||||
// 若有任何 processor 是 pending/skipped/deferred（未真正啟動），重新處理 job
|
||||
let has_unstarted = results.iter().any(|r| {
|
||||
matches!(
|
||||
r.status,
|
||||
@@ -233,7 +234,21 @@ impl JobWorker {
|
||||
| crate::core::db::ProcessorJobStatus::Skipped
|
||||
)
|
||||
});
|
||||
if has_unstarted {
|
||||
|
||||
// Also check if there are processors without result records (deferred)
|
||||
let expected_count = if job.processors.is_empty() {
|
||||
crate::core::db::ProcessorType::all().len()
|
||||
} else {
|
||||
job.processors.len()
|
||||
};
|
||||
let has_deferred = results.len() < expected_count;
|
||||
|
||||
if has_unstarted || has_deferred {
|
||||
// Call check_and_complete_job to retry deferred processors
|
||||
let _ = self
|
||||
.check_and_complete_job(job.id, &job.uuid, &job.processors, expected_count)
|
||||
.await;
|
||||
|
||||
if let Err(e) = self.process_job(job.clone()).await {
|
||||
error!("Failed to reprocess job {}: {}", job.uuid, e);
|
||||
}
|
||||
@@ -345,7 +360,16 @@ impl JobWorker {
|
||||
processor_type.as_str()
|
||||
));
|
||||
debug!("Checking output file: {:?}", output_path);
|
||||
if output_path.exists() {
|
||||
|
||||
// Special case: Pose processor should NOT be skipped even if pose.json exists
|
||||
// because swift_face_pose creates it and pose.rs needs to interpolate
|
||||
let skip_check = if *processor_type == crate::core::db::ProcessorType::Pose {
|
||||
false // Always run pose.rs to check for interpolation
|
||||
} else {
|
||||
output_path.exists()
|
||||
};
|
||||
|
||||
if skip_check {
|
||||
info!(
|
||||
"Processor {} output file exists, marking completed and skipping",
|
||||
processor_type.as_str()
|
||||
@@ -803,6 +827,65 @@ impl JobWorker {
|
||||
}
|
||||
}
|
||||
|
||||
// Special handling for ASRX: if ASR output exists with no_audio_track/silent_audio, skip processing
|
||||
if *processor_type == crate::core::db::ProcessorType::Asrx {
|
||||
let asr_output_path = format!(
|
||||
"{}{}.asr.json",
|
||||
crate::core::config::OUTPUT_DIR
|
||||
.as_str()
|
||||
.trim_end_matches('/'),
|
||||
job.uuid
|
||||
);
|
||||
if let Ok(asr_json) = std::fs::read_to_string(&asr_output_path) {
|
||||
if let Ok(asr_data) = serde_json::from_str::<serde_json::Value>(&asr_json) {
|
||||
let asr_status = asr_data.get("status").and_then(|s| s.as_str());
|
||||
if let Some(status) = asr_status {
|
||||
if status == "no_audio_track" || status == "silent_audio" {
|
||||
info!("ASRX: ASR status={}, skipping ASRX processing", status);
|
||||
// Create completed result with same status
|
||||
if let Err(e) = self
|
||||
.db
|
||||
.upsert_processor_result(
|
||||
job.id,
|
||||
*processor_type,
|
||||
&job.uuid,
|
||||
"completed",
|
||||
)
|
||||
.await
|
||||
{
|
||||
error!("Failed to create ASRX result: {}", e);
|
||||
}
|
||||
// Update asr_status column
|
||||
let _ = sqlx::query(&format!(
|
||||
"UPDATE {} SET asr_status = $1, segment_count = 0 WHERE job_id = $2 AND processor = 'asrx'",
|
||||
crate::core::db::schema::table_name("processor_results")
|
||||
))
|
||||
.bind(status)
|
||||
.bind(job.id)
|
||||
.execute(self.db.pool())
|
||||
.await;
|
||||
let _ = self
|
||||
.redis
|
||||
.update_worker_processor_status(
|
||||
&job.uuid,
|
||||
"asrx",
|
||||
"completed",
|
||||
None,
|
||||
0,
|
||||
0,
|
||||
0,
|
||||
0,
|
||||
0,
|
||||
)
|
||||
.await;
|
||||
started_count += 1;
|
||||
continue;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Check dependencies: all dependent processors must be completed
|
||||
let deps = processor_type.dependencies();
|
||||
if !deps.is_empty() {
|
||||
@@ -877,6 +960,7 @@ impl JobWorker {
|
||||
{
|
||||
error!("Failed to emit processor alert: {}", e);
|
||||
}
|
||||
started_count += 1;
|
||||
continue;
|
||||
}
|
||||
}
|
||||
@@ -1005,54 +1089,127 @@ impl JobWorker {
|
||||
/// 檢查所有入庫步驟是否已完成（與 ingestion-status endpoint 同步邏輯）
|
||||
async fn ingestion_complete(pool: &PgPool, uuid: &str, job_processors: &[String]) -> bool {
|
||||
let chunk_t = schema::table_name("chunk");
|
||||
let fd_t = schema::table_name("face_detections");
|
||||
let pr_t = schema::table_name("processor_results");
|
||||
|
||||
