删除 batch_detector.py
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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批量视频检测器
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简化版本，专注于高效处理大量视频文件
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"""
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import os
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import cv2
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import numpy as np
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from pathlib import Path
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from ultralytics import YOLO
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import json
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import time
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from datetime import datetime
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from config import config, DetectionConfig
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from PIL import Image, ImageDraw, ImageFont
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class BatchVideoDetector:
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def __init__(self, model_path=None, confidence=0.5):
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"""
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初始化批量视频检测器
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Args:
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model_path (str): 模型路径，如果为None则使用配置文件中的路径
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confidence (float): 置信度阈值
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"""
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self.model_path = model_path or config.get_model_path()
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self.confidence = confidence
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# 创建必要文件夹
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config.create_folders()
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# 加载模型
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self.load_model()
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# 时间间隔过滤变量
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self.last_saved_timestamp = 0
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# 初始化场景类别变量
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self.last_scene_classes = set()
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# 统计信息
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self.stats = {
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'start_time': None,
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'end_time': None,
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'total_videos': 0,
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'processed_videos': 0,
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'total_frames': 0,
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'detected_frames': 0,
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'saved_images': 0,
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'filtered_frames': 0,
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'errors': []
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}
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# 视频详细信息列表
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self.video_details = []
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def load_model(self):
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"""加载YOLO模型"""
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try:
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print(f"正在加载模型: {self.model_path}")
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self.model = YOLO(self.model_path)
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print(f"模型加载成功!")
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# 检查是否有缓存的模型信息
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model_filename = os.path.basename(self.model_path)
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info_path = os.path.join(os.path.dirname(self.model_path), f"{os.path.splitext(model_filename)[0]}_info.json")
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cached_info_loaded = False
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if os.path.exists(info_path):
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try:
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with open(info_path, 'r', encoding='utf-8') as f:
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import json
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cached_info = json.load(f)
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if cached_info.get('analyzed', False):
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print(f"使用缓存的模型信息，跳过重复分析")
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print(f"模型类别数量: {cached_info.get('class_count', 0)}")
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print(f"检测场景类型: {list(cached_info.get('classes', {}).values())}")
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cached_info_loaded = True
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except Exception as e:
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print(f"读取缓存模型信息失败: {e}，将重新分析")
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# 如果没有缓存信息，则进行完整分析
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if not cached_info_loaded:
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# 动态加载模型类别信息到配置中
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DetectionConfig.load_model_classes(self.model_path)
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if hasattr(self.model, 'names'):
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print(f"\n=== 模型检测场景分析 ===")
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print(f"模型类别数量: {len(self.model.names)}")
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print(f"模型原始类别: {list(self.model.names.values())}")
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print(f"\n=== 中文类别映射 ===")
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for class_id, class_name in self.model.names.items():
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class_name_cn = config.get_class_name_cn(class_id)
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print(f"类别 {class_id}: {class_name} -> {class_name_cn}")
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print(f"\n=== 检测场景总结 ===")
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scene_types = list(DetectionConfig.get_all_classes().values())
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print(f"本模型可检测的场景类型: {scene_types}")
