import os import cv2 import torch import numpy as np from redis import Redis import json from kafka import KafkaConsumer import threading import redis import torch import mediapipe as mp from mediapipe.tasks import python from mediapipe.tasks.python import vision from config import * # 配置 MODEL_PATH = MEDIAPIPE_MODEL_PATH KAFKA_BROKER = KAFKA_BROKER KAFKA_TOPIC = WORKER_CONFIGS["mediapipe"]["kafka_topic"] KAFKA_GROUP_ID = f"mediapipe_{KAFKA_GROUP_ID_PREFIX}" REDIS_HOST = REDIS_HOST REDIS_PORT = REDIS_PORT REDIS_PASSWORD = REDIS_PASSWORD REDIS_DB = WORKER_CONFIGS["mediapipe"]["redis_db"] # Worker使用的Redis DB MAIN_REDIS_DB = MAIN_REDIS_DB # 主Redis DB UPLOAD_DIR = UPLOAD_DIR RESULT_DIR = RESULT_DIR # Ensure directories exist os.makedirs(UPLOAD_DIR, exist_ok=True) os.makedirs(RESULT_DIR, exist_ok=True) # Initialize Kafka consumer = KafkaConsumer( KAFKA_TOPIC, bootstrap_servers=[KAFKA_BROKER], group_id=KAFKA_GROUP_ID, auto_offset_reset='earliest', enable_auto_commit=True, value_deserializer=lambda x: json.loads(x.decode('utf-8')) ) # 初始化 Redis redis_client = Redis( host=REDIS_HOST, port=REDIS_PORT, password=REDIS_PASSWORD, db=REDIS_DB ) main_redis_client = Redis( host=REDIS_HOST, port=REDIS_PORT, password=REDIS_PASSWORD, db=MAIN_REDIS_DB ) class mediapipeEmbedder: def __init__(self, model_path): base_options = python.BaseOptions(model_asset_path=model_path) options = vision.FaceLandmarkerOptions( base_options=base_options, output_face_blendshapes=True, output_facial_transformation_matrixes=True, num_faces=1 ) self.detector = vision.FaceLandmarker.create_from_options(options) def get_mediapipe_landmarks(self, image): mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=image) detection_result = self.detector.detect(mp_image) if detection_result.face_landmarks: return np.array([(lm.x, lm.y, lm.z) for lm in detection_result.face_landmarks[0]]) return None def process_image(self, image_data): img = cv2.imdecode(np.frombuffer(image_data, np.uint8), cv2.IMREAD_COLOR) landmarks = self.get_mediapipe_landmarks(img) if landmarks is not None: # Calculate a more detailed mediapipe embedding embedding = self.calculate_detailed_embedding(landmarks) # Draw landmarks on the image for lm in landmarks: cv2.circle(img, (int(lm[0]*img.shape[1]), int(lm[1]*img.shape[0])), 2, (0,255,0), -1) return { "embedding": embedding, "landmarks": landmarks.tolist() }, img else: return None, img def calculate_detailed_embedding(self, landmarks): # Calculate various statistical features mean = np.mean(landmarks, axis=0) std = np.std(landmarks, axis=0) median = np.median(landmarks, axis=0) min_vals = np.min(landmarks, axis=0) max_vals = np.max(landmarks, axis=0) # Calculate pairwise distances between key facial landmarks nose_tip = landmarks[4] left_eye = landmarks[159] right_eye = landmarks[386] left_mouth = landmarks[61] right_mouth = landmarks[291] eye_distance = np.linalg.norm(left_eye - right_eye) mouth_width = np.linalg.norm(left_mouth - right_mouth) nose_to_mouth = np.linalg.norm(nose_tip - (left_mouth + right_mouth) / 2) # Calculate face shape features face_width = np.max(landmarks[:, 0]) - np.min(landmarks[:, 0]) face_height = np.max(landmarks[:, 1]) - np.min(landmarks[:, 1]) face_depth = np.max(landmarks[:, 2]) - np.min(landmarks[:, 2]) # Combine all features into a single embedding embedding = np.concatenate([ mean, std, median, min_vals, max_vals, [eye_distance, mouth_width, nose_to_mouth, face_width, face_height, face_depth] ]) return embedding.tolist() embedder = mediapipeEmbedder(MODEL_PATH) def process_image(image_data, filename): try: results, annotated_img = embedder.process_image(image_data) if results: # Save annotated image annotated_filename = f"mediapipe_{filename}" annotated_path = os.path.join(RESULT_DIR, annotated_filename) cv2.imwrite(annotated_path, annotated_img) return results, annotated_filename else: print(f"No face landmarks detected in image: {filename}") return None, None except Exception as e: print(f"Error processing image {filename}: {str(e)}") import traceback traceback.print_exc() return None, None def process_video(video_data, filename): try: temp_video_path = os.path.join(UPLOAD_DIR, f"mediapipe_{filename}") with open(temp_video_path, 