steering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from random import randint\n",
    "import os.path\n",
    "df=pd.read_csv(\"C:\\\\Carla\\\\CARLA_0.9.14\\\\WindowsNoEditor\\\\PythonAPI\\\\examples\\\\behaviour cloning\\\\data.csv\")\n",
    "df.shape\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.models import Sequential\n",
    "from keras.optimizers import Adam\n",
    "from keras.layers import Conv2D, MaxPool2D, Dropout, Flatten, Dense\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.layers import Activation\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "import matplotlib.image as mpimg\n",
    "from imgaug import augmenters as iaa\n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def img_random_brightness(image):\n",
    "  brightness = iaa.Multiply((0.2, 1.2))\n",
    "  image = brightness.augment_image(image)\n",
    "  return image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def flip_image(image, steering_angle):\n",
    "  image = cv2.flip(image, 1)\n",
    "  steering_angle = -steering_angle\n",
    "  return image, steering_angle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def random_augment(image, steering_angle):\n",
    "  if np.random.rand() < 0.5:\n",
    "    if np.random.rand() > 0.5:\n",
    "      image = img_random_brightness(image)\n",
    "\n",
    "  if np.random.rand() > 0.5:\n",
    "    if np.random.rand() < 0.5:\n",
    "      image, steering_angle = flip_image(image, steering_angle)\n",
    "\n",
    "  return image, steering_angle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def im_the_generator(dataset1,dataset2,batch_size,istraining):\n",
    "\n",
    "    while True:\n",
    "        batch_img = []\n",
    "        batch_steering = []\n",
    "\n",
    "        for i in range(batch_size):\n",
    "            random_index = randint(0, len(dataset1)-1)\n",
    "\n",
    "            if istraining:\n",
    "                image, steering = dataset1[random_index], dataset2[random_index]\n",
    "\n",
    "            else:\n",
    "                image = (dataset1[random_index])\n",
    "                steering = dataset2[random_index]\n",
    "\n",
    "        batch_img.append(image)\n",
    "        batch_steering.append(steering)\n",
    "\n",
    "        yield(np.asarray(batch_img), np.asarray(batch_steering))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([14975.,  1134.,  1314.,  2055.,  3152.,  2364.,  1307.,  1058.,\n",
       "          918.,  7541.]),\n",
       " array([0.        , 0.085     , 0.17      , 0.25500001, 0.34000001,\n",
       "        0.42500001, 0.51000001, 0.59500002, 0.68000002, 0.76500002,\n",
