Test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#测试摇杆接收\n",
    "import pygame\n",
    "import pygame\n",
    "import win32api\n",
    "# 初始化Pygame\n",
    "pygame.init()\n",
    "\n",
    "# 获取摇杆控制器\n",
    "joystick = pygame.joystick.Joystick(0)\n",
    "joystick.init()\n",
    "\n",
    "# 创建日志文件\n",
    "#log_file = open(\"./yaoganshuju/joystick_log.txt\", \"w\")\n",
    "flag=True\n",
    "while flag:\n",
    "    \n",
    "    for event in pygame.event.get():\n",
    "            if event.type == pygame.JOYAXISMOTION:\n",
    "                if event.axis ==0:\n",
    "                    axis = event.axis\n",
    "                    value = event.value\n",
    "                    \n",
    "                    if round(value,3)-round(last,3)>0.002 or round(last,3)-round(value,3)>0.002:\n",
    "                        #log_file.write(\"Axis {:}: {:.3f}\\n\".format(axis,value))\n",
    "                        print(\"Axis {:}: {:.3f}\\n\".format(axis,value))\n",
    "                    last = event.value\n",
    "\n",
    "            '''elif event.type == pygame.JOYBUTTONDOWN:\n",
    "                button = event.button\n",
    "                log_file.write(f\"Button {button} down\\n\")\n",
    "            elif event.type == pygame.JOYBUTTONUP:\n",
    "                button = event.button\n",
    "                log_file.write(f\"Button {button} up\\n\")'''\n",
    "\n",
    "            # 如果按下摇杆的13号按钮（SELECT），退出程序\n",
    "            if event.type == pygame.JOYBUTTONDOWN and event.button == 13:\n",
    "                flag=False\n",
    "                #log_file.close()\n",
    "                #pygame.quit()      #这个是导致会话崩溃的根本原因\n",
    "                break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#录制，截图并获取摇杆信息\n",
    "import numpy as np\n",
    "import pyautogui\n",
    "import cv2\n",
    "import pygetwindow as gw\n",
    "import pygame\n",
    "import win32api\n",
    "# 初始化Pygame\n",
    "pygame.init()\n",
    "\n",
    "# 获取摇杆控制器\n",
    "joystick = pygame.joystick.Joystick(1)\n",
    "joystick.init()\n",
    "\n",
    "# 创建日志文件\n",
    "log_file = open(\"./yaoganshuju/joystick_log.txt\", \"w\")\n",
    "\n",
    "# 监听摇杆事件\n",
    "flag = True\n",
    "i=0\n",
    "REC=False\n",
    "while flag:\n",
    "    ind = 0\n",
    "    if win32api.GetAsyncKeyState(0x30):#按0激活\n",
    "        if REC==False and ind==0:\n",
    "            REC=True\n",
    "            ind=1\n",
    "        if REC==True and ind==0:\n",
    "            REC=False\n",
    "            ind=1\n",
    "    print(REC)\n",
    "    if REC==True:    \n",
    "        # 获取屏幕截图\n",
    "        target_window = gw.getWindowsWithTitle('American Truck Simulator')[0]  # Replace '目标窗口标题' with the actual title of the window\n",
    "    \n",
    "        window_rect = (target_window.left, target_window.top, target_window.width, target_window.height)\n",
    "    \n",
    "        screenshot = pyautogui.screenshot()\n",
    "        screenshot = screenshot.crop((window_rect[0]+11, window_rect[1]+39, window_rect[0] + window_rect[2]-11, window_rect[1] + window_rect[3]-11))\n",
    "    \n",
    "        # 将截图转换成OpenCV图像\n",
    "        frame = cv2.cvtColor(np.array(screenshot), cv2.COLOR_RGB2BGR)\n",
    "        resize = cv2.resize(frame, (320,180))#[:270,:480]\n",
    "        # 显示图像\n",
    "        cv2.imshow('Screen Capture', resize)\n",
    "    \n",
    "\n",
    "        # 检测按键事件，按下q键退出循环\n",
    "        if cv2.waitKey(1) == ord('q'):\n",
    "            break\n",
    "      \n",
    "    \n",
    "    #接收摇杆信息\n",
    "        for event in pygame.event.get():\n",
    "            if event.type == pygame.JOYAXISMOTION:\n",
    "                if event.axis ==0:\n",
    "                    axis = event.axis\n",
    "                    value = event.value\n",
    "                    log_file.write(\"Axis {:}: {:.3f}\\n\".format(axis,value))\n",
    "                    cv2.imwrite(f'./capture/capture_{i}.png',resize)\n",
    "            '''elif event.type == pygame.JOYBUTTONDOWN:\n",
    "                button = event.button\n",
    "                log_file.write(f\"Button {button} down\\n\")\n",
    "            elif event.type == pygame.JOYBUTTONUP:\n",
    "                button = event.button\n",
    "                log_file.write(f\"Button {button} up\\n\")'''\n",
    "\n",
    "            # 如果按下摇杆的6号按钮（SELECT），退出程序\n",
    "            if event.type == pygame.JOYBUTTONDOWN and event.button == 6:\n",
    "                flag=False\n",
    "                log_file.close()\n",
    "                #pygame.quit()      #这个是导致会话崩溃的根本原因\n",
    "                break\n",
    "                #exit()\n",
    "            i+=1       \n",
    "    #i+=1\n",
    "# 清理资源\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.256 -0.231 -0.206 -0.181 -0.156 -0.131 -0.106 -0.081 -0.056 -0.031\n",
      " -0.006  0.019  0.044  0.069  0.094  0.119  0.144  0.169  0.194  0.219\n",
      "  0.244]\n",
      "220\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#数据直方图\n",
    "import matplotlib.pyplot as plt\n",
    "y = []\n",
    "with open('./yaoganshuju/joystick_log.txt','r') as file:\n",
    "    lines = file.readlines()\n",
    "    for line in lines:\n",
    "        y.append(float(line.strip().split(':')[1]))\n",
    "binwidth = 0.025\n",
    "dt = []\n",
    "for i in y:\n",
    "    dt.append(float(i))\n",
    "\n",
    "print(np.arange(min(dt), max(dt) + binwidth, binwidth))\n",
    "print(len(set(dt)))\n",
    "#直方图\n",
    "plt.hist(dt,bins=151)\n",
    "plt.xlim(-0.3,0.3)\n",
    "plt.title('Number of images per steering angle')\n",
    "plt.xlabel('Steering Angle')\n",
    "plt.ylabel('# Frames')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#键盘监听（学习）\n",
    "from pynput import keyboard, mouse\n",
    "\n",
    "def on_keyboard_press(key):\n",
    "    print(f'键盘按下：{str(key)}')\n",
    "\n",
    "def on_mouse_move(x, y):\n",
    "    print(f'鼠标移动至：({x}, {y})')\n",
    "\n",
    "def on_mouse_click(x, y, button, pressed):\n",
