Image Processing project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "import cv2\n",
    "from matplotlib import pyplot as mlt\n",
    "# comment something to push\n",
    "pip install opencv-python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x18b8ba82160>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gd5ILk7yA3sacuybcpy1L8qIkL16+DbwNeIjeOh3oFjsAfGYyPRyKtdblLuB93V6I1wPfW55mzIoV20Aup/fZQW/d9iV5YZIL6W1M/dK4+zcup0/yzavqZJJrgM8BpwEHq+rhSfZpQOcCdyaB3s/2U1X12ST3A7cnuQL4DvDuCfZx05LcClwMnJVkCfgg8GFWX5e7gUvpbYR7Fnj/2Dv8PKyxbhcnmac3NXgCuBKgqh5OcjvwNeAkcHVVnZpEv8fBIxolNSY9fZA0ZQwFSQ1DQVLDUJDUMBQkNQwFSQ1DQVLDUJDU+H8IC5XUmelWfAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img_path = 'five.jpg'\n",
    "image = cv2.imread(img_path,0)\n",
    "# mlt.imshow(image)\n",
    "# print(image.shape)\n",
    "# image = np.reshape(a, (-1, 28)).astype(np.uint8)\n",
    "boudary = [(20,218),(20,218)]\n",
    "#[(20,218),(20,218)]\n",
    "if np.sum(boudary) != 0:\n",
    "    image = image[boudary[0][0]:boudary[0][1],boudary[1][0]:boudary[1][1]]\n",
    "# print(image[0,0])\n",
    "if image[0,0] > 80:\n",
    "    ret, img = cv2.threshold(image, 200,255, cv2.THRESH_BINARY_INV)\n",
    "else:\n",
    "    ret, img = cv2.threshold(image, 60,255, cv2.THRESH_BINARY)\n",
    "# image = image[20:218,20:218]\n",
    "# mlt.imshow(image, cmap='Greys')\n",
    "mlt.imshow(img, cmap='Greys')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " ...\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]]\n"
     ]
    }
   ],
   "source": [
    "print(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x18b884bd208>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ret,img = cv2.threshold(image,60,255,cv2.THRESH_BINARY)\n",
    "mlt.imshow(img, cmap = 'Greys')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[255   0   0 ...   0   0 255]\n",
      " [  0   0   0 ...   0   0   0]\n",
      " [  0   0   0 ...   0   0   0]\n",
      " ...\n",
      " [  0   0   0 ...   0   0   0]\n",
      " [  0   0   0 ...   0   0   0]\n",
      " [255   0   0 ...   0   0 255]]\n"
     ]
    }
   ],
   "source": [
    "print(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x186b6d1ccc0>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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69UHSlDEUJDUMBUkNQ0FSw1CQ1DAUJDUMBUkNQ0FS4/8AyWRrZ7ed8mYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kernel = np.ones((10,10),np.uint8)\n",
    "temp = cv2.dilate(img, kernel, iterations = 1)\n",
    "mlt.imshow(temp, cmap = 'Greys')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x18b8bbdb860>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kernel = np.ones((10,10),np.uint8)\n",
    "# closing = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)\n",
    "# dilation = cv2.dilate(img,kernel,iterations = 1)\n",
    "closing = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)\n",
    "\n",
    "mlt.imshow(closing,cmap = 'Greys')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(198, 198)\n"
     ]
    }
   ],
   "source": [
    "img = closing\n",
    "print((img.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " ...\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]]\n"
     ]
    }
   ],
   "source": [
    "print(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(23, 109) 255\n"
     ]
    }
   ],
   "source": [
    "for i, row in enumerate(img):\n",
    "    for j, value in enumerate(row):\n",
    "#         cv2.circle(img,(i,j),5,150,1)\n",
    "        if value == 255:\n",
    "            start_point = (i, j)\n",
    "            print(start_point, value)\n",
    "            break\n",
    "    else:\n",
    "        continue\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x18b8a747390>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv2.circle(img,start_point[::-1],5,150,2)\n",
    "mlt.imshow(img, cmap='Greys')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "directions = [ 0,  1,  2,\n",
    "               7,      3,\n",
    "               6,  5,  4]\n",
    "dir2idx = dict(zip(directions, range(len(directions))))\n",
    "# print(dir2idx)\n",
    "temp = 1\n",
    "change_j =   [-temp,  0, temp, # x or columns\n",
    "              -temp,    temp,\n",
    "              -temp,  0, temp]\n",
    "\n",
    "change_i =   [-temp, -temp, -temp, # y or rows\n",
    "               0,      0,\n",
    "               temp, temp,temp]\n",
    "\n",
    "border = []\n",
    "chain = []\n",
    "curr_point = start_point\n",
    "for direction in directions:\n",
    "#     print(direction)\n",
