save_files.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import imageio\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import math\n",
    "import os\n",
    "import sys\n",
    "from IPython.display import display, Image\n",
    "from six.moves import cPickle as pickle\n",
    "from scipy.misc import imread, imsave, imresize\n",
    "from scipy import signal\n",
    "from skimage import data as dt, transform as transform\n",
    "from PIL import Image as Image1, ImageEnhance\n",
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Resize image to 32 by 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".\\resized_dataset\\Sample002\\img002-00001 (3).png\n",
      ".\\resized_dataset\\Sample002\\img002-00001 - Copy.png\n",
      ".\\resized_dataset\\Sample002\\img002-00002 (3).png\n",
      ".\\resized_dataset\\Sample002\\img002-00002 - Copy.png\n",
      ".\\resized_dataset\\Sample002\\img002-00003 (3).png\n",
      ".\\resized_dataset\\Sample002\\img002-00003 - Copy.png\n",
      ".\\resized_dataset\\Sample002\\img002-00004 (3).png\n",
      ".\\resized_dataset\\Sample002\\img002-00004 - Copy.png\n",
      ".\\resized_dataset\\Sample002\\img002-00005 (3).png\n",
      ".\\resized_dataset\\Sample002\\img002-00005 - Copy.png\n",
      ".\\resized_dataset\\Sample002\\img002-00006 (3).png\n",
      ".\\resized_dataset\\Sample002\\img002-00006 - Copy.png\n",
      ".\\resized_dataset\\Sample002\\img002-00007 (3).png\n",
      ".\\resized_dataset\\Sample002\\img002-00007 - Copy.png\n",
      ".\\resized_dataset\\Sample002\\img002-00008 (3).png\n"
     ]
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     "name": "stderr",
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     "text": [
      "C:\\Users\\shivangi pandey\\Anaconda3\\lib\\site-packages\\imageio\\core\\util.py:78: UserWarning: Lossy conversion from float64 to uint8, range [0, 1]\n",
      "  dtype_str, out_type.__name__))\n",
      "C:\\Users\\shivangi pandey\\Anaconda3\\lib\\site-packages\\skimage\\transform\\_warps.py:84: UserWarning: The default mode, 'constant', will be changed to 'reflect' in skimage 0.15.\n",
      "  warn(\"The default mode, 'constant', will be changed to 'reflect' in \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      ".\\resized_dataset\\Sample002\\img002-00992.png\n",
      ".\\resized_dataset\\Sample002\\img002-00993 - Copy.png\n",
      ".\\resized_dataset\\Sample002\\img002-00993.png\n",
      ".\\resized_dataset\\Sample002\\img002-00994 - Copy.png\n",
      ".\\resized_dataset\\Sample002\\img002-00994.png\n",
      ".\\resized_dataset\\Sample002\\img002-00995 - Copy.png\n",
      ".\\resized_dataset\\Sample002\\img002-00995.png\n",
      ".\\resized_dataset\\Sample002\\img002-00996 - Copy.png\n",
      ".\\resized_dataset\\Sample002\\img002-00996.png\n",
      ".\\resized_dataset\\Sample002\\img002-00997 - Copy.png\n",
      ".\\resized_dataset\\Sample002\\img002-00997.png\n",
      ".\\resized_dataset\\Sample002\\img002-00998 - Copy.png\n",
      ".\\resized_dataset\\Sample002\\img002-00998.png\n",
      ".\\resized_dataset\\Sample002\\img002-00999 - Copy.png\n",
      ".\\resized_dataset\\Sample002\\img002-00999.png\n",
      ".\\resized_dataset\\Sample002\\img002-01000 - Copy.png\n",
      ".\\resized_dataset\\Sample002\\img002-01000.png\n",
      ".\\resized_dataset\\Sample002\\img002-01001 - Copy.png\n",
      ".\\resized_dataset\\Sample002\\img002-01001.png\n",
      ".\\resized_dataset\\Sample002\\img002-01002 - Copy.png\n",
      ".\\resized_dataset\\Sample002\\img002-01002.png\n",
      ".\\resized_dataset\\Sample002\\img002-01003 - Copy.png\n",
      ".\\resized_dataset\\Sample002\\img002-01003.png\n",
      ".\\resized_dataset\\Sample002\\img002-01004 - Copy.png\n",
      ".\\resized_dataset\\Sample002\\img002-01004.png\n",
      ".\\resized_dataset\\Sample002\\img002-01005 - Copy.png\n",
      ".\\resized_dataset\\Sample002\\img002-01005.png\n",
      ".\\resized_dataset\\Sample002\\img002-01006 - Copy.png\n",
      ".\\resized_dataset\\Sample002\\img002-01006.png\n",
      ".\\resized_dataset\\Sample002\\img002-01007 - Copy.png\n",
      ".\\resized_dataset\\Sample002\\img002-01007.png\n",
      ".\\resized_dataset\\Sample002\\img002-01008 - Copy.png\n",
      ".\\resized_dataset\\Sample002\\img002-01008.png\n",
      ".\\resized_dataset\\Sample002\\img002-01009 - Copy.png\n",
      ".\\resized_dataset\\Sample002\\img002-01009.png\n",
      ".\\resized_dataset\\Sample002\\img002-01010 - Copy.png\n",
      ".\\resized_dataset\\Sample002\\img002-01010.png\n",