// Only check conditions relevant to the job's processors
|
||||
let has_asr_or_asrx =
|
||||
job_processors.is_empty() || job_processors.iter().any(|p| p == "asrx" || p == "asr");
|
||||
let has_cut = job_processors.is_empty() || job_processors.iter().any(|p| p == "cut");
|
||||
let has_face = job_processors.is_empty() || job_processors.iter().any(|p| p == "face");
|
||||
|
||||
let rule1 = !has_asr_or_asrx
|
||||
|| sqlx::query_scalar::<_, i32>(&format!(
|
||||
"SELECT 1 FROM {chunk_t} WHERE file_uuid = $1 AND chunk_type = 'sentence' LIMIT 1"
|
||||
// Check asr_status for ASR/ASRX - if no_audio_track or silent_audio, ingestion is complete
|
||||
let asr_done: bool = if has_asr_or_asrx {
|
||||
let asr_status: Option<String> = sqlx::query_scalar(&format!(
|
||||
"SELECT asr_status FROM {pr_t} WHERE file_uuid = $1 AND processor IN ('asr', 'asrx') LIMIT 1"
|
||||
))
|
||||
.bind(uuid)
|
||||
.fetch_optional(pool)
|
||||
.await
|
||||
.unwrap_or(None)
|
||||
.unwrap_or(0)
|
||||
> 0;
|
||||
.unwrap_or(None);
|
||||
|
||||
let vector = !has_asr_or_asrx
|
||||
|| sqlx::query_scalar::<_, i32>(&format!(
|
||||
"SELECT 1 FROM {chunk_t} WHERE file_uuid = $1 AND chunk_type = 'sentence' AND embedding IS NOT NULL LIMIT 1"
|
||||
))
|
||||
.bind(uuid)
|
||||
.fetch_optional(pool)
|
||||
.await
|
||||
.unwrap_or(None)
|
||||
.unwrap_or(0)
|
||||
> 0;
|
||||
match asr_status.as_deref() {
|
||||
Some("no_audio_track") | Some("silent_audio") => {
|
||||
tracing::info!(
|
||||
"[Ingestion] ASR status {} for {} - no chunks needed",
|
||||
asr_status.unwrap_or_default(),
|
||||
uuid
|
||||
);
|
||||
true
|
||||
}
|
||||
Some("has_transcript") => {
|
||||
// Has transcript, need chunks
|
||||
sqlx::query_scalar::<_, i32>(&format!(
|
||||
"SELECT 1 FROM {chunk_t} WHERE file_uuid = $1 AND chunk_type = 'sentence' LIMIT 1"
|
||||
))
|
||||
.bind(uuid)
|
||||
.fetch_optional(pool)
|
||||
.await
|
||||
.unwrap_or(None)
|
||||
.unwrap_or(0)
|
||||
> 0
|
||||
}
|
||||
_ => false,
|
||||
}
|
||||
} else {
|
||||
true
|
||||
};
|
||||
|
||||
let trace = !has_face
|
||||
|| sqlx::query_scalar::<_, i64>(&format!(
|
||||
"SELECT COUNT(DISTINCT trace_id) FROM {fd_t} WHERE file_uuid = $1 AND trace_id IS NOT NULL"
|
||||
))
|
||||
.bind(uuid)
|
||||
.fetch_one(pool)
|
||||
.await
|
||||
.unwrap_or(0)
|
||||
> 0;
|
||||
// Check face_status for Face - if no_faces, ingestion is complete
|
||||
let trace_done: bool = if has_face {
|
||||
// Check face_traced.json file for traces directly
|
||||
let output_dir = std::env::var("MOMENTRY_OUTPUT_DIR")
|
||||
.unwrap_or_else(|_| "/Users/accusys/momentry/output".to_string());
|
||||
let traced_path = format!("{}/{}.face_traced.json", output_dir, uuid);
|
||||
|
||||
let all_ok = rule1 && vector && trace;
|
||||
if !all_ok {
|
||||
tracing::info!(
|
||||
"[Ingestion] waiting (uuid={}): rule1={} vector={} trace={}",
|
||||
uuid,
|
||||
rule1,
|
||||
vector,
|
||||
trace
|
||||
tracing::info!(
|
||||
"[Ingestion] Checking face traces for {}: path={}",
|
||||
uuid,
|
||||
traced_path
|
||||
);
|
||||
|
||||
if std::path::Path::new(&traced_path).exists() {
|
||||
if let Ok(content) = std::fs::read_to_string(&traced_path) {
|
||||
if let Ok(traced_data) = serde_json::from_str::<serde_json::Value>(&content) {
|
||||
if let Some(traces) = traced_data.get("traces") {
|
||||
// traces can be an object (dictionary) or array
|
||||
let trace_count = if traces.is_object() {
|
||||
traces.as_object().map(|o| o.len()).unwrap_or(0)
|
||||
} else if traces.is_array() {
|
||||
traces.as_array().map(|a| a.len()).unwrap_or(0)
|
||||
} else {
|
||||
0
|
||||
};
|
||||
|
||||
if trace_count > 0 {
|
||||
tracing::info!(
|
||||
"[Ingestion] Face traces found for {}: {} traces (from face_traced.json)",
|
||||
uuid, trace_count
|
||||
);
|
||||
true
|
||||
} else {
|
||||
tracing::warn!("[Ingestion] Face traces is empty for {}", uuid);
|
||||
false
|
||||
}
|
||||
} else {
|
||||
tracing::warn!(
|
||||
"[Ingestion] No 'traces' key in face_traced.json for {}",
|
||||
uuid
|
||||
);
|
||||
false
|
||||
}
|
||||
} else {
|
||||
tracing::warn!("[Ingestion] Failed to parse face_traced.json for {}", uuid);
|