       "        0.85000002]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9XtXV1amsrMyu2bVrl1pbW5WQkGDXFBcXq7m52a4pLCzU8OHD1b9/f7vmy+/TVtP2PgAAoGfrcMg5deqUysvLVV5eLunvNxuXl5fr+PHjCgsL05w5c/Too4/qtdde0+HDh3X77bfL4/HYT2CNHDlSN910k2bPnq19+/Zp9+7dyszM1PTp0+XxeCRJM2bMUGRkpNLS0lRRUaFNmzZpxYoVysrKsvu49957VVBQoGXLluno0aNasmSJDhw4oMzMzG//UwEAACEvoqMHHDhwQNdff739ui14zJo1S2vXrtUDDzyghoYGpaenq66uTtdee60KCgoUHR1tH7N+/XplZmbqxhtvVHh4uKZNm6aVK1fa406nUzt37lRGRobGjx+vQYMGKTs7O+C7dK6++mpt2LBBixYt0h/+8Adddtll2rp1q6644ooL+kEAAACzfKvvyQl1fE9OIL4nBwAQCrrle3IAAAC+Kwg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADBS0EPOmTNn9OCDDyo+Pl59+vTRpZdeqkceeUSWZdk1lmUpOztbgwcPVp8+fZSYmKgPPvggYJ7a2lqlpqbK4XAoJiZGaWlpOnXqVEDNoUOHNGnSJEVHRysuLk65ubnBPh0AABCigh5ynnjiCa1evVrPPfecjhw5oieeeEK5ubl69tln7Zrc3FytXLlSeXl5Ki0tVd++fZWUlKTTp0/bNampqaqoqFBhYaHy8/NVXFys9PR0e9zv92vKlCkaOnSoysrK9OSTT2rJkiVas2ZNsE8JAACEoDDry5dYguCnP/2pXC6XXnzxRXvftGnT1KdPH/35z3+WZVnyeDy677779Pvf/16SVF9fL5fLpbVr12r69Ok6cuSIRo0apf3792vChAmSpIKCAk2dOlWffPKJPB6PVq9erYULF8rn8ykyMlKSNH/+fG3dulVHjx49r179fr+cTqfq6+vlcDiC+WPQsPnbgjpfVzi2NLm7WwAA4Bud7+d30K/kXH311SoqKtL7778vSfqv//ovvf322/rJT34iSaqqqpLP51NiYqJ9jNPpVEJCgkpKSiRJJSUliomJsQOOJCUmJio8PFylpaV2zeTJk+2AI0lJSUmqrKzUyZMn2+2tsbFRfr8/YAMAAGaKCPaE8+fPl9/v14gRI9SrVy+dOXNGjz32mFJTUyVJPp9PkuRyuQKOc7lc9pjP51NsbGxgoxERGjBgQEBNfHz8WXO0jfXv3/+s3nJycvTQQw8F4SwBAMB3XdCv5Lzyyitav369NmzYoIMHD2rdunV66qmntG7dumC/VYctWLBA9fX19nbixInubgkAAHSSoF/Juf/++zV//nxNnz5dkjR69Gh9/PHHysnJ0axZs+R2uyVJ1dXVGjx4sH1cdXW1xo4dK0lyu92qqakJmLelpUW1tbX28W63W9XV1QE1ba/bar4qKipKUVFR3/4kAQDAd17Qr+R88cUXCg8PnLZXr15qbW2VJMXHx8vtdquoqMge9/v9Ki0tldfrlSR5vV7V1dWprKzMrtm1a5daW1uVkJBg1xQXF6u5udmuKSws1PDhw9v9VRUAAOhZgh5ybr75Zj322GPatm2bjh07pi1btujpp5/WP/7jP0qSwsLCNGfOHD366KN67bXXdPjwYd1+++3yeDxKSUmRJI0cOVI33XSTZs+erX379mn37t3KzMzU9OnT5fF4JEkzZsxQZGSk0tLSVFFRoU2bNmnFihXKysoK9ikBAIAQFPRfVz377LN68MEHdffdd6umpkYej0e//e1vlZ2dbdc88MADamhoUHp6uurq6nTttdeqoKBA0dHRds369euVmZmpG2+8UeHh4Zo2bZpWrlxpjzudTu3cuVMZGRkaP368Bg0apOzs7IDv0gEAAD1X0L8nJ5TwPTmB+J4cAEAo6LbvyQEAAPguIOQAAAAjEXIAAICRCDkAAMBIQX+6CgAABB8PtHQcV3IAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEbqlJDz17/+Vb/61a80cOBA9enTR6NHj9aBAwfsccuylJ2drcGDB6tPnz5KTEzUBx98EDBHbW2tUlNT5XA4FBMTo7S0NJ06dSqg5tChQ5o0aZKio6MVFxen3NzczjgdAAAQgoIeck6ePKlrrrlGvXv31r//+7/rvffe07Jly9S/f3+7Jjc3VytXrlReXp5KS0vVt29fJSUl6fTp03ZNamqqKioqVFhYqPz8fBUXFys9Pd0e9/v9mjJlioYOHaqysjI9+eSTWrJkidasWRPsUwIAACEoItgTPvHEE4qLi9PLL79s74uPj7f/s2VZWr58uRYtWqSf//znkqR/+Zd/kcvl0tatWzV9+nQdOXJEBQUF2r9/vyZMmCBJevbZZzV16lQ99dRT8ng8Wr9+vZqamvTSSy8pMjJSl19+ucrLy/X0008HhCEAANAzBf1KzmuvvaYJEybon/7pnxQbG6tx48bp+eeft8erqqrk8/mUmJho73M6nUpISFBJSYkkqaSkRDExMXbAkaTExESFh4ertLTUrpk8ebIiIyPtmqSkJFVWVurkyZPt9tbY2Ci/3x+wAQAAMwU95Hz00UdavXq1LrvsMu3YsUN33XWXfve732ndunWSJJ/PJ0lyuVwBx7lcLnvM5/MpNjY2YDwiIkIDBgwIqGlvji+/x1fl5OTI6XTaW1xc3Lc8WwAA8F0V9JDT2tqqK6+8Uo8//rjGjRun9PR0zZ49W3l5ecF+qw5bsGCB6uvr7e3EiRPd3RIAAOgkQQ85gwcP1qhRowL2jRw5UsePH5ckud1uSVJ1dXVATXV1tT3mdrtVU1MTMN7S0qLa2tqAmvbm+PJ7fFVUVJQcDkfABgAAzBT0kHPNNdeosrIyYN/777+voUOHSvr7Tchut1tFRUX2uN/vV2lpqbxeryTJ6/Wqrq5OZWVlds2uXbvU2tqqhIQEu6a4uFjNzc12TWFhoYYPHx7wJBcAAOiZgh5y5s6dq7179+rxxx/Xhx9+qA0bNmjNmjXKyMiQJIWFhWnOnDl69NFH9dprr+nw4cO6/fbb5fF4lJKSIunvV35uuukmzZ49W/v27dPu3buVmZmp6dOny+PxSJJmzJihyMhIpaWlqaKiQps2bdKKFSuUlZUV7FMCAAAhKOiPkP/oRz/Sli1btGDBAj388MOKj4/X8uXLlZqaatc88MADamhoUHp6uurq6nTttdeqoKBA0dHRds369euVmZmpG2+8UeHh4Zo2bZpWrlxpjzudTu3cuVMZGRkaP368Bg0apOzsbB4fBwAAkqQwy7Ks7m6iu/j9fjmdTtXX1wf9/pxh87cFdb6ucGxpcne3AAA4Bz5X/s/5fn7zt6sAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMFKnh5ylS5cqLCxMc+bMsfedPn1aGRkZGjhwoC6++GJNmzZN1dXVAccdP35cycnJuuiiixQbG6v7779fLS0tATVvvvmmrrzySkVFRen73/++1q5d29mnAwAAQkSnhpz9+/frT3/6k374wx8G7J87d65ef/11bd68WW+99ZY+/fRT/eIXv7DHz5w5o+TkZDU1NWnPnj1at26d1q5dq+zsbLumqqpKycnJuv7661VeXq45c+bojjvu0I4dOzrzlAAAQIjotJBz6tQppaam6vnnn1f//v3t/fX19XrxxRf19NNP64YbbtD48eP18ssva8+ePdq7d68kaefOnXrvvff05z//WWPHjtVPfvITPfLII1q1apWampokSXl5eYqPj9eyZcs0cuRIZWZm6pe//KWeeeaZzjolAAAQQjot5GRkZCg5OVmJiYkB+8vKytTc3Bywf8SIERoyZIhKSkokSSUlJRo9erRcLpddk5SUJL/fr4qKCrvmq3MnJSXZc7SnsbFRfr8/YAMAAGaK6IxJN27cqIMHD2r//v1njfl8PkVGRiomJiZgv8vlks/ns2u+HHDaxtvGvq7G7/frb3/7m/r06XPWe+fk5Oihhx664PMCAAChI+hXck6cOKF7771X69evV3R0dLCn/1YWLFig+vp6eztx4kR3twQAADpJ0ENOWVmZampqdOWVVyoiIkIRERF66623tHLlSkVERMjlcqmpqUl1dXUBx1VXV8vtdkuS3G73WU9btb3+phqHw9HuVRxJioqKksPhCNgAAICZgh5ybrzxRh0+fFjl5eX2NmHCBKWmptr/uXfv3ioqKrKPqays1PHjx+X1eiVJXq9Xhw8fVk1NjV1TWFgoh8OhUaNG2TVfnqOtpm0OAADQswX9npx+/frpiiuuCNjXt29fDRw40N6flpamrKwsDRgwQA6HQ/fcc4+8Xq8mTpwoSZoyZYpGjRqlmTNnKjc3Vz6fT4sWLVJGRoaioqIkSXfeeaeee+45PfDAA/rNb36jXbt26ZVXXtG2bduCfUoAACAEdcqNx9/kmWeeUXh4uKZNm6bGxkYlJSXpj3/8oz3eq1cv5efn66677pLX61Xfvn01a9YsPfzww3ZNfHy8tm3bprlz52rFihW65JJL9MILLygpKak7TgkAAHzHhFmWZXV3E93F7/fL6XSqvr4+6PfnDJsfeleUji1N7u4WAADnwOfK/znfz2/+dhUAADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAI0V0dwMAQgN/ARlAqOFKDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABgporsbAIDOMmz+tu5uocOOLU3u7hYAY3AlBwAAGImQAwAAjETIAQAARgp6yMnJydGPfvQj9evXT7GxsUpJSVFlZWVAzenTp5WRkaGBAwfq4osv1rRp01RdXR1Qc/z4cSUnJ+uiiy5SbGys7r//frW0tATUvPnmm7ryyisVFRWl73//+1q7dm2wTwcAAISooIect956SxkZGdq7d68KCwvV3NysKVOmqKGhwa6ZO3euXn/9dW3evFlvvfWWPv30U/3iF7+wx8+cOaPk5GQ1NTVpz549WrdundauXavs7Gy7pqqqSsnJybr++utVXl6uOXPm6I477tCOHTuCfUoAACAEBf3pqoKCgoDXa9euVWxsrMrKyjR58mTV19frxRdf1IYNG3TDDTdIkl5++WWNHDlSe/fu1cSJE7Vz50699957euONN+RyuTR27Fg98sgjmjdvnpYsWaLIyEjl5eUpPj5ey5YtkySNHDlSb7/9tp555hklJSUF+7QAAECI6fRHyOvr6yVJAwYMkCSVlZWpublZiYmJds2IESM0ZMgQlZSUaOLEiSopKdHo0aPlcrnsmqSkJN11112qqKjQuHHjVFJSEjBHW82cOXPO2UtjY6MaGxvt136/PxinCHRIKD7WDAChqFNvPG5tbdWcOXN0zTXX6IorrpAk+Xw+RUZGKiYmJqDW5XLJ5/PZNV8OOG3jbWNfV+P3+/W3v/2t3X5ycnLkdDrtLS4u7lufIwAA+G7q1JCTkZGhd999Vxs3buzMtzlvCxYsUH19vb2dOHGiu1sCAACdpNN+XZWZman8/HwVFxfrkksusfe73W41NTWprq4u4GpOdXW13G63XbNv376A+dqevvpyzVefyKqurpbD4VCfPn3a7SkqKkpRUVHf+twAAMB3X9Cv5FiWpczMTG3ZskW7du1SfHx8wPj48ePVu3dvFRUV2fsqKyt1/Phxeb1eSZLX69Xhw4dVU1Nj1xQWFsrhcGjUqFF2zZfnaKtpmwMAAPRsQb+Sk5GRoQ0bNujf/u3f1K9fP/seGqfTqT59+sjpdCotLU1ZWVkaMGCAHA6H7rnnHnm9Xk2cOFGSNGXKFI0aNUozZ85Ubm6ufD6fFi1apIyMDPtKzJ133qnnnntODzzwgH7zm99o165deuWVV7RtGzd1AgCATriSs3r1atXX1+u6667T4MGD7W3Tpk12zTPPPKOf/vSnmjZtmiZPniy3261XX33VHu/Vq5fy8/PVq1cveb1e/epXv9Ltt9+uhx9+2K6Jj4/Xtm3bVFhYqDFjxmjZsmV64YUXeHwcAABI6oQrOZZlfWNNdHS0Vq1apVWrVp2zZujQodq+ffvXznPdddfpnXfe6XCPAADAfPztKgAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgpE77K+RAVxg2n79VBgBoHyEHAL5DQjG4H1ua3N0tAO3i11UAAMBIhBwAAGAkQg4AADAS9+TAFor3AgAAcC6EHADAtxKK/weJm6V7Bn5dBQAAjETIAQAARiLkAAAAI3FPDgCgxwnF+4jQcVzJAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIwU8iFn1apVGjZsmKKjo5WQkKB9+/Z1d0sAAOA7IKRDzqZNm5SVlaXFixfr4MGDGjNmjJKSklRTU9PdrQEAgG4W0iHn6aef1uzZs/XrX/9ao0aNUl5eni666CK99NJL3d0aAADoZhHd3cCFampqUllZmRYsWGDvCw8PV2JiokpKSto9prGxUY2Njfbr+vp6SZLf7w96f62NXwR9TgAAQklnfL5+eV7Lsr62LmRDzmeffaYzZ87I5XIF7He5XDp69Gi7x+Tk5Oihhx46a39cXFyn9AgAQE/mXN6583/++edyOp3nHA/ZkHMhFixYoKysLPt1a2uramtrNXDgQIWFhQXtffx+v+Li4nTixAk5HI6gzYvgYp1CA+sUGlin0GHCWlmWpc8//1wej+dr60I25AwaNEi9evVSdXV1wP7q6mq53e52j4mKilJUVFTAvpiYmM5qUQ6HI2T/C9STsE6hgXUKDaxT6Aj1tfq6KzhtQvbG48jISI0fP15FRUX2vtbWVhUVFcnr9XZjZwAA4LsgZK/kSFJWVpZmzZqlCRMm6KqrrtLy5cvV0NCgX//6193dGgAA6GYhHXJuvfVW/e///q+ys7Pl8/k0duxYFRQUnHUzcleLiorS4sWLz/rVGL5bWKfQwDqFBtYpdPSktQqzvun5KwAAgBAUsvfkAAAAfB1CDgAAMBIhBwAAGImQAwAAjETIuUCrVq3SsGHDFB0drYSEBO3bt+9r6zdv3qwRI0YoOjpao0eP1vbt27uo056tI+v0/PPPa9KkSerfv7/69++vxMTEb1xXBEdH/z212bhxo8LCwpSSktK5DUJSx9eprq5OGRkZGjx4sKKiovSDH/yA/+3rAh1dp+XLl2v48OHq06eP4uLiNHfuXJ0+fbqLuu1kFjps48aNVmRkpPXSSy9ZFRUV1uzZs62YmBirurq63frdu3dbvXr1snJzc6333nvPWrRokdW7d2/r8OHDXdx5z9LRdZoxY4a1atUq65133rGOHDli/fM//7PldDqtTz75pIs771k6uk5tqqqqrH/4h3+wJk2aZP385z/vmmZ7sI6uU2NjozVhwgRr6tSp1ttvv21VVVVZb775plVeXt7FnfcsHV2n9evXW1FRUdb69eutqqoqa8eOHdbgwYOtuXPndnHnnYOQcwGuuuoqKyMjw3595swZy+PxWDk5Oe3W33LLLVZycnLAvoSEBOu3v/1tp/bZ03V0nb6qpaXF6tevn7Vu3brOahHWha1TS0uLdfXVV1svvPCCNWvWLEJOF+joOq1evdr63ve+ZzU1NXVVi7A6vk4ZGRnWDTfcELAvKyvLuuaaazq1z67Cr6s6qKmpSWVlZUpMTLT3hYeHKzExUSUlJe0eU1JSElAvSUlJSeesx7d3Iev0VV988YWam5s1YMCAzmqzx7vQdXr44YcVGxurtLS0rmizx7uQdXrttdfk9XqVkZEhl8ulK664Qo8//rjOnDnTVW33OBeyTldffbXKysrsX2l99NFH2r59u6ZOndolPXe2kP7G4+7w2Wef6cyZM2d9q7LL5dLRo0fbPcbn87Vb7/P5Oq3Pnu5C1umr5s2bJ4/Hc1ZARfBcyDq9/fbbevHFF1VeXt4FHUK6sHX66KOPtGvXLqWmpmr79u368MMPdffdd6u5uVmLFy/uirZ7nAtZpxkzZuizzz7TtddeK8uy1NLSojvvvFN/+MMfuqLlTseVHKAdS5cu1caNG7VlyxZFR0d3dzv4/z7//HPNnDlTzz//vAYNGtTd7eBrtLa2KjY2VmvWrNH48eN16623auHChcrLy+vu1vAlb775ph5//HH98Y9/1MGDB/Xqq69q27ZteuSRR7q7taDgSk4HDRo0SL169VJ1dXXA/urqarnd7naPcbvdHarHt3ch69Tmqaee0tKlS/XGG2/ohz/8YWe22eN1dJ3++7//W8eOHdPNN99s72ttbZUkRUREqLKyUpdeemnnNt0DXci/p8GDB6t3797q1auXvW/kyJHy+XxqampSZGRkp/bcE13IOj344IOaOXOm7rjjDknS6NGj1dDQoPT0dC1cuFDh4aF9LSS0u+8GkZGRGj9+vIqKiux9ra2tKioqktfrbfcYr9cbUC9JhYWF56zHt3ch6yRJubm5euSRR1RQUKAJEyZ0Ras9WkfXacSIETp8