CellTreatment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import os\n",
    "import cv2\n",
    "import glob\n",
    "\n",
    "from keras.models import load_model\n",
    "model = load_model('./training_models/NN_digit.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-3-4851bc000386>, line 12)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-3-4851bc000386>\"\u001b[0;36m, line \u001b[0;32m12\u001b[0m\n\u001b[0;31m    model.predict_classes(cell70.reshape((, 784)))\u001b[0m\n\u001b[0m                                          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "cell00 = np.load('./SudokuCells/cell_np0 0.npy')\n",
    "cell04 = np.load('./SudokuCells/cell_np0 4.npy')\n",
    "cell08 = np.load('./SudokuCells/cell_np0 8.npy')\n",
    "cell26 = np.load('./SudokuCells/cell_np2 6.npy')\n",
    "cell43 = np.load('./SudokuCells/cell_np4 3.npy')\n",
    "cell41 = np.load('./SudokuCells/cell_np4 1.npy')\n",
    "cell62 = np.load('./SudokuCells/cell_np6 2.npy')\n",
    "cell78 = np.load('./SudokuCells/cell_np8 8.npy')\n",
    "cell47 = np.load('./SudokuCells/cell_np4 7.npy')\n",
    "cell70 = np.load('./SudokuCells/cell_np7 0.npy')\n",
    "\n",
    "model.predict_classes(cell70.reshape((, 784)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def show_mask(mask):\n",
    "    plt.figure(figsize=(5, 5))\n",
    "    plt.imshow(mask, cmap='gray')\n",
    "\n",
    "def find_biggest_contour(image):\n",
    "    \n",
    "    # Copy to prevent modification\n",
    "    image = image.copy()\n",
    "    contours, hierarchy = cv2.findContours(image, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)\n",
    "    print len(contours)\n",
    "\n",
    "    # Isolate largest contour\n",
    "    contour_sizes = [(cv2.contourArea(contour), contour) for contour in contours]\n",
    "    biggest_contour = max(contour_sizes, key=lambda x: x[0])[1]\n",
    " \n",
    "    mask = np.zeros(image.shape, np.uint8)\n",
    "    cv2.drawContours(mask, [biggest_contour], -1, 255, -1)\n",
    "    return biggest_contour, mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "4\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "cell01 = np.load('./SudokuCells/cell_np0 0.npy')\n",
    "big_contour01, red_mask01 = find_biggest_contour(cell01.reshape((28, 28)))\n",
    "# big_contour00, red_mask00 = find_biggest_contour(cell00.reshape((28, 28)))\n",
    "# big_contour04, red_mask04 = find_biggest_contour(cell04.reshape((28, 28)))\n",
    "big_contour08, red_mask08 = find_biggest_contour(cell08.reshape((28, 28)))\n",
    "# big_contour26, red_mask26 = find_biggest_contour(cell26.reshape((28, 28)))\n",
    "# big_contour41, red_mask41 = find_biggest_contour(cell41.reshape((28, 28)))\n",
    "# big_contour43, red_mask43 = find_biggest_contour(cell43.reshape((28, 28)))\n",
    "# big_contour47, red_mask47 = find_biggest_contour(cell47.reshape((28, 28)))\n",
    "# big_contour62, red_mask62 = find_biggest_contour(cell62.reshape((28, 28)))\n",
    "# big_contour78, red_mask78 = find_biggest_contour(cell78.reshape((28, 28)))\n",
    "\n",
    "# area = [cv2.contourArea(big_contour00), cv2.contourArea(big_contour04), cv2.contourArea(big_contour08),\n",
    "#        cv2.contourArea(big_contour26), cv2.contourArea(big_contour41), cv2.contourArea(big_contour43),\n",
    "#        cv2.contourArea(big_contour47), cv2.contourArea(big_contour62), cv2.contourArea(big_contour78)]\n",
    "\n",
    "print \"-\"*80"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Error when checking : expected dense_7_input to have shape (None, 784) but got array with shape (28, 28)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-17-1a3d7a02136a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mbig_contour70\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mred_mask70\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfind_biggest_contour\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcell70\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m28\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m28\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_classes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcell70\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/keras/models.pyc\u001b[0m in \u001b[0;36mpredict_classes\u001b[0;34m(self, x, batch_size, verbose)\u001b[0m\n\u001b[1;32m   1012\u001b[0m             \u001b[0mA\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0marray\u001b[0m \u001b[0mof\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1013\u001b[0m         \"\"\"\n\u001b[0;32m-> 1014\u001b[0;31m         \u001b[0mproba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1015\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mproba\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1016\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mproba\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/keras/models.pyc\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, x, batch_size, verbose)\u001b[0m\n\u001b[1;32m    907\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    