      ".\\resized_dataset\\Sample002\\img002-01011 - Copy.png\n",
      ".\\resized_dataset\\Sample002\\img002-01011.png\n",
      ".\\resized_dataset\\Sample002\\img002-01012 - Copy.png\n",
      ".\\resized_dataset\\Sample002\\img002-01012.png\n",
      ".\\resized_dataset\\Sample002\\img002-01013 - Copy.png\n",
      ".\\resized_dataset\\Sample002\\img002-01013.png\n",
      ".\\resized_dataset\\Sample002\\img002-01014 - Copy.png\n",
      ".\\resized_dataset\\Sample002\\img002-01014.png\n",
      ".\\resized_dataset\\Sample002\\img002-01015 - Copy.png\n",
      ".\\resized_dataset\\Sample002\\img002-01015.png\n",
      ".\\resized_dataset\\Sample002\\img002-01016 - Copy.png\n",
      ".\\resized_dataset\\Sample002\\img002-01016.png\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def change_contrast(img, level):\n",
    "    factor = (259 * (level + 127)) / (127 * (259 - level))\n",
    "    def contrast(c):\n",
    "        return 128 + factor * (c - 128)\n",
    "    return img.point(contrast)\n",
    "\n",
    "#change_contrast(Image.open(os.path.join(t,os.listdir(t)[11])), 150)\n",
    "\n",
    "data_root = '.'\n",
    "\n",
    "img_rows, img_cols = 32, 32\n",
    "\n",
    "path = os.path.join(data_root ,\"dataset\") \n",
    "\n",
    "# Create the directories if needed\n",
    "if not os.path.exists(path):\n",
    "    os.makedirs(path)\n",
    "\n",
    "path3 = os.path.join(path,'good')\n",
    "### Images preprocessing ###\n",
    "\n",
    "for filename in os.listdir(path3):\n",
    "    files = os.listdir(os.path.join(path3,filename))\n",
    "    \n",
    "    data = np.zeros((len(files), img_rows,img_cols))\n",
    "\n",
    "    \n",
    "    for i, file in enumerate(files):\n",
    "        filepath = os.path.join(path3,filename,files[i])\n",
    "        core = os.path.join(data_root,'resized_dataset',filename)\n",
    "        newName = os.path.join(core,files[i])\n",
    "        if not os.path.exists(core):\n",
    "            os.makedirs(core)\n",
    "        if not os.path.exists(newName):    \n",
    "            print(newName)\n",
    "            imageio.imwrite(newName, imageResized)\n",
    "            image = dt.imread(filepath,as_grey=True)\n",
    "            #imageContrast = np.array(change_contrast(Image1.open(filepath),100).convert('L'))\n",
    "            imageResized = transform.resize(image, (img_rows,img_cols))\n",
    "            # Applying median filter\n",
    "            #imageResized = signal.medfilt(imageResized)\n",
    "            if imageResized.shape == data[i].shape:\n",
    "                data[i] = imageResized\n",
    "            #core = os.path.join(data_root,'resized_dataset1',filename)\n",
    "root = os.path.join(data_root,'resized_dataset')\n",
    "#root = os.path.join(data_root,'resized_dataset1')\n",
    "data_folders = [\n",
    "    os.path.join(root,d) for d in sorted(os.listdir(root))\n",
    "    if os.path.isdir(os.path.join(root,d))\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".\\resized_dataset\\Sample001.pickle already present - Skipping pickling.\n",
      "Pickling .\\resized_dataset\\Sample002.pickle.\n",
      "Full dataset tensor: (2160, 32, 32)\n",
      "Mean: 0.3587543\n",
      "Standard deviation: 0.32249236\n",
      ".\\resized_dataset\\Sample003.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample004.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample005.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample006.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample007.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample008.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample009.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample010.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample011.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample012.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample013.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample014.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample015.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample016.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample017.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample018.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample019.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample020.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample021.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample022.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample023.