TestImages.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\yosep\\Documents\\Junior 1\\ELEC301\\Homework\\CatDogClassification\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "..\\CatDogDataSet\\images\\Abyssinian_49.jpg\n..\\CatDogDataSet\\annotations\\trimaps\\Abyssinian_51.png\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "TRI_DIR = Path('../CatDogDataSet/annotations/trimaps')\n",
    "IMG_DIR = Path('../CatDogDataSet/images')\n",
    "BASE_DIR = Path('.')\n",
    "print(BASE_DIR.resolve())\n",
    "tri_img = [x for x in TRI_DIR.iterdir() if TRI_DIR.is_dir()]\n",
    "train_img = [x for x in IMG_DIR.iterdir() if IMG_DIR.is_dir()]\n",
    "test_img = [x for x in tri_img if '._' not in str(x)]\n",
    "print(train_img[150])\n",
    "print(test_img[150])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'Image' is not defined",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-ee7530afe817>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mim\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_img\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[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mIMREAD_COLOR\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mim\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mim\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m128\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m128\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mINTER_CUBIC\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mim2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_img\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[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0mim2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mim2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muint8\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mim2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'Image' is not defined"
     ],
     "output_type": "error"
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "datamat = np.ndarray((4, 128, 128))\n",
    "coord = 10\n",
    "im = cv2.imread(str(train_img[150]), cv2.IMREAD_COLOR)\n",
    "im = cv2.resize(im, (128, 128), interpolation=cv2.INTER_CUBIC)\n",
    "im2 = Image.open(test_img[150])\n",
    "im2 = np.asarray(im2,dtype=np.uint8)\n",
    "print(im2.shape)\n",
    "im2 = cv2.resize(im2, (128, 128), interpolation=cv2.INTER_CUBIC)\n",
    "\n",
    "print(im2.shape)\n",
    "# When you take the transpose it is now RGB, Column, Row.\n",
    "data = im2.ravel()\n",
    "for i in range(4):\n",
    "    datamat[i] = im2\n",
    "print(datamat.shape)\n",
    "datamat = datamat.reshape(4*128**2)\n",
    "datamat = datamat[0:16384]\n",
    "print(datamat.shape)\n",
    "print(im2[coord])\n",
    "print(data.shape)\n",
    "data = datamat.reshape(128,128)\n",
    "print(data[coord])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<PIL.Image.Image image mode=L size=600x400 at 0x25B806CF2E8>\nNone (600, 400) F\nMatrix Dimensions: (700, 700)\nType Pix: <class 'PixelAccess'>\nPixelArray: <PixelAccess object at 0x0000025B805ED530>\n<class 'numpy.ndarray'>\n0\n"
     ]
    },
    {
     "ename": "SystemError",
     "evalue": "new style getargs format but argument is not a tuple",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mSystemError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-113-8447a633e90f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     39\u001b[0m         \u001b[0mcurr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpix\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcurr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m         \u001b[0mpix\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mcurr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     42\u001b[0m \u001b[1;31m# pix[200, 600] = 2\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m \u001b[1;31m# print(pix[600, 400])\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mSystemError\u001b[0m: new style getargs format but argument is not a tuple"
     ],
     "output_type": "error"
    }
   ],
   "source": [