    "    if pressed:\n",
    "        print(f'鼠标按下：({x}, {y}) - {button}')\n",
    "    else:\n",
    "        print(f'鼠标松开：({x}, {y}) - {button}')\n",
    "    \n",
    "# 创建键盘监听器\n",
    "keyboard_listener = keyboard.Listener(on_press=on_keyboard_press)\n",
    "\n",
    "# 创建鼠标监听器\n",
    "mouse_listener = mouse.Listener(on_move=on_mouse_move, on_click=on_mouse_click)\n",
    "\n",
    "# 启动监听器\n",
    "keyboard_listener.start()\n",
    "mouse_listener.start()\n",
    "\n",
    "# 进入监听状态，直到按下Escape键退出程序\n",
    "keyboard_listener.join()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#摇杆监听（学习）\n",
    "import pygame\n",
    "import cv2\n",
    "\n",
    "# 初始化Pygame\n",
    "pygame.init()\n",
    "\n",
    "# 获取摇杆控制器\n",
    "joystick = pygame.joystick.Joystick(0)\n",
    "joystick.init()\n",
    "\n",
    "# 创建日志文件\n",
    "log_file = open(\"joystick_log.txt\", \"w\")\n",
    "\n",
    "# 监听摇杆事件\n",
    "flag=True\n",
    "while flag:\n",
    "\n",
    "    for event in pygame.event.get():\n",
    "        if event.type == pygame.JOYAXISMOTION:\n",
    "            if event.axis ==0:\n",
    "                axis = event.axis\n",
    "                value = event.value\n",
    "                log_file.write(\"Axis {:}: {:.3f}\\n\".format(axis,value))\n",
    "        if event.type == pygame.JOYBUTTONDOWN:\n",
    "            button = event.button\n",
    "            log_file.write(f\"Button {button} down\\n\")\n",
    "        if event.type == pygame.JOYBUTTONUP:\n",
    "            button = event.button\n",
    "            log_file.write(f\"Button {button} up\\n\")\n",
    "\n",
    "        # 如果按下摇杆的0号按钮（示例），退出程序\n",
    "        if event.type == pygame.JOYBUTTONDOWN and button == 0:\n",
    "            flag=False\n",
    "            log_file.close()\n",
    "            #pygame.quit()      #这个是导致会话崩溃的根本原因\n",
    "            break\n",
    "            #exit()\n",
    "    \n",
    "\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#数据增强处理\n",
    "\n",
    "from keras.preprocessing.image import random_channel_shift\n",
    "\n",
    "def horizontal_flip(img, steering_angle):\n",
    "    flipped_image = cv2.flip(img, 1)\n",
    "    steering_angle = -1 * steering_angle\n",
    "    return flipped_image, steering_angle\n",
    "\n",
    "def channel_shift(img, channel_shift_range=0.02):\n",
    "    img_channel_index = 2 # tf indexing\n",
    "    channel_shifted_image = random_channel_shift(img, channel_shift_range, img_channel_index)\n",
    "    return channel_shifted_image\n",
    "\n",
    "# shift height/width of the image by a small fraction\n",
    "def height_width_shift(img, steering_angle):\n",
    "    rows, cols, channels = img.shape\n",
    "    \n",
    "    # Translation\n",
    "    tx = 100 * np.random.uniform() - 100 / 2\n",
    "    ty = 40 * np.random.uniform() - 40 / 2\n",
    "    steering_angle = steering_angle + tx / 100 * 2 * .2\n",
    "    \n",
    "    transform_matrix = np.float32([[1, 0, tx],\n",
    "                                   [0, 1, ty]])\n",
    "    \n",
    "    translated_image = cv2.warpAffine(img, transform_matrix, (cols, rows))\n",
    "    return translated_image, steering_angle\n",
    "\n",
    "def brightness_shift(img, bright_value=None):\n",
    "    img = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)\n",
    "    \n",
    "    if bright_value:\n",
    "        img[:,:,2] += bright_value\n",
    "    else:\n",
    "        random_bright = .25 + np.random.uniform()\n",
    "        img[:,:,2] = img[:,:,2] * random_bright\n",
    "    \n",
    "    img = cv2.cvtColor(img, cv2.COLOR_HSV2RGB)\n",
    "    return img\n",
    "\n",
    "def apply_random_transformation(img, steering_angle):\n",
    "    \n",
    "    transformed_image, steering_angle = height_width_shift(img, steering_angle)\n",
    "    transformed_image = brightness_shift(transformed_image)\n",
    "    if np.random.random() < 0.5:\n",
    "        transformed_image, steering_angle = horizontal_flip(transformed_image, steering_angle)\n",
    "    \n",
    "    return transformed_image, steering_angle\n",
    "\n",
    "def load_and_augment_image(OX,OY):\n",
    "    NX = []\n",
    "    NY = []\n",
    "    for x,y in zip(OX,OY):\n",
    "        nx, ny = apply_random_transformation(x, y)\n",
    "        NX.append(nx)\n",
    "        NY.append(ny)\n",
    "    NX = np.array(NX)\n",
    "    NY = np.array(NY)\n",
    "    return NX, NY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5391, 256, 256, 3)\n",
      "(5391, 256, 256, 3) (5391,)\n"
     ]
    }
   ],
   "source": [
    "#加载数据集\n",
    "import os\n",
    "import time\n",
    "from PIL import Image\n",
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "def read_images_by_time(folder_path):\n",
    "    images = []\n",
    "    file_times = {}\n",
    "\n",
    "    for filename in os.listdir(folder_path):\n",
    "        if filename.endswith(\".jpg\") or filename.endswith(\".png\"):\n",
    "            file_path = os.path.join(folder_path, filename)\n",
    "            file_times[file_path] = os.path.getmtime(file_path)\n",
    "\n",
    "    sorted_files = sorted(file_times.items(), key=lambda x: x[1])\n",
    "\n",
    "    for file_path, _ in sorted_files:\n",
    "        image = Image.open(file_path)\n",
    "        image_array = np.array(image)\n",
    "        image_array = image_array[80:,:]#割掉天空\n",
    "        image_array = cv2.resize(image_array,(256,256))\n",
    "        images.append(image_array)\n",
    "    images_array = np.array(images)\n",
    "    return images_array\n",
    "\n",
    "folder_path = \"./NEcapture/\"\n",
    "\n",
    "images_array=read_images_by_time(folder_path)\n",
    "\n",
    "print(images_array.shape)\n",
    "\n",
    "splitrate = 0.80\n",
    "X = images_array\n",
    "\n",
    "y = []\n",
    "with open('./yaoganshuju/Nomal+enhancejoystick_log.txt','r') as file:\n",
    "    lines = file.readlines()\n",
    "    for line in lines:\n",
    "        y.append(float(line.strip().split(':')[1]))\n",