    "    idx = dir2idx[direction]\n",
    "#     print(idx)\n",
    "    new_point = (start_point[0]+change_i[idx], start_point[1]+change_j[idx])\n",
    "#     print(\"New point: {}, {}\".format(new_point, img[new_point]))\n",
    "    if img[new_point] != 0: # if is ROI\n",
    "        border.append(new_point)\n",
    "        chain.append(direction)\n",
    "        curr_point = new_point\n",
    "        break\n",
    "\n",
    "count = 0\n",
    "while(count < 30):\n",
    "    while curr_point != start_point:\n",
    "        #figure direction to start search\n",
    "    #     cv2.circle(img,curr_point[::-1],5,150,2)\n",
    "\n",
    "        b_direction = (direction + 5) % 8\n",
    "        b_direction += 1\n",
    "        dirs_1 = range(b_direction, 8)\n",
    "        dirs_2 = range(0, b_direction)\n",
    "        dirs = []\n",
    "        dirs.extend(dirs_1)\n",
    "        dirs.extend(dirs_2)\n",
    "        for direction in dirs:\n",
    "            idx = dir2idx[direction]\n",
    "            new_point = (curr_point[0]+change_i[idx], curr_point[1]+change_j[idx])\n",
    "            if img[new_point] != 0:\n",
    "                border.append(new_point)\n",
    "                chain.append(direction)\n",
    "                curr_point = new_point\n",
    "                break\n",
    "        count += 1\n",
    "    temp+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1104\n",
      "[3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 7, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n"
     ]
    }
   ],
   "source": [
    "print(count)\n",
    "print(chain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x280a71dbd30>]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mlt.imshow(img, cmap='Greys')\n",
    "mlt.plot([i[1] for i in border], [i[0] for i in border])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_list=[\"Alex:1990:London\", \"Tony:1993:NYC\", \"Kate:2001:Beijing\", \"Tony:2001:LA\", \"Alex:1978:Shanghai\"]\n",
    "dict = {}\n",
    "for idx, content in enumerate(my_list):\n",
    "    name = content[:(content.index(':'))]\n",
    "    if name not in dict:\n",
    "        dict[name] = []\n",
    "    dict[name].append(my_list[idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def properNounCorrection(noun):\n",
    "    noun = noun.lower()\n",
    "    a = list(noun)\n",
    "    a[0]= a[0].upper()\n",
    "    a = ''.join(a)\n",
    "    print(a)\n",
    "properNounCorrection('oNAD')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6, 0, 0, 0, 6, 2, 6, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 6, 2, 6, 0, 0, 0, 0, 6, 2, 0, 6, 2]\n"
     ]
    }
   ],
   "source": [
    "a = [0,1,2,3,4,5,6,7]\n",
    "b = [0,0,0,0,6,0,6,6,6,6,6,6,6,6,4,4,4,4,4,4,2,4,2,2,2,2,2,0,2,2,0,2]\n",
    "c = [6,0,0,0,6,2,6,0,0,0,0,0,0,0,6,0,0,0,0,0,6,2,6,0,0,0,0,6,2,0,6,2]\n",
    "l = len(b)\n",
    "p = []\n",
    "def _different(direction, chain_code):\n",
    "    result = []\n",
    "    direction = list(range(direction))\n",
    "    len_code = len(chain_code)\n",
    "    for i in range(len_code):\n",
    "        step = [direction.index(chain_code[i]), direction.index(chain_code[i-1])]\n",
    "        if i ==0:\n",
    "            step = [direction.index(chain_code[len_code-1]), direction.index(chain_code[0])]\n",
    "        temp = (direction.index(chain_code[i])-direction.index(chain_code[i-1]))\n",
    "        if temp < 0:\n",
    "            minimum = min(step)\n",
    "            maximum = max(step)\n",
    "            temp = len(direction[:minimum])+len(direction[maximum:])\n",
    "        result.append(temp)\n",
    "    return result\n",
    "d = _different(8,b)\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "00006066666666444444242222202202\n",
      "60006260000000600000626000062062\n"
     ]
    }
   ],
   "source": [
    "print((''.join([str(i) for i in b])))\n",
    "print((''.join([str(i) for i in d])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "temp = [1,2,3]\n",
    "print([0]+temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function, division\n",
    "import numpy as np\n",
    "import math\n",
    "import cv2\n",
    "from matplotlib import pyplot as mlt\n",
    "\n",
    "class FreemanChainCode(object):\n",
    "    def __init__(self, image_path, **kwargs):\n",
    "        \"\"\"\n",
    "        Required arguments:\n",
    "        - image_path: the location of image, remember '/' with Linux and '\\\\' with Windows\n",
    "        Optional arguments:\n",
    "        - direction: number of direction of chaincode\n",
