Test Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DM\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "C:\\Users\\DM\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "C:\\Users\\DM\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "C:\\Users\\DM\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "C:\\Users\\DM\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "C:\\Users\\DM\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\DM\\Autopilot-TensorFlow-master\\model.py:15: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\DM\\Autopilot-TensorFlow-master\\model.py:5: The name tf.truncated_normal is deprecated. Please use tf.random.truncated_normal instead.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DM\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "C:\\Users\\DM\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "C:\\Users\\DM\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "C:\\Users\\DM\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "C:\\Users\\DM\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "C:\\Users\\DM\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\DM\\Autopilot-TensorFlow-master\\model.py:58: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
      "WARNING:tensorflow:From C:\\Users\\DM\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\tensorflow\\python\\training\\saver.py:1276: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use standard file APIs to check for files with this prefix.\n",
      "INFO:tensorflow:Restoring parameters from save/model.ckpt\n",
      "Starting frameofvideo:1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DM\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\ipykernel_launcher.py:42: DeprecationWarning: `imresize` is deprecated!\n",
      "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
      "Use ``skimage.transform.resize`` instead.\n"
     ]
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "No such file: 'C:\\Users\\DM\\Pictures\\DM auto\\0 (1245).jpg'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-5a578d479a4e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     39\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     40\u001b[0m \u001b[1;32mwhile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwaitKey\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mord\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'q'\u001b[0m\u001b[1;33m)\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[1;32m---> 41\u001b[1;33m     \u001b[0mfull_image\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mimageio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"C:/Users/DM/Pictures/DM auto/\"\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'0 ('\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\").jpg\"\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     42\u001b[0m     \u001b[0mimage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscipy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmisc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimresize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfull_image\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m150\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m66\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m200\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m/\u001b[0m \u001b[1;36m255.0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     43\u001b[0m     \u001b[0mdegrees\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mimage\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeep_prob\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;36m180.0\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mscipy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpi\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\imageio\\core\\functions.py\u001b[0m in \u001b[0;36mimread\u001b[1;34m(uri, format, **kwargs)\u001b[0m\n\u001b[0;32m    263\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    264\u001b[0m     \u001b[1;31m# Get reader and read first\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 265\u001b[1;33m     \u001b[0mreader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0muri\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"i\"\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    266\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mreader\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    267\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mreader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\imageio\\core\\functions.py\u001b[0m in \u001b[0;36mget_reader\u001b[1;34m(uri, format, mode, **kwargs)\u001b[0m\n\u001b[0;32m    170\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    171\u001b[0m     \u001b[1;31m# Create request object\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 172\u001b[1;33m     \u001b[0mrequest\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mRequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0muri\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"r\"\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mmode\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    173\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    174\u001b[0m     \u001b[1;31m# Get format\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\imageio\\core\\request.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, uri, mode, **kwargs)\u001b[0m\n\u001b[0;32m    119\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    120\u001b[0m         \u001b[1;31m# Parse what was given\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 121\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_parse_uri\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0muri\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    122\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    123\u001b[0m         \u001b[1;31m# Set extension\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\deeplearning\\lib\\site-packages\\imageio\\core\\request.py\u001b[0m in \u001b[0;36m_parse_uri\u001b[1;34m(self, uri)\u001b[0m\n\u001b[0;32m    255\u001b[0m                 \u001b[1;31m# Reading: check that the file exists (but is allowed a dir)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    256\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfn\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[1;32m--> 257\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0mFileNotFoundError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"No such file: '%s'\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mfn\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    258\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    259\u001b[0m                 \u001b[1;31m# Writing: check that the directory to write to does exist\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: No such file: 'C:\\Users\\DM\\Pictures\\DM auto\\0 (1245).jpg'"
     ]
    }
   ],
   "source": [
    "#pip3 install opencv-python\n",
    "\n",
    "import tensorflow as tf\n",
    "import scipy.misc \n",
    "import model\n",
    "import cv2\n",
    "from subprocess import call\n",
    "import math\n",
    "import imageio\n",
    "\n",
    "sess = tf.InteractiveSession()\n",
    "saver = tf.train.Saver()\n",
    "saver.restore(sess, \"save/model.ckpt\")\n",
    "\n",
    "img = cv2.imread('steering_wheel_image.jpg',0)\n",
    "rows,cols = img.shape\n",
    "\n",
    "smoothed_angle = 0\n",
    "\n",
    "\n",
    "#read data.txt\n",
    "xs = []\n",
    "ys = []\n",
    "with open(\"driving_dataset/data.txt\") as f:\n",
    "    for line in f:\n",
    "        xs.append(\"driving_dataset/\" + line.split()[0])\n",
    "        #the paper by Nvidia uses the inverse of the turning radius,\n",
    "        #but steering wheel angle is proportional to the inverse of turning radius\n",
    "        #so the steering wheel angle in radians is used as the output\n",
    "        ys.append(float(line.split()[1]) * scipy.pi / 180)\n",
    "\n",
    "#get number of images\n",
    "num_images = len(xs)\n",
    "\n",
    "\n",
    "#i = math.ceil(num_images*0.2)\n",
    "i=1\n",
    "print(\"Starting frameofvideo:\" +str(i))\n",
    "\n",
    "while(cv2.waitKey(10) != ord('q')):\n",
    "    full_image = imageio.imread(\"C:/Users/DM/Pictures/DM auto/\" + str('0 (')+ str(i) + \").jpg\")\n",
    "    image = scipy.misc.imresize(full_image[-150:], [66, 200]) / 255.0\n",
    "    degrees = model.y.eval(feed_dict={model.x: [image], model.keep_prob: 1.0})[0][0] * 180.0 / scipy.pi\n",
    "    #call(\"clear\")\n",
    "    #print(\"Predicted Steering angle: \" + str(degrees))\n",
    "    #print(\"Steering angle: \" + str(degrees) + \" (pred)\\t\" + str(ys[i]*180/scipy.pi) + \" (actual)\")\n",
    "    cv2.imshow(\"frame\", cv2.cvtColor(full_image, cv2.COLOR_RGB2BGR))\n",
    "    #make smooth angle transitions by turning the steering wheel based on the difference of the current angle\n",
    "    #and the predicted angle\n",
    "    smoothed_angle += 0.2 * pow(abs((degrees - smoothed_angle)), 2.0 / 3.0) * (degrees - smoothed_angle) / abs(degrees - smoothed_angle)\n",
    "    M = cv2.getRotationMatrix2D((cols/2,rows/2),-smoothed_angle,1)\n",
    "    dst = cv2.warpAffine(img,M,(cols,rows))\n",
    "    cv2.imshow(\"steering wheel\", dst)\n",
    "    i += 1\n",
    "\n",
    "cv2.destroyAllWindows()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.4.1'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import  PIL\n",
    "scipy.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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