# video_face_recognition.py import cv2 import numpy as np import time from insightface.app import FaceAnalysis from typing import List, Dict, Tuple import os class VideoFaceRecognition: """ 视频人脸识别系统 支持实时视频流和视频文件处理 """ def __init__(self, model_name: str = 'buffalo_l', use_gpu: bool = True): # 初始化人脸识别模型 self.app = FaceAnalysis(name=model_name) self.app.prepare( ctx_id=0 if use_gpu else -1, det_thresh=0.3, det_size=(640, 640) ) self.target_embedding = None self.target_id = None self.similarity_threshold = 0.3 # 性能统计 self.frame_count = 0 self.processing_times = [] print(f"✅ 视频人脸识别系统初始化完成 - GPU: {use_gpu}") def set_target_face(self, image_path: str, person_id: str = "target") -> bool: """设置目标人脸""" img = cv2.imread(image_path) if img is None: print(f"❌ 无法读取目标图像: {image_path}") return False faces = self.app.get(img) if not faces: print(f"❌ 目标图像中未检测到人脸: {image_path}") return False self.target_embedding = faces[0].embedding self.target_id = person_id print(f"✅ 目标人脸设置: {person_id}") return True def process_frame(self, frame: np.ndarray) -> Tuple[np.ndarray, List[Dict]]: """ 处理单帧图像 返回: (处理后的帧, 识别结果列表) """ start_time = time.time() # 人脸检测和识别 faces = self.app.get(frame) results = [] for face in faces: similarity = 0.0 if self.target_embedding is not None: # 计算相似度 emb1 = face.embedding / np.linalg.norm(face.embedding) emb2 = self.target_embedding / np.linalg.norm(self.target_embedding) similarity = float(np.dot(emb1, emb2)) result = { 'bbox': face.bbox.astype(int).tolist(), 'similarity': similarity, 'is_match': similarity >= self.similarity_threshold, 'gender': 'Male' if face.gender == 1 else 'Female', 'age': int(face.age), 'det_score': float(face.det_score) } results.append(result) # 在帧上绘制结果 frame = self._draw_detection(frame, result) # 性能统计 processing_time = (time.time() - start_time) * 1000 self.processing_times.append(processing_time) self.frame_count += 1 return frame, results def _draw_detection(self, frame: np.ndarray, result: Dict) -> np.ndarray: """在帧上绘制检测结果""" bbox = result['bbox'] similarity = result['similarity'] is_match = result['is_match'] # 选择颜色 if is_match: color = (0, 255, 0) # 绿色 - 匹配 # elif similarity > self.similarity_threshold/2: # color = (0, 255, 255) # 黄色 - 中等相似度 else: color = (0, 0, 255) # 红色 - 不匹配 # 绘制人脸框 x1, y1, x2, y2 = bbox cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2) # 绘制信息文本 if self.target_id: status = f"MATCH: {similarity:.3f}" if is_match else f"NO MATCH: {similarity:.3f}" # text = f"{self.target_id}: {status}" text = status else: text = f"Similarity: {similarity:.3f}" # 文本背景 text_size = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 0.6, 2)[0] cv2.rectangle(frame, (x1, y1 - text_size[1] - 10), (x1 + text_size[0], y1), color, -1) # 文本 cv2.putText(frame, text, (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2) # 详细信息 info_text = f"{result['gender']}/{result['age']}" cv2.putText(frame, info_text, (x1, y2 + 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1) return frame def process_video_file(self, video_path: str, output_path: str = None, skip_frames: int = 0, show_preview: bool = True): """ 处理视频文件 Args: video_path: 输入视频路径 output_path: 输出视频路径 skip_frames: 跳帧数，用于提高处理速度 show_preview: 是否显示实时预览 """ if not os.path.exists(video_path): print(f"❌ 视频文件不存在: {video_path}") return # 打开视频文件 cap = cv2.VideoCapture(video_path) if not cap.isOpened(): print(f"❌ 无法打开视频文件: {video_path}") return # 获取视频信息 fps = cap.get(cv2.CAP_PROP_FPS) width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) print(f"📹 视频信息: {width}x{height}, {fps:.1f}FPS, 总帧数: {total_frames}") # 