    "Y = np.array(y)\n",
    "\n",
    "#X, Y = load_and_augment_image(X, Y) #是否增强\n",
    "\n",
    "print(X.shape,Y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#数据集直方图\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.hist(Y,bins=200)\n",
    "plt.xlim(-0.7,0.7)\n",
    "plt.title('Number of images per steering angle')\n",
    "plt.xlabel('Steering Angle')\n",
    "plt.ylabel('# Frames')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.075\n"
     ]
    }
   ],
   "source": [
    "#查看某张图像及其标签\n",
    "change = cv2.cvtColor(X[0],cv2.COLOR_BGR2RGB)\n",
    "cv2.imshow('pic1',change)\n",
    "print(Y[0])\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#模型定义\n",
    "from keras.models import Sequential\n",
    "from keras.layers import *\n",
    "import tensorflow\n",
    "from keras.callbacks import EarlyStopping\n",
    "\n",
    "#early_stopping = EarlyStopping(monitor='val_loss', patience=20)\n",
    "\n",
    "model = Sequential()\n",
    "#model.add(BatchNormalization(input_shape=(128,128,3)))\n",
    "model.add(Conv2D(32,(5,5),padding='Same',activation='relu',strides=2,input_shape=(256,256,3)))\n",
    "model.add(Conv2D(32,(3,3),padding='Same',activation='relu',strides=2))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(Conv2D(64,(3,3),padding='Same',activation='relu',strides=2))\n",
    "model.add(Conv2D(128,(3,3),padding='Same',activation='relu',strides=2))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(Conv2D(256,(3,3),padding='Same',activation='relu',strides=1))\n",
    "model.add(Conv2D(256,(3,3),padding='Same',activation='relu',strides=1))\n",
    "#model.add(Conv2D(256,(3,3),padding='Same',activation='relu',strides=1))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "#model.add(Dropout(0.25))\n",
    "#model.add(Conv2D(128,(3,3),padding='Same',activation='relu',strides=1))\n",
    "#model.add(Conv2D(64,(3,3),padding='Same',activation='relu',strides=1))\n",
    "#model.add(MaxPooling2D(pool_size=2))\n",
    "#model.add(Dropout(0.25))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(2048,activation='relu'))\n",
    "\n",
    "model.add(Dropout(0.4))\n",
    "model.add(Dense(2048,activation='relu'))\n",
    "\n",
    "#model.add(Dropout(0.4))\n",
    "model.add(Dense(512,activation='relu'))\n",
    "model.add(BatchNormalization())\n",
    "#model.add(Dropout(0.3))\n",
    "model.add(Dense(1,activation='linear'))\n",
    "\n",
    "# model.compile(loss=tensorflow.keras.losses.categorical_crossentropy, optimizer=tensorflow.keras.optimizers.SGD(),metrics=['accuracy'])\n",
    "model.compile(loss=tensorflow.keras.losses.mse, optimizer=tensorflow.keras.optimizers.Adam(),metrics=['mse'])\n",
    "#model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "54/54 [==============================] - 8s 67ms/step - loss: 0.4845 - mse: 0.4845\n",
      "Epoch 2/200\n",
      "54/54 [==============================] - 3s 58ms/step - loss: 0.0078 - mse: 0.0078\n",
      "Epoch 3/200\n",
      "54/54 [==============================] - 3s 58ms/step - loss: 0.0054 - mse: 0.0054\n",
      "Epoch 4/200\n",
      "54/54 [==============================] - 3s 58ms/step - loss: 0.0046 - mse: 0.0046\n",
      "Epoch 5/200\n",
      "54/54 [==============================] - 3s 57ms/step - loss: 0.0038 - mse: 0.0038\n",
      "Epoch 6/200\n",
      "54/54 [==============================] - 3s 57ms/step - loss: 0.0037 - mse: 0.0037\n",
      "Epoch 7/200\n",
      "54/54 [==============================] - 3s 57ms/step - loss: 0.0055 - mse: 0.0055\n",
      "Epoch 8/200\n",
      "54/54 [==============================] - 3s 58ms/step - loss: 0.0050 - mse: 0.0050\n",
      "Epoch 9/200\n",
      "54/54 [==============================] - 3s 58ms/step - loss: 0.0042 - mse: 0.0042\n",
      "Epoch 10/200\n",
      "54/54 [==============================] - 3s 58ms/step - loss: 0.0037 - mse: 0.0037\n",
      "Epoch 11/200\n",
      "54/54 [==============================] - 3s 58ms/step - loss: 0.0030 - mse: 0.0030\n",
      "Epoch 12/200\n",
      "54/54 [==============================] - 3s 58ms/step - loss: 0.0029 - mse: 0.0029\n",
      "Epoch 13/200\n",
      "54/54 [==============================] - 3s 58ms/step - loss: 0.0026 - mse: 0.0026\n",
      "Epoch 14/200\n",
      "54/54 [==============================] - 3s 58ms/step - loss: 0.0023 - mse: 0.0023\n",
      "Epoch 15/200\n",
      "54/54 [==============================] - 3s 58ms/step - loss: 0.0024 - mse: 0.0024\n",
      "Epoch 16/200\n",
      "54/54 [==============================] - 3s 58ms/step - loss: 0.0026 - mse: 0.0026\n",
      "Epoch 17/200\n",
      "54/54 [==============================] - 3s 58ms/step - loss: 0.0020 - mse: 0.0020\n",
      "Epoch 18/200\n",
      "54/54 [==============================] - 3s 58ms/step - loss: 0.0022 - mse: 0.0022\n",
      "Epoch 19/200\n",
      "54/54 [==============================] - 3s 58ms/step - loss: 0.0017 - mse: 0.0017\n",
      "Epoch 20/200\n",
      "54/54 [==============================] - 3s 58ms/step - loss: 0.0017 - mse: 0.0017\n",
      "Epoch 21/200\n",
      "54/54 [==============================] - 3s 58ms/step - loss: 0.0019 - mse: 0.0019\n",
      "Epoch 22/200\n",
      "54/54 [==============================] - 3s 58ms/step - loss: 0.0017 - mse: 0.0017\n",
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      "54/54 [==============================] - 3s 58ms/step - loss: 9.0655e-04 - mse: 9.0655e-04\n",
      "Epoch 196/200\n",
      "54/54 [==============================] - 3s 58ms/step - loss: 0.0013 - mse: 0.0013\n",
      "Epoch 197/200\n",
      "54/54 [==============================] - 3s 58ms/step - loss: 8.7583e-04 - mse: 8.7583e-04\n",
      "Epoch 198/200\n",
      "54/54 [==============================] - 3s 58ms/step - loss: 8.6733e-04 - mse: 8.6733e-04\n",
      "Epoch 199/200\n",
      "54/54 [==============================] - 3s 58ms/step - loss: 0.0012 - mse: 0.0012\n",
      "Epoch 200/200\n",