'wb') as temp_video: temp_video.write(video_data) cap = cv2.VideoCapture(temp_video_path) frame_count = 0 results = [] fps = int(cap.get(cv2.CAP_PROP_FPS)) width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) annotated_filename = f"mediapipe_{filename}" output_path = os.path.join(RESULT_DIR, annotated_filename) out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (width, height)) while cap.isOpened(): ret, frame = cap.read() if not ret: break if frame_count % fps == 0: frame_results, annotated_frame = embedder.process_image(cv2.imencode('.jpg', frame)[1].tobytes()) if frame_results: results.append({"frame": frame_count, "results": frame_results}) else: annotated_frame = frame out.write(annotated_frame) frame_count += 1 cap.release() out.release() os.remove(temp_video_path) return results, annotated_filename except Exception as e: print(f"Error processing video: {str(e)}") return None, None def process_task(): print("开始处理任务,等待Kafka消息...") for message in consumer: print(f"收到Kafka消息: topic={message.topic}, partition={message.partition}, offset={message.offset}") task = message.value task_id = task['task_id'] filename = task['filename'] file_type = task['file_type'] print(f"解析任务信息: ID={task_id}, 文件名={filename}, 类型={file_type}") file_path = os.path.join(UPLOAD_DIR, filename) # 检查键类型并更新状态 task_key = f"task:{task_id}" try: key_type = main_redis_client.type(task_key) if key_type != b'hash': main_redis_client.delete(task_key) main_redis_client.hset(f"task:{task_id}", "status", "processing") print(f"任务 {task_id} 状态更新为 'processing'") except redis.exceptions.ResponseError as e: print(f"更新任务 {task_id} 状态时出错: {str(e)}") continue # 跳过这个任务，继续处理下一个 try: if file_type == "image": print(f"开始处理图像: {filename}") with open(file_path, 'rb') as f: image_data = f.read() json_results, annotated_filename = process_image(image_data, filename) if json_results and annotated_filename is not None: result_path = os.path.join(RESULT_DIR, annotated_filename) redis_client.hset(f"mediapipe_result:{task_id}", mapping={ "result": json.dumps(json_results), "result_file": annotated_filename }) main_redis_client.hset(f"task:{task_id}", "status", "completed") main_redis_client.hset(f"task:{task_id}", "result_type", "mediapipe") main_redis_client.hset(f"task:{task_id}", "result_key", f"mediapipe_result:{task_id}") print(f"图像 {filename} 处理完成,结果已保存") else: print(f"图像 {filename} 处理失败") main_redis_client.hset(f"task:{task_id}", "status", "failed") else: # video print(f"开始处理视频: {filename}") with open(file_path, 'rb') as f: video_data = f.read() json_results, annotated_filename = process_video(video_data, filename) if json_results and annotated_filename: redis_client.hset(f"mediapipe_result:{task_id}", mapping={ "result": json.dumps(json_results), "result_file": annotated_filename }) main_redis_client.hset(f"task:{task_id}", "status", "completed") main_redis_client.hset(f"task:{task_id}", "result_type", "mediapipe") main_redis_client.hset(f"task:{task_id}", "result_key", f"mediapipe_result:{task_id}") print(f"视频 {filename} 处理完成,结果已保存") else: print(f"视频 {filename} 处理失败") main_redis_client.hset(f"task:{task_id}", "status", "failed") except Exception as e: print(f"处理任务 {task_id} 时出错: {str(e)}") main_redis_client.hset(f"task:{task_id}", { "status": "failed", "error": str(e) }) print(f"任务 {task_id} 处理完毕,等待下一个Kafka消息...") def listen_redis_changes(): pubsub = redis_client.pubsub() pubsub.psubscribe('__keyspace@3__:mediapipe_result:*') # 监听所有fall_result键的变化 for message in pubsub.listen(): if message['type'] == 'pmessage': key = message['channel'].decode('utf-8').split(':')[-1] operation = message['data'].decode('utf-8') if operation == 'hset': value = redis_client.hgetall(f"mediapipe_result:{key}") if value: result = {k.decode(): v.decode() for k, v in value.items()} print(f"Result update for task {key}: {result}") if __name__ == "__main__": print("mediapipe处理程序启动...") # 启动处理任务的线程 task_thread = threading.Thread(target=process_task, daemon=True) task_thread.start() print("任务处理线程已启动") # 启动Redis监听线程 redis_thread = threading.Thread(target=listen_redis_changes, daemon=True) redis_thread.start() print("Redis监听线程已启动") print("主程序进入等待状态...") # 保持主线程运行 task_thread.join() redis_thread.join()