908\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 909\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    910\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    911\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpredict_on_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/keras/engine/training.pyc\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, x, batch_size, verbose)\u001b[0m\n\u001b[1;32m   1497\u001b[0m         x = _standardize_input_data(x, self._feed_input_names,\n\u001b[1;32m   1498\u001b[0m                                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_feed_input_shapes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1499\u001b[0;31m                                     check_batch_axis=False)\n\u001b[0m\u001b[1;32m   1500\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstateful\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1501\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/keras/engine/training.pyc\u001b[0m in \u001b[0;36m_standardize_input_data\u001b[0;34m(data, names, shapes, check_batch_axis, exception_prefix)\u001b[0m\n\u001b[1;32m    138\u001b[0m                             \u001b[0;34m' to have shape '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshapes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    139\u001b[0m                             \u001b[0;34m' but got array with shape '\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 140\u001b[0;31m                             str(array.shape))\n\u001b[0m\u001b[1;32m    141\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    142\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Error when checking : expected dense_7_input to have shape (None, 784) but got array with shape (28, 28)"
     ]
    }
   ],
   "source": [
    "big_contour70, red_mask70 = find_biggest_contour(cell70.reshape((28, 28)))\n",
    "model.predict_classes(cell70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fcd4c61f710>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4k4QUGkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcd4cab7c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = cell08.reshape((28, 28))\n",
    "if(cv2.contourArea(big_contour08) < 5.0):\n",
    "    img[:, :] = 0\n",
    "else:\n",
    "    pass\n",
    "\n",
    "\n",
    "plt.imshow(img, 'gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'big_contour' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-3f25beb69c46>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mM\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmoments\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbig_contour\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mM\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'big_contour' is not defined"
     ]
    }
   ],
   "source": [
    "M = cv2.moments(big_contour)\n",
    "print( M )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'M' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-6b5b522ea2f4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Centroid of the biggest blob\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mcx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mM\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'm10'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mM\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'm00'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mcy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mM\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'm01'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mM\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'm00'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'M' is not defined"
     ]
    }
   ],
   "source": [
    "# Centroid of the biggest blob\n",
    "cx = int(M['m10']/M['m00'])\n",
    "cy = int(M['m01']/M['m00'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fcd48ce9910>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcd4cab78d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = cell78.reshape((28, 28))\n",
    "plt.imshow(img, 'gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "[[[19 10]]\n",
      "\n",
      " [[18 11]]\n",
      "\n",
      " [[18 12]]\n",
      "\n",
      " [[19 12]]\n",
      "\n",
      " [[20 11]]\n",
      "\n",
      " [[20 10]]]\n"
     ]
    }
   ],
   "source": [
    "cell00 = np.load('./SudokuCells/cell_np0 0.npy')\n",
    "image = cell00.reshape((28, 28))\n",
    "\n",
    "contours, hierarchy = cv2.findContours(image, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)\n",
    "print len(contours)\n",
    "\n",
    "# Isolate largest contour\n",
    "try:\n",
    "    contour_sizes = [(cv2.contourArea(contour), contour) for contour in contours]\n",
    "    biggest_contour = max(contour_sizes, key=lambda x: x[0])[1]\n",
    "except:\n",
    "    biggest_contour = 0\n",
    "print biggest_contour \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
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   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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