+LDKy8vt7Wc/+5muv/56lZeXKy4urivb7zEu5N/TNddcow8//NAOoZL0/vvva/DgwQScTnIh6/TFF1+cFWTagqllwp+27O47n0PRxo0braioKGvt2rXWe++9Z6Wnp1sxMTGWz+ezLMuyZs6cac2fP9+u3717txUREWE99dRT1pEjR6zFixfzCHkX6Og6LV261IqMjLT+8pe/WP/zP/9jb59//nl3nUKP0NF1+iqeruoaHV2n48ePW/369bMyMzOtyspKKz8/34qNjbUeffTR7jqFHqGj67R48WKrX79+1r/+679aH330kbVz507r0ksvtW655ZbuOoWgIuRcoGeffdYaMmSIFRkZaV111VXW3r177bEf//jH1qxZswLqX3nlFesHP/iBFRkZaV1++eXWtm3burjjnqkj6zR06FBL0lnb4sWLu77xHqaj/56+jJDTdTq6Tnv27LESEhKsqKgo63vf+5712GOPWS0tLV3cdc/TkXVqbm62lixZYl166aVWdHS0FRcXZ919993WyZMnu77xThBmWSZcjwIAAAjEPTkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGOn/ASbC8vLc85asAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df[\"Throttle\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([4.000e+00, 1.000e+00, 1.000e+00, 1.000e+00, 0.000e+00, 1.000e+00,\n",
       "        1.000e+00, 1.000e+00, 0.000e+00, 2.000e+00, 1.000e+00, 0.000e+00,\n",
       "        0.000e+00, 2.000e+00, 6.000e+00, 6.000e+00, 1.600e+01, 4.300e+01,\n",
       "        6.700e+01, 1.640e+02, 7.870e+02, 2.207e+03, 2.856e+03, 2.200e+04,\n",
       "        6.062e+03, 6.330e+02, 3.940e+02, 1.620e+02, 8.100e+01, 8.400e+01,\n",
       "        8.400e+01, 6.300e+01, 4.400e+01, 2.300e+01, 1.200e+01, 3.000e+00,\n",
       "        2.000e+00, 2.000e+00, 1.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n",
       "        0.000e+00, 0.000e+00, 1.000e+00]),\n",
       " array([-8.00000012e-01, -7.66649773e-01, -7.33299535e-01, -6.99949296e-01,\n",
       "        -6.66599058e-01, -6.33248819e-01, -5.99898581e-01, -5.66548342e-01,\n",
       "        -5.33198104e-01, -4.99847865e-01, -4.66497627e-01, -4.33147388e-01,\n",
       "        -3.99797150e-01, -3.66446911e-01, -3.33096673e-01, -2.99746434e-01,\n",
       "        -2.66396195e-01, -2.33045957e-01, -1.99695718e-01, -1.66345480e-01,\n",
       "        -1.32995241e-01, -9.96450028e-02, -6.62947643e-02, -3.29445257e-02,\n",
       "         4.05712800e-04,  3.37559513e-02,  6.71061899e-02,  1.00456428e-01,\n",
       "         1.33806667e-01,  1.67156905e-01,  2.00507144e-01,  2.33857383e-01,\n",
       "         2.67207621e-01,  3.00557860e-01,  3.33908098e-01,  3.67258337e-01,\n",
       "         4.00608575e-01,  4.33958814e-01,  4.67309052e-01,  5.00659291e-01,\n",
       "         5.34009529e-01,  5.67359768e-01,  6.00710006e-01,  6.34060245e-01,\n",
       "         6.67410483e-01,  7.00760722e-01]),\n",
       " <BarContainer object of 45 artists>)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df[\"Steering\"],bins=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2adbf457c48>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_bins = 13\n",
    "avg_samples_per_bin = len(df[\"Steering\"])/num_bins\n",
    "hist, bins = np.histogram(df[\"Steering\"], num_bins)\n",
    "samples_per_bin=2693\n",
