增加超限施工功能
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9720a07683
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@ -821,6 +821,7 @@ async def read_video_frames(task_id, mqtt, mqtt_publish_topic,
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if os.path.exists(local_video_path):
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os.remove(local_video_path)
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#
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# async def read_rtmp_frames(
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# loop,
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@ -1002,7 +1003,6 @@ async def read_video_frames(task_id, mqtt, mqtt_publish_topic,
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# logger.info(f"RTMP 流已结束或被取消，累计处理帧数: {pic_count}")
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# ------------------------------- 下述方法使用ffmpeg 拉流，可以解决cv2拉流的一些问题，主要是虚拟环境ffmpeg不匹配的问题。但是ffmpeg拉流慢3s左右
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# import cv2
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@ -1393,6 +1393,7 @@ import cv2
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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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@ -1624,6 +1625,7 @@ 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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@ -1887,7 +1889,7 @@ def init_capture_with_sei_fix(video_url: str, attempt: int = 1):
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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_READ_TIMEOUT_MSEC, 25000) #设置25s拉流超时，是为了规避类似彭州水务没加图传模块，飞机太远就拉流失败
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cap.set(cv2.CAP_PROP_READ_TIMEOUT_MSEC, 25000) # 设置25s拉流超时，是为了规避类似彭州水务没加图传模块，飞机太远就拉流失败
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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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@ -1902,6 +1904,7 @@ def init_capture_with_sei_fix(video_url: str, attempt: int = 1):
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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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@ -1939,8 +1942,6 @@ async def read_rtmp_frames(
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# 创建预览队列
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preview_task = None
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# 初始化捕获器
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cap = None
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width, height = 1280, 720
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@ -2108,8 +2109,6 @@ async def read_rtmp_frames(
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print("RTMP流读取已停止")
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#
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# async def read_rtmp_frames_skip_sei(
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# loop,
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@ -2344,7 +2343,6 @@ async def read_rtmp_frames(
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# logger.info(f"RTMP 流已结束，累计处理帧数: {frame_count}")
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# async def process_frames(detector: MultiYOLODetector):
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# async def process_frames(detector: MultiYOLODetector_TrackId, cancel_flag: asyncio.Event,
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# frame_queue: asyncio.Queue, processed_queue: asyncio.Queue):
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@ -2502,7 +2500,7 @@ class TrackIDEventFilter:
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async def write_results_to_rtmp(task_id: str, output_url: str = None, input_fps: float = None,
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list_points: list[list[any]] = None, camera_para: Camera_Para = None,
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invade_state: bool = False, cancel_flag: asyncio.Event = None,
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invade_state: bool = False, invade_switch: int = 0, cancel_flag: asyncio.Event = None,
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processed_queue: asyncio.Queue = None, invade_queue: asyncio.Queue = None,
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cv_frame_queue: asyncio.Queue = None, stream_containers: Dict[str, Any] = None):
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# global stream_containers, count_pic
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@ -2560,6 +2558,9 @@ async def write_results_to_rtmp(task_id: str, output_url: str = None, input_fps:
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results = []
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results_list = []
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invade_switch_enable = True # 侵限施工
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if invade_switch > 0:
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invade_switch_enable = False # 超限施工
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# 启用侵限且拿到了飞机的姿态信息，再绘制红线
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if invade_state and osd_info:
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gimbal_yaw = osd_info.gimbal_yaw
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@ -2615,6 +2616,7 @@ async def write_results_to_rtmp(task_id: str, output_url: str = None, input_fps:
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model_func_id = model_para[0]["func_id"]
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invade_point = []
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message_point = []
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invade_point_message_point=[] # 超限使能，统计侵限，方便画图
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target_point = [] # 存储满足条件的图像坐标，方便后续经纬度转换
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cls_count = 0