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample024.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample025.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample026.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample027.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample028.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample029.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample030.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample031.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample032.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample033.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample034.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample035.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample036.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample037.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample038.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample039.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample040.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample041.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample042.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample043.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample044.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample045.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample046.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample047.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample048.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample049.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample050.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample051.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample052.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample053.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample054.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample055.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample056.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample057.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample058.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample059.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample060.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample061.pickle already present - Skipping pickling.\n",
      ".\\resized_dataset\\Sample062.pickle already present - Skipping pickling.\n"
     ]
    }
   ],
   "source": [
    "image_size = 32\n",
    "#image_size = 64\n",
    "pixel_depth = 255.0\n",
    "\n",
    "def load_letter(folder):\n",
    "    # Load data for single letter label\n",
    "    image_files = os.listdir(folder)\n",
    "    dataset = np.ndarray(shape=(len(image_files), image_size,image_size),dtype=np.float32)\n",
    "    num_images = 0\n",
    "    for image in image_files:\n",
    "        image_file = os.path.join(folder,image)\n",
    "        try:\n",
    "            image_data = ((imageio.imread(image_file).astype(float) - pixel_depth / 2) / pixel_depth)\n",
    "            if image_data.shape != (image_size,image_size):\n",
    "                 raise Exception('Unexpected image shape: %s' % str(image_data.shape))\n",
    "            dataset[num_images, :, :] = image_data\n",
    "            num_images += 1\n",
    "        except (IOError,ValueError) as e:\n",
    "            print('Could not read:',image_file,\":\",e,'- it\\'s ok, skipping.')\n",
    "    dataset = dataset[0:num_images,:,:]\n",
    "    print('Full dataset tensor:',dataset.shape)\n",
    "    print('Mean:',np.mean(dataset))\n",
    "    print('Standard deviation:',np.std(dataset))\n",
    "    return dataset\n",
    "\n",
    "def maybe_pickle(data_folders, force =False):\n",
    "    dataset_names = []\n",
    "    for folder in data_folders:\n",
    "        set_filename = folder+'.pickle'\n",
    "        dataset_names.append(set_filename)\n",
    "        if os.path.exists(set_filename) and not force:\n",
    "            print('%s already present - Skipping pickling.' % set_filename)\n",
    "        else:\n",
    "            print('Pickling %s.' % set_filename)\n",
    "            dataset = load_letter(folder)\n",
    "            try:\n",
    "                with open(set_filename,'wb') as f:\n",
    "                    pickle.dump(dataset, f, pickle.HIGHEST_PROTOCOL)\n",
    "            except Exaception as e:\n",
    "                print('Unable to save data to', set_filename,\":\",e)\n",
    "    return dataset_names\n",
    "\n",
