code/Course_92.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "with open('kaptcha.jpg', 'wb') as f:\n",
    "    res = requests.get('http://gcis.nat.gov.tw/pub/kaptcha.jpg')\n",
    "    f.write(res.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Jm3sPyRiNeQgileIgEdOo4AFT9IRTMietH3zht/V6JXhmy4vPfP6hdNZteJNG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\n",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=120x40 at 0x89EDC50>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from PIL import Image\n",
    "image = Image.open('kaptcha.jpg')\n",
    "image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import PIL\n",
    "import numpy\n",
    "pil_image = PIL.Image.open('kaptcha.jpg').convert('RGB') \n",
    "open_cv_image = numpy.array(pil_image) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[  0,   0,   0],\n",
       "        [ 10,  10,  10],\n",
       "        [  0,   0,   0],\n",
       "        ..., \n",
       "        [  0,   0,   0],\n",
       "        [ 10,  10,  10],\n",
       "        [  0,   0,   0]],\n",
       "\n",
       "       [[ 14,  14,  14],\n",
       "        [188, 188, 188],\n",
       "        [215, 215, 215],\n",
       "        ..., \n",
       "        [215, 215, 215],\n",
       "        [188, 188, 188],\n",
       "        [ 14,  14,  14]],\n",
       "\n",
       "       [[  0,   0,   0],\n",
       "        [217, 217, 217],\n",
       "        [223, 223, 223],\n",
       "        ..., \n",
       "        [223, 223, 223],\n",
       "        [217, 217, 217],\n",
       "        [  0,   0,   0]],\n",
       "\n",
       "       ..., \n",
       "       [[  0,   0,   0],\n",
       "        [217, 217, 217],\n",
       "        [223, 223, 223],\n",
       "        ..., \n",
       "        [223, 223, 223],\n",
       "        [217, 217, 217],\n",
       "        [  0,   0,   0]],\n",
       "\n",
       "       [[ 14,  14,  14],\n",
       "        [188, 188, 188],\n",
       "        [215, 215, 215],\n",
       "        ..., \n",
       "        [215, 215, 215],\n",
       "        [188, 188, 188],\n",
       "        [ 14,  14,  14]],\n",
       "\n",
       "       [[  0,   0,   0],\n",
       "        [ 10,  10,  10],\n",
       "        [  0,   0,   0],\n",
       "        ..., \n",
       "        [  0,   0,   0],\n",
       "        [ 10,  10,  10],\n",
       "        [  0,   0,   0]]], dtype=uint8)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open_cv_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\programdata\\anaconda3\\envs\\opencvtest\\lib\\site-packages\\IPython\\core\\magics\\pylab.py:160: UserWarning: pylab import has clobbered these variables: ['f']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n",
      "  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x827a400>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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u9+8fYfqxkSwjNqaLPLyMp3gPU3B9MbKFW9WeBZyy6x/kbu/Pf2Zohqt/61Ks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\n0SMFhqiqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7a55208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%pylab inline\n",
    "from matplotlib import pyplot as plt\n",
    "plt.imshow(open_cv_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "imgray = cv2.cvtColor(open_cv_image, cv2.COLOR_BGR2GRAY)\n",
    "ret, thresh = cv2.threshold(imgray, 127, 255, 0)\n",
    "image, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cnts = sorted([(c, cv2.boundingRect(c)[0]) for c in contours], key=lambda x:x[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 1, 118, 38)\n",
      "(14, 9, 18, 24)\n",
      "(19, 21, 8, 8)\n",
      "(32, 9, 16, 24)\n",
      "(48, 9, 15, 24)\n",
      "(62, 9, 16, 24)\n",
      "(67, 21, 6, 8)\n",
      "(78, 9, 17, 24)\n",
      "(95, 9, 15, 24)\n"
     ]
    }
   ],
   "source": [
    "ary = []\n",
    "for (c,_) in cnts:\n",
    "    (x,y,w,h) = cv2.boundingRect(c)\n",
    "    print((x,y,w,h))\n",
    "    if w >= 15 and h == 24:\n",
    "        ary.append((x,y,w,h) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(14, 9, 18, 24), (32, 9, 16, 24), (48, 9, 15, 24), (62, 9, 16, 24), (78, 9, 17, 24), (95, 9, 15, 24)]\n"
     ]
    }
   ],
   "source": [
    "print(ary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x82cbd30>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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u9+8fYfqxkSwjNqaLPLyMp3gPU3B9MbKFW9WeBZyy6x/kbu/Pf2Zohqt/61Ks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\n0SMFhqiqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8289908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%pylab inline\n",
    "plt.imshow(open_cv_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(14, 9, 18, 24),\n",
       " (32, 9, 16, 24),\n",
       " (48, 9, 15, 24),\n",
       " (62, 9, 16, 24),\n",
       " (78, 9, 17, 24),\n",
       " (95, 9, 15, 24)]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VuOl3Z1iOdxqkFaXo+sKi7bXQq4QQZQCQ/tvmywGoH1VKOVVKOTUvr7C93XZJ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\nNmzYsNFHYCt0GzZs2Ogj+H+CVmCUA9VySQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x82eec18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "fig = plt.figure()\n",
    "for id, (x,y,w,h) in enumerate(ary):\n",
    "    roi = open_cv_image[y:y+h, x:x+w]\n",
    "    thresh = roi.copy()\n",
    "    a = fig.add_subplot(1, len(ary), id+1)\n",
    "    plt.imshow(thresh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "from datetime import datetime\n",
    "ct = int(time.mktime(datetime.now().timetuple()))\n",
    "for id, (x,y,w,h) in enumerate(ary):\n",
    "    fig = plt.figure()\n",
    "    roi = open_cv_image[y:y+h, x:x+w]\n",
    "    thresh = roi.copy()\n",
    "    plt.imshow(thresh)\n",
    "    plt.savefig('{}_{}.jpg'.format(ct, id+1), dpi=100)"
   ]
  },
  {
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}