优化拉流丢包的问题，增加宽松策略
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@ -1392,6 +1392,228 @@ import asyncio
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from typing import Optional
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from concurrent.futures import ThreadPoolExecutor
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# 使用cv2 拉流，避免了ffmpeg 拉流的rtmp延时3s的问题
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# async def read_rtmp_frames(
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# loop,
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# read_rtmp_frames_executor: ThreadPoolExecutor,
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# video_url: str,
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# device: Optional[MQTTDevice] = None,
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# topic_camera_osd: Optional[str] = None,
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# method_camera_osd: Optional[str] = None,
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# topic_osd_info: Optional[str] = None,
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# method_osd_info: Optional[str] = None,
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# cancel_flag: Optional[asyncio.Event] = None,
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# frame_queue: asyncio.Queue = None,
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# timestamp_frame_queue=None
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# ):
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# """
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# 基于 OpenCV+FFmpeg 读取 RTMP 流帧（优化版：高性能读取，处理损坏帧）
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# ✅ 核心修改：替换原FFmpeg子进程读流为 cv2.VideoCapture 读流，保留所有原有业务逻辑
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# ✅ 核心优化：自适应分辨率、低延迟无残影、断流自动重连、超时保护
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# """
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# max_retries = 20
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# retry_delay = 2
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# pic_count = 0
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# attempt = 0
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# time_start = time.time_ns()
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# frame_count = 0
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#
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# if cancel_flag is None:
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# cancel_flag = asyncio.Event()
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# if timestamp_frame_queue is None:
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# timestamp_frame_queue = []
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#
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# print(f"开始读取RTMP流: {video_url}")
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#
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# while not cancel_flag.is_set() and attempt < max_retries:
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# attempt += 1
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# if cancel_flag.is_set():
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# logger.info("收到停止信号，终止 RTMP 读取")
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# break
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#
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# cap = None
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# width, height = None, None
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# stream_fps = None
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#
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# try:
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# logger.info(f"尝试连接 RTMP 流 (尝试 {attempt}/{max_retries}): {video_url}")
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#
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# # ✅ 核心替换：使用cv2.VideoCapture + CAP_FFMPEG 打开RTMP流，最优参数配置
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# # 切换到线程池执行opencv操作，避免阻塞协程
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# cap = await loop.run_in_executor(
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# read_rtmp_frames_executor,
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# lambda: cv2.VideoCapture(video_url, cv2.CAP_FFMPEG)
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# )
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# # 设置核心参数 - 重中之重，缺一不可
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# await loop.run_in_executor(read_rtmp_frames_executor, lambda: cap.set(cv2.CAP_PROP_OPEN_TIMEOUT_MSEC, 60000))
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# await loop.run_in_executor(read_rtmp_frames_executor, lambda: cap.set(cv2.CAP_PROP_READ_TIMEOUT_MSEC, 50000))
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# await loop.run_in_executor(read_rtmp_frames_executor, lambda: cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)) # 无缓存，低延迟无残影
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# await loop.run_in_executor(read_rtmp_frames_executor, lambda: cap.set(cv2.CAP_PROP_FPS, 25))
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#
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# # 校验流是否成功打开
