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尝试从卡点rtsp中移除算法

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zqc
2026-02-02 12:49:02 +08:00
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biz/checkpoint/checkpoint_biz.py Normal file
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import cv2
import numpy as np
from typing import Dict, Any
# -------------------------- 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
# ========================= 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'
# 默认相对ROI（与原文件一致）
#ROI_RELATIVE = np.array([
# [0.10989583333333333, 0.006481481481481481],
# [0.421875, 0.005555555555555556],
# [0.9921875, 0.9888888888888889],
# [0.3411458333333333, 0.9861111111111112]
#])
ROI_RELATIVE=np.array([
[0.15,0.001],
[0.5,0.001],
[1.0,0.8],
[0.35,1.0]
])
ALERT_PUSH_INTERVAL = 5.0
# 输入尺寸
PERSON_CAR_INPUT_SIZE = 640
POSE_INPUT_SIZE = 640
RTSP_TARGET_FPS = 10.0
# ========================= Kadian TrafficMonitor（精简版，专为服务设计） =========================
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.pose_detector = YOLOv8_Pose_ONNX(POSE_MODEL_PATH, conf_threshold=0.7, iou_threshold=0.6,
input_size=POSE_INPUT_SIZE)
# Tracker
class TrackerArgs:
track_thresh = 0.25
track_buffer = 30
match_thresh = 0.8
mot20 = False
self.tracker = BYTETracker(TrackerArgs(), frame_rate=10.0)
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 = 3.0 # 单人单检判定时长
self.TIME_THRESHOLD_NOBODY = 2.0 # 无人检查判定时长
# 后备箱检查判定阈值
self.TIME_THRESHOLD_TRUNK_OPEN = 0.5
# 新增：手机检测判定阈值
self.TIME_THRESHOLD_PHONE = 1.0 # 手机检测持续1秒（30帧 @30fps）
self.TIME_TOLERANCE_PHONE = 0.5 # 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 = 1.0 # 制服不合规判定时长
self.TIME_TOLERANCE_UNIFORM = 0.5 # 制服合规恢复缓冲时间
# 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 = 3.0
# 2. Person 丢帧缓冲
self.TIME_TOLERANCE_PERSON = 1.0
# 3. Car 丢帧/ID维持缓冲
self.TIME_TOLERANCE_CAR = 0.5
# --- 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)
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' (视为Nobody) 最小停留: {self.frame_thresh_car_min_duration} 帧")
self.current_frame_idx = 0
self.cnt_frame_one_person = 0
self.cnt_frame_nobody = 0
self.cnt_missing_buffer_person = 0
# 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 Nobody)
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:
# 使用默认相对坐标
default_rel = np.array([
[0.15, 0.01],
[0.45, 0.01],
[0.95, 0.95],
[0.35, 0.95]
], dtype=np.float64)
roi_abs = default_rel * np.array([frame_width, frame_height])
else:
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 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
# ==========================================
# 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']
c_box = car_info['box']
trunk_found_for_this_car = False
for t_pt in current_trunks:
if self.is_point_in_box(t_pt, c_box):
trunk_found_for_this_car = True
break
if trunk_found_for_this_car:
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():
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:
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 Nobody (Too Fast)
if duration_frames < self.frame_thresh_car_min_duration:
print(f"ALARM: Car {car_id} passed too fast -> Regarded as Nobody Checked!")
self.fast_pass_alerts[car_id] = self.current_frame_idx + int(3.0 * 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(3.0 * 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)
# ==========================================
status_text = ""
if effective_car_count > 0:
# --- Only One ---
if current_roi_person_count == 1:
self.cnt_frame_one_person += 1
self.cnt_missing_buffer_person = 0
self.cnt_frame_nobody = 0
# --- Nobody ---
elif current_roi_person_count == 0:
if self.cnt_frame_one_person > 0 and self.cnt_missing_buffer_person < self.frame_buffer_limit_person:
self.cnt_frame_one_person += 1
self.cnt_missing_buffer_person += 1
self.cnt_frame_nobody = 0
status_text = f"Person Buffer ({self.cnt_missing_buffer_person}/{self.frame_buffer_limit_person})"
else:
self.cnt_frame_one_person = 0
self.cnt_missing_buffer_person = 0
self.cnt_frame_nobody += 1
else:
self.cnt_frame_one_person = 0
self.cnt_missing_buffer_person = 0
self.cnt_frame_nobody = 0
else:
self.cnt_frame_one_person = 0
self.cnt_missing_buffer_person = 0
self.cnt_frame_nobody = 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
# 第一层：实时状态 (Real-time Status)
# ------------------------------------------------
# A. 显示 Only One
if self.cnt_frame_one_person >= self.frame_thresh_one:
current_frame_alerts.append(
{
'time': current_time_sec,
'action': "Only One",
}
)
self.draw_alert(frame, "Only One", (0, 255, 255), status_text, offset_y=alert_offset)
alert_offset += 100
# B. 显示 Nobody (实时状态)
elif self.cnt_frame_nobody >= self.frame_thresh_nobody:
current_frame_alerts.append(
{
'time': current_time_sec,
'action': "Nobody",
}
)
self.draw_alert(frame, "Nobody", (0, 0, 255), 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), sub_text, offset_y=alert_offset)
alert_offset += 100
# E. 新增：显示 Unvaild Uniform!!
if self.uniform_alert_active:
# 显示具体数量差异
diff = self.pose_person_count - current_roi_person_count
sub_text = f"Missing {diff} uniform(s)"
current_frame_alerts.append(
{
'time': current_time_sec,
'action': "Unvaild Uniform!!",
}
)
self.draw_alert(frame, "Unvaild Uniform!!", (255, 165, 0), sub_text, 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 Nobody (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"Nobody (ID:{list(self.fast_pass_alerts.keys())})"
current_frame_alerts.append(
{
'time': current_time_sec,
'action': "Nobody",
}
)
self.draw_alert(frame, alert_text, (0, 0, 255), offset_y=alert_offset)
alert_offset += 100
return {
"image": frame,
"alerts":current_frame_alerts
}