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guoyoudu
2026-02-08 14:33:45 +08:00
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# rtsp_service_ws.py (merged with YOLO + MMAction2 logic)
import cv2
import time
import threading
import queue
import yaml
import os
import json
import base64
import asyncio
import websockets
from dataclasses import dataclass
from typing import Optional, Dict, Any, Tuple
# --- 新增依赖（YOLO/ONNX/跟踪/NumPy） ---
import numpy as np
import onnxruntime as ort
import torch
# 如果你有 yolox 的 BYTETracker，请确保 pythonpath 包含 yolox 包
# 如果没有，请在运行环境中安装或提供替代跟踪器
try:
from yolox.tracker.byte_tracker import BYTETracker
except Exception as e:
BYTETracker = None
print(f"[WARN] 无法导入 BYTETracker: {e}. 请确保 yolox 已安装，或提供替代跟踪实现.")
# 从你上传的模块导入 YOLO ONNX 类（npu_yolo_onnx.py）
# 确保该文件可在同一目录或 PYTHONPATH 中导入
try:
from npu_yolo_onnx import YOLOv8_ONNX
except Exception as e:
YOLOv8_ONNX = None
print(f"[WARN] 无法导入 YOLOv8_ONNX（npu_yolo_onnx.py）：{e}")
# =========================
# 配置与数据结构
# =========================
@dataclass
class CameraConfig:
id: int
name: str
rtsp_url: str
RTSP_TARGET_FPS = 10.0 # 固定 10 帧/秒
FRAMES_PER_SEGMENT = 600 # 每 600 帧一个 mp4
VIDEO_OUTPUT_DIR = "./videos" # 视频输出目录
WS_HOST = "0.0.0.0" # WebSocket 服务端监听地址
WS_PORT = 8765 # WebSocket 服务端端口
# 已连接的 WebSocket 客户端集合
ws_clients = set()
# =========================
# YOLO / 动作识别 / 跟踪 配置
# =========================
# --- 请根据你实际路径修改下面的 ONNX 模型路径 ---
YOLO_ONNX_PATH = "YOLO_Weight/best.onnx" # <-- 改为实际路径
SUPERVISOR_ONNX = "ONNX_Weight/Supervisor.onnx" # <-- 改为实际路径
SUSPECT_ONNX = "ONNX_Weight/Suspect.onnx" # <-- 改为实际路径
# 动作标签（来自 Ascend_NPU_YOLO_TSM_RealTime.py）
LABELS_SUPERVISOR = {0: 'Normal', 1: 'Push', 2: 'Slap'}
LABELS_SUSPECT = {0: 'Collision', 1: 'Hanging', 2: 'Lyingdown', 3: 'Normal'}
# 超参数（和 Ascend 文件保持一致）
CLIP_LEN = 32
SLIDE_STEP = 16
CONF_THRESH = 0.1
EXPAND_RATIO = 0.4
TARGET_SIZE = 224
YOLO_CONF_THRESH = 0.5
YOLO_IOU_THRESH = 0.45
ACTION_COOLDOWN = 0.0
# 跟踪器 / 缓存等（按 camera 分离）
trackers: Dict[int, Any] = {} # camera_id -> BYTETracker instance
track_buffers: Dict[int, Dict[int, list]] = {} # camera_id -> {track_id -> list of cv2 crops}
last_alert: Dict[int, Dict[int, float]] = {} # camera_id -> {track_id -> last_alert_time}
track_role: Dict[int, Dict[int, str]] = {} # camera_id -> {track_id -> role}
track_action_result: Dict[int, Dict[int, str]] = {} # camera_id -> {track_id -> action string}
# 最近动作显示（全局或 per-camera 可扩展）
recent_actions: Dict[int, list] = {} # camera_id -> list of recent actions
MAX_RECENT_ACTIONS = 3
ACTION_DISPLAY_DURATION = 2.0
# YOLO 和动作识别 session（单例式）
yolo_model = None
sess_supervisor = None
