diff --git a/biz/checkpoint/checkpoint02_biz.py b/biz/checkpoint/checkpoint02_biz.py deleted file mode 100644 index 95d565d..0000000 --- a/biz/checkpoint/checkpoint02_biz.py +++ /dev/null @@ -1,888 +0,0 @@ -# rtsp_service_kadian.py -# 融合 Kadian_Detect_1221.py + rtsp_service_ws.py -# 支持多路RTSP、抽帧、分段保存MP4、WebSocket推送图像与告警 - -import cv2 -import numpy as np -import time -import threading -import queue - -import base64 - -from typing import Dict, Any, Tuple, List - - -# -------------------------- Kadian 检测相关导入 -------------------------- -from algorithm.checkpoint.npu_yolo_onnx_person_car_phone import YOLOv8_ONNX # 主检测模型（人/车/后备箱/手机） -from algorithm.checkpoint.npu_yolo_pose_onnx import YOLOv8_Pose_ONNX # Pose 专用模型 -from yolox.tracker.byte_tracker import BYTETracker - -from utils.logger import get_logger -logger = get_logger(__name__) - -# ========================= 配置区 ========================= -# Kadian 模型路径与ROI（可根据实际情况修改） -DETECT_MODEL_PATH = 'YOLO_Weight/car_opentrunk_person_phone.onnx' -POSE_MODEL_PATH = 'YOLO_Weight/yolov8l-pose.onnx' - - -# 三十家子101警务工作站1 -ROI_RELATIVE=np.array([ - [0.15,0.001], - [0.6,0.001], - [1.0, 0.7], - [1.0,1.0], - [0.35,1.0] -]) - -# 0088 -# ROI_RELATIVE=np.array([ -# [0.03,0.65], -# [0.25,0.60], -# -# [0.30,0.72], -# [0.05,0.87] -# ]) - -# 1008 -# ROI_RELATIVE=np.array([ -# [0.4,0.4], -# [0.58,0.4], -# -# [0.85,1.0], -# [0.55,1.0] -# ]) - -# 2108 -# ROI_RELATIVE=np.array([ -# [0.5,0.25], -# [0.63,0.25], -# -# [0.70,0.48], -# [0.5,0.48] -# ]) - - -# 6782 -# ROI_RELATIVE=np.array([ -# [0.4,0.2], -# [1.0,0.33], -# -# [1.0,0.99], -# [0.32,0.75] -# ]) - -# 新增：告警推送频率限制（秒） -ALERT_PUSH_INTERVAL = 1.0 # 相同action 5秒内仅推送一次 - -# 输入尺寸 -PERSON_CAR_INPUT_SIZE = 640 -POSE_INPUT_SIZE = 640 - -# RTSP 服务配置 -RTSP_TARGET_FPS = 10.0 - - -class KadianDetector: - def __init__(self, roi_points=ROI_RELATIVE): - # 模型加载 - # self.detector = YOLOv8_ONNX(DETECT_MODEL_PATH, conf_threshold=0.25, iou_threshold=0.45,input_size=PERSON_CAR_INPUT_SIZE) - self.detector = YOLOv8_ONNX(DETECT_MODEL_PATH, conf_threshold=0.15, iou_threshold=0.65, - input_size=PERSON_CAR_INPUT_SIZE) - - # self.pose_detector = YOLOv8_Pose_ONNX(POSE_MODEL_PATH, conf_threshold=0.7, iou_threshold=0.6,input_size=POSE_INPUT_SIZE) - self.pose_detector = YOLOv8_Pose_ONNX(POSE_MODEL_PATH, conf_threshold=0.45, iou_threshold=0.6, - input_size=POSE_INPUT_SIZE) - - # Tracker - # class TrackerArgs: - # track_thresh = 0.25 # 必须大于等于yolo的conf_threshold - # track_buffer = 30 - # match_thresh = 0.8 - # mot20 = False - class TrackerArgs: - track_thresh = 0.2 # 必须大于等于yolo的conf_threshold - track_buffer = 60 - match_thresh = 0.9 - mot20 = True - - self.tracker = BYTETracker(TrackerArgs(), frame_rate=RTSP_TARGET_FPS) - - self.track_role = {} - - self.fps = RTSP_TARGET_FPS - - # ROI 处理（支持相对/绝对） - # self.roi_points = roi_points.astype(np.int32) - self.roi_points = np.array(roi_points, dtype=np.float64) if roi_points is not None else None - - # ========================================== - # 超参数设置 (Hyperparameters) - # ========================================== - - # 1. 业务判定时间阈值 - self.TIME_THRESHOLD_ONLY_ONE = 10.0 # 单人单检判定时长 - self.TIME_THRESHOLD_NOBODY = 10.0 # 无人检查判定时长 - - # 后备箱检查判定阈值 - self.TIME_THRESHOLD_TRUNK_OPEN = 0.1 - - # 新增：手机检测判定阈值 - self.TIME_THRESHOLD_PHONE = 3.0 # 手机检测持续1秒（30帧 @30fps） - self.TIME_TOLERANCE_PHONE = 1.5 # 手机丢失缓冲时间（防抖动） - - # 新增：制服检测判定阈值 - self.TIME_THRESHOLD_UNIFORM = 2.0 # 制服不合规判定时长 - self.TIME_TOLERANCE_UNIFORM = 1.0 # 制服合规恢复缓冲时间 - - # 2. Person 丢帧缓冲 - self.TIME_TOLERANCE_PERSON = 3.0 - - # 车辆最小停留时间阈值 (小于此时间视为无人检查/直接通过) - self.TIME_THRESHOLD_CAR_MIN_DURATION = 10.0 - - # 3. Car 丢帧/ID维持缓冲 - self.TIME_TOLERANCE_CAR = 10.0 - - # 4 OnlyOne 丢帧缓冲 - self.TIME_TOLERANCE_ONLY_ONE_DURATION = 3.0 - - # --- 计算对应的帧数阈值 --- - self.frame_thresh_one = int(self.TIME_THRESHOLD_ONLY_ONE * self.fps) - self.frame_thresh_nobody = int(self.TIME_THRESHOLD_NOBODY * self.fps) - self.frame_thresh_trunk_valid = int(self.TIME_THRESHOLD_TRUNK_OPEN * self.fps) - - # 新增：手机检测帧数阈值 - self.frame_thresh_phone = int(self.TIME_THRESHOLD_PHONE * self.fps) - self.frame_buffer_phone = int(self.TIME_TOLERANCE_PHONE * self.fps) - - # 新增：制服检测帧数阈值 - self.frame_thresh_uniform = int(self.TIME_THRESHOLD_UNIFORM * self.fps) - self.frame_buffer_uniform = int(self.TIME_TOLERANCE_UNIFORM * self.fps) - - self.frame_thresh_car_min_duration = int(self.TIME_THRESHOLD_CAR_MIN_DURATION * self.fps) - - self.frame_buffer_limit_person = int(self.TIME_TOLERANCE_PERSON * self.fps) - self.frame_buffer_limit_car = int(self.TIME_TOLERANCE_CAR * self.fps) - self.frame_buffer_limit_onlyOne = int(self.TIME_TOLERANCE_ONLY_ONE_DURATION * self.fps) - - print(f"\n超参数设置:") - print(f" FPS: {self.fps:.2f}") - print(f" 判定 'Only One' / 'Nobody' 需连续: {self.frame_thresh_one} 帧") - print(f" 判定 'Trunk Checked' 需累计检测: {self.frame_thresh_trunk_valid} 帧") - print(f" 判定 'Phone Detected' 需累计检测: {self.frame_thresh_phone} 帧") - print(f" 手机丢失缓冲帧数: {self.frame_buffer_phone} 帧") - print(f" 判定 'Uniform Invalid' 需连续检测: {self.frame_thresh_uniform} 帧") - print(f" 制服合规恢复缓冲帧数: {self.frame_buffer_uniform} 帧") - print(f" 判定 'Too Fast' 最小停留: {self.frame_thresh_car_min_duration} 帧") - - self.onlyone_counter = 0 - # self.onlyone_lost_counter = 0 - # self.onlyone_buffer_limit = self.frame_buffer_limit_person # 10帧（1秒） - self.onlyone_thresh = self.frame_thresh_one # 30帧（3秒） - - self.nobody_counter = 0 - self.nobody_present_counter = 0 - self.nobody_buffer_limit = self.frame_buffer_limit_onlyOne - self.nobody_thresh = self.frame_thresh_nobody # 20帧（2秒） - - self.current_frame_idx = 0 - - self.ignore_show_seconds = 0.5 # 未检测的警告显示时长 - self.openTrunk_show_seconds = 0.5 # 打开后备箱的警告显示时长 - - # 手机检测状态变量（独立于车辆） - self.phone_detection_frames = 0 # 连续检测到手机的帧数 - self.phone_missing_frames = 0 # 连续未检测到手机的帧数 - self.phone_alert_active = False # 手机报警是否激活 - - # 新增：制服检测状态变量 - self.pose_person_count = 0 # 骨骼点模型检测的ROI内人员数量 - self.uniform_alert_active = False # 制服报警是否激活 - self.uniform_detection_frames = 0 # 连续检测到制服不合规的帧数 - self.uniform_recovery_frames = 0 # 连续恢复合规的帧数 - - # 车辆注册表 (字典) - self.roi_car_registry = {} - - # 违规车辆记录 (后备箱未检) - self.unchecked_trunk_alerts = {} - - # 违规车辆记录 (通过过快 -> 归类为 Ignore) - self.fast_pass_alerts = {} - - def _get_roi_points(self, frame_width: int, frame_height: int): - """ - 每帧动态计算正确的 ROI 绝对坐标，并确保类型为 np.int32 - 用于 pointPolygonTest 和 polylines - """ - if self.roi_points is None: - raise ValueError("ROI points must be provided; cannot be None.") - - if self.roi_points.max() <= 1.0: - # 相对坐标 → 转换为绝对 - roi_abs = self.roi_points * np.array([frame_width, frame_height]) - else: - # 绝对坐标，直接使用 - roi_abs = self.roi_points.copy() - - # 强制转为 int32（关键！解决 OpenCV 断言错误） - return roi_abs.astype(np.int32) - - def check_point_in_roi(self, roi_points, point): - return cv2.pointPolygonTest(roi_points, point, False) >= 0 - - def compute_iou(self, boxA, boxB): - # box = [x1, y1, x2, y2] - xA = max(boxA[0], boxB[0]) - yA = max(boxA[1], boxB[1]) - xB = min(boxA[2], boxB[2]) - yB = min(boxA[3], boxB[3]) - - interW = max(0, xB - xA) - interH = max(0, yB - yA) - interArea = interW * interH - - boxAArea = (boxA[2] - boxA[0]) * (boxA[3] - boxA[1]) - boxBArea = (boxB[2] - boxB[0]) * (boxB[3] - boxB[1]) - - unionArea = boxAArea + boxBArea - interArea - if unionArea == 0: - return 0.0 - - return interArea / unionArea - - def draw_alert(self, frame, text, color=(0, 0, 255), sub_text=None, offset_y=0): - """在右上角绘制警告文字 (支持垂直偏移，防止文字重叠)""" - font_scale = 1.5 - thickness = 3 - font = cv2.FONT_HERSHEY_SIMPLEX - - (text_w, text_h), _ = cv2.getTextSize(text, font, font_scale, thickness) - x = self.width - text_w - 20 - y = 50 + text_h + offset_y # 增加 Y 轴偏移 - - cv2.rectangle(frame, (x - 10, y - text_h - 10), (x + text_w + 10, y + 10), (0, 0, 0), -1) - cv2.putText(frame, text, (x, y), font, font_scale, color, thickness) - - if sub_text: - cv2.putText(frame, sub_text, (x, y + 40), font, 0.7, (200, 200, 200), 2) - - def is_point_in_box(self, point, box): - px, py = point - x1, y1, x2, y2 = box - return x1 < px < x2 and y1 < py < y2 - - def is_pose_inside_detector_person(self, pose_bbox, dets_xyxy, dets_roles): - """ - 判断一个 pose 人是否位于 detector 的 person 框内部（中心点匹配） - - 参数： - pose_bbox: [x1, y1, x2, y2] - dets_xyxy: detector 输出的所有 bbox 列表 - dets_roles: 对应的类别列表（如 "person", "car"...） - - 返回： - True -> 在某个人体框内部 - False -> 不在任何人体框内部 - """ - - px1, py1, px2, py2 = pose_bbox - cx, cy = (px1 + px2) // 2, (py1 + py2) // 2 - - for box, role in zip(dets_xyxy, dets_roles): - if role != "person": - continue - - dx1, dy1, dx2, dy2 = map(int, box) - - # 判断中心点是否在 detector person 框内 - if dx1 <= cx <= dx2 and dy1 <= cy <= dy2: - return True - - return False - - def count_pose_inside_detector_person(self, pose_results, dets_xyxy, dets_roles): - """ - 统计有多少个pose框在detector person框内部 - - 参数： - pose_results: pose检测结果列表，每个元素为字典，包含'bbox'键，值为[x1, y1, x2, y2] - dets_xyxy: detector输出的所有bbox列表 - dets_roles: 对应的类别列表（如 "person", "car"...） - - 返回： - int: 在detector person框内部的pose框数量 - """ - count = 0 - for pose in pose_results: - pose_bbox = pose['bbox'] # [x1, y1, x2, y2] - if self.is_pose_inside_detector_person(pose_bbox, dets_xyxy, dets_roles): - count += 1 - return count - - def process_frame(self, frame, camera_id: int, timestamp: float) -> Dict[str, Any]: - h, w = frame.shape[:2] - self.width, self.height = w, h - - self.current_frame_idx += 1 - # ========= 每帧动态获取正确的 ROI（int32）========= - roi_points_int32 = self._get_roi_points(w, h) # shape: (4, 2), dtype: int32 - roi_points_draw = roi_points_int32.reshape((-1, 1, 2)) # shape: (4, 1, 2) 用于绘制 - - current_time_sec = timestamp - - pose_results = self.pose_detector(frame) - - # ========= 主检测 ========= - detections = self.detector(frame) - - dets_xyxy = [] - dets_roles = [] - dets_for_tracker = [] - - # ========= 当前帧所有警告列表（关键改动）========== - current_frame_alerts = [] # 每帧清空，重新收集 - - if detections: - for det in detections: - x1, y1, x2, y2, conf, cls_id = det # x1, y1, x2, y2为角点坐标，x1 y1为左上角，x2 y2为右下角 - dets_xyxy.append([x1, y1, x2, y2]) - dets_for_tracker.append([x1, y1, x2, y2, conf]) - if cls_id == 0: - dets_roles.append("car") - - elif cls_id == 1: - dets_roles.append("opentrunk") - - elif cls_id == 2: - dets_roles.append("person") - - elif cls_id == 3: - dets_roles.append("phone") - # print(f'dets_roles: {dets_roles}') - - dets = np.array(dets_for_tracker, dtype=np.float32) if len(dets_for_tracker) else np.empty((0, 5)) - - tracks = self.tracker.update( - dets, - [self.height, self.width], - [self.height, self.width] - ) - # print("tracks: {}".format(tracks)) - # 绘制骨骼 - frame = YOLOv8_Pose_ONNX.draw_keypoints(frame, pose_results) - # ========= 绘制 ROI ========= - cv2.polylines(frame, [roi_points_draw], isClosed=True, color=(255, 0, 0), thickness=3) - - # ========= 单帧统计变量 ========= - current_roi_person_count = 0 - current_roi_trunk_count = 0 - current_roi_phone_count = 0 - - # 临时存储本帧的目标，用于后续关联分析 - current_cars = [] # {'id':, 'box':} - current_trunks = [] # (cx, cy) - - for t in tracks: - # print("t: {}".format(t)) - tid = t.track_id - # cls_id = -1 - - # IoU 匹配角色 - # if tid not in track_role and dets_xyxy: - REVALIDATE_FRAME_INTERVAL = 10 - # if tid not in self.track_role: - if (self.current_frame_idx % REVALIDATE_FRAME_INTERVAL == 0) or (tid not in self.track_role): - best_iou = 0 - best_role = "unknown" - - t_box = list(map(float, t.tlbr)) # [x1,y1,x2,y2] - - for i, box in enumerate(dets_xyxy): - iou_val = self.compute_iou(t_box, box) - if iou_val > best_iou: - best_iou = iou_val - best_role = dets_roles[i] - if best_iou > 0.1: - self.track_role[tid] = best_role - else: - self.track_role[tid] = "unknown" - - role = self.track_role.get(tid, "unknown") - cls_id = -1 - if role == "car": - cls_id = 0 - elif role == "opentrunk": - cls_id = 1 - elif role == "person": - cls_id = 2 - elif role == "phone": - cls_id = 3 - # print("tid: {}, role: {}, cls: {}".format(tid, role,cls_id)) - - x1, y1, x2, y2 = map(int, t.tlbr) - - cx, cy = (x1 + x2) // 2, (y1 + y2) // 2 - - color = None - label = None - - if self.check_point_in_roi(roi_points_int32, (cx, cy)): - if cls_id == 0: # Car - color = (0, 255, 0) - - current_cars.append({'id': tid, 'box': [x1, y1, x2, y2]}) - - if tid not in self.roi_car_registry: - self.roi_car_registry[tid] = { - 'first_seen': self.current_frame_idx, - 'last_seen': self.current_frame_idx, - 'trunk_frames': 0, - 'is_checked': False, - } - else: - self.roi_car_registry[tid]['last_seen'] = self.current_frame_idx - - label = f"Car:{tid} IN" - - elif cls_id == 1: # Opentrunk - current_roi_trunk_count += 1 - color = (255, 165, 0) - current_trunks.append((cx, cy)) - label = "OpenTrunk IN" - - elif cls_id == 2: # Person - current_roi_person_count += 1 - color = (255, 0, 255) - label = "Person IN" - - elif cls_id == 3: # Phone（主模型已支持） - current_roi_phone_count += 1 - color = (0, 0, 139) - - else: - color = (255, 255, 255) - label = "Unknown" - - # label = f"ID:{tid} IN" - - # 特殊显示: 如果这辆车已经合格，框变蓝色 - if cls_id == 0 and tid in self.roi_car_registry and self.roi_car_registry[tid][ - 'is_checked']: - color = (255, 255, 0) # Cyan for checked cars - label += " (Checked)" - else: - color = (0, 0, 255) - label = "OUT" - - cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2) - cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2) - - # ========================================== - # 4. 从骨骼点模型中统计ROI内人员数量 - # ========================================== - self.pose_person_count = 0 - # if pose_results[0].boxes is not None: - # pose_boxes = pose_results[0].boxes - # for box in pose_boxes: - # # 获取人体检测框的中心点 - # x1, y1, x2, y2 = map(int, box.xyxy[0]) - # cx, cy = (x1 + x2) // 2, (y1 + y2) // 2 - # - # # 判断中心点是否在ROI内 - # if self.check_point_in_roi((cx, cy)): - # self.pose_person_count += 1 - - if pose_results: - for pose in pose_results: - - x1, y1, x2, y2 = pose['bbox'][0], pose['bbox'][1], pose['bbox'][2], pose['bbox'][3] - cx, cy = (x1 + x2) // 2, (y1 + y2) // 2 - # 判断中心点是否在ROI内 - if self.check_point_in_roi(roi_points_int32, (cx, cy)): - self.pose_person_count += 1 - - # 统计pose框在detector person框内部的数量 - pose_inside_count = self.count_pose_inside_detector_person(pose_results, dets_xyxy, dets_roles) - - # ========================================== - # 5. 关联分析: 哪个后备箱属于哪辆车? - # ========================================== - for car_info in current_cars: - c_id = car_info['id'] # 车的id - c_box = car_info['box'] # 车的框 - - trunk_found_for_this_car = False # 开后备箱标记 - for t_pt in current_trunks: - if self.is_point_in_box(t_pt, c_box): # 如果开后备箱的框在车的框内,就设置开后备箱标记为true - trunk_found_for_this_car = True - break - - if trunk_found_for_this_car: # 如果当前车辆的开后备箱标记为true了,就设置开了后备箱的帧数+1,凑够了判断“开后备箱”这个动作的帧数之后,就设置该车"已检查" - self.roi_car_registry[c_id]['trunk_frames'] += 1 - if self.roi_car_registry[c_id]['trunk_frames'] >= self.frame_thresh_trunk_valid: - self.roi_car_registry[c_id]['is_checked'] = True - - # ========================================== - # 6. 独立的手机检测逻辑（不与车辆绑定） - # ========================================== - if current_roi_phone_count > 0: - # 检测到手机框 - self.phone_detection_frames += 1 - self.phone_missing_frames = 0 # 重置丢失计数器 - - # 当检测累计达到阈值时，激活报警 - if self.phone_detection_frames >= self.frame_thresh_phone: - self.phone_alert_active = True - else: - # 未检测到手机框 - self.phone_missing_frames += 1 - - # 如果之前检测到手机，重置检测计数器 - if self.phone_detection_frames > 0: - # 只有在连续丢失超过缓冲帧数时才重置 - if self.phone_missing_frames >= self.frame_buffer_phone: - self.phone_detection_frames = 0 - self.phone_alert_active = False - else: - # 从未检测到手机，保持状态 - pass - - # ========================================== - # 7. 制服检测逻辑（比较两个模型的人员数量） - # ========================================== - # 比较骨骼点模型和业务检测模型的人员数量 - uniform_invalid = False - - if self.pose_person_count > current_roi_person_count: - # 骨骼点模型检测到的人员多于业务检测模型 - # 说明有人没穿执勤制服 - uniform_invalid = True - self.uniform_detection_frames += 1 - self.uniform_recovery_frames = 0 # 重置恢复计数器 - - # 当连续检测不合规达到阈值时，激活报警 - if self.uniform_detection_frames >= self.frame_thresh_uniform: - self.uniform_alert_active = True - else: - # 人员数量匹配或业务模型检测更多（理论上不会） - self.uniform_recovery_frames += 1 - - # 如果之前有不合规检测，检查是否需要关闭报警 - if self.uniform_detection_frames > 0: - # 只有在连续合规超过缓冲帧数时才重置 - if self.uniform_recovery_frames >= self.frame_buffer_uniform: - self.uniform_detection_frames = 0 - self.uniform_alert_active = False - else: - # 从未检测到不合规，保持状态 - pass - - # ========================================== - # 8. 维护车辆注册表 & 生成离场报警 - # ========================================== - active_car_ids = [] - cars_to_remove = [] - - for car_id, info in self.roi_car_registry.items(): - # 遍历所有车辆，如果当前帧时间-该车辆最后可见的时间得到的值大于车辆消失时间阈值的话,就把该车添加到移除列表中,否则添加到活跃列表中 - last_seen = info['last_seen'] - - if (self.current_frame_idx - last_seen) <= self.frame_buffer_limit_car: - active_car_ids.append(car_id) - else: - cars_to_remove.append(car_id) - - # 执行删除 并 检查违规 - for car_id in cars_to_remove: - # 遍历所有移除列表中的车辆, - # 如果该车辆最后出现时间-最早出现时间的值小于车辆最小存在时间,则判断为ignore, - # 如果该车辆的“已检查”标记为true,则 - # 最后在所有车辆列表中删除该车辆 - - car_info = self.roi_car_registry[car_id] - - duration_frames = car_info['last_seen'] - car_info['first_seen'] - - # 情况1：通过时间太短 -> 归类为 Ignore (Too Fast) - if duration_frames < self.frame_thresh_car_min_duration: - print(f"ALARM: Car {car_id} passed too fast -> Regarded as Ignore Checked!") - self.fast_pass_alerts[car_id] = self.current_frame_idx + int(self.ignore_show_seconds * self.fps) - - # 情况2：时间够长，但没检查后备箱 -> Unchecked Trunk - elif not car_info['is_checked']: - print(f"ALARM: Car {car_id} left without checking trunk!") - self.unchecked_trunk_alerts[car_id] = self.current_frame_idx + int( - self.openTrunk_show_seconds * self.fps) - - del self.roi_car_registry[car_id] - - effective_car_count = len(active_car_ids) - - # ========================================== - # 9. 业务逻辑判定 (Only One / Nobody) - 重构版 - # ========================================== - if effective_car_count >= 0: # 只要没人就检测,不用等到来了车再检测 - # ----- 定义条件 ----- - onlyone_condition = (pose_inside_count == 1) - nobody_condition = (current_roi_person_count == 0 and self.pose_person_count == 0) - - # ----- Onlyone 计数器更新 ----- - if onlyone_condition: # 如果骨骼点和检测框都检测到了只有一个人时,onlyone+1,当onlyone累计够了之后触发报警 - self.onlyone_counter += 1 - # self.onlyone_lost_counter = 0 - elif current_roi_person_count > 1 or self.pose_person_count > 1: - self.onlyone_counter = 0 - # if self.onlyone_counter > 0: - # self.onlyone_lost_counter += 1 - # if self.onlyone_lost_counter > self.onlyone_buffer_limit: - # self.onlyone_counter = 0 - # self.onlyone_lost_counter = 0 - - # ----- Nobody 计数器更新 ----- - if nobody_condition: - self.nobody_counter += 1 - # self.nobody_present_counter = 0 - elif current_roi_person_count > 0 or self.pose_person_count > 0: - self.nobody_counter = 0 - # if self.nobody_counter > 0: - # self.nobody_present_counter += 1 - # if self.nobody_present_counter > self.nobody_buffer_limit: - # self.nobody_counter = 0 - # self.nobody_present_counter = 0 - - else: - # 无活跃车辆，清零所有计数器 - self.onlyone_counter = 0 - # self.onlyone_lost_counter = 0 - self.nobody_counter = 0 - self.nobody_present_counter = 0 - - # ========================================== - # 10. 显示报警 (UI分层优化) - # ========================================== - - # 更新调试信息，包含所有检测状态 - phone_status = f"Phone: {current_roi_phone_count}" - if self.phone_alert_active: - phone_status += " (ALERT)" - - uniform_status = f"Uniform: Pose={self.pose_person_count}, Model={current_roi_person_count}" - if self.uniform_alert_active: - uniform_status += " (INVALID!)" - - debug_info = f"Cars: {len(active_car_ids)} | Person: {current_roi_person_count} | Trunk: {current_roi_trunk_count} | {phone_status}" - cv2.putText(frame, debug_info, (20, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2) - cv2.putText(frame, uniform_status, (20, 70), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2) - - # 使用 offset 实现报警堆叠，防止遮挡 - alert_offset = 0 - - # A. 显示 Only One（当累积帧数达到阈值时） - if self.onlyone_counter >= self.onlyone_thresh: - current_frame_alerts.append({'time': current_time_sec, 'action': "Only One"}) - self.draw_alert(frame, "Only One", (0, 255, 255), None, offset_y=alert_offset) - alert_offset += 100 - - # B. 显示 Nobody（当累积帧数达到阈值时） - elif self.nobody_counter >= self.nobody_thresh: - current_frame_alerts.append({'time': current_time_sec, 'action': "Nobody"}) - self.draw_alert(frame, "Nobody", (0, 0, 255), None, offset_y=alert_offset) - alert_offset += 100 - - # C. 显示 Trunk Checked (在车辆存活期间) - for car_id in active_car_ids: - if car_id in self.roi_car_registry and self.roi_car_registry[car_id]['is_checked']: - current_frame_alerts.append( - { - 'time': current_time_sec, - 'action': "Trunk Checked", - } - ) - self.draw_alert(frame, "Trunk Checked!!", (0, 255, 0), offset_y=alert_offset) - alert_offset += 100 - break # 只显示一次 - - # D. 显示 Playing Phone（独立检测，不与车辆绑定） - if self.phone_alert_active: - # 可以显示检测的持续时间 - duration_seconds = self.phone_detection_frames / self.fps - # sub_text = f"Detected for {duration_seconds:.1f}s" - current_frame_alerts.append( - { - 'time': current_time_sec, - 'action': "Playing Phone", - } - ) - self.draw_alert(frame, "Playing Phone", (255, 0, 0), None, offset_y=alert_offset) - alert_offset += 100 - - # E. 新增：显示 Unvaild Uniform!! - if self.uniform_alert_active: - current_frame_alerts.append( - { - 'time': current_time_sec, - 'action': "Unvaild Uniform!!", - } - ) - self.draw_alert(frame, "Unvaild Uniform!!", (255, 165, 0), None, offset_y=alert_offset) - alert_offset += 100 - - # 第二层：离场违规 (Post-Event Alerts) - # ------------------------------------------------ - - # F. 显示 Unchecked Trunk - expired_alerts = [cid for cid, end_frame in self.unchecked_trunk_alerts.items() if - self.current_frame_idx > end_frame] - for cid in expired_alerts: - del self.unchecked_trunk_alerts[cid] - - if len(self.unchecked_trunk_alerts) > 0: - alert_text = f"Unchecked Trunk! (ID:{list(self.unchecked_trunk_alerts.keys())})" - current_frame_alerts.append( - { - 'time': current_time_sec, - 'action': "Unchecked Trunk", - } - ) - self.draw_alert(frame, alert_text, (0, 0, 255), offset_y=alert_offset) - alert_offset += 100 - - # G. 显示 Ignore (离场结果) - expired_fast_alerts = [cid for cid, end_frame in self.fast_pass_alerts.items() if - self.current_frame_idx > end_frame] - for cid in expired_fast_alerts: - del self.fast_pass_alerts[cid] - - if len(self.fast_pass_alerts) > 0: - alert_text = f"Ignore: (ID:{list(self.fast_pass_alerts.keys())})" - current_frame_alerts.append( - { - 'time': current_time_sec, - 'action': "Ignore", - } - ) - self.draw_alert(frame, alert_text, (0, 0, 255), offset_y=alert_offset) - alert_offset += 100 - - return { - "image": frame, - "alerts": current_frame_alerts, - } - - -# ========================= 帧处理线程 ========================= -class FrameProcessorWorker(threading.Thread): - def __init__(self, raw_queue: queue.Queue, ws_queue: queue.Queue, stop_event: threading.Event): - super().