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引入监控室biz

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2026-02-27 14:59:18 +08:00
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# 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()