      "54/54 [==============================] - 3s 58ms/step - loss: 8.8251e-04 - mse: 8.8251e-04\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x280fa971090>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#基础模型训练\n",
    "#model.fit(x_train,y_train,batch_size=32,validation_data=(x_test, y_test), callbacks=[early_stopping],verbose=2, epochs=100)\n",
    "\n",
    "model.fit(X,Y,batch_size=100,verbose=1, epochs=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#扩展训练数据集\n",
    "XTR=X;YTR=Y\n",
    "RATE=0.8\n",
    "x_train,y_train,x_test, y_test = XTR[:int(XTR.shape[0]*RATE)],YTR[:int(XTR.shape[0]*RATE)],XTR[int(XTR.shape[0]*RATE):],YTR[int(XTR.shape[0]*RATE):]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<keras.layers.convolutional.conv2d.Conv2D object at 0x0000018D1388ABC0>\n",
      "<keras.layers.convolutional.conv2d.Conv2D object at 0x0000018D1388A440>\n",
      "<keras.layers.pooling.max_pooling2d.MaxPooling2D object at 0x0000018D13889FF0>\n",
      "<keras.layers.convolutional.conv2d.Conv2D object at 0x0000018B72771030>\n",
      "<keras.layers.convolutional.conv2d.Conv2D object at 0x0000018B727720E0>\n",
      "<keras.layers.pooling.max_pooling2d.MaxPooling2D object at 0x0000018B72771CF0>\n",
      "<keras.layers.convolutional.conv2d.Conv2D object at 0x0000018B72794700>\n",
      "<keras.layers.convolutional.conv2d.Conv2D object at 0x0000018B72794280>\n",
      "<keras.layers.pooling.max_pooling2d.MaxPooling2D object at 0x0000018B72794E80>\n",
      "<keras.layers.reshaping.flatten.Flatten object at 0x0000018B727A0DF0>\n",
      "Epoch 1/100\n",
      "22/22 [==============================] - 2s 47ms/step - loss: 0.0141 - mse: 0.0141 - val_loss: 0.0520 - val_mse: 0.0520\n",
      "Epoch 2/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0091 - mse: 0.0091 - val_loss: 0.0506 - val_mse: 0.0506\n",
      "Epoch 3/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0075 - mse: 0.0075 - val_loss: 0.0480 - val_mse: 0.0480\n",
      "Epoch 4/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0069 - mse: 0.0069 - val_loss: 0.0495 - val_mse: 0.0495\n",
      "Epoch 5/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0071 - mse: 0.0071 - val_loss: 0.0546 - val_mse: 0.0546\n",
      "Epoch 6/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0080 - mse: 0.0080 - val_loss: 0.0508 - val_mse: 0.0508\n",
      "Epoch 7/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0065 - mse: 0.0065 - val_loss: 0.0468 - val_mse: 0.0468\n",
      "Epoch 8/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0076 - mse: 0.0076 - val_loss: 0.0506 - val_mse: 0.0506\n",
      "Epoch 9/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0088 - mse: 0.0088 - val_loss: 0.0535 - val_mse: 0.0535\n",
      "Epoch 10/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0087 - mse: 0.0087 - val_loss: 0.0463 - val_mse: 0.0463\n",
      "Epoch 11/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0099 - mse: 0.0099 - val_loss: 0.0656 - val_mse: 0.0656\n",
      "Epoch 12/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0072 - mse: 0.0072 - val_loss: 0.0482 - val_mse: 0.0482\n",
      "Epoch 13/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0068 - mse: 0.0068 - val_loss: 0.0587 - val_mse: 0.0587\n",
      "Epoch 14/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0071 - mse: 0.0071 - val_loss: 0.0509 - val_mse: 0.0509\n",
      "Epoch 15/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0068 - mse: 0.0068 - val_loss: 0.0484 - val_mse: 0.0484\n",
      "Epoch 16/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0079 - mse: 0.0079 - val_loss: 0.0476 - val_mse: 0.0476\n",
      "Epoch 17/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0075 - mse: 0.0075 - val_loss: 0.0496 - val_mse: 0.0496\n",
      "Epoch 18/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0068 - mse: 0.0068 - val_loss: 0.0504 - val_mse: 0.0504\n",
      "Epoch 19/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0069 - mse: 0.0069 - val_loss: 0.0510 - val_mse: 0.0510\n",
      "Epoch 20/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0067 - mse: 0.0067 - val_loss: 0.0470 - val_mse: 0.0470\n",
      "Epoch 21/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0068 - mse: 0.0068 - val_loss: 0.0556 - val_mse: 0.0556\n",
      "Epoch 22/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0068 - mse: 0.0068 - val_loss: 0.0555 - val_mse: 0.0555\n",
      "Epoch 23/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0064 - mse: 0.0064 - val_loss: 0.0488 - val_mse: 0.0488\n",
      "Epoch 24/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0069 - mse: 0.0069 - val_loss: 0.0489 - val_mse: 0.0489\n",
      "Epoch 25/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0088 - mse: 0.0088 - val_loss: 0.0477 - val_mse: 0.0477\n",
      "Epoch 26/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0064 - mse: 0.0064 - val_loss: 0.0491 - val_mse: 0.0491\n",
      "Epoch 27/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0084 - mse: 0.0084 - val_loss: 0.0612 - val_mse: 0.0612\n",
      "Epoch 28/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0097 - mse: 0.0097 - val_loss: 0.0496 - val_mse: 0.0496\n",
      "Epoch 29/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0072 - mse: 0.0072 - val_loss: 0.0486 - val_mse: 0.0486\n",
      "Epoch 30/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0071 - mse: 0.0071 - val_loss: 0.0529 - val_mse: 0.0529\n",
      "Epoch 31/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0069 - mse: 0.0069 - val_loss: 0.0501 - val_mse: 0.0501\n",
      "Epoch 32/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0063 - mse: 0.0063 - val_loss: 0.0513 - val_mse: 0.0513\n",
      "Epoch 33/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0071 - mse: 0.0071 - val_loss: 0.0520 - val_mse: 0.0520\n",
      "Epoch 34/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0078 - mse: 0.0078 - val_loss: 0.0548 - val_mse: 0.0548\n",
      "Epoch 35/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0068 - mse: 0.0068 - val_loss: 0.0535 - val_mse: 0.0535\n",
      "Epoch 36/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0066 - mse: 0.0066 - val_loss: 0.0498 - val_mse: 0.0498\n",