    "from PIL import Image\n",
    "import numpy as np\n",
    "\n",
    "# Pixel Annotations: 1: Foreground 2:Background 3: Not classified\n",
    "\n",
    "size = 700\n",
    "img = Image.open(test_img[0])\n",
    "\n",
    "img_y, img_b, img_r = img.convert('YCbCr').split()\n",
    "print(img_y)\n",
    "\n",
    "img_ybr = Image.merge('YCbCr', (img_y, img_b, img_r))\n",
    "# img.show()\n",
    "n_x, n_y = img.size\n",
    "\n",
    "if n_y > n_x:\n",
    "    n_y_new = size\n",
    "    n_x_new = round(size * n_x / n_y)\n",
    "else:\n",
    "    n_x_new = size\n",
    "    n_y_new = round(size * n_y / n_x)\n",
    "img_np = np.asarray(img).astype(float)\n",
    "img_np = np.abs(img_np - 2)\n",
    "img = Image.fromarray(img_np)\n",
    "\n",
    "print(img.format, img.size, img.mode)\n",
    "img_pad = Image.new('L', (size,size), 0)\n",
    "ulc = ((size - n_x_new) // 2, (size - n_y_new) // 2)\n",
    "img_pad.paste(img, ulc)\n",
    "img_mat = np.asarray(img_pad, dtype=np.uint8)\n",
    "print(\"Matrix Dimensions:\", img_mat.shape)\n",
    "pix = img_pad.load()\n",
    "print(\"Type Pix:\", type(pix))\n",
    "pixarray = np.asarray(pix)\n",
    "print(\"PixelArray:\", pixarray)\n",
    "print(type(pixarray))\n",
    "npixarray = np.ndarray()\n",
    "for x in range(200):\n",
    "    for y in range(600):\n",
    "        curr = pix[x, y]\n",
    "        print(curr)\n",
    "        pix[x, y] = [curr, x, y]\n",
    "# pix[200, 600] = 2\n",
    "# print(pix[600, 400])\n",
    "# # img_change[10:100, 10:100] = 4\n",
    "# # pixarray1 = Image.fromarray(pixarray, 'L')\n",
    "# plt.imshow(img_pad)\n",
    "# plt.show()\n",
    "# # img_pad.save('TEST.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 394, 3)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 ..., 3 3 3]\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "Invalid dimensions for image data",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-22-302213ede5ff>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[1;31m# It looks like it treats it as row, column, rather than x, y.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtri\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtri\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\yosep\\Anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mimshow\u001b[0;34m(X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, shape, filternorm, filterrad, imlim, resample, url, hold, data, **kwargs)\u001b[0m\n\u001b[1;32m   3027\u001b[0m                         \u001b[0mfilternorm\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfilternorm\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilterrad\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfilterrad\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   3028\u001b[0m                         \u001b[0mimlim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mimlim\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresample\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mresample\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3029\u001b[0;31m                         **kwargs)\n\u001b[0m\u001b[1;32m   3030\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   3031\u001b[0m         \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhold\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwashold\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\yosep\\Anaconda3\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1816\u001b[0m                     warnings.warn(msg % (label_namer, func.__name__),\n\u001b[1;32m   1817\u001b[0m                                   RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1818\u001b[0;31m             \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[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[0m\n\u001b[0m\u001b[1;32m   1819\u001b[0m         \u001b[0mpre_doc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minner\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1820\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\yosep\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mimshow\u001b[0;34m(self, X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, shape, filternorm, filterrad, imlim, resample, url, **kwargs)\u001b[0m\n\u001b[1;32m   4920\u001b[0m                               resample=resample, **kwargs)\n\u001b[1;32m   4921\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4922\u001b[0;31m         \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[0m\n\u001b[0m\u001b[1;32m   4923\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[0m\n\u001b[1;32m   4924\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[0m\n",