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print(f"检测置信度阈值: {self.confidence}")
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print("=" * 40)
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else:
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# 使用缓存信息更新配置
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DetectionConfig.load_model_classes(self.model_path)
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except Exception as e:
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print(f"模型加载失败: {e}")
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raise
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def get_video_files(self, input_folder="input_videos"):
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"""获取所有视频文件"""
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input_path = Path(input_folder)
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if not input_path.exists():
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print(f"输入文件夹不存在: {input_folder}")
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return []
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video_files = []
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for ext in config.SUPPORTED_VIDEO_FORMATS:
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video_files.extend(input_path.rglob(f"*{ext}"))
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print(f"找到 {len(video_files)} 个视频文件")
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return video_files
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def detect_and_save_frame(self, frame, frame_number, timestamp, video_name, output_dir):
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"""检测单帧并保存结果"""
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try:
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# 进行检测
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results = self.model(frame, conf=self.confidence, verbose=False)
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result = results[0] if results else None
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# 如果检测到目标
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if result is not None and result.boxes is not None and len(result.boxes) > 0:
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# 获取当前帧的检测类别
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current_scene_classes = set()
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for box in result.boxes:
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class_id = int(box.cls[0].cpu().numpy())
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current_scene_classes.add(class_id)
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# 检查时间间隔
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time_diff = timestamp - self.last_saved_timestamp
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# 如果时间间隔小于最小间隔，检查是否为新场景
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if time_diff < config.MIN_FRAME_INTERVAL and self.last_saved_timestamp > 0:
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# 如果当前帧的场景类别与上一帧相同，则过滤
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if hasattr(self, 'last_scene_classes') and current_scene_classes == self.last_scene_classes:
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self.stats['filtered_frames'] += 1
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# 仍然返回检测信息，但不保存图像
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detections = []
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for box in result.boxes:
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class_id = int(box.cls[0].cpu().numpy())
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confidence = float(box.conf[0].cpu().numpy())
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bbox = box.xyxy[0].cpu().numpy().tolist()
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class_name = self.model.names.get(class_id, f"class_{class_id}")
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class_name_cn = config.get_class_name_cn(class_id)
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detections.append({
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'class_id': class_id,
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'class_name': class_name,
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'class_name_cn': class_name_cn,
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'confidence': confidence,
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'bbox': bbox,
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'filtered': True
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})
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return {
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'frame_number': frame_number,
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'timestamp': timestamp,
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'detections': detections,
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'filtered': True
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}
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# 更新最后检测到的场景类别
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self.last_scene_classes = current_scene_classes
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# 更新最后保存的时间戳
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self.last_saved_timestamp = timestamp
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# 创建文件名
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base_name = f"{video_name}_frame_{frame_number:06d}_t{timestamp:.2f}s"
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# 获取检测到的场景类别，用于分类保存
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detected_scenes = set()
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for box in result.boxes:
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class_id = int(box.cls[0].cpu().numpy())
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class_name_cn = config.get_class_name_cn(class_id)
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detected_scenes.add(class_name_cn)
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# 为每个检测到的场景创建对应的文件夹并保存图片
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for scene_name in detected_scenes:
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# 创建视频名称文件夹
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video_folder = output_dir / video_name
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video_folder.mkdir(parents=True, exist_ok=True)
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# 创建场景文件夹
