From 9b81c102484491a644a8dc57c17453611f82c7be Mon Sep 17 00:00:00 2001 From: zqc <835569504@qq.com> Date: Fri, 9 Jan 2026 13:32:49 +0800 Subject: [PATCH] =?UTF-8?q?=E5=BC=95=E5=85=A5yolo=E7=9B=91=E7=8B=B1?= =?UTF-8?q?=E8=AF=86=E5=88=AB=E4=BB=A3=E7=A0=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- npu_yolo_onnx_person_car_phone.py | 153 +++++++ rtsp/service.py | 3 +- rtsp_service_ws_0108.py | 644 ++++++++++++++++++++++++++++++ 3 files changed, 799 insertions(+), 1 deletion(-) create mode 100644 npu_yolo_onnx_person_car_phone.py create mode 100644 rtsp_service_ws_0108.py diff --git a/npu_yolo_onnx_person_car_phone.py b/npu_yolo_onnx_person_car_phone.py new file mode 100644 index 0000000..c0ad169 --- /dev/null +++ b/npu_yolo_onnx_person_car_phone.py @@ -0,0 +1,153 @@ +# 文件名: npu_yolo_onnx.py +import cv2 +import numpy as np +import onnxruntime as ort +import os +import time + +def letterbox(img, new_shape=(640, 640), color=(114, 114, 114)): + shape = img.shape[:2] # h, w + r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) + new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) + dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] + dw /= 2 + dh /= 2 + if shape[::-1] != new_unpad: + img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR) + top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) + left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) + img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) + return img, r, (dw, dh) + +class YOLOv8_ONNX: + def __init__(self, onnx_path, conf_threshold=0.25, iou_threshold=0.45, input_size=640): + providers = [("CANNExecutionProvider", { + "device_id": 0, + "arena_extend_strategy": "kNextPowerOfTwo", + "npu_mem_limit": 16 * 1024 * 1024 * 1024, + "precision_mode": "allow_fp32_to_fp16", + "op_select_impl_mode": "high_precision", + "enable_cann_graph": True, + }), + "CUDAExecutionProvider", + "CPUExecutionProvider", + ] + + self.session = ort.InferenceSession(onnx_path, providers=providers) + actual_providers = self.session.get_providers() + print("YOLO Providers:", actual_providers) + + if "CANNExecutionProvider" in actual_providers: + print("[INFO] YOLO 使用 CANNExecutionProvider（昇腾 NPU）") + elif 'CUDAExecutionProvider' in actual_providers: + print("[INFO] YOLO 使用 CUDAExecutionProvider（NVIDIA GPU）") + else: + print("[INFO] YOLO 使用 CPUExecutionProvider") + + self.conf_threshold = conf_threshold + self.iou_threshold = iou_threshold + self.input_name = self.session.get_inputs()[0].name + self.input_size = (input_size, input_size) if isinstance(input_size, int) else input_size + + print(f"模型输入名称: {self.input_name}") + print(f"模型输入形状: {self.session.get_inputs()[0].shape}") + print(f"模型输出形状: {self.session.get_outputs()[0].shape}") + + def preprocess(self, img): + self.orig_shape = img.shape[:2] + img, self.ratio, (self.dw, self.dh) = letterbox(img, self.input_size) + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + img = img.transpose(2, 0, 1).astype(np.float32) + img /= 255.0 + img = np.expand_dims(img, axis=0) + return img + + def postprocess(self, pred, im0_shape): + # 1. 转置：从 [1, 4+cls, 8400] -> [8400, 4+cls] + pred = pred[0].T + + # 2. 获取数据 + boxes = pred[:, :4] # cx, cy, w, h + scores = pred[:, 4:] + + # 3. 获取最大置信度和类别 + conf = np.max(scores, axis=1) + class_pred = np.argmax(scores, axis=1) + + # 4. 初步过滤 + mask = conf > self.conf_threshold + if not mask.any(): + return [] + + boxes = boxes[mask] + conf = conf[mask] + class_pred = class_pred[mask] + + # ========================================================= + # 还原坐标 (逆 Letterbox) + # ========================================================= + boxes[:, 0] = (boxes[:, 0] - self.dw) / self.ratio # cx + boxes[:, 1] = (boxes[:, 1] - self.dh) / self.ratio # cy + boxes[:, 2] = boxes[:, 2] / self.ratio # w + boxes[:, 3] = boxes[:, 3] / self.ratio # h + + # 转换格式：Center(cx,cy) -> TopLeft(x,y) + x = boxes[:, 0] - boxes[:, 2] / 2 + y = boxes[:, 1] - boxes[:, 3] / 2 + w = boxes[:, 2] + h = boxes[:, 3] + + # 原始框（用于最终输出） + bboxes_original = np.stack([x, y, w, h], axis=1) + + # ========================================================= + # 【核心修复】：Class-Aware NMS (偏移量法) + # 给不同类别的框增加不同的偏移量，使得不同类别的框绝对不会重叠 + # 从而避免 "车" 把 "人" 过滤掉的情况 + # ========================================================= + max_wh = 4096 # 只要大于图片最大分辨率即可 + class_offset = class_pred * max_wh + + # NMS 专用的框坐标 (加上了偏移量) + bboxes_for_nms = bboxes_original.copy() + bboxes_for_nms[:, 0] += class_offset + bboxes_for_nms[:, 1] += class_offset + + # ========================================================= + # 执行 NMS + # ========================================================= + indices = cv2.dnn.NMSBoxes( + bboxes_for_nms.tolist(), + conf.tolist(), + self.conf_threshold, + self.iou_threshold + ) + + result = [] + if len(indices) > 0: + indices = indices.flatten() + for i in indices: + # 注意：这里取数据要从 bboxes_original 取 (没有加偏移量的) + bx, by, bw, bh = bboxes_original[i] + + # 转换回 x1, y1, x2, y2 供业务代码画图使用 + x1 = np.clip(bx, 0, im0_shape[1]) + y1 = np.clip(by, 0, im0_shape[0]) + x2 = np.clip(bx + bw, 0, im0_shape[1]) + y2 = np.clip(by + bh, 0, im0_shape[0]) + + result.append([ + float(x1), + float(y1), + float(x2), + float(y2), + float(conf[i]), + int(class_pred[i]) + ]) + return result + + def __call__(self, frame): + input_data = self.preprocess(frame) + pred = self.session.run(None, {self.input_name: input_data})[0] + results = self.postprocess(pred, frame.shape[:2]) + return results \ No newline at end of file diff --git a/rtsp/service.py b/rtsp/service.py index 1f36237..5259375 100644 --- a/rtsp/service.py +++ b/rtsp/service.py @@ -4,7 +4,8 @@ RTSP 服务模块 - 简洁版本，直接使用原始服务 import threading # from rtsp_service_ws_1217 import RTSPService -from rtsp_service_ws_prison import RTSPService +# from rtsp_service_ws_prison import RTSPService +from rtsp_service_ws_0108 import RTSPService class SimpleRTSPServer: diff --git a/rtsp_service_ws_0108.py b/rtsp_service_ws_0108.py new file mode 100644 index 0000000..d916442 --- /dev/null +++ b/rtsp_service_ws_0108.py @@ -0,0 +1,644 @@ +# 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 +from datetime import datetime + +# 导入人脸识别算法 +try: + from api.routes.algorithm_router import video_face_prison_biz + + print("[INFO] 成功导入人脸识别算法") +except Exception as e: + print(f"[WARN] 无法导入人脸识别算法: {e}") + +# -------------------------- Kadian 检测相关导入 -------------------------- +from 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 + + +# ========================= 配置区 ========================= +# Kadian 模型路径与ROI（可根据实际情况修改） +police_prisoner_model_path = 'YOLO_Weight/prisoner_model.onnx' + +FACE_RECOGNITION_ENABLED = True # 是否启用人脸识别 + +# 输入尺寸 +police_prisoner_input_size = 1280 + +# RTSP 服务配置 +RTSP_TARGET_FPS = 30.0 +FRAMES_PER_SEGMENT = 1800 +VIDEO_OUTPUT_DIR = "./videos" +WS_HOST = "0.0.0.0" +WS_PORT = 8765 + +# WebSocket 客户端集合 +ws_clients = set() + + +# ========================= 数据结构 ========================= +@dataclass +class CameraConfig: + id: int + name: str + rtsp_url: str + + +# ========================= Kadian TrafficMonitor（精简版，专为服务设计） ========================= +class KadianDetector: + def __init__(self): + # 模型加载 + + self.police_prisoner_detector = YOLOv8_ONNX(police_prisoner_model_path, conf_threshold=0.5, iou_threshold=0.45, + input_size=police_prisoner_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) + # ========================================== + + # 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_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 = 0.5 # 犯人丢失缓冲时间（防抖动） + + # 无人在场帧数阈值 + # self.frame_thresh_nobody = int(self.TIME_THRESHOLD_NOBODY * self.fps) + + # 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 'Nobody' 需连续: {self.frame_thresh_nobody} 帧") + 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 + + # 无人在场检测状态变量 + self.cnt_frame_nobody = 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 + + + 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 + + # ========= 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] + ) + # ========= 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: + # print("t: {}".format(t)) + tid = t.track_id + # cls_id = -1 + + # IoU 匹配角色 + + if 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 + # 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_police_count += 1 + color = (255, 0, 255) + label = "police" + + elif cls_id == 1: # Phone（主模型已支持） + current_prisoner_count += 1 + color = (0, 0, 139) + label = "prisoner" + 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) + + # ========================================== + # 犯人检测 + # ========================================== + if current_prisoner_count > 0: + # 检测到犯人框 + self.prisoner_detection_frames += 1 + self.prisoner_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.prisoner_detection_frames >= self.frame_thresh_prisoner: + self.prisoner_alert_active = True + else: + # 未检测到犯人框 + self.prisoner_missing_frames += 1 + + # 如果之前检测到手机，重置检测计数器 + if self.prisoner_detection_frames > 0: + # 只有在连续丢失超过缓冲帧数时才重置 + if self.prisoner_missing_frames >= self.frame_buffer_prisoner: + self.prisoner_detection_frames = 0 + self.prisoner_alert_active = False + else: + # 从未检测到犯人，保持状态 + pass + # ========================================== + # 警察检测 + # ========================================== + if current_police_count > 0: + # 检测到犯人框 + self.police_detection_frames += 1 + self.police_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.police_detection_frames >= self.frame_thresh_police: + self.police_alert_active = True + else: + # 未检测到犯人框 + self.police_missing_frames += 1 + + # 如果之前检测到手机，重置检测计数器 + if self.police_detection_frames > 0: + # 只有在连续丢失超过缓冲帧数时才重置 + if self.police_missing_frames >= self.frame_buffer_police: + self.police_detection_frames = 0 + self.police_alert_active = False + else: + # 从未检测到犯人，保持状态 + pass + + + + 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, # 固定为1.0（规则判定） + 'details': f"Detected for {duration_seconds:.1f}s" + } + ) + self.draw_alert(frame, "prisoner", (0, 0, 255), offset_y=alert_offset) + alert_offset += 100 + + # ========================================== + # 11. 统一显示当前帧所有警告（可替换原分层显示） + # ========================================== + debug_info = f" prisoner: {current_prisoner_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 == 'prisoner': + color = (255, 255, 255) + + 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 + } + +# ========================= WebSocket 服务线程 ========================= +class WebSocketSender(threading.Thread): + def __init__(self, send_queue: queue.Queue, stop_event: threading.Event): + super().__init__(daemon=True) + self.send_queue = send_queue + self.stop_event = stop_event + + async def _ws_handler(self, websocket): + ws_clients.add(websocket) + try: + async for _ in websocket: + pass + finally: + ws_clients.discard(websocket) + + async def _broadcaster(self): + while not self.stop_event.is_set(): + try: + msg = await asyncio.to_thread(self.send_queue.get, timeout=0.5) + except queue.Empty: + continue + data = json.dumps(msg) + dead = [] + for ws in list(ws_clients): + try: + await ws.send(data) + except: + dead.append(ws) + for ws in dead: + ws_clients.discard(ws) + self.send_queue.task_done() + + async def _run_async(self): + async with websockets.serve(self._ws_handler, WS_HOST, WS_PORT): + print(f"[INFO] WebSocket server started at ws://{WS_HOST}:{WS_PORT}") + await self._broadcaster() + + def run(self): + asyncio.run(self._run_async()) + + +# ========================= RTSP 抓流线程 ========================= +class RTSPCaptureWorker(threading.Thread): + def __init__(self, camera_cfg: CameraConfig, raw_queue: queue.Queue, stop_event: threading.Event): + super().__init__(daemon=True) + self.camera_cfg = camera_cfg + self.raw_queue = raw_queue + self.stop_event = stop_event + + def run(self): + cap = cv2.VideoCapture(self.camera_cfg.rtsp_url, cv2.CAP_FFMPEG) + if not cap.isOpened(): + print(f"[ERROR] Cannot open RTSP: {self.camera_cfg.rtsp_url}") + return + print(f"[INFO] Capturing {self.camera_cfg.name} (ID:{self.camera_cfg.id})") + while not self.stop_event.is_set(): + ret, frame = cap.read() + if not ret: + time.sleep(0.2) + continue + item = { + "camera_id": self.camera_cfg.id, + "camera_name": self.camera_cfg.name, + "timestamp": time.time(), + "frame": frame, + } + try: + self.raw_queue.put(item, timeout=1.0) + except queue.Full: + pass + cap.release() + + +# ========================= 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.video_writers: Dict[int, cv2.VideoWriter] = {} + self.video_counts: Dict[int, int] = {} + self.last_ts: Dict[int, float] = {} + self.video_files: Dict[int, str] = {} + + + os.makedirs(VIDEO_OUTPUT_DIR, exist_ok=True) + + # 每个摄像头一个独立的 Kadian 检测器实例 + self.kadian_detectors: Dict[int, KadianDetector] = {} + + def _get_writer(self, camera_id: int, frame) -> Tuple[cv2.VideoWriter, str]: + if camera_id in self.video_writers: + return self.video_writers[camera_id], self.video_files[camera_id] + + h, w = frame.shape[:2] + ts_str = datetime.now().strftime("%Y%m%d_%H%M%S") + filepath = os.path.join(VIDEO_OUTPUT_DIR, f"{ts_str}_cam{camera_id}.mp4") + writer = cv2.VideoWriter(filepath, cv2.VideoWriter_fourcc(*"mp4v"), RTSP_TARGET_FPS, (w, h)) + + self.video_writers[camera_id] = writer + self.video_files[camera_id] = filepath + self.video_counts[camera_id] = 0 + print(f"[INFO] New segment: {filepath}") + return writer, filepath + + def _close_segment_if_needed(self, camera_id: int): + count = self.video_counts.get(camera_id, 0) + if count >= FRAMES_PER_SEGMENT: + writer = self.video_writers.get(camera_id) + if writer is not None: + writer.release() + print(f"[INFO] Close segment: camera={camera_id}, file={self.video_files[camera_id]}") + + self.video_writers.pop(camera_id, None) + self.video_counts.pop(camera_id, None) + self.last_ts.pop(camera_id, None) + self.video_files.pop(camera_id, None) + + 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.kadian_detectors: + self.kadian_detectors[cam_id] = KadianDetector() + detector = self.kadian_detectors[cam_id] + + # 2) 进行人脸识别（如果启用） + current_face_alert = None + face_results = [] + face_processing_time = 0 + if video_face_prison_biz is not None and FACE_RECOGNITION_ENABLED: + try: + # 处理当前帧 - 获取人脸识别结果 + processed_frame_for_face, face_results, face_processing_time = video_face_prison_biz.process_frame( + frame.copy()) + + for result in face_results: + if result['has_passed']: + print(f"[INFO] 犯人带出: {result['passed_person_id']}") + + # 记录当前帧人脸告警信息 + current_face_alert = { + "person_name": result['passed_person_id'], + "timestamp": ts + } + + except Exception as e: + print(f"[WARN] 人脸识别处理失败: {e}") + + # 执行检测 + result = detector.process_frame(frame.copy(), cam_id, ts) + + result_img = result["image"] + result_type = result["alerts"] + + # 绘制人脸识别结果 + if video_face_prison_biz is not None and face_results: + result_img = video_face_prison_biz.draw_detections(result_img, face_results) + + # 添加人脸识别统计信息 + match_count = sum(1 for r in face_results if r['is_match']) + face_info_text = f"Faces: {len(face_results)} | Matches: {match_count}" + cv2.putText(result_img, face_info_text, (10, result_img.shape[0] - 20), + cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 255), 2) + + # 写视频 + writer, video_path = self._get_writer(cam_id, frame) + writer.write(result_img) + self.video_counts[cam_id] = self.video_counts.get(cam_id, 0) + 1 + + # 5) 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: + # 将abnormal_actions对象数组转换为字符串数组 + action_names = [action_info['action'] for action_info in result_type] + + if current_face_alert is not None: + action_names.append("face") + + msg = { + "msg_type": "frame", + "camera_id": cam_id, + "timestamp": ts, + "result_type": action_names, + "image_base64": img_b64, + } + try: + self.ws_queue.put(msg, timeout=1.0) + except queue.Full: + print("[WARN] ws_send_queue full, drop frame message") + + + + self._close_segment_if_needed(cam_id) + self.raw_queue.task_done() + + self.video_counts[cam_id] = self.video_counts.get(cam_id, 0) + 1 + + # 清理 + for w in self.video_writers.values(): + w.release() + + +# ========================= 服务主类 ========================= +class RTSPService: + def __init__(self, config_path: str = "config.yaml"): + with open(config_path, "r", encoding="utf-8") as f: + cfg = yaml.safe_load(f) + self.cameras = [CameraConfig(id=c["id"], name=c.get("name", f"cam_{c['id']}"), rtsp_url=c["rtsp_url"]) + for c in cfg.get("cameras", [])] + + self.stop_event = threading.Event() + self.raw_queue = queue.Queue(maxsize=500) + self.ws_queue = queue.Queue(maxsize=1000) + + self.capture_workers = [] + self.processor = FrameProcessorWorker(self.raw_queue, self.ws_queue, self.stop_event) + self.ws_sender = WebSocketSender(self.ws_queue, self.stop_event) + + def start(self): + self.ws_sender.start() + self.processor.start() + for cam in self.cameras: + w = RTSPCaptureWorker(cam, self.raw_queue, self.stop_event) + w.start() + self.capture_workers.append(w) + print("[INFO] Kadian RTSP Service started") + + def stop(self): + self.stop_event.set() + self.raw_queue.join() + self.ws_queue.join() + for w in self.capture_workers: + w.join(timeout=2.0) + self.processor.join(timeout=2.0) + self.ws_sender.join(timeout=2.0) + print("[INFO] Service stopped") + + +if __name__ == "__main__": + service = RTSPService("config.yaml") + service.start() + try: + while True: + time.sleep(1) + except KeyboardInterrupt: + service.stop() \ No newline at end of file