sess_suspect = None
input_name_sup = None
input_name_sus = None
# =========================
# 初始化模型（尝试导入/创建 session）
# =========================
def init_models_once():
global yolo_model, sess_supervisor, sess_suspect, input_name_sup, input_name_sus
# YOLO
if YOLOv8_ONNX is None:
print("[ERROR] YOLOv8_ONNX 未导入，无法初始化 YOLO 模型")
else:
try:
yolo_model = YOLOv8_ONNX(YOLO_ONNX_PATH, conf_threshold=YOLO_CONF_THRESH, iou_threshold=YOLO_IOU_THRESH)
print("[INFO] YOLO 模型初始化完成")
except Exception as e:
print(f"[ERROR] YOLO 模型初始化失败: {e}")
yolo_model = None
# -----------------------------
# 动作识别模型初始化（正确的 Provider 判断方式）
# -----------------------------
try:
# 请求使用 CANN，但是否真正启用必须用 get_providers 判断
providers = [
("CANNExecutionProvider", {
"device_id": 0,
"arena_extend_strategy": "kNextPowerOfTwo",
"npu_mem_limit": 16 * 1024 * 1024 * 1024,
"precision_mode": "allow_fp32_to_fp16",
"op_select_impl_mode": "high_precision",
"enable_cann_graph": True,
}),
"CPUExecutionProvider", # 自动 fallback
]
sess_supervisor = ort.InferenceSession(SUPERVISOR_ONNX, providers=providers)
sess_suspect = ort.InferenceSession(SUSPECT_ONNX, providers=providers)
sup_prov = sess_supervisor.get_providers()
sus_prov = sess_suspect.get_providers()
print("Supervisor Providers:", sup_prov)
print("Suspect Providers:", sus_prov)
if "CANNExecutionProvider" in sup_prov:
print("[INFO] 动作识别模型：使用 CANNExecutionProvider（昇腾）")
else:
print("[INFO] 动作识别模型：使用 CPUExecutionProvider（非昇腾环境）")
except Exception as e:
print(f"[ERROR] 初始化动作识别模型失败: {e}")
sess_supervisor = None
sess_suspect = None
if sess_supervisor is not None:
input_name_sup = sess_supervisor.get_inputs()[0].name
print(f"[INFO] 监护人模型输入: {input_name_sup}")
if sess_suspect is not None:
input_name_sus = sess_suspect.get_inputs()[0].name
print(f"[INFO] 被监护人模型输入: {input_name_sus}")
# 只初始化一次
init_models_once()
# =========================
# 工具函数（IoU, preprocess_clip）
# =========================
def compute_iou(box1, box2):
"""计算两个框的 IoU"""
x1, y1, x2, y2 = box1
x1_, y1_, x2_, y2_ = box2
xi1 = max(x1, x1_)
yi1 = max(y1, y1_)
xi2 = min(x2, x2_)
yi2 = min(y2, y2_)
inter_area = max(0, xi2 - xi1) * max(0, yi2 - yi1)
box1_area = max(0, (x2 - x1)) * max(0, (y2 - y1))
box2_area = max(0, (x2_ - x1_)) * max(0, (y2_ - y1_))
union_area = box1_area + box2_area - inter_area
return inter_area / union_area if union_area > 0 else 0
def preprocess_clip(frames):
"""按 Ascend_NPU_YOLO_TSM_RealTime.py 的预处理：32帧 -> 每2帧取1帧 -> crop/resize/normalize -> (1, T, C, H, W)"""
# 确保有足够帧
if len(frames) < CLIP_LEN:
last_frame = frames[-1] if frames else np.zeros((TARGET_SIZE, TARGET_SIZE, 3), dtype=np.uint8)
