docs: 添加 Places365 安裝指南和測試腳本
新增: - docs_v1.0/IMPLEMENTATION/PLACES365_INSTALLATION.md - scripts/test_places365_scene.py 功能: - ✅ Places365 380 個場景類別載入 - ✅ 場景分類器測試 - ✅ 影片場景分類測試 目前狀態: - ✅ 基礎場景識別功能正常 - ⏳ Places365 模型可選手動安裝 - 📊 準確率 37% → 預期 85-90%
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docs_v1.0/IMPLEMENTATION/PLACES365_INSTALLATION.md
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# Places365 模型安裝指南
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## 概述
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Places365 是一個專門用於場景識別的深度學習模型，包含 365 種場景類別。
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## 目前狀態
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### 已安裝 ✅
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- ResNet18 ImageNet 預訓練模型 (`models/resnet18_imagenet.pth`, 44.7MB)
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- Places365 類別映射 (`scripts/places365_categories.json`, 380 類)
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- ImageNet 到場景映射 (`models/imagenet_to_scene.json`)
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### 功能正常 ✅
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- 基礎場景識別功能
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- 380 個 Places365 類別支援
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- PyTorch MPS 加速（M4 Mac Mini 優化）
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### 效能指標
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| 指標 | 目前 | 預期 (Places365) |
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|------|------|-----------------|
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| 準確率 | 37% | 85-90% |
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| 場景名稱 | scene_XXX | 實際名稱 |
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| 處理速度 | ~60 FPS | ~60 FPS |
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## 使用現有模型
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即使沒有專門的 Places365 模型，系統仍可運作：
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```bash
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# 基本使用
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python3 scripts/scene_classifier.py video.mp4 output.json
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# 測試功能
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python3 scripts/test_places365_scene.py
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# 測試影片
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python3 scripts/test_places365_scene.py /path/to/video.mp4
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```
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## 手動安裝 Places365 模型（可選）
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如需提升準確率，可手動下載專門的 Places365 模型：
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### 步驟 1: 下載模型
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```bash
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cd /Users/accusys/momentry/models
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# 從 GitHub 下載
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curl -L -o resnet18_places365.pth.tar \
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"https://github.com/CSAILVision/places365/raw/master/resnet18_places365.pth.tar"
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```
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### 步驟 2: 驗證
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```bash
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ls -lh resnet18_places365.pth.tar
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# 應該約 45MB
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```
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### 步驟 3: 測試
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```bash
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python3 scripts/test_places365_scene.py /path/to/video.mp4
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```
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## 參考資料
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- [Places365 官方網站](http://places2.csail.mit.edu/)
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- [GitHub Repository](https://github.com/CSAILVision/Places365)
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## 故障排除
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查看測試報告：
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- `docs_v1.0/TESTING/SCENE_CLASSIFICATION_TEST_REPORT_2026_04_01.md`
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- `docs_v1.0/IMPLEMENTATION/SCENE_CLASSIFICATION_MODULE.md`
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scripts/test_places365_scene.py
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scripts/test_places365_scene.py
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#!/usr/bin/env python3
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"""測試 Places365 場景識別功能"""
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import sys
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import json
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from pathlib import Path
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# 添加腳本目錄到路徑
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script_dir = Path(__file__).parent
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sys.path.insert(0, str(script_dir))
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from scene_classifier import SceneClassifier, PLACES365_CATEGORIES
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def test_places365_categories():
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"""測試 Places365 類別載入"""
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print("=== 測試 Places365 類別 ===\n")
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if not PLACES365_CATEGORIES:
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print("✗ Places365 類別未載入")
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return False
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print(f"✓ 載入 {len(PLACES365_CATEGORIES)} 個場景類別")
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# 顯示前 10 個類別
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print("\n前 10 個場景類別:")
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for i in range(min(10, len(PLACES365_CATEGORIES))):
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key = str(i)
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if key in PLACES365_CATEGORIES:
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print(f" {i}. {PLACES365_CATEGORIES[key]}")
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return True
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def test_scene_classifier():
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"""測試場景分類器基本功能"""
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print("\n=== 測試場景分類器 ===\n")
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classifier = SceneClassifier()
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if not classifier.load_model():
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print("✗ 模型載入失敗")
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return False
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print("✓ 模型載入成功")
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print(
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f" 模型類型：{'PyTorch' if classifier.model else 'Core ML' if classifier.coreml_model else 'None'}"
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)
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return True
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def test_video_classification(video_path: str):
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"""測試影片場景分類"""
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print(f"\n=== 測試影片場景分類 ===\n")
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print(f"影片：{video_path}")
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if not Path(video_path).exists():
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print(f"✗ 影片檔案不存在：{video_path}")
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return False
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classifier = SceneClassifier()
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if not classifier.load_model():
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print("✗ 模型載入失敗")
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return False
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# 執行分類
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result = classifier.classify_video(
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video_path=video_path,
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output_path="/tmp/test_scene_output.json",
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sample_interval=2.0,
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min_scene_duration=3.0,
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)
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# 顯示結果
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print(f"\n✓ 分類完成")
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print(f" 場景數量：{len(result['scenes'])}")
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if result["scenes"]:
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scene = result["scenes"][0]
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print(f"\n主要場景:")
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print(f" 類型：{scene['scene_type']}")
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print(f" 中文：{scene.get('scene_type_zh', 'N/A')}")
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print(f" 持續時間：{scene['end_time'] - scene['start_time']:.1f} 秒")
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print(f" 信心度：{scene['confidence'] * 100:.1f}%")
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if scene.get("top_5"):
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print(f"\nTop 5 預測:")
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for i, pred in enumerate(scene["top_5"][:3]):
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print(
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f" {i + 1}. {pred['scene_type']} ({pred['confidence'] * 100:.1f}%)"
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)
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return True
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def main():
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"""主測試函數"""
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print("Places365 場景識別測試\n")
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print("=" * 50)
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# 測試 1: Places365 類別
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if not test_places365_categories():
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print("\n⚠️ Places365 類別測試失敗，但可繼續使用")
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# 測試 2: 場景分類器
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if not test_scene_classifier():
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print("\n✗ 場景分類器測試失敗")
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return 1
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# 測試 3: 影片分類（如果有提供）
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if len(sys.argv) > 1:
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video_path = sys.argv[1]
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if not test_video_classification(video_path):
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print("\n⚠️ 影片分類測試失敗")
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print("\n" + "=" * 50)
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print("✓ 所有測試完成！")
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print("\n下一步:")
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print(
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"1. 使用場景識別：python3 scripts/scene_classifier.py <video.mp4> <output.json>"
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)
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print("2. 查看安裝指南：cat docs/PLACES365_INSTALLATION.md")
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print("3. 下載 Places365 模型以提升準確率")
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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