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完成ab门迁移

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zqc
2026-03-06 11:11:09 +08:00
parent 124672332f
commit 845814e6c1
2 changed files with 102 additions and 287 deletions
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386
biz/prison/ab_biz.py
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@@ -6,6 +6,7 @@ import threading
import time
import queue
import requests
from biz.base_frame_processor import BaseFrameProcessorWorker
# -------------------------- Kadian 检测相关导入 --------------------------
from algorithm.common.npu_yolo_onnx_person_car_phone import YOLOv8_ONNX # 主检测模型（人/车/后备箱/手机）
@@ -16,10 +17,10 @@ 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）
detector_model_path = 'YOLO_Weight/prisoner_model.onnx'
detector_model_path = 'YOLO_Weight/bag_model.onnx'
# 输入尺寸
input_size = 1280
input_size = 640
RTSP_TARGET_FPS = 10.0
@@ -27,12 +28,15 @@ RTSP_TARGET_FPS = 10.0
ALERT_PUSH_INTERVAL = 5.0 # 相同action 5秒内仅推送一次
class ZoulangDetector:
def __init__(self):
class AbDetector:
def __init__(self, params=None):
# 摄像头额外参数
self.params = params if params is not None else {}
# 模型加载
self.police_prisoner_detector = YOLOv8_ONNX(detector_model_path, conf_threshold=0.5, iou_threshold=0.45,
input_size=input_size)
self.detector = YOLOv8_ONNX(detector_model_path, conf_threshold=0.5, iou_threshold=0.45,
input_size=input_size)
# ByteTracker
class TrackerArgs:
@@ -41,9 +45,7 @@ class ZoulangDetector:
match_thresh = 0.8
mot20 = False
self.police_prisoner_track_role = {}
self.track_role = {}
self.fps = RTSP_TARGET_FPS
@@ -52,53 +54,30 @@ class ZoulangDetector:
# ==========================================
# 超参数设置 (Hyperparameters)
# ==========================================
self.TIME_THRESHOLD_BLACKBAG = 1.0 # 黑包判定时长（秒）
self.TIME_TOLERANCE_BLACKBAG = 0.5 # 黑包丢失缓冲时间
# 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_THRESHOLD_POLICE = 1.0 # 警察判定时长
self.TIME_TOLERANCE_POLICE = 0.5 # 警察失缓冲时间（防抖动）
self.TIME_THRESHOLD_PRISONER = 1.0 # 犯人判定时长
self.TIME_TOLERANCE_PRISONER = 0.5 # 犯人丢失缓冲时间（防抖动）
# 无人在场帧数阈值
# self.frame_thresh_nobody = int(self.TIME_THRESHOLD_NOBODY * self.fps)
# 警察检测帧数阈值
self.frame_thresh_police = int(self.TIME_THRESHOLD_POLICE * self.fps)
self.frame_buffer_police = int(self.TIME_TOLERANCE_POLICE * self.fps)
# 犯人检测帧数阈值
self.frame_thresh_prisoner = int(self.TIME_THRESHOLD_PRISONER * self.fps)
self.frame_buffer_prisoner = int(self.TIME_TOLERANCE_PRISONER * self.fps)
# 转换为帧数阈值
self.frame_thresh_blackbag = int(self.TIME_THRESHOLD_BLACKBAG * self.fps)
self.frame_buffer_blackbag = int(self.TIME_TOLERANCE_BLACKBAG * 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 'police Detected' 需累计检测: {self.frame_thresh_police} 帧")
print(f" 警察丢失缓冲帧数: {self.frame_buffer_police} 帧")
print(f" 判定 'prisoner Detected' 需累计检测: {self.frame_thresh_prisoner} 帧")
print(f" 犯人丢失缓冲帧数: {self.frame_buffer_prisoner} 帧")
print(f" 判定 'BlackBag Detected' 需累计检测: {self.frame_thresh_blackbag} 帧")
print(f" 黑包丢失缓冲帧数: {self.frame_buffer_blackbag} 帧")
# ==========================================
# 状态变量初始化
# ==========================================
self.current_frame_idx = 0
# 无人在场检测状态变量
self.cnt_frame_nobody = 0
# 黑包检测状态
self.blackbag_detection_frames = 0
self.blackbag_missing_frames = 0
self.blackbag_alert_active = False
# 警察检测状态变量
self.police_detection_frames = 0 # 连续检测到警察的帧数
self.police_missing_frames = 0 # 连续未检测到警察的帧数
self.police_alert_active = False # 警察报警是否激活
# 犯人检测状态变量
self.prisoner_detection_frames = 0 # 连续检测到犯人的帧数
self.prisoner_missing_frames = 0 # 连续未检测到犯人的帧数
self.prisoner_alert_active = False # 犯人报警是否激活
# 人员统计变量
self.current_person_count = 0
def compute_iou(self,boxA, boxB):
@@ -145,184 +124,120 @@ class ZoulangDetector:
current_time_sec = timestamp
# ========= 警察和犯人检测 =========
police_prisoner_results = self.police_prisoner_detector(frame)
# ========= 检测推理（黑包+人）=========
detect_results = self.detector(frame)
police_prisoner_dets_xyxy = []