__init__(daemon=True) - self.raw_queue = raw_queue - self.ws_queue = ws_queue - self.stop_event = stop_event - - self.last_ts: Dict[int, float] = {} - - # 每个摄像头一个独立的 Kadian 检测器实例 - self.kadian_detectors: Dict[int, KadianDetector] = {} - - self.last_alert_push_time: Dict[int, Dict[str, float]] = {} - - def _encode_base64(self, img): - _, buf = cv2.imencode(".jpg", img) - return base64.b64encode(buf).decode("ascii") - - def run(self): - target_interval = 1.0 / RTSP_TARGET_FPS - while not self.stop_event.is_set(): - try: - item = self.raw_queue.get(timeout=0.5) - except queue.Empty: - continue - - try: - cam_id = item["camera_id"] - ts = item["timestamp"] - frame = item["frame"] - - # 抽帧控制 - if ts - self.last_ts.get(cam_id, 0) < target_interval: - # self.raw_queue.task_done() - continue - self.last_ts[cam_id] = ts - - # 获取检测器实例 - if cam_id not in self.kadian_detectors: - self.kadian_detectors[cam_id] = KadianDetector() - detector = self.kadian_detectors[cam_id] - - # 执行检测 - # detect_start = time.time() - result = detector.process_frame(frame.copy(), cam_id, ts) - # detect_time = (time.time() - detect_start) * 1000 - - result_img = result["image"] - result_type = result["alerts"] - # logger.debug(f"alerts: {result_type}") - - # ========= 核心修改：过滤5秒内重复的action ========= - # 初始化当前摄像头的推送时间记录 - if cam_id not in self.last_alert_push_time: - self.last_alert_push_time[cam_id] = {} - - # 筛选出符合推送条件的action（5秒内未推送过） - push_actions = [] - current_time = time.time() - for alert in result_type: - action = alert['action'] - last_push = self.last_alert_push_time[cam_id].get(action, 0) - # 检查是否超过推送间隔 - if current_time - last_push >= ALERT_PUSH_INTERVAL: - push_actions.append(action) - # 更新该action的最后推送时间 - self.last_alert_push_time[cam_id][action] = current_time - - # 通过 WebSocket 发送帧结果 - # encode_start = time.time() - try: - img_b64 = self._encode_base64(result_img) - except Exception as e: - logger.error(f"[ERROR] Encode image failed: {e}") - img_b64 = None - # encode_time = (time.time() - encode_start) * 1000 - - if img_b64 is not None: - # 将abnormal_actions对象数组转换为字符串数组 - # action_names = [action_info['action'] for action_info in push_actions] - - msg = { - "msg_type": "frame", - "camera_id": 0, - "timestamp": ts, - # "result_type": action_names, - "result_type": push_actions, - "image_base64": img_b64, - } - try: - self.ws_queue.put(msg, timeout=1.0) - # if push_actions and len(push_actions) > 0: - # self.ws_queue_2.put(msg, timeout=1.0) - except queue.Full: - logger.warning("[WARN] ws_send_queue full, drop frame message") - - # # 打印关键操作的耗时 - # total_time = detect_time + encode_time - # logger.info(f"[PERF] Camera {cam_id} - Total: {total_time:.1f}ms | " - # f"Detect: {detect_time:.1f}ms | " - # f"Encode: {encode_time:.1f}ms | ") - - except Exception as e: - logger.error( - f"[ERROR] Frame processing failed for camera {cam_id if 'cam_id' in locals() else 'unknown'}: {e}") - logger.exception("Exception details:") # 打印完整的堆栈跟踪 - # 继续处理下一帧，不要退出循环 - finally: - self.raw_queue.task_done() - - - diff --git a/biz/supervisionRoom/supervisionRoom_biz.py b/biz/supervisionRoom/supervisionRoom_biz.py deleted file mode 100644 index b6b8973..0000000 --- a/biz/supervisionRoom/supervisionRoom_biz.py +++ /dev/null @@ -1,606 +0,0 @@ -# rtsp_service_kadian.py -# 融合 Kadian_Detect_1221.py + rtsp_service_ws.py -# 支持多路RTSP、抽帧、分段保存MP4、WebSocket推送图像与告警 - -import cv2 -import numpy as np -import os -import time -import threading -import queue -import yaml -import json -import base64 - -from typing import Dict, Any, Tuple, List - - -# -------------------------- Kadian 检测相关导入 -------------------------- -from algorithm.checkpoint.npu_yolo_onnx_person_car_phone import YOLOv8_ONNX # 主检测模型（人/车/后备箱/手机） - -from yolox.tracker.byte_tracker import BYTETracker -from utils.logger import get_logger -logger = get_logger(__name__) - -# ========================= 配置区 ========================= -Person_Phone_Model = r'D:\Python_Save\PoliceProject\Yolo_Weight\person_phone_model.onnx' # 人和手机的检测模型 -Smoke_Model = r'D:\Python_Save\PoliceProject\Yolo_Weight\smoke_model.onnx' # 抽烟检测模型 - -person_phone_input_size = 1280 # 模型输入尺寸，与训练时的模型一致 -smoke_input_size = 1280 # 模型输入尺寸，与训练时的模型一致 - -# RTSP 服务配置 -RTSP_TARGET_FPS = 5.0 - - -# 新增：告警推送频率限制（秒） -ALERT_PUSH_INTERVAL = 5.0 # 相同action 5秒内仅推送一次 - - -class ZhihuishiDetector: - def __init__(self): - # 模型加载 - - # 人和手机检测模型 - print(f"加载人和手机检测模型: {Person_Phone_Model}") - self.person_phone_detector = YOLOv8_ONNX(Person_Phone_Model, conf_threshold=0.6, iou_threshold=0.45, - input_size=person_phone_input_size) - - # 抽烟检测模型 - print(f"加载抽烟检测模型: {Smoke_Model}") - self.smoke_detector = YOLOv8_ONNX(Smoke_Model, conf_threshold=0.4, iou_threshold=0.65, - input_size=smoke_input_size) - - # ByteTracker - class TrackerArgs: - track_thresh = 0.25 - track_buffer = 30 - match_thresh = 0.8 - mot20 = False - - self.fps = RTSP_TARGET_FPS - - self.person_phone_tracker = BYTETracker(TrackerArgs(), frame_rate=self.fps) - self.smoke_tracker = BYTETracker(TrackerArgs(), frame_rate=self.fps) - - self.person_phone_track_role = {} - self.smoke_track_role = {} - - - # ========================================== - # 超参数设置 (Hyperparameters) - # ========================================== - - # 1. 业务判定时间阈值 - self.TIME_THRESHOLD_NOBODY = 2.0 # 无人在场判定时长 - self.TIME_TOLERANCE_NOBODY = 2.0 # 人丢失缓冲时间 - - self.TIME_THRESHOLD_SMOKE = 1.0 # 抽烟判定时长 - self.TIME_TOLERANCE_SMOKE = 0.5 # 烟丢失缓冲时间（防抖动） - - self.TIME_THRESHOLD_PHONE = 1.0 # 玩手机判定时长 - self.TIME_TOLERANCE_PHONE = 0.5 # 手机丢失缓冲时间（防抖动） - - # 无人在场帧数阈值 - self.frame_thresh_nobody = int(self.TIME_THRESHOLD_NOBODY * self.fps) - self.frame_buffer_nobody = int(self.TIME_TOLERANCE_NOBODY * self.fps) - - # 抽烟检测帧数阈值 - self.frame_thresh_smoke = int(self.TIME_THRESHOLD_SMOKE * self.fps) - self.frame_buffer_smoke = int(self.TIME_TOLERANCE_SMOKE * self.fps) - - # 手机检测帧数阈值 - self.frame_thresh_phone = int(self.TIME_THRESHOLD_PHONE * self.fps) - self.frame_buffer_phone = int(self.TIME_TOLERANCE_PHONE * self.fps) - - print(f"\n超参数设置:") - print(f" FPS: {self.fps:.2f}") - print(f" 判定 'Nobody' 需连续: {self.frame_thresh_nobody} 帧") - print(f" 判定 'Smoke Detected' 需累计检测: {self.frame_thresh_smoke} 帧") - print(f" 抽烟丢失缓冲帧数: {self.frame_buffer_smoke} 帧") - print(f" 判定 'Phone Detected' 需累计检测: {self.frame_thresh_phone} 帧") - print(f" 手机丢失缓冲帧数: {self.frame_buffer_phone} 帧") - - # ========================================== - # 状态变量初始化 - # ========================================== - - self.current_frame_idx = 0 - - # 无人在场检测状态变量 - self.nobody_detection_frames = 0 - self.nobody_missing_frames = 0 # 连续未检测到手机的帧数 - self.nobody_alert_active = False # 手机报警是否激活 - - # 手机检测状态变量 - self.phone_detection_frames = 0 # 连续检测到手机的帧数 - self.phone_missing_frames = 0 # 连续未检测到手机的帧数 - self.phone_alert_active = False # 手机报警是否激活 - - # 抽烟检测状态变量 - self.smoke_detection_frames = 0 # 连续检测到手机的帧数 - self.smoke_missing_frames = 0 # 连续未检测到手机的帧数 - self.smoke_alert_active = False # 手机报警是否激活 - - - def compute_iou(self,boxA, boxB): - # box = [x1, y1, x2, y2] - xA = max(boxA[0], boxB[0]) - yA = max(boxA[1], boxB[1]) - xB = min(boxA[2], boxB[2]) - yB = min(boxA[3], boxB[3]) - - interW = max(0, xB - xA) - interH = max(0, yB - yA) - interArea = interW * interH - - boxAArea = (boxA[2] - boxA[0]) * (boxA[3] - boxA[1]) - boxBArea = (boxB[2] - boxB[0]) * (boxB[3] - boxB[1]) - - unionArea = boxAArea + boxBArea - interArea - if unionArea == 0: - return 0.0 - - return interArea / unionArea - - def draw_alert(self, frame, text, color=(0, 0, 255), sub_text=None, offset_y=0): - """在右上角绘制警告文字 (支持垂直偏移，防止文字重叠)""" - font_scale = 1.5 - thickness = 3 - font = cv2.FONT_HERSHEY_SIMPLEX - - (text_w, text_h), _ = cv2.getTextSize(text, font, font_scale, thickness) - x = self.width - text_w - 20 - y = 50 + text_h + offset_y # 增加 Y 轴偏移 - - cv2.rectangle(frame, (x - 10, y - text_h - 10), (x + text_w + 10, y + 10), (0, 0, 0), -1) - cv2.putText(frame, text, (x, y), font, font_scale, color, thickness) - - if sub_text: - cv2.putText(frame, sub_text, (x, y + 40), font, 0.7, (200, 200, 200), 2) - - def process_frame(self, frame, camera_id: int, timestamp: float) -> Dict[str, Any]: - h, w = frame.shape[:2] - self.width, self.height = w, h - - self.current_frame_idx += 1 - - current_time_sec = timestamp - - # ========= 人和手机检测 ========= - person_phone_results = self.person_phone_detector(frame) - - # ========= 抽烟检测 ========= - smoke_results = self.smoke_detector(frame) - - person_phone_dets_xyxy = [] - person_phone_dets_roles = [] - person_phone_dets_for_tracker = [] - - smoke_dets_xyxy = [] - smoke_dets_roles = [] - smoke_dets_for_tracker = [] - - # ========= 当前帧所有警告列表（关键改动）========== - current_frame_alerts = [] # 每帧清空，重新收集 - - # 收集 人和手机的检测结果 - if person_phone_results: - for det in person_phone_results: - x1, y1, x2, y2, conf, cls_id = det # x1, y1, x2, y2为角点坐标，x1 y1为左上角，x2 y2为右下角 - person_phone_dets_xyxy.append([x1, y1, x2, y2]) - person_phone_dets_for_tracker.append([x1, y1, x2, y2, conf]) - if cls_id == 0: - person_phone_dets_roles.append("phone") - elif cls_id == 1: - person_phone_dets_roles.append("police") - - person_phone_dets = np.array(person_phone_dets_for_tracker, dtype=np.float32) if len( - person_phone_dets_for_tracker) else np.empty((0, 5)) - - person_phone_tracks = self.person_phone_tracker.update( - person_phone_dets, - [self.height, self.width], - [self.height, self.width] - ) - - # 收集 抽烟的检测结果 - if smoke_results: - for det in smoke_results: - x1, y1, x2, y2, conf, cls_id = det - smoke_dets_xyxy.append([x1, y1, x2, y2]) - smoke_dets_for_tracker.append([x1, y1, x2, y2, conf]) - if cls_id == 0: - smoke_dets_roles.append("smoke") - - smoke_dets = np.array(smoke_dets_for_tracker, dtype=np.float32) if len( - smoke_dets_for_tracker) else np.empty((0, 5)) - - smoke_tracks = self.smoke_tracker.update( - smoke_dets, - [self.height, self.width], - [self.height, self.width] - ) - - # ========= 单帧统计变量 ========= - current_person_count = 0 - current_phone_count = 0 - current_smoke_count = 0 - - # ========= 人和手机检测 ========= - for t in person_phone_tracks: - # print("t: {}".format(t)) - tid = t.track_id - # cls_id = -1 - - # IoU 匹配角色 - # IoU匹配跟踪ID和类别 - REVALIDATE_FRAME_INTERVAL = 10 - if (self.current_frame_idx % REVALIDATE_FRAME_INTERVAL == 0) or (tid not in self.person_phone_track_role): - #if tid not in self.person_phone_track_role: - best_iou = 0 - best_role = "unknown" - - t_box = list(map(float, t.tlbr)) # [x1,y1,x2,y2] - - for i, box in enumerate(person_phone_dets_xyxy): - iou_val = self.compute_iou(t_box, box) - if iou_val > best_iou: - best_iou = iou_val - best_role = person_phone_dets_roles[i] - if best_iou > 0.1: - self.person_phone_track_role[tid] = best_role - else: - self.person_phone_track_role[tid] = "unknown" - - role = self.person_phone_track_role.get(tid, "unknown") - cls_id = -1 - if role == "phone": - cls_id = 0 - elif role == "police": - cls_id = 1 - # print("tid: {}, role: {}, cls: {}".format(tid, role,cls_id)) - - x1, y1, x2, y2 = map(int, t.tlbr) - - cx, cy = (x1 + x2) // 2, (y1 + y2) // 2 - - color = None - label = None - - if cls_id == 0: # Person - current_phone_count += 1 - color = (255, 0, 255) - label = "Phone" - - elif cls_id == 1: # Phone（主模型已支持） - current_person_count += 1 - color = (0, 0, 139) - label = "Person" - - else: - color = (255, 255, 255) - label = "Unknown" - - # label = f"ID:{tid} IN" - - cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2) - cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2) - - # ========= 抽烟检测 ========= - for t in smoke_tracks: - # print("t: {}".format(t)) - tid = t.track_id - # cls_id = -1 - - # IoU 匹配角色 - # IoU匹配跟踪ID和类别 - REVALIDATE_FRAME_INTERVAL = 10 - if (self.current_frame_idx % REVALIDATE_FRAME_INTERVAL == 0) or (tid not in self.smoke_track_role): - #if tid not in self.smoke_track_role: - best_iou = 0 - best_role = "unknown" - - t_box = list(map(float, t.tlbr)) # [x1,y1,x2,y2] - - for i, box in enumerate(smoke_dets_xyxy): - iou_val = self.compute_iou(t_box, box) - if iou_val > best_iou: - best_iou = iou_val - best_role = smoke_dets_roles[i] - # self.smoke_track_role[tid] = best_role - if best_iou > 0.1: - self.smoke_track_role[tid] = best_role - else: - self.smoke_track_role[tid] = "unknown" - - role = self.smoke_track_role.get(tid, "unknown") - cls_id = -1 - if role == "smoke": - cls_id = 0 - - x1, y1, x2, y2 = map(int, t.tlbr) - - cx, cy = (x1 + x2) // 2, (y1 + y2) // 2 - - color = None - label = None - - if cls_id == 0: # 抽烟 - current_smoke_count += 1 - color = (255, 255, 0) - label = "Smoke" - - else: - color = (255, 255, 255) - label = "Unknown" - - # label = f"ID:{tid} IN" - - cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2) - cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2) - - # ========================================== - # 手机检测 - # ========================================== - if current_phone_count > 0: - # 检测到手机框 - self.phone_detection_frames += 1 - self.phone_missing_frames = 0 # 重置丢失计数器 - - # 当检测累计达到阈值时，激活报警 - if self.phone_detection_frames >= self.frame_thresh_phone: - self.phone_alert_active = True - else: - # 未检测到手机框 - self.phone_missing_frames += 1 - - # 如果之前检测到手机，重置检测计数器 - if self.phone_detection_frames > 0: - # 只有在连续丢失超过缓冲帧数时才重置 - if self.phone_missing_frames >= self.frame_buffer_phone: - self.phone_detection_frames = 0 - self.phone_alert_active = False - else: - # 从未检测到手机，保持状态 - pass - - # ========================================== - # 抽烟检测 - # ========================================== - if current_smoke_count > 0: - # 检测到抽烟框 - self.smoke_detection_frames += 1 - self.smoke_missing_frames = 0 # 重置丢失计数器 - - # 当检测累计达到阈值时，激活报警 - if self.smoke_detection_frames >= self.frame_thresh_smoke: - self.smoke_alert_active = True - else: - # 未检测到抽烟框 - self.smoke_missing_frames += 1 - - # 如果之前检测到抽烟，重置检测计数器 - if self.smoke_detection_frames > 0: - # 只有在连续丢失超过缓冲帧数时才重置 - if self.smoke_missing_frames >= self.frame_buffer_smoke: - self.smoke_detection_frames = 0 - self.smoke_alert_active = False - else: - # 从未检测到抽烟，保持状态 - pass - - # ========================================== - # 9. 业务逻辑判定 (Only One / Nobody) - # ========================================== - status_text = "" - - if current_person_count == 0: - self.nobody_detection_frames += 1 - self.nobody_missing_frames = 0 - - if self.nobody_detection_frames >= self.frame_thresh_nobody: - self.nobody_alert_active = True - else: - self.nobody_missing_frames += 1 - - if self.nobody_detection_frames > 0: - if self.nobody_missing_frames >= self.frame_buffer_nobody: - self.nobody_detection_frames = 0 - self.nobody_alert_active = False - else: - pass - - - # if current_person_count == 0: - # self.cnt_frame_nobody += 1 - # else: - # self.cnt_frame_nobody = 0 - - # ========================================== - # 10. 收集并生成结构化警告（核心改动） - # ========================================== - - alert_offset = 0 - - # A. Playing Phone - if self.phone_alert_active: - duration_seconds = self.phone_detection_frames / self.fps - current_frame_alerts.append( - { - 'time': current_time_sec, - 'action': 'Playing Phone', - 'confidence': 1.0, # 固定为1.0（规则判定） - 'details': f"Detected for {duration_seconds:.1f}s" - } - ) - - # A. Playing Phone - if self.smoke_alert_active: - duration_seconds = self.smoke_detection_frames / self.fps - current_frame_alerts.append( - { - 'time': current_time_sec, - 'action': 'Smoke', - 'confidence': 1.0, # 固定为1.0（规则判定） - 'details': f"Detected for {duration_seconds:.1f}s" - } - ) - - - # D. Nobody Checking - if self.nobody_alert_active: - duration_seconds = self.nobody_detection_frames / self.fps - current_frame_alerts.append({ - 'time': current_time_sec, - 'action': 'Nobody Checking', - 'confidence': 1.0, - 'details': f"Detected for {duration_seconds:.1f}s" - }) - - # ========================================== - # 11. 统一显示当前帧所有警告（可替换原分层显示） - # ========================================== - debug_info = f"Person: {current_person_count} | Phone: {current_phone_count} | Smoke: {current_smoke_count}" - cv2.putText(frame, debug_info, (20, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2) - - # 统一警告显示区 - alert_y_start = 150 - for i, alert in enumerate(current_frame_alerts): - action = alert['action'] - details = alert.get('details', '') - color = (0, 0, 255) # 默认红色警告 - - if action == 'Nobody Checking': - color = (255, 255, 255) - elif action == 'Smoke': - color = (0, 0, 255) - elif action == 'Playing Phone': - color = (255, 0, 0) - - main_text = action - if details: - main_text += f" ({details})" - - y_pos = alert_y_start + i * 50 - cv2.rectangle(frame, (20, y_pos - 40), (900, y_pos + 10), (0, 0, 0), -1) - cv2.putText(frame, main_text, (30, y_pos), cv2.FONT_HERSHEY_SIMPLEX, 1.0, color, 2) - - return { - "image": frame, - - "alerts":current_frame_alerts - } - - -# ========================= 帧处理线程 ========================= -class FrameProcessorWorker(threading.Thread): - def __init__(self, raw_queue: queue.Queue, ws_queue: queue.Queue, stop_event: threading.Event): - super().__init__(daemon=True) - self.raw_queue = raw_queue - self.ws_queue = ws_queue - self.stop_event = stop_event - - self.last_ts: Dict[int, float] = {} - - # 每个摄像头一个独立的 Kadian 检测器实例 - self.kadian_detectors: Dict[int, ZhihuishiDetector] = {} - - self.last_alert_push_time: Dict[int, Dict[str, float]] = {} - - def _encode_base64(self, img): - _, buf = cv2.imencode(".jpg", img) - return base64.b64encode(buf).decode("ascii") - - def run(self): - target_interval = 1.0 / RTSP_TARGET_FPS - while not self.stop_event.is_set(): - try: - item = self.raw_queue.get(timeout=0.5) - except queue.Empty: - continue - - try: - cam_id = item["camera_id"] - ts = item["timestamp"] - frame = item["frame"] - - # 抽帧控制 - if ts - self.last_ts.get(cam_id, 0) < target_interval: - # self.raw_queue.task_done() - continue - self.last_ts[cam_id] = ts - - # 获取检测器实例 - if cam_id not in self.kadian_detectors: - self.kadian_detectors[cam_id] = ZhihuishiDetector() - detector = self.kadian_detectors[cam_id] - - # 执行检测 - # detect_start = time.time() - result = detector.process_frame(frame.copy(), cam_id, ts) - # detect_time = (time.time() - detect_start) * 1000 - - result_img = result["image"] - result_type = result["alerts"] - # logger.debug(f"alerts: {result_type}") - - # ========= 核心修改：过滤5秒内重复的action ========= - # 初始化当前摄像头的推送时间记录 - if cam_id not in self.last_alert_push_time: - self.last_alert_push_time[cam_id] = {} - - # 筛选出符合推送条件的action（5秒内未推送过） - push_actions = [] - current_time = time.time() - for alert in result_type: - action = alert['action'] - last_push = self.last_alert_push_time[cam_id].get(action, 0) - # 检查是否超过推送间隔 - if current_time - last_push >= ALERT_PUSH_INTERVAL: - push_actions.append(action) - # 更新该action的最后推送时间 - self.last_alert_push_time[cam_id][action] = current_time - - # 通过 WebSocket 发送帧结果 - # encode_start = time.time() - try: - img_b64 = self._encode_base64(result_img) - except Exception as e: - logger.error(f"[ERROR] Encode image failed: {e}") - img_b64 = None - # encode_time = (time.time() - encode_start) * 1000 - - if img_b64 is not None: - # 将abnormal_actions对象数组转换为字符串数组 - # action_names = [action_info['action'] for action_info in push_actions] - - msg = { - "msg_type": "frame", - "camera_id": 0, - "timestamp": ts, - # "result_type": action_names, - "result_type": push_actions, - "image_base64": img_b64, - } - try: - self.ws_queue.put(msg, timeout=1.0) - # if push_actions and len(push_actions) > 0: - # self.ws_queue_2.put(msg, timeout=1.0) - except queue.Full: - logger.warning("[WARN] ws_send_queue full, drop frame message") - - # # 打印关键操作的耗时 - # total_time = detect_time + encode_time - # logger.info(f"[PERF] Camera {cam_id} - Total: {total_time:.1f}ms | " - # f"Detect: {detect_time:.1f}ms | " - # f"Encode: {encode_time:.1f}ms | ") - - except Exception as e: - logger.error( - f"[ERROR] Frame processing failed for camera {cam_id if 'cam_id' in locals() else 'unknown'}: {e}") - logger.exception("Exception details:") # 打印完整的堆栈跟踪 - # 继续处理下一帧，不要退出循环 - finally: - self.raw_queue.task_done() - diff --git a/biz/trajectory/trajectory01_biz.py b/biz/trajectory/trajectory01_biz.py deleted file mode 100644 index 1fed5ff..0000000 --- a/biz/trajectory/trajectory01_biz.py +++ /dev/null @@ -1,578 +0,0 @@ -# rtsp_service_kadian.py -# 融合 Kadian_Detect_1221.py + rtsp_service_ws.py -# 支持多路RTSP、抽帧、分段保存MP4、WebSocket推送图像与告警 - -import cv2 -import numpy as np -import os -import time -import threading -import queue -import yaml -import json -import base64 -import asyncio -import websockets -from dataclasses import dataclass -from typing import Dict, Any, Tuple, List -from datetime import datetime - -# -------------------------- Kadian 检测相关导入 -------------------------- -from algorithm.checkpoint.npu_yolo_onnx_person_car_phone import YOLOv8_ONNX # 主检测模型（人/车/后备箱/手机） -# from rtsp_service_ws_0108 import WS_PORT - -from yolox.tracker.byte_tracker import BYTETracker - -# ========================= 配置区 ========================= -# Kadian 模型路径与ROI（可根据实际情况修改） -detector_model_path = 'YOLO_Weight/prisoner_model.onnx' - -# 输入尺寸 -input_size = 640 - -# RTSP 服务配置 -RTSP_TARGET_FPS = 10.0 - -# 新增：告警推送频率限制（秒） -ALERT_PUSH_INTERVAL = 5.0 # 相同action 5秒内仅推送一次 - -ALERT_PUSH_URL = "http://123.57.151.210:10000/picenter/websocket/test/process" - - - -class TrajectoryDetector: - def __init__(self): - # 模型加载 - self.police_prisoner_detector = YOLOv8_ONNX(detector_model_path, conf_threshold=0.5, iou_threshold=0.45, - input_size=input_size) - - # ByteTracker - class TrackerArgs: - track_thresh = 0.25 - track_buffer = 30 - match_thresh = 0.8 - mot20 = False - - self.police_prisoner_track_role = {} - - self.fps = RTSP_TARGET_FPS - - self.tracker = BYTETracker(TrackerArgs(), frame_rate=self.fps) - - # ========================================== - # 超参数设置 (Hyperparameters) - # ========================================== - self.TIME_THRESHOLD_POLICE = 1.0 # 警察判定时长 - self.TIME_TOLERANCE_POLICE = 0.5 # 警察失缓冲时间（防抖动） - - self.TIME_THRESHOLD_PRISONER = 1.0 # 犯人判定时长 - self.TIME_TOLERANCE_PRISONER = 1.0 # 犯人丢失缓冲时间（防抖动） - - # 警察检测帧数阈值 - self.frame_thresh_police = int(self.TIME_THRESHOLD_POLICE * self.fps) - self.frame_buffer_police = int(self.TIME_TOLERANCE_POLICE * self.fps) - - # 犯人检测帧数阈值 - self.frame_thresh_prisoner = int(self.TIME_THRESHOLD_PRISONER * self.fps) - self.frame_buffer_prisoner = int(self.TIME_TOLERANCE_PRISONER * self.fps) - - print(f"\n超参数设置:") - print(f" FPS: {self.fps:.2f}") - print(f" 判定 'police Detected' 需累计检测: {self.frame_thresh_police} 帧") - print(f" 警察丢失缓冲帧数: {self.frame_buffer_police} 帧") - print(f" 判定 'prisoner Detected' 需累计检测: {self.frame_thresh_prisoner} 帧") - print(f" 犯人丢失缓冲帧数: {self.frame_buffer_prisoner} 帧") - - # ========================================== - # 状态变量初始化 - # ========================================== - self.current_frame_idx = 0 - - # 警察检测状态变量 - self.police_detection_frames = 0 # 连续检测到警察的帧数 - self.police_missing_frames = 0 # 连续未检测到警察的帧数 - self.police_alert_active = False # 警察报警是否激活 - - # 犯人检测状态变量 - self.prisoner_detection_frames = 0 # 连续检测到犯人的帧数 - self.prisoner_missing_frames = 0 # 连续未检测到犯人的帧数 - self.prisoner_alert_active = False # 犯人报警是否激活 - - # ========================= - # 路线 ROI + 状态机初始化 - # ========================= - # ⚠️ 改为相对坐标（0-1区间），按 [x, y] 格式，x/y 范围 0~1 - # 示例：原 (50,100) 在 960x480 分辨率下 → x=50/960≈0.052, y=100/480≈0.208 - self.route_rois = [ - { - "name": "entry", - "polygon_rel": [(0.4, 0.05), (0.6, 0.05), (0.6, 0.35), (0.4, 0.35)] # 相对坐标 - }, - { - "name": "corridor", - "polygon_rel": [(0.4, 0.4), (0.6, 0.4), (0.6, 0.6), (0.4, 0.6)] # 相对坐标 - }, - { - "name": "exit", # finish区域 - "polygon_rel": [(0.55, 0.3), (0.75, 0.3), (0.75, 0.7), (0.55, 0.7)] # 相对坐标 - } - ] - - # 帧尺寸（动态更新） - self.width = 0 - self.height = 0 - - print(f"相对坐标 ROI: {self.route_rois}") - - # 每个犯人（track_id）一套路线状态 - self.prisoner_route_state = {} - - # 新增：记录所有曾经出现过的犯人track_id及其状态 - self.all_prisoner_tracks = {} - # 新增：记录已触发违规的track_id，避免重复告警 - self.violated_tracks = set() - - def _get_abs_polygon(self, rel_polygon): - """将相对坐标(0-1)转换为绝对像素坐标""" - return [ - (int(x * self.width), int(y * self.height)) - for x, y in rel_polygon - ] - - def compute_iou(self, boxA, boxB): - # box = [x1, y1, x2, y2] - xA = max(boxA[0], boxB[0]) - yA = max(boxA[1], boxB[1]) - xB = min(boxA[2], boxB[2]) - yB = min(boxB[3], boxB[3]) - - interW = max(0, xB - xA) - interH = max(0, yB - yA) - interArea = interW * interH - - boxAArea = (boxA[2] - boxA[0]) * (boxA[3] - boxA[1]) - boxBArea = (boxB[2] - boxB[0]) * (boxB[3] - boxB[1]) - - unionArea = boxAArea + boxBArea - interArea - if unionArea == 0: - return 0.0 - - return interArea / unionArea - - def draw_alert(self, frame, text, color=(0, 0, 255), sub_text=None, offset_y=0): - """在右上角绘制警告文字 (支持垂直偏移，防止文字重叠)""" - font_scale = 1.5 - thickness = 3 - font = cv2.FONT_HERSHEY_SIMPLEX - - (text_w, text_h), _ = cv2.getTextSize(text, font, font_scale, thickness) - x = self.width - text_w - 20 - y = 50 + text_h + offset_y # 增加 Y 轴偏移 - - cv2.rectangle(frame, (x - 10, y - text_h - 10), (x + text_w + 10, y + 10), (0, 0, 0), -1) - cv2.putText(frame, text, (x, y), font, font_scale, color, thickness) - - if sub_text: - cv2.putText(frame, sub_text, (x, y + 40), font, 0.7, (200, 200, 200), 2) - - def _point_in_polygon(self, point, polygon): - """ - 判断点是否在多边形内 - polygon: 绝对像素坐标的多边形 - """ - return cv2.pointPolygonTest( - np.array(polygon, dtype=np.int32), - point, - False - ) >= 0 - - def _draw_route_rois(self, frame): - """ - 在画面中绘制路线 ROI（动态转换为绝对坐标） - """ - for idx, roi in enumerate(self.route_rois): - # 相对坐标转绝对坐标 - abs_polygon = self._get_abs_polygon(roi["polygon_rel"]) - pts = np.array(abs_polygon, np.int32).reshape((-1, 1, 2)) - - # ROI 边框 - cv2.polylines( - frame, - [pts], - isClosed=True, - color=(0, 255, 255), - thickness=2 - ) - - # 标注名称 - text_pos = abs_polygon[0] - cv2.putText( - frame, - f"{idx + 1}:{roi['name']}", - (text_pos[0], text_pos[1] - 5), - cv2.FONT_HERSHEY_SIMPLEX, - 0.7, - (0, 255, 255), - 2 - ) - - def _update_prisoner_route(self, tid, point, timestamp): - """ - 路线状态机： - 必须按顺序进入 route_rois - """ - # 初始化状态 - if tid not in self.prisoner_route_state: - self.prisoner_route_state[tid] = { - "stage": 0, # 当前应进入的 ROI 索引 - "finished": False, # 是否完成路线 - "violation": False, # 是否违规 - "entered_entry": False, # 是否进入过entry区域 - "last_seen": timestamp # 最后出现时间 - } - # 记录所有犯人track - self.all_prisoner_tracks[tid] = self.prisoner_route_state[tid] - - state = self.prisoner_route_state[tid] - state["last_seen"] = timestamp # 更新最后出现时间 - - # 已完成或已违规，不再处理 - # 已完成或已违规，不再处理并删除该tid的状态 - if state["finished"] or state["violation"]: - # 关键修改：删除当前tid的状态记录 - if tid in self.prisoner_route_state: - del self.prisoner_route_state[tid] - # 可选：同时清理all_prisoner_tracks和已标记的违规/完成记录，避免内存泄漏 - if tid in self.all_prisoner_tracks: - del self.all_prisoner_tracks[tid] - self.violated_tracks.discard(tid) # 移除违规标记 - - return - - current_stage = state["stage"] - - # 所有阶段完成 - if current_stage >= len(self.route_rois): - state["finished"] = True - return - - # 当前应进入的 ROI（转换为绝对坐标） - current_roi_rel = self.route_rois[current_stage]["polygon_rel"] - current_roi_abs = self._get_abs_polygon(current_roi_rel) - - # 是否进入当前 ROI - if self._point_in_polygon(point, current_roi_abs): - # 标记是否进入entry区域（第一个ROI） - if current_stage == 0: - state["entered_entry"] = True - state["stage"] += 1 - - # 如果刚好完成最后一个 ROI (exit/finish) - if state["stage"] == len(self.route_rois): - state["finished"] = True - - def _check_prisoner_violation(self, current_time): - """ - 检查消失的犯人是否违规： - 1. 进入过entry区域 - 2. 未完成整个路线（未进入exit/finish） - 3. 已经消失（超过track buffer时间） - """ - violations = [] - # 遍历所有曾经出现过的犯人track - for tid, state in list(self.all_prisoner_tracks.items()): - # 跳过已完成、已违规或未进入entry的track - if state["finished"] or state["violation"] or not state["entered_entry"]: - continue - - # 检查是否已消失（超过track buffer时间，这里用3秒作为消失判定） - if current_time - state["last_seen"] > 2.0 and tid not in self.violated_tracks: - state["violation"] = True - self.violated_tracks.add(tid) - violations.append({ - 'time': current_time, - 'action': 'violation', - 'confidence': 1.0, - 'details': f"" - }) - return violations - - def process_frame(self, frame, camera_id: int, timestamp: float) -> Dict[str, Any]: - h, w = frame.shape[:2] - self.width, self.height = w, h # 更新帧尺寸 - - self.current_frame_idx += 1 - current_time_sec = timestamp - - # ========= 警察和犯人检测 ========= - police_prisoner_results = self.police_prisoner_detector(frame) - - police_prisoner_dets_xyxy = [] - police_prisoner_dets_roles = [] - police_prisoner_dets_for_tracker = [] - - # ========= 当前帧所有警告列表（关键改动）========== - current_frame_alerts = [] # 每帧清空，重新收集 - - if police_prisoner_results: - for det in police_prisoner_results: - x1, y1, x2, y2, conf, cls_id = det # x1, y1, x2, y2为角点坐标，x1 y1为左上角，x2 y2为右下角 - police_prisoner_dets_xyxy.append([x1, y1, x2, y2]) - police_prisoner_dets_for_tracker.append([x1, y1, x2, y2, conf]) - if cls_id == 0: - police_prisoner_dets_roles.append("police") - elif cls_id == 1: - police_prisoner_dets_roles.append("prisoner") - - ppolice_prisoner_dets = np.array(police_prisoner_dets_for_tracker, dtype=np.float32) if len( - police_prisoner_dets_for_tracker) else np.empty((0, 5)) - - police_prisoner_dets_tracks = self.tracker.update( - ppolice_prisoner_dets, - [self.height, self.width], - [self.height, self.width] - ) - - # 重置当前帧的犯人track标记 - current_frame_prisoner_tids = set() - - # ========= 单帧统计变量 ========= - current_police_count = 0 - current_prisoner_count = 0 - - # ========= 警察和犯人检测 ========= - for t in police_prisoner_dets_tracks: - tid = t.track_id - - # IoU 匹配角色 - REVALIDATE_FRAME_INTERVAL = 10 - if (self.current_frame_idx % REVALIDATE_FRAME_INTERVAL == 0) or ( - tid not in self.police_prisoner_track_role): - best_iou = 0 - best_role = "unknown" - - t_box = list(map(float, t.tlbr)) # [x1,y1,x2,y2] - - for i, box in enumerate(police_prisoner_dets_xyxy): - iou_val = self.compute_iou(t_box, box) - if iou_val > best_iou: - best_iou = iou_val - best_role = police_prisoner_dets_roles[i] - if best_iou > 0.1: - self.police_prisoner_track_role[tid] = best_role - else: - self.police_prisoner_track_role[tid] = "unknown" - - role = self.police_prisoner_track_role.get(tid, "unknown") - cls_id = -1 - if role == "police": - cls_id = 0 - elif role == "prisoner": - cls_id = 1 - - x1, y1, x2, y2 = map(int, t.tlbr) - cx, cy = (x1 + x2) // 2, (y1 + y2) // 2 - - color = None - label = None - - if cls_id == 0: # police - current_police_count += 1 - color = (255, 0, 255) - label = "police" - - elif cls_id == 1: # prisoner - current_prisoner_count += 1 - color = (0, 0, 139) - label = "prisoner" - current_frame_prisoner_tids.add(tid) - # ===== 路线状态机更新 ===== - self._update_prisoner_route( - tid=tid, - point=(cx, cy), - timestamp=current_time_sec - ) - else: - color = (255, 255, 255) - label = "Unknown" - - cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2) - cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2) - - # ========================================== - # 检查犯人违规（进入entry但未到exit就消失） - # ========================================== - violation_alerts = self._check_prisoner_violation(current_time_sec) - current_frame_alerts.extend(violation_alerts) - - # ========================================== - # 犯人检测 - # ========================================== - if current_prisoner_count > 0: - self.prisoner_detection_frames += 1 - self.prisoner_missing_frames = 0 - if self.prisoner_detection_frames >= self.frame_thresh_prisoner: - self.prisoner_alert_active = True - else: - self.prisoner_missing_frames += 1 - if self.prisoner_detection_frames > 0: - if self.prisoner_missing_frames >= self.frame_buffer_prisoner: - self.prisoner_detection_frames = 0 - self.prisoner_alert_active = False - - # ========================================== - # 警察检测 - # ========================================== - if current_police_count > 0: - self.police_detection_frames += 1 - self.police_missing_frames = 0 - if self.police_detection_frames >= self.frame_thresh_police: - self.police_alert_active = True - else: - self.police_missing_frames += 1 - if self.police_detection_frames > 0: - if self.police_missing_frames >= self.frame_buffer_police: - self.police_detection_frames = 0 - self.police_alert_active = False - - alert_offset = 0 - - # A. 有犯人 - if self.prisoner_alert_active: - duration_seconds = self.prisoner_detection_frames / self.fps - current_frame_alerts.append( - { - 'time': current_time_sec, - 'action': 'prisoner', - 'confidence': 1.0, - 'details': f"Detected for {duration_seconds:.1f}s" - } - ) - self.draw_alert(frame, "prisoner", (0, 0, 255), offset_y=alert_offset) - alert_offset += 100 - - # B. 路线违规告警 - for tid, state in self.prisoner_route_state.items(): - if state["finished"]: - current_frame_alerts.append({ - "time": current_time_sec, - "action": "finished", - "confidence": 1.0, - "details": "" - }) - #state["finished"] = False - self.draw_alert(frame, "finished", (0, 255, 0), offset_y=alert_offset) - alert_offset += 100 - - # C. 路线违规告警 - for violation in violation_alerts: - self.draw_alert(frame, "ROUTE VIOLATION!", (0, 0, 255), - sub_text=violation['details'], offset_y=alert_offset) - alert_offset += 100 - - # ========================= - # 绘制路线 ROI（始终显示） - # ========================= - self._draw_route_rois(frame) - - return { - "image": frame, - "alerts": current_frame_alerts - } - - -# ========================= 帧处理线程 ========================= -class FrameProcessorWorker(threading.Thread): - def __init__(self, - raw_frame_queue: "queue.Queue[Dict[str, Any]]", - ws_send_queue: "queue.Queue[Dict[str, Any]]", - stop_event: threading.Event): - super().