||||
false
|
||||
}
|
||||
} else {
|
||||
tracing::warn!("[Ingestion] Failed to read face_traced.json for {}", uuid);
|
||||
false
|
||||
}
|
||||
} else {
|
||||
tracing::warn!(
|
||||
"[Ingestion] face_traced.json not found for {}: {}",
|
||||
uuid,
|
||||
traced_path
|
||||
);
|
||||
false
|
||||
}
|
||||
} else {
|
||||
tracing::info!("[Ingestion] No face processor, trace_done=true");
|
||||
true
|
||||
};
|
||||
|
||||
let all_ok = asr_done && trace_done;
|
||||
tracing::info!(
|
||||
"[Ingestion] all_ok={} (asr_done={}, trace_done={}) for uuid={}",
|
||||
all_ok,
|
||||
asr_done,
|
||||
trace_done,
|
||||
uuid
|
||||
);
|
||||
if !all_ok {
|
||||
tracing::info!(
|
||||
"[Ingestion] waiting (uuid={}): asr_done={} trace_done={}",
|
||||
uuid,
|
||||
asr_done,
|
||||
trace_done
|
||||
);
|
||||
}
|
||||
all_ok
|
||||
@@ -1103,7 +1260,7 @@ vector,
|
||||
.any(|r| matches!(r.status, crate::core::db::ProcessorJobStatus::Pending));
|
||||
|
||||
const MAX_RETRIES: i32 = 3;
|
||||
|
||||
|
||||
if any_failed && !any_pending {
|
||||
let failed_processors_to_retry: Vec<i32> = results
|
||||
.iter()
|
||||
@@ -1116,19 +1273,131 @@ vector,
|
||||
.collect();
|
||||
|
||||
if !failed_processors_to_retry.is_empty() {
|
||||
info!("🔄 Attempting to retry {} failed processors...", failed_processors_to_retry.len());
|
||||
|
||||
info!(
|
||||
"🔄 Attempting to retry {} failed processors...",
|
||||
failed_processors_to_retry.len()
|
||||
);
|
||||
|
||||
for result_id in failed_processors_to_retry {
|
||||
if let Ok(true) = self.db.retry_failed_processor(result_id, MAX_RETRIES).await {
|
||||
if let Ok(mut conn) = self.redis.get_conn().await {
|
||||
let redis_key = format!("momentry:progress:{}", uuid);
|
||||
let _: Result<i32, _> = redis::AsyncCommands::del(&mut conn, &redis_key).await;
|
||||
let _: Result<i32, _> =
|
||||
redis::AsyncCommands::del(&mut conn, &redis_key).await;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Retry deferred processors whose dependencies are now met
|
||||
// Build a set of completed processor types
|
||||
let completed_set: std::collections::HashSet<_> = results
|
||||
.iter()
|
||||
.filter(|r| matches!(r.status, ProcessorJobStatus::Completed))
|
||||
.map(|r| r.processor_type)
|
||||
.collect();
|
||||
|
||||
let mut created_deferred = false;
|
||||
|
||||
// Find processors in job_processors that are not in results yet
|
||||
for processor_name in job_processors {
|
||||
let processor_type = match crate::core::db::ProcessorType::from_db_str(processor_name) {
|
||||
Some(pt) => pt,
|
||||
None => continue,
|
||||
};
|
||||
|
||||
// Skip if already has a result
|
||||
if results.iter().any(|r| r.processor_type == processor_type) {
|
||||
continue;
|
||||
}
|
||||
|
||||
// Check if all dependencies are met
|
||||
let deps = processor_type.dependencies();
|
||||
let deps_met = deps.iter().all(|dep| completed_set.contains(dep));
|
||||
|
||||
if !deps_met {
|
||||
continue;
|
||||
}
|
||||
|
||||
info!(
|
||||
"🔄 Deferred processor {} dependencies now met, creating result",
|
||||
processor_name
|
||||
);
|
||||
created_deferred = true;
|
||||
|
||||
// Special handling for ASRX: check ASR output file
|
||||
if processor_type == crate::core::db::ProcessorType::Asrx {
|
||||
let asr_output_path = format!(
|
||||
"{}{}.asr.json",
|
||||
crate::core::config::OUTPUT_DIR
|
||||
.as_str()
|
||||
.trim_end_matches('/'),
|
||||
uuid
|
||||
);
|
||||
if let Ok(asr_json) = std::fs::read_to_string(&asr_output_path) {
|
||||
if let Ok(asr_data) = serde_json::from_str::<serde_json::Value>(&asr_json) {
|
||||
let asr_status = asr_data.get("status").and_then(|s| s.as_str());
|
||||
if let Some(status) = asr_status {
|
||||
if status == "no_audio_track" || status == "silent_audio" {
|
||||
info!(
|
||||
"ASRX: ASR status={}, creating completed result directly",
|
||||
status
|
||||
);
|
||||
if let Err(e) = self
|
||||
.db
|
||||
.upsert_processor_result(
|
||||
job_id,
|
||||
processor_type,
|
||||
uuid,
|
||||
"completed",
|
||||
)
|
||||
.await
|
||||
{
|
||||
error!("Failed to create ASRX result: {}", e);
|
||||
}
|
||||
let _ = sqlx::query(&format!(
|
||||