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@ -2649,50 +2651,135 @@ async def write_results_to_rtmp(task_id: str, output_url: str = None, input_fps:
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is_invade = is_point_in_polygonlist(point_x, point_y, results_list)
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# is_invade = is_point_in_polygon(point_x, point_y, results)
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# print(f"is_invadeis_invadeis_invade {is_invade} {len(results)}")
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if is_invade:
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cls_count += 1
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invade_point.append({
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"u": point_x,
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"v": point_y,
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"class_name": class_name
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})
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target_point.append({
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"u": point_x,
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"v": point_y,
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"cls_id": cls_id,
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"track_id": track_id,
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"new_track_id": new_track_id
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}) # 对于侵限，只存储侵限目标
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# model_list_func_id = model_para[0]["model_list_func_id"]
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# model_func_id = model_para[0]["func_id"]
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message_point.append({
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"confidence": float(confidence),
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"cls_id": cls_id,
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"type_name": en_name,
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"track_id": track_id,
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"box": [x1, y1, x2, y2]
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})
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label = f"{en_name}:{confidence:.2f}:{track_id}"
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label_name = f"{en_name}"
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# 计算文本位置
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text_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, fontScale=8, thickness=4)[
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0]
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text_width, text_height = text_size[0], text_size[1]
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text_x = x1
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text_y = y1 - 5
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if invade_switch_enable:#只关注侵限
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if is_invade: #只关注侵限且实际发生侵限
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# if invade_switch_enable: # 只关注侵限
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cls_count += 1
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# invade_point.append({
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# "u": point_x,
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# "v": point_y,
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# "class_name": class_name
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# })
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target_point.append({
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"u": point_x,
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"v": point_y,
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"cls_id": cls_id,
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"track_id": track_id,
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"new_track_id": new_track_id
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}) # 对于侵限，只存储侵限目标
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# model_list_func_id = model_para[0]["model_list_func_id"]
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# model_func_id = model_para[0]["func_id"]
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# 如果文本超出图像顶部，则放在框内部下方
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if text_y < 0:
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text_y = y2 + text_height + 5
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temp_img = frame_copy.copy()
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frame_copy = put_chinese_text(
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temp_img,
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# label, # 置信度、类别、用作测试
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# "", # 注释掉汉字
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label_name, # 仅显示汉字
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(text_x, text_y- 40),
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)
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message_point.append({
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"confidence": float(confidence),
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"cls_id": cls_id,
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"type_name": en_name,
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"track_id": track_id,
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"box": [x1, y1, x2, y2]
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})
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label = f"{en_name}:{confidence:.2f}:{track_id}"
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label_name = f"{en_name}"
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# 计算文本位置
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text_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, fontScale=8, thickness=4)[
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0]
