优化拉流丢包的问题，增加宽松策略
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@ -1613,10 +1613,309 @@ from concurrent.futures import ThreadPoolExecutor
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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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# SEI修复核心配置
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os.environ["OPENCV_FFMPEG_LOG_LEVEL"] = "ERROR" # 屏蔽SEI错误日志
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MAX_RETRIES = 5
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RETRY_DELAY = 2
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BUFFER_SIZE = 1 # 最小缓冲区减少SEI积压
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TARGET_FPS = 25
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FOURCC = cv2.VideoWriter_fourcc(*'H264')
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MAX_CORRUPTED = 30
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#
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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: TimestampedQueue = None
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# ):
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# """优化版RTMP流读取（集成SEI修复+完整逻辑）
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# 核心修复：
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# 1. 屏蔽FFmpeg SEI截断日志
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# 2. 精简OpenCV参数，仅保留Python支持的核心配置
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# 3. 增强帧格式校验和异常处理
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# 4. 修复事件循环嵌套运行的致命错误
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# 5. 优化重连机制和SEI帧跳过逻辑
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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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# # ✅ 关键修复1：设置FFmpeg全局参数，屏蔽SEI帧截断日志
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# os.environ["OPENCV_FFMPEG_LOG_LEVEL"] = "ERROR" # 只输出致命错误，屏蔽SEI相关警告
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#
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# def ensure_cv8uc3(frame):
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# """确保帧格式为CV_8UC3（增强版修复）"""
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# if frame is None:
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# return None
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# if frame.dtype != np.uint8:
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# frame = frame.astype(np.uint8)
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# if len(frame.shape) == 2: # 灰度图转彩色
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# frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2BGR)
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# elif frame.shape[2] == 4: # RGBA 转 BGR
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# frame = cv2.cvtColor(frame, cv2.COLOR_RGBA2BGR)
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# return frame
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#
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# # ✅ 关键修复2：将init_capture改为同步函数（核心！解决事件循环冲突）
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# def init_capture(attempt: int = 1):
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# """初始化捕获器（同步版本，带SEI修复参数+重连逻辑）"""
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# print(f"第 {attempt}/{MAX_RETRIES} 次尝试初始化RTMP捕获器")
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#
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# # 指定FFmpeg后端创建捕获器
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# cap = cv2.VideoCapture(video_url, cv2.CAP_FFMPEG)
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# if not cap.isOpened():
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# raise RuntimeError(f"无法打开RTMP流 (第{attempt}次尝试)")
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#
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# # 仅保留Python版OpenCV支持的核心参数
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# cap.set(cv2.CAP_PROP_BUFFERSIZE, BUFFER_SIZE) # 缓冲区设为1，减少SEI帧积压
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# cap.set(cv2.CAP_PROP_FOURCC, FOURCC) # 指定H264解码器，减少SEI解析开销
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# cap.set(cv2.CAP_PROP_FPS, TARGET_FPS) # 同步流帧率，避免冗余处理
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#
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# # 获取分辨率（增加异常处理）
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# try:
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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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# if width <= 0 or height <= 0:
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# width, height = 1280, 720 # 默认分辨率兜底
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# except Exception as e:
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# print(f"获取分辨率失败，使用默认值: {e}")
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# width, height = 1280, 720
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#
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# print(f"RTMP捕获器初始化成功，分辨率: {width}x{height}")
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# return cap, (width, height)
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#
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# # 初始化捕获器（支持重连）
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# cap = None
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# width, height = 1280, 720
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# for attempt in range(1, MAX_RETRIES + 1):
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# try:
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# # ✅ 关键修复3：直接在线程池执行同步的init_capture，不再嵌套事件循环
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# cap, (width, height) = await loop.run_in_executor(
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# read_rtmp_frames_executor,
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# init_capture, # 直接传函数，不再用lambda嵌套loop.run_until_complete
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# attempt # 传递attempt参数
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# )
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# break
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# except RuntimeError as e:
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# print(f"初始化失败: {e}")
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# if attempt >= MAX_RETRIES:
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# raise RuntimeError(f"所有{MAX_RETRIES}次初始化尝试均失败")
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# await asyncio.sleep(RETRY_DELAY)
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#
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# try:
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# last_valid_frame = np.zeros((height, width, 3), dtype=np.uint8)
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# consecutive_corrupted_frames = 0
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# frame_count = 0
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# time_start = time.time_ns()
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#
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# while not cancel_flag.is_set():
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# try:
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# # 读取帧（使用线程池避免阻塞事件循环）
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# ret, frame = await loop.run_in_executor(
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# read_rtmp_frames_executor,
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# cap.read # 直接传递方法，更简洁
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# )
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#
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# # 增强SEI帧/损坏帧处理逻辑
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# current_frame = last_valid_frame.copy()
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# if ret and frame is not None and frame.size > 0:
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# # 正常帧：格式转换 + 更新兜底帧
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# processed_frame = ensure_cv8uc3(frame)
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# if processed_frame is not None:
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# current_frame = processed_frame
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# last_valid_frame = current_frame.copy()
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# consecutive_corrupted_frames = 0
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# else:
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# consecutive_corrupted_frames += 1
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# else:
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# # SEI帧/损坏帧：使用兜底帧 + 计数
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# consecutive_corrupted_frames += 1
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# if consecutive_corrupted_frames % 15 == 0:
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# print(f"跳过SEI/损坏帧 (连续: {consecutive_corrupted_frames})")
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#
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# # 获取OSD信息（保留原逻辑）
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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 = await loop.run_in_executor(
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# read_rtmp_frames_executor,
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# device.get_latest_message,
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# topic_osd_info,
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# method_osd_info
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# )
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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 e:
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# print(f"获取OSD信息失败: {str(e)}")
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#
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# # 时间戳和队列处理（优化超时逻辑）
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# timestamp = time.time_ns()
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# if frame_queue is not None and timestamp_frame_queue is not None:
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# try:
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# # 非阻塞放入队列，避免长时间等待
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# if not frame_queue.full():
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# await asyncio.wait_for(
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# frame_queue.put((current_frame, osd_info, timestamp)),
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# timeout=0.01
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# )
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# timestamp_frame_queue.append({
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# "timestamp": timestamp,
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# "frame": current_frame
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# })
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# frame_count += 1
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#
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# # 每秒打印一次帧数统计
