commit ad6282c6d523dc08bc6c7933b28fe5238ae41737
Author: 16337 <1633794139@qq.com>
Date: Mon Jan 12 17:38:39 2026 +0800
Initial commit
diff --git a/.idea/.gitignore b/.idea/.gitignore
new file mode 100644
index 0000000..b6b1ecf
--- /dev/null
+++ b/.idea/.gitignore
@@ -0,0 +1,10 @@
+# 默认忽略的文件
+/shelf/
+/workspace.xml
+# 已忽略包含查询文件的默认文件夹
+/queries/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml
+# 基于编辑器的 HTTP 客户端请求
+/httpRequests/
diff --git a/.idea/Detector.iml b/.idea/Detector.iml
new file mode 100644
index 0000000..0dd87bd
--- /dev/null
+++ b/.idea/Detector.iml
@@ -0,0 +1,8 @@
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/inspectionProfiles/Project_Default.xml b/.idea/inspectionProfiles/Project_Default.xml
new file mode 100644
index 0000000..710d32f
--- /dev/null
+++ b/.idea/inspectionProfiles/Project_Default.xml
@@ -0,0 +1,12 @@
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml
new file mode 100644
index 0000000..105ce2d
--- /dev/null
+++ b/.idea/inspectionProfiles/profiles_settings.xml
@@ -0,0 +1,6 @@
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/misc.xml b/.idea/misc.xml
new file mode 100644
index 0000000..68ee80e
--- /dev/null
+++ b/.idea/misc.xml
@@ -0,0 +1,7 @@
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/modules.xml b/.idea/modules.xml
new file mode 100644
index 0000000..8fdd359
--- /dev/null
+++ b/.idea/modules.xml
@@ -0,0 +1,8 @@
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
new file mode 100644
index 0000000..94a25f7
--- /dev/null
+++ b/.idea/vcs.xml
@@ -0,0 +1,6 @@
+
+
+
+
+
+
\ No newline at end of file
diff --git a/__pycache__/detector.cpython-310.pyc b/__pycache__/detector.cpython-310.pyc
new file mode 100644
index 0000000..f3a3898
Binary files /dev/null and b/__pycache__/detector.cpython-310.pyc differ
diff --git a/__pycache__/sort.cpython-310.pyc b/__pycache__/sort.cpython-310.pyc
new file mode 100644
index 0000000..0a9760e
Binary files /dev/null and b/__pycache__/sort.cpython-310.pyc differ
diff --git a/config.yaml b/config.yaml
new file mode 100644
index 0000000..ff37923
--- /dev/null
+++ b/config.yaml
@@ -0,0 +1,43 @@
+model:
+ path: "C:/Users/16337/PycharmProjects/Security/yolo11n.pt"
+ imgsz: 480
+ conf_threshold: 0.5
+ device: "cuda" # cuda, cpu
+
+common:
+ working_hours: [9, 17] # 工作时间：9:00 ~ 17:00（24小时制）
+ process_every_n_frames: 3 # 每3帧处理1帧
+ alert_cooldown_sec: 300 # 离岗告警冷却（秒）
+ crowd_cooldown_sec: 180 # 聚集告警冷却（秒）
+ entry_grace_period_sec: 1.0 # 入岗保护期（防漏检）
+
+cameras:
+ - id: "cam_01"
+ rtsp_url: "rtsp://admin:admin@172.16.8.19:554/cam/realmonitor?channel=16&subtype=1"
+ roi_points: [[380, 50], [530, 100], [550, 550], [140, 420]] # 离岗检测区域
+ crowd_roi_points: [[220, 50], [380, 60], [180, 525], [0, 500]] # 聚集检测区域
+ off_duty_threshold_sec: 300 # 离岗超时告警（秒）
+ on_duty_confirm_sec: 5 # 上岗确认时间（秒）
+ process_every_n_frames: 5
+ off_duty_confirm_sec: 30 # 离岗确认时间（秒）
+ crowd_threshold: 5 # 聚集人数阈值（最低触发）
+
+
+ - id: "cam_02"
+ rtsp_url: "rtsp://admin:admin@172.16.8.13:554/cam/realmonitor?channel=7&subtype=1"
+ roi_points: [ [ 380, 50 ], [ 530, 100 ], [ 550, 550 ], [ 140, 420 ] ] # 离岗检测区域
+ crowd_roi_points: [ [ 220, 50 ], [ 380, 60 ], [ 180, 525 ], [ 0, 500 ] ] # 聚集检测区域
+ off_duty_threshold_sec: 600
+ on_duty_confirm_sec: 10
+ off_duty_confirm_sec: 20
+ crowd_threshold: 3
+
+
+ - id: "cam_03"
+ rtsp_url: "rtsp://admin:admin@172.16.8.26:554/cam/realmonitor?channel=3&subtype=1"