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text_width, text_height = text_size[0], text_size[1]
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text_x = x1
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text_y = y1 - 5
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# 如果文本超出图像顶部，则放在框内部下方
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if text_y < 0:
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text_y = y2 + text_height + 5
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temp_img = frame_copy.copy()
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frame_copy = put_chinese_text(
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temp_img,
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# label, # 置信度、类别、用作测试
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# "", # 注释掉汉字
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label_name, # 仅显示汉字
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(text_x, text_y - 40),
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)
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else: #只关注超限
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if is_invade: #只关注超限的情况下，发生了侵限行为，只在图像上展示侵限，不做行为记录
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# if invade_switch_enable: # 只关注侵限
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# cls_count += 1
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# target_point.append({
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# "u": point_x,
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# "v": point_y,
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# "cls_id": cls_id,
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# "track_id": track_id,
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# "new_track_id": new_track_id
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# }) # 对于侵限，只存储侵限目标
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# model_list_func_id = model_para[0]["model_list_func_id"]
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# model_func_id = model_para[0]["func_id"]
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invade_point_message_point.append({
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"confidence": float(confidence),
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"cls_id": cls_id,
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"type_name": en_name,
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"track_id": track_id,
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"box": [x1, y1, x2, y2]
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})
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label = f"{en_name}:{confidence:.2f}:{track_id}"
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label_name = f"{en_name}"
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# 计算文本位置
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text_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, fontScale=8, thickness=4)[
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0]
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text_width, text_height = text_size[0], text_size[1]
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text_x = x1
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text_y = y1 - 5
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# 如果文本超出图像顶部，则放在框内部下方
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if text_y < 0:
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text_y = y2 + text_height + 5
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temp_img = frame_copy.copy()
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frame_copy = put_chinese_text(
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temp_img,
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# label, # 置信度、类别、用作测试
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# "", # 注释掉汉字
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label_name, # 仅显示汉字
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(text_x, text_y - 40),
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)
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else: # 超限使能且识别到了超限发生
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print("超限使能且识别到了超限发生")
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cls_count += 1
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target_point.append({
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"u": point_x,
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"v": point_y,
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"cls_id": cls_id,
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"track_id": track_id,
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"new_track_id": new_track_id
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}) # 对于侵限，只存储侵限目标
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# model_list_func_id = model_para[0]["model_list_func_id"]
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# model_func_id = model_para[0]["func_id"]
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message_point.append({
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"confidence": float(confidence),
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"cls_id": cls_id,
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"type_name": en_name,
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"track_id": track_id,
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"box": [x1, y1, x2, y2]
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})
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label = f"{en_name}:{confidence:.2f}:{track_id}"
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label_name = f"{en_name}"
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# 计算文本位置
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text_size = \
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cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, fontScale=8, thickness=4)[
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0]
