620 lines
19 KiB
Python
620 lines
19 KiB
Python
#!/usr/bin/env python3
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"""
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場景識別處理器 (Scene Classification Processor)
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使用 Core ML + Places365 模型進行場景識別
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支援 Apple Silicon M4 優化
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- Core ML 模型 (原生)
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- PyTorch + MPS (備案)
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"""
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import argparse
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import json
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import sys
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import time
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from datetime import datetime
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from pathlib import Path
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from typing import Dict, List, Optional, Any
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# 嘗試導入 Core ML
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try:
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import coremltools as ct
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HAS_COREML = True
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except ImportError:
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HAS_COREML = False
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# 嘗試導入 PyTorch (備案)
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try:
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import torch
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from torchvision import transforms, models
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HAS_TORCH = True
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DEVICE = torch.device("mps" if torch.backends.mps.is_available() else "cpu")
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except ImportError:
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HAS_TORCH = False
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DEVICE = torch.device("cpu")
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# 嘗試導入 Pillow 用於圖像處理
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try:
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from PIL import Image
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HAS_PIL = True
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except ImportError:
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HAS_PIL = False
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# 嘗試導入 OpenCV 用於影片處理
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try:
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import cv2
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HAS_CV = True
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except ImportError:
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HAS_CV = False
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# 場景類型中英文對照
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SCENE_TYPE_ZH = {
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"hospital_room": "醫院病房",
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"pharmacy": "藥房",
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"classroom": "教室",
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"office": "辦公室",
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"kitchen": "廚房",
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"living_room": "客廳",
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"bedroom": "臥室",
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"bathroom": "浴室",
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"restaurant": "餐廳",
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"gym": "健身房",
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"supermarket": "超市",
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"basketball_court": "籃球場",
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"football_field": "足球場",
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"tennis_court": "網球場",
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"swimming_pool": "游泳池",
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"park": "公園",
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"street": "街道",
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"beach": "海灘",
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"mountain": "山地",
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"forest": "森林",
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"airport": "機場",
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"train_station": "火車站",
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"subway_station": "地鐵站",
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"gas_station": "加油站",
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"parking_lot": "停車場",
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"auditorium": "禮堂",
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"library": "圖書館",
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"laboratory": "實驗室",
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"art_studio": "藝術工作室",
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"music_store": "音樂商店",
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"computer_room": "電腦室",
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"conference_room": "會議室",
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"playground": "遊樂場",
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"ski_slope": "滑雪坡",
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"ice_rink": "溜冰場",
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"boxing_ring": "拳擊場",
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"volleyball_court": "排球場",
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"baseball_field": "棒球場",
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}
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# 場景類別（Places365 子集）
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SCENE_CATEGORIES = [
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"hospital_room",
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"pharmacy",
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"classroom",
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"office",
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"kitchen",
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"living_room",
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"bedroom",
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"bathroom",
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"restaurant",
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"gym",
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"supermarket",
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"basketball_court",
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"football_field",
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"tennis_court",
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"swimming_pool",
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"park",
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"street",
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"beach",
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"mountain",
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"forest",
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"airport",
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"train_station",
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"subway_station",
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"gas_station",
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"parking_lot",
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"auditorium",
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"library",
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"laboratory",
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"art_studio",
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"music_store",
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"computer_room",
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"conference_room",
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"playground",
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"ski_slope",
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"ice_rink",
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"boxing_ring",
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"volleyball_court",
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"baseball_field",
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]
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class SceneClassifier:
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"""場景識別器"""
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def __init__(self, model_path: Optional[str] = None):
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"""
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初始化場景識別器
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Args:
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model_path: Core ML 模型路徑 (可選)
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"""
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self.model_path = model_path
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self.model = None
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self.coreml_model = None
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self.transform = None
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# 圖像預處理
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self.transform = transforms.Compose(
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[
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transforms.Resize((224, 224)),
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transforms.ToTensor(),
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transforms.Normalize(
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mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
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),
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]
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)
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def load_model(self) -> bool:
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"""
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載入模型
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Returns:
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bool: 是否成功載入
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"""
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# 優先使用 Core ML
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if HAS_COREML and self.model_path and Path(self.model_path).exists():
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try:
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print(f"[SCENE] Loading Core ML model: {self.model_path}")
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self.coreml_model = ct.models.MLModel(self.model_path)
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print("[SCENE] Core ML model loaded successfully")
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return True
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except Exception as e:
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print(f"[SCENE] Warning: Failed to load Core ML model: {e}")
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# 備案：使用 PyTorch + ResNet
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if HAS_TORCH:
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try:
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print(f"[SCENE] Loading PyTorch model on {DEVICE}")
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# 使用預訓練的 ResNet18
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self.model = models.resnet18(pretrained=True)
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self.model.to(DEVICE)
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self.model.eval()
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print("[SCENE] PyTorch model loaded successfully")
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return True
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except Exception as e:
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print(f"[SCENE] Warning: Failed to load PyTorch model: {e}")
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print("[SCENE] Error: No model available")
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return False
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def predict_frame(self, frame: Any) -> List[Dict[str, Any]]:
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"""
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預測單幀圖像的場景類型
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Args:
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frame: 圖像幀 (OpenCV ndarray 或 PIL)
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Returns:
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List[Dict]: 前 5 個預測結果
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"""
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if self.coreml_model is None and self.model is None:
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print("[SCENE] Warning: No model loaded")
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return []
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# 轉換為 PIL Image
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if isinstance(frame, str):
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img = Image.open(frame).convert("RGB")
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elif HAS_CV and hasattr(frame, "shape") and len(frame.shape) == 3:
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# OpenCV frame (BGR ndarray)
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img = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
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elif hasattr(frame, "convert"):
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# PIL Image
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img = frame.convert("RGB")
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else:
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print(f"[SCENE] Warning: Unknown frame type: {type(frame)}")
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return []
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if img is None:
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print("[SCENE] Warning: Failed to convert to PIL Image")
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return []
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# 使用 Core ML
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if self.coreml_model is not None:
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try:
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# Core ML 需要 dict 輸入
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input_dict = {"image": img}
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output = self.coreml_model.predict(input_dict)
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# 解析輸出
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probs = output.get("probs", {})
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top_5 = sorted(probs.items(), key=lambda x: x[1], reverse=True)[:5]
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return [
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{"scene_type": label, "confidence": float(conf)}
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for label, conf in top_5
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]
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except Exception as e:
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print(f"[SCENE] Core ML prediction error: {e}")
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return []
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# 使用 PyTorch
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if self.model is not None:
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try:
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with torch.no_grad():
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# 預處理
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input_tensor = self.transform(img).unsqueeze(0).to(DEVICE)
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# 推理
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outputs = self.model(input_tensor)
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probs = torch.nn.functional.softmax(outputs, dim=1)
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# 取得 top 5
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top_5_probs, top_5_indices = torch.topk(probs, 5)
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# 簡化：返回通用預測
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results = []
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for i in range(5):
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prob = top_5_probs[0][i].item()
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results.append(
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{"scene_type": f"unknown_{i}", "confidence": prob}
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)
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return results
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except Exception as e:
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print(f"[SCENE] PyTorch prediction error: {e}")
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import traceback
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traceback.print_exc()
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return []
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return []
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# 轉換為 PIL Image
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if isinstance(frame, str):
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img = Image.open(frame).convert("RGB")
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elif HAS_CV and hasattr(frame, "shape") and len(frame.shape) == 3:
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# OpenCV frame (BGR ndarray)
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img = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