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# if timestamp - time_start > 1000000000:
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# fps = frame_count / ((timestamp - time_start) / 1000000000)
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# print(f"读取帧数: {frame_count} | 实时FPS: {fps:.2f}")
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# frame_count = 0
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# time_start = timestamp
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# else:
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# # 队列满时短暂休眠，避免CPU占用过高
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# await asyncio.sleep(0.001)
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# except asyncio.TimeoutError:
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# print("帧队列已满，跳过此帧")
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#
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# # 连续损坏帧触发重连逻辑（增强版）
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# if consecutive_corrupted_frames > MAX_CORRUPTED:
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# print(f"连续{MAX_CORRUPTED}帧异常，尝试重新初始化捕获器")
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# # 释放旧捕获器
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# if cap and cap.isOpened():
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# await loop.run_in_executor(
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# read_rtmp_frames_executor,
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# cap.release
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# )
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# # 重新初始化
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# reconnected = False
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# for attempt in range(1, MAX_RETRIES + 1):
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# try:
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# cap, (width, height) = await loop.run_in_executor(
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# read_rtmp_frames_executor,
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# init_capture,
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# attempt
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# )
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# consecutive_corrupted_frames = 0
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# last_valid_frame = np.zeros((height, width, 3), dtype=np.uint8)
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# reconnected = True
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# break
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# except RuntimeError as e:
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# print(f"重连失败 (第{attempt}次): {e}")
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# if attempt >= MAX_RETRIES:
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# raise RuntimeError("重连次数超限，停止尝试")
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# await asyncio.sleep(RETRY_DELAY)
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# if not reconnected:
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# break
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#
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# except Exception as e:
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# print(f"帧处理错误: {str(e)}")
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# await asyncio.sleep(0.1)
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#
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# except asyncio.CancelledError:
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# print("读取任务已取消")
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# except Exception as e:
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# print(f"流读取异常: {str(e)}")
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# # 异常恢复机制（增强版）
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# if not cancel_flag.is_set():
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# await asyncio.sleep(RETRY_DELAY)
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# # 重新初始化捕获器
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# reconnected = False
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# for attempt in range(1, MAX_RETRIES + 1):
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# try:
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# cap, (width, height) = await loop.run_in_executor(
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# read_rtmp_frames_executor,
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# init_capture,
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# attempt
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# )
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# reconnected = True
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# break
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# except RuntimeError as e:
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# print(f"异常恢复重连失败 (第{attempt}次): {e}")
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# if attempt >= MAX_RETRIES:
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# raise
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# await asyncio.sleep(RETRY_DELAY)
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# if reconnected:
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# # 恢复后继续运行（递归调用自身，保持逻辑完整）
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# await read_rtmp_frames(
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# loop=loop,
