From 59ab409d52780591671231bc509c8587f904f5b8 Mon Sep 17 00:00:00 2001 From: zqc <835569504@qq.com> Date: Wed, 1 Apr 2026 17:00:36 +0800 Subject: [PATCH] =?UTF-8?q?=E6=A8=A1=E5=9E=8B=E3=80=81=E5=8C=BA=E5=9F=9F?= =?UTF-8?q?=E7=82=B9=E3=80=81=E9=A2=9C=E8=89=B2=E4=BB=8E=E9=85=8D=E7=BD=AE?= =?UTF-8?q?=E4=B8=AD=E8=AF=BB=E5=8F=96?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- biz/prison/indoor_biz.py | 54 +++++++++++++++++++--------------- config.yaml | 63 +++++++++++++++++++++++++++++++++++----- 2 files changed, 86 insertions(+), 31 deletions(-) diff --git a/biz/prison/indoor_biz.py b/biz/prison/indoor_biz.py index c58d8c6..0fe5898 100644 --- a/biz/prison/indoor_biz.py +++ b/biz/prison/indoor_biz.py @@ -5,9 +5,10 @@ import requests from biz.base_frame_processor import BaseFrameProcessorWorker from algorithm.common.npu_yolo_onnx_person_car_phone import YOLOv8_ONNX from yolox.tracker.byte_tracker import BYTETracker +from common.constants import MODEL_ROOT_PATH # ========================= 走廊场景专属配置 ========================= -MODEL_PATH = 'YOLO_Weight/kanshousuo.onnx' # 犯人检测onnx模型路径 +DETECT_MODEL_PATH = 'YOLO_Weight/kanshousuo.onnx' # 犯人检测onnx模型路径 INPUT_SIZE = 640 # 模型输入尺寸 RTSP_FPS = 10 # 视频流目标FPS ALERT_PUSH_INTERVAL = 5 # 相同报警5秒内仅推送1次 @@ -15,24 +16,35 @@ ALERT_PUSH_URL = "http://123.57.151.210:10000/picenter/websocket/test/process" # 消失判定：中心点在ROI内消失后，持续无检测的帧数（1.0秒，可微调） ROI_LOST_FRAMES_THRESH = int(0.5 * RTSP_FPS) -# ========================= 5个ROI区域配置（相对坐标，适配任意分辨率） ========================= -# 格式：{ROI名称: [[x1,y1], [x2,y2], ...], ...} （多边形顶点，顺/逆时针均可） -# 相对坐标：x/y 0~1（0=左/上，1=右/下），可直接根据场景调整 -ROI_CONFIG = { - "left_door_1": [[0.195, 0.242], [0.265, 0.17], [0.3, 0.63] ,[0.248, 0.8]], # 左侧1门ROI - "left_door_2": [[0.3, 0.1], [0.34, 0.08], [0.35, 0.43], [0.322, 0.52]], # 左侧2门 ROI - "left_door_3": [[0.355, 0.06], [0.42, 0], [0.42, 0.18], [0.362, 0.36]], # 左侧3门ROI - "right_door_1": [[0.735, 0.142], [0.81, 0.22], [0.78, 0.8], [0.715, 0.65]], # 右侧1门 ROI - "right_door_2": [[0.65, 0.06], [0.7, 0.09], [0.69, 0.5], [0.65, 0.4]] # 右侧2门ROI +# ========================= 默认ROI区域配置（当config.yaml未配置时使用） ========================= +DEFAULT_DOOR_ROIS = { + "left_door_1": { + "points": [[0.195, 0.242], [0.265, 0.17], [0.3, 0.63], [0.248, 0.8]], + "color": [255, 0, 0] + } } # ================================================================================== class PrisonerDoorDetector: def __init__(self, params=None): self.params = params or {} - # 1. 加载YOLO模型（仅提取犯人检测结果） + + # 0. 从params解析ROI配置，无则使用默认值 + door_rois_config = self.params.get('door_rois', DEFAULT_DOOR_ROIS) + self.roi_config = {} + self.roi_colors = {} + for door_name, door_cfg in door_rois_config.items(): + self.roi_config[door_name] = door_cfg['points'] + self.roi_colors[door_name] = tuple(door_cfg['color']) + + model_path = self.params.get('model_path') + if model_path: + full_model_path = f"{MODEL_ROOT_PATH}/{model_path}" + else: + full_model_path = DETECT_MODEL_PATH + self.detector = YOLOv8_ONNX( - MODEL_PATH, + full_model_path, conf_threshold=0.5, # 置信度阈值，可根据模型精度调整 iou_threshold=0.45, # IOU阈值 input_size=INPUT_SIZE @@ -70,9 +82,9 @@ class PrisonerDoorDetector: def _get_roi_abs(self, roi_name): """相对坐标转绝对像素坐标（适配当前帧分辨率，OpenCV要求int32）""" - if roi_name not in ROI_CONFIG: + if roi_name not in self.roi_config: return None - roi_rel = np.array(ROI_CONFIG[roi_name], dtype=np.float64) + roi_rel = np.array(self.roi_config[roi_name], dtype=np.float64) roi_abs = roi_rel * np.array([self.frame_width, self.frame_height]) return roi_abs.astype(np.int32) @@ -119,23 +131,19 @@ class PrisonerDoorDetector: self.frame_height, self.frame_width = frame.shape[:2] current_frame_alerts = [] # 本帧报警信息 - # ========================= 1. 