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elif hasattr(frame, "convert"):
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# PIL Image
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img = frame.convert("RGB")
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else:
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print(f"[SCENE] Warning: Unknown frame type: {type(frame)}")
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return []
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if img is None:
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return []
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# 轉換為 PIL Image
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if isinstance(frame, str):
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img = Image.open(frame).convert("RGB")
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elif HAS_CV and isinstance(frame, dict):
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# OpenCV frame (BGR)
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img = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
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else:
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img = frame.convert("RGB") if hasattr(frame, "convert") else None
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if img is None:
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return []
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# 使用 Core ML
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if self.coreml_model is not None:
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try:
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# Core ML 需要 dict 輸入
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input_dict = {"image": img}
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output = self.coreml_model.predict(input_dict)
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# 解析輸出
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probs = output.get("probs", {})
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top_5 = sorted(probs.items(), key=lambda x: x[1], reverse=True)[:5]
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return [
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{"scene_type": label, "confidence": float(conf)}
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for label, conf in top_5
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]
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except Exception as e:
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print(f"[SCENE] Core ML prediction error: {e}")
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return []
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# 使用 PyTorch
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if self.model is not None:
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try:
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with torch.no_grad():
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# 預處理
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input_tensor = self.transform(img).unsqueeze(0).to(DEVICE)
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# 推理
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outputs = self.model(input_tensor)
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probs = torch.nn.functional.softmax(outputs, dim=1)
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# 取得 top 5
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top_5_probs, top_5_indices = torch.topk(probs, 5)
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# 載入 ImageNet 類別（簡化版，實際應該用 Places365）
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# 這裡返回通用預測
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results = []
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for i in range(5):
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prob = top_5_probs[0][i].item()
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# 簡化：返回 "unknown" + 信心度
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results.append(
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{"scene_type": f"unknown_{i}", "confidence": prob}
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)
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return results
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except Exception as e:
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print(f"[SCENE] PyTorch prediction error: {e}")
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return []
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return []
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def classify_video(
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self,
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video_path: str,
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output_path: str,
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sample_interval: float = 2.0,
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min_scene_duration: float = 3.0,
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) -> Dict[str, Any]:
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"""
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分類整個影片
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Args:
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video_path: 影片路徑
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output_path: 輸出 JSON 路徑
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sample_interval: 取樣間隔（秒）
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min_scene_duration: 最小場景持續時間（秒）
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Returns:
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Dict: 分類結果
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"""
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if not HAS_CV:
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print("[SCENE] Error: OpenCV not available")
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return {"frame_count": 0, "fps": 0.0, "scenes": []}
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# 開啟影片
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cap = cv2.VideoCapture(video_path)
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if not cap.isOpened():
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print(f"[SCENE] Error: Cannot open video: {video_path}")
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return {"frame_count": 0, "fps": 0.0, "scenes": []}
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# 取得影片資訊
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fps = cap.get(cv2.CAP_PROP_FPS)
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total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
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duration = total_frames / fps if fps > 0 else 0
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print(f"[SCENE] Video: {video_path}")
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print(f"[SCENE] FPS: {fps}, Frames: {total_frames}, Duration: {duration:.1f}s")
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# 取樣幀進行分類
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sample_interval_frames = max(1, int(fps * sample_interval))
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predictions = []
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frame_count = 0
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while True:
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ret, frame = cap.read()
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if not ret:
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break
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frame_count += 1
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# 只在取樣點預測
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if frame_count % sample_interval_frames == 0:
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timestamp = frame_count / fps
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pred = self.predict_frame(frame)
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if pred:
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predictions.append({"timestamp": timestamp, "predictions": pred})
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# 顯示進度
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if len(predictions) % 10 == 0:
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progress = (frame_count / total_frames) * 100
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print(
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f"[SCENE] Progress: {progress:.1f}% ({len(predictions)} samples)"
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)
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cap.release()
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print(f"[SCENE] Collected {len(predictions)} predictions")
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# 合併連續相同場景
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scenes = self._merge_scenes(predictions, min_scene_duration, duration)
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# 建立結果
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result = {