    "        Default is 8\n",
    "        - object_location: list of tuple elements location of object in image\n",
    "        Default is [(20,218),(20,218)]\n",
    "        - threshold: choose a threshold for detect object clearly with environment\n",
    "        Default is 60\n",
    "        - kernel_size: the size of kernel use for opening and closing to let picture clear, this kernel with have square shape\n",
    "        Default is 10\n",
    "        - morno: choose that you will use closing or opening or none\n",
    "        Default is 'closing'\n",
    "        \"\"\"\n",
    "        self.path = image_path\n",
    "        self.direction = kwargs.pop('direction', 8)\n",
    "        self.object_location = kwargs.pop('location', [(20,218),(20,218)])\n",
    "        self.thresh = kwargs.pop('threshold', 60)\n",
    "        self.kernel_size = kwargs.pop('kernel_size', 10)\n",
    "        self.mor = kwargs.pop('morno', 'closing')\n",
    "        self.length = kwargs.pop('shape_length', 1)\n",
    "\n",
    "    def _preprocessing(self):\n",
    "        image = cv2.imread(self.path,0)\n",
    "        boudary = self.object_location\n",
    "        if np.sum(boudary) != 0:\n",
    "            image = image[boudary[0][0]:boudary[0][1],boudary[1][0]:boudary[1][1]]\n",
    "        if image[0,0] > 80:\n",
    "            ret, img = cv2.threshold(image, self.thresh,255, cv2.THRESH_BINARY_INV)\n",
    "        else:\n",
    "            ret, img = cv2.threshold(image, self.thresh,255, cv2.THRESH_BINARY)\n",
    "        return img\n",
    "    \n",
    "    def _start_point(self):\n",
    "        if self.mor == 'closing':\n",
    "            img = self._closing()\n",
    "        if self.mor == 'opening':\n",
    "            img = self._opening()\n",
    "        if self.mor == 'None':\n",
    "            img = self._preprocessing()\n",
    "        for i, row in enumerate(img):\n",
    "            for j, value in enumerate(row):\n",
    "                if value == 255:\n",
    "                    start_point = (i, j)\n",
    "                    break\n",
    "            else:\n",
    "                continue\n",
    "            break\n",
    "        return start_point, img\n",
    "    def _closing(self):\n",
    "        kernel = np.ones((self.kernel_size,self.kernel_size),np.uint8)\n",
    "        img = self._preprocessing()\n",
    "        closing = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)\n",
    "        return closing\n",
    "    def _opening(self):\n",
    "        kernel = np.ones((self.kernel_size,self.kernel_size),np.uint8)\n",
    "        img = self._preprocessing()\n",
    "        opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)\n",
    "        return opening\n",
    "    def _get_chain_code(self):\n",
    "        '''\n",
    "        Get chain code by direction\n",
    "        '''\n",
    "        start_point, img = self._start_point()\n",
    "        if self.direction == 4:\n",
    "            dicrections = [   0,\n",
    "                           1,    2,\n",
    "                              3    ]\n",
    "            num_necess = 3\n",
    "        elif self.direction == 8:\n",
    "            directions = [ 0,  1,  2,\n",
    "                           7,      3,\n",
    "                           6,  5,  4]\n",
    "            num_necess = 5\n",
    "        dir2idx = dict(zip(directions, range(len(directions))))\n",
    "        move = self.length\n",
    "        # Move the current point follow the direction\n",
    "        if self.direction == 8:\n",
    "            # Columns\n",
    "            change_x =   [-move,  0, move, \n",
    "                          -move,     move,\n",
    "                          -move,  0, move]\n",
    "\n",
    "            # Rows\n",
    "            change_y =   [-move,-move,-move,\n",
    "                           0,             0,\n",
    "                           move, move, move]\n",
    "        elif self.direction == 4:\n",
    "            # Columns\n",
    "            change_x = [       0,    \n",
    "                        -move,    move,\n",
    "                               0       ]\n",
    "            # Rows\n",
    "            change_y = [     -move,\n",
    "                         0,        0,\n",
    "                              move    ]\n",
    "\n",
    "        # Border for drawing the boundary when finish the job\n",
    "        border = []\n",
    "        chain = []\n",
    "        curr_point = start_point\n",
