260 lines
9.1 KiB
Python
260 lines
9.1 KiB
Python
import cv2
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import numpy as np
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import base64
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from typing import Dict, Any
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import threading
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import time
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import queue
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import requests
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from biz.base_frame_processor import BaseFrameProcessorWorker
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# -------------------------- Kadian 检测相关导入 --------------------------
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from algorithm.common.npu_yolo_onnx_person_car_phone import YOLOv8_ONNX # 主检测模型（人/车/后备箱/手机）
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from common.constants import ALERT_PUSH_URL
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from yolox.tracker.byte_tracker import BYTETracker
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# ========================= 配置区 =========================
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# Kadian 模型路径与ROI（可根据实际情况修改）
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detector_model_path = 'YOLO_Weight/bag_model.onnx'
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# 输入尺寸
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input_size = 640
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RTSP_TARGET_FPS = 10.0
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# 新增：告警推送频率限制（秒）
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ALERT_PUSH_INTERVAL = 5.0 # 相同action 5秒内仅推送一次
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class AbDetector:
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def __init__(self, params=None):
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# 摄像头额外参数
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self.params = params if params is not None else {}
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# 模型加载
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self.detector = YOLOv8_ONNX(detector_model_path, conf_threshold=0.5, iou_threshold=0.45,
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input_size=input_size)
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# ByteTracker
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class TrackerArgs:
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track_thresh = 0.25
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track_buffer = 30
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match_thresh = 0.8
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mot20 = False
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self.track_role = {}
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self.fps = RTSP_TARGET_FPS
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self.tracker = BYTETracker(TrackerArgs(), frame_rate=self.fps)
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# ==========================================
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# 超参数设置 (Hyperparameters)
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# ==========================================
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self.TIME_THRESHOLD_BLACKBAG = 1.0 # 黑包判定时长（秒）
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self.TIME_TOLERANCE_BLACKBAG = 0.5 # 黑包丢失缓冲时间
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# 转换为帧数阈值
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self.frame_thresh_blackbag = int(self.TIME_THRESHOLD_BLACKBAG * self.fps)
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self.frame_buffer_blackbag = int(self.TIME_TOLERANCE_BLACKBAG * self.fps)
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print(f"\n超参数设置:")
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print(f" FPS: {self.fps:.2f}")
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print(f" 判定 'BlackBag Detected' 需累计检测: {self.frame_thresh_blackbag} 帧")
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print(f" 黑包丢失缓冲帧数: {self.frame_buffer_blackbag} 帧")
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# ==========================================
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# 状态变量初始化
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# ==========================================
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self.current_frame_idx = 0
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# 黑包检测状态
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self.blackbag_detection_frames = 0
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self.blackbag_missing_frames = 0
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self.blackbag_alert_active = False
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# 人员统计变量
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self.current_person_count = 0
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def compute_iou(self,boxA, boxB):
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# box = [x1, y1, x2, y2]
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xA = max(boxA[0], boxB[0])
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yA = max(boxA[1], boxB[1])
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xB = min(boxA[2], boxB[2])
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yB = min(boxA[3], boxB[3])
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interW = max(0, xB - xA)
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interH = max(0, yB - yA)
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interArea = interW * interH
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boxAArea = (boxA[2] - boxA[0]) * (boxA[3] - boxA[1])
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boxBArea = (boxB[2] - boxB[0]) * (boxB[3] - boxB[1])
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unionArea = boxAArea + boxBArea - interArea
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if unionArea == 0:
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return 0.0
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return interArea / unionArea
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def draw_alert(self, frame, text, color=(0, 0, 255), sub_text=None, offset_y=0):
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"""在右上角绘制警告文字 (支持垂直偏移，防止文字重叠)"""
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font_scale = 1.5
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thickness = 3
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font = cv2.FONT_HERSHEY_SIMPLEX
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(text_w, text_h), _ = cv2.getTextSize(text, font, font_scale, thickness)
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x = self.width - text_w - 20
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y = 50 + text_h + offset_y # 增加 Y 轴偏移
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cv2.rectangle(frame, (x - 10, y - text_h - 10), (x + text_w + 10, y + 10), (0, 0, 0), -1)
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cv2.putText(frame, text, (x, y), font, font_scale, color, thickness)
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if sub_text:
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cv2.putText(frame, sub_text, (x, y + 40), font, 0.7, (200, 200, 200), 2)
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def process_frame(self, frame, camera_id: int, timestamp: float) -> Dict[str, Any]:
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h, w = frame.shape[:2]
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self.width, self.height = w, h
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self.current_frame_idx += 1
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current_time_sec = timestamp
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# ========= 检测推理（黑包+人）=========
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detect_results = self.detector(frame)
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# 初始化检测结果存储
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dets_xyxy = []
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dets_roles = []
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dets_for_tracker = []
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current_frame_alerts = []
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# 解析检测结果（黑包cls_id=0，人员cls_id=1）
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if detect_results:
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for det in detect_results:
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x1, y1, x2, y2, conf, cls_id = det
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dets_xyxy.append([x1, y1, x2, y2])
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dets_for_tracker.append([x1, y1, x2, y2, conf])
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if cls_id == 0:
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dets_roles.append("black_bag")
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elif cls_id == 1:
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dets_roles.append("person")
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# 跟踪器更新
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dets = np.array(dets_for_tracker, dtype=np.float32) if len(dets_for_tracker) else np.empty((0, 5))
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tracks = self.tracker.update(
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dets,
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[self.height, self.width],
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[self.height, self.width]
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)
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# ========= 单帧统计初始化 =========
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self.current_person_count = 0
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current_blackbag_count = 0
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# ========= 跟踪结果绘制与统计 =========
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for t in tracks:
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tid = t.track_id
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# IoU匹配跟踪ID和类别
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REVALIDATE_FRAME_INTERVAL = 10
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#if tid not in self.track_role:
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if (self.current_frame_idx % REVALIDATE_FRAME_INTERVAL == 0) or (tid not in self.track_role):
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best_iou = 0
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best_role = "unknown"
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t_box = list(map(float, t.tlbr))
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for i, box in enumerate(dets_xyxy):
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iou_val = self.compute_iou(t_box, box)
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if iou_val > best_iou:
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best_iou = iou_val
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best_role = dets_roles[i]
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self.track_role[tid] = best_role if best_iou > 0.1 else "unknown"
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role = self.track_role.get(tid, "unknown")
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x1, y1, x2, y2 = map(int, t.tlbr)
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color = (255, 255, 255)
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label = "Unknown"
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# 人员检测（cls_id=1）
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if role == "person":
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self.current_person_count += 1
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color = (255, 0, 255) # 紫色框
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label = "Person"
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# 黑包检测（cls_id=0）
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elif role == "black_bag":
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current_blackbag_count += 1
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color = (0, 128, 0) # 绿色框
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label = "Black Bag"
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# 绘制检测框和标签
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cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)
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cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)
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# ==========================================
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# 黑包状态更新
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# ==========================================
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if current_blackbag_count > 0:
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self.blackbag_detection_frames += 1
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self.blackbag_missing_frames = 0
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if self.blackbag_detection_frames >= self.frame_thresh_blackbag:
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self.blackbag_alert_active = True
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else:
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self.blackbag_missing_frames += 1
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if self.blackbag_missing_frames >= self.frame_buffer_blackbag:
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self.blackbag_detection_frames = 0
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self.blackbag_alert_active = False
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# ==========================================
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# 警告信息收集
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# ==========================================
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if self.blackbag_alert_active:
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duration_seconds = self.blackbag_detection_frames / self.fps
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current_frame_alerts.append(
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{
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'time': current_time_sec,
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'action': 'Black Bag',
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'details': f"Detected for {duration_seconds:.1f}s"
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}
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)
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self.draw_alert(frame, "Black Bag Alert", (0, 0, 255), sub_text=f"Detected for {duration_seconds:.1f}s")
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# ==========================================
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# 绘制信息
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# ==========================================
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# 实时统计
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debug_info = f"Person: {self.current_person_count} | BlackBag: {current_blackbag_count}"
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cv2.putText(frame, debug_info, (20, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
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# 警告信息
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alert_y_start = 150
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for i, alert in enumerate(current_frame_alerts):
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action = alert['action']
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details = alert.get('details', '')
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color = (0, 0, 255) # 红色警告
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main_text = f"{action} ({details})"
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y_pos = alert_y_start + i * 50
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cv2.rectangle(frame, (20, y_pos - 40), (900, y_pos + 10), (0, 0, 0), -1)
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cv2.putText(frame, main_text, (30, y_pos), cv2.FONT_HERSHEY_SIMPLEX, 1.0, color, 2)
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return {
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"image": frame,
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"alerts":current_frame_alerts
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}
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# ========================= 帧处理线程 =========================
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class FrameProcessorWorker(BaseFrameProcessorWorker):
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"""轨迹检测帧处理线程"""
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# 子类配置
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DETECTOR_FACTORY = lambda params: AbDetector(params)
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POST_TYPE = 2
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TARGET_FPS = RTSP_TARGET_FPS
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