      "Epoch 37/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0068 - mse: 0.0068 - val_loss: 0.0569 - val_mse: 0.0569\n",
      "Epoch 38/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0070 - mse: 0.0070 - val_loss: 0.0489 - val_mse: 0.0489\n",
      "Epoch 39/100\n",
      "22/22 [==============================] - 1s 34ms/step - loss: 0.0062 - mse: 0.0062 - val_loss: 0.0497 - val_mse: 0.0497\n",
      "Epoch 40/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0070 - mse: 0.0070 - val_loss: 0.0540 - val_mse: 0.0540\n",
      "Epoch 41/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0080 - mse: 0.0080 - val_loss: 0.0511 - val_mse: 0.0511\n",
      "Epoch 42/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0060 - mse: 0.0060 - val_loss: 0.0499 - val_mse: 0.0499\n",
      "Epoch 43/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0074 - mse: 0.0074 - val_loss: 0.0564 - val_mse: 0.0564\n",
      "Epoch 44/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0066 - mse: 0.0066 - val_loss: 0.0531 - val_mse: 0.0531\n",
      "Epoch 45/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0071 - mse: 0.0071 - val_loss: 0.0475 - val_mse: 0.0475\n",
      "Epoch 46/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0073 - mse: 0.0073 - val_loss: 0.0483 - val_mse: 0.0483\n",
      "Epoch 47/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0074 - mse: 0.0074 - val_loss: 0.0530 - val_mse: 0.0530\n",
      "Epoch 48/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0064 - mse: 0.0064 - val_loss: 0.0551 - val_mse: 0.0551\n",
      "Epoch 49/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0065 - mse: 0.0065 - val_loss: 0.0520 - val_mse: 0.0520\n",
      "Epoch 50/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0065 - mse: 0.0065 - val_loss: 0.0575 - val_mse: 0.0575\n",
      "Epoch 51/100\n",
      "22/22 [==============================] - 1s 34ms/step - loss: 0.0064 - mse: 0.0064 - val_loss: 0.0542 - val_mse: 0.0542\n",
      "Epoch 52/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0064 - mse: 0.0064 - val_loss: 0.0551 - val_mse: 0.0551\n",
      "Epoch 53/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0077 - mse: 0.0077 - val_loss: 0.0570 - val_mse: 0.0570\n",
      "Epoch 54/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0062 - mse: 0.0062 - val_loss: 0.0478 - val_mse: 0.0478\n",
      "Epoch 55/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0078 - mse: 0.0078 - val_loss: 0.0534 - val_mse: 0.0534\n",
      "Epoch 56/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0065 - mse: 0.0065 - val_loss: 0.0499 - val_mse: 0.0499\n",
      "Epoch 57/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0072 - mse: 0.0072 - val_loss: 0.0538 - val_mse: 0.0538\n",
      "Epoch 58/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0075 - mse: 0.0075 - val_loss: 0.0578 - val_mse: 0.0578\n",
      "Epoch 59/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0077 - mse: 0.0077 - val_loss: 0.0545 - val_mse: 0.0545\n",
      "Epoch 60/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0074 - mse: 0.0074 - val_loss: 0.0523 - val_mse: 0.0523\n",
      "Epoch 61/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0080 - mse: 0.0080 - val_loss: 0.0495 - val_mse: 0.0495\n",
      "Epoch 62/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0071 - mse: 0.0071 - val_loss: 0.0503 - val_mse: 0.0503\n",
      "Epoch 63/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0079 - mse: 0.0079 - val_loss: 0.0477 - val_mse: 0.0477\n",
      "Epoch 64/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0061 - mse: 0.0061 - val_loss: 0.0489 - val_mse: 0.0489\n",
      "Epoch 65/100\n",
      "22/22 [==============================] - 1s 34ms/step - loss: 0.0067 - mse: 0.0067 - val_loss: 0.0504 - val_mse: 0.0504\n",
      "Epoch 66/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0063 - mse: 0.0063 - val_loss: 0.0503 - val_mse: 0.0503\n",
      "Epoch 67/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0071 - mse: 0.0071 - val_loss: 0.0512 - val_mse: 0.0512\n",
      "Epoch 68/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0065 - mse: 0.0065 - val_loss: 0.0514 - val_mse: 0.0514\n",
      "Epoch 69/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0082 - mse: 0.0082 - val_loss: 0.0495 - val_mse: 0.0495\n",
      "Epoch 70/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0062 - mse: 0.0062 - val_loss: 0.0483 - val_mse: 0.0483\n",
      "Epoch 71/100\n",
      "22/22 [==============================] - 1s 34ms/step - loss: 0.0070 - mse: 0.0070 - val_loss: 0.0523 - val_mse: 0.0523\n",
      "Epoch 72/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0090 - mse: 0.0090 - val_loss: 0.0514 - val_mse: 0.0514\n",
      "Epoch 73/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0062 - mse: 0.0062 - val_loss: 0.0589 - val_mse: 0.0589\n",
      "Epoch 74/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0068 - mse: 0.0068 - val_loss: 0.0539 - val_mse: 0.0539\n",
      "Epoch 75/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0065 - mse: 0.0065 - val_loss: 0.0529 - val_mse: 0.0529\n",
      "Epoch 76/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0076 - mse: 0.0076 - val_loss: 0.0522 - val_mse: 0.0522\n",
      "Epoch 77/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0074 - mse: 0.0074 - val_loss: 0.0537 - val_mse: 0.0537\n",
      "Epoch 78/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0060 - mse: 0.0060 - val_loss: 0.0522 - val_mse: 0.0522\n",
      "Epoch 79/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0061 - mse: 0.0061 - val_loss: 0.0542 - val_mse: 0.0542\n",
      "Epoch 80/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0065 - mse: 0.0065 - val_loss: 0.0567 - val_mse: 0.0567\n",
      "Epoch 81/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0067 - mse: 0.0067 - val_loss: 0.0525 - val_mse: 0.0525\n",
      "Epoch 82/100\n",
      "22/22 [==============================] - 1s 34ms/step - loss: 0.0063 - mse: 0.0063 - val_loss: 0.0536 - val_mse: 0.0536\n",