    "        '''\n",
    "        This find the first shape, the direction we choose to move our chain code\n",
    "        '''\n",
    "        for direction in directions:\n",
    "            idx = dir2idx[direction]\n",
    "            # Move to each directions to find the true way\n",
    "            new_point = (start_point[0]+change_y[idx], start_point[1]+change_x[idx])\n",
    "            if img[new_point] != 0:\n",
    "                border.append(new_point)\n",
    "                chain.append(direction)\n",
    "                curr_point = new_point\n",
    "                break\n",
    "        count = 0\n",
    "        while(count < 30):\n",
    "            while curr_point != start_point:\n",
    "                # Make sure the direction always be the first opposite with the current direction\n",
    "                b_direction = (direction + num_necess) % self.direction\n",
    "                if self.direction > 4:\n",
    "                    b_direction += 1\n",
    "                dirs_1 = range(b_direction, 8)\n",
    "                dirs_2 = range(0, b_direction)\n",
    "                dirs = []\n",
    "                dirs.extend(dirs_1)\n",
    "                dirs.extend(dirs_2)\n",
    "                for direction in dirs:\n",
    "                    idx = dir2idx[direction]\n",
    "                    new_point = (curr_point[0]+change_y[idx], curr_point[1]+change_x[idx])\n",
    "                    if img[new_point] != 0:\n",
    "                        border.append(new_point)\n",
    "                        chain.append(direction)\n",
    "                        curr_point = new_point\n",
    "                        break\n",
    "                count += 1\n",
    "            if self.length == 1:\n",
    "                move+=1\n",
    "            else:\n",
    "                break\n",
    "        return border, chain, img, move\n",
    "    def _different(self):\n",
    "        '''\n",
    "        Return the different from chain code\n",
    "        '''\n",
    "        directions = self.direction\n",
    "        _,chain_code,_,_ = self._get_chain_code()\n",
    "        result = []\n",
    "        dir = list(range(directions))\n",
    "        len_code = len(chain_code)\n",
    "        for i in range(len_code):\n",
    "            step = [dir.index(chain_code[i]), dir.index(chain_code[i-1])]\n",
    "            if i ==0:\n",
    "                step = [dir.index(chain_code[len_code-1]), dir.index(chain_code[0])]\n",
    "            temp = (dir.index(chain_code[i])-dir.index(chain_code[i-1]))\n",
    "            if temp < 0:\n",
    "                minimum = min(step)\n",
    "                maximum = max(step)\n",
    "                temp = len(dir[:minimum])+len(dir[maximum:])\n",
    "            result.append(temp)\n",
    "        return result        \n",
    "    def result(self):\n",
    "        '''\n",
    "        This function give the final result, return the boundary point of object\n",
    "        '''\n",
    "        border, chain, img, length = self._get_chain_code()\n",
    "        diff = self._different()\n",
    "        shape = [diff[len(diff)-1]] + diff[:(len(diff)-1)]\n",
    "        chain_code = (''.join([str(i) for i in chain]))\n",
    "        differ = (''.join([str(i) for i in diff]))\n",
    "        shape_no = (''.join([str(i) for i in chain_code]))\n",
    "        return border, chain_code, differ, shape_no, img, length\n",
    "    def show(self):\n",
    "        '''\n",
    "        Show the final solution\n",
    "        '''\n",
    "        #temp\n",
    "        border, chain_code, differ, shape_no, img, length = self.result()\n",
    "        print(\"Our chain code is {}:\".format(chain_code))\n",
    "        print(\"Different {}:\".format(differ))\n",
    "        print(\"Shape number {}:\".format(shape_no))\n",
    "        print(\"Length of shape is: {}\".format(length))\n",
    "        mlt.imshow(img, cmap='Greys')\n",
    "        mlt.plot([i[1] for i in border], [i[0] for i in border])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.35968992248062015\n"
     ]
    }
   ],
   "source": [
    "from assignment import FreemanChainCode\n",
    "from stringmatching import *\n",
    "seven = FreemanChainCode(image_path = 'seven.png', location = [(0,0),(0,0)], morno = 'opening')\n",
    "five = FreemanChainCode(image_path = 'five.jpg')\n",
    "zero = FreemanChainCode(image_path = 'zero.png')\n",
    "_,_,_,shape_1,_ = seven.result()\n",
    "_,_,_,shape_2,_ = five.result()\n",