+ roi_points: [ [ 380, 50 ], [ 530, 100 ], [ 550, 550 ], [ 140, 420 ] ] # 离岗检测区域
+ crowd_roi_points: [ [ 220, 50 ], [ 380, 60 ], [ 180, 525 ], [ 0, 500 ] ] # 聚集检测区域
+ off_duty_threshold_sec: 600
+ on_duty_confirm_sec: 10
+ off_duty_confirm_sec: 20
+ crowd_threshold: 3
\ No newline at end of file
diff --git a/detector.py b/detector.py
new file mode 100644
index 0000000..f729806
--- /dev/null
+++ b/detector.py
@@ -0,0 +1,218 @@
+import cv2
+import numpy as np
+from ultralytics import YOLO
+from sort import Sort
+import time
+import datetime
+import threading
+import queue
+import torch
+from collections import deque
+
+
+class ThreadedFrameReader:
+ def __init__(self, src, maxsize=1):
+ self.cap = cv2.VideoCapture(src, cv2.CAP_FFMPEG)
+ self.cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)
+ self.q = queue.Queue(maxsize=maxsize)
+ self.running = True
+ self.thread = threading.Thread(target=self._reader)
+ self.thread.daemon = True
+ self.thread.start()
+
+ def _reader(self):
+ while self.running:
+ ret, frame = self.cap.read()
+ if not ret:
+ time.sleep(0.1)
+ continue
+ if not self.q.empty():
+ try:
+ self.q.get_nowait()
+ except queue.Empty:
+ pass
+ self.q.put(frame)
+
+ def read(self):
+ if not self.q.empty():
+ return True, self.q.get()
+ return False, None
+
+ def release(self):
+ self.running = False
+ self.cap.release()
+
+
+def is_point_in_roi(x, y, roi):
+ return cv2.pointPolygonTest(roi, (int(x), int(y)), False) >= 0
+
+
+class OffDutyCrowdDetector:
+ def __init__(self, config, model, device, use_half):
+ self.config = config
+ self.model = model
+ self.device = device
+ self.use_half = use_half
+
+ # 解析 ROI
+ self.roi = np.array(config["roi_points"], dtype=np.int32)
+ self.crowd_roi = np.array(config["crowd_roi_points"], dtype=np.int32)
+
+ # 状态变量
+ self.tracker = Sort(
+ max_age=30,
+ min_hits=2,
+ iou_threshold=0.3
+ )
+
+ self.is_on_duty = False
+ self.on_duty_start_time = None
+ self.is_off_duty = True
+ self.last_no_person_time = None
+ self.off_duty_timer_start = None
+ self.last_alert_time = 0
+
+ self.last_crowd_alert_time = 0
+ self.crowd_history = deque(maxlen=1500) # 自动限制5分钟（假设5fps）
+
+ self.last_person_seen_time = None
+ self.frame_count = 0
+
+ # 缓存配置
+ self.working_start_min = config.get("working_hours", [9, 17])[0] * 60
+ self.working_end_min = config.get("working_hours", [9, 17])[1] * 60
+ self.process_every = config.get("process_every_n_frames", 3)
+
+ def in_working_hours(self):
+ now = datetime.datetime.now()
+ total_min = now.hour * 60 + now.minute
+ return self.working_start_min <= total_min <= self.working_end_min
+
+ def count_people_in_roi(self, boxes, roi):
+ count = 0
+ for box in boxes:
+ x1, y1, x2, y2 = box.xyxy[0].cpu().numpy()
+ cx, cy = (x1 + x2) / 2, (y1 + y2) / 2
+ if is_point_in_roi(cx, cy, roi):
+ count += 1
+ return count
+
+ def run(self):
+ """主循环：供线程调用"""
+ frame_reader = ThreadedFrameReader(self.config["rtsp_url"])
+ try:
+ while True:
+ ret, frame = frame_reader.read()
+ if not ret:
+ time.sleep(0.01)
+ continue
+
+ self.frame_count += 1
+ if self.frame_count % self.process_every != 0:
+ continue
+
+ current_time = time.time()
+ now = datetime.datetime.now()
+
+ # YOLO 推理
+ results = self.model(
+ frame,
+ imgsz=self.config.get("imgsz", 480),
+ conf=self.config.get("conf_thresh", 0.4),
+ verbose=False,
+ device=self.device,
+ half=self.use_half,
+ classes=[0] # person class
+ )