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scene_folder = video_folder / scene_name
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scene_folder.mkdir(parents=True, exist_ok=True)
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# 保存原始帧
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if config.SAVE_ORIGINAL_FRAMES:
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original_path = scene_folder / f"{base_name}_original.jpg"
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cv2.imwrite(str(original_path), frame, [cv2.IMWRITE_JPEG_QUALITY, config.IMAGE_QUALITY])
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# 绘制并保存标注帧
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if config.SAVE_ANNOTATED_FRAMES:
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annotated_frame = self.draw_detections(frame.copy(), result)
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annotated_path = scene_folder / f"{base_name}_detected.jpg"
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cv2.imwrite(str(annotated_path), annotated_frame, [cv2.IMWRITE_JPEG_QUALITY, config.IMAGE_QUALITY])
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# 收集检测信息
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detections = []
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for box in result.boxes:
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class_id = int(box.cls[0].cpu().numpy())
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confidence = float(box.conf[0].cpu().numpy())
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bbox = box.xyxy[0].cpu().numpy().tolist()
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class_name = self.model.names.get(class_id, f"class_{class_id}")
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class_name_cn = config.get_class_name_cn(class_id)
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detections.append({
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'class_id': class_id,
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'class_name': class_name,
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'class_name_cn': class_name_cn,
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'confidence': confidence,
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'bbox': bbox
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})
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return {
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'frame_number': frame_number,
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'timestamp': timestamp,
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'detections': detections
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}
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return None
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except Exception as e:
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print(f"处理帧 {frame_number} 时出错: {e}")
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return None
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def draw_detections(self, frame, result):
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"""在帧上绘制检测结果（绘制边界框和中文标注）"""
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if result is None or result.boxes is None:
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return frame
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# 转换为PIL图像
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pil_image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
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draw = ImageDraw.Draw(pil_image)
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# 尝试加载中文字体
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try:
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# Windows系统常用中文字体路径
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font_paths = [
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"C:/Windows/Fonts/simhei.ttf", # 黑体
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"C:/Windows/Fonts/simsun.ttc", # 宋体
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"C:/Windows/Fonts/msyh.ttc", # 微软雅黑
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]
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font = None
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for font_path in font_paths:
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try:
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font = ImageFont.truetype(font_path, 20)
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break
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except:
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continue
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if font is None:
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font = ImageFont.load_default()
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except:
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font = ImageFont.load_default()
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for box in result.boxes:
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# 获取坐标和信息
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x1, y1, x2, y2 = box.xyxy[0].cpu().numpy().astype(int)
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class_id = int(box.cls[0].cpu().numpy())
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confidence = float(box.conf[0].cpu().numpy())
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# 获取颜色和类别名称
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color = config.get_class_color(class_id)
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bg_color = tuple(reversed(color)) # BGR转RGB
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class_name_cn = config.get_class_name_cn(class_id)
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# 绘制边界框
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draw.rectangle([x1, y1, x2, y2], outline=bg_color, width=2)
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# 准备标注文字
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label_text = f"{class_name_cn} {confidence:.2f}"
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# 计算文字背景框大小
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bbox = draw.textbbox((0, 0), label_text, font=font)
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text_width = bbox[2] - bbox[0]
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text_height = bbox[3] - bbox[1]
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# 确保标注不超出图像边界
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label_x = max(0, x1)
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label_y = max(0, y1 - text_height - 5)