police_prisoner_dets_roles = []
police_prisoner_dets_for_tracker = []
# 初始化检测结果存储
dets_xyxy = []
dets_roles = []
dets_for_tracker = []
current_frame_alerts = []
# ========= 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 police_prisoner_results:
for det in police_prisoner_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
police_prisoner_dets_xyxy.append([x1, y1, x2, y2])
police_prisoner_dets_for_tracker.append([x1, y1, x2, y2, conf])
# 解析检测结果（黑包cls_id=0，人员cls_id=1）
if detect_results:
for det in detect_results:
x1, y1, x2, y2, conf, cls_id = det
dets_xyxy.append([x1, y1, x2, y2])
dets_for_tracker.append([x1, y1, x2, y2, conf])
if cls_id == 0:
police_prisoner_dets_roles.append("police")
dets_roles.append("black_bag")
elif cls_id == 1:
police_prisoner_dets_roles.append("prisoner")
dets_roles.append("person")
ppolice_prisoner_dets = np.array(police_prisoner_dets_for_tracker, dtype=np.float32) if len(
police_prisoner_dets_for_tracker) else np.empty((0, 5))
police_prisoner_dets_tracks = self.tracker.update(
ppolice_prisoner_dets,
# 跟踪器更新
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]
)
# ========= 单帧统计变量 =========
current_police_count = 0
current_prisoner_count = 0
# ========= 警察和犯人检测 =========
for t in police_prisoner_dets_tracks:
# print("t: {}".format(t))
# ========= 单帧统计初始化 =========
self.current_person_count = 0
current_blackbag_count = 0
# ========= 跟踪结果绘制与统计 =========
for t in tracks:
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
REVALIDATE_FRAME_INTERVAL = 10
if (self.current_frame_idx % REVALIDATE_FRAME_INTERVAL == 0) or (
tid not in self.police_prisoner_track_role):
#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(police_prisoner_dets_xyxy):
t_box = list(map(float, t.tlbr))
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 = police_prisoner_dets_roles[i]
if best_iou > 0.1:
self.police_prisoner_track_role[tid] = best_role
else:
self.police_prisoner_track_role[tid] = "unknown"
role = self.police_prisoner_track_role.get(tid, "unknown")
cls_id = -1
if role == "police":
cls_id = 0
elif role == "prisoner":
cls_id = 1
# print("tid: {}, role: {}, cls: {}".format(tid, role,cls_id))
best_role = dets_roles[i]
self.track_role[tid] = best_role if best_iou > 0.1 else "unknown"
role = self.track_role.get(tid, "unknown")
x1, y1, x2, y2 = map(int, t.tlbr)
color = (255, 255, 255)
label = "Unknown"
cx, cy = (x1 + x2) // 2, (y1 + y2) // 2
color = None
label = None
if cls_id == 0: # Person
current_police_count += 1
color = (255, 0, 255)
label = "police"
elif cls_id == 1: # Phone（主模型已支持）
current_prisoner_count += 1
color = (0, 0, 139)
label = "prisoner"
else:
color = (255, 255, 255)
label = "Unknown"
# label = f"ID:{tid} IN"
# 人员检测（cls_id=1）
if role == "person":
self.current_person_count += 1
color = (255, 0, 255) # 紫色框
label = "Person"
# 黑包检测（cls_id=0）
elif role == "black_bag":
current_blackbag_count += 1
color = (0, 128, 0) # 绿色框
label = "Black Bag"
# 绘制检测框和标签
cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)
cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)
# ==========================================
# 犯人检测
# 黑包状态更新
# ==========================================
if current_prisoner_count > 0:
# 检测到犯人框
self.prisoner_detection_frames += 1
self.prisoner_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.prisoner_detection_frames >= self.frame_thresh_prisoner:
self.prisoner_alert_active = True
if current_blackbag_count > 0:
self.blackbag_detection_frames += 1
self.blackbag_missing_frames = 0
if self.blackbag_detection_frames >= self.frame_thresh_blackbag:
self.blackbag_alert_active = True
else:
# 未检测到犯人框
self.prisoner_missing_frames += 1
self.blackbag_missing_frames += 1
if self.blackbag_missing_frames >= self.frame_buffer_blackbag:
self.blackbag_detection_frames = 0
self.blackbag_alert_active = False
# 如果之前检测到手机，重置检测计数器
if self.prisoner_detection_frames > 0:
# 只有在连续丢失超过缓冲帧数时才重置
if self.prisoner_missing_frames >= self.frame_buffer_prisoner:
self.prisoner_detection_frames = 0
self.prisoner_alert_active = False
else:
# 从未检测到犯人，保持状态
pass
# ==========================================
# 警察检测
# 警告信息收集
# ==========================================
if current_police_count > 0:
# 检测到犯人框
self.police_detection_frames += 1
self.police_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.police_detection_frames >= self.frame_thresh_police:
self.police_alert_active = True
else:
# 未检测到犯人框
self.police_missing_frames += 1
# 如果之前检测到手机，重置检测计数器
if self.police_detection_frames > 0:
# 只有在连续丢失超过缓冲帧数时才重置
if self.police_missing_frames >= self.frame_buffer_police:
self.police_detection_frames = 0
self.police_alert_active = False
else:
# 从未检测到犯人，保持状态
pass
alert_offset = 0
# A. 有犯人
if self.prisoner_alert_active:
duration_seconds = self.prisoner_detection_frames / self.fps
if self.blackbag_alert_active:
duration_seconds = self.blackbag_detection_frames / self.fps
current_frame_alerts.append(
{
'time': current_time_sec,
'action': 'prisoner',
'confidence': 1.0, # 固定为1.0（规则判定）
'action': 'Black Bag',
'details': f"Detected for {duration_seconds:.1f}s"
}
)
self.draw_alert(frame, "prisoner", (0, 0, 255), offset_y=alert_offset)
alert_offset += 100
self.draw_alert(frame, "Black Bag Alert", (0, 0, 255), sub_text=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）
# 绘制信息
# ==========================================
debug_info = f" prisoner: {current_prisoner_count}"
# 实时统计
debug_info = f"Person: {self.current_person_count} | BlackBag: {current_blackbag_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
# 警告信息
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 == 'prisoner':
color = (255, 255, 255)
main_text = action
if details:
main_text += f" ({details})"
color = (0, 0, 255) # 红色警告
main_text = f"{action} ({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)
@@ -334,114 +249,11 @@ class ZoulangDetector:
}
# ========================= 帧处理线程 =========================
class FrameProcessorWorker(threading.Thread):
def __init__(self,
raw_frame_queue: "queue.Queue[Dict[str, Any]]",
ws_send_queue: "queue.Queue[Dict[str, Any]]",
stop_event: threading.Event):
super().__init__(daemon=True)
self.raw_queue = raw_frame_queue
self.ws_queue = ws_send_queue
self.stop_event = stop_event
class FrameProcessorWorker(BaseFrameProcessorWorker):
"""轨迹检测帧处理线程"""
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, ZoulangDetector] = {}
# 新增：维护每个摄像头每个action的最后推送时间 {camera_id: {action: last_push_time}}
self.last_alert_push_time: Dict[int, Dict[str, float]] = {}
def _encode_image_to_base64(self, image) -> str:
ok, buf = cv2.imencode(".jpg", image)
if not ok:
raise RuntimeError("Failed to encode image to JPEG")
return base64.b64encode(buf.tobytes()).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
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] = ZoulangDetector()
detector = self.kadian_detectors[cam_id]
# 执行检测
result = detector.process_frame(frame.copy(), cam_id, ts)
result_img = result["image"]
result_type = result["alerts"]
# ========= 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
try:
img_b64 = self._encode_image_to_base64(result_img)
except Exception as e:
print(f"[ERROR] Encode image failed: {e}")
img_b64 = None
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": item["camera_index"],
"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:
# 发送POST请求
post_msg = msg.copy()
post_msg['type'] = 2
try:
response = requests.post(ALERT_PUSH_URL, json=post_msg, timeout=5.0)
if response.status_code == 200:
print(f"[INFO] POST alert sent successfully for actions: {push_actions}")
else:
print(f"[WARN] POST alert failed with status: {response.status_code}")
except Exception as e:
print(f"[ERROR] POST alert request failed: {e}")
except queue.Full:
print("[WARN] ws_send_queue full, drop frame message")
self.raw_queue.task_done()
# 子类配置
DETECTOR_FACTORY = lambda params: AbDetector(params)
POST_TYPE = 2
TARGET_FPS = RTSP_TARGET_FPS