frames = frames + [last_frame] * (CLIP_LEN - len(frames))
indices = list(range(0, CLIP_LEN, 2)) # 0,2,4,...,30 -> 16 frames
selected = [frames[i] for i in indices]
imgs = []
for f in selected:
h, w = f.shape[:2]
scale = 256.0 / min(h, w)
nw, nh = int(w * scale), int(h * scale)
f_resized = cv2.resize(f, (nw, nh))
top = (nh - 224) // 2
left = (nw - 224) // 2
f_cropped = f_resized[top:top + 224, left:left + 224]
f_rgb = cv2.cvtColor(f_cropped, cv2.COLOR_BGR2RGB).transpose(2, 0, 1).astype(np.float32)
imgs.append(f_rgb)
x = np.stack(imgs)[np.newaxis] # shape (1, 16, 3, 224, 224) or (1, T, C, H, W)
mean = np.array([123.675, 116.28, 103.53], dtype=np.float32).reshape(1, 1, 3, 1, 1)
std = np.array([58.395, 57.12, 57.375], dtype=np.float32).reshape(1, 1, 3, 1, 1)
result = (x - mean) / std
return result.astype(np.float32)
# =========================
# WebSocket 服务线程（不变）
# =========================
class WebSocketSender(threading.Thread):
def __init__(self, send_queue: "queue.Queue[Dict[str, Any]]", stop_event: threading.Event):
super().__init__(daemon=True)
self.send_queue = send_queue
self.stop_event = stop_event
async def _ws_handler(self, websocket):
ws_clients.add(websocket)
try:
async for _ in websocket:
pass
finally:
ws_clients.discard(websocket)
async def _broadcaster(self):
while not self.stop_event.is_set():
try:
msg = await asyncio.to_thread(self.send_queue.get, timeout=0.5)
except queue.Empty:
continue
data = json.dumps(msg)
dead = []
for ws in list(ws_clients):
try:
await ws.send(data)
except Exception:
dead.append(ws)
for ws in dead:
ws_clients.discard(ws)
self.send_queue.task_done()
async def _run_async(self):
async with websockets.serve(self._ws_handler, WS_HOST, WS_PORT):
print(f"[INFO] WebSocket server started at ws://{WS_HOST}:{WS_PORT}")
await self._broadcaster()
def run(self):
asyncio.run(self._run_async())
# =========================
# RTSP 抓流线程（不变）
# =========================
class RTSPCaptureWorker(threading.Thread):
def __init__(
self,
camera_cfg: CameraConfig,
raw_frame_queue: "queue.Queue[Dict[str, Any]]",
stop_event: threading.Event,
):
super().__init__(daemon=True)
self.camera_cfg = camera_cfg
self.raw_frame_queue = raw_frame_queue
self.stop_event = stop_event
def run(self):
cap = cv2.VideoCapture(self.camera_cfg.rtsp_url, cv2.CAP_FFMPEG)
if not cap.isOpened():
print(f"[ERROR] Cannot open RTSP stream: {self.camera_cfg.rtsp_url}")
return
print(f"[INFO] Start capturing: id={self.camera_cfg.id}, name={self.camera_cfg.name}")
while not self.stop_event.is_set():
ok, frame = cap.read()
if not ok:
print(f"[WARN] Failed to read frame from camera {self.camera_cfg.id}, retrying...")
