365 lines
15 KiB
Plaintext
365 lines
15 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "80978b97",
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"metadata": {},
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"outputs": [],
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"source": [
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"import time\n",
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"import cv2\n",
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"import numpy as np\n",
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"from mss import mss\n",
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"from collections import deque"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "ea32fa13",
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"metadata": {},
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"outputs": [],
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"source": [
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"from typing import Callable, Optional"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "27c4af4c",
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"import threading\n",
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"import websocket\n",
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"from queue import Queue\n",
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"\n",
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"class WebSocketClient:\n",
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" def __init__(self, server_url):\n",
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" self.server_url = server_url\n",
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" self.ws = None\n",
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" self.connected = False\n",
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" self.message_queue = Queue()\n",
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" self.lock = threading.Lock()\n",
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" self.reconnect_delay = 3 # 重连延迟(秒)\n",
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" self.running = False\n",
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"\n",
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" def on_open(self, ws):\n",
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" \"\"\"连接成功回调\"\"\"\n",
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" print(\"WebSocket 连接成功\")\n",
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" self.connected = True\n",
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"\n",
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" def on_message(self, ws, message):\n",
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" \"\"\"接收消息回调\"\"\"\n",
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" print(f\"收到前端消息: {message}\")\n",
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"\n",
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" def on_error(self, ws, error):\n",
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" \"\"\"错误回调\"\"\"\n",
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" print(f\"WebSocket 错误: {error}\")\n",
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" self.connected = False\n",
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"\n",
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" def on_close(self, ws, close_status_code, close_msg):\n",
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" \"\"\"关闭回调\"\"\"\n",
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" print(f\"WebSocket 关闭: {close_status_code} - {close_msg}\")\n",
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" self.connected = False\n",
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" if self.running:\n",
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" self.reconnect()\n",
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"\n",
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" def reconnect(self):\n",
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" \"\"\"断线重连\"\"\"\n",
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" print(f\"{self.reconnect_delay}秒后尝试重连...\")\n",
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" time.sleep(self.reconnect_delay)\n",
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" self.connect()\n",
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"\n",
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" def connect(self):\n",
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" \"\"\"连接WebSocket服务器\"\"\"\n",
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" self.running = True\n",
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" self.ws = websocket.WebSocketApp(\n",
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" self.server_url,\n",
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" on_open=self.on_open,\n",
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" on_message=self.on_message,\n",
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" on_error=self.on_error,\n",
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" on_close=self.on_close\n",
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" )\n",
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" # 在独立线程中运行WebSocket\n",
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" threading.Thread(target=self.ws.run_forever, daemon=True).start()\n",
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"\n",
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" def send(self, data):\n",
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" \"\"\"发送消息(线程安全)\"\"\"\n",
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" with self.lock:\n",
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" if self.connected and self.ws:\n",
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" try:\n",
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" self.ws.send(\"xtleto|\"+json.dumps(data))\n",
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" return True\n",
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" except Exception as e:\n",
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" print(f\"消息发送失败: {e}\")\n",
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" return False\n",
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" return False\n",
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"\n",
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" def stop(self):\n",
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" \"\"\"停止WebSocket连接\"\"\"\n",
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" self.running = False\n",
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" if self.ws:\n",
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" self.ws.close()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "88cabf89",
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"metadata": {},
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"outputs": [],
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"source": [
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"class DynamicAdjuster:\n",
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" def __init__(self):\n",
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" # 参数跟踪窗口（保留最近20帧数据）\n",
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" self.diff_history = deque(maxlen=20)\n",
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" self.area_history = deque(maxlen=20)\n",
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" \n",
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" # 初始参数值\n",
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" self.threshold = 10\n",
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" self.min_area = 100\n",
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" \n",
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" # 调整速率系数\n",
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" self.THRESH_STEP = 2 # 阈值调整步长\n",
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" self.AREA_STEP = 5 # 面积调整步长\n",
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" self.TARGET_FPS = 15 # 目标处理速度（用于CPU优化）\n",
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"\n",
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" def adjust_by_fps(self, actual_fps):\n",
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" \"\"\"根据实际帧率动态调整参数\"\"\"\n",
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" if actual_fps < self.TARGET_FPS * 0.8:\n",