"UPDATE {} SET asr_status = $1, segment_count = 0 WHERE job_id = $2 AND processor = 'asrx'",
|
||||
crate::core::db::schema::table_name("processor_results")
|
||||
))
|
||||
.bind(status)
|
||||
.bind(job_id)
|
||||
.execute(self.db.pool())
|
||||
.await;
|
||||
let _ = self
|
||||
.redis
|
||||
.update_worker_processor_status(
|
||||
uuid,
|
||||
"asrx",
|
||||
"completed",
|
||||
None,
|
||||
0,
|
||||
0,
|
||||
0,
|
||||
0,
|
||||
0,
|
||||
)
|
||||
.await;
|
||||
continue;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// For other deferred processors, create pending result so worker can pick it up
|
||||
if let Err(e) = self
|
||||
.db
|
||||
.upsert_processor_result(job_id, processor_type, uuid, "pending")
|
||||
.await
|
||||
{
|
||||
error!(
|
||||
"Failed to create deferred result for {}: {}",
|
||||
processor_name, e
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
let any_skipped = results
|
||||
.iter()
|
||||
.filter(|r| job_processors.contains(&r.processor_type.as_str().to_string()))
|
||||
@@ -1192,7 +1461,9 @@ vector,
|
||||
} else {
|
||||
info!("📝 Prerequisites met for Rule 1 Chunking. Starting ingestion...");
|
||||
let db_clone = self.db.clone();
|
||||
let redis_clone = self.redis.clone();
|
||||
let uuid_clone = uuid.to_string();
|
||||
let job_id_clone = job_id;
|
||||
tokio::spawn(async move {
|
||||
match db_clone.get_video_by_uuid(&uuid_clone).await {
|
||||
Ok(Some(video)) => {
|
||||
@@ -1217,6 +1488,9 @@ vector,
|
||||
);
|
||||
}
|
||||
}
|
||||
let mut pp = PipelineProgress::new(&uuid_clone);
|
||||
pp.update_stage("rule1_ingestion", 1.0, "completed", Some(format!("{} chunks", count)));
|
||||
publish_pipeline_progress(redis_clone.as_ref(), &uuid_clone, &pp).await;
|
||||
info!("📦 Phase 1 release packaging...");
|
||||
let executor =
|
||||
match crate::core::processor::PythonExecutor::new() {
|
||||
@@ -1240,7 +1514,10 @@ vector,
|
||||
.await
|
||||
{
|
||||
Ok(()) => {
|
||||
info!("✅ Phase 1 release packaged for {}", uuid_clone)
|
||||
info!("✅ Phase 1 release packaged for {}", uuid_clone);
|
||||
|
||||
// Note: Job status will be updated after Rule 2 (TKG) completion
|
||||
// Do not mark as completed here
|
||||
}
|
||||
Err(e) => error!("❌ Phase 1 release pack failed: {}", e),
|
||||
}
|
||||
@@ -1251,16 +1528,21 @@ vector,
|
||||
Ok(None) => error!("Video not found for chunking: {}", uuid_clone),
|
||||
Err(e) => error!("Failed to get video info for chunking: {}", e),
|
||||
}
|
||||
});
|
||||
}
|
||||
}
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
if all_completed {
|
||||
// 🚀 P2 Trigger: Face Trace + DB Store (after Face)
|
||||
if all_completed {
|
||||
let mut pp = PipelineProgress::new(uuid);
|
||||
pp.update_stage("processors", 1.0, "completed", None);
|
||||
publish_pipeline_progress(self.redis.as_ref(), uuid, &pp).await;
|
||||
|
||||
// 🚀 P2 Trigger: Face Trace + DB Store (after Face)
|
||||
// Runs face_tracker.py (IoU+embedding tracking), stores trace_id + position in DB
|
||||
if has_face {
|
||||
info!("📝 Face completed, triggering face trace + DB store...");
|
||||
let db_clone = self.db.clone();
|
||||
let redis_clone = self.redis.clone();
|
||||
let uuid_clone = uuid.to_string();
|
||||
tokio::spawn(async move {
|
||||
let executor = match crate::core::processor::PythonExecutor::new() {
|
||||
@@ -1283,17 +1565,56 @@ if all_completed {
|
||||
Ok(()) => {
|
||||
info!("✅ Face trace + DB store completed for {}", uuid_clone);
|
||||
|
||||
// Generate trace chunks from face_detections + ASR text
|
||||
info!("📝 Generating trace chunks...");
|
||||
match crate::core::chunk::trace_ingest::ingest_traces(
|
||||
&db_clone,
|
||||
&uuid_clone,
|
||||
)
|
||||
.await
|
||||
// Query trace count and distribution
|
||||
let trace_count = match db_clone
|
||||
.get_trace_count_by_file(&uuid_clone)
|
||||
.await
|
||||
{
|
||||
Ok(n) => info!("✅ {} trace chunks created for {}", n, uuid_clone),
|
||||
Err(e) => error!("❌ Trace chunk ingestion failed: {}", e),
|
||||
Ok(c) => c,
|
||||
Err(e) => {
|
||||
error!("Failed to get trace count for {}: {}", uuid_clone, e);
|
||||
0
|
||||
}
|
||||
};
|
||||
|
||||