设置输出视频 if output_path: fourcc = cv2.VideoWriter_fourcc(*'mp4v') out = cv2.VideoWriter(output_path, fourcc, fps/(skip_frames), (width, height)) else: out = None # 处理视频帧 frame_index = 0 processed_frames = 0 start_time = time.time() print("🚀 开始处理视频...") while True: ret, frame = cap.read() if not ret: break # 跳帧处理 if skip_frames > 0 and frame_index % (skip_frames + 1) != 0: frame_index += 1 continue # 处理当前帧 processed_frame, results = self.process_frame(frame) # 写入输出视频 if out: out.write(processed_frame) # 显示预览 if show_preview: # 添加性能信息 fps_text = f"Frame: {frame_index}/{total_frames} | Faces: {len(results)}" cv2.putText(processed_frame, fps_text, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2) cv2.imshow('Video Face Recognition', processed_frame) if cv2.waitKey(1) & 0xFF == ord('q'): break frame_index += 1 processed_frames += 1 # 进度显示 if frame_index % 30 == 0: progress = (frame_index / total_frames) * 100 print(f"📊 处理进度: {progress:.1f}% ({frame_index}/{total_frames})") # 清理资源 cap.release() if out: out.release() if show_preview: cv2.destroyAllWindows() # 性能统计 total_time = time.time() - start_time avg_processing_time = np.mean(self.processing_times) if self.processing_times else 0 print(f"\n🎉 视频处理完成!") print(f"📊 性能统计:") print(f" 总处理帧数: {processed_frames}") print(f" 总耗时: {total_time:.1f}秒") print(f" 平均每帧: {avg_processing_time:.1f}ms") print(f" 实际FPS: {processed_frames / total_time:.1f}") if output_path: print(f" 输出视频: {output_path}") def process_webcam(self, camera_id: int = 0, output_path: str = None): """ 处理摄像头实时视频流 """ cap = cv2.VideoCapture(camera_id) if not cap.isOpened(): print(f"❌ 无法打开摄像头 {camera_id}") return # 设置摄像头分辨率（可选） cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280) cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 720) # 设置输出视频 if output_path: fps = cap.get(cv2.CAP_PROP_FPS) width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) fourcc = cv2.VideoWriter_fourcc(*'mp4v') out = cv2.VideoWriter(output_path, fourcc, fps, (width, height)) else: out = None print("🎥 开始摄像头实时识别 (按 'q' 退出)...") while True: ret, frame = cap.read() if not ret: print("❌ 无法读取摄像头帧") break # 处理当前帧 processed_frame, results = self.process_frame(frame) # 添加实时信息 current_fps = 1000 / self.processing_times[-1] if self.processing_times else 0 info_text = f"FPS: {current_fps:.1f} | Faces: {len(results)}" cv2.putText(processed_frame, info_text, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2) # 写入输出 if out: out.write(processed_frame) # 显示预览 cv2.imshow('Real-time Face Recognition', processed_frame) # 按'q'退出 if cv2.waitKey(1) & 0xFF == ord('q'): break # 清理资源 cap.release() if out: out.release() cv2.destroyAllWindows() print("✅ 摄像头处理结束") # 使用示例 def main(): # 创建视频识别系统 video_system = VideoFaceRecognition(use_gpu=True) # 设置目标人脸（可选） # target_image = "test_data/register/person1.png" target_image = "test_data/register/sy.jpg" if os.path.exists(target_image): video_system.set_target_face(target_image, "目标人物") # 选择处理模式 print("请选择处理模式:") # print("1. 处理视频文件") # print("2. 实时摄像头") # choice = input("请输入选择 (1 或 2): ").strip() choice = "1"; if choice == "1": # 处理视频文件 video_path = "test_data/video/video_1.mp4" output_path = "test_data/output_video/video_1.mp4" # 性能优化：跳帧处理 skip_frames = 1 # 每2帧处理1帧，提高速度 video_system.process_video_file( video_path=video_path, output_path=output_path, skip_frames=skip_frames, show_preview=True ) elif choice == "2": # 实时摄像头 output_path = "webcam_recording.mp4" # 可选：保存录制 video_system.process_webcam( camera_id=0, output_path=output_path ) else: print("❌ 无效选择") if __name__ == "__main__": main()