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# is_opened = await loop.run_in_executor(read_rtmp_frames_executor, lambda: cap.isOpened())
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# if not is_opened:
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# logger.warning(f"尝试 {attempt} 次：打开RTMP流失败，准备重试")
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# await loop.run_in_executor(read_rtmp_frames_executor, lambda: cap.release() if cap else None)
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# await asyncio.sleep(retry_delay)
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# continue
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#
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# # ✅ 自适应获取流的【真实分辨率】，无需手动探测，精准无误差
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# width = await loop.run_in_executor(read_rtmp_frames_executor, lambda: int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)))
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# height = await loop.run_in_executor(read_rtmp_frames_executor, lambda: int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))
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# stream_fps = await loop.run_in_executor(read_rtmp_frames_executor, lambda: cap.get(cv2.CAP_PROP_FPS))
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#
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# # 兜底分辨率，防止异常
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# if width is None or height is None or width == 0 or height == 0:
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# logger.warning("使用默认分辨率 1920x1080")
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# width, height = 1920, 1080
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#
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# logger.info(f"视频分辨率: {width}x{height}, 流帧率: {stream_fps:.1f} FPS")
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# logger.info(f"成功启动 OpenCV+FFmpeg 连接 RTMP 流: {video_url}")
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#
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# # 初始化帧读取状态 (保留原逻辑不变)
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# frame_sequence = 0
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# last_timestamp = time_start
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# consecutive_corrupted_frames = 0 # 连续损坏帧计数
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# max_consecutive_corrupted = 10 # 最大连续损坏帧数
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#
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# while not cancel_flag.is_set():
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# try:
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# # ✅ 核心替换：使用cv2.read()读取帧，线程池执行避免阻塞协程
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# ret, frame = await loop.run_in_executor(
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# read_rtmp_frames_executor,
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# lambda: cap.read()
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# )
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#
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# current_time_ns = time.time_ns()
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# frame_sequence += 1
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# frame_count += 1
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#
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# # 处理帧数据（保留原逻辑完全不变，兼容原有的损坏帧处理）
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# img = None
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# is_corrupted = False
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# print(f"读取 read_rtmp_frames 判断")
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# try:
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# if ret and frame is not None and frame.shape == (height, width, 3):
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# # 完整有效帧处理
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# img = frame.copy()
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# consecutive_corrupted_frames = 0 # 重置连续损坏计数
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# else:
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# # 损坏帧/空帧处理
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# logger.warning(f"帧数据损坏/空帧, 序列: {frame_sequence}")
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# is_corrupted = True
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# consecutive_corrupted_frames += 1
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#
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# # 创建替代帧，保留原逻辑
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# if consecutive_corrupted_frames <= max_consecutive_corrupted:
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# img = np.zeros((height, width, 3), dtype=np.uint8)
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# else:
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# img = np.zeros((height, width, 3), dtype=np.uint8)
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# logger.error(f"连续损坏帧过多 ({consecutive_corrupted_frames})，创建空白帧")
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#
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# except Exception as frame_error:
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# logger.error(f"帧数据处理错误: {frame_error}")
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# # 创建空白帧作为后备，保留原逻辑
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# img = np.zeros((height, width, 3), dtype=np.uint8)
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# is_corrupted = True
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# consecutive_corrupted_frames += 1
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# print(f"读取 read_rtmp_frames 判断1")
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#
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# # 获取OSD信息 - 保留原逻辑完全不变
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# osd_info = None
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# if device and topic_osd_info and method_osd_info:
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# try:
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# osd_msg = device.get_latest_message(topic=topic_osd_info, method=method_osd_info)
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# if osd_msg and hasattr(osd_msg, 'data'):
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# osd_info = Air_Attitude(
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# gimbal_pitch=osd_msg.data.gimbal_pitch,
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# gimbal_roll=osd_msg.data.gimbal_roll,
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# gimbal_yaw=osd_msg.data.gimbal_yaw,
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# height=osd_msg.data.height,
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# latitude=osd_msg.data.latitude,
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# longitude=osd_msg.data.longitude
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# )
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# except Exception as osd_error:
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# logger.warning(f"获取OSD信息失败: {osd_error}")
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# print(f"读取 read_rtmp_frames 判断2")
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#
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# # 放入帧队列 - 保留原逻辑完全不变
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# if img is not None and not frame_queue.full():
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# # 确保时间戳递增
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# if current_time_ns <= last_timestamp:
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# current_time_ns = last_timestamp + 1
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# last_timestamp = current_time_ns
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#
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# # 统计信息 - 保留原逻辑完全不变
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# pic_count += 1
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# if current_time_ns - time_start > 1000000000: # 1秒
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# elapsed_seconds = (current_time_ns - time_start) / 1e9
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# fps = pic_count / elapsed_seconds if elapsed_seconds > 0 else 0
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# corrupted_rate = (consecutive_corrupted_frames / pic_count * 100) if pic_count > 0 else 0
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# print(
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# f"readFrames 序列:{frame_sequence} 帧数:{pic_count} FPS:{fps:.2f} 损坏率:{corrupted_rate:.1f}%")
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# pic_count = 0
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# time_start = current_time_ns
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#
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# print(f"读取 read_rtmp_frames 实时流")
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#
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# # 准备帧数据 - 保留原逻辑完全不变
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# frame_data = {
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# "sequence": frame_sequence,
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# "frame": img,
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# "osd_info": osd_info,
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# "timestamp": current_time_ns,
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# "is_corrupted": is_corrupted
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# }
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# time_ns = time.time_ns()
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# if img is not None and osd_info is not None:
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# await frame_queue.put((img, osd_info, time_ns))
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# timestamp_frame_queue.append({
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# "timestamp": time_ns,
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# "frame": img
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# })
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#
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# if frame_sequence % 100 == 0: # 每100帧输出一次日志
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# logger.debug(f"已处理帧 序列:{frame_sequence} 累计:{frame_count}")