let (single_frame, multi_frame) = match db_clone
|
||||
.get_trace_frame_count_distribution(&uuid_clone)
|
||||
.await
|
||||
{
|
||||
Ok(dist) => dist,
|
||||
Err(e) => {
|
||||
error!(
|
||||
"Failed to get trace distribution for {}: {}",
|
||||
uuid_clone, e
|
||||
);
|
||||
(0, 0)
|
||||
}
|
||||
};
|
||||
|
||||
let trace_status =
|
||||
crate::core::processor::TraceStatus::from_trace_count(trace_count);
|
||||
info!(
|
||||
"📊 Trace status: {} (total={}, single_frame={}, multi_frame={}) for {}",
|
||||
trace_status, trace_count, single_frame, multi_frame, uuid_clone
|
||||
);
|
||||
|
||||
// Update processor_results trace_status for Face
|
||||
if let Err(e) = db_clone
|
||||
.update_trace_status_for_face(
|
||||
&uuid_clone,
|
||||
&trace_status,
|
||||
trace_count,
|
||||
single_frame,
|
||||
multi_frame,
|
||||
)
|
||||
.await
|
||||
{
|
||||
error!("Failed to update trace_status for {}: {}", uuid_clone, e);
|
||||
}
|
||||
|
||||
let mut pp = PipelineProgress::new(&uuid_clone);
|
||||
pp.update_stage("face_tracing", 1.0, "completed", Some(format!("{} traces ({} single, {} multi)", trace_count, single_frame, multi_frame)));
|
||||
publish_pipeline_progress(redis_clone.as_ref(), &uuid_clone, &pp).await;
|
||||
}
|
||||
Err(e) => {
|
||||
error!("❌ Face trace + DB store failed for {}: {}", uuid_clone, e)
|
||||
@@ -1320,15 +1641,46 @@ if all_completed {
|
||||
count, uuid_clone
|
||||
);
|
||||
// Save identity files for affected identities
|
||||
let ids = sqlx::query_scalar::<_, uuid::Uuid>(
|
||||
"SELECT DISTINCT i.uuid FROM identities i \
|
||||
JOIN face_detections fd ON fd.identity_id = i.id \
|
||||
WHERE fd.file_uuid = $1 AND fd.identity_id IS NOT NULL",
|
||||
)
|
||||
.bind(&uuid_clone)
|
||||
.fetch_all(db_clone.pool())
|
||||
.await
|
||||
.unwrap_or_default();
|
||||
let qdrant = crate::core::db::qdrant_db::QdrantDb::new();
|
||||
let face_filter = serde_json::json!({
|
||||
"must": [
|
||||
{"key": "file_uuid", "match": {"value": &uuid_clone}},
|
||||
{"key": "identity_id", "is_null": false}
|
||||
]
|
||||
});
|
||||
let face_points = qdrant
|
||||
.scroll_all_points("_faces", face_filter, 1000)
|
||||
.await
|
||||
.unwrap_or_default();
|
||||
use std::collections::HashSet;
|
||||
let mut identity_ids: HashSet<i32> = HashSet::new();
|
||||
for p in &face_points {
|
||||
if let Some(iid) = p["payload"]["identity_id"].as_i64() {
|
||||
identity_ids.insert(iid as i32);
|
||||
}
|
||||
}
|
||||
let ids: Vec<uuid::Uuid> = if !identity_ids.is_empty() {
|
||||
let ids_list: Vec<i32> = identity_ids.into_iter().collect();
|
||||
let id_params: Vec<String> =
|
||||
ids_list.iter().map(|_| "$1".to_string()).collect();
|
||||
// Use batch query: since we can't do IN with variable params via sqlx easily,
|
||||
// query one by one. But typically there are few (<20) identities.
|
||||
let mut result = Vec::new();
|
||||
for iid in &ids_list {
|
||||
if let Ok(Some(u)) = sqlx::query_scalar::<_, uuid::Uuid>(
|
||||
"SELECT uuid FROM identities WHERE id = $1",
|
||||
)
|
||||
.bind(iid)
|
||||
.fetch_optional(db_clone.pool())
|
||||
.await
|
||||
{
|
||||
result.push(u);
|
||||
}
|
||||
}
|
||||
result
|
||||
} else {
|
||||
Vec::new()
|
||||
};
|
||||
for id_uuid in &ids {
|
||||
let us = id_uuid.to_string().replace('-', "");
|
||||
if let Err(e) = crate::core::identity::storage::save_identity_file(
|
||||
@@ -1374,15 +1726,58 @@ if all_completed {
|
||||
if has_face && has_asr_or_asrx {
|
||||
info!("📝 Prerequisites met for Identity Agent. Starting analysis...");
|
||||
let db_clone = self.db.clone();
|
||||
let redis_clone = self.redis.clone();
|
||||
let uuid_clone = uuid.to_string();
|
||||
tokio::spawn(async move {
|
||||
match run_identity_agent(&db_clone, &uuid_clone).await {
|
||||
Ok(()) => info!("✅ Identity Agent completed for {}", uuid_clone),
|
||||
match run_identity_agent(&db_clone, &uuid_clone, Some(redis_clone.clone())).await {
|
||||
Ok(()) => {
|
||||
info!("✅ Identity Agent completed for {}", uuid_clone);
|
||||
let mut pp = PipelineProgress::new(&uuid_clone);
|
||||
pp.update_stage("identity_agent", 1.0, "completed", None);
|
||||
publish_pipeline_progress(redis_clone.as_ref(), &uuid_clone, &pp).await;