      "Epoch 83/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0061 - mse: 0.0061 - val_loss: 0.0584 - val_mse: 0.0584\n",
      "Epoch 84/100\n",
      "22/22 [==============================] - 1s 34ms/step - loss: 0.0064 - mse: 0.0064 - val_loss: 0.0529 - val_mse: 0.0529\n",
      "Epoch 85/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0074 - mse: 0.0074 - val_loss: 0.0504 - val_mse: 0.0504\n",
      "Epoch 86/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0063 - mse: 0.0063 - val_loss: 0.0512 - val_mse: 0.0512\n",
      "Epoch 87/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0062 - mse: 0.0062 - val_loss: 0.0520 - val_mse: 0.0520\n",
      "Epoch 88/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0061 - mse: 0.0061 - val_loss: 0.0559 - val_mse: 0.0559\n",
      "Epoch 89/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0071 - mse: 0.0071 - val_loss: 0.0517 - val_mse: 0.0517\n",
      "Epoch 90/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0064 - mse: 0.0064 - val_loss: 0.0507 - val_mse: 0.0507\n",
      "Epoch 91/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0062 - mse: 0.0062 - val_loss: 0.0535 - val_mse: 0.0535\n",
      "Epoch 92/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0062 - mse: 0.0062 - val_loss: 0.0573 - val_mse: 0.0573\n",
      "Epoch 93/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0060 - mse: 0.0060 - val_loss: 0.0540 - val_mse: 0.0540\n",
      "Epoch 94/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0059 - mse: 0.0059 - val_loss: 0.0610 - val_mse: 0.0610\n",
      "Epoch 95/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0063 - mse: 0.0063 - val_loss: 0.0536 - val_mse: 0.0536\n",
      "Epoch 96/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0061 - mse: 0.0061 - val_loss: 0.0540 - val_mse: 0.0540\n",
      "Epoch 97/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0067 - mse: 0.0067 - val_loss: 0.0566 - val_mse: 0.0566\n",
      "Epoch 98/100\n",
      "22/22 [==============================] - 1s 34ms/step - loss: 0.0067 - mse: 0.0067 - val_loss: 0.0534 - val_mse: 0.0534\n",
      "Epoch 99/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0057 - mse: 0.0057 - val_loss: 0.0598 - val_mse: 0.0598\n",
      "Epoch 100/100\n",
      "22/22 [==============================] - 1s 33ms/step - loss: 0.0070 - mse: 0.0070 - val_loss: 0.0518 - val_mse: 0.0518\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x18b72770340>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#扩展训练\n",
    "import tensorflow\n",
    "from keras.models import load_model\n",
    "model = load_model('./1745_steering_model.h5')\n",
    "#model.summary()\n",
    "for layer in model.layers[:10]:  # n是卷积层数量，前n层为卷积层\n",
    "    print(layer)\n",
    "    layer.trainable = False\n",
    "    \n",
    "model.compile(loss=tensorflow.keras.losses.mse, optimizer=tensorflow.keras.optimizers.Adam(),metrics=['mse'])\n",
    "model.fit(x_train,y_train,batch_size=128,verbose=1, epochs=100,validation_data=(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制训练的均方差\n",
    "#plt.figure(figsize=(18,10))\n",
    "plt.plot(model.history.history['mse'])\n",
    "#plt.plot(model.history.history['val_mse'])\n",
    "plt.title('Model MSE')\n",
    "plt.ylabel('Mse')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#保存模型\n",
    "model.save(\"771637_steering_model.h5\", save_format=\"h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 256, 256, 3)\n",
      "1/1 [==============================] - 0s 132ms/step\n",
      "[[0.02170889]]\n"
     ]
    }
   ],
   "source": [
    "#指定结果预览\n",
    "from keras.models import load_model\n",
    "import cv2\n",
    "import numpy as np\n",
    "# 载入模型\n",
    "model = load_model('./2058_steering_model.h5')\n",
    "img = cv2.imread('./capture/capture_20340.png')\n",
    "img = img[75:,:]\n",
    "img = cv2.resize(img,(256,256))\n",
    "\n",
    "img = np.expand_dims(img, axis=0)\n",
    "\n",
    "print(img.shape)\n",
    "print(model.predict(img))\n",
    "\n",
    "yp = model.predict(X[3300:3301])\n",
    "print(yp.shape)\n",
    "print(np.max(yp))\n",
    "#print(model.evaluate(x_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#执行函数，检测并将数据输入ATS\n",
    "import numpy as np\n",
    "import pyautogui\n",
    "import cv2\n",
    "import pygetwindow as gw\n",
    "import pygame\n",
    "import pyvjoy\n",
    "from keras.models import load_model\n",
    "import win32api\n",
    "import time\n",
    "\n",
    "global j\n",
    "j = pyvjoy.VJoyDevice(1)\n",
    "\n",
    "model = load_model('./steering_model.h5')\n",
    "\n",
    "def set_joystick_axis(x_value, y_value):\n",
    "    # 创建 vJoy 设备实例\n",
    "    joystick = pyvjoy.VJoyDevice(1)  # 1 表示设备 ID，根据你的设置进行调整\n",
    "    \n",
    "    # 设置左摇杆的值\n",
    "    joystick.set_axis(pyvjoy.HID_USAGE_X, x_value)  # 设置 X 轴的值\n",
    "    joystick.set_axis(pyvjoy.HID_USAGE_Y, y_value)  # 设置 Y 轴的值\n",
    "    \n",
    "    joystick.update()\n",
    "    # 释放 vJoy 设备\n",
    "    joystick.__del__\n",
    "\n",
    "def stop_joystick():\n",
    "    # 创建 vJoy 设备实例\n",
    "    joystick = pyvjoy.VJoyDevice(1)  # 1 表示设备 ID，根据你的设置进行调整\n",
    "    \n",
    "    # 将摇杆值设置为中心位置\n",
    "    x_value = 16384  # X 轴中心位置的值\n",
    "    y_value = 16384  # Y 轴中心位置的值\n",
    "    \n",
    "    joystick.set_axis(pyvjoy.HID_USAGE_X, x_value)  # 设置 X 轴的值\n",
    "    joystick.set_axis(pyvjoy.HID_USAGE_Y, y_value)  # 设置 Y 轴的值\n",
    "    \n",
    "    joystick.update()\n",
    "    # 释放 vJoy 设备\n",
    "    joystick.__del__\n",
    "\n",
    "#img = cv2.imread('./P1.png')\n",
    "AP = False\n",
    "SETL = False\n",
    "SETR = False\n",
    "flag=True\n",
    "while flag:\n",
    "    if win32api.GetAsyncKeyState(0x36):#按6激活\n",
    "        print('6pressed')\n",
    "        AP=True\n",
    "    if win32api.GetAsyncKeyState(0x31):#按1暂停\n",
    "        print('1pressed')\n",
    "        SETL=SETR=False\n",
    "        AP=False\n",
    "    if win32api.GetAsyncKeyState(0x30):#按0结束\n",
