BlobDetection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from keras.models import load_model\n",
    "model = load_model('./training_models/NN_digit.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f2ebd3a3190>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nq3UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2ebe263890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cell57 = np.load('./SudokuCells/cell_np5 7.npy')\n",
    "\n",
    "image = cell57.reshape((28, 28))\n",
    "plt.imshow(image, 'gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f2ebc29a590>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2ebd360ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.07685136795 (16.10780906677246, 12.81128215789795)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f2ebc1e37d0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2ebc333050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "params = cv2.SimpleBlobDetector_Params()\n",
    "params.filterByArea = True\n",
    "params.minArea = 3\n",
    "params.maxArea = 37\n",
    "params.filterByCircularity = False\n",
    "params.filterByConvexity = False\n",
    "\n",
    "detector = cv2.SimpleBlobDetector(params)\n",
    "\n",
    "keypoints = detector.detect(image)\n",
    "\n",
    "for ix in keypoints:\n",
    "    print ix.size, ix.pt\n",
    "\n",
    "im_with_keypoints = cv2.drawKeypoints(image, keypoints, np.array([]), (0,0,255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)\n",
    "\n",
    "plt.imshow(im_with_keypoints, 'gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "edcbxb"
   ]
  }
 ],
 "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
}