+ boxes = results[0].boxes
+
+ # 更新 tracker（可选，用于ID跟踪）
+ dets = []
+ for box in boxes:
+ x1, y1, x2, y2 = box.xyxy[0].cpu().numpy()
+ conf = float(box.conf)
+ dets.append([x1, y1, x2, y2, conf])
+ dets = np.array(dets) if dets else np.empty((0, 5))
+ self.tracker.update(dets)
+
+ # === 离岗检测 ===
+ if self.in_working_hours():
+ roi_has_person = self.count_people_in_roi(boxes, self.roi) > 0
+ if roi_has_person:
+ self.last_person_seen_time = current_time
+
+ # 入岗保护期
+ effective_on_duty = (
+ self.last_person_seen_time is not None and
+ (current_time - self.last_person_seen_time) < 1.0
+ )
+
+ if effective_on_duty:
+ self.last_no_person_time = None
+ if self.is_off_duty:
+ if self.on_duty_start_time is None:
+ self.on_duty_start_time = current_time
+ elif current_time - self.on_duty_start_time >= self.config.get("on_duty_confirm", 5):
+ self.is_on_duty = True
+ self.is_off_duty = False
+ self.on_duty_start_time = None
+ print(f"[{self.config['id']}] ✅ 上岗确认")
+ else:
+ self.on_duty_start_time = None
+ self.last_person_seen_time = None
+ if not self.is_off_duty:
+ if self.last_no_person_time is None:
+ self.last_no_person_time = current_time
+ elif current_time - self.last_no_person_time >= self.config.get("off_duty_confirm", 30):
+ self.is_off_duty = True
+ self.is_on_duty = False
+ self.off_duty_timer_start = current_time
+ print(f"[{self.config['id']}] ⏳ 开始离岗计时")
+
+ # 离岗告警
+ if self.is_off_duty and self.off_duty_timer_start:
+ elapsed = current_time - self.off_duty_timer_start
+ if elapsed >= self.config.get("off_duty_threshold", 300):
+ if current_time - self.last_alert_time >= self.config.get("alert_cooldown", 300):
+ print(f"[{self.config['id']}] 🚨 离岗告警！已离岗 {elapsed/60:.1f} 分钟")
+ self.last_alert_time = current_time
+
+ # === 聚集检测 ===
+ crowd_count = self.count_people_in_roi(boxes, self.crowd_roi)
+ self.crowd_history.append((current_time, crowd_count))
+
+ # 动态阈值
+ if crowd_count >= 10:
+ req_dur = 60
+ elif crowd_count >= 7:
+ req_dur = 120
+ elif crowd_count >= 5:
+ req_dur = 300
+ else:
+ req_dur = float('inf')
+
+ if req_dur < float('inf'):
+ recent = [(t, c) for t, c in self.crowd_history if current_time - t <= req_dur]
+ if recent:
+ valid = [c for t, c in recent if c >= 4]
+ ratio = len(valid) / len(recent)
+ if ratio >= 0.9 and (current_time - self.last_crowd_alert_time) >= self.config.get("crowd_cooldown", 180):
+ print(f"[{self.config['id']}] 🚨 聚集告警！{crowd_count}人持续{req_dur//60}分钟")
+ self.last_crowd_alert_time = current_time
+
+ # 可视化（可选，部署时可关闭）
+ if True: # 设为 True 可显示窗口
+ vis = results[0].plot()
+ overlay = vis.copy()
+ cv2.fillPoly(overlay, [self.roi], (0,255,0))
+ cv2.fillPoly(overlay, [self.crowd_roi], (0,0,255))
+ cv2.addWeighted(overlay, 0.2, vis, 0.8, 0, vis)
+ cv2.imshow(f"Monitor - {self.config['id']}", vis)
+ if cv2.waitKey(1) & 0xFF == ord('q'):
+ break
+
+ except Exception as e:
+ print(f"[{self.config['id']}] Error: {e}")
+ finally:
+ frame_reader.release()
+ cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/main.py b/main.py
new file mode 100644
index 0000000..eb389a0
--- /dev/null
+++ b/main.py
@@ -0,0 +1,16 @@
+# 这是一个示例 Python 脚本。
+
+# 按 Shift+F10 执行或将其替换为您的代码。
+# 按 双击 Shift 在所有地方搜索类、文件、工具窗口、操作和设置。
+
+
+def print_hi(name):
+ # 在下面的代码行中使用断点来调试脚本。
+ print(f'Hi, {name}') # 按 Ctrl+F8 切换断点。
+
+
+# 按装订区域中的绿色按钮以运行脚本。
+if __name__ == '__main__':
+ print_hi('PyCharm')
+
+# 访问 https://www.jetbrains.com/help/pycharm/ 获取 PyCharm 帮助