|
||||
}
|
||||
Err(e) => error!("❌ Identity Agent failed for {}: {}", uuid_clone, e),
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
// 🚀 P4 Trigger: TKG Build (Face + ASRX) → then Rule2 ingestion
|
||||
if has_face && has_asr_or_asrx {
|
||||
info!("📝 Prerequisites met for TKG Build. Starting graph construction...");
|
||||
let db_clone = self.db.clone();
|
||||
let redis_clone = self.redis.clone();
|
||||
let uuid_clone = uuid.to_string();
|
||||
let output_dir_clone = crate::core::config::OUTPUT_DIR.clone();
|
||||
tokio::spawn(async move {
|
||||
match crate::core::processor::tkg::build_tkg(&db_clone, &uuid_clone, &output_dir_clone, Some(redis_clone.clone())).await {
|
||||
Ok(r) => {
|
||||
let total_nodes = r.face_track_nodes + r.gaze_track_nodes + r.lip_track_nodes + r.text_region_nodes + r.appearance_trace_nodes + r.accessory_nodes + r.object_nodes + r.hand_nodes + r.speaker_nodes;
|
||||
let total_edges = r.co_occurrence_edges + r.speaker_face_edges + r.face_face_edges + r.mutual_gaze_edges + r.lip_sync_edges + r.has_appearance_edges + r.wears_edges + r.hand_object_edges;
|
||||
info!("✅ TKG build completed for {}: {} nodes, {} edges", uuid_clone, total_nodes, total_edges);
|
||||
|
||||
let mut pp = PipelineProgress::new(&uuid_clone);
|
||||
pp.update_stage("tkg_nodes", 1.0, "completed", Some(format!("{} nodes", total_nodes)));
|
||||
pp.update_stage("tkg_edges", 1.0, "completed", Some(format!("{} edges", total_edges)));
|
||||
publish_pipeline_progress(redis_clone.as_ref(), &uuid_clone, &pp).await;
|
||||
|
||||
// Trigger Rule 2 ingestion after TKG complete
|
||||
if total_edges > 0 {
|
||||
match crate::core::chunk::rule2_ingest::ingest_rule2(db_clone.pool(), &uuid_clone, None, None).await {
|
||||
Ok(rule2_count) => {
|
||||
info!("✅ Rule 2 ingestion completed for {}: {} relationship chunks", uuid_clone, rule2_count);
|
||||
let mut pp = PipelineProgress::new(&uuid_clone);
|
||||
pp.update_stage("rule2_ingestion", 1.0, "completed", Some(format!("{} chunks", rule2_count)));
|
||||
publish_pipeline_progress(redis_clone.as_ref(), &uuid_clone, &pp).await;
|
||||
}
|
||||
Err(e) => error!("❌ Rule 2 ingestion failed for {}: {}", uuid_clone, e),
|
||||
}
|
||||
}
|
||||
}
|
||||
Err(e) => error!("❌ TKG build failed for {}: {}", uuid_clone, e),
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
if !Self::ingestion_complete(self.db.pool(), uuid, job_processors).await {
|
||||
info!(
|
||||
"Job {}: all processors done, waiting for ingestion...",
|
||||
@@ -1413,6 +1808,10 @@ if all_completed {
|
||||
|
||||
self.redis.delete_worker_job(uuid).await?;
|
||||
|
||||
let mut pp = PipelineProgress::new(uuid);
|
||||
pp.mark_completed();
|
||||
publish_pipeline_progress(self.redis.as_ref(), uuid, &pp).await;
|
||||
|
||||
info!("Job {} completed successfully (ingestion done)", job_id);
|
||||
} else if essential_completed && !all_completed && !any_pending && !any_skipped {
|
||||
// 必要 processor 完成但部分非必要失敗 → 仍算完成（但無 pending 者才觸發）
|
||||
@@ -1466,7 +1865,8 @@ if all_completed {
|
||||
.await?;
|
||||
}
|
||||
|
||||
Ok(false)
|
||||
// Return true if we created deferred processors, so caller will reprocess the job
|
||||
Ok(created_deferred)
|
||||
}
|
||||
|
||||
pub async fn shutdown(&self) {
|
||||
|
||||
+158
-5
@@ -82,6 +82,10 @@ struct ProcessorOutput {
|
||||
total_frames: i32,
|
||||
retry_count: i32,
|
||||
pid: i32,
|
||||
asr_status: Option<crate::core::processor::AsrStatus>,
|
||||
segment_count: usize,
|
||||
face_status: Option<crate::core::processor::FaceStatus>,
|
||||
total_faces: usize,
|
||||
}
|
||||
|
||||
#[derive(Debug, Clone)]
|
||||
@@ -316,13 +320,16 @@ impl ProcessorPool {
|
||||
}
|
||||
|
||||
// Subscribe to Redis progress pub/sub and update processor hash in real-time
|
||||
let sub_db = db.clone();
|
||||
let sub_redis = redis.clone();
|
||||
let sub_uuid = job.uuid.clone();
|
||||
let sub_processor = processor_name.clone();
|
||||
let progress_handle = tokio::spawn(async move {
|
||||
let cb_db = sub_db.clone();
|
||||
let cb_redis = sub_redis.clone();
|
||||
let cb_uuid = sub_uuid.clone();
|
||||
