282 lines
9.9 KiB
Python
282 lines
9.9 KiB
Python
import os
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import cv2
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import torch
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import numpy as np
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from redis import Redis
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from ultralytics import YOLO
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import json
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from kafka import KafkaConsumer
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import threading
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import redis
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from config import *
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# 配置
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MODEL_PATH = YOLO_MODEL_PATH
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KAFKA_BROKER = KAFKA_BROKER
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KAFKA_TOPIC = WORKER_CONFIGS["yolo"]["kafka_topic"]
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KAFKA_GROUP_ID = f"yolo_{KAFKA_GROUP_ID_PREFIX}"
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REDIS_HOST = REDIS_HOST
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REDIS_PORT = REDIS_PORT
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REDIS_PASSWORD = REDIS_PASSWORD
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REDIS_DB = WORKER_CONFIGS["yolo"]["redis_db"] # Worker使用的Redis DB
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MAIN_REDIS_DB = MAIN_REDIS_DB # 主Redis DB
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UPLOAD_DIR = UPLOAD_DIR
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RESULT_DIR = RESULT_DIR
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# 确保目录存在
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os.makedirs(UPLOAD_DIR, exist_ok=True)
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os.makedirs(RESULT_DIR, exist_ok=True)
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KAFKA_TOPIC = 'yolo'
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# 初始化 Kafka Consumer
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consumer = KafkaConsumer(
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KAFKA_TOPIC,
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bootstrap_servers=[KAFKA_BROKER],
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group_id=KAFKA_GROUP_ID,
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auto_offset_reset='earliest',
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enable_auto_commit=True,
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value_deserializer=lambda x: json.loads(x.decode('utf-8'))
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)
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# 初始化 Redis
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redis_client = Redis(
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host=REDIS_HOST,
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port=REDIS_PORT,
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password=REDIS_PASSWORD,
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db=REDIS_DB
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)
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main_redis_client = Redis(
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host=REDIS_HOST,
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port=REDIS_PORT,
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password=REDIS_PASSWORD,
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db=MAIN_REDIS_DB
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)
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# YOLO detector class
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class yoloDetector:
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def __init__(self, model_path):
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self.model = YOLO(model_path)
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def detect(self, frame):
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results = self.model(frame)
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return results
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def format_results(self, results, original_shape):
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formatted_results = []
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for r in results:
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boxes = r.boxes
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for box in boxes:
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x1, y1, x2, y2 = box.xyxy[0].tolist()
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x1, x2 = [x * original_shape[1] / 640 for x in [x1, x2]]
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y1, y2 = [y * original_shape[0] / 640 for y in [y1, y2]]
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conf = box.conf.item()
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cls = int(box.cls.item())
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name = self.model.names[cls]
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formatted_results.append({
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"class": name,
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"confidence": conf,
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"bbox": [x1, y1, x2, y2]
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})
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return formatted_results
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def draw_results(self, frame, formatted_results):
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for result in formatted_results:
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x1, y1, x2, y2 = map(int, result['bbox'])
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name = result['class']
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conf = result['confidence']
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cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
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label = f"{name} {conf:.2f}"
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(text_width, text_height), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 1, 2)
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cv2.rectangle(frame, (x1, y1 - text_height - 5), (x1 + text_width, y1), (0, 255, 0), -1)
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cv2.putText(frame, label, (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2)
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return frame
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detector = yoloDetector(MODEL_PATH)
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def process_image(image_path):
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try:
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original_img = cv2.imread(image_path)
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original_shape = original_img.shape
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img = cv2.cvtColor(original_img, cv2.COLOR_BGR2RGB)
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img = cv2.resize(img, (640, 640))
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img = img.transpose((2, 0, 1))
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img = np.ascontiguousarray(img)
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img = torch.from_numpy(img).float()
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img /= 255.0
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img = img.unsqueeze(0)
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results = detector.detect(img)
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json_results = detector.format_results(results, original_shape)
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annotated_img = detector.draw_results(original_img, json_results)
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return json_results, annotated_img
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except Exception as e:
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print(f"处理图像时出错: {str(e)}")
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return None, None
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def process_video(video_path):
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try:
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cap = cv2.VideoCapture(video_path)
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frame_count = 0
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json_results = []
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fps = int(cap.get(cv2.CAP_PROP_FPS))
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width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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original_shape = (height, width)
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out = cv2.VideoWriter(video_path.replace(UPLOAD_DIR, RESULT_DIR), cv2.VideoWriter_fourcc(*'mp4v'), fps, (width, height))
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while cap.isOpened():
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ret, frame = cap.read()
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if not ret:
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break
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if frame_count % fps == 0:
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preprocessed_frame = preprocess_frame(frame)
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results = detector.detect(preprocessed_frame)
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frame_json_results = detector.format_results(results, original_shape)
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json_results.append({"frame": frame_count, "detections": frame_json_results})
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annotated_frame = detector.draw_results(frame, frame_json_results)
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out.write(annotated_frame)
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frame_count += 1
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cap.release()
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out.release()
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return json_results
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except Exception as e:
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print(f"处理视频时出错: {str(e)}")
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return None
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def preprocess_frame(frame):
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frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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frame_resized = cv2.resize(frame_rgb, (640, 640))
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frame_transposed = frame_resized.transpose((2, 0, 1))
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frame_contiguous = np.ascontiguousarray(frame_transposed)
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frame_tensor = torch.from_numpy(frame_contiguous).float()
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frame_normalized = frame_tensor / 255.0
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frame_batched = frame_normalized.unsqueeze(0)
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return frame_batched
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def process_task():
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print("开始处理任务,等待Kafka消息...")
