C-30 Self Driving Car.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import cv2 as cv\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from tensorflow.keras.models import Sequential,load_model\n",
    "from tensorflow.keras.layers import Convolution2D,Flatten,Dense\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.load('final_data.npy')\n",
    "\n",
    "img = data[0]\n",
    "steering = data[1]\n",
    "\n",
    "x_train,x_test,y_train,y_test = train_test_split(img,steering,test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#steering  = []\n",
    "#images = []\n",
    "\n",
    "#for i in data :\n",
    "    #images.append(i[0])\n",
    "    #steering.append(i[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#def crop_img(path):   \n",
    "    \n",
    "   # img = cv.imread(imgs[0])\n",
    "   # crop = img[60:138,:,:]    # top :bottom, \n",
    "    #return crop\n",
    "\n",
    "#hsv = cv.cvtColor(crop,cv.COLOR_BGR2HSV) for greyscale\n",
    "#def bgr2yuv(crop):\n",
    "     #yuv = cv.cvtColor(crop,cv.COLOR_BGR2YUV)\n",
    "     #return yuv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#crop = crop_img('D:\\\\STUDY\\\\AUTONOMOUS\\\\DRIVE\\\\IMG\\\\center_2021_05_22_12_41_33_391.jpg')\n",
    "\n",
    "#yuv = bgr2yuv(crop)\n",
    "#plt.imshow(yuv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#yuv = bgr2yuv(crop)\n",
    "#plt.imshow(yuv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#brightness_cont = np.random.randint(1,4)  #low  to high\n",
    "\n",
    "#plt.imshow(yuv//brightness_cont)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#img = cv.imread('D:\\\\STUDY\\\\AUTONOMOUS\\\\DRIVE\\\\IMG\\\\center_2021_05_22_12_41_33_391.jpg')\n",
    "#plt.imshow(img)\n",
    "#float(steering[391])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#flip = cv.flip(img,1)  #to flip horizintal 0,flip vertical 1\n",
    "#plt.imshow(flip)\n",
    "\n",
    "#-float(steering[391])  #steering angle reversed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AUGMENTATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def batch_generator(imgs,steering,batch_size): \n",
    "    \n",
    "   \n",
    "    \n",
    "    while True:                                        #will run till no of batches\n",
    "        \n",
    "        x = []\n",
    "        y = []\n",
    "        \n",
    "        for i in range(batch_size):                   # size if given 50 then 50 imgs will assign and loop 50 times    \n",
    "    \n",
    "            index = np.random.randint(0,len(imgs)-1)  #coz 0 se indexing will start\n",
    "            img = cv.imread(imgs[index])\n",
    "            \n",
    "            img = cv.cvtColor(img,cv.COLOR_BGR2YUV) #or img = bgr2yuv()\n",
    "            img = img[60:138,:,:]                   #or img = crop_img(img)\n",
    "            \n",
    "            img = cv.resize(img,(200,66))\n",
    "        \n",
    "            if(np.random.rand() > 0.5):\n",
    "    \n",
    "                brightness_cont = np.random.randint(1,4)\n",
    "                img = img//brightness_cont\n",
    "    \n",
    "            if(np.random.rand() > 0.5):   \n",
    "    \n",
    "                img = cv.flip(img,1)\n",
    "                steering_angle = -float(steering[index])\n",
    "        \n",
    "            else:\n",
    "                steering_angle = float(steering[index])\n",
    "    \n",
    "            x.append(img)\n",
    "            y.append(steering_angle)\n",
    "        \n",
    "        x = np.array(x)                            #converting to numpy array\n",
    "        y = np.array(y)\n",
    "        \n",
    "        yield x,y                                  #will return values and run indefinitly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Image data of dtype <U64 cannot be converted to float",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-5-23af6c37b3d0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mD:\\STUDY\\Anaconda\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mimshow\u001b[1;34m(X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, filternorm, filterrad, resample, url, data, **kwargs)\u001b[0m\n\u001b[0;32m   2722\u001b[0m         \u001b[0mfilternorm\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilterrad\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m4.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresample\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2723\u001b[0m         data=None, **kwargs):\n\u001b[1;32m-> 2724\u001b[1;33m     __ret = gca().imshow(\n\u001b[0m\u001b[0;32m   2725\u001b[0m         \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcmap\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnorm\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnorm\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maspect\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maspect\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2726\u001b[0m         \u001b[0minterpolation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minterpolation\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvmin\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\STUDY\\Anaconda\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36minner\u001b[1;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1436\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1437\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1438\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1439\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1440\u001b[0m         \u001b[0mbound\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\STUDY\\Anaconda\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mimshow\u001b[1;34m(self, X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, filternorm, filterrad, resample, url, **kwargs)\u001b[0m\n\u001b[0;32m   5521\u001b[0m                               resample=resample, **kwargs)\n\u001b[0;32m   5522\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5523\u001b[1;33m         \u001b[0mim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5524\u001b[0m         \u001b[0mim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_alpha\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5525\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_clip_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\STUDY\\Anaconda\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36mset_data\u001b[1;34m(self, A)\u001b[0m\n\u001b[0;32m    698\u001b[0m         if (self._A.dtype != np.uint8 and\n\u001b[0;32m    699\u001b[0m                 not np.can_cast(self._A.dtype, float, \"same_kind\")):\n\u001b[1;32m--> 700\u001b[1;33m             raise TypeError(\"Image data of dtype {} cannot be converted to \"\n\u001b[0m\u001b[0;32m    701\u001b[0m                             \"float\".format(self._A.dtype))\n\u001b[0;32m    702\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: Image data of dtype <U64 cannot be converted to float"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()                               #66 height x 200 width with 3 channel\n",
    "\n",
    "model.add(Convolution2D(24,(5,5),strides = (2,2), input_shape = (66,200,3),activation = 'elu'))  #how many pixel to shift kernal is strides\n",
    "model.add(Convolution2D(36,(5,5),strides = (2,2), activation = 'elu'))\n",
    "model.add(Convolution2D(48,(5,5),strides = (2,2), activation = 'elu'))\n",
    "\n",
    "model.add(Convolution2D(64,(3,3), activation = 'elu'))\n",
    "model.add(Convolution2D(64,(3,3), activation = 'elu'))\n",
    "\n",
    "model.add(Flatten())  #to convert 2d to 1d\n",
    "\n",
    "model.add(Dense(100,activation = 'elu'))           #100 neurons\n",
    "model.add(Dense(50,activation = 'elu'))\n",
    "model.add(Dense(10,activation = 'elu'))\n",
    "model.add(Dense(1))\n",
    "\n",
    "model.compile(optimizer = 'adam', loss = 'mse', metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 31, 98, 24)        1824      \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 14, 47, 36)        21636     \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 5, 22, 48)         43248     \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 3, 20, 64)         27712     \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 1, 18, 64)         36928     \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 1152)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 100)               115300    \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 50)                5050      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                510       \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 11        \n",
      "=================================================================\n",
      "Total params: 252,219\n",
      "Trainable params: 252,219\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "300/300 [==============================] - 261s 867ms/step - loss: 1.4041 - accuracy: 0.2762 - val_loss: 0.1444 - val_accuracy: 0.2512\n",
      "Epoch 2/10\n",
      "300/300 [==============================] - 268s 894ms/step - loss: 0.1578 - accuracy: 0.2796 - val_loss: 0.1446 - val_accuracy: 0.2524\n",
      "Epoch 3/10\n",
      "300/300 [==============================] - 274s 914ms/step - loss: 0.1549 - accuracy: 0.2858 - val_loss: 0.1439 - val_accuracy: 0.2475\n",
      "Epoch 4/10\n",
      "300/300 [==============================] - 257s 857ms/step - loss: 0.1588 - accuracy: 0.2846 - val_loss: 0.1437 - val_accuracy: 0.2492\n",
      "Epoch 5/10\n",
      "300/300 [==============================] - 237s 791ms/step - loss: 0.1563 - accuracy: 0.2836 - val_loss: 0.1457 - val_accuracy: 0.2531\n",
      "Epoch 6/10\n",
      "300/300 [==============================] - 236s 788ms/step - loss: 0.1543 - accuracy: 0.2841 - val_loss: 0.1449 - val_accuracy: 0.2522\n",
      "Epoch 7/10\n",
      "300/300 [==============================] - 235s 783ms/step - loss: 0.1563 - accuracy: 0.2849 - val_loss: 0.1441 - val_accuracy: 0.2500\n",
      "Epoch 8/10\n",
      "300/300 [==============================] - 236s 789ms/step - loss: 0.1544 - accuracy: 0.2809 - val_loss: 0.1461 - val_accuracy: 0.2514\n",
      "Epoch 9/10\n",
      "300/300 [==============================] - 237s 791ms/step - loss: 0.1567 - accuracy: 0.2802 - val_loss: 0.1445 - val_accuracy: 0.2542\n",
      "Epoch 10/10\n",
      "300/300 [==============================] - 241s 805ms/step - loss: 0.1583 - accuracy: 0.2779 - val_loss: 0.1443 - val_accuracy: 0.2512\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(batch_generator(x_train, y_train, 181), steps_per_epoch = 300, epochs = 10,\n",
    "           validation_data = batch_generator(x_test,y_test,181),validation_steps = 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x_train)/181"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5792"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "181 * 8 * 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = cv.imread(x_train[0])\n",
    "img = img[60:135,:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "history.history['loss']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['val_loss'])\n",
    "\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('12_Epoch_Model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = load_model('12_Epoch_Model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}