    "datasets = maybe_pickle(data_folders)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'datasets' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-1c4b8440c9b4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpickle_file\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdatasets\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m28\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpickle_file\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'rb'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0mletter_set\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m#unppickle\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0msample_idx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m23\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0msample_image\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mletter_set\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0msample_idx\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[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'datasets' is not defined"
     ]
    }
   ],
   "source": [
    "pickle_file = datasets[28]\n",
    "with open(pickle_file,'rb') as f:\n",
    "    letter_set = pickle.load(f) #unppickle\n",
    "    sample_idx = 23\n",
    "    sample_image = letter_set[sample_idx,:,:]\n",
    "    plt.figure()\n",
    "    plt.imshow(sample_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_arrays(nb_rows, img_size):\n",
    "    if nb_rows:\n",
    "        dataset = np.ndarray((nb_rows,img_size,img_size), dtype=np.float32)\n",
    "        labels = np.ndarray(nb_rows, dtype=np.int32)\n",
    "    else:\n",
    "        dataset, labels = None,None\n",
    "    return dataset, labels\n",
    "\n",
    "def merge_datasets(pickle_files, test_percent, valid_percent=0):\n",
    "    num_classes = len(pickle_files)\n",
    "    \n",
    "    path = os.path.join('.','resized_dataset')\n",
    "    #path = os.path.join('.','resized_dataset1')\n",
    "    t = os.listdir(path)\n",
    "\n",
    "    test_size, valid_size, train_size = 0,0,0\n",
    "\n",
    "    for i in t:\n",
    "        if os.path.isdir(os.path.join(path,i)):\n",
    "            leng = len(os.listdir(os.path.join(path,i)))\n",
    "            t = math.floor(leng * (test_percent/100))\n",
    "            v = math.floor(leng * (valid_percent/100))\n",
    "            test_size += t\n",
    "            valid_size += v\n",
    "            train_size += leng - (t + v)\n",
    "   \n",
    "    valid_dataset, valid_labels  = make_arrays(valid_size, image_size)\n",
    "    train_dataset, train_labels = make_arrays(train_size, image_size)\n",
    "    test_dataset, test_labels = make_arrays(test_size, image_size)\n",
    "    \n",
    "    start_v,start_train,start_test = 0,0,0\n",
    "    end_v,end_train,end_test = 0,0,0\n",
    "    for label, pickle_file in enumerate(pickle_files):\n",
    "        try:\n",
    "            with open(pickle_file, 'rb') as f:\n",
    "                letter_set = pickle.load(f)\n",
    "                np.random.shuffle(letter_set)\n",
    "                length = len(letter_set)\n",
    "                vsize_per_class = math.floor((valid_percent/100)*length)\n",
    "                test_size_per_class = math.floor((test_percent/100)*length)\n",
    "                train_size_per_class = length-(vsize_per_class+test_size_per_class)\n",
    "               \n",
    "                if valid_dataset is not None:\n",
    "                    end_v += vsize_per_class\n",
    "                    valid_letter = letter_set[:vsize_per_class, :, :]\n",
    "                    valid_dataset[start_v:end_v, :, :] = valid_letter\n",
    "                    valid_labels[start_v:end_v] = label\n",
    "                    #print(valid_labels[start_v:end_v])\n",
    "                    start_v += vsize_per_class\n",
    "                \n",
    "                end_test += test_size_per_class\n",
    "                end_tv = vsize_per_class + test_size_per_class\n",
    "                test_letter = letter_set[vsize_per_class:end_tv, :, :]\n",
    "                test_dataset[start_test:end_test, :, :] = test_letter\n",
    "                test_labels[start_test:end_test] = label \n",
    "                start_test += test_size_per_class\n",
    "                \n",
    "                end_train += train_size_per_class\n",
    "                end_tvt = end_tv + train_size_per_class\n",
    "                train_letter = letter_set[end_tv:end_tvt, :, :]\n",