初始化ROI绝对坐标并绘制5个ROI ========================= - roi_colors = { # 各ROI绘制颜色（自定义区分） - "left_door_1": (255, 0, 0), "left_door_2": (0, 255, 0), - "left_door_3": (0, 0, 255), "right_door_1": (255, 255, 0), - "right_door_2": (255, 165, 0) - } + # ========================= 1. 初始化ROI绝对坐标并绘制ROI ========================= self.roi_abs_cache.clear() - for roi_name, _ in ROI_CONFIG.items(): + for roi_name in self.roi_config: roi_abs = self._get_roi_abs(roi_name) if roi_abs is None: continue self.roi_abs_cache[roi_name] = roi_abs # 绘制ROI多边形（闭合）+ ROI名称标签 roi_draw = roi_abs.reshape((-1, 1, 2)) # OpenCV绘制要求形状 (n,1,2) - cv2.polylines(frame, [roi_draw], isClosed=True, color=roi_colors[roi_name], thickness=2) + color = self.roi_colors.get(roi_name, (255, 255, 255)) + cv2.polylines(frame, [roi_draw], isClosed=True, color=color, thickness=2) cv2.putText(frame, roi_name, (roi_abs[0][0], roi_abs[0][1] - 5), - cv2.FONT_HERSHEY_SIMPLEX, 0.5, roi_colors[roi_name], 2) + cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2) # ========================= 2. 模型推理：仅提取犯人检测框 ========================= detect_results = self.detector(frame) diff --git a/config.yaml b/config.yaml index b3f1cc5..9e5b83c 100644 --- a/config.yaml +++ b/config.yaml @@ -48,22 +48,69 @@ video_clip_retention_seconds: 3600 # 视频文件 video_clip_default_segment_duration: 2 # 默认分片时长fallback（秒） service_groups: -- name: "kadian_group" # 服务组名称 +#- name: "kadian_group" # 服务组名称 +# video_source_type: "hls" +# ws_host: "0.0.0.0" # WebSocket 服务地址 +# ws_port: 8765 # WebSocket 服务端口 +# algorithm: "checkpoint" # 算法类型 +# cameras: # 该组下的摄像头列表 +# - id: 8 +# index: "12345" +# name: Entrance +# params: +# model_path: "Kadian_sanshijiazi.onnx" +# roi_points: +# - [0.15, 0.001] +# - [0.5, 0.001] +# - [1.0, 0.8] +# - [0.35, 1.0] +- name: "indoor_group" # 服务组名称 video_source_type: "hls" ws_host: "0.0.0.0" # WebSocket 服务地址 ws_port: 8765 # WebSocket 服务端口 - algorithm: "checkpoint" # 算法类型 + algorithm: "indoor" # 算法类型 cameras: # 该组下的摄像头列表 - id: 8 index: "12345" name: Entrance params: - model_path: "Kadian_sanshijiazi.onnx" - roi_points: - - [0.15, 0.001] - - [0.5, 0.001] - - [1.0, 0.8] - - [0.35, 1.0] + model_path: "kanshousuo.onnx" + door_rois: + left_door_1: + points: + - [0.195, 0.242] + - [0.265, 0.17] + - [0.3, 0.63] + - [0.248, 0.8] + color: [255, 0, 0] + left_door_2: + points: + - [0.3, 0.1] + - [0.34, 0.08] + - [0.35, 0.43] + - [0.322, 0.52] + color: [0, 255, 0] + left_door_3: + points: + - [0.355, 0.06] + - [0.42, 0.0] + - [0.42, 0.18] + - [0.362, 0.36] + color: [0, 0, 255] + right_door_1: + points: + - [0.735, 0.142] + - [0.81, 0.22] + - [0.78, 0.8] + - [0.715, 0.65] + color: [255, 255, 0] + right_door_2: + points: + - [0.65, 0.06] + - [0.7, 0.09] + - [0.69, 0.5] + - [0.65, 0.4] + color: [255, 165, 0] #- name: "prison_group" # 服务组名称 # video_source_type: "hls" # ws_host: "0.0.0.0" # WebSocket 服务地址