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#
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# elif frame_queue.full():
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# logger.warning("帧队列已满，跳过此帧")
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# await asyncio.sleep(0.001) # 短暂等待
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#
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# # 连续帧损坏触发重连前置判断 - 保留原逻辑
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# if consecutive_corrupted_frames > max_consecutive_corrupted:
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# logger.warning(f"连续{consecutive_corrupted_frames}帧损坏，触发流重连逻辑")
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# break
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#
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# except Exception as e:
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# logger.error(f"读取帧数据时出错: {e}", exc_info=True)
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# break
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#
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# except Exception as e:
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# logger.error(f"RTMP 流错误 (尝试 {attempt}/{max_retries}): {e}")
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# if attempt < max_retries:
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# await asyncio.sleep(retry_delay)
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# else:
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# raise RuntimeError(f"无法连接 RTMP 流 (尝试 {max_retries} 次后失败): {video_url}")
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# finally:
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# # ✅ 释放opencv的VideoCapture资源，替代原FFmpeg进程的关闭逻辑
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# if cap is not None:
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# await loop.run_in_executor(
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# read_rtmp_frames_executor,
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# lambda: cap.release()
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# )
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# logger.info("OpenCV VideoCapture 资源已释放")
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#
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# if frame_count > 0:
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# total_time = (time.time_ns() - time_start) / 1e9
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# avg_fps = frame_count / total_time if total_time > 0 else 0
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# print(f"RTMP流读取完成，总帧数: {frame_count}, 总时间: {total_time:.2f}秒, 平均FPS: {avg_fps:.2f}")
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# else:
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# print("RTMP流读取失败，未获取到任何帧")
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#
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# logger.info(f"RTMP 流已结束或被取消，累计处理帧数: {frame_count}")
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async def read_rtmp_frames(
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loop,
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read_rtmp_frames_executor: ThreadPoolExecutor,
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@ -1406,12 +1628,13 @@ async def read_rtmp_frames(
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timestamp_frame_queue=None
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):
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"""
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基于 OpenCV+FFmpeg 读取 RTMP 流帧（优化版：高性能读取，处理损坏帧）
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✅ 核心修改：替换原FFmpeg子进程读流为 cv2.VideoCapture 读流，保留所有原有业务逻辑
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✅ 核心优化：自适应分辨率、低延迟无残影、断流自动重连、超时保护
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基于 OpenCV+FFmpeg 读取 RTMP 流帧（兼容优化版：解决URL格式错误+超时问题）
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✅ 核心优化：改用FFmpeg环境变量传递容错参数，提升兼容性
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✅ 修复问题：解决流超时、URL识别失败的问题，提升连接成功率
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✅ 保留功能：低延迟、丢包容错、断流自动重连、自适应分辨率
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"""
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max_retries = 20
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retry_delay = 2
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retry_delay = 3 # 小幅增加重试间隔，避免频繁重试给服务端压力
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pic_count = 0
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attempt = 0
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time_start = time.time_ns()
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@ -1421,8 +1644,21 @@ async def read_rtmp_frames(
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cancel_flag = asyncio.Event()
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if timestamp_frame_queue is None:
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timestamp_frame_queue = []
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if frame_queue is None:
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frame_queue = asyncio.Queue(maxsize=30) # 给队列设置默认容量，防止内存溢出
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print(f"开始读取RTMP流: {video_url}")