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if label_y < 0:
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label_y = y1 + 5
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# 绘制文字背景
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draw.rectangle(
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[label_x, label_y, label_x + text_width + 4, label_y + text_height + 4],
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fill=bg_color
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)
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# 绘制文字（白色）
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draw.text((label_x + 2, label_y + 2), label_text, fill=(255, 255, 255), font=font)
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# 转换回OpenCV格式
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frame = cv2.cvtColor(np.array(pil_image), cv2.COLOR_RGB2BGR)
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return frame
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def process_video(self, video_path, output_base_dir="output_frames"):
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"""处理单个视频"""
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print(f"\n开始处理: {video_path.name}")
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# 创建输出目录
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output_dir = Path(output_base_dir) / video_path.stem
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output_dir.mkdir(parents=True, exist_ok=True)
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# 重置时间间隔过滤变量，确保每个视频独立计算时间间隔
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self.last_saved_timestamp = 0
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# 重置场景类别变量，确保每个视频独立分类场景
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self.last_scene_classes = set()
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# 打开视频
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cap = cv2.VideoCapture(str(video_path))
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if not cap.isOpened():
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error_msg = f"无法打开视频: {video_path}"
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print(error_msg)
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self.stats['errors'].append(error_msg)
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return
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# 获取视频信息
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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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print(f"视频信息: {total_frames} 帧, {fps:.2f} FPS")
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# 处理统计
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frame_count = 0
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detected_frames = 0
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saved_images = 0
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filtered_frames = 0
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all_detections = []
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start_time = time.time()
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try:
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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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timestamp = frame_count / fps if fps > 0 else frame_count
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# 显示进度
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if frame_count % config.PROGRESS_INTERVAL == 0:
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progress = (frame_count / total_frames) * 100
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elapsed = time.time() - start_time
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fps_current = frame_count / elapsed
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print(f"进度: {progress:.1f}% ({frame_count}/{total_frames}), 处理速度: {fps_current:.1f} FPS")
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# 更新进度回调
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if hasattr(self, 'update_progress'):
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self.update_progress(f"处理视频帧", None, None, None, frame_count)
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# 检测并保存
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detection_result = self.detect_and_save_frame(
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frame, frame_count, timestamp, video_path.stem, output_dir
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)
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if detection_result:
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detected_frames += 1
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all_detections.append(detection_result)
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# 检查是否是被过滤的帧
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if detection_result.get('filtered', False):
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filtered_frames += 1
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else:
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# 计算保存的图片数量
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if config.SAVE_ORIGINAL_FRAMES:
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saved_images += 1
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if config.SAVE_ANNOTATED_FRAMES:
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saved_images += 1
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finally:
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cap.release()
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# 保存检测结果JSON
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if config.SAVE_DETECTION_JSON and all_detections:
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json_path = output_dir / f"{video_path.stem}_detections.json"
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detection_summary = {
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'video_name': video_path.name,
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'total_frames': frame_count,
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'detected_frames': detected_frames,
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'fps': fps,
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'detections': all_detections,
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'processing_time': time.time() - start_time
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}
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with open(json_path, 'w', encoding='utf-8') as f:
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json.dump(detection_summary, f, ensure_ascii=False, indent=2)
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# 更新统计
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self.stats['processed_videos'] += 1
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self.stats['total_frames'] += frame_count
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self.stats['detected_frames'] += detected_frames
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self.stats['saved_images'] += saved_images
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self.stats['filtered_frames'] += filtered_frames
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processing_time = time.time() - start_time
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print(f"处理完成: {video_path.name}")
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print(f"检测到目标的帧数: {detected_frames}/{frame_count}, 保存图片: {saved_images} 张")
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print(f"时间间隔过滤帧数: {filtered_frames} 帧 (间隔阈值: {config.MIN_FRAME_INTERVAL}秒)")
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print(f"处理时间: {processing_time:.2f} 秒")
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# 记录视频详细信息
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video_info = {
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'video_name': video_path.name,
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'video_path': str(video_path),
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'total_frames': frame_count,
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'detected_frames': detected_frames,
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'saved_images': saved_images,
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'filtered_frames': filtered_frames,
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'processing_time': processing_time,
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'fps': fps,
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'duration': frame_count / fps if fps > 0 else 0,
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'output_directory': str(output_dir)
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}
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self.video_details.append(video_info)
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||||
|
||||
def process_all_videos(self, input_folder="input_videos", output_folder="output_frames"):
|
||||
"""批量处理所有视频"""
|
||||
self.stats['start_time'] = datetime.now()
|
||||
|
||||
video_files = self.get_video_files(input_folder)
|
||||
if not video_files:
|
||||
print("没有找到视频文件")
|
||||
return
|
||||
|
||||
self.stats['total_videos'] = len(video_files)
|
||||
|
||||
print(f"\n开始批量处理 {len(video_files)} 个视频文件")
|
||||
print(f"模型: {self.model_path}")
|
||||
print(f"置信度阈值: {self.confidence}")
|
||||
print(f"输出目录: {output_folder}")
|
||||
print("=" * 50)
|
||||
|
||||
for i, video_path in enumerate(video_files, 1):
|
||||
print(f"\n[{i}/{len(video_files)}] 处理视频")
|
||||
self.process_video(video_path, output_folder)
|
||||
|
||||
self.stats['end_time'] = datetime.now()
|
||||
report_path = self.save_final_report(output_folder)
|
||||
self.print_summary()
|
||||
return report_path
|
||||
|
||||
def save_final_report(self, output_folder):
|
||||
"""保存最终批处理报告"""
|
||||
timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
|
||||
report_path = Path(output_folder) / f"batch_detection_report_{timestamp}.json"
|
||||
|
||||
# 计算总处理时间
|
||||
total_seconds = 0
|
||||
if self.stats['start_time'] and self.stats['end_time']:
|
||||
total_seconds = (self.stats['end_time'] - self.stats['start_time']).total_seconds()
|
||||
|
||||
# 创建报告数据
|
||||
report_data = {
|
||||
'timestamp': timestamp,
|
||||
'model_path': self.model_path,
|
||||
'confidence_threshold': self.confidence,
|
||||
'frame_interval_threshold': config.MIN_FRAME_INTERVAL,
|
||||
'filter_strategy': '相同场景在指定时间间隔内只保存一次',
|
||||
'statistics': {
|
||||
'total_videos': self.stats['total_videos'],
|
||||
'processed_videos': self.stats['processed_videos'],
|
||||
'total_frames': self.stats['total_frames'],
|
||||
'detected_frames': self.stats['detected_frames'],
|
||||
'filtered_frames': self.stats['filtered_frames'],
|
||||
'saved_images': self.stats['saved_images'],
|
||||
'processing_time_seconds': total_seconds
|
||||
},
|
||||
'videos': self.video_details,
|
||||
'errors': self.stats['errors']
|
||||
}
|
||||
|
||||
# 保存为JSON
|
||||
with open(report_path, 'w', encoding='utf-8') as f:
|
||||
json.dump(report_data, f, ensure_ascii=False, indent=2)
|
||||
|
||||
print(f"\n批处理报告已保存: {report_path}")
|
||||
return str(report_path)
|
||||
|
||||
def print_summary(self):
|
||||
"""打印处理摘要"""
|
||||
if not self.stats['end_time'] or not self.stats['start_time']:
|
||||
return
|
||||
|
||||
total_time = (self.stats['end_time'] - self.stats['start_time']).total_seconds()
|
||||
hours, remainder = divmod(total_time, 3600)
|
||||
minutes, seconds = divmod(remainder, 60)
|
||||
|
||||
print("\n" + "=" * 50)
|
||||
print("批量处理摘要")
|
||||
print("=" * 50)
|
||||
print(f"处理视频数量: {self.stats['processed_videos']}/{self.stats['total_videos']}")
|
||||
print(f"总处理帧数: {self.stats['total_frames']}")
|
||||
print(f"检测到目标的帧数: {self.stats['detected_frames']}")
|
||||
print(f"时间间隔过滤帧数: {self.stats['filtered_frames']} (间隔阈值: {config.MIN_FRAME_INTERVAL}秒)")
|
||||
print(f"过滤策略: 相同场景在{config.MIN_FRAME_INTERVAL}秒内只保存一次")
|
||||
print(f"实际保存的图片数量: {self.stats['saved_images']}")
|
||||
print(f"总处理时间: {int(hours)}小时 {int(minutes)}分钟 {seconds:.2f}秒")
|
||||
|
||||
if self.stats['errors']:
|
||||
print("\n处理过程中的错误:")
|
||||
for error in self.stats['errors']:
|
||||
print(f"- {error}")
|
||||
|
||||
print("=" * 50)
|
||||
|
||||
def main():
|
||||
"""主函数"""
|
||||
import argparse
|
||||
|
||||
parser = argparse.ArgumentParser(description='批量视频检测系统')
|
||||
parser.add_argument('--model', type=str, help='模型文件路径')
|
||||
parser.add_argument('--confidence', type=float, default=0.5, help='置信度阈值')
|
||||
parser.add_argument('--input', type=str, default='input_videos', help='输入视频文件夹')
|
||||
parser.add_argument('--output', type=str, default='output_frames', help='输出图片文件夹')
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
# 创建检测器
|
||||
detector = BatchVideoDetector(
|
||||
model_path=args.model,
|
||||
confidence=args.confidence
|
||||
)
|
||||
|
||||
# 开始批量处理
|
||||
detector.process_all_videos(args.input, args.output)
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
Loading…
Reference in New Issue
Block a user