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# read_rtmp_frames_executor=read_rtmp_frames_executor,
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# video_url=video_url,
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# device=device,
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# topic_camera_osd=topic_camera_osd,
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# method_camera_osd=method_camera_osd,
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# topic_osd_info=topic_osd_info,
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# method_osd_info=method_osd_info,
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# cancel_flag=cancel_flag,
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# frame_queue=frame_queue,
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# timestamp_frame_queue=timestamp_frame_queue
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# )
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# finally:
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# # 确保资源释放
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# if cap:
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# await loop.run_in_executor(
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# read_rtmp_frames_executor,
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# lambda: cap.release() if cap.isOpened() else None
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# )
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# print("RTMP流读取已停止")
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def init_capture_with_sei_fix(video_url: str, attempt: int = 1):
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"""
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修复SEI错误的VideoCapture初始化
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"""
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print(f"\n===== 第 {attempt}/{MAX_RETRIES} 次尝试连接 =====")
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# ✅ 关键：设置FFmpeg全局参数，屏蔽SEI帧截断日志
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os.environ["OPENCV_FFMPEG_LOG_LEVEL"] = "ERROR" # 只输出FFmpeg致命错误，屏蔽警告（包括SEI截断）
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# 初始化VideoCapture，指定FFmpeg后端
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cap = cv2.VideoCapture(video_url, cv2.CAP_FFMPEG)
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if not cap.isOpened():
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raise RuntimeError(f"无法打开RTMP流 (第{attempt}次尝试)")
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# 设置核心参数
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cap.set(cv2.CAP_PROP_BUFFERSIZE, BUFFER_SIZE) # 小缓冲区，实时推帧
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cap.set(cv2.CAP_PROP_FOURCC, FOURCC) # 指定H264解码器
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cap.set(cv2.CAP_PROP_FPS, TARGET_FPS) # 同步流帧率
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# 获取流分辨率
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try:
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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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except:
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width, height = 1440, 1080 # 默认分辨率
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print(f"拉流成功：分辨率 {width}x{height}")
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return cap, (width, height)
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def ensure_cv8uc3(frame):
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"""确保帧格式为8位3通道BGR"""
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if frame is None or frame.size == 0:
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return None
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if frame.dtype != np.uint8:
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frame = frame.astype(np.uint8)
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if len(frame.shape) == 2: # 灰度图转彩色
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frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2BGR)
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elif frame.shape[2] == 4: # RGBA转BGR
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frame = cv2.cvtColor(frame, cv2.COLOR_RGBA2BGR)
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return frame
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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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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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@ -1625,151 +1924,90 @@ async def read_rtmp_frames(
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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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timestamp_frame_queue: TimestampedQueue = None
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):
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"""
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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 = 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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frame_count = 0
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"""异步RTMP读取（修复SEI错误）"""
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print(f"开始测试 RTMP 拉流（验证 SEI 丢包问题）: {video_url}")
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print("测试说明：1. 控制台无 SEI 截断警告即为正常 2. 观察 imshow 窗口画面是否稳定 3. 按 'q' 退出")
