Road_Crack_Detection - Test model.ipynb

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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
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    {
     "name": "stderr",
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     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from tensorflow import keras\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from PIL import Image\n",
    "import imutils\n",
    "from openpyxl import Workbook\n",
    "import os, os.path\n",
    "import re\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout, Activation\n",
    "from keras.utils import to_categorical\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function for displaying an image\n",
    "def show(show_img):\n",
    "    cv2.imshow(\"Image\", show_img) \n",
    "    cv2.waitKey(0)\n",
    "\n",
    "#Function for getting the x,y,w,h values for ROI\n",
    "def get_crdnt(image):\n",
    "    r = cv2.selectROI(\"Image\", image)\n",
    "    (x,y,w,h) = r\n",
    "    # Crop image\n",
    "    imCrop = image[int(r[1]):int(r[1]+r[3]), int(r[0]):int(r[0]+r[2])]\n",
    "    # Display cropped image\n",
    "    show(imCrop)\n",
    "    print(\"x\",x,\"y\",y, \"w\", w,\"h\",h )\n",
    "\n",
    "#Function for cropping an image based on x,y,w,h values\n",
    "def crop_road(x,y,w,h,image_full):\n",
    "    r = (x,y,w,h)\n",
    "    imCrop = image_full[int(r[1]):int(r[1]+r[3]), int(r[0]):int(r[0]+r[2])]\n",
    "    return imCrop \n",
    "\n",
    "#Function for reading an image from folder and then cropping an image\n",
    "def get_image(filepath,filename):  \n",
    "    image = cv2.imread(filepath+filename)\n",
    "    image = cv2.resize(image, (960, 540)) \n",
    "    cropped = crop_road(4,122,956,244,image)\n",
    "    return cropped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "source": [
    "#Create a pandas dataframe for 244x956 = 233265 pixels\n",
    "col = [x for x in range(233264)]\n",
    "\n",
    "road_pixels = pd.DataFrame(columns=col)\n",
    "road_pixels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./Predict/IMG_20191006_112601-Copy1.jpg\n",
      "./Predict/IMG_20191006_114117-Copy1.jpg\n",
      "./Predict/IMG_20191006_114148-Copy1.jpg\n",
      "./Predict/Screen Shot 2019-11-01 at 2-Copy1.43.45 PM.png\n"
     ]
    }
   ],
   "source": [
    "z = 0\n",
    "PATH = r\"./Predict/\"\n",
    "filenames = os.listdir(PATH)\n",
    "for filename in filenames:\n",
    "    print(PATH + filename)\n",
    "\n",
    "    if filename == \".ipynb_checkpoints\":\n",
    "        continue\n",
    "\n",
    "    road = get_ipythonmage(PATH,filename)\n",
    "    img = road\n",
    "#         show(img)\n",
    "\n",
    "    #convert BGR to HSV\n",
    "    imgHSV= cv2.cvtColor(img,cv2.COLOR_BGR2HSV)\n",
    "\n",
    "#         show(imgHSV)\n",
    "    #get mean and standard deviation of pixel colour values\n",
    "    means, stddevs  = cv2.meanStdDev(imgHSV)\n",
    "\n",
    "    # Set lowest value of colur\n",
    "    lowerBound= means - stddevs\n",
    "    # Set highest value of colur\n",
    "    upperBound= means + stddevs\n",
    "\n",
    "    #kernel of 5x5 matrix of ones\n",
    "    kernelOpen=np.ones((5,5))\n",
    "    #kernel of 20x20 matrix of ones\n",
    "    kernelClose=np.ones((20,20))\n",
    "\n",
    "    # create the Mask\n",
    "    mask=cv2.inRange(imgHSV,lowerBound,upperBound)\n",
    "    #morphology\n",
    "    maskOpen=cv2.morphologyEx(mask,cv2.MORPH_OPEN,kernelOpen)\n",
    "    maskClose=cv2.morphologyEx(maskOpen,cv2.MORPH_CLOSE,kernelClose)\n",
    "    #Use maskClose\n",
    "    maskFinal=maskClose\n",
    "    kernel = np.ones((5,5),np.uint8)\n",
    "    erosion = cv2.erode(maskFinal,kernel,iterations = 7)\n",
    "\n",
    "    #get contours\n",
    "    conts,h=cv2.findContours(erosion.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)\n",
    "\n",
    "\n",
    "\n",
    "    hull = []\n",
    "\n",
    "    # calculate hull for each contour\n",
    "    for i in range(len(conts)):\n",
    "        # creating convex hull object for each contour\n",
    "        hull.append(cv2.convexHull(conts[i], False))\n",
    "\n",
    "    # draw contours and hull points\n",
    "    # for i in range(len(conts)):\n",
    "    #     #cv2.drawContours(img,conts,-1,(0,0,255),3)\n",
    "    #     # draw ith convex hull object\n",
    "    #     cv2.drawContours(img, hull, i, (0,0,255), 1, 8)\n",
    "\n",
    "\n",
    "    #create matrix with a shape of img.shape\n",
    "    stencil = np.zeros(img.shape).astype(img.dtype)\n",
    "\n",
    "    #set color black for areas outside hull\n",
    "    color = [255, 255, 255]\n",
    "    cv2.fillPoly(stencil, hull, color)\n",
    "    result = cv2.bitwise_and(img, stencil)\n",
    "\n",
    "\n",
    "#         show(result)\n",
    "    # convert the processed image into Grayscale\n",
    "    resultGray= cv2.cvtColor(result,cv2.COLOR_BGR2GRAY)\n",
    "    block_size = 293\n",
    "    constant = 2\n",
    "    #Threshold converted image\n",
    "    th1 = cv2.adaptiveThreshold(resultGray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, block_size, constant)\n",
    "    th2 = cv2.adaptiveThreshold (resultGray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, block_size, constant)\n",
    "#         show(th2)\n",
    "\n",
    "    #convert the numpy array of image pixel data to list\n",
    "    row = th2.ravel()\n",
    "    row_as_list = row.tolist()\n",
    "\n",
    "    row_as_list\n",
    "\n",
    "    #convert the lists to pandas dataframe\n",
    "    road_pixels.loc[z] = row_as_list\n",
    "\n",
    "    z+=1"
   ]
  },
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     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "road_pixels = road_pixels.reset_index(drop=True)\n",
    "road_pixels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X_train\n",
    "predict = road_pixels.to_numpy().reshape(len(road_pixels),956,244,1).astype('float32')\n",
    "predict /= 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From c:\\users\\user\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\tensorflow\\python\\ops\\init_ops.py:97: calling GlorotUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
      "WARNING:tensorflow:From c:\\users\\user\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\tensorflow\\python\\ops\\init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
      "WARNING:tensorflow:From c:\\users\\user\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\tensorflow\\python\\ops\\init_ops.py:97: calling Zeros.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
     ]
    }
   ],
   "source": [
    "model = keras.models.load_model(\"trained_model.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#predict for train set\n",
    "predicted_categories = model.predict(predict)\n",
    "predicted_categories = np.argmax(np.round(predicted_categories),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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     "execution_count": 13,
     "metadata": {},
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   ],
   "source": [
    "#predictaed labels for train set\n",
    "predicted_categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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