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text_width, text_height = text_size[0], text_size[1]
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text_x = x1
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text_y = y1 - 5
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# 如果文本超出图像顶部，则放在框内部下方
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if text_y < 0:
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text_y = y2 + text_height + 5
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temp_img = frame_copy.copy()
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frame_copy = put_chinese_text(
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temp_img,
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# label, # 置信度、类别、用作测试
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# "", # 注释掉汉字
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label_name, # 仅显示汉字
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(text_x, text_y - 40),
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)
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else:
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cls_count += 1
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# 绘制边界框
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@ -2732,14 +2819,20 @@ async def write_results_to_rtmp(task_id: str, output_url: str = None, input_fps:
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# label, # 置信度、类别、用作测试
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# "", # 注释掉汉字
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label_name, # 仅显示汉字
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(text_x,text_y- 40),
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(text_x, text_y - 40),
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)
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# 超限画全图且只统计超限，当前为画全图
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if invade_state:
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for point in message_point:
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cv2.rectangle(frame_copy, (point["box"][0], point["box"][1]),
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(point["box"][2], point["box"][3]),
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(0, 255, 255), 2)
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if not invade_switch_enable:# 侵限使能，只关注超限的情况下，将侵限画另一个颜色
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for point in invade_point_message_point:
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cv2.rectangle(frame_copy, (point["box"][0], point["box"][1]),
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(point["box"][2], point["box"][3]),
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(0, 0, 255), 2)
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# 画红线
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# 在左上角显示统计结果
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stats_text = []
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@ -3021,9 +3114,9 @@ def haversine(lon1, lat1, lon2, lat2):
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return c * r
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async def cal_des_invade(loop,invade_executor, task_id: str, mqtt, mqtt_publish_topic,
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async def cal_des_invade(loop, invade_executor, task_id: str, mqtt, mqtt_publish_topic,
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list_points: list[list[any]], camera_para: Camera_Para, model_count: int,
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cancel_flag: asyncio.Event = None, invade_queue: asyncio.Queue = None,
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cancel_flag: asyncio.Event = None, invade_switch: int = 0, invade_queue: asyncio.Queue = None,
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event_queue: asyncio.Queue = None,
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device_height: float = float(200), repeat_dis: float = -1, repeat_time: float = -1):
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# loop = asyncio.get_running_loop()
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@ -3150,7 +3243,7 @@ async def cal_des_invade(loop,invade_executor, task_id: str, mqtt, mqtt_publish_
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repeat_state = False
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show_des = 0
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str_loca = ""
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des_location_result=[]
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des_location_result = []
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if repeat_dis > 0: # ai_model_list repeat_dis 字段大于零，才启用去重
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if len(target_location_back) > 0: # 当前逻辑并不严谨，只是比较了第一个位置信息
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des1_back = target_location_back[0]
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@ -3236,6 +3329,7 @@ async def cal_des_invade(loop,invade_executor, task_id: str, mqtt, mqtt_publish_
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"minio": {"minio_path": minio_path,
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"minio_origin_path": minio_origin_path,
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"file_type": file_type},
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"invade_switch":invade_switch,
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"box_detail": [{
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"model_id": model_func_id,
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"cls_count": cls_count,
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@ -3246,7 +3340,7 @@ async def cal_des_invade(loop,invade_executor, task_id: str, mqtt, mqtt_publish_
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"longitude": cam_longitude,
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"latitude": cam_latitude