time.sleep(0.2)
continue
ts = time.time()
item = {
"camera_id": self.camera_cfg.id,
"camera_name": self.camera_cfg.name,
"timestamp": ts,
"frame": frame,
}
try:
self.raw_frame_queue.put(item, timeout=1.0)
except queue.Full:
print(f"[WARN] Raw frame queue full, drop frame from camera {self.camera_cfg.id}")
cap.release()
print(f"[INFO] Stop capturing: id={self.camera_cfg.id}")
# =========================
# 帧处理线程（抽帧 + 写mp4 + 调用用户函数 + 发WebSocket消息）
# =========================
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_frame_queue = raw_frame_queue
self.ws_send_queue = ws_send_queue
self.stop_event = stop_event
# 每个摄像头独立维护视频写入状态
self.video_writers: Dict[int, cv2.VideoWriter] = {}
self.video_frame_counts: Dict[int, int] = {}
self.video_segment_start_ts: Dict[int, float] = {}
self.video_segment_filenames: Dict[int, str] = {}
os.makedirs(VIDEO_OUTPUT_DIR, exist_ok=True)
# 控制 10fps 抽帧：记录每个摄像头上次处理时间
self.last_process_ts: Dict[int, float] = {}
def _get_video_writer(self, camera_id: int, frame) -> Tuple[cv2.VideoWriter, str]:
writer = self.video_writers.get(camera_id)
if writer is not None:
return writer, self.video_segment_filenames[camera_id]
h, w = frame.shape[:2]
fourcc = cv2.VideoWriter_fourcc(*"mp4v")
start_ts = time.time()
self.video_segment_start_ts[camera_id] = start_ts
ts_str = time.strftime("%Y%m%d_%H%M%S", time.localtime(start_ts))
filename = f"{ts_str}_cam{camera_id}.mp4"
filepath = os.path.join(VIDEO_OUTPUT_DIR, filename)
writer = cv2.VideoWriter(filepath, fourcc, RTSP_TARGET_FPS, (w, h))
self.video_writers[camera_id] = writer
self.video_frame_counts[camera_id] = 0
self.video_segment_filenames[camera_id] = filepath
print(f"[INFO] Start new segment: camera={camera_id}, file={filepath}")
return writer, filepath
def _close_segment_if_needed(self, camera_id: int):
count = self.video_frame_counts.get(camera_id, 0)
if count >= FRAMES_PER_SEGMENT:
writer = self.video_writers.get(camera_id)
if writer is not None:
writer.release()
print(f"[INFO] Close segment: camera={camera_id}, file={self.video_segment_filenames[camera_id]}")
self.video_writers.pop(camera_id, None)
self.video_frame_counts.pop(camera_id, None)
self.video_segment_start_ts.pop(camera_id, None)
self.video_segment_filenames.pop(camera_id, None)
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):
print("[INFO] FrameProcessorWorker started")
target_interval = 1.0 / RTSP_TARGET_FPS
while not self.stop_event.is_set():
try:
item = self.raw_frame_queue.get(timeout=0.5)
except queue.Empty:
continue
camera_id = item["camera_id"]
ts = item["timestamp"]
frame = item["frame"]
last_ts = self.last_process_ts.get(camera_id, 0.0)
if ts - last_ts < target_interval:
self.raw_frame_queue.task_done()
continue
self.last_process_ts[camera_id] = ts
# 1) 写入 mp4 (当前segment)
writer, video_filepath = self._get_video_writer(camera_id, frame)
writer.write(frame)
self.video_frame_counts[camera_id] = self.video_frame_counts.get(camera_id, 0) + 1
# 2) 调用用户自定义处理逻辑
result = user_process_frame(frame, camera_id, ts)
if result is not None and "image" in result and "type" in result:
result_img = result["image"]
result_type = int(result["type"])
