新增业务算法
This commit is contained in:
888
biz/checkpoint/checkpoint02_biz.py
Normal file
888
biz/checkpoint/checkpoint02_biz.py
Normal file
@@ -0,0 +1,888 @@
|
||||
# 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()
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user