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" # 当帧率过低时激进调节参数\n",
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" self.THRESH_STEP = max(3, self.THRESH_STEP + 1)\n",
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" self.min_area = min(300, self.min_area + 10)\n",
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" elif actual_fps > self.TARGET_FPS * 1.2:\n",
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" # 当帧率过高时放松限制\n",
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" self.THRESH_STEP = max(1, self.THRESH_STEP - 1)\n",
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" \n",
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" def update(self, diff_frame, detected_areas):\n",
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" \"\"\"根据当前帧数据动态调整参数\"\"\"\n",
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" # 计算当前帧的运动强度\n",
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" curr_diff = cv2.mean(diff_frame)[0]\n",
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" self.diff_history.append(curr_diff)\n",
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" \n",
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" # 计算当前检测区域面积\n",
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" curr_area = sum([(x2-x1)*(y2-y1) for (x1,y1,x2,y2) in detected_areas])\n",
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" self.area_history.append(curr_area)\n",
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" \n",
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" # 计算平均运动强度（最近N帧的指数加权平均值）\n",
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" avg_diff = sum(self.diff_history) / len(self.diff_history) if self.diff_history else 0\n",
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" \n",
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" # 自动调整阈值（运动强度越低，阈值越高）\n",
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" if avg_diff < 10: # 低运动量阶段\n",
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" self.threshold = min(15, self.threshold + self.THRESH_STEP)\n",
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" elif avg_diff > 30: # 高运动量阶段\n",
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" self.threshold = max(15, self.threshold - self.THRESH_STEP*2)\n",
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" else: # 正常调整\n",
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" if len(detected_areas) > 5:\n",
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" self.threshold = max(10, self.threshold - self.THRESH_STEP)\n",
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" elif not detected_areas:\n",
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" self.threshold = min(50, self.threshold + self.THRESH_STEP)\n",
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" \n",
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" # 根据检测区域面积自动调整最小面积\n",
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" avg_area = sum(self.area_history) / len(self.area_history) if self.area_history else 0\n",
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" if avg_area < 500: # 小范围变化\n",
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" self.min_area = min(200, self.min_area + self.AREA_STEP)\n",
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" else: # 大范围变化\n",
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" self.min_area = max(50, self.min_area - self.AREA_STEP)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "857f6a30",
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"metadata": {},
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"outputs": [],
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"source": [
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"class FrameDiffProcessor:\n",
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" def __init__(self, ws_client: Optional[WebSocketClient] = None):\n",
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" self.prev_frame = None\n",
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" self.adjuster = DynamicAdjuster()\n",
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" self.kernel_size = (21, 21)\n",
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" self.last_diff = None\n",
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" self.ws_client = ws_client\n",
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"\n",
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" def _preprocess(self, frame):\n",
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" gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
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" return cv2.GaussianBlur(gray, self.kernel_size, 0)\n",
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"\n",
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" def process(self, frame):\n",
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" processed = self._preprocess(frame)\n",
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" if self.prev_frame is None:\n",
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" self.prev_frame = processed\n",
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" return [], self.adjuster.threshold, self.adjuster.min_area, 0\n",
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"\n",
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" # 计算原始差异\n",
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" raw_diff = cv2.absdiff(self.prev_frame, processed)\n",
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" self.last_diff = raw_diff.copy()\n",
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"\n",
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" # 应用当前阈值\n",
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" _, thresh = cv2.threshold(\n",
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" raw_diff, self.adjuster.threshold, 255, cv2.THRESH_BINARY\n",
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" )\n",
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"\n",
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" # 形态学优化\n",
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" kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))\n",
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" thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)\n",
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"\n",
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" # 查找轮廓并过滤\n",
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" contours, _ = cv2.findContours(\n",
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" thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE\n",
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" )\n",
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" rects = []\n",
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" for cnt in contours:\n",
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" area = cv2.contourArea(cnt)\n",
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" if area > self.adjuster.min_area:\n",
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" x, y, w, h = cv2.boundingRect(cnt)\n",
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" rects.append((x, y, x + w, y + h))\n",
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"\n",
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" if rects:\n",
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" payload = {\n",
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" \"type\": \"motion_detection\",\n",
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" \"timestamp\": int(time.time()*1000),\n",
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" \"regions\": [],\n",
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" \"stats\": {\n",
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" \"threshold\": self.adjuster.threshold,\n",
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" \"min_area\": self.adjuster.min_area,\n",
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" \"fps\": self.adjuster.TARGET_FPS,\n",
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" },\n",
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" }\n",
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" for (x1, y1, x2, y2) in rects:\n",
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" roi = frame[y1:y2, x1:x2]\n",
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" # 将图像转换为base64编码\n",