diff --git a/monitor.py b/monitor.py
new file mode 100644
index 0000000..d1aa976
--- /dev/null
+++ b/monitor.py
@@ -0,0 +1,291 @@
+import cv2
+import numpy as np
+import yaml
+import torch
+from ultralytics import YOLO
+import time
+import datetime
+import threading
+import queue
+import sys
+import argparse
+from collections import defaultdict
+
+
+class ThreadedFrameReader:
+ def __init__(self, cam_id, rtsp_url):
+ self.cam_id = cam_id
+ self.rtsp_url = rtsp_url
+ self.cap = cv2.VideoCapture(rtsp_url, cv2.CAP_FFMPEG)
+ self.cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)
+ self.q = queue.Queue(maxsize=2)
+ self.running = True
+ self.thread = threading.Thread(target=self._reader, daemon=True)
+ self.thread.start()
+
+ def _reader(self):
+ while self.running:
+ ret, frame = self.cap.read()
+ if not ret:
+ time.sleep(0.1)
+ continue
+ if self.q.full():
+ try:
+ self.q.get_nowait()
+ except queue.Empty:
+ pass
+ self.q.put(frame)
+
+ def read(self):
+ if not self.q.empty():
+ return True, self.q.get()
+ return False, None
+
+ def release(self):
+ self.running = False
+ self.cap.release()
+
+
+class MultiCameraMonitor:
+ def __init__(self, config_path):
+ with open(config_path, 'r', encoding='utf-8') as f:
+ self.cfg = yaml.safe_load(f)
+
+ # === 全局模型（只加载一次）===
+ model_cfg = self.cfg['model']
+ self.device = model_cfg.get('device', 'auto')
+ if self.device == 'auto' or not self.device:
+ self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
+ print(f"🚀 全局加载模型到 {self.device}...")
+ self.model = YOLO(model_cfg['path'])
+ self.model.to(self.device)
+ self.use_half = (self.device == 'cuda')
+ if self.use_half:
+ print("✅ 启用 FP16 推理")
+
+ self.imgsz = model_cfg['imgsz']
+ self.conf_thresh = model_cfg['conf_threshold']
+
+ # === 初始化所有摄像头 ===
+ self.common = self.cfg['common']
+ self.cameras = {}
+ self.frame_readers = {}
+ self.queues = {} # cam_id -> queue for detection results
+
+ for cam_cfg in self.cfg['cameras']:
+ cam_id = cam_cfg['id']
+ self.cameras[cam_id] = CameraLogic(cam_id, cam_cfg, self.common)
+ self.frame_readers[cam_id] = ThreadedFrameReader(cam_id, cam_cfg['rtsp_url'])
+ self.queues[cam_id] = queue.Queue(maxsize=1) # 存放检测结果
+
+ # === 控制信号 ===
+ self.running = True
+ self.inference_thread = threading.Thread(target=self._inference_loop, daemon=True)
+ self.inference_thread.start()
+
+ def _inference_loop(self):
+ """统一推理线程：轮询各摄像头最新帧，逐个推理"""
+ while self.running:
+ processed = False
+ for cam_id, reader in self.frame_readers.items():
+ ret, frame = reader.read()
+ if not ret:
+ continue
+
+ # 获取该摄像头是否需要处理此帧
+ cam_logic = self.cameras[cam_id]
+ if cam_logic.should_skip_frame():
+ continue
+
+ # 执行推理
+ results = self.model(
+ frame,
+ imgsz=self.imgsz,
+ conf=self.conf_thresh,
+ verbose=False,
+ device=self.device,
+ half=self.use_half,
+ classes=[0] # person
+ )
+
+ # 将结果送回对应摄像头逻辑
+ if not self.queues[cam_id].full():
+ self.queues[cam_id].put((frame.copy(), results[0]))
+ processed = True
+
+ if not processed:
+ time.sleep(0.01)
+
+ def run(self):
+ """启动所有摄像头的显示和告警逻辑（主线程）"""
+ try:
+ while self.running:
+ for cam_id, cam_logic in self.cameras.items():
+ if not self.queues[cam_id].empty():
+ frame, results = self.queues[cam_id].get()
+ cam_logic.process(frame, results)
+ # 让 OpenCV 刷新所有窗口
+ key = cv2.waitKey(1) & 0xFF
+ if key == ord('q'): # 可选：按 q 退出
+ break
+ time.sleep(0.01) # 避免 CPU 占用过高