    "_,_,_,shape_3,_ = zero.result()\n",
    "print(similarity(shape_2,shape_1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matching shape number of triangle and rectangle is: 0.3574051407588739\n",
      "Matching shape number of triangle and square is: 0.14566115702479338\n",
      "Matching shape number of rectangle and square is: 0.14984391259105098\n",
      "Matching shape number of big apple and small apple is: 0.13473423980222496\n",
      "Matching shape number of small apple and pear is: 0.05456634118322803\n"
     ]
    }
   ],
   "source": [
    "from assignment import FreemanChainCode\n",
    "from stringmatching import *\n",
    "triangle = FreemanChainCode(image_path = 'triangle.png', threshold = 200, location = [(0,0),(0,0)])\n",
    "rectangle = FreemanChainCode(image_path= 'rectangle.png', location = [(0,0),(0,0)])\n",
    "square = FreemanChainCode(image_path= 'square.png', location = [(0,0),(0,0)])\n",
    "apple = FreemanChainCode(image_path='apple.jpg', threshold = 200, location = [(0,0),(0,0)])\n",
    "apple_1 = FreemanChainCode(image_path='apple_2.jpg', threshold = 200, location = [(0,0),(0,0)])\n",
    "le = FreemanChainCode(image_path='le.jpg', threshold = 200, location = [(0,0),(0,0)])\n",
    "_,_,_,shape_1,_,_ = triangle.result()\n",
    "_,_,_,shape_2,_,_ = rectangle.result()\n",
    "_,_,_,shape_3,_,_ = square.result()\n",
    "_,_,_,shape_4,_,_ = apple.result()\n",
    "_,_,_,shape_5,_,_ = apple_1.result()\n",
    "_,_,_,shape_6,_,_ = le.result()\n",
    "tri_rec = similarity(shape_1,shape_2)\n",
    "tri_sq  = similarity(shape_1,shape_3)\n",
    "rec_sq  = similarity(shape_2,shape_3)\n",
    "ap_ap1  = similarity(shape_4,shape_5)\n",
    "ap1_le  = similarity(shape_5,shape_6) \n",
    "\n",
    "print(\"Matching shape number of triangle and rectangle is: {}\".format(tri_rec))\n",
    "print(\"Matching shape number of triangle and square is: {}\".format(tri_sq))\n",
    "print(\"Matching shape number of rectangle and square is: {}\".format(rec_sq))\n",
    "print(\"Matching shape number of big apple and small apple is: {}\".format(ap_ap1))\n",
    "print(\"Matching shape number of small apple and pear is: {}\".format(ap1_le))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.44242424242424244\n",
      "0.26620689655172414\n"
     ]
    }
   ],
   "source": [
    "from assignment import FreemanChainCode\n",
    "from stringmatching import *\n",
    "apple = FreemanChainCode(image_path='apple.jpg', threshold = 200, location = [(0,0),(0,0)])\n",
    "apple_1 = FreemanChainCode(image_path='apple_2.jpg', threshold = 200, location = [(0,0),(0,0)])\n",
    "le = FreemanChainCode(image_path='le.jpg', threshold = 200, location = [(0,0),(0,0)])\n",
    "_,_,_,shape_1,_,_ = apple.result()\n",
    "_,_,_,shape_2,_,_ = apple_1.result()\n",
    "_,_,_,shape_3,_,_ = le.result()\n",
    "print(similarity(c,f))\n",
    "print(similarity(d,f))\n",
    "# apple.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function, division\n",
    "import numpy as np\n",
    "import math\n",
    "import cv2\n",
    "from matplotlib import pyplot as mlt\n",
    "\n",
    "class FreemanChainCode(object):\n",
    "    def __init__(self, image_path, **kwargs):\n",
    "        \"\"\"\n",
    "        Required arguments:\n",
    "        - image_path: the location of image, remember '/' with Linux and '\\\\' with Windows\n",
    "        Optional arguments:\n",
    "        - direction: number of direction of chaincode\n",
    "        Default is 8\n",
    "        - object_location: list of tuple elements location of object in image\n",
    "        Default is [(20,218),(20,218)]\n",
    "        - threshold: choose a threshold for detect object clearly with environment\n",
    "        Default is 60\n",
    "        - kernel_size: the size of kernel use for opening and closing to let picture clear, this kernel with have square shape\n",
    "        Default is 10\n",
    "        - morno: choose that you will use closing or opening or none\n",
    "        Default is 'closing'\n",
    "        \"\"\"\n",
    "        self.path = image_path\n",
    "        self.direction = kwargs.pop('direction', 8)\n",
    "        self.object_location = kwargs.pop('location', [(20,218),(20,218)])\n",
    "        self.thresh = kwargs.pop('threshold', 60)\n",
    "        self.kernel_size = kwargs.pop('kernel_size', 10)\n",
    "        self.mor = kwargs.pop('morno', 'closing')\n",
    "        self.length = kwargs.pop('shape_length', 1)\n",
    "\n",
    "    def _preprocessing(self):\n",
    "        image = cv