      "\u001b[0;32mC:\\Users\\yosep\\Anaconda3\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36mset_data\u001b[0;34m(self, A)\u001b[0m\n\u001b[1;32m    451\u001b[0m         if (self._A.ndim not in (2, 3) or\n\u001b[1;32m    452\u001b[0m                 (self._A.ndim == 3 and self._A.shape[-1] not in (3, 4))):\n\u001b[0;32m--> 453\u001b[0;31m             \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Invalid dimensions for image data\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    454\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    455\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_imcache\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Invalid dimensions for image data"
     ],
     "output_type": "error"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from scipy.misc import imread\n",
    "from sklearn.cluster import KMeans\n",
    "tri = imread(train_img[2])\n",
    "print(tri.shape)\n",
    "tri = tri.reshape(500*394, 3)\n",
    "kmeans = KMeans().predict(tri)\n",
    "tri = kmeans.predict(tri)\n",
    "\n",
    "# print(tri)\n",
    "# It looks like it treats it as row, column, rather than x, y.\n",
    "print(tri)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "197000\n(197000,)\n(500, 394)\n[0 0 0 0 0 0 0 7 4 4 4 4 4 4 4 7 0 0 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n 2 2 2 2 2 2 2 2 2 2 2 2 2 0 2 0 0 0 0 0 0 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 7 7 6 7 7 7 7 7 7 7 7 7 7 6 6 7 7\n 7 7 0 7 7 0 0 7 7 6 7 6 7 7 6 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7\n 7 4 7 7 7 7 7 7 4 7 7 4 7 7 7 7 4 4 7 7 7 4 1 4 4 4 1 4 7 7 7 7 7 7 7 7 7\n 7 7 7 7 4 4 4 7 7 6 6 6 7 7 7 7 7 7 4 7 7 7 7 7 7 4 7 7 7 7 7 7 7 7 7 7 7\n 7 7 4 7 7 7 7 4 7 7 7 7 7 7 4 7 7 7 7 7 0 4 0 7 0 4 7 7 4 7 7 7 4 4 7 4 4\n 7 7 7 7 7 7 7 7 7 7 7 7 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 2 2 2 2 2 2 2 2 0 0 0 0 0 0 0 0 2 2 0\n 0 0 0 0 0 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 0 0 3 3 3 0 3 3 5 3 0 3\n 5 5 5 5 5 5 3 3 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 3 3 3 3 2 2 2 2 2 3 5\n 5 5 5 5 5 5 5 3 5 3 5 5 5 3 5 5 5 5 5 5 5 5 5 5 5 5 3 3 5 5 5 5 5 5 5 5 5\n 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5]\n(394, 500)\n"
     ]
    }
   ],
   "source": [
    "print(500*394)\n",
    "from PIL import Image\n",
    "print(tri.shape)\n",
    "tri1 = tri.reshape((500, 394))\n",
    "print(tri1.shape)\n",
    "print(tri1[:,300])\n",
    "imnew = Image.fromarray(tri1).convert('L')\n",
    "print(imnew.size)\n",
    "# imnew.show()\n",
    "plt.imshow(imnew)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<PIL.Image.Image image mode=L size=600x400 at 0x25BFBF203C8>\nJPEG (600, 400) RGB\nType Pix: <class 'PixelAccess'>\nPixelArray: <PixelAccess object at 0x0000025BFBEBBDD0>\n"
     ]
    }
   ],
   "source": [
    "# Pixel Annotations: 1: Foreground 2:Background 3: Not classified\n",
    "\n",
    "size = 700\n",
    "img = Image.open(train_img[0])\n",
    "\n",
    "img_y, img_b, img_r = img.convert('YCbCr').split()\n",
    "print(img_y)\n",
    "\n",
    "img_ybr = Image.merge('YCbCr', (img_y, img_b, img_r))\n",
    "# img.show()\n",
    "n_x, n_y = img.size\n",
    "print(img.format, img.size, img.mode)\n",
    "img_pad = Image.new('RGB', (size, size), (128, 128, 128))\n",
    "ulc = ((size - n_x) // 2, (size - n_y) // 2)\n",
    "img_pad.paste(img, ulc)\n",
    "pix = img.load()\n",
    "print(\"Type Pix:\", type(pix))\n",
    "pixarray = np.asarray(pix)\n",
    "print(\"PixelArray:\", pixarray)\n",