+ except KeyboardInterrupt:
+ pass
+ finally:
+ self.stop()
+
+ def stop(self):
+ self.running = False
+ for reader in self.frame_readers.values():
+ reader.release()
+ cv2.destroyAllWindows()
+
+
+class CameraLogic:
+ def __init__(self, cam_id, cam_cfg, common_cfg):
+ self.cam_id = cam_id
+ self.roi_off_duty = np.array(cam_cfg['roi_points'], dtype=np.int32)
+ self.roi_crowd = np.array(cam_cfg['crowd_roi_points'], dtype=np.int32)
+ self.off_duty_threshold_sec = cam_cfg['off_duty_threshold_sec']
+ self.on_duty_confirm_sec = cam_cfg['on_duty_confirm_sec']
+ self.off_duty_confirm_sec = cam_cfg['off_duty_confirm_sec']
+ self.crowd_threshold_min = cam_cfg['crowd_threshold']
+
+ self.working_hours = common_cfg['working_hours']
+ self.process_every_n = cam_cfg.get('process_every_n_frames', common_cfg['process_every_n_frames'])
+ self.alert_cooldown_sec = cam_cfg.get('alert_cooldown_sec', common_cfg['alert_cooldown_sec'])
+ self.crowd_cooldown_sec = cam_cfg.get('crowd_cooldown_sec', common_cfg['crowd_cooldown_sec'])
+
+ self.frame_count = 0
+ self.is_on_duty = False
+ self.is_off_duty = True
+ self.on_duty_start_time = None
+ self.last_no_person_time = None
+ self.off_duty_timer_start = None
+ self.last_alert_time = 0
+ self.last_crowd_alert_time = 0
+ self.crowd_history = []
+ self.last_person_seen_time = None
+
+ def should_skip_frame(self):
+ self.frame_count += 1
+ return self.frame_count % self.process_every_n != 0
+
+ def is_point_in_roi(self, x, y, roi):
+ return cv2.pointPolygonTest(roi, (int(x), int(y)), False) >= 0
+
+ def in_working_hours(self):
+ now = datetime.datetime.now()
+ h = now.hour
+ start_h, end_h = self.working_hours
+ return start_h <= h < end_h or (start_h == end_h == 0)
+
+ def detect_crowd_count(self, boxes, roi):
+ count = 0
+ for box in boxes:
+ x1, y1, x2, y2 = box.xyxy[0].cpu().numpy()
+ cx, cy = (x1 + x2) / 2, (y1 + y2) / 2
+ if self.is_point_in_roi(cx, cy, roi):
+ count += 1
+ return count
+
+ def process(self, frame, results):
+ current_time = time.time()
+ now = datetime.datetime.now()
+ in_work = self.in_working_hours()
+ boxes = results.boxes
+
+ # === 离岗逻辑 ===
+ roi_has_person_raw = any(
+ self.is_point_in_roi((b.xyxy[0][0] + b.xyxy[0][2]) / 2,
+ (b.xyxy[0][1] + b.xyxy[0][3]) / 2,
+ self.roi_off_duty)
+ for b in boxes
+ )
+
+ if in_work:
+ if roi_has_person_raw:
+ self.last_person_seen_time = current_time
+ effective = (
+ self.last_person_seen_time is not None and
+ (current_time - self.last_person_seen_time) < 1.0 # grace period
+ )
+
+ if effective:
+ self.last_no_person_time = None
+ if self.is_off_duty:
+ if self.on_duty_start_time is None:
+ self.on_duty_start_time = current_time
+ elif current_time - self.on_duty_start_time >= self.on_duty_confirm_sec:
+ self.is_on_duty, self.is_off_duty = True, False
+ self.on_duty_start_time = None
+ print(f"[{self.cam_id}] ✅ 上岗确认成功 ({now.strftime('%H:%M:%S')})")
+ else:
+ self.on_duty_start_time = None
+ self.last_person_seen_time = None
+ if not self.is_off_duty:
+ if self.last_no_person_time is None:
+ self.last_no_person_time = current_time
+ elif current_time - self.last_no_person_time >= self.off_duty_confirm_sec:
+ self.is_off_duty, self.is_on_duty = True, False
+ self.last_no_person_time = None
+ self.off_duty_timer_start = current_time
+ print(f"[{self.cam_id}] 🚪 进入离岗计时")
+
+ # === 聚集检测 ===
+ crowd_count = self.detect_crowd_count(boxes, self.roi_crowd)