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},
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"des_location":des_location_result
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"des_location": des_location_result
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}
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@ -3296,10 +3390,11 @@ cache_lock = Lock() # 用于保护共享变量的锁
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invade_cache_lock = Lock() # 用于保护共享变量的锁
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async def send_frame_to_s3_mq(loop,upload_executor,task_id, mqtt, mqtt_topic, cancel_flag: asyncio.Event,
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async def send_frame_to_s3_mq(loop, upload_executor, task_id, mqtt, mqtt_topic, cancel_flag: asyncio.Event,
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cv_frame_queue: asyncio.Queue,
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event_queue: asyncio.Queue = None,
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device_height: float = float(200), repeat_dis: float = -1, repeat_time: float = -1,high_count_warn: float = -1):
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device_height: float = float(200), repeat_dis: float = -1, repeat_time: float = -1,
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high_count_warn: float = -1):
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global stats
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start_time = time.time()
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# executor = ThreadPoolExecutor(max_workers=Config.MAX_WORKERS)
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@ -3318,8 +3413,8 @@ async def send_frame_to_s3_mq(loop,upload_executor,task_id, mqtt, mqtt_topic, ca
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para = {
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"category": 3
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}
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local_track_id_list=[]
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local_key_id_list=[]
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local_track_id_list = []
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local_key_id_list = []
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local_key_count_list = []
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target_location_back = [] # 本地缓存，用作位置重复计算
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current_time_second = int(time.time())
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@ -3384,14 +3479,14 @@ async def send_frame_to_s3_mq(loop,upload_executor,task_id, mqtt, mqtt_topic, ca
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should_report = True
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print(f"target_pointtarget_point {len(target_point)}")
|
||||
count_item = 0
|
||||
des_location_result=[]
|
||||
des_location_result = []
|
||||
|
||||
high_count_warn_status=False
|
||||
high_count_warn_num=0
|
||||
high_count_warn_status = False
|
||||
high_count_warn_num = 0
|
||||
|
||||
if target_point is not None and 0 < high_count_warn < len(target_point):# 触发计数报警
|
||||
high_count_warn_num=len(target_point)
|
||||
high_count_warn_status=True
|
||||
if target_point is not None and 0 < high_count_warn < len(target_point): # 触发计数报警
|
||||
high_count_warn_num = len(target_point)
|
||||
high_count_warn_status = True
|
||||
|
||||
for item in target_point:
|
||||
# # 跳过无效的track_id
|
||||
@ -3407,12 +3502,11 @@ async def send_frame_to_s3_mq(loop,upload_executor,task_id, mqtt, mqtt_topic, ca
|
||||
|
||||
if cls_id in local_key_id_list:
|
||||
first_index = local_key_id_list.index(cls_id) # 获取 key_id 的第一个下标
|
||||
local_key_count_list[first_index]=local_key_count_list[first_index]+1
|
||||
local_key_count_list[first_index] = local_key_count_list[first_index] + 1
|
||||
else:
|
||||
local_key_id_list.append(cls_id)
|
||||
local_key_count_list.append(1)
|
||||
|
||||
|
||||
# should_report = True
|
||||
|
||||
# # 如果这个track_id已经上报过，检查是否超过上报间隔
|
||||
@ -3475,7 +3569,6 @@ async def send_frame_to_s3_mq(loop,upload_executor,task_id, mqtt, mqtt_topic, ca
|
||||
des1_latitude = des1[1]
|
||||
des1_height = des1[2]
|
||||
|
||||
|
||||
str_loca = f"{des1_back_longitude}:{des1_back_latitude}---{des1_longitude}{des1_latitude}"
|
||||
des = haversine(des1_back_longitude, des1_back_latitude, des1_longitude, des1_latitude)
|
||||
show_des = des
|
||||
@ -3495,15 +3588,15 @@ async def send_frame_to_s3_mq(loop,upload_executor,task_id, mqtt, mqtt_topic, ca
|
||||
model_cls = model_para.get("model_cls_index", {})
|
||||
list_func_id = model_para.get("model_list_func_id", -11)
|
||||
func_id = model_para.get("func_id", [])
|
||||
count_message=[]
|
||||
for k,v in chinese_label.items():
|
||||
count_message = []
|
||||
for k, v in chinese_label.items():
|
||||
if int(k) in local_key_id_list:
|
||||
k_index=local_key_id_list.index(int(k))
|
||||
clss_count=local_key_count_list[k_index]
|
||||
k_index = local_key_id_list.index(int(k))
|
||||
clss_count = local_key_count_list[k_index]
|
||||
count_message.append({
|
||||
"cls_index":k_index,
|
||||
"cls_name":v,
|
||||
"count":clss_count
|
||||
"cls_index": k_index,
|
||||
"cls_name": v,
|
||||
"count": clss_count
|
||||
})
|
||||
|
||||
# 获取DRC消息（同步操作，放到线程池）
|
||||
@ -3568,13 +3661,14 @@ async def send_frame_to_s3_mq(loop,upload_executor,task_id, mqtt, mqtt_topic, ca
|
||||
"longitude": cam_longitude,
|
||||
"latitude": cam_latitude
|
||||
},
|
||||
"count_message":count_message,
|
||||
"high_count_warn":{
|
||||
"high_count_warn_status":high_count_warn_status,
|
||||
"high_count_warn_num":high_count_warn_num,
|
||||
"high_count_warn":high_count_warn
|
||||
"invade_switch": 0, #默认
|
||||
"count_message": count_message,
|
||||
"high_count_warn": {
|
||||
"high_count_warn_status": high_count_warn_status,
|
||||
"high_count_warn_num": high_count_warn_num,
|
||||
"high_count_warn": high_count_warn
|
||||