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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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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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# ✅ 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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# 创建预览队列
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preview_task = None
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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:
|
||||
attempt += 1
|
||||
if cancel_flag.is_set():
|
||||
logger.info("收到停止信号，终止 RTMP 读取")
|
||||
break
|
||||
|
||||
# 初始化捕获器
|
||||
cap = None
|
||||
width, height = None, None
|
||||
stream_fps = None
|
||||
width, height = 1280, 720
|
||||
|
||||
for attempt in range(1, MAX_RETRIES + 1):
|
||||
try:
|
||||
cap, (width, height) = await loop.run_in_executor(
|
||||
executor,
|
||||
init_capture_with_sei_fix,
|
||||
video_url,
|
||||
attempt
|
||||
)
|
||||
break
|
||||
except RuntimeError as e:
|
||||
print(f"第 {attempt} 次尝试失败：{e}，{RETRY_DELAY} 秒后重试")
|
||||
if attempt >= MAX_RETRIES:
|
||||
raise RuntimeError(f"所有{MAX_RETRIES}次初始化尝试均失败")
|
||||
await asyncio.sleep(RETRY_DELAY)
|
||||
|
||||
try:
|
||||
logger.info(f"尝试连接 RTMP 流 (尝试 {attempt}/{max_retries}): {video_url}")
|
||||
last_valid_frame = np.zeros((height, width, 3), dtype=np.uint8)
|
||||
consecutive_corrupted_frames = 0
|
||||
frame_count = 0
|
||||
fps_count = 0
|
||||
time_start = time.time_ns()
|
||||
fps_start = time.time()
|
||||
|
||||
# ✅ 关键改动2：恢复原始RTMP URL，避免格式识别错误
|
||||
cap = await loop.run_in_executor(
|
||||
read_rtmp_frames_executor,
|
||||
lambda: cv2.VideoCapture(video_url, cv2.CAP_FFMPEG)
|
||||
)
|
||||
|
||||
# ✅ 关键改动3：优化超时和缓存参数，解决30秒超时问题
|
||||
# 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）
|
||||
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）
|
||||
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）
|
||||
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）
|
||||
await loop.run_in_executor(read_rtmp_frames_executor, lambda: cap.set(cv2.CAP_PROP_FPS, 25))
|
||||
# 关闭自动处理功能，减少额外开销
|
||||
await loop.run_in_executor(read_rtmp_frames_executor, lambda: cap.set(cv2.CAP_PROP_AUTO_WB, 0))
|
||||
await loop.run_in_executor(read_rtmp_frames_executor, lambda: cap.set(cv2.CAP_PROP_AUTO_EXPOSURE, 0))
|
||||
|
||||
# ✅ 关键改动4：流连接预热，提升首次打开成功率
|
||||
await asyncio.sleep(0.5) # 短暂等待，让FFmpeg完成底层初始化
|
||||
|
||||
# 校验流是否成功打开
|
||||
is_opened = await loop.run_in_executor(read_rtmp_frames_executor, lambda: cap.isOpened())
|
||||
if not is_opened:
|
||||
logger.warning(f"尝试 {attempt} 次：打开RTMP流失败，准备重试")
|
||||
# 释放资源前先短暂等待，避免资源泄露
|
||||
await asyncio.sleep(0.1)
|
||||
await loop.run_in_executor(read_rtmp_frames_executor, lambda: cap.release() if cap else None)
|
||||
await asyncio.sleep(retry_delay)
|
||||
continue
|
||||
|
||||
# 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))
|
||||
|
||||
# 兜底分辨率，防止异常
|
||||
if width is None or height is None or width == 0 or height == 0:
|
||||
logger.warning("使用默认分辨率 1920x1080")
|
||||
width, height = 1920, 1080
|
||||
|
||||
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
|
||||
frame_sequence = 0
|
||||
last_timestamp = time_start
|
||||
consecutive_corrupted_frames = 0 # 连续损坏帧计数
|
||||
max_consecutive_corrupted = 20 # 适配丢包场景的最大连续损坏帧数
|
||||
print(f"拉流成功：分辨率 {width}x{height}，开始预览（按 'q' 退出）")
|
||||
|
||||
while not cancel_flag.is_set():
|
||||
try:
|
||||
# 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()
|
||||
)
|
||||
# 异步读取帧
|
||||
ret, frame = await loop.run_in_executor(executor, cap.read)
|
||||
|
||||
current_time_ns = time.time_ns()
|
||||
frame_sequence += 1
|
||||
# 帧有效性判断 + 格式转换
|
||||
current_frame = last_valid_frame.copy()
|
||||
if ret and frame is not None and frame.size > 0:
|
||||
frame = ensure_cv8uc3(frame)
|
||||
current_frame = frame.copy()
|
||||
last_valid_frame = current_frame
|
||||
consecutive_corrupted_frames = 0
|
||||
frame_count += 1
|
||||
|
||||
# 处理帧数据（微调损坏帧判断，提升容错）
|
||||
img = None
|
||||
is_corrupted = False
|
||||
try:
|
||||
# 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
|
||||
fps_count += 1
|
||||
else:
|
||||
# 损坏帧/空帧处理
|
||||
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
|
||||
if consecutive_corrupted_frames % 15 == 0:
|
||||
print(f"跳过 SEI 帧/临时解码异常，连续异常帧：{consecutive_corrupted_frames}（画面稳定）")
|
||||
|
||||
# 创建替代帧
|
||||
if consecutive_corrupted_frames <= max_consecutive_corrupted:
|
||||
img = np.zeros((height, width, 3), dtype=np.uint8)
|
||||
else:
|
||||
img = np.zeros((height, width, 3), dtype=np.uint8)
|
||||
logger.error(f"连续损坏帧过多 ({consecutive_corrupted_frames})，创建空白帧")
|
||||
# # 放入预览队列
|
||||
# if enable_preview and preview_queue is not None:
|
||||
# try:
|
||||
# # 清空旧帧，只保留最新的
|
||||
# while not preview_queue.empty():
|
||||
# try:
|
||||
# preview_queue.get_nowait()
|
||||
# except asyncio.QueueEmpty:
|
||||
# break
|
||||
#
|
||||
# if not preview_queue.full():
|
||||
# await asyncio.wait_for(preview_queue.put(current_frame), timeout=0.001)
|
||||
# except (asyncio.QueueFull, asyncio.TimeoutError):
|
||||
# pass
|
||||
|
||||
except Exception as frame_error:
|
||||
logger.error(f"帧数据处理错误: {frame_error}")
|
||||
img = np.zeros((height, width, 3), dtype=np.uint8)
|
||||
is_corrupted = True
|
||||
consecutive_corrupted_frames += 1
|
||||
|
||||
# 获取OSD信息 - 保留原逻辑完全不变
|
||||
# OSD信息
|
||||
osd_info = None
|
||||
if device and topic_osd_info and method_osd_info:
|
||||
try:
|
||||
osd_msg = device.get_latest_message(topic=topic_osd_info, method=method_osd_info)
|
||||
osd_msg = await loop.run_in_executor(
|
||||
executor, device.get_latest_message, topic_osd_info, method_osd_info
|
||||
)
|
||||
if osd_msg and hasattr(osd_msg, 'data'):
|
||||
osd_info = Air_Attitude(
|
||||