    "width = 0.7 * (bins[1] - bins[0])\n",
    "center = (bins[:-1] + bins[1:]) / 2\n",
    "plt.bar(center, hist, align='center', width=width)\n",
    "plt.plot((np.min(df[\"Steering\"]), np.max(df[\"Steering\"])), (avg_samples_per_bin, avg_samples_per_bin), 'k-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "removed: 29758\n",
      "remainind df:  6060\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2adbf622848>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "remove_list = []\n",
    "for j in range(num_bins):\n",
    "  list_ = []\n",
    "  for i in range(len(df['Steering'])):\n",
    "    if df['Steering'][i] >= bins[j] and df['Steering'][i] <= bins[j+1]:\n",
    "      list_.append(i)\n",
    "  list_ = shuffle(list_)\n",
    "  list_ = list_[samples_per_bin:]\n",
    "  remove_list.extend(list_)\n",
    "\n",
    "print('removed:', len(remove_list))\n",
    "df.drop(df.index[remove_list], inplace = True)\n",
    "print('remainind df: ', len(df))\n",
    "hist, _ = np.histogram(df['Steering'], num_bins)\n",
    "plt.bar(center, hist, width = 0.05)\n",
    "plt.plot((np.min(df['Steering']), np.max(df['Steering'])), (samples_per_bin, samples_per_bin))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cam_path    pokemon/034373.png\n",
      "Throttle              0.139771\n",
      "Steering                   0.0\n",
      "Braking                    0.0\n",
      "Name: 20, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df.iloc[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "imagepath=np.asarray(df[\"cam_path\"])\n",
    "angle=np.asarray(df[\"Steering\"])\n",
    "throttle=df[\"Throttle\"]\n",
    "brake=df[\"Braking\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pokemon/034173.png\n",
      "0\n",
      "pokemon/034373.png\n",
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      "2007\n",
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      "2008\n",
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      "2009\n",
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      "2010\n",
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      "2011\n",
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      "2012\n",
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      "2013\n",
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      "2014\n",
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      "2015\n",
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      "2016\n",
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      "2017\n",
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     ]
    }
   ],
   "source": [
    "imagedata=[]\n",
    "angle1=[]\n",
    "for i in range(len(imagepath)):\n",
    "    if os.path.exists(imagepath[i]):\n",
    "        try:\n",
    "            image=cv2.imread(imagepath[i])\n",