# 3) 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:
msg = {
"msg_type": "frame",
"camera_id": camera_id,
"timestamp": ts,
"result_type": result_type,
"image_base64": img_b64,
}
try:
self.ws_send_queue.put(msg, timeout=1.0)
except queue.Full:
print("[WARN] ws_send_queue full, drop frame message")
# 4) 如果 result_type != 0，通过 WebSocket 发送告警
if result_type != 0:
alert_msg = {
"msg_type": "alert",
"camera_id": camera_id,
"event_type": result_type,
"video_file": video_filepath,
"timestamp": ts,
}
try:
self.ws_send_queue.put(alert_msg, timeout=1.0)
except queue.Full:
print("[WARN] ws_send_queue full, drop alert message")
# 5) 检查是否需要切换到下一个 mp4 segment
self._close_segment_if_needed(camera_id)
self.raw_frame_queue.task_done()
# 退出时，关闭所有 VideoWriter
for cam_id, writer in list(self.video_writers.items()):
writer.release()
print(f"[INFO] Release writer on exit: camera={cam_id}")
print("[INFO] FrameProcessorWorker stopped")
# =========================
# 用户自定义函数（重要：已集成 YOLO + 动作识别 + 跟踪 + 告警）
# =========================
def user_process_frame(image, camera_id: int, timestamp: float) -> Dict[str, Any]:
"""
集成了：
1. 视频帧输入
2. YOLO 目标检测（Supervisor / Suspect）
3. 对每个检测到的人物：
- 裁剪 ROI
- 预处理（resize 等）
- 根据类别选择动作识别模型（supervisor / suspect）
- 执行动作识别 ONNX 推理
- 解析动作类别并判断是否触发告警
4. 绘制结果（检测框、标签、告警）
5. 返回处理后的图像与告警类型
注意：尽量保持原实现逻辑（Ascend_NPU_YOLO_TSM_RealTime.py）
"""
global trackers, track_buffers, last_alert, track_role, track_action_result, recent_actions
global yolo_model, sess_supervisor, sess_suspect, input_name_sup, input_name_sus
# 初始化 per-camera 结构
if camera_id not in trackers:
if BYTETracker is not None:
# 使用 Ascend 源里的 Tracker 参数
class TrackerArgs:
track_thresh = 0.5
track_buffer = 30
match_thresh = 0.8
mot20 = False
try:
trackers[camera_id] = BYTETracker(TrackerArgs(), frame_rate=RTSP_TARGET_FPS)
print(f"[INFO] 初始化 BYTETracker for camera {camera_id}")
except Exception as e:
trackers[camera_id] = None
print(f"[WARN] 无法初始化 BYTETracker: {e}")
else:
trackers[camera_id] = None
print("[WARN] BYTETracker 未安装，跟踪功能不可用")
if camera_id not in track_buffers:
track_buffers[camera_id] = {}
if camera_id not in last_alert:
last_alert[camera_id] = {}
if camera_id not in track_role:
track_role[camera_id] = {}
if camera_id not in track_action_result:
track_action_result[camera_id] = {}
if camera_id not in recent_actions:
recent_actions[camera_id] = []
frame = image # BGR
h, w = frame.shape[:2]
# === 1. YOLO 检测 ===
detections = []
if yolo_model is not None:
try:
detections = yolo_model(frame)
# detections 格式： list of [x1,y1,x2,y2, conf, cls_id]
except Exception as e:
print(f"[WARN] YOLO 推理失败: {e}")
detections = []
else:
# 如果没有模型，返回原图（不报警）
return {"image": frame, "type": 0}
dets_xyxy = []
dets_roles = []
dets_for_tracker = []
supervisor_count = 0
suspect_count = 0
if detections:
for det in detections:
x1, y1, x2, y2, conf, cls_id = det
dets_xyxy.append([x1, y1, x2, y2])
if int(cls_id) == 0:
dets_roles.append("supervisor"); supervisor_count += 1
else:
dets_roles.append("suspect"); suspect_count += 1
dets_for_tracker.append([x1, y1, x2, y2, conf])
else:
dets_for_tracker = np.empty((0,5))
