CellTreatment01.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "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": 17,
   "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": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fa9dd0e2d90>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8EtF/R+ogfF4LzeiBgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa9dd23edd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cell57 = np.load('./SudokuCells/cell_np6 6.npy')\n",
    "image = cell57.reshape((28, 28))\n",
    "plt.imshow(image, 'gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fa9dc5cf690>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Gv4E8zQ64X1XV34drGJ0rWwlW5O0mVfWT3vQXk3w0yeqq2u8NlLPy9WczsLGb3gh8fuEK\nSVYlObKbXg2cCdw92AiXbim3JvT39yJga3VnzqbYAfdrwXmG9cB3Bhzfs2kz8LbuKtArgcd7X9dn\n1n5ur9m/SZ+BXuJZ6mOBW4AdwJeBY7r5c8A13fSrge2MrjpsBy6Z9Lj3sz/nA/cyOoq6opt3JbC+\nmz4K+AywE/gGcPKkx9xov/4SuKv7N/oK8OJJj3mJ+3U98BDwv4zOl1wCvAN4R7c8jP7Y2H3dZ29u\n0mNutF+X9f69tgGvXsr7+mv6kpqala8/kmaEoSKpKUNFUlOGiqSmDBVJTRkqkpoyVCQ19X/d4XpS\nWYD5ggAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa9dc6c6d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "contours, hierarchy = cv2.findContours(image, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)\n",
    "cnt = max(contours, key = cv2.contourArea)\n",
    "\n",
    "x,y,w,h = cv2.boundingRect(cnt)\n",
    "\n",
    "\n",
    "plt.imshow(roi, 'gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fa9ddbde790>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa9dddb0710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kernel = np.ones((2, 2), np.uint8)\n",
    "opening = cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel)\n",
    "plt.imshow(opening, 'gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}