    "        print('0pressed')\n",
    "\n",
    "        AP=False\n",
    "        flag = False\n",
    "    \n",
    "    #进行设置\n",
    "    if win32api.GetAsyncKeyState(0x37):\n",
    "        print('set left')\n",
    "        SETL = True\n",
    "        SETR = False\n",
    "    if win32api.GetAsyncKeyState(0x38):\n",
    "        print('set right')\n",
    "        SETR = True\n",
    "        SETL = False\n",
    "\n",
    "    if SETL==True:\n",
    "        j.data.wAxisX = int(0)\n",
    "        j.update()\n",
    "        \n",
    "    if SETR==True:\n",
    "        j.data.wAxisX = int(1*32767)\n",
    "        j.update()\n",
    "        \n",
    "    \n",
    "    if AP==True:\n",
    "        target_window = gw.getWindowsWithTitle('American Truck Simulator')[0]  # Replace '目标窗口标题' with the actual title of the window\n",
    "        #target_window = gw.getWindowsWithTitle('大作业')[0]  # Replace '目标窗口标题' with the actual title of the window\n",
    "        window_rect = (target_window.left, target_window.top, target_window.width, target_window.height)\n",
    "    \n",
    "        screenshot = pyautogui.screenshot()\n",
    "        screenshot = screenshot.crop((window_rect[0]+11, window_rect[1]+39, window_rect[0] + window_rect[2]-11, window_rect[1] + window_rect[3]-11))\n",
    "    \n",
    "        # 将截图转换成OpenCV图像\n",
    "        frame = cv2.cvtColor(np.array(screenshot), cv2.COLOR_RGB2BGR)\n",
    "        img = cv2.resize(frame,(320,180))\n",
    "        img = cv2.resize(img[72:,:],(256,256))\n",
    "\n",
    "        FD_img = np.expand_dims(img, axis=0)\n",
    "\n",
    "        #print(FD_img.shape)\n",
    "        # 显示图像\n",
    "        cv2.imshow('Screen Capture', img)\n",
    "        steering_value=model.predict(FD_img)[0][0]\n",
    "        print(int(steering_value*16384 +16384),steering_value)\n",
    "        # 设置虚拟手柄的输入\n",
    "        #set_joystick_axis(int(steering_value*32767), 0)\n",
    "        j.data.wAxisX = int(steering_value * 16384 +16384)\n",
    "        #j.data.wAxisY = int(0.5*32767)\n",
    "        j.update()\n",
    "\n",
    "    if AP==False and SETL==False and SETR==False:\n",
    "        j.data.wAxisX = int(0.5*32767)\n",
    "        j.data.wAxisY = int(0.5*32767)\n",
    "        j.update()\n",
    "    if cv2.waitKey(1) == ord('q'):\n",
    "        flag = False\n",
    "        break\n",
    "\n",
    "cv2.destroyAllWindows()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3086, 256, 256, 3)\n",
      "(3086, 256, 256, 3) (3086, 1)\n"
     ]
    }
   ],
   "source": [
    "#VGG16尝试，数据集加载\n",
    "import os\n",
    "import time\n",
    "from PIL import Image\n",
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "def read_images_by_time(folder_path):\n",
    "    images = []\n",
    "    file_times = {}\n",
    "\n",
    "    for filename in os.listdir(folder_path):\n",
    "        if filename.endswith(\".jpg\") or filename.endswith(\".png\"):\n",
    "            file_path = os.path.join(folder_path, filename)\n",
    "            file_times[file_path] = os.path.getmtime(file_path)\n",
    "\n",
    "    sorted_files = sorted(file_times.items(), key=lambda x: x[1])\n",
    "\n",
    "    for file_path, _ in sorted_files:\n",
    "        image = Image.open(file_path)\n",
    "        image_array = np.array(image)\n",
    "        image_array = image_array[80:,:]#割掉天空\n",
    "        image_array = cv2.resize(image_array,(256,256))\n",
    "        images.append(image_array)\n",
    "    images_array = np.array(images)\n",
    "    return images_array\n",
    "\n",
    "folder_path = \"./capture/\"\n",
    "\n",
    "images_array=read_images_by_time(folder_path)\n",
    "\n",
    "print(images_array.shape)\n",
    "\n",
    "splitrate = 0.80\n",
    "XTR = images_array\n",
    "\n",
    "y = []\n",
    "with open('./yaoganshuju/joystick_log.txt','r') as file:\n",
    "    lines = file.readlines()\n",
    "    for line in lines:\n",
    "        y.append(float(line.strip().split(':')[1]))\n",
    "YTR = np.array(y)\n",
    "YTR = YTR.reshape((YTR.shape[0],1))\n",
    "#X, Y = load_and_augment_image(X, Y) #是否增强\n",
    "\n",
    "print(XTR.shape,YTR.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# vgg16D 尝试\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "#数据准备\n",
    "#(x_train,y_train), (x_test, y_test)=tf.keras.datasets.cifar100.load_data() \n",
    "RATE=0.8\n",
    "x_train,y_train,x_test, y_test = XTR[:int(XTR.shape[0]*RATE)],YTR[:int(XTR.shape[0]*RATE)],XTR[int(XTR.shape[0]*RATE):],YTR[int(XTR.shape[0]*RATE):]\n",
    "\n",
    "# 数据预处理部分 z-score标准化\n",
    "mean = np.mean(x_train, axis=(0, 1, 2, 3))#四个维度 批数 像素x像素 通道数\n",
    "std = np.std(x_train, axis=(0, 1, 2, 3))\n",
    "x_train = (x_train - mean) / (std + 1e-7)#trick 加小数点 避免出现整数 \n",
    "x_test = (x_test - mean) / (std + 1e-7) \n",
    "\n",
    "#转换one-hot独热映射\n",
    "y_train = tf.keras.utils.to_categorical(y_train, 100)\n",
    "y_test = tf.keras.utils.to_categorical(y_test, 100)\n",
    "\n",
    "# 构建模型\n",
    "def vgg16D():\n",
    "    model = tf.keras.Sequential()\n",
    "    # 第一块\n",
    "    #conv1\n",
    "    model.add(tf.keras.layers.Conv2D(filters=64, kernel_size=[3, 3], input_shape=(256, 256, 3), strides=1,activation='relu',padding='SAME', kernel_regularizer=tf.keras.regularizers.l2(0.0005)))\n",
    "    model.add(tf.keras.layers.BatchNormalization()) \n",
    "    model.add(tf.keras.layers.Dropout(0.4)) \n",
    "    \n",
    "    model.add(tf.keras.layers.Conv2D(filters=64, kernel_size=[3, 3], strides=1,activation='relu',\n",
    "                                            padding='SAME', kernel_regularizer=tf.keras.regularizers.l2(0.0005)))\n",
    "    model.add(tf.keras.layers.BatchNormalization()) \n",
    "    #最大池化层1\n",
    "    model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "    # 第二块\n",
    "    #conv3\n",
    "    model.add(tf.keras.layers.Conv2D(filters=128, kernel_size=[3, 3], strides=1,activation='relu',\n",
    "                                            padding='SAME', kernel_regularizer=tf.keras.regularizers.l2(0.0005)))\n",