    "        except cv2.error:\n",
    "            throttle.pop(i)\n",
    "            brake.pop(i)\n",
    "            continue\n",
    "        except AssertionError:\n",
    "            throttle.pop(i)\n",
    "            brake.pop(i)\n",
    "            continue\n",
    "        else:\n",
    "            print(imagepath[i])\n",
    "            print(i)\n",
    "            image = cv2.GaussianBlur(image, (3,3), 0)\n",
    "            image= cv2.cvtColor(image, cv2.COLOR_BGR2YUV)\n",
    "            image,angle[i]=random_augment(image,angle[i])\n",
    "            image_fromarray=Image.fromarray(image,\"RGB\")\n",
    "            resize_image=image_fromarray.resize((400,225))\n",
    "            imagedata.append(np.array(resize_image))\n",
    "            angle1.append(angle[i])\n",
    "    else :\n",
    "        throttle.pop(i)\n",
    "        brake.pop(i)\n",
    "        continue\n",
    "imagedata=np.asarray(imagedata)\n",
    "angle1=np.asarray(angle)\n",
    "throttle=np.asarray(throttle)\n",
    "brake=np.asarray(brake)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((6060, 225, 400, 3), (6060,), (6060,), (6060,))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imagedata.shape,angle.shape,throttle.shape,brake.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_val,y_train,y_val=train_test_split(imagedata,angle,test_size=.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Validation Set')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize = (12, 4))\n",
    "axs[0].hist(y_train, bins = num_bins, width = 0.05, color = 'blue')\n",
    "axs[0].set_title('Training Set')\n",
    "axs[1].hist(y_val, bins = num_bins, width = 0.05, color = 'red')\n",
    "axs[1].set_title('Validation Set')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\L\\anaconda3\\envs\\carla-sim\\lib\\site-packages\\keras\\optimizers\\optimizer_v2\\adam.py:117: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super().__init__(name, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "def nvidia_model():\n",
    "    model = Sequential()\n",
    "\n",
    "    model.add(Conv2D(24,(5,5), strides=(2, 2), input_shape=(225, 400, 3), activation='elu'))\n",
    "    model.add(Conv2D(36, (5,5), strides=(2, 2), activation='elu'))\n",
    "\n",
    "    model.add(Conv2D(48, (5,5), strides=(2, 2), activation='elu'))\n",
    "    model.add(Conv2D(64, (3,3), activation='elu'))\n",
    "\n",
    "    model.add(MaxPool2D())\n",
    "\n",
    "\n",
    "    model.add(Flatten())\n",
    "\n",
    "\n",
    "    model.add(Dense(1))\n",
    "\n",
    "    optimizer = Adam(lr=1e-3)\n",
    "    model.compile(loss='mse', optimizer=optimizer)\n",
    "    return model\n",
    "model = nvidia_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/16\n",
      "300/300 - 10s - loss: 65.9501 - val_loss: 0.1821 - 10s/epoch - 33ms/step\n",