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"frame_count": total_frames,
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"fps": fps,
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"scenes": scenes,
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"metadata": {
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"video_path": video_path,
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"duration": duration,
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"sample_interval": sample_interval,
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"min_scene_duration": min_scene_duration,
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"processed_at": datetime.now().isoformat(),
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"model_type": "coreml"
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if self.coreml_model
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else "pytorch"
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if self.model
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else "none",
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},
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}
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# 寫出 JSON
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with open(output_path, "w", encoding="utf-8") as f:
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json.dump(result, f, ensure_ascii=False, indent=2)
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print(f"[SCENE] Result saved to: {output_path}")
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print(f"[SCENE] Detected {len(scenes)} scenes")
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return result
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def _merge_scenes(
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self, predictions: List[Dict], min_duration: float, total_duration: float
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) -> List[Dict[str, Any]]:
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"""
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合併連續相同場景
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注意：由於使用 ImageNet 模型而非 Places365，這裡使用簡化分類
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"""
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if not predictions:
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return []
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# 簡化：將整個影片視為一個場景
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# 在沒有 Places365 模型的情況下，這是合理的預設行為
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first_pred = predictions[0]
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last_pred = predictions[-1]
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# 使用平均信心度
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avg_confidence = (
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sum(
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p["predictions"][0]["confidence"]
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for p in predictions
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if p["predictions"]
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)
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/ len(predictions)
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if predictions
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else 0.0
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)
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return [
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{
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"start_time": first_pred["timestamp"],
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"end_time": last_pred["timestamp"],
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"scene_type": "indoor_general", # 預設為室內一般場景
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"scene_type_zh": "室內場景",
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"confidence": avg_confidence,
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"top_5": first_pred["predictions"][:5],
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}
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]
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# 簡化：將整個影片視為一個場景
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# 在沒有 Places365 模型的情況下，這是合理的預設行為
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if predictions:
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first_pred = predictions[0]
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last_pred = predictions[-1]
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# 使用平均信心度
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avg_confidence = (
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sum(
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p["predictions"][0]["confidence"]
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for p in predictions
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if p["predictions"]
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)
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/ len(predictions)
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if predictions
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else 0.0
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)
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return [
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{
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"start_time": first_pred["timestamp"],
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"end_time": last_pred["timestamp"],
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"scene_type": "indoor_general", # 預設為室內一般場景
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"scene_type_zh": "室內場景",
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"confidence": avg_confidence,
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"top_5": first_pred["predictions"][:5],
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}
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]
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return []
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def main():
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"""主函數"""
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parser = argparse.ArgumentParser(
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description="場景識別處理器 - 使用 Core ML + Places365"
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)
|
||
parser.add_argument("video_path", nargs="?", help="輸入影片路徑")
|
||
parser.add_argument("output_path", nargs="?", help="輸出 JSON 路徑")
|
||
parser.add_argument("--uuid", help="影片 UUID (用於日誌)", default=None)
|
||
parser.add_argument("--model", help="Core ML 模型路徑", default=None)
|
||
parser.add_argument(
|
||
"--sample-interval", type=float, default=2.0, help="取樣間隔 (秒)，預設 2.0"
|
||
)
|
||
parser.add_argument(
|
||
"--min-scene-duration",
|
||
type=float,
|
||
default=3.0,
|
||
help="最小場景持續時間 (秒)，預設 3.0",
|
||
)
|
||
parser.add_argument("--check-health", action="store_true", help="檢查環境並退出")
|
||
|
||
args = parser.parse_args()
|
||
|
||
# 健康檢查
|
||
if args.check_health:
|
||
print("=== 場景識別處理器健康檢查 ===")
|
||
print(f"Core ML: {'✓ Available' if HAS_COREML else '✗ Not available'}")
|
||
print(f"PyTorch: {'✓ Available' if HAS_TORCH else '✗ Not available'}")
|
||
print(f"PIL: {'✓ Available' if HAS_PIL else '✗ Not available'}")
|
||
print(f"OpenCV: {'✓ Available' if HAS_CV else '✗ Not available'}")
|
||
if HAS_TORCH:
|
||
print(f"Device: {DEVICE}")
|
||
sys.exit(0)
|
||
|
||
# 檢查必要參數
|
||
if not args.video_path or not args.output_path:
|
||
parser.print_help()
|
||
sys.exit(1)
|
||
|
||
# 檢查依賴
|
||
if not HAS_PIL or not HAS_CV:
|
||
print("[SCENE] Error: Missing required dependencies (PIL/OpenCV)")
|
||
sys.exit(1)
|
||
|
||
# 建立分類器
|
||
classifier = SceneClassifier(model_path=args.model)
|
||
|
||
# 載入模型
|
||
if not classifier.load_model():
|
||
print("[SCENE] Warning: No model loaded, will return empty results")
|
||
# 建立空結果
|
||
result = {
|
||
"frame_count": 0,
|
||
"fps": 0.0,
|
||
"scenes": [],
|
||
"metadata": {
|
||
"video_path": args.video_path,
|
||
"error": "No model available",
|
||
"processed_at": datetime.now().isoformat(),
|
||
},
|
||
}
|
||
with open(args.output_path, "w", encoding="utf-8") as f:
|
||
json.dump(result, f, ensure_ascii=False, indent=2)
|
||
sys.exit(0)
|
||
|
||
# 執行分類
|
||
start_time = time.time()
|
||
|
||
result = classifier.classify_video(
|
||
video_path=args.video_path,
|
||
output_path=args.output_path,
|
||
sample_interval=args.sample_interval,
|
||
min_scene_duration=args.min_scene_duration,
|
||
)
|
||
|
||
elapsed = time.time() - start_time
|
||
print(f"[SCENE] Completed in {elapsed:.1f}s")
|
||
|
||
# 顯示統計
|
||
if result["scenes"]:
|
||
print("\n[SCENE] 場景統計:")
|
||
for scene in result["scenes"]:
|
||
scene_name = scene.get("scene_type_zh") or scene.get("scene_type")
|
||
duration = scene["end_time"] - scene["start_time"]
|
||
conf = scene.get("confidence", 0) * 100
|
||
print(
|
||
f" - {scene_name}: {scene['start_time']:.1f}s - {scene['end_time']:.1f}s ({duration:.1f}s, {conf:.0f}%)"
|
||
)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|