__init__(daemon=True) - self.raw_queue = raw_frame_queue - self.ws_queue = ws_send_queue - self.stop_event = stop_event - - self.last_ts: Dict[int, float] = {} - - # 每个摄像头一个独立的 Kadian 检测器实例 - self.trajectory_detectors: Dict[int, TrajectoryDetector] = {} - - # 新增：维护每个摄像头每个action的最后推送时间 {camera_id: {action: last_push_time}} - self.last_alert_push_time: Dict[int, Dict[str, float]] = {} - - def _encode_image_to_base64(self, image) -> str: - ok, buf = cv2.imencode(".jpg", image) - if not ok: - raise RuntimeError("Failed to encode image to JPEG") - return base64.b64encode(buf.tobytes()).decode("ascii") - - def run(self): - target_interval = 1.0 / RTSP_TARGET_FPS - while not self.stop_event.is_set(): - try: - item = self.raw_queue.get(timeout=0.5) - except queue.Empty: - continue - - cam_id = item["camera_id"] - ts = item["timestamp"] - frame = item["frame"] - - # 抽帧控制 - if ts - self.last_ts.get(cam_id, 0) < target_interval: - self.raw_queue.task_done() - continue - self.last_ts[cam_id] = ts - - # 获取检测器实例 - if cam_id not in self.trajectory_detectors: - self.trajectory_detectors[cam_id] = TrajectoryDetector() - detector = self.trajectory_detectors[cam_id] - - # 执行检测 - result = detector.process_frame(frame.copy(), cam_id, ts) - - result_img = result["image"] - result_type = result["alerts"] - - # ========= 核心修改：过滤5秒内重复的action ========= - # 初始化当前摄像头的推送时间记录 - if cam_id not in self.last_alert_push_time: - self.last_alert_push_time[cam_id] = {} - - # 筛选出符合推送条件的action（5秒内未推送过） - push_actions = [] - current_time = time.time() - for alert in result_type: - action = alert['action'] - last_push = self.last_alert_push_time[cam_id].get(action, 0) - # 检查是否超过推送间隔 - if current_time - last_push >= ALERT_PUSH_INTERVAL: - push_actions.append(action) - # 更新该action的最后推送时间 - self.last_alert_push_time[cam_id][action] = current_time - - # 通过 WebSocket 发送帧结果 - try: - img_b64 = self._encode_image_to_base64(result_img) - except Exception as e: - print(f"[ERROR] Encode image failed: {e}") - img_b64 = None - - if img_b64 is not None: - msg = { - "msg_type": "frame", - "camera_id": 1, - "timestamp": ts, - "result_type": push_actions, - "image_base64": img_b64, - } - try: - self.ws_queue.put(msg, timeout=1.0) - # if push_actions and len(push_actions) > 0: - # self.ws_queue_2.put(msg, timeout=1.0) - except queue.Full: - print("[WARN] ws_send_queue full, drop frame message") - - self.raw_queue.task_done() - diff --git a/biz/trajectory/trajectory02_biz.py b/biz/trajectory/trajectory02_biz.py deleted file mode 100644 index d9ab7b5..0000000 --- a/biz/trajectory/trajectory02_biz.py +++ /dev/null @@ -1,562 +0,0 @@ -# rtsp_service_kadian.py -# 融合 Kadian_Detect_1221.py + rtsp_service_ws.py -# 支持多路RTSP、抽帧、分段保存MP4、WebSocket推送图像与告警 -# 修改为单一区域监控：犯人离开指定区域即报警 - -import cv2 -import numpy as np -import os -import time -import threading -import queue -import yaml -import json -import base64 -import asyncio -import websockets -from dataclasses import dataclass -from typing import Dict, Any, Tuple, List -from datetime import datetime - -# -------------------------- Kadian 检测相关导入 -------------------------- -from algorithm.checkpoint.npu_yolo_onnx_person_car_phone import YOLOv8_ONNX # 主检测模型（人/车/后备箱/手机） -# from rtsp_service_ws_0108 import WS_PORT - -from yolox.tracker.byte_tracker import BYTETracker - -# ========================= 配置区 ========================= -# Kadian 模型路径与ROI（可根据实际情况修改） -detector_model_path = 'YOLO_Weight/prisoner_model.onnx' - -# 输入尺寸 -input_size = 640 - -# RTSP 服务配置 -RTSP_TARGET_FPS = 10.0 - -# 新增：告警推送频率限制（秒） -ALERT_PUSH_INTERVAL = 5.0 # 相同action 5秒内仅推送一次 - -ALERT_PUSH_URL = "http://123.57.151.210:10000/picenter/websocket/test/process" - - -class TrajectoryDetector: - def __init__(self): - # 模型加载 - self.police_prisoner_detector = YOLOv8_ONNX(detector_model_path, conf_threshold=0.5, iou_threshold=0.45, - input_size=input_size) - - # ByteTracker - class TrackerArgs: - track_thresh = 0.25 - track_buffer = 30 - match_thresh = 0.8 - mot20 = False - - self.police_prisoner_track_role = {} - - self.fps = RTSP_TARGET_FPS - - self.tracker = BYTETracker(TrackerArgs(), frame_rate=self.fps) - - # ========================================== - # 超参数设置 (Hyperparameters) - # ========================================== - self.TIME_THRESHOLD_POLICE = 1.0 # 警察判定时长 - self.TIME_TOLERANCE_POLICE = 0.5 # 警察失缓冲时间（防抖动） - - self.TIME_THRESHOLD_PRISONER = 1.0 # 犯人判定时长 - self.TIME_TOLERANCE_PRISONER = 1.0 # 犯人丢失缓冲时间（防抖动） - - # 警察检测帧数阈值 - self.frame_thresh_police = int(self.TIME_THRESHOLD_POLICE * self.fps) - self.frame_buffer_police = int(self.TIME_TOLERANCE_POLICE * self.fps) - - # 犯人检测帧数阈值 - self.frame_thresh_prisoner = int(self.TIME_THRESHOLD_PRISONER * self.fps) - self.frame_buffer_prisoner = int(self.TIME_TOLERANCE_PRISONER * self.fps) - - print(f"\n超参数设置:") - print(f" FPS: {self.fps:.2f}") - print(f" 判定 'police Detected' 需累计检测: {self.frame_thresh_police} 帧") - print(f" 警察丢失缓冲帧数: {self.frame_buffer_police} 帧") - print(f" 判定 'prisoner Detected' 需累计检测: {self.frame_thresh_prisoner} 帧") - print(f" 犯人丢失缓冲帧数: {self.frame_buffer_prisoner} 帧") - - # ========================================== - # 状态变量初始化 - # ========================================== - self.current_frame_idx = 0 - - # 警察检测状态变量 - self.police_detection_frames = 0 # 连续检测到警察的帧数 - self.police_missing_frames = 0 # 连续未检测到警察的帧数 - self.police_alert_active = False # 警察报警是否激活 - - # 犯人检测状态变量 - self.prisoner_detection_frames = 0 # 连续检测到犯人的帧数 - self.prisoner_missing_frames = 0 # 连续未检测到犯人的帧数 - self.prisoner_alert_active = False # 犯人报警是否激活 - - # ========================= - # 区域 ROI + 状态机初始化（修改为单一区域） - # ========================= - # ⚠️ 改为相对坐标（0-1区间），按 [x, y] 格式，x/y 范围 0~1 - # 示例：原 (50,100) 在 960x480 分辨率下 → x=50/960≈0.052, y=100/480≈0.208 - self.route_rois = [ - { - "name": "zone", # 单一区域，犯人离开即报警 - "polygon_rel": [(0.47, 0.35), (0.5, 0.35), (0.7, 1.0), (0.3, 1.0)] # 相对坐标，可自定义 - } - ] - - # 帧尺寸（动态更新） - self.width = 0 - self.height = 0 - - print(f"相对坐标 ROI: {self.route_rois}") - - # 每个犯人（track_id）一套状态 - self.prisoner_route_state = {} - - # 新增：记录所有曾经出现过的犯人track_id及其状态 - self.all_prisoner_tracks = {} - # 新增：记录已触发违规的track_id，避免重复告警 - self.violated_tracks = set() - - def _get_abs_polygon(self, rel_polygon): - """将相对坐标(0-1)转换为绝对像素坐标""" - return [ - (int(x * self.width), int(y * self.height)) - for x, y in rel_polygon - ] - - def compute_iou(self, boxA, boxB): - # box = [x1, y1, x2, y2] - xA = max(boxA[0], boxB[0]) - yA = max(boxA[1], boxB[1]) - xB = min(boxA[2], boxB[2]) - yB = min(boxB[3], boxB[3]) - - interW = max(0, xB - xA) - interH = max(0, yB - yA) - interArea = interW * interH - - boxAArea = (boxA[2] - boxA[0]) * (boxA[3] - boxA[1]) - boxBArea = (boxB[2] - boxB[0]) * (boxB[3] - boxB[1]) - - unionArea = boxAArea + boxBArea - interArea - if unionArea == 0: - return 0.0 - - return interArea / unionArea - - def draw_alert(self, frame, text, color=(0, 0, 255), sub_text=None, offset_y=0): - """在右上角绘制警告文字 (支持垂直偏移，防止文字重叠)""" - font_scale = 1.5 - thickness = 3 - font = cv2.FONT_HERSHEY_SIMPLEX - - (text_w, text_h), _ = cv2.getTextSize(text, font, font_scale, thickness) - x = self.width - text_w - 20 - y = 50 + text_h + offset_y # 增加 Y 轴偏移 - - cv2.rectangle(frame, (x - 10, y - text_h - 10), (x + text_w + 10, y + 10), (0, 0, 0), -1) - cv2.putText(frame, text, (x, y), font, font_scale, color, thickness) - - if sub_text: - cv2.putText(frame, sub_text, (x, y + 40), font, 0.7, (200, 200, 200), 2) - - def _point_in_polygon(self, point, polygon): - """ - 判断点是否在多边形内 - polygon: 绝对像素坐标的多边形 - """ - return cv2.pointPolygonTest( - np.array(polygon, dtype=np.int32), - point, - False - ) >= 0 - - def _draw_route_rois(self, frame): - """ - 在画面中绘制路线 ROI（动态转换为绝对坐标） - """ - for idx, roi in enumerate(self.route_rois): - # 相对坐标转绝对坐标 - abs_polygon = self._get_abs_polygon(roi["polygon_rel"]) - pts = np.array(abs_polygon, np.int32).reshape((-1, 1, 2)) - - # ROI 边框 - cv2.polylines( - frame, - [pts], - isClosed=True, - color=(0, 255, 255), - thickness=2 - ) - - # 标注名称 - text_pos = abs_polygon[0] - cv2.putText( - frame, - f"{idx + 1}:{roi['name']}", - (text_pos[0], text_pos[1] - 5), - cv2.FONT_HERSHEY_SIMPLEX, - 0.7, - (0, 255, 255), - 2 - ) - - def _update_prisoner_route(self, tid, point, timestamp): - """ - 区域监控状态机（修改为单一区域）： - 只监控一个区域，如果犯人进入过该区域，后来离开（连续多帧不在区域内或消失），则触发违规。 - """ - # 初始化状态 - if tid not in self.prisoner_route_state: - self.prisoner_route_state[tid] = { - "entered_zone": False, # 是否曾进入区域 - "in_zone": False, # 当前是否在区域内 - "out_frames": 0, # 连续不在区域内的帧数 - "violation": False, # 是否已触发离开违规 - "last_seen": timestamp # 最后出现时间 - } - # 记录所有犯人track - self.all_prisoner_tracks[tid] = self.prisoner_route_state[tid] - - state = self.prisoner_route_state[tid] - state["last_seen"] = timestamp - - # 如果已经触发违规，不再处理（可保留但不重复触发） - if state["violation"]: - return - - # 获取当前唯一区域的多边形（绝对坐标） - current_roi_rel = self.route_rois[0]["polygon_rel"] - current_roi_abs = self._get_abs_polygon(current_roi_rel) - - # 判断点是否在区域内 - in_zone = self._point_in_polygon(point, current_roi_abs) - - if in_zone: - # 在区域内 - state["in_zone"] = True - state["out_frames"] = 0 - if not state["entered_zone"]: - state["entered_zone"] = True - else: - # 不在区域内 - if state["entered_zone"]: - # 曾进入过区域，开始计数离开帧数 - state["out_frames"] += 1 - # 如果离开帧数超过阈值，触发违规 - # 使用 frame_buffer_prisoner 作为离开判定缓冲（可自定义） - if state["out_frames"] >= self.frame_buffer_prisoner: - state["violation"] = True - state["in_zone"] = False - # 如果还未进入区域，忽略 - - def _check_prisoner_violation(self, current_time): - """ - 检查消失的犯人是否违规（离开区域）： - 1. 曾进入过区域 - 2. 未触发过违规 - 3. 已经消失（超过track buffer时间） - """ - violations = [] - # 遍历所有曾经出现过的犯人track - for tid, state in list(self.all_prisoner_tracks.items()): - # 跳过已违规或未进入区域的track - if state["violation"] or not state["entered_zone"]: - continue - - # 检查是否已消失（超过track buffer时间，这里用2秒作为消失判定） - if current_time - state["last_seen"] > 2.0 and tid not in self.violated_tracks: - state["violation"] = True - self.violated_tracks.add(tid) - violations.append({ - 'time': current_time, - 'action': 'violation', - 'confidence': 1.0, - 'details': "Prisoner left zone (disappeared)" - }) - return violations - - def process_frame(self, frame, camera_id: int, timestamp: float) -> Dict[str, Any]: - h, w = frame.shape[:2] - self.width, self.height = w, h # 更新帧尺寸 - - self.current_frame_idx += 1 - current_time_sec = timestamp - - # ========= 警察和犯人检测 ========= - police_prisoner_results = self.police_prisoner_detector(frame) - - police_prisoner_dets_xyxy = [] - police_prisoner_dets_roles = [] - police_prisoner_dets_for_tracker = [] - - # ========= 当前帧所有警告列表 ========== - current_frame_alerts = [] # 每帧清空，重新收集 - - if police_prisoner_results: - for det in police_prisoner_results: - x1, y1, x2, y2, conf, cls_id = det # x1, y1, x2, y2为角点坐标，x1 y1为左上角，x2 y2为右下角 - police_prisoner_dets_xyxy.append([x1, y1, x2, y2]) - police_prisoner_dets_for_tracker.append([x1, y1, x2, y2, conf]) - if cls_id == 0: - police_prisoner_dets_roles.append("police") - elif cls_id == 1: - police_prisoner_dets_roles.append("prisoner") - - ppolice_prisoner_dets = np.array(police_prisoner_dets_for_tracker, dtype=np.float32) if len( - police_prisoner_dets_for_tracker) else np.empty((0, 5)) - - police_prisoner_dets_tracks = self.tracker.update( - ppolice_prisoner_dets, - [self.height, self.width], - [self.height, self.width] - ) - - # 重置当前帧的犯人track标记 - current_frame_prisoner_tids = set() - - # ========= 单帧统计变量 ========= - current_police_count = 0 - current_prisoner_count = 0 - - # ========= 警察和犯人检测 ========= - for t in police_prisoner_dets_tracks: - tid = t.track_id - - # IoU 匹配角色 - REVALIDATE_FRAME_INTERVAL = 10 - if (self.current_frame_idx % REVALIDATE_FRAME_INTERVAL == 0) or ( - tid not in self.police_prisoner_track_role): - best_iou = 0 - best_role = "unknown" - - t_box = list(map(float, t.tlbr)) # [x1,y1,x2,y2] - - for i, box in enumerate(police_prisoner_dets_xyxy): - iou_val = self.compute_iou(t_box, box) - if iou_val > best_iou: - best_iou = iou_val - best_role = police_prisoner_dets_roles[i] - if best_iou > 0.1: - self.police_prisoner_track_role[tid] = best_role - else: - self.police_prisoner_track_role[tid] = "unknown" - - role = self.police_prisoner_track_role.get(tid, "unknown") - cls_id = -1 - if role == "police": - cls_id = 0 - elif role == "prisoner": - cls_id = 1 - - x1, y1, x2, y2 = map(int, t.tlbr) - cx, cy = (x1 + x2) // 2, (y1 + y2) // 2 - - color = None - label = None - - if cls_id == 0: # police - current_police_count += 1 - color = (255, 0, 255) - label = "police" - - elif cls_id == 1: # prisoner - current_prisoner_count += 1 - color = (0, 0, 139) - label = "prisoner" - current_frame_prisoner_tids.add(tid) - # ===== 区域状态机更新 ===== - self._update_prisoner_route( - tid=tid, - point=(cx, cy), - timestamp=current_time_sec - ) - else: - color = (255, 255, 255) - label = "Unknown" - - cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2) - cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2) - - # ========================================== - # 检查犯人违规（进入区域后离开） - # ========================================== - violation_alerts = self._check_prisoner_violation(current_time_sec) - - # 遍历所有状态，收集刚刚触发的 violation（那些在更新中被标记但尚未加入 violated_tracks 的） - for tid, state in self.prisoner_route_state.items(): - if state["violation"] and tid not in self.violated_tracks: - self.violated_tracks.add(tid) - violation_alerts.append({ - 'time': current_time_sec, - 'action': 'violation', - 'confidence': 1.0, - 'details': "Prisoner left zone" - }) - - current_frame_alerts.extend(violation_alerts) - - # ========================================== - # 犯人检测 - # ========================================== - if current_prisoner_count > 0: - self.prisoner_detection_frames += 1 - self.prisoner_missing_frames = 0 - if self.prisoner_detection_frames >= self.frame_thresh_prisoner: - self.prisoner_alert_active = True - else: - self.prisoner_missing_frames += 1 - if self.prisoner_detection_frames > 0: - if self.prisoner_missing_frames >= self.frame_buffer_prisoner: - self.prisoner_detection_frames = 0 - self.prisoner_alert_active = False - - # ========================================== - # 警察检测 - # ========================================== - if current_police_count > 0: - self.police_detection_frames += 1 - self.police_missing_frames = 0 - if self.police_detection_frames >= self.frame_thresh_police: - self.police_alert_active = True - else: - self.police_missing_frames += 1 - if self.police_detection_frames > 0: - if self.police_missing_frames >= self.frame_buffer_police: - self.police_detection_frames = 0 - self.police_alert_active = False - - alert_offset = 0 - - # A. 有犯人 - if self.prisoner_alert_active: - duration_seconds = self.prisoner_detection_frames / self.fps - current_frame_alerts.append( - { - 'time': current_time_sec, - 'action': 'prisoner', - 'confidence': 1.0, - 'details': f"Detected for {duration_seconds:.1f}s" - } - ) - self.draw_alert(frame, "prisoner", (0, 0, 255), offset_y=alert_offset) - alert_offset += 100 - - # C. 区域违规告警（离开区域） - for violation in violation_alerts: - self.draw_alert(frame, "ZONE VIOLATION!", (0, 0, 255), - sub_text=violation['details'], offset_y=alert_offset) - alert_offset += 100 - - # ========================= - # 绘制区域 ROI（始终显示） - # ========================= - self._draw_route_rois(frame) - - return { - "image": frame, - "alerts": current_frame_alerts - } - - -# ========================= 帧处理线程 ========================= -class FrameProcessorWorker(threading.Thread): - def __init__(self, - raw_frame_queue: "queue.Queue[Dict[str, Any]]", - ws_send_queue: "queue.Queue[Dict[str, Any]]", - stop_event: threading.Event): - super().__init__(daemon=True) - self.raw_queue = raw_frame_queue - self.ws_queue = ws_send_queue - self.stop_event = stop_event - - self.last_ts: Dict[int, float] = {} - - # 每个摄像头一个独立的 Kadian 检测器实例 - self.trajectory_detectors: Dict[int, TrajectoryDetector] = {} - - # 新增：维护每个摄像头每个action的最后推送时间 {camera_id: {action: last_push_time}} - self.last_alert_push_time: Dict[int, Dict[str, float]] = {} - - def _encode_image_to_base64(self, image) -> str: - ok, buf = cv2.imencode(".jpg", image) - if not ok: - raise RuntimeError("Failed to encode image to JPEG") - return base64.b64encode(buf.tobytes()).decode("ascii") - - def run(self): - target_interval = 1.0 / RTSP_TARGET_FPS - while not self.stop_event.is_set(): - try: - item = self.raw_queue.get(timeout=0.5) - except queue.Empty: - continue - - cam_id = item["camera_id"] - ts = item["timestamp"] - frame = item["frame"] - - # 抽帧控制 - if ts - self.last_ts.get(cam_id, 0) < target_interval: - self.raw_queue.task_done() - continue - self.last_ts[cam_id] = ts - - # 获取检测器实例 - if cam_id not in self.trajectory_detectors: - self.trajectory_detectors[cam_id] = TrajectoryDetector() - detector = self.trajectory_detectors[cam_id] - - # 执行检测 - result = detector.process_frame(frame.copy(), cam_id, ts) - - result_img = result["image"] - result_type = result["alerts"] - - # ========= 核心修改：过滤5秒内重复的action ========= - # 初始化当前摄像头的推送时间记录 - if cam_id not in self.last_alert_push_time: - self.last_alert_push_time[cam_id] = {} - - # 筛选出符合推送条件的action（5秒内未推送过） - push_actions = [] - current_time = time.time() - for alert in result_type: - action = alert['action'] - last_push = self.last_alert_push_time[cam_id].get(action, 0) - # 检查是否超过推送间隔 - if current_time - last_push >= ALERT_PUSH_INTERVAL: - push_actions.append(action) - # 更新该action的最后推送时间 - self.last_alert_push_time[cam_id][action] = current_time - - # 通过 WebSocket 发送帧结果 - try: - img_b64 = self._encode_image_to_base64(result_img) - except Exception as e: - print(f"[ERROR] Encode image failed: {e}") - img_b64 = None - - if img_b64 is not None: - msg = { - "msg_type": "frame", - "camera_id": 1, - "timestamp": ts, - "result_type": push_actions, - "image_base64": img_b64, - } - try: - self.ws_queue.put(msg, timeout=1.0) - # if push_actions and len(push_actions) > 0: - # self.ws_queue_2.put(msg, timeout=1.0) - except queue.Full: - print("[WARN] ws_send_queue full, drop frame message") - - self.raw_queue.task_done() \ No newline at end of file diff --git a/biz/trajectory/trajectory03_biz.py b/biz/trajectory/trajectory03_biz.py deleted file mode 100644 index ad8bbf9..0000000 --- a/biz/trajectory/trajectory03_biz.py +++ /dev/null @@ -1,562 +0,0 @@ -# rtsp_service_kadian.py -# 融合 Kadian_Detect_1221.py + rtsp_service_ws.py -# 支持多路RTSP、抽帧、分段保存MP4、WebSocket推送图像与告警 -# 修改为单一区域监控：犯人离开指定区域即报警 - -import cv2 -import numpy as np -import os -import time -import threading -import queue -import yaml -import json -import base64 -import asyncio -import websockets -from dataclasses import dataclass -from typing import Dict, Any, Tuple, List -from datetime import datetime - -# -------------------------- Kadian 检测相关导入 -------------------------- -from algorithm.checkpoint.npu_yolo_onnx_person_car_phone import YOLOv8_ONNX # 主检测模型（人/车/后备箱/手机） -# from rtsp_service_ws_0108 import WS_PORT - -from yolox.tracker.byte_tracker import BYTETracker - -# ========================= 配置区 ========================= -# Kadian 模型路径与ROI（可根据实际情况修改） -detector_model_path = 'YOLO_Weight/prisoner_model.onnx' - -# 输入尺寸 -input_size = 640 - -# RTSP 服务配置 -RTSP_TARGET_FPS = 10.0 - -# 新增：告警推送频率限制（秒） -ALERT_PUSH_INTERVAL = 5.0 # 相同action 5秒内仅推送一次 - -ALERT_PUSH_URL = "http://123.57.151.210:10000/picenter/websocket/test/process" - - -class TrajectoryDetector: - def __init__(self): - # 模型加载 - self.police_prisoner_detector = YOLOv8_ONNX(detector_model_path, conf_threshold=0.5, iou_threshold=0.45, - input_size=input_size) - - # ByteTracker - class TrackerArgs: - track_thresh = 0.25 - track_buffer = 30 - match_thresh = 0.8 - mot20 = False - - self.police_prisoner_track_role = {} - - self.fps = RTSP_TARGET_FPS - - self.tracker = BYTETracker(TrackerArgs(), frame_rate=self.fps) - - # ========================================== - # 超参数设置 (Hyperparameters) - # ========================================== - self.TIME_THRESHOLD_POLICE = 1.0 # 警察判定时长 - self.TIME_TOLERANCE_POLICE = 0.5 # 警察失缓冲时间（防抖动） - - self.TIME_THRESHOLD_PRISONER = 1.0 # 犯人判定时长 - self.TIME_TOLERANCE_PRISONER = 1.0 # 犯人丢失缓冲时间（防抖动） - - # 警察检测帧数阈值 - self.frame_thresh_police = int(self.TIME_THRESHOLD_POLICE * self.fps) - self.frame_buffer_police = int(self.TIME_TOLERANCE_POLICE * self.fps) - - # 犯人检测帧数阈值 - self.frame_thresh_prisoner = int(self.TIME_THRESHOLD_PRISONER * self.fps) - self.frame_buffer_prisoner = int(self.TIME_TOLERANCE_PRISONER * self.fps) - - print(f"\n超参数设置:") - print(f" FPS: {self.fps:.2f}") - print(f" 判定 'police Detected' 需累计检测: {self.frame_thresh_police} 帧") - print(f" 警察丢失缓冲帧数: {self.frame_buffer_police} 帧") - print(f" 判定 'prisoner Detected' 需累计检测: {self.frame_thresh_prisoner} 帧") - print(f" 犯人丢失缓冲帧数: {self.frame_buffer_prisoner} 帧") - - # ========================================== - # 状态变量初始化 - # ========================================== - self.current_frame_idx = 0 - - # 警察检测状态变量 - self.police_detection_frames = 0 # 连续检测到警察的帧数 - self.police_missing_frames = 0 # 连续未检测到警察的帧数 - self.police_alert_active = False # 警察报警是否激活 - - # 犯人检测状态变量 - self.prisoner_detection_frames = 0 # 连续检测到犯人的帧数 - self.prisoner_missing_frames = 0 # 连续未检测到犯人的帧数 - self.prisoner_alert_active = False # 犯人报警是否激活 - - # ========================= - # 区域 ROI + 状态机初始化（修改为单一区域） - # ========================= - # ⚠️ 改为相对坐标（0-1区间），按 [x, y] 格式，x/y 范围 0~1 - # 示例：原 (50,100) 在 960x480 分辨率下 → x=50/960≈0.052, y=100/480≈0.208 - self.route_rois = [ - { - "name": "zone", # 单一区域，犯人离开即报警 - "polygon_rel": [(0.48, 0.18), (0.54, 0.18), (0.75, 1.0), (0.25, 1.0)] # 相对坐标，可自定义 - } - ] - - # 帧尺寸（动态更新） - self.width = 0 - self.height = 0 - - print(f"相对坐标 ROI: {self.route_rois}") - - # 每个犯人（track_id）一套状态 - self.prisoner_route_state = {} - - # 新增：记录所有曾经出现过的犯人track_id及其状态 - self.all_prisoner_tracks = {} - # 新增：记录已触发违规的track_id，避免重复告警 - self.violated_tracks = set() - - def _get_abs_polygon(self, rel_polygon): - """将相对坐标(0-1)转换为绝对像素坐标""" - return [ - (int(x * self.width), int(y * self.height)) - for x, y in rel_polygon - ] - - def compute_iou(self, boxA, boxB): - # box = [x1, y1, x2, y2] - xA = max(boxA[0], boxB[0]) - yA = max(boxA[1], boxB[1]) - xB = min(boxA[2], boxB[2]) - yB = min(boxB[3], boxB[3]) - - interW = max(0, xB - xA) - interH = max(0, yB - yA) - interArea = interW * interH - - boxAArea = (boxA[2] - boxA[0]) * (boxA[3] - boxA[1]) - boxBArea = (boxB[2] - boxB[0]) * (boxB[3] - boxB[1]) - - unionArea = boxAArea + boxBArea - interArea - if unionArea == 0: - return 0.0 - - return interArea / unionArea - - def draw_alert(self, frame, text, color=(0, 0, 255), sub_text=None, offset_y=0): - """在右上角绘制警告文字 (支持垂直偏移，防止文字重叠)""" - font_scale = 1.5 - thickness = 3 - font = cv2.FONT_HERSHEY_SIMPLEX - - (text_w, text_h), _ = cv2.getTextSize(text, font, font_scale, thickness) - x = self.width - text_w - 20 - y = 50 + text_h + offset_y # 增加 Y 轴偏移 - - cv2.rectangle(frame, (x - 10, y - text_h - 10), (x + text_w + 10, y + 10), (0, 0, 0), -1) - cv2.putText(frame, text, (x, y), font, font_scale, color, thickness) - - if sub_text: - cv2.putText(frame, sub_text, (x, y + 40), font, 0.7, (200, 200, 200), 2) - - def _point_in_polygon(self, point, polygon): - """ - 判断点是否在多边形内 - polygon: 绝对像素坐标的多边形 - """ - return cv2.pointPolygonTest( - np.array(polygon, dtype=np.int32), - point, - False - ) >= 0 - - def _draw_route_rois(self, frame): - """ - 在画面中绘制路线 ROI（动态转换为绝对坐标） - """ - for idx, roi in enumerate(self.route_rois): - # 相对坐标转绝对坐标 - abs_polygon = self._get_abs_polygon(roi["polygon_rel"]) - pts = np.array(abs_polygon, np.int32).reshape((-1, 1, 2)) - - # ROI 边框 - cv2.polylines( - frame, - [pts], - isClosed=True, - color=(0, 255, 255), - thickness=2 - ) - - # 标注名称 - text_pos = abs_polygon[0] - cv2.putText( - frame, - f"{idx + 1}:{roi['name']}", - (text_pos[0], text_pos[1] - 5), - cv2.FONT_HERSHEY_SIMPLEX, - 0.7, - (0, 255, 255), - 2 - ) - - def _update_prisoner_route(self, tid, point, timestamp): - """ - 区域监控状态机（修改为单一区域）： - 只监控一个区域，如果犯人进入过该区域，后来离开（连续多帧不在区域内或消失），则触发违规。 - """ - # 初始化状态 - if tid not in self.prisoner_route_state: - self.prisoner_route_state[tid] = { - "entered_zone": False, # 是否曾进入区域 - "in_zone": False, # 当前是否在区域内 - "out_frames": 0, # 连续不在区域内的帧数 - "violation": False, # 是否已触发离开违规 - "last_seen": timestamp # 最后出现时间 - } - # 记录所有犯人track - self.all_prisoner_tracks[tid] = self.prisoner_route_state[tid] - - state = self.prisoner_route_state[tid] - state["last_seen"] = timestamp - - # 如果已经触发违规，不再处理（可保留但不重复触发） - if state["violation"]: - return - - # 获取当前唯一区域的多边形（绝对坐标） - current_roi_rel = self.route_rois[0]["polygon_rel"] - current_roi_abs = self._get_abs_polygon(current_roi_rel) - - # 判断点是否在区域内 - in_zone = self._point_in_polygon(point, current_roi_abs) - - if in_zone: - # 在区域内 - state["in_zone"] = True - state["out_frames"] = 0 - if not state["entered_zone"]: - state["entered_zone"] = True - else: - # 不在区域内 - if state["entered_zone"]: - # 曾进入过区域，开始计数离开帧数 - state["out_frames"] += 1 - # 如果离开帧数超过阈值，触发违规 - # 使用 frame_buffer_prisoner 作为离开判定缓冲（可自定义） - if state["out_frames"] >= self.frame_buffer_prisoner: - state["violation"] = True - state["in_zone"] = False - # 如果还未进入区域，忽略 - - def _check_prisoner_violation(self, current_time): - """ - 检查消失的犯人是否违规（离开区域）： - 1. 曾进入过区域 - 2. 未触发过违规 - 3. 已经消失（超过track buffer时间） - """ - violations = [] - # 遍历所有曾经出现过的犯人track - for tid, state in list(self.all_prisoner_tracks.items()): - # 跳过已违规或未进入区域的track - if state["violation"] or not state["entered_zone"]: - continue - - # 检查是否已消失（超过track buffer时间，这里用2秒作为消失判定） - if current_time - state["last_seen"] > 2.0 and tid not in self.violated_tracks: - state["violation"] = True - self.violated_tracks.add(tid) - violations.append({ - 'time': current_time, - 'action': 'violation', - 'confidence': 1.0, - 'details': "Prisoner left zone (disappeared)" - }) - return violations - - def process_frame(self, frame, camera_id: int, timestamp: float) -> Dict[str, Any]: - h, w = frame.shape[:2] - self.width, self.height = w, h # 更新帧尺寸 - - self.current_frame_idx += 1 - current_time_sec = timestamp - - # ========= 警察和犯人检测 ========= - police_prisoner_results = self.police_prisoner_detector(frame) - - police_prisoner_dets_xyxy = [] - police_prisoner_dets_roles = [] - police_prisoner_dets_for_tracker = [] - - # ========= 当前帧所有警告列表 ========== - current_frame_alerts = [] # 每帧清空，重新收集 - - if police_prisoner_results: - for det in police_prisoner_results: - x1, y1, x2, y2, conf, cls_id = det # x1, y1, x2, y2为角点坐标，x1 y1为左上角，x2 y2为右下角 - police_prisoner_dets_xyxy.append([x1, y1, x2, y2]) - police_prisoner_dets_for_tracker.append([x1, y1, x2, y2, conf]) - if cls_id == 0: - police_prisoner_dets_roles.append("police") - elif cls_id == 1: - police_prisoner_dets_roles.append("prisoner") - - ppolice_prisoner_dets = np.array(police_prisoner_dets_for_tracker, dtype=np.float32) if len( - police_prisoner_dets_for_tracker) else np.empty((0, 5)) - - police_prisoner_dets_tracks = self.tracker.update( - ppolice_prisoner_dets, - [self.height, self.width], - [self.height, self.width] - ) - - # 重置当前帧的犯人track标记 - current_frame_prisoner_tids = set() - - # ========= 单帧统计变量 ========= - current_police_count = 0 - current_prisoner_count = 0 - - # ========= 警察和犯人检测 ========= - for t in police_prisoner_dets_tracks: - tid = t.track_id - - # IoU 匹配角色 - REVALIDATE_FRAME_INTERVAL = 10 - if (self.current_frame_idx % REVALIDATE_FRAME_INTERVAL == 0) or ( - tid not in self.police_prisoner_track_role): - best_iou = 0 - best_role = "unknown" - - t_box = list(map(float, t.tlbr)) # [x1,y1,x2,y2] - - for i, box in enumerate(police_prisoner_dets_xyxy): - iou_val = self.compute_iou(t_box, box) - if iou_val > best_iou: - best_iou = iou_val - best_role = police_prisoner_dets_roles[i] - if best_iou > 0.1: - self.police_prisoner_track_role[tid] = best_role - else: - self.police_prisoner_track_role[tid] = "unknown" - - role = self.police_prisoner_track_role.get(tid, "unknown") - cls_id = -1 - if role == "police": - cls_id = 0 - elif role == "prisoner": - cls_id = 1 - - x1, y1, x2, y2 = map(int, t.tlbr) - cx, cy = (x1 + x2) // 2, (y1 + y2) // 2 - - color = None - label = None - - if cls_id == 0: # police - current_police_count += 1 - color = (255, 0, 255) - label = "police" - - elif cls_id == 1: # prisoner - current_prisoner_count += 1 - color = (0, 0, 139) - label = "prisoner" - current_frame_prisoner_tids.add(tid) - # ===== 区域状态机更新 ===== - self._update_prisoner_route( - tid=tid, - point=(cx, cy), - timestamp=current_time_sec - ) - else: - color = (255, 255, 255) - label = "Unknown" - - cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2) - cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2) - - # ========================================== - # 检查犯人违规（进入区域后离开） - # ========================================== - violation_alerts = self._check_prisoner_violation(current_time_sec) - - # 遍历所有状态，收集刚刚触发的 violation（那些在更新中被标记但尚未加入 violated_tracks 的） - for tid, state in self.prisoner_route_state.items(): - if state["violation"] and tid not in self.violated_tracks: - self.violated_tracks.add(tid) - violation_alerts.append({ - 'time': current_time_sec, - 'action': 'violation', - 'confidence': 1.0, - 'details': "Prisoner left zone" - }) - - current_frame_alerts.extend(violation_alerts) - - # ========================================== - # 犯人检测 - # ========================================== - if current_prisoner_count > 0: - self.prisoner_detection_frames += 1 - self.prisoner_missing_frames = 0 - if self.prisoner_detection_frames >= self.frame_thresh_prisoner: - self.prisoner_alert_active = True - else: - self.prisoner_missing_frames += 1 - if self.prisoner_detection_frames > 0: - if self.prisoner_missing_frames >= self.frame_buffer_prisoner: - self.prisoner_detection_frames = 0 - self.prisoner_alert_active = False - - # ========================================== - # 警察检测 - # ========================================== - if current_police_count > 0: - self.police_detection_frames += 1 - self.police_missing_frames = 0 - if self.police_detection_frames >= self.frame_thresh_police: - self.police_alert_active = True - else: - self.police_missing_frames += 1 - if self.police_detection_frames > 0: - if self.police_missing_frames >= self.frame_buffer_police: - self.police_detection_frames = 0 - self.police_alert_active = False - - alert_offset = 0 - - # A. 有犯人 - if self.prisoner_alert_active: - duration_seconds = self.prisoner_detection_frames / self.fps - current_frame_alerts.append( - { - 'time': current_time_sec, - 'action': 'prisoner', - 'confidence': 1.0, - 'details': f"Detected for {duration_seconds:.1f}s" - } - ) - self.draw_alert(frame, "prisoner", (0, 0, 255), offset_y=alert_offset) - alert_offset += 100 - - # C. 区域违规告警（离开区域） - for violation in violation_alerts: - self.draw_alert(frame, "ZONE VIOLATION!", (0, 0, 255), - sub_text=violation['details'], offset_y=alert_offset) - alert_offset += 100 - - # ========================= - # 绘制区域 ROI（始终显示） - # ========================= - self._draw_route_rois(frame) - - return { - "image": frame, - "alerts": current_frame_alerts - } - - -# ========================= 帧处理线程 ========================= -class FrameProcessorWorker(threading.Thread): - def __init__(self, - raw_frame_queue: "queue.Queue[Dict[str, Any]]", - ws_send_queue: "queue.Queue[Dict[str, Any]]", - stop_event: threading.Event): - super().__init__(daemon=True) - self.raw_queue = raw_frame_queue - self.ws_queue = ws_send_queue - self.stop_event = stop_event - - self.last_ts: Dict[int, float] = {} - - # 每个摄像头一个独立的 Kadian 检测器实例 - self.trajectory_detectors: Dict[int, TrajectoryDetector] = {} - - # 新增：维护每个摄像头每个action的最后推送时间 {camera_id: {action: last_push_time}} - self.last_alert_push_time: Dict[int, Dict[str, float]] = {} - - def _encode_image_to_base64(self, image) -> str: - ok, buf = cv2.imencode(".jpg", image) - if not ok: - raise RuntimeError("Failed to encode image to JPEG") - return base64.b64encode(buf.tobytes()).decode("ascii") - - def run(self): - target_interval = 1.0 / RTSP_TARGET_FPS - while not self.stop_event.is_set(): - try: - item = self.raw_queue.get(timeout=0.5) - except queue.Empty: - continue - - cam_id = item["camera_id"] - ts = item["timestamp"] - frame = item["frame"] - - # 抽帧控制 - if ts - self.last_ts.get(cam_id, 0) < target_interval: - self.raw_queue.task_done() - continue - self.last_ts[cam_id] = ts - - # 获取检测器实例 - if cam_id not in self.trajectory_detectors: - self.trajectory_detectors[cam_id] = TrajectoryDetector() - detector = self.trajectory_detectors[cam_id] - - # 执行检测 - result = detector.process_frame(frame.copy(), cam_id, ts) - - result_img = result["image"] - result_type = result["alerts"] - - # ========= 核心修改：过滤5秒内重复的action ========= - # 初始化当前摄像头的推送时间记录 - if cam_id not in self.last_alert_push_time: - self.last_alert_push_time[cam_id] = {} - - # 筛选出符合推送条件的action（5秒内未推送过） - push_actions = [] - current_time = time.time() - for alert in result_type: - action = alert['action'] - last_push = self.last_alert_push_time[cam_id].get(action, 0) - # 检查是否超过推送间隔 - if current_time - last_push >= ALERT_PUSH_INTERVAL: - push_actions.append(action) - # 更新该action的最后推送时间 - self.last_alert_push_time[cam_id][action] = current_time - - # 通过 WebSocket 发送帧结果 - try: - img_b64 = self._encode_image_to_base64(result_img) - except Exception as e: - print(f"[ERROR] Encode image failed: {e}") - img_b64 = None - - if img_b64 is not None: - msg = { - "msg_type": "frame", - "camera_id": 1, - "timestamp": ts, - "result_type": push_actions, - "image_base64": img_b64, - } - try: - self.ws_queue.put(msg, timeout=1.0) - # if push_actions and len(push_actions) > 0: - # self.ws_queue_2.put(msg, timeout=1.0) - except queue.Full: - print("[WARN] ws_send_queue full, drop frame message") - - self.raw_queue.task_done() \ No newline at end of file