    "    model.add(tf.keras.layers.BatchNormalization()) \n",
    "    model.add(tf.keras.layers.Dropout(0.4))\n",
    "    #conv4\n",
    "    model.add(tf.keras.layers.Conv2D(filters=128, kernel_size=[3, 3], strides=1,activation='relu',\n",
    "                                            padding='SAME', kernel_regularizer=tf.keras.regularizers.l2(0.0005)))\n",
    "    model.add(tf.keras.layers.BatchNormalization()) \n",
    "    #最大池化层2\n",
    "    model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "\n",
    "\n",
    "    #conv5\n",
    "    model.add(tf.keras.layers.Conv2D(filters=256, kernel_size=[3, 3], strides=1,activation='relu',\n",
    "                                            padding='SAME', kernel_regularizer=tf.keras.regularizers.l2(0.0005)))\n",
    "    model.add(tf.keras.layers.BatchNormalization()) \n",
    "    model.add(tf.keras.layers.Dropout(0.4))\n",
    "    #conv6\n",
    "    model.add(tf.keras.layers.Conv2D(filters=256, kernel_size=[3, 3], strides=1,activation='relu',\n",
    "                                            padding='SAME', kernel_regularizer=tf.keras.regularizers.l2(0.0005)))\n",
    "    model.add(tf.keras.layers.BatchNormalization()) \n",
    "    model.add(tf.keras.layers.Dropout(0.4))\n",
    "    #conv7\n",
    "    model.add(tf.keras.layers.Conv2D(filters=256, kernel_size=[3, 3], strides=1,activation='relu',\n",
    "                                            padding='SAME', kernel_regularizer=tf.keras.regularizers.l2(0.0005)))\n",
    "    model.add(tf.keras.layers.BatchNormalization()) \n",
    "    #最大池化层3\n",
    "    model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "    #conv8\n",
    "    model.add(tf.keras.layers.Conv2D(filters=512, kernel_size=[3, 3], strides=1,activation='relu',\n",
    "                                            padding='SAME', kernel_regularizer=tf.keras.regularizers.l2(0.0005)))\n",
    "    model.add(tf.keras.layers.BatchNormalization()) \n",
    "    model.add(tf.keras.layers.Dropout(0.4))\n",
    "    #conv9\n",
    "    model.add(tf.keras.layers.Conv2D(filters=512, kernel_size=[3, 3], strides=1,activation='relu',\n",
    "                                            padding='SAME', kernel_regularizer=tf.keras.regularizers.l2(0.0005)))\n",
    "    model.add(tf.keras.layers.BatchNormalization()) \n",
    "    model.add(tf.keras.layers.Dropout(0.4))\n",
    "    #conv10\n",
    "    model.add(tf.keras.layers.Conv2D(filters=512, kernel_size=[3, 3], strides=1,activation='relu',\n",
    "                                            padding='SAME', kernel_regularizer=tf.keras.regularizers.l2(0.0005)))\n",
    "    model.add(tf.keras.layers.BatchNormalization()) \n",
    "    #最大池化层4\n",
    "    model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "\n",
    "    #conv11\n",
    "    model.add(tf.keras.layers.Conv2D(filters=512, kernel_size=[3, 3], strides=1,activation='relu',\n",
    "                                            padding='SAME', kernel_regularizer=tf.keras.regularizers.l2(0.0005)))\n",
    "    model.add(tf.keras.layers.BatchNormalization()) \n",
    "    model.add(tf.keras.layers.Dropout(0.4))\n",
    "    #conv12\n",
    "    model.add(tf.keras.layers.Conv2D(filters=512, kernel_size=[3, 3], strides=1,activation='relu',\n",
    "                                            padding='SAME', kernel_regularizer=tf.keras.regularizers.l2(0.0005)))\n",
    "    model.add(tf.keras.layers.BatchNormalization()) \n",
    "    model.add(tf.keras.layers.Dropout(0.4))\n",
    "    #conv13\n",
    "    model.add(tf.keras.layers.Conv2D(filters=512, kernel_size=[3, 3], strides=1,activation='relu',\n",
    "                                            padding='SAME', kernel_regularizer=tf.keras.regularizers.l2(0.0005)))\n",
    "    model.add(tf.keras.layers.BatchNormalization()) \n",
    "    #最大池化层5\n",
    "    model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "    #f-c 三层 \n",
    "    model.add(tf.keras.layers.Flatten())\n",
    "    model.add(tf.keras.layers.Dropout(rate=0.5))\n",
    "\n",
    "    model.add(tf.keras.layers.Dense(units=512,activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.0005)))\n",
    "    model.add(tf.keras.layers.BatchNormalization())\n",
    "\n",
    "\n",
    "    model.add(tf.keras.layers.Dense(units=100))\n",
    "    model.add(tf.keras.layers.Activation('softmax'))\n",
    "    #查看摘要\n",
    "    model.summary()\n",
    "    return model\n",
    "    \n",
    "#超参设置\n",
    "training_epochs = 100\n",
    "batch_size = 128\n",
    "learning_rate = 0.01\n",
    "momentum = 0.9 #SGD加速动量\n",
    "lr_decay = 1e-6 #学习衰减\n",
    "lr_drop = 20 #衰减倍数\n",
    "model = vgg16D()\n",
    "\n",
    "# 每20个epoch 学习率缩小为原来的一半\n",
    "def lr_scheduler(epoch):\n",
    "    return learning_rate * (0.5 ** (epoch // lr_drop))\n",
    "\n",
    "reduce_lr = tf.keras.callbacks.LearningRateScheduler(lr_scheduler)\n",
    "'''# 数据增强\n",
    "datagen = ImageDataGenerator(\n",
    "    rotation_range=15,  # 旋转的范围\n",
    "    width_shift_range=0.1, \n",
    "    height_shift_range=0.1,  \n",
    "    horizontal_flip=True,  # 随机水平翻转。\n",
    ")\n",
    "datagen.fit(x_train)'''\n",
    "# 使用sgd优化器\n",
    "optimizer = tf.keras.optimizers.SGD(learning_rate=learning_rate,\n",
    "                                    decay=1e-6, momentum=momentum, nesterov=True)\n",
    "model.compile(loss='mse', optimizer=optimizer, metrics=['mse'])\n",
    "\n",
    "history = model.fit(x_train,y_train, epochs=training_epochs, callbacks=[reduce_lr],\n",
    "                    steps_per_epoch=x_train.shape[0] // batch_size, validation_data=(x_test, y_test))   \n",
    "# 绘制训练 && 验证的均方差\n",
    "plt.plot(history.history['mse'])\n",
    "plt.plot(history.history['val_mse'])\n",
    "plt.title('Model MSE')\n",
    "plt.ylabel('Mse')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "# 绘制训练 && 验证的损失值\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "plt.show()\n"
   ]
  }
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