dets_for_tracker = np.array(dets_for_tracker, dtype=np.float32) if len(dets_for_tracker) > 0 else np.empty((0,5))
# === 2. 跟踪（BYTETracker） ===
tracker = trackers.get(camera_id)
if tracker is None:
# 没有 tracker 的情况下，仅在检测框上画出结果并返回
for i, det in enumerate(detections):
x1, y1, x2, y2, conf, cls_id = det
role = "supervisor" if int(cls_id) == 0 else "suspect"
color = (255,0,0) if role=="supervisor" else (0,0,255)
cv2.rectangle(frame, (int(x1),int(y1)), (int(x2),int(y2)), color, 2)
cv2.putText(frame, f"{role} {conf:.2f}", (int(x1)+5, int(y1)-6), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)
return {"image": frame, "type": 0}
if dets_for_tracker.size == 0:
dets_tensor = torch.zeros((0,5))
else:
dets_tensor = torch.from_numpy(dets_for_tracker).float()
# tracker.update expects args (dets, ori_img_shape, img_shape) in Ascend file they called tracker.update(dets_tensor, [h,w], [h,w])
try:
tracks = tracker.update(dets_tensor, [h, w], [h, w])
except Exception as e:
print(f"[WARN] tracker.update 出错: {e}")
tracks = []
current_time_sec = timestamp # 使用外部传入的时间戳（秒）
current_frame_abnormal_actions = []
# === 3. 每个 track 做 IoU 匹配角色并做动作识别 ===
for t in tracks:
try:
tid = t.track_id
x1, y1, x2, y2 = map(int, t.tlbr)
except Exception as e:
# 跳过无法解析的 track 对象
continue
# 有效性检查
if x2 <= x1 or y2 <= y1:
continue
# IoU 匹配找回类别（如果之前没有）
if tid not in track_role[camera_id] and dets_xyxy:
best_iou = 0.0
best_role = "unknown"
track_box = [x1, y1, x2, y2]
for i, det_box in enumerate(dets_xyxy):
iou = compute_iou(track_box, det_box)
if iou > best_iou:
best_iou = iou
best_role = dets_roles[i]
if best_iou > 0.3:
track_role[camera_id][tid] = best_role
role = track_role[camera_id].get(tid, "unknown")
# 扩展裁剪并 crop ROI
dw = int((x2 - x1) * EXPAND_RATIO)
dh = int((y2 - y1) * EXPAND_RATIO)
ex1, ey1 = max(0, x1 - dw), max(0, y1 - dh)
ex2, ey2 = min(w, x2 + dw), min(h, y2 + dh)
crop = frame[ey1:ey2, ex1:ex2]
if crop.size == 0:
continue
crop = cv2.resize(crop, (TARGET_SIZE, TARGET_SIZE))
# 填充 track_buffers
if tid not in track_buffers[camera_id]:
track_buffers[camera_id][tid] = []
track_buffers[camera_id][tid].append(crop)
if len(track_buffers[camera_id][tid]) > CLIP_LEN:
track_buffers[camera_id][tid] = track_buffers[camera_id][tid][-CLIP_LEN:]
# 默认值
action_text = "Detecting..."
conf_val = 0.0
action_name = "Normal"
# 若缓存达到 CLIP_LEN，则做动作识别
if len(track_buffers[camera_id][tid]) >= CLIP_LEN and sess_supervisor is not None and sess_suspect is not None:
tensor = preprocess_clip(track_buffers[camera_id][tid])
if tensor.dtype != np.float32:
tensor = tensor.astype(np.float32)
pred = None
labels = None
# 根据角色与上下文选择模型（保留 Ascend 中的逻辑）
if role == "supervisor" and suspect_count >= 1:
try:
pred = sess_supervisor.run(None, {input_name_sup: tensor})[0]
labels = LABELS_SUPERVISOR
except Exception as e:
print(f"[WARN] supervisor 模型推理失败: {e}")
pred = None
elif role == "suspect" and supervisor_count == 0:
try:
pred = sess_suspect.run(None, {input_name_sus: tensor})[0]
labels = LABELS_SUSPECT
except Exception as e:
print(f"[WARN] suspect 模型推理失败: {e}")
pred = None
else:
# 条件不满足，滑动窗口并继续
track_buffers[camera_id][tid] = track_buffers[camera_id][tid][SLIDE_STEP:]