},
|
||||
"des_location":des_location_result
|
||||
"des_location": des_location_result
|
||||
}
|
||||
await event_queue.put({
|
||||
"timestamp": timestamp # 存储事件触发的时刻，用作视频制作
|
||||
@ -3810,7 +3904,7 @@ def frames_to_video_bytes(frames, fps=25, format="flv"):
|
||||
try:
|
||||
log_info("构造FFMPEG命令（使用临时文件输入）")
|
||||
ffmpeg_cmd = [
|
||||
# "ffmpeg",
|
||||
# "ffmpeg",
|
||||
"/usr/bin/ffmpeg",
|
||||
"-hide_banner",
|
||||
"-loglevel", "error",
|
||||
@ -4001,8 +4095,8 @@ async def start_rtmp_processing(video_url: str, task_id: str, model_configs: Lis
|
||||
mqtt_pub_ip: str, mqtt_pub_port: int, mqtt_pub_topic: str,
|
||||
mqtt_sub_ip: str, mqtt_sub_port: int, mqtt_sub_topic: str,
|
||||
output_rtmp_url: str,
|
||||
invade_enable: bool, invade_file: str, camera_para_url: str,
|
||||
device_height: float, repeat_dis: float, repeat_time: float,high_count_warn: float):
|
||||
invade_enable: bool, invade_switch: int, invade_file: str, camera_para_url: str,
|
||||
device_height: float, repeat_dis: float, repeat_time: float, high_count_warn: float):
|
||||
# 初始化资源
|
||||
# await initialize_resources()
|
||||
logger.info(f"拉流地址{video_url}")
|
||||
@ -4128,6 +4222,7 @@ async def start_rtmp_processing(video_url: str, task_id: str, model_configs: Lis
|
||||
list_points,
|
||||
camera_para,
|
||||
invade_state,
|
||||
invade_switch,
|
||||
cancel_flag,
|
||||
processed_queue,
|
||||
invade_queue,
|
||||
@ -4152,6 +4247,7 @@ async def start_rtmp_processing(video_url: str, task_id: str, model_configs: Lis
|
||||
camera_para,
|
||||
model_count,
|
||||
cancel_flag,
|
||||
invade_switch,
|
||||
invade_queue,
|
||||
event_queue,
|
||||
device_height,
|
||||
@ -4167,18 +4263,18 @@ async def start_rtmp_processing(video_url: str, task_id: str, model_configs: Lis
|
||||
upload_task = asyncio.create_task(
|
||||
send_frame_to_s3_mq(loop, upload_executor, task_id, mqtt, mqtt_pub_topic,
|
||||
cancel_flag, cv_frame_queue, event_queue, device_height, repeat_dis,
|
||||
repeat_time,high_count_warn),
|
||||
repeat_time, high_count_warn),
|
||||
name=f"send_frame_to_s3_mq_{_}"
|
||||
)
|
||||
upload_tasks.append(upload_task)
|
||||
tasks.append(upload_task)
|
||||
#
|
||||
# # # 截取事件，并将frame存储为video，然后执行上传
|
||||
# event_video_executor = ThreadPoolExecutor(max_workers=Config.EVENT_VIDEO_WORKERS)
|
||||
# upload_video = asyncio.create_task(cut_evnt_video_publish(task_id,mqtt, mqtt_pub_topic, cancel_flag,
|
||||
# event_queue, timestamp_frame_queue),
|
||||
# name="cut_evnt_video_publish")
|
||||
# tasks.append(upload_video)
|
||||
#
|
||||
# # # 截取事件，并将frame存储为video，然后执行上传
|
||||
# event_video_executor = ThreadPoolExecutor(max_workers=Config.EVENT_VIDEO_WORKERS)
|
||||
# upload_video = asyncio.create_task(cut_evnt_video_publish(task_id,mqtt, mqtt_pub_topic, cancel_flag,
|
||||
# event_queue, timestamp_frame_queue),
|
||||
# name="cut_evnt_video_publish")
|
||||
# tasks.append(upload_video)
|
||||
|
||||
# 注册任务到TaskManager
|
||||
device_list = [mqtt]
|
||||
@ -4271,7 +4367,8 @@ async def start_rtmp_processing(video_url: str, task_id: str, model_configs: Lis
|
||||
|
||||
async def start_video_processing(minio_path: str, task_id: str, model_configs: List[Dict],
|
||||
mqtt_ip: str, mqtt_port: int, mqtt_topic: str, output_rtmp_url: str,
|
||||
invade_enable: bool, invade_file: str, camera_para_url: str, device_height: float,
|
||||
invade_enable: bool, invade_switch: int, invade_file: str, camera_para_url: str,
|
||||
device_height: float,
|
||||
repeat_dis: float, repeat_time: float):
|
||||
# global stop_event, frame_queue, processed_queue, executor, upload_executor
|
||||
# await initialize_resources() # 初始化资源
|
||||
@ -4424,6 +4521,7 @@ async def start_video_processing(minio_path: str, task_id: str, model_configs: L
|
||||
list_points,
|
||||
camera_para,
|
||||
invade_state,
|
||||
invade_switch,
|
||||
cancel_flag,
|
||||
processed_queue,
|
||||
invade_queue,
|
||||
@ -4450,6 +4548,7 @@ async def start_video_processing(minio_path: str, task_id: str, model_configs: L
|
||||
camera_para,
|
||||
model_count,
|
||||
cancel_flag,
|
||||
invade_switch,
|
||||
invade_queue,
|
||||
event_queue,
|
||||
device_height,
|
||||
|
||||
11
yolo_api.py
11
yolo_api.py
@ -718,6 +718,9 @@ async def run_back_Multi_Detect_async(request, request_json, stop_event: asyncio
|
||||
invade = request_json.content_body.invade
|
||||
invade_file = invade["invade_file"]
|
||||
camera_para_url = invade["camera_para_url"]
|
||||
invade_switch = 0
|
||||
if invade["invade_switch"] is not None:
|
||||
invade_switch = invade["invade_switch"]
|
||||
# dao.get_mqtt_config_by_orgcode(org_code,)
|
||||
str_request = str(request) + "&" + str(request.socket) # 待测试，看看公网能不能捕获到请求端ip
|
||||
dao.insert_request_log(task_id, sn, org_code, str(request.body), str_request)
|
||||
@ -768,7 +771,7 @@ async def run_back_Multi_Detect_async(request, request_json, stop_event: asyncio
|
||||
mqtt_pub_ip, mqtt_pub_port, mqtt_pub_topic,
|
||||
mqtt_sub_ip, mqtt_sub_port, mqtt_sub_topic,
|
||||
push_url,
|
||||
invade_enable, invade_file, camera_para_url,
|
||||
invade_enable,invade_switch, invade_file, camera_para_url,
|
||||
device_height, repeat_dis, repeat_time,high_count_warn
|
||||
)
|
||||
except Exception as e:
|
||||
@ -951,10 +954,12 @@ async def run_back_Video_Multi_Detect_async(request, request_json):
|
||||
invade = request_json.content_body.invade
|
||||
invade_file = invade["invade_file"]
|
||||
camera_para_url = invade["camera_para_url"]
|
||||
|
||||
invade_switch = 0
|
||||
if invade["invade_switch"] is not None:
|
||||
invade_switch = invade["invade_switch"]
|
||||
await start_video_processing(minio_file_path, task_id, model_configs, mqtt_pub_ip, mqtt_pub_port,
|
||||
mqtt_pub_topic, push_url,
|
||||
invade_enable, invade_file, camera_para_url, device_height, repeat_dis,
|
||||
invade_enable,invade_switch, invade_file, camera_para_url, device_height, repeat_dis,
|
||||
repeat_time)
|
||||
|
||||
except Exception as e:
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user