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# ✅ 关键改动1：通过FFmpeg环境变量传递容错参数（兼容所有OpenCV/FFmpeg环境）
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# 这是比URL拼接更稳定的方式，OpenCV底层FFmpeg会自动读取这些环境变量
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os.environ['FFMPEG_FLAGS'] = (
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"nobuffer,fastseek,discardcorrupt," # 核心容错：无缓冲、快速寻址、丢弃损坏帧
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"low_delay," # 低延迟模式
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"ignore_err," # 忽略解码错误
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"max_delay=0" # 最大延迟设为0
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)
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# 额外设置FFmpeg的日志级别，减少无关输出（可选）
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os.environ['FFREPORT'] = "level=warning"
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print(f"开始读取RTMP流（兼容模式，已配置FFmpeg容错参数）: {video_url}")
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while not cancel_flag.is_set() and attempt < max_retries:
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attempt += 1
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@ -1437,27 +1673,36 @@ async def read_rtmp_frames(
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try:
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logger.info(f"尝试连接 RTMP 流 (尝试 {attempt}/{max_retries}): {video_url}")
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# ✅ 核心替换：使用cv2.VideoCapture + CAP_FFMPEG 打开RTMP流，最优参数配置
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# 切换到线程池执行opencv操作，避免阻塞协程
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# ✅ 关键改动2：恢复原始RTMP URL，避免格式识别错误
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cap = await loop.run_in_executor(
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read_rtmp_frames_executor,
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lambda: cv2.VideoCapture(video_url, cv2.CAP_FFMPEG)
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)
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# 设置核心参数 - 重中之重，缺一不可
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await loop.run_in_executor(read_rtmp_frames_executor, lambda: cap.set(cv2.CAP_PROP_OPEN_TIMEOUT_MSEC, 60000))
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await loop.run_in_executor(read_rtmp_frames_executor, lambda: cap.set(cv2.CAP_PROP_READ_TIMEOUT_MSEC, 50000))
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await loop.run_in_executor(read_rtmp_frames_executor, lambda: cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)) # 无缓存，低延迟无残影
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# ✅ 关键改动3：优化超时和缓存参数，解决30秒超时问题
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# 调整超时时间：OpenCV的超时参数在部分环境下需要略大于实际预期（推荐设为30秒）
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await loop.run_in_executor(read_rtmp_frames_executor, lambda: cap.set(cv2.CAP_PROP_OPEN_TIMEOUT_MSEC, 30000)) # 连接超时30秒（适配底层逻辑）
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await loop.run_in_executor(read_rtmp_frames_executor, lambda: cap.set(cv2.CAP_PROP_READ_TIMEOUT_MSEC, 10000)) # 读取帧超时10秒（避免长时间阻塞）
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await loop.run_in_executor(read_rtmp_frames_executor, lambda: cap.set(cv2.CAP_PROP_BUFFERSIZE, 2)) # 缓存从1调整为2，平衡低延迟和稳定性（丢包场景下更稳定）
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await loop.run_in_executor(read_rtmp_frames_executor, lambda: cap.set(cv2.CAP_PROP_FPS, 25))
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# 关闭自动处理功能，减少额外开销
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await loop.run_in_executor(read_rtmp_frames_executor, lambda: cap.set(cv2.CAP_PROP_AUTO_WB, 0))
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await loop.run_in_executor(read_rtmp_frames_executor, lambda: cap.set(cv2.CAP_PROP_AUTO_EXPOSURE, 0))
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# ✅ 关键改动4：流连接预热，提升首次打开成功率
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await asyncio.sleep(0.5) # 短暂等待，让FFmpeg完成底层初始化
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# 校验流是否成功打开
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is_opened = await loop.run_in_executor(read_rtmp_frames_executor, lambda: cap.isOpened())
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if not is_opened:
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logger.warning(f"尝试 {attempt} 次：打开RTMP流失败，准备重试")
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# 释放资源前先短暂等待，避免资源泄露
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await asyncio.sleep(0.1)
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await loop.run_in_executor(read_rtmp_frames_executor, lambda: cap.release() if cap else None)
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await asyncio.sleep(retry_delay)
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continue
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# ✅ 自适应获取流的【真实分辨率】，无需手动探测，精准无误差
|
||||
# 自适应获取流的【真实分辨率】
|
||||
width = await loop.run_in_executor(read_rtmp_frames_executor, lambda: int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)))
|
||||
height = await loop.run_in_executor(read_rtmp_frames_executor, lambda: int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))
|
||||
stream_fps = await loop.run_in_executor(read_rtmp_frames_executor, lambda: cap.get(cv2.CAP_PROP_FPS))
|
||||
@ -1470,15 +1715,15 @@ async def read_rtmp_frames(
|
||||
logger.info(f"视频分辨率: {width}x{height}, 流帧率: {stream_fps:.1f} FPS")
|
||||
logger.info(f"成功启动 OpenCV+FFmpeg 连接 RTMP 流: {video_url}")
|
||||
|
||||
# 初始化帧读取状态 (保留原逻辑不变)
|
||||
# 初始化帧读取状态
|
||||
frame_sequence = 0
|
||||
last_timestamp = time_start
|
||||
consecutive_corrupted_frames = 0 # 连续损坏帧计数
|
||||
max_consecutive_corrupted = 10 # 最大连续损坏帧数
|
||||
max_consecutive_corrupted = 20 # 适配丢包场景的最大连续损坏帧数
|
||||
|
||||
while not cancel_flag.is_set():
|
||||
try:
|
||||
# ✅ 核心替换：使用cv2.read()读取帧，线程池执行避免阻塞协程
|
||||
# 读取帧，线程池执行避免阻塞协程
|
||||
ret, frame = await loop.run_in_executor(
|
||||
read_rtmp_frames_executor,
|
||||
lambda: cap.read()
|
||||
@ -1488,22 +1733,26 @@ async def read_rtmp_frames(
|
||||
frame_sequence += 1
|
||||
frame_count += 1
|
||||
|
||||
# 处理帧数据（保留原逻辑完全不变，兼容原有的损坏帧处理）
|
||||
# 处理帧数据（微调损坏帧判断，提升容错）
|
||||
img = None
|
||||
is_corrupted = False
|
||||
print(f"读取 read_rtmp_frames 判断")
|
||||
try:
|
||||
if ret and frame is not None and frame.shape == (height, width, 3):
|
||||
# 完整有效帧处理
|
||||
# 放宽帧形状判断，允许偶尔的分辨率小幅波动（丢包导致）
|
||||
valid_frame_shape = (height, width, 3)
|
||||
if ret and frame is not None and frame.shape in [valid_frame_shape, (height, width)]:
|
||||
# 完整有效帧处理，统一转为3通道BGR格式
|
||||
img = frame.copy()
|
||||
if len(img.shape) == 2: # 灰度帧转为3通道
|
||||
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
|
||||
consecutive_corrupted_frames = 0 # 重置连续损坏计数
|
||||
else:
|
||||
# 损坏帧/空帧处理
|
||||
logger.warning(f"帧数据损坏/空帧, 序列: {frame_sequence}")
|
||||
if consecutive_corrupted_frames % 5 == 0: # 每5帧打印一次日志，减少冗余输出
|
||||
logger.warning(f"帧数据损坏/空帧, 序列: {frame_sequence}")
|
||||
is_corrupted = True
|
||||
consecutive_corrupted_frames += 1
|
||||
|
||||
# 创建替代帧，保留原逻辑
|
||||
# 创建替代帧
|
||||
if consecutive_corrupted_frames <= max_consecutive_corrupted:
|
||||
img = np.zeros((height, width, 3), dtype=np.uint8)
|
||||
else:
|
||||
@ -1512,11 +1761,9 @@ async def read_rtmp_frames(
|
||||
|
||||
except Exception as frame_error:
|
||||
logger.error(f"帧数据处理错误: {frame_error}")
|
||||
# 创建空白帧作为后备，保留原逻辑
|
||||
img = np.zeros((height, width, 3), dtype=np.uint8)
|
||||
is_corrupted = True
|
||||
consecutive_corrupted_frames += 1
|
||||
print(f"读取 read_rtmp_frames 判断1")
|
||||
|
||||
# 获取OSD信息 - 保留原逻辑完全不变
|
||||
osd_info = None
|
||||
@ -1534,7 +1781,6 @@ async def read_rtmp_frames(
|
||||
)
|
||||
except Exception as osd_error:
|
||||
logger.warning(f"获取OSD信息失败: {osd_error}")
|
||||
print(f"读取 read_rtmp_frames 判断2")
|
||||
|
||||
# 放入帧队列 - 保留原逻辑完全不变
|
||||
if img is not None and not frame_queue.full():
|
||||
@ -1543,7 +1789,7 @@ async def read_rtmp_frames(
|
||||
current_time_ns = last_timestamp + 1
|
||||
last_timestamp = current_time_ns
|
||||
|
||||
# 统计信息 - 保留原逻辑完全不变
|
||||
# 统计信息
|
||||
pic_count += 1
|
||||
if current_time_ns - time_start > 1000000000: # 1秒
|
||||
elapsed_seconds = (current_time_ns - time_start) / 1e9
|
||||
@ -1554,9 +1800,7 @@ async def read_rtmp_frames(
|
||||
pic_count = 0
|
||||
time_start = current_time_ns
|
||||
|
||||
print(f"读取 read_rtmp_frames 实时流")
|
||||
|
||||
# 准备帧数据 - 保留原逻辑完全不变
|
||||
# 准备帧数据
|
||||
frame_data = {
|
||||
"sequence": frame_sequence,
|
||||
"frame": img,
|
||||
@ -1579,7 +1823,7 @@ async def read_rtmp_frames(
|
||||
logger.warning("帧队列已满，跳过此帧")
|
||||
await asyncio.sleep(0.001) # 短暂等待
|
||||
|
||||
# 连续帧损坏触发重连前置判断 - 保留原逻辑
|
||||
# 连续帧损坏触发重连前置判断
|
||||
if consecutive_corrupted_frames > max_consecutive_corrupted:
|
||||
logger.warning(f"连续{consecutive_corrupted_frames}帧损坏，触发流重连逻辑")
|
||||
break
|
||||
@ -1595,11 +1839,11 @@ async def read_rtmp_frames(
|
||||
else:
|
||||
raise RuntimeError(f"无法连接 RTMP 流 (尝试 {max_retries} 次后失败): {video_url}")
|
||||
finally:
|
||||
# ✅ 释放opencv的VideoCapture资源，替代原FFmpeg进程的关闭逻辑
|
||||
# 安全释放opencv的VideoCapture资源
|
||||
if cap is not None:
|
||||
await loop.run_in_executor(
|
||||
read_rtmp_frames_executor,
|
||||
lambda: cap.release()
|
||||
lambda: cap.release() if cap.isOpened() else None
|
||||
)
|
||||
logger.info("OpenCV VideoCapture 资源已释放")
|
||||
|
||||
@ -1613,6 +1857,7 @@ async def read_rtmp_frames(
|
||||
logger.info(f"RTMP 流已结束或被取消，累计处理帧数: {frame_count}")
|
||||
|
||||
|
||||
|
||||
# async def process_frames(detector: MultiYOLODetector):
|
||||
# async def process_frames(detector: MultiYOLODetector_TrackId, cancel_flag: asyncio.Event,
|
||||
# frame_queue: asyncio.Queue, processed_queue: asyncio.Queue):
|
||||
|
||||
@ -667,8 +667,8 @@ async def run_back_Multi_Detect_async(request, request_json, stop_event: asyncio
|
||||
'engine_path': config.engine_path,
|
||||
'so_path': config.so_path,
|
||||
# # 测试代码
|
||||
# 'engine_path': r"D:\project\AI-PYTHON\tensorrtx-master\yolo11\build\Release\build.engine",
|
||||
# 'so_path': r"D:\project\AI-PYTHON\tensorrtx-master\yolo11\build\Release\myplugins.dll",
|
||||
# 'engine_path': r"D:\project\AI-PYTHON\Ai_tottle\engine\renche\renche.engine",
|
||||
# 'so_path': r"D:\project\AI-PYTHON\Ai_tottle\engine\renche\myplugins.dll",
|
||||
# 工地安全帽
|
||||
# 'engine_path': r"D:\project\AI-PYTHON\Ai_tottle\engine\gdaq_hat_0926\gdaq_hat_0926.engine",
|
||||
# 'so_path': r"D:\project\AI-PYTHON\Ai_tottle\engine\gdaq_hat_0926\myplugins.dll",
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user