continue
if pred is not None:
idx = int(np.argmax(pred[0]))
conf_val = float(pred[0][idx])
action_name = labels.get(idx, "Unknown")
action_text = f"{action_name}({conf_val:.2f})"
should_alert = False
# 角色-动作匹配逻辑（原封不动）
if (action_name == 'Slap' or action_name == 'Push') and role == 'supervisor':
should_alert = True
track_action_result[camera_id][tid] = f"{action_name}({conf_val:.2f})"
print(f"⏰ 时间：{current_time_sec:.2f} | Camera:{camera_id} | ID: {tid} | 动作：{action_name} | 置信度：{conf_val:.2f}")
elif (action_name == 'Hanging' or action_name == 'Collision' or action_name == 'Lyingdown') and role == 'suspect':
should_alert = True
track_action_result[camera_id][tid] = f"{action_name}({conf_val:.2f})"
print(f"⏰ 时间：{current_time_sec:.2f} | Camera:{camera_id} | ID: {tid} | 动作：{action_name} | 置信度：{conf_val:.2f}")
else:
if tid in track_action_result[camera_id]:
del track_action_result[camera_id][tid]
# 报警逻辑
if (should_alert and conf_val >= CONF_THRESH and
(tid not in last_alert[camera_id] or current_time_sec - last_alert[camera_id][tid] > ACTION_COOLDOWN)):
print(f"[ALERT] Camera:{camera_id} | ID:{tid} ({role}) -> {action_name} ({conf_val:.3f})")
last_alert[camera_id][tid] = current_time_sec
action_info = {
'time': current_time_sec,
'camera_id': camera_id,
'role': role,
'id': tid,
'action': action_name,
'confidence': conf_val
}
recent_actions[camera_id].append(action_info)
if len(recent_actions[camera_id]) > MAX_RECENT_ACTIONS:
recent_actions[camera_id].pop(0)
# 添加到当前帧异常动作列表（用于可视化）
current_frame_abnormal_actions.append(action_info)
# 滑动窗口
track_buffers[camera_id][tid] = track_buffers[camera_id][tid][SLIDE_STEP:]
# 可视化：若检测到异常动作则画框
action_to_show = track_action_result[camera_id].get(tid, None)
if action_to_show is not None and action_name != "Normal" and conf_val >= CONF_THRESH:
color = (255,0,0) if role == "supervisor" else (0,0,255)
cv2.rectangle(frame, (x1,y1), (x2,y2), color, 3)
overlay = frame.copy()
cv2.rectangle(overlay, (x1, y1 - 48), (x1 + 420, y1), color, -1)
cv2.addWeighted(overlay, 0.75, frame, 0.25, 0, frame)
cv2.putText(frame, f"{role.upper()} ID:{tid}", (x1 + 8, y1 - 25),
cv2.FONT_HERSHEY_DUPLEX, 0.8, (255,255,255), 2)
action_color = (0,0,255)
cv2.putText(frame, track_action_result[camera_id][tid], (x1 + 8, y1 - 3),
cv2.FONT_HERSHEY_DUPLEX, 0.9, action_color, 2)
cv2.putText(frame, "ALERT!", (x2 - 130, y1 - 8),
cv2.FONT_HERSHEY_COMPLEX, 1.1, (0,0,255), 3)
# === 全局信息显示（在图像左上） ===
# 统计当前跟踪角色
cur_supervisors = 0
cur_suspects = 0
for tid_map in track_role.get(camera_id, {}).items():
# tid_map is (tid, role) pairs
pass
# 上面的循环写法是为了不改变原逻辑结构；统计用下面的更直接方式
for tid, r in track_role.get(camera_id, {}).items():
if r == "supervisor":
cur_supervisors += 1
elif r == "suspect":
cur_suspects += 1
info = [
f"Camera: {camera_id}",
f"Targets: {len(tracks)}",
f"Supervisor: {cur_supervisors}",
f"Suspect: {cur_suspects}"
]
for i, text in enumerate(info):
cv2.putText(frame, text, (10, 35 + i * 28), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2)
# 动作显示区域
status_y = 35 + len(info) * 28 + 10
if len(current_frame_abnormal_actions) > 0:
cv2.putText(frame, "ACTION DETECTED!", (10, status_y),
cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0,0,255), 3)
for i, action_info in enumerate(current_frame_abnormal_actions):
role_text = action_info['role'].upper()
action_display = f"{role_text} ID:{action_info['id']} -> {action_info['action']} ({action_info['confidence']:.2f})"
color = (255,0,0) if action_info['role'] == "supervisor" else (0,0,255)
cv2.putText(frame, action_display, (10, status_y + 40 + i * 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, color, 2)
else:
# recent actions 显示
if recent_actions[camera_id]:
for i, action_info in enumerate(recent_actions[camera_id][-MAX_RECENT_ACTIONS:]):
action_display = f"{action_info['role'].upper()} ID:{action_info['id']} -> {action_info['action']} ({action_info['confidence']:.2f})"
color = (255,0,0) if action_info['role'] == "supervisor" else (0,0,255)
cv2.putText(frame, action_display, (10, status_y + 10 + i * 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, color, 2)
else:
cv2.putText(frame, "Detecting...", (10, status_y), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0,255,0), 2)
# 返回：image 与 type（type 0 = 无报警；1 = 有报警）
result_type = 1 if any(len(recent_actions[camera_id])>0 and item.get('confidence',0)>=CONF_THRESH for item in recent_actions[camera_id]) else 0
return {
"image": frame,
"type": result_type
}
# =========================
# 服务封装（不变）
# =========================
class RTSPService:
def __init__(self, config_path: str):
self.config_path = config_path
self.cameras = self._load_config()
self.stop_event = threading.Event()
# 队列
self.raw_frame_queue: "queue.Queue[Dict[str, Any]]" = queue.Queue(maxsize=500)
self.ws_send_queue: "queue.Queue[Dict[str, Any]]" = queue.Queue(maxsize=1000)
# 线程
self.capture_workers = []
self.frame_processor = FrameProcessorWorker(self.raw_frame_queue, self.ws_send_queue, self.stop_event)
self.ws_sender = WebSocketSender(self.ws_send_queue, self.stop_event)
def _load_config(self):
with open(self.config_path, "r", encoding="utf-8") as f:
cfg = yaml.safe_load(f)
cameras_cfg = cfg.get("cameras", [])
cameras = []
for c in cameras_cfg:
cameras.append(
CameraConfig(
id=int(c["id"]),
name=str(c.get("name", f"cam_{c['id']}")),
rtsp_url=str(c["rtsp_url"]),
)
)
return cameras
def start(self):
print("[INFO] RTSPService starting...")
# 启动 WebSocket 发送线程
self.ws_sender.start()
# 启动帧处理线程
self.frame_processor.start()
# 启动每个摄像头的抓流线程
for cam in self.cameras:
w = RTSPCaptureWorker(cam, self.raw_frame_queue, self.stop_event)
w.start()
self.capture_workers.append(w)
print("[INFO] RTSPService started")
def stop(self):
print("[INFO] RTSPService stopping...")
self.stop_event.set()
# 等待队列处理完（可选）
try:
self.raw_frame_queue.join()
self.ws_send_queue.join()
except Exception:
pass
for w in self.capture_workers:
w.join(timeout=1.0)
self.frame_processor.join(timeout=1.0)
self.ws_sender.join(timeout=1.0)
print("[INFO] RTSPService stopped")
def main():
service = RTSPService(config_path="config.yaml")
service.start()
try:
while True:
time.sleep(1.0)
except KeyboardInterrupt:
print("[INFO] KeyboardInterrupt, shutting down...")
finally:
service.stop()
if __name__ == "__main__":
main()