+ self.crowd_history.append((current_time, crowd_count))
+ self.crowd_history = [(t, c) for t, c in self.crowd_history if current_time - t <= 300]
+
+ if in_work and crowd_count >= self.crowd_threshold_min:
+ recent = [c for t, c in self.crowd_history[-10:]]
+ if len(recent) >= 5 and sum(c >= self.crowd_threshold_min for c in recent[-5:]) >= 4:
+ if current_time - self.last_crowd_alert_time >= self.crowd_cooldown_sec:
+ print(f"[{self.cam_id}] 🚨 聚集告警：{crowd_count}人")
+ self.last_crowd_alert_time = current_time
+
+ # === 离岗告警 ===
+ if in_work and self.is_off_duty and self.off_duty_timer_start:
+ if (current_time - self.off_duty_timer_start) >= self.off_duty_threshold_sec:
+ if (current_time - self.last_alert_time) >= self.alert_cooldown_sec:
+ print(f"[{self.cam_id}] 🚨 离岗告警！")
+ self.last_alert_time = current_time
+
+ # === 可视化 ===
+ vis = results.plot()
+ overlay = vis.copy()
+ cv2.fillPoly(overlay, [self.roi_off_duty], (0, 255, 0))
+ cv2.fillPoly(overlay, [self.roi_crowd], (0, 0, 255))
+ cv2.addWeighted(overlay, 0.2, vis, 0.8, 0, vis)
+
+ status = "OUT OF HOURS"
+ color = (128, 128, 128)
+ if in_work:
+ if self.is_on_duty:
+ status, color = "ON DUTY", (0, 255, 0)
+ elif self.is_off_duty:
+ if self.off_duty_timer_start:
+ elapsed = int(current_time - self.off_duty_timer_start)
+ if elapsed >= self.off_duty_threshold_sec:
+ status, color = "OFF DUTY!", (0, 0, 255)
+ else:
+ status = f"IDLE - {elapsed}s"
+ color = (0, 255, 255)
+ else:
+ status, color = "OFF DUTY", (255, 0, 0)
+
+ cv2.putText(vis, f"[{self.cam_id}] {status}", (20, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, color, 2)
+ cv2.imshow(f"Monitor - {self.cam_id}", vis)
+
+
+def main():
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--config", default="config.yaml", help="配置文件路径")
+ args = parser.parse_args()
+
+ monitor = MultiCameraMonitor(args.config)
+ monitor.run()
+
+
+if __name__ == "__main__":
+ main()
\ No newline at end of file
diff --git a/run_detectors.py b/run_detectors.py
new file mode 100644
index 0000000..3269d8e
--- /dev/null
+++ b/run_detectors.py
@@ -0,0 +1,52 @@
+import yaml
+import threading
+from ultralytics import YOLO
+import torch
+from detector import OffDutyCrowdDetector
+import os
+
+
+def load_config(config_path="config.yaml"):
+ with open(config_path, "r", encoding="utf-8") as f:
+ return yaml.safe_load(f)
+
+
+def main():
+ config = load_config()
+
+ # 全局模型（共享）
+ model_path = config["model"]["path"]
+ device = config["model"].get("device", "cuda" if torch.cuda.is_available() else "cpu")
+ use_half = (device == "cuda")
+
+ print(f"Loading model {model_path} on {device} (FP16: {use_half})")
+ model = YOLO(model_path)
+ model.to(device)
+ if use_half:
+ model.model.half()
+
+ # 启动每个摄像头的检测线程
+ threads = []
+ for cam_cfg in config["cameras"]:
+ # 合并 common 配置
+ full_cfg = {**config.get("common", {}), **cam_cfg}
+ full_cfg["imgsz"] = config["model"]["imgsz"]
+ full_cfg["conf_thresh"] = config["model"]["conf_thresh"]
+ full_cfg["working_hours"] = config["common"]["working_hours"]
+
+ detector = OffDutyCrowdDetector(full_cfg, model, device, use_half)
+ thread = threading.Thread(target=detector.run, daemon=True)
+ thread.start()
+ threads.append(thread)
+ print(f"Started detector for {cam_cfg['id']}")
+
+ print(f"✅ 已启动 {len(threads)} 路摄像头检测，按 Ctrl+C 退出")
+ try:
+ for t in threads:
+ t.join()
+ except KeyboardInterrupt:
+ print("\n🛑 Shutting down...")