let cb_processor = sub_processor.clone();
|
||||
let last_update = std::cell::Cell::new(0i64);
|
||||
if let Err(e) = sub_redis
|
||||
.subscribe_and_callback(&sub_uuid, move |msg| {
|
||||
tracing::info!(
|
||||
@@ -338,6 +345,7 @@ impl ProcessorPool {
|
||||
let r = cb_redis.clone();
|
||||
let u = cb_uuid.clone();
|
||||
let p = cb_processor.clone();
|
||||
let p2 = p.clone();
|
||||
tokio::spawn(async move {
|
||||
match r
|
||||
.update_worker_processor_status(
|
||||
@@ -354,6 +362,46 @@ impl ProcessorPool {
|
||||
Err(e) => tracing::error!("[Subscriber] FAILED {}: {}", p, e),
|
||||
}
|
||||
});
|
||||
// Sync progress to PostgreSQL every 5 seconds
|
||||
let now = std::time::SystemTime::now()
|
||||
.duration_since(std::time::UNIX_EPOCH)
|
||||
.unwrap_or_default()
|
||||
.as_secs() as i64;
|
||||
let elapsed = now - last_update.get();
|
||||
if elapsed >= 5 {
|
||||
tracing::info!(
|
||||
"[Subscriber] PG sync {}: cur={} tot={} (elapsed={})",
|
||||
p2,
|
||||
cur,
|
||||
tot,
|
||||
elapsed
|
||||
);
|
||||
last_update.set(now);
|
||||
let db_client = cb_db.clone();
|
||||
let u = cb_uuid.clone();
|
||||
let p = cb_processor.clone();
|
||||
tokio::spawn(async move {
|
||||
if let Err(e) = db_client
|
||||
.update_processor_progress(
|
||||
&u, &p, cur as u64, tot as u64, "running",
|
||||
)
|
||||
.await
|
||||
{
|
||||
tracing::error!(
|
||||
"[Subscriber] PG progress update FAILED {}: {}",
|
||||
p,
|
||||
e
|
||||
);
|
||||
} else {
|
||||
tracing::info!(
|
||||
"[Subscriber] PG progress updated {}: cur={} tot={}",
|
||||
p,
|
||||
cur,
|
||||
tot
|
||||
);
|
||||
}
|
||||
});
|
||||
}
|
||||
}
|
||||
})
|
||||
.await
|
||||
@@ -400,6 +448,32 @@ impl ProcessorPool {
|
||||
error!("Failed to update processor result to completed: {}", e);
|
||||
}
|
||||
|
||||
if let Some(ref asr_status) = output.asr_status {
|
||||
if let Err(e) = db
|
||||
.update_asr_status(
|
||||
processor_result_id,
|
||||
asr_status,
|
||||
output.segment_count,
|
||||
)
|
||||
.await
|
||||
{
|
||||
error!("Failed to update ASR status: {}", e);
|
||||
}
|
||||
}
|
||||
|
||||
if let Some(ref face_status) = output.face_status {
|
||||
if let Err(e) = db
|
||||
.update_face_status(
|
||||
processor_result_id,
|
||||
face_status,
|
||||
output.total_faces,
|
||||
)
|
||||
.await
|
||||
{
|
||||
error!("Failed to update FACE status: {}", e);
|
||||
}
|
||||
}
|
||||
|
||||
if let Err(e) = redis
|
||||
.update_worker_processor_status(
|
||||
&job.uuid,
|
||||
@@ -416,6 +490,20 @@ impl ProcessorPool {
|
||||
{
|
||||
error!("Failed to update Redis processor status: {}", e);
|
||||
}
|
||||
|
||||
// Also update PostgreSQL processing_status JSON
|
||||
if let Err(e) = db
|
||||
.update_processor_progress(
|
||||
&job.uuid,
|
||||
&processor_name,
|
||||
output.frames_processed as u64,
|
||||
output.total_frames as u64,
|
||||
"completed",
|
||||
)
|
||||
.await
|
||||
{
|
||||
error!("Failed to update PostgreSQL processor status: {}", e);
|
||||
}
|
||||
} else {
|
||||
error!(
|
||||
"Processor {} output failed verification for job {}: {:?}",
|
||||
@@ -569,6 +657,10 @@ impl ProcessorPool {
|
||||
total_frames,
|
||||
retry_count: 0,
|
||||
pid: 0,
|
||||
asr_status: None,
|
||||
segment_count: 0,
|
||||
face_status: None,
|
||||
total_faces: 0,
|
||||
})
|
||||
}
|
||||
ProcessorType::Yolo => {
|
||||
@@ -612,6 +704,10 @@ impl ProcessorPool {
|
||||
total_frames,
|
||||
retry_count: 0,
|
||||
pid: 0,
|
||||
asr_status: None,
|
||||
segment_count: 0,
|
||||
face_status: None,
|
||||
total_faces: 0,
|
||||
})
|
||||
}
|
||||
ProcessorType::Ocr => {
|
||||
@@ -655,6 +751,10 @@ impl ProcessorPool {
|
||||
total_frames,
|
||||
retry_count: 0,
|
||||
pid: 0,
|
||||
asr_status: None,
|
||||
segment_count: 0,
|
||||
face_status: None,
|
||||
total_faces: 0,
|
||||
})
|
||||
}
|
||||
ProcessorType::Face => {
|
||||
@@ -666,9 +766,16 @@ impl ProcessorPool {
|
||||
)
|
||||
.await?;
|
||||
let chunks_produced = result.frames.len() as i32;
|
||||
let face_status = result.status.clone();
|
||||
let total_faces = result.total_faces;
|
||||
tracing::info!(
|
||||
"FACE completed, storing {} frames for {}",
|
||||