gimbal_pitch=osd_msg.data.gimbal_pitch,
|
||||
@ -1779,82 +2017,328 @@ async def read_rtmp_frames(
|
||||
latitude=osd_msg.data.latitude,
|
||||
longitude=osd_msg.data.longitude
|
||||
)
|
||||
except Exception as osd_error:
|
||||
logger.warning(f"获取OSD信息失败: {osd_error}")
|
||||
|
||||
# 放入帧队列 - 保留原逻辑完全不变
|
||||
if img is not None and not frame_queue.full():
|
||||
# 确保时间戳递增
|
||||
if current_time_ns <= last_timestamp:
|
||||
current_time_ns = last_timestamp + 1
|
||||
last_timestamp = current_time_ns
|
||||
|
||||
# 统计信息
|
||||
pic_count += 1
|
||||
if current_time_ns - time_start > 1000000000: # 1秒
|
||||
elapsed_seconds = (current_time_ns - time_start) / 1e9
|
||||
fps = pic_count / elapsed_seconds if elapsed_seconds > 0 else 0
|
||||
corrupted_rate = (consecutive_corrupted_frames / pic_count * 100) if pic_count > 0 else 0
|
||||
print(
|
||||
f"readFrames 序列:{frame_sequence} 帧数:{pic_count} FPS:{fps:.2f} 损坏率:{corrupted_rate:.1f}%")
|
||||
pic_count = 0
|
||||
time_start = current_time_ns
|
||||
|
||||
# 准备帧数据
|
||||
frame_data = {
|
||||
"sequence": frame_sequence,
|
||||
"frame": img,
|
||||
"osd_info": osd_info,
|
||||
"timestamp": current_time_ns,
|
||||
"is_corrupted": is_corrupted
|
||||
}
|
||||
time_ns = time.time_ns()
|
||||
if img is not None and osd_info is not None:
|
||||
await frame_queue.put((img, osd_info, time_ns))
|
||||
timestamp_frame_queue.append({
|
||||
"timestamp": time_ns,
|
||||
"frame": img
|
||||
})
|
||||
|
||||
if frame_sequence % 100 == 0: # 每100帧输出一次日志
|
||||
logger.debug(f"已处理帧 序列:{frame_sequence} 累计:{frame_count}")
|
||||
|
||||
elif frame_queue.full():
|
||||
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
|
||||
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
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"读取帧数据时出错: {e}", exc_info=True)
|
||||
break
|
||||
if consecutive_corrupted_frames < 5: # 减少日志输出
|
||||
print(f"获取OSD信息失败: {str(e)}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"RTMP 流错误 (尝试 {attempt}/{max_retries}): {e}")
|
||||
if attempt < max_retries:
|
||||
await asyncio.sleep(retry_delay)
|
||||
else:
|
||||
raise RuntimeError(f"无法连接 RTMP 流 (尝试 {max_retries} 次后失败): {video_url}")
|
||||
finally:
|
||||
# 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() if cap.isOpened() else None
|
||||
# 帧数统计
|
||||
timestamp = time.time_ns()
|
||||
if frame_queue is not None and timestamp_frame_queue is not None and ret:
|
||||
try:
|
||||
if not frame_queue.full():
|
||||
await asyncio.wait_for(
|
||||
frame_queue.put((current_frame, osd_info, timestamp)),
|
||||
timeout=0.01
|
||||
)
|
||||
logger.info("OpenCV VideoCapture 资源已释放")
|
||||
timestamp_frame_queue.append({"timestamp": timestamp, "frame": current_frame})
|
||||
except (asyncio.QueueFull, asyncio.TimeoutError):
|
||||
pass
|
||||
|
||||
if frame_count > 0:
|
||||
total_time = (time.time_ns() - time_start) / 1e9
|
||||
avg_fps = frame_count / total_time if total_time > 0 else 0
|
||||
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}")
|
||||
else:
|
||||
print("RTMP流读取失败，未获取到任何帧")
|
||||
# 实时FPS统计
|
||||
fps_elapsed = time.time() - fps_start
|
||||
if fps_elapsed >= 1.0:
|
||||
fps = fps_count / fps_elapsed
|
||||
timestamp_elapsed = (timestamp - time_start) / 1000000000
|
||||
if timestamp_elapsed > 0:
|
||||
timestamp_fps = frame_count / timestamp_elapsed
|
||||
print(f"实时 FPS：{fps:.2f} | 累计FPS：{timestamp_fps:.2f} | 分辨率：{width}x{height}")
|
||||
|
||||
logger.info(f"RTMP 流已结束或被取消，累计处理帧数: {frame_count}")
|
||||
fps_count = 0
|
||||
fps_start = time.time()
|
||||
|
||||
# 连续损坏帧重连
|
||||
if consecutive_corrupted_frames > MAX_CORRUPTED:
|
||||
print(f"连续 {MAX_CORRUPTED} 帧异常，停止预览，尝试重连")
|
||||
if cap and cap.isOpened():
|
||||
await loop.run_in_executor(executor, cap.release)
|
||||
|
||||
reconnected = False
|
||||
for retry_attempt in range(1, MAX_RETRIES + 1):
|
||||
try:
|
||||
cap, (width, height) = await loop.run_in_executor(
|
||||
executor, init_capture_with_sei_fix, video_url, retry_attempt
|
||||
)
|
||||
consecutive_corrupted_frames = 0
|
||||
last_valid_frame = np.zeros((height, width, 3), dtype=np.uint8)
|
||||
reconnected = True
|
||||
print(f"重连成功 (第{retry_attempt}次)")
|
||||
break
|
||||
except RuntimeError as e:
|
||||
print(f"重连失败 (第{retry_attempt}次): {e}")
|
||||
if retry_attempt >= MAX_RETRIES:
|
||||
print("重连次数超限，退出")
|
||||
cancel_flag.set()
|
||||
await asyncio.sleep(RETRY_DELAY)
|
||||
if not reconnected:
|
||||
break
|
||||
|
||||
# 小幅延迟，避免CPU占用过高
|
||||
await asyncio.sleep(0.001)
|
||||
|
||||
except asyncio.CancelledError:
|
||||
print("读取任务被取消")
|
||||
break
|
||||
except Exception as e:
|
||||
print(f"帧处理错误: {str(e)}")
|
||||
await asyncio.sleep(0.1)
|
||||
|
||||
except asyncio.CancelledError:
|
||||
print("读取任务已取消")
|
||||
except Exception as e:
|
||||
print(f"流读取异常: {str(e)}")
|
||||
finally:
|
||||
# 资源释放
|
||||
if cap:
|
||||
await loop.run_in_executor(executor, lambda: cap.release() if cap.isOpened() else None)
|
||||
print("释放VideoCapture资源")
|
||||
|
||||
if preview_task is not None:
|
||||
preview_task.cancel()
|
||||
try:
|
||||
await preview_task
|
||||
except asyncio.CancelledError:
|
||||
pass
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
await loop.run_in_executor(executor, cv2.destroyAllWindows)
|
||||
print("RTMP流读取已停止")
|
||||
|
||||
|
||||
|
||||
|
||||
#
|
||||
# async def read_rtmp_frames_skip_sei(
|
||||
# loop,
|
||||
# read_rtmp_frames_executor: ThreadPoolExecutor,
|
||||
# video_url: str,
|
||||
# device: Optional[MQTTDevice] = None,
|
||||
# topic_camera_osd: Optional[str] = None,
|
||||
# method_camera_osd: Optional[str] = None,
|
||||
# topic_osd_info: Optional[str] = None,
|
||||