    "                train_dataset[start_train:end_train, :, :] = train_letter\n",
    "                train_labels[start_train:end_train] = label\n",
    "                start_train += train_size_per_class\n",
    "                \n",
    "        except Exception as e:\n",
    "            print('Unable to process data from', pickle_file, ':',e)\n",
    "            raise\n",
    "    \n",
    "    return valid_dataset,valid_labels,test_dataset,test_labels,train_dataset,train_labels\n",
    "\n",
    "test_percent = 20\n",
    "valid_percent = 20\n",
    "\n",
    "valid_dataset,valid_labels,test_dataset,test_labels,train_dataset,train_labels = merge_datasets(\n",
    "        datasets, test_percent, valid_percent)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training:  (45931, 32, 32) (45931,)\n",
      "Validation:  (15268, 32, 32) (15268,)\n",
      "Testing:  (15268, 32, 32) (15268,)\n"
     ]
    }
   ],
   "source": [
    "print('Training: ', train_dataset.shape, train_labels.shape)\n",
    "print('Validation: ', valid_dataset.shape, valid_labels.shape)\n",
    "print('Testing: ', test_dataset.shape, test_labels.shape)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xaW3S3LauYB5wNXM1nSuprwN/MKIaPkpnpOEV4Dtt1gE8SOfl40/pvBK6FlgEbABerX4v\nHFEdXwM2ARvphG9pC3V8gs5L2I3Ay9XP6raPSY86Wj0mwC/QmRR3I50/NH804//s88BrwN8BHxhm\nP97hJxXKO/ykQhl+qVCGXyqU4ZcKZfilQhl+qVCGXyqU4ZcK9X96WLWJAFgQAAAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c680452ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21\n"
     ]
    }
   ],
   "source": [
    "def randomize(dataset, labels):\n",
    "    permutation = np.random.permutation(labels.shape[0])\n",
    "    shuffled_dataset = dataset[permutation, :, :]\n",
    "    shuffled_labels = labels[permutation]\n",
    "    return shuffled_dataset, shuffled_labels\n",
    "\n",
    "train_dataset, train_labels = randomize(train_dataset, train_labels)\n",
    "test_dataset, test_labels = randomize(test_dataset, test_labels)\n",
    "valid_dataset, valid_labels = randomize(valid_dataset, valid_labels)\n",
    "\n",
    "\n",
    "plt.imshow(signal.medfilt(train_dataset[0]), interpolation='nearest')\n",
    "plt.show() \n",
    "print(train_labels[0])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pickle_file = os.path.join(data_root,'complete_dataset.pickle')\n",
    "#pickle_file = os.path.join(data_root,'complete_dataset1.pickle')\n",
    "try:\n",
    "    f = open(pickle_file, 'wb')\n",
    "    save = {\n",
    "        'train_dataset' : train_dataset,\n",
    "        'train_labels' : train_labels,\n",
    "        'valid_dataset' : valid_dataset,\n",
    "        'valid_labels' : valid_labels,\n",
    "        'test_dataset' : test_dataset,\n",
    "        'test_labels' : test_labels,\n",
    "        }\n",
    "    pickle.dump(save, f, pickle.HIGHEST_PROTOCOL)\n",
    "    f.close()\n",
    "except Exception as e :\n",
    "    print('Unable to save data to', pickle_file, ':', e)\n",
    "    raise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compresed pickle size:  313515202\n"
     ]
    }
   ],
   "source": [
    "statinfo = os.stat(pickle_file)\n",
    "print('Compresed pickle size: ', statinfo.st_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PIL.Image.Image"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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j7Uy0NSIWAVQ/t42iiMzcWv3H2wXcRkvHJCLm0QncnZl5f7W49WMyUx2jOibVvve6aW6v\nRhH+p4ETqiuXBwIXAw+1XUREHBwRh+5+DHwKeKH7VkP1EJ1GqDDChqi7w1a5kBaOSUQEnR6Q6zPz\nxmlDrR6TujraPiatNc1t6wrmHlczz6NzJfVV4E9HVMPH6Mw0PA+82GYdwF10Xj6+R+eV0GXAfOBR\n4IfVzyNGVMffAeuAtXTCt6iFOj5B5yXsWuC56t95bR+TLnW0ekyAk+k0xV1L5w/NtdP+zz4FbAD+\nHvjgIPvxE35SofyEn1Qowy8VyvBLhTL8UqEMv1Qowy8VyvBLhTL8UqH+D9gh0X7HDjooAAAAAElF\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c6dde06390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from PIL import Image, ImageEnhance\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "\n",
    "path = os.path.join('.','resized_dataset')\n",
    "t = os.path.join(path,os.listdir(path)[0])\n",
    "\n",
    "plt.imshow(mpimg.imread(os.path.join(t,os.listdir(t)[7])))\n",
    "\n",
    "\n",
    "def change_contrast(img, level):\n",
    "    factor = (259 * (level + 127)) / (127 * (259 - level))\n",
    "    def contrast(c):\n",
    "        return 128 + factor * (c - 128)\n",
    "    return img.point(contrast)\n",
    "\n",
    "im = change_contrast(Image.open(os.path.join(t,os.listdir(t)[100])), 100)\n",
    "type(im.convert('L'))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}