+
+
+if __name__ == "__main__":
+ main()
\ No newline at end of file
diff --git a/sort.py b/sort.py
new file mode 100644
index 0000000..fb41847
--- /dev/null
+++ b/sort.py
@@ -0,0 +1,214 @@
+import numpy as np
+from scipy.optimize import linear_sum_assignment
+from filterpy.kalman import KalmanFilter
+
+def linear_assignment(cost_matrix):
+ x, y = linear_sum_assignment(cost_matrix)
+ return np.array(list(zip(x, y)))
+
+def iou_batch(bb_test, bb_gt):
+ """
+ From SORT: Computes IOU between two bboxes in the form [x1,y1,x2,y2]
+ """
+ bb_test = np.expand_dims(bb_test, 1)
+ bb_gt = np.expand_dims(bb_gt, 0)
+ xx1 = np.maximum(bb_test[..., 0], bb_gt[..., 0])
+ yy1 = np.maximum(bb_test[..., 1], bb_gt[..., 1])
+ xx2 = np.minimum(bb_test[..., 2], bb_gt[..., 2])
+ yy2 = np.minimum(bb_test[..., 3], bb_gt[..., 3])
+ w = np.maximum(0., xx2 - xx1)
+ h = np.maximum(0., yy2 - yy1)
+ wh = w * h
+ o = wh / ((bb_test[..., 2] - bb_test[..., 0]) * (bb_test[..., 3] - bb_test[..., 1])
+ + (bb_gt[..., 2] - bb_gt[..., 0]) * (bb_gt[..., 3] - bb_gt[..., 1]) - wh)
+ return o
+
+def convert_bbox_to_z(bbox):
+ """
+ Takes a bounding box in the form [x1,y1,x2,y2] and returns z in the form
+ [x,y,s,r] where x,y is the centre of the box and s is the scale/area and r is
+ the aspect ratio
+ """
+ w = bbox[2] - bbox[0]
+ h = bbox[3] - bbox[1]
+ x = bbox[0] + w / 2.
+ y = bbox[1] + h / 2.
+ s = w * h # scale is just area
+ r = w / float(h)
+ return np.array([x, y, s, r]).reshape((4, 1))
+
+def convert_x_to_bbox(x, score=None):
+ """
+ Takes a bounding box in the centre form [x,y,s,r] and returns it in the form
+ [x1,y1,x2,y2] where x1,y1 is the top left and x2,y2 is the bottom right
+ """
+ w = np.sqrt(x[2] * x[3])
+ h = x[2] / w
+ if score is None:
+ return np.array([x[0] - w / 2., x[1] - h / 2., x[0] + w / 2., x[1] + h / 2.]).reshape((1, 4))
+ else:
+ return np.array([x[0] - w / 2., x[1] - h / 2., x[0] + w / 2., x[1] + h / 2., score]).reshape((1, 5))
+
+class KalmanBoxTracker(object):
+ """
+ This class represents the internal state of individual tracked objects observed as bbox.
+ """
+ count = 0
+
+ def __init__(self, bbox):
+ """
+ Initialises a tracker using initial bounding box.
+ """
+ self.kf = KalmanFilter(dim_x=7, dim_z=4)
+ self.kf.F = np.array([[1, 0, 0, 0, 1, 0, 0],
+ [0, 1, 0, 0, 0, 1, 0],
+ [0, 0, 1, 0, 0, 0, 1],
+ [0, 0, 0, 1, 0, 0, 0],
+ [0, 0, 0, 0, 1, 0, 0],
+ [0, 0, 0, 0, 0, 1, 0],
+ [0, 0, 0, 0, 0, 0, 1]])
+ self.kf.H = np.array([[1, 0, 0, 0, 0, 0, 0],
+ [0, 1, 0, 0, 0, 0, 0],
+ [0, 0, 1, 0, 0, 0, 0],
+ [0, 0, 0, 1, 0, 0, 0]])
+
+ self.kf.R[2:, 2:] *= 10.
+ self.kf.P[4:, 4:] *= 1000. # give high uncertainty to the unobservable initial velocities
+ self.kf.P *= 10.
+ self.kf.Q[-1, -1] *= 0.01
+ self.kf.Q[4:, 4:] *= 0.01
+
+ self.kf.x[:4] = convert_bbox_to_z(bbox)
+ self.time_since_update = 0
+ self.id = KalmanBoxTracker.count
+ KalmanBoxTracker.count += 1
+ self.history = []
+ self.hits = 0
+ self.hit_streak = 0
+ self.age = 0
+
+ def update(self, bbox):
+ """
+ Updates the state vector with observed bbox.
+ """
+ self.time_since_update = 0
+ self.history = []
+ self.hits += 1
+ self.hit_streak += 1
+ self.kf.update(convert_bbox_to_z(bbox))
+
+ def predict(self):
+ """
+ Advances the state vector and returns the predicted bounding box estimate.