      "Epoch 2/16\n",
      "300/300 - 9s - loss: 0.3377 - val_loss: 0.0455 - 9s/epoch - 31ms/step\n",
      "Epoch 3/16\n",
      "300/300 - 9s - loss: 0.1578 - val_loss: 0.1435 - 9s/epoch - 29ms/step\n",
      "Epoch 4/16\n",
      "300/300 - 9s - loss: 0.1392 - val_loss: 0.0864 - 9s/epoch - 29ms/step\n",
      "Epoch 5/16\n",
      "300/300 - 9s - loss: 0.1041 - val_loss: 0.0368 - 9s/epoch - 29ms/step\n",
      "Epoch 6/16\n",
      "300/300 - 9s - loss: 0.1213 - val_loss: 0.0676 - 9s/epoch - 29ms/step\n",
      "Epoch 7/16\n",
      "300/300 - 9s - loss: 0.0823 - val_loss: 0.0596 - 9s/epoch - 30ms/step\n",
      "Epoch 8/16\n",
      "300/300 - 9s - loss: 0.0752 - val_loss: 0.0296 - 9s/epoch - 29ms/step\n",
      "Epoch 9/16\n",
      "300/300 - 9s - loss: 0.0711 - val_loss: 0.0719 - 9s/epoch - 30ms/step\n",
      "Epoch 10/16\n",
      "300/300 - 9s - loss: 0.1060 - val_loss: 0.0202 - 9s/epoch - 29ms/step\n",
      "Epoch 11/16\n",
      "300/300 - 9s - loss: 0.0613 - val_loss: 0.3516 - 9s/epoch - 30ms/step\n",
      "Epoch 12/16\n",
      "300/300 - 9s - loss: 0.1046 - val_loss: 0.0615 - 9s/epoch - 29ms/step\n",
      "Epoch 13/16\n",
      "300/300 - 9s - loss: 0.0536 - val_loss: 0.0223 - 9s/epoch - 29ms/step\n",
      "Epoch 14/16\n",
      "300/300 - 9s - loss: 0.0585 - val_loss: 0.0609 - 9s/epoch - 29ms/step\n",
      "Epoch 15/16\n",
      "300/300 - 9s - loss: 0.0696 - val_loss: 0.0401 - 9s/epoch - 29ms/step\n",
      "Epoch 16/16\n",
      "300/300 - 9s - loss: 0.0464 - val_loss: 0.0518 - 9s/epoch - 29ms/step\n"
     ]
    }
   ],
   "source": [
    "mode=model.fit(im_the_generator(X_train,y_train,100,1),validation_data=im_the_generator(X_val,y_val,100,0),epochs=16,steps_per_epoch=300,validation_steps=100,verbose=2,shuffle=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 111, 198, 24)      1824      \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 54, 97, 36)        21636     \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 25, 47, 48)        43248     \n",
      "                                                                 \n",
      " conv2d_3 (Conv2D)           (None, 23, 45, 64)        27712     \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 11, 22, 64)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 15488)             0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 1)                 15489     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 109,909\n",
      "Trainable params: 109,909\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save_weights('./modela.h5')\n",
    "\n",
    "model.save('model_a.h5')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "carla-sim",
   "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": "3.7.16"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}