# method_osd_info: Optional[str] = None,
|
||||
# cancel_flag: Optional[asyncio.Event] = None,
|
||||
# frame_queue: asyncio.Queue = None,
|
||||
# timestamp_frame_queue=None
|
||||
# ):
|
||||
# """
|
||||
# 优化版（兼容 Python OpenCV + 屏蔽 SEI 帧错误）
|
||||
# ✅ 核心修复：1. 移除 Python 不支持的 CAP_PROP_OUTPUT_FORMAT 2. 屏蔽 FFmpeg SEI 截断日志
|
||||
# ✅ 核心实现：配置解码器参数忽略 SEI 帧，保证实时拉流无性能损耗，保留异步逻辑和队列功能
|
||||
# ✅ 解决问题：[h264 @ xxx] SEI type 245 truncated 错误，同时保证拉流实时性和有效性
|
||||
# """
|
||||
# max_retries = 20
|
||||
# retry_delay = 3
|
||||
# pic_count = 0
|
||||
# attempt = 0
|
||||
# time_start = cv2.getTickCount()
|
||||
# frame_count = 0
|
||||
#
|
||||
# # ✅ 关键优化：设置 FFmpeg 全局参数，屏蔽 SEI 帧截断日志（Python OpenCV 间接控制 FFmpeg）
|
||||
# # 禁止 FFmpeg 输出冗余警告，仅保留致命错误，彻底隐藏 SEI type 245 truncated 信息
|
||||
# os.environ["OPENCV_FFMPEG_LOG_LEVEL"] = "ERROR"
|
||||
#
|
||||
# # 核心拉流参数（沿用同步测试代码的有效配置）
|
||||
# BUFFER_SIZE = 1 # 减少缓存，不积压 SEI 帧
|
||||
# TARGET_FPS = 30 # 适配 RTMP 流帧率
|
||||
# FOURCC = cv2.VideoWriter_fourcc(*'H264') # 兼容 YUV420P 像素格式，减少 SEI 解析冗余
|
||||
#
|
||||
# # 初始化默认参数
|
||||
# if cancel_flag is None:
|
||||
# cancel_flag = asyncio.Event()
|
||||
# if timestamp_frame_queue is None:
|
||||
# timestamp_frame_queue = []
|
||||
# if frame_queue is None:
|
||||
# frame_queue = asyncio.Queue(maxsize=5) # 小队列减少缓存，保证实时性
|
||||
#
|
||||
# print(f"开始读取 RTMP 流（OpenCV 优化版，跳过 SEI 帧错误）: {video_url}")
|
||||
#
|
||||
# while not cancel_flag.is_set() and attempt < max_retries:
|
||||
# attempt += 1
|
||||
# if cancel_flag.is_set():
|
||||
# logger.info("收到停止信号，终止 RTMP 读取")
|
||||
# break
|
||||
#
|
||||
# cap = None
|
||||
# consecutive_corrupted_frames = 0
|
||||
# max_consecutive_corrupted = 30
|
||||
# last_valid_frame = None
|
||||
#
|
||||
# try:
|
||||
# logger.info(f"尝试连接 RTMP 流 (尝试 {attempt}/{max_retries}): {video_url}")
|
||||
#
|
||||
# # ✅ 步骤1：初始化 VideoCapture，指定 FFmpeg 后端，修复 Python 兼容问题
|
||||
# def init_capture_with_sei_skip():
|
||||
# # 1. 指定 FFmpeg 后端，启用高级参数配置
|
||||
# cap = cv2.VideoCapture(video_url, cv2.CAP_FFMPEG)
|
||||
# if not cap.isOpened():
|
||||
# return None
|
||||
#
|
||||
# # ✅ 步骤2：配置解码器参数（仅保留 Python 支持的核心参数，移除 CAP_PROP_OUTPUT_FORMAT）
|
||||
# # 2.1 关键配置：缓冲区大小设为 1，实时推帧，不缓存 SEI 帧
|
||||
# cap.set(cv2.CAP_PROP_BUFFERSIZE, BUFFER_SIZE)
|
||||
#
|
||||
# # 2.2 配置像素格式，确保与硬件兼容（对应 PIX_FMT_YUV420P）
|
||||
# cap.set(cv2.CAP_PROP_FOURCC, FOURCC)
|
||||
#
|
||||
# # 2.3 配置帧率，与流帧率同步，避免冗余处理
|
||||
# cap.set(cv2.CAP_PROP_FPS, TARGET_FPS)
|
||||
#
|
||||
# # ✅ 步骤3：可选启用硬件加速（保留原逻辑，增加异常兼容）
|
||||
# try:
|
||||
# # 启用 CUDA 硬件加速（对应 cv::CAP_PROP_HW_ACCELERATION）
|
||||
# cap.set(cv2.CAP_PROP_HW_ACCELERATION, cv2.VIDEO_ACCELERATION_CUDA)
|
||||
# logger.info("已启用 CUDA 硬件加速，降低 SEI 帧解析负担")
|
||||
# except (AttributeError, cv2.error):
|
||||
# try:
|
||||
# # 备用：启用 VA-API 硬件加速（Intel 平台）
|
||||
# cap.set(cv2.CAP_PROP_HW_ACCELERATION, cv2.VIDEO_ACCELERATION_VAAPI)
|
||||
# logger.info("已启用 VA-API 硬件加速，降低 SEI 帧解析负担")
|
||||
# except (AttributeError, cv2.error):
|
||||
# logger.info("未检测到硬件加速模块，使用软件解码（仍可跳过 SEI 帧错误）")
|
||||
#
|
||||
# # ✅ 步骤4：替代 CAP_PROP_OUTPUT_FORMAT：定义帧格式转换函数（确保 CV_8UC3 格式）
|
||||
# global ensure_cv8uc3 # 全局声明，方便后续帧处理调用
|
||||
# def ensure_cv8uc3(frame):
|
||||
# if frame.dtype != np.uint8:
|
||||
# frame = frame.astype(np.uint8)
|
||||
# if len(frame.shape) == 2: # 灰度图转彩色
|
||||
# frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2BGR)
|
||||
# elif frame.shape[2] == 4: # RGBA 转 BGR
|
||||
# frame = cv2.cvtColor(frame, cv2.COLOR_RGBA2BGR)
|
||||
# return frame
|
||||
#
|
||||
# # ✅ 步骤5：获取流分辨率，初始化兜底帧（增加容错，防止获取失败）
|
||||
# try:
|
||||
# width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
||||
# height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
||||
# except:
|
||||
# width, height = 1280, 720 # 默认分辨率，提升鲁棒性
|
||||
# nonlocal last_valid_frame
|
||||
# last_valid_frame = np.zeros((height, width, 3), dtype=np.uint8)
|
||||
#
|
||||
# return cap
|
||||
#
|
||||
# # 在线程池中初始化捕获器，避免阻塞事件循环
|
||||
# cap = await loop.run_in_executor(
|
||||
# read_rtmp_frames_executor,
|
||||
# init_capture_with_sei_skip
|
||||
# )
|
||||
#
|
||||
# if cap is None or not cap.isOpened():
|
||||
# logger.warning(f"尝试 {attempt} 次：OpenCV 捕获器初始化失败，准备重试")
|
||||
# await asyncio.sleep(retry_delay)
|
||||
# continue
|
||||
#
|
||||
# # 获取有效分辨率，打印启动日志
|
||||
# try:
|
||||
# width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
||||
# height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
||||
# except:
|
||||
# width, height = 1280, 720
|
||||
# logger.info(f"OpenCV 捕获器已启动（跳过 SEI 帧），分辨率：{width}x{height}")
|
||||
# logger.info("解码器已配置：忽略 SEI 帧，仅处理核心视频帧，无截断错误日志")
|
||||
#
|
||||
# # ✅ 步骤6：读取视频流，跳过 SEI 帧影响（整合格式转换，保留异步逻辑）
|
||||
# while not cancel_flag.is_set():
|
||||
# # 读取一帧（解码器自动跳过 SEI 帧，仅返回核心解码帧）
|
||||
# def read_frame_core():
|
||||
# ret, frame = cap.read()
|
||||
# return ret, frame
|
||||
#
|
||||
# ret, frame = await loop.run_in_executor(
|
||||
# read_rtmp_frames_executor,
|
||||
# read_frame_core
|
||||
# )
|
||||
#
|
||||
# current_time = cv2.getTickCount()
|
||||
# frame_count += 1
|
||||
# img = None
|
||||
# is_corrupted = False
|
||||
#
|
||||
# # ✅ 流程判断：帧有效性检查 + 格式转换（替代 CAP_PROP_OUTPUT_FORMAT）
|
||||
# if ret and frame is not None and frame.size > 0:
|
||||
# # 非 SEI 帧，解码成功，转换为 CV_8UC3 格式
|
||||
# frame = ensure_cv8uc3(frame)
|
||||
# img = frame.copy()
|
||||
# last_valid_frame = img.copy()
|
||||
# consecutive_corrupted_frames = 0
|
||||
# is_corrupted = False
|
||||
# else:
|
||||
# # SEI 帧或解码异常，使用兜底帧（避免解码器状态异常）
|
||||
# img = last_valid_frame.copy()
|
||||
# is_corrupted = True
|
||||
# consecutive_corrupted_frames += 1
|
||||
# if consecutive_corrupted_frames % 15 == 0:
|
||||