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" _, buffer = cv2.imencode('.webp', roi, [cv2.IMWRITE_WEBP_QUALITY, 80])\n",
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" payload[\"regions\"].append({\n",
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" \"x\": x1,\n",
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" \"y\": y1,\n",
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" \"width\": x2 - x1,\n",
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" \"height\": y2 - y1,\n",
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" \"image\": buffer.tobytes().hex() # 转换为十六进制字符串\n",
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" })\n",
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" \n",
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" # 通过WebSocket发送\n",
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" self.ws_client.send(payload)\n",
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" # 动态参数调整\n",
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" self.adjuster.update(raw_diff, rects)\n",
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"\n",
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" self.prev_frame = processed\n",
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" return (\n",
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" rects,\n",
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" self.adjuster.threshold,\n",
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" self.adjuster.min_area,\n",
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" self.adjuster.TARGET_FPS,\n",
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" )\n",
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"\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "108febd8",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"WebSocket 连接成功\n"
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]
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},
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{
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"ename": "KeyboardInterrupt",
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"evalue": "",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[1;32mIn[6], line 14\u001b[0m\n\u001b[0;32m 11\u001b[0m sct_img \u001b[38;5;241m=\u001b[39m sct\u001b[38;5;241m.\u001b[39mgrab(monitor)\n\u001b[0;32m 12\u001b[0m frame \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(sct_img)\n\u001b[1;32m---> 14\u001b[0m rects, curr_thresh, curr_area, fps \u001b[38;5;241m=\u001b[39m processor\u001b[38;5;241m.\u001b[39mprocess(frame)\n\u001b[0;32m 16\u001b[0m \u001b[38;5;66;03m# 计算实际FPS\u001b[39;00m\n\u001b[0;32m 17\u001b[0m curr_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n",
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"Cell \u001b[1;32mIn[5], line 14\u001b[0m, in \u001b[0;36mFrameDiffProcessor.process\u001b[1;34m(self, frame)\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mprocess\u001b[39m(\u001b[38;5;28mself\u001b[39m, frame):\n\u001b[1;32m---> 14\u001b[0m processed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_preprocess(frame)\n\u001b[0;32m 15\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprev_frame \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 16\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprev_frame \u001b[38;5;241m=\u001b[39m processed\n",
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"Cell \u001b[1;32mIn[5], line 10\u001b[0m, in \u001b[0;36mFrameDiffProcessor._preprocess\u001b[1;34m(self, frame)\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_preprocess\u001b[39m(\u001b[38;5;28mself\u001b[39m, frame):\n\u001b[1;32m---> 10\u001b[0m gray \u001b[38;5;241m=\u001b[39m cv2\u001b[38;5;241m.\u001b[39mcvtColor(frame, cv2\u001b[38;5;241m.\u001b[39mCOLOR_BGR2GRAY)\n\u001b[0;32m 11\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m cv2\u001b[38;5;241m.\u001b[39mGaussianBlur(gray, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkernel_size, \u001b[38;5;241m0\u001b[39m)\n",
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"\u001b[1;31mKeyboardInterrupt\u001b[0m: "
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"WebSocket 错误: Connection to remote host was lost.\n",
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"WebSocket 关闭: None - None\n",
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"3秒后尝试重连...\n",
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"WebSocket 连接成功\n"
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]
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}
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],
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"source": [
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"# 初始化捕获\n",
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"sct = mss()\n",
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"monitor = sct.monitors[1]\n",
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"ws_client = WebSocketClient(\"ws://43.248.184.71:48905?mac=server\")\n",
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"ws_client.connect() \n",
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"processor = FrameDiffProcessor(ws_client)\n",
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"\n",
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"cv2.namedWindow(\"Adaptive Detection\")\n",
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"prev_time = time.time()\n",
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"while True:\n",
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" sct_img = sct.grab(monitor)\n",
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" frame = np.array(sct_img)\n",
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"\n",
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" rects, curr_thresh, curr_area, fps = processor.process(frame)\n",
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"\n",
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" # 计算实际FPS\n",
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" curr_time = time.time()\n",
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" actual_fps = 1 / (curr_time - prev_time)\n",
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" prev_time = curr_time\n",
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"\n",
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" # 调节参数\n",
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" processor.adjuster.adjust_by_fps(actual_fps)\n",
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" # 可视化\n",
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" display = frame.copy()\n",
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" for (x1, y1, x2, y2) in rects:\n",
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" cv2.rectangle(display, (x1, y1), (x2, y2), (0, 255, 0), 2)\n",
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"\n",
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" # 显示调整信息\n",
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" status = f\"Thresh: {curr_thresh} | MinArea: {curr_area}| FPS:{ fps}\"\n",
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" cv2.putText(display, status, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)\n",
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"\n",
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" # 显示差异图（可选）\n",
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" # if processor.last_diff is not None:\n",
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" # diff_display = cv2.normalize(processor.last_diff, None, 0, 255, cv2.NORM_MINMAX)\n",
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" # cv2.imshow(\"Difference\", diff_display.astype(np.uint8))\n",
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"\n",
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" cv2.imshow(\"Adaptive Detection\", display)\n",
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"\n",
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" if cv2.waitKey(25) & 0xFF == 27:\n",
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" break\n",
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"\n",
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"cv2.destroyAllWindows()\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "base",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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|
"version": "3.12.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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