    "# print(type(pixarray))\n",
    "# for x in range(200):\n",
    "#     for y in range(600):\n",
    "#         pix[x, y] = 0\n",
    "# pix[200, 600] = 2\n",
    "# print(pix[10, 10])\n",
    "# img_change[10:100, 10:100] = 4\n",
    "# pixarray1 = Image.fromarray(pixarray, 'L')\n",
    "plt.imshow(img_pad)\n",
    "plt.show()\n",
    "# img_pad.save('TEST.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n[29 37 26]\n(400, 600, 3)\n[29 37 26]\n(240000, 3)\n(7200,)\n(400, 600, 3)\n(720000,)\n"
     ]
    }
   ],
   "source": [
    "data = np.asarray(img, dtype=\"int32\")\n",
    "print(type(data))\n",
    "print(data[2, 0])\n",
    "print(data.shape)\n",
    "# How to reshape a dataMatrix into a Vector.\n",
    "dataVector = data.reshape(-1)\n",
    "mMat = data.reshape(400*600, 3)\n",
    "print(mMat[1200])\n",
    "print(mMat.shape)\n",
    "\n",
    "# This will randomly get indexes to reduce the size by 100.\n",
    "randArray = np.random.choice(data.size, data.size // 100)\n",
    "print(randArray.shape)\n",
    "print(data.shape)\n",
    "print(dataVector.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<zip object at 0x0000025BFC42F288>\n(10, 1)\n(12, 2)\n(14, 3)\n(16, 4)\n"
     ]
    }
   ],
   "source": [
    "l = [10,12,14,16,18,20]\n",
    "l1 = np.add(range(4),1)\n",
    "l2 = zip(l, l1)\n",
    "print(l2)\n",
    "for i in l2:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[WindowsPath('../CatDogDataSet/test_images/1.jpg'), WindowsPath('../CatDogDataSet/test_images/10.jpg'), WindowsPath('../CatDogDataSet/test_images/11.jpg'), WindowsPath('../CatDogDataSet/test_images/12.jpg'), WindowsPath('../CatDogDataSet/test_images/13.jpg'), WindowsPath('../CatDogDataSet/test_images/14.jpg'), WindowsPath('../CatDogDataSet/test_images/15.jpg'), WindowsPath('../CatDogDataSet/test_images/16.jpg'), WindowsPath('../CatDogDataSet/test_images/17.jpg'), WindowsPath('../CatDogDataSet/test_images/18.jpg'), WindowsPath('../CatDogDataSet/test_images/19.jpg'), WindowsPath('../CatDogDataSet/test_images/2.jpg'), WindowsPath('../CatDogDataSet/test_images/20.jpg'), WindowsPath('../CatDogDataSet/test_images/21.jpg'), WindowsPath('../CatDogDataSet/test_images/22.jpg'), WindowsPath('../CatDogDataSet/test_images/23.jpg'), WindowsPath('../CatDogDataSet/test_images/24.jpg'), WindowsPath('../CatDogDataSet/test_images/25.jpg'), WindowsPath('../CatDogDataSet/test_images/26.jpg'), WindowsPath('../CatDogDataSet/test_images/27.jpg'), WindowsPath('../CatDogDataSet/test_images/28.jpg'), WindowsPath('../CatDogDataSet/test_images/29.jpg'), WindowsPath('../CatDogDataSet/test_images/3.jpg'), WindowsPath('../CatDogDataSet/test_images/30.jpg'), WindowsPath('../CatDogDataSet/test_images/31.jpg'), WindowsPath('../CatDogDataSet/test_images/32.jpg'), WindowsPath('../CatDogDataSet/test_images/33.jpg'), WindowsPath('../CatDogDataSet/test_images/34.jpg'), WindowsPath('../CatDogDataSet/test_images/35.jpg'), WindowsPath('../CatDogDataSet/test_images/36.jpg'), WindowsPath('../CatDogDataSet/test_images/37.jpg'), WindowsPath('../CatDogDataSet/test_images/38.jpg'), WindowsPath('../CatDogDataSet/test_images/39.jpg'), WindowsPath('../CatDogDataSet/test_images/4.jpg'), WindowsPath('../CatDogDataSet/test_images/40.jpg'), WindowsPath('../CatDogDataSet/test_images/41.jpg'), WindowsPath('../CatDogDataSet/test_images/42.jpg'), WindowsPath('../CatDogDataSet/test_images/43.jpg'), WindowsPath('../CatDogDataSet/test_images/44.jpg'), WindowsPath('../CatDogDataSet/test_images/5.jpg'), WindowsPath('../CatDogDataSet/test_images/6.jpg'), WindowsPath('../CatDogDataSet/test_images/7.jpg'), WindowsPath('../CatDogDataSet/test_images/8.jpg'), WindowsPath('../CatDogDataSet/test_images/9.jpg')]\n"
     ]
    }
   ],
   "source": [
    "TRI_DIR = Path('../CatDogDataSet/test_images')\n",
    "test_img = [x for x in TRI_DIR.iterdir() if TRI_DIR.is_dir() and '.jpg' in str(x).lower()]\n",
    "# test_img  = [x for x in test_img if '.jpg' in str(x).lower()]\n",
    "print(test_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    ""
   ]
  }
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