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for message in consumer:
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print(f"收到Kafka消息: topic={message.topic}, partition={message.partition}, offset={message.offset}")
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task = message.value
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task_id = task['task_id']
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filename = task['filename']
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file_type = task['file_type']
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print(f"解析任务信息: ID={task_id}, 文件名={filename}, 类型={file_type}")
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file_path = os.path.join(UPLOAD_DIR, filename)
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# 检查键类型并更新状态
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task_key = f"task:{task_id}"
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try:
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key_type = main_redis_client.type(task_key)
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if key_type != b'hash':
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main_redis_client.delete(task_key)
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main_redis_client.hset(task_key, "status", "processing")
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print(f"任务 {task_id} 状态更新为 'processing'")
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except redis.exceptions.ResponseError as e:
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print(f"更新任务 {task_id} 状态时出错: {str(e)}")
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continue # 跳过这个任务，继续处理下一个
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try:
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if file_type == "image":
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print(f"开始处理图像: {filename}")
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json_results, annotated_img = process_image(file_path)
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if json_results and annotated_img is not None:
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result_filename = f"yolo_{filename}"
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result_path = os.path.join(RESULT_DIR, result_filename)
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cv2.imwrite(result_path, annotated_img)
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redis_client.hmset(f"yolo_result:{task_id}", {
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"result": json.dumps(json_results),
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"result_file": result_filename
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})
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main_redis_client.hmset(f"task:{task_id}", {
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"status": "completed",
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"result_type": "yolo",
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"result_key": f"yolo_result:{task_id}"
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})
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print(f"图像 {filename} 处理完成,结果已保存")
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else:
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print(f"图像 {filename} 处理失败")
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main_redis_client.hset(f"task:{task_id}", "status", "failed")
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else: # video
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print(f"开始处理视频: {filename}")
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json_results = process_video(file_path)
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if json_results:
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result_filename = f"yolo_{filename}"
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redis_client.hmset(f"yolo_result:{task_id}", {
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"result": json.dumps(json_results),
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"result_file": result_filename
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})
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main_redis_client.hmset(f"task:{task_id}", {
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"status": "completed",
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"result_type": "yolo",
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"result_key": f"yolo_result:{task_id}"
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})
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print(f"视频 {filename} 处理完成,结果已保存")
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else:
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print(f"视频 {filename} 处理失败")
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main_redis_client.hset(f"task:{task_id}", "status", "failed")
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except Exception as e:
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print(f"处理任务 {task_id} 时出错: {str(e)}")
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main_redis_client.hmset(f"task:{task_id}", {
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"status": "failed",
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"error": str(e)
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})
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print(f"任务 {task_id} 处理完毕,等待下一个Kafka消息...")
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def listen_redis_changes():
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pubsub = redis_client.pubsub()
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pubsub.psubscribe('__keyspace@3__:yolo_result:*') # 监听所有yolo_result键的变化
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for message in pubsub.listen():
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if message['type'] == 'pmessage':
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key = message['channel'].decode('utf-8').split(':')[-1]
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operation = message['data'].decode('utf-8')
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if operation == 'hmset':
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value = redis_client.hgetall(f"yolo_result:{key}")
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if value:
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result = {k.decode(): v.decode() for k, v in value.items()}
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print(f"Result update for task {key}: {result}")
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if __name__ == "__main__":
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print("YOLO处理程序启动...")
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# 启动处理任务的线程
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task_thread = threading.Thread(target=process_task, daemon=True)
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task_thread.start()
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print("任务处理线程已启动")
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# 启动Redis监听线程
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redis_thread = threading.Thread(target=listen_redis_changes, daemon=True)
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redis_thread.start()
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print("Redis监听线程已启动")
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print("主程序进入等待状态...")
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# 保持主线程运行
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task_thread.join()
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redis_thread.join() |