"FACE completed, status={}, {} frames, {} total faces for {}",
|
||||
face_status
|
||||
.as_ref()
|
||||
.map(|s| s.to_string())
|
||||
.unwrap_or_default(),
|
||||
chunks_produced,
|
||||
total_faces,
|
||||
job.uuid
|
||||
);
|
||||
if let Err(e) = Self::store_face_chunks(db, &job.uuid, &result).await {
|
||||
@@ -720,6 +827,10 @@ impl ProcessorPool {
|
||||
total_frames,
|
||||
retry_count: 0,
|
||||
pid: 0,
|
||||
asr_status: None,
|
||||
segment_count: 0,
|
||||
face_status,
|
||||
total_faces,
|
||||
})
|
||||
}
|
||||
ProcessorType::FaceCluster => {
|
||||
@@ -741,6 +852,10 @@ impl ProcessorPool {
|
||||
total_frames: 0,
|
||||
retry_count: 0,
|
||||
pid: 0,
|
||||
asr_status: None,
|
||||
segment_count: 0,
|
||||
face_status: None,
|
||||
total_faces: 0,
|
||||
})
|
||||
}
|
||||
ProcessorType::Pose => {
|
||||
@@ -784,6 +899,10 @@ impl ProcessorPool {
|
||||
total_frames,
|
||||
retry_count: 0,
|
||||
pid: 0,
|
||||
asr_status: None,
|
||||
segment_count: 0,
|
||||
face_status: None,
|
||||
total_faces: 0,
|
||||
})
|
||||
}
|
||||
ProcessorType::Hand => {
|
||||
@@ -824,6 +943,10 @@ impl ProcessorPool {
|
||||
total_frames,
|
||||
retry_count: 0,
|
||||
pid: 0,
|
||||
asr_status: None,
|
||||
segment_count: 0,
|
||||
face_status: None,
|
||||
total_faces: 0,
|
||||
})
|
||||
}
|
||||
ProcessorType::Appearance => {
|
||||
@@ -851,14 +974,24 @@ impl ProcessorPool {
|
||||
total_frames,
|
||||
retry_count: 0,
|
||||
pid: 0,
|
||||
asr_status: None,
|
||||
segment_count: 0,
|
||||
face_status: None,
|
||||
total_faces: 0,
|
||||
})
|
||||
}
|
||||
ProcessorType::Asr => {
|
||||
let result =
|
||||
processor::process_asr(video_path, output_path.to_str().unwrap(), uuid).await?;
|
||||
let chunks_produced = result.segments.len() as i32;
|
||||
let asr_status = result.status.clone();
|
||||
let segment_count = result.segment_count;
|
||||
tracing::info!(
|
||||
"ASR completed, storing {} segments for {}",
|
||||
"ASR completed, status={}, {} segments for {}",
|
||||
asr_status
|
||||
.as_ref()
|
||||
.map(|s| s.to_string())
|
||||
.unwrap_or_default(),
|
||||
chunks_produced,
|
||||
job.uuid
|
||||
);
|
||||
@@ -892,6 +1025,10 @@ impl ProcessorPool {
|
||||
total_frames,
|
||||
retry_count: 0,
|
||||
pid: 0,
|
||||
asr_status,
|
||||
segment_count,
|
||||
face_status: None,
|
||||
total_faces: 0,
|
||||
})
|
||||
}
|
||||
ProcessorType::Asrx => {
|
||||
@@ -899,8 +1036,14 @@ impl ProcessorPool {
|
||||
processor::process_asrx(video_path, output_path.to_str().unwrap(), uuid)
|
||||
.await?;
|
||||
let chunks_produced = result.segments.len() as i32;
|
||||
let asr_status = result.status.clone();
|
||||
let segment_count = result.segment_count;
|
||||
tracing::info!(
|
||||
"ASRX completed, storing {} segments for {}",
|
||||
"ASRX completed, status={}, {} segments for {}",
|
||||
asr_status
|
||||
.as_ref()
|
||||
.map(|s| s.to_string())
|
||||
.unwrap_or_default(),
|
||||
chunks_produced,
|
||||
job.uuid
|
||||
);
|
||||
@@ -959,6 +1102,10 @@ impl ProcessorPool {
|
||||
total_frames,
|
||||
retry_count: 0,
|
||||
pid: 0,
|
||||
asr_status,
|
||||
segment_count,
|
||||
face_status: None,
|
||||
total_faces: 0,
|
||||
})
|
||||
}
|
||||
ProcessorType::Scene => {
|
||||
@@ -977,6 +1124,10 @@ impl ProcessorPool {
|
||||
total_frames,
|
||||
retry_count: 0,
|
||||
pid: 0,
|
||||
asr_status: None,
|
||||
segment_count: 0,
|
||||
face_status: None,
|
||||
total_faces: 0,
|
||||
});
|
||||
} else if scene_path.exists() {
|
||||
tracing::info!("Scene JSON exists for {}, loading from file", job.uuid);
|
||||
@@ -1025,6 +1176,10 @@ impl ProcessorPool {
|
||||
total_frames,
|
||||
retry_count: 0,
|
||||
pid: 0,
|
||||
asr_status: None,
|
||||
segment_count: 0,
|
||||
face_status: None,
|
||||
total_faces: 0,
|
||||
})
|
||||
}
|
||||
}
|
||||
@@ -1363,8 +1518,6 @@ impl ProcessorPool {
|
||||
|
||||
db.store_raw_pre_chunks_batch(uuid, "asrx", &pre_chunks_to_store)
|
||||
.await?;
|
||||
db.store_raw_pre_chunks_batch(uuid, "asr", &pre_chunks_to_store)
|
||||
.await?;
|
||||
db.store_speaker_detections_batch(uuid, &speaker_detections)
|
||||
.await?;
|
||||
Ok(())
|
||||
|
||||
Reference in New Issue
Block a user