# logger.warning(f"跳过 SEI 帧/解码异常，连续损坏帧: {consecutive_corrupted_frames}（解码器状态稳定）")
|
||||
#
|
||||
# # 强制兜底，保证帧非空
|
||||
# if img is None or img.size == 0:
|
||||
# img = np.zeros((height, width, 3), dtype=np.uint8)
|
||||
#
|
||||
# # 获取 OSD 信息（保留原逻辑，实时性优先）
|
||||
# osd_info = None
|
||||
# if device and topic_osd_info and method_osd_info:
|
||||
# try:
|
||||
# osd_msg = device.get_latest_message(topic=topic_osd_info, method=method_osd_info)
|
||||
# if osd_msg and hasattr(osd_msg, 'data'):
|
||||
# osd_info = Air_Attitude(
|
||||
# gimbal_pitch=osd_msg.data.gimbal_pitch,
|
||||
# gimbal_roll=osd_msg.data.gimbal_roll,
|
||||
# gimbal_yaw=osd_msg.data.gimbal_yaw,
|
||||
# height=osd_msg.data.height,
|
||||
# latitude=osd_msg.data.latitude,
|
||||
# longitude=osd_msg.data.longitude
|
||||
# )
|
||||
# except Exception as osd_error:
|
||||
# pass
|
||||
#
|
||||
# # 实时放入队列，满队列丢弃旧帧（保证实时性，保留原逻辑）
|
||||
# if img is not None:
|
||||
# try:
|
||||
# if frame_queue.full():
|
||||
# frame_queue.get_nowait()
|
||||
# current_time_ns = time.time_ns()
|
||||
# await frame_queue.put((img, osd_info, current_time_ns), timeout=0.001)
|
||||
# except asyncio.TimeoutError:
|
||||
# pass
|
||||
#
|
||||
# # 统计实时 FPS，验证性能（无额外 CPU 开销）
|
||||
# pic_count += 1
|
||||
# tick_count = cv2.getTickCount() - time_start
|
||||
# elapsed_seconds = tick_count / cv2.getTickFrequency()
|
||||
# if elapsed_seconds >= 1.0:
|
||||
# fps = pic_count / elapsed_seconds
|
||||
# print(f"实时统计：FPS:{fps:.2f} 分辨率:{img.shape[1]}x{img.shape[0]} 无 SEI 帧截断错误日志")
|
||||
# pic_count = 0
|
||||
# time_start = cv2.getTickCount()
|
||||
#
|
||||
# # 连续损坏帧触发重连（解码器状态异常时重试）
|
||||
# if consecutive_corrupted_frames > max_consecutive_corrupted:
|
||||
# logger.warning(f"连续{consecutive_corrupted_frames}帧异常，触发捕获器重连")
|
||||
# break
|
||||
#
|
||||
# except Exception as e:
|
||||
# logger.error(f"RTMP 流错误 (尝试 {attempt}/{max_retries}): {e}")
|
||||
# if attempt < max_retries:
|
||||
# await asyncio.sleep(retry_delay)
|
||||
# else:
|
||||
# raise RuntimeError(f"无法连接 RTMP 流 (尝试 {max_retries} 次后失败): {video_url}")
|
||||
# finally:
|
||||
# # 安全释放捕获器资源
|
||||
# if cap is not None and hasattr(cap, 'isOpened') and cap.isOpened():
|
||||
# cap.release()
|
||||
# logger.info("OpenCV 捕获器资源已释放")
|
||||
#
|
||||
# # 统计最终结果
|
||||
# total_elapsed = (cv2.getTickCount() - time_start) / cv2.getTickFrequency()
|
||||
# if frame_count > 0 and frame_count > max_retries:
|
||||
# avg_fps = frame_count / total_elapsed if total_elapsed > 0 else 0
|
||||
# print(f"\nRTMP 流读取完成，总有效帧数: {frame_count}, 平均 FPS: {avg_fps:.2f}，无 SEI 帧截断错误日志")
|
||||
# else:
|
||||
# print("\nRTMP 流读取失败，未获取到有效帧")
|
||||
#
|
||||
# logger.info(f"RTMP 流已结束，累计处理帧数: {frame_count}")
|
||||
|
||||
|
||||
|
||||
@ -1942,7 +2426,7 @@ from PIL import Image, ImageDraw, ImageFont
|
||||
|
||||
chinese_font = None
|
||||
try:
|
||||
chinese_font = ImageFont.truetype("config/SIMSUN.TTC", 60)
|
||||
chinese_font = ImageFont.truetype("config/SIMSUN.TTC", 20)
|
||||
except:
|
||||
chinese_font = ImageFont.load_default()
|
||||
|
||||
@ -2187,6 +2671,7 @@ async def write_results_to_rtmp(task_id: str, output_url: str = None, input_fps:
|
||||
"box": [x1, y1, x2, y2]
|
||||
})
|
||||
label = f"{en_name}:{confidence:.2f}:{track_id}"
|
||||
label_name = f"{en_name}"
|
||||
# 计算文本位置
|
||||
text_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, fontScale=8, thickness=4)[
|
||||
0]
|
||||
@ -2201,8 +2686,9 @@ async def write_results_to_rtmp(task_id: str, output_url: str = None, input_fps:
|
||||
frame_copy = put_chinese_text(
|
||||
temp_img,
|
||||
# label, # 置信度、类别、用作测试
|
||||
"", # 注释掉汉字
|
||||
(text_x, text_y - text_height),
|
||||
# "", # 注释掉汉字
|
||||
label_name, # 仅显示汉字
|
||||
(text_x, text_y- 40),
|
||||
)
|
||||
else:
|
||||
cls_count += 1
|
||||
@ -2225,6 +2711,7 @@ async def write_results_to_rtmp(task_id: str, output_url: str = None, input_fps:
|
||||
# 准备标签文本
|
||||
# label = f"{chinese_label.get(cls_id, class_name)}: {confidence:.2f}:{track_id}"
|
||||
label = f"{confidence:.2f}:{track_id}"
|
||||
label_name = f"{en_name}"
|
||||
# 计算文本位置
|
||||
text_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, fontScale=8, thickness=8)[0]
|
||||
text_width, text_height = text_size[0], text_size[1]
|
||||
@ -2240,8 +2727,9 @@ async def write_results_to_rtmp(task_id: str, output_url: str = None, input_fps:
|
||||
frame_copy = put_chinese_text(
|
||||
temp_img,
|
||||
# label, # 置信度、类别、用作测试
|
||||
"", # 注释掉汉字
|
||||
(text_x, text_y - text_height),
|
||||
# "", # 注释掉汉字
|
||||
label_name, # 仅显示汉字
|
||||
(text_x,text_y- 40),
|
||||
)
|
||||
|
||||
if invade_state:
|
||||
|
||||
10
yolo_api.py
10
yolo_api.py
@ -664,11 +664,11 @@ async def run_back_Multi_Detect_async(request, request_json, stop_event: asyncio
|
||||
{
|
||||
|
||||
'path': config.model_path,
|
||||
'engine_path': config.engine_path,
|
||||
'so_path': config.so_path,
|
||||
# 'engine_path': config.engine_path,
|
||||
# 'so_path': config.so_path,
|
||||
# # 测试代码
|
||||
# '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",
|
||||
'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",
|
||||
@ -1657,6 +1657,8 @@ async def websocket_endpoint(request: Request, ws):
|
||||
camera_para_url = "meta_data/camera_para/xyzj_camera_para.txt"
|
||||
if model2 == "M4D":
|
||||
camera_para_url = "meta_data/camera_para/xyzj_camera_para.txt"
|
||||
elif model2 == "M3TD":
|
||||
camera_para_url = "meta_data/camera_para/hami_camera_para .txt"
|
||||
elif model2 == "M4TD":
|
||||
camera_para_url = "meta_data/camera_para/hami_camera_para .txt"
|
||||
camera_file_path = downFile(camera_para_url)
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user