+ """
+ if (self.kf.x[6] + self.kf.x[2]) <= 0:
+ self.kf.x[6] *= 0.0
+ self.kf.predict()
+ self.age += 1
+ if self.time_since_update > 0:
+ self.hit_streak = 0
+ self.time_since_update += 1
+ self.history.append(convert_x_to_bbox(self.kf.x))
+ return self.history[-1]
+
+ def get_state(self):
+ """
+ Returns the current bounding box estimate.
+ """
+ return convert_x_to_bbox(self.kf.x)
+
+class Sort(object):
+ def __init__(self, max_age=5, min_hits=2, iou_threshold=0.3):
+ """
+ Sets key parameters for SORT
+ """
+ self.max_age = max_age
+ self.min_hits = min_hits
+ self.iou_threshold = iou_threshold
+ self.trackers = []
+ self.frame_count = 0
+
+ def update(self, dets=np.empty((0, 5))):
+ """
+ Params:
+ dets - a numpy array of detections in the format [[x1,y1,x2,y2,score],...]
+ Requires: this method must be called once for each frame even with empty detections.
+ Returns the a similar array, where the last column is the object ID.
+
+ NOTE: The number of objects returned may differ from the number of detections provided.
+ """
+ self.frame_count += 1
+ trks = np.zeros((len(self.trackers), 5))
+ to_del = []
+ ret = []
+ for t, trk in enumerate(trks):
+ pos = self.trackers[t].predict()[0]
+ trk[:] = [pos[0], pos[1], pos[2], pos[3], 0]
+ if np.any(np.isnan(pos)):
+ to_del.append(t)
+ trks = np.ma.compress_rows(np.ma.masked_invalid(trks))
+ for t in reversed(to_del):
+ self.trackers.pop(t)
+ matched, unmatched_dets, unmatched_trks = associate_detections_to_trackers(dets, trks, self.iou_threshold)
+
+ # update matched trackers with assigned detections
+ for m in matched:
+ self.trackers[m[1]].update(dets[m[0], :])
+
+ # create and initialise new trackers for unmatched detections
+ for i in unmatched_dets:
+ trk = KalmanBoxTracker(dets[i, :])
+ self.trackers.append(trk)
+ i = len(self.trackers)
+ for trk in reversed(self.trackers):
+ d = trk.get_state()[0]
+ if (trk.time_since_update < 1) and (trk.hit_streak >= self.min_hits or self.frame_count <= self.min_hits):
+ ret.append(np.concatenate((d, [trk.id + 1])).reshape(1, -1)) # +1 as MOT benchmark requires positive
+ i -= 1
+ # remove dead tracklet
+ if trk.time_since_update > self.max_age:
+ self.trackers.pop(i)
+ if len(ret) > 0:
+ return np.concatenate(ret)
+ return np.empty((0, 5))
+
+def associate_detections_to_trackers(detections, trackers, iou_threshold=0.3):
+ """
+ Assigns detections to tracked object (both represented as bounding boxes)
+ Returns 3 lists of matches, unmatched_detections, unmatched_trackers
+ """
+ if len(trackers) == 0:
+ return np.empty((0, 2), dtype=int), np.arange(len(detections)), np.empty((0, 1), dtype=int)
+ iou_matrix = iou_batch(detections, trackers)
+
+ if min(iou_matrix.shape) > 0:
+ a = (iou_matrix > iou_threshold).astype(np.int32)
+ if a.sum(1).max() == 1 and a.sum(0).max() == 1:
+ matched_indices = np.stack(np.where(a), axis=1)
+ else:
+ matched_indices = linear_assignment(-iou_matrix)
+ else:
+ matched_indices = np.empty(shape=(0, 2))
+
+ unmatched_detections = []
+ for d, det in enumerate(detections):
+ if d not in matched_indices[:, 0]:
+ unmatched_detections.append(d)
+ unmatched_trackers = []
+ for t, trk in enumerate(trackers):
+ if t not in matched_indices[:, 1]:
+ unmatched_trackers.append(t)
+
+ matches = []
+ for m in matched_indices:
+ if iou_matrix[m[0], m[1]] < iou_threshold:
+ unmatched_detections.append(m[0])
+ unmatched_trackers.append(m[1])
+ else:
+ matches.append(m.reshape(1, 2))
+ if len(matches) == 0:
+ matches = np.empty((0, 2), dtype=int)
+ else:
+ matches = np.concatenate(matches, axis=0)
+
+ return matches, np.array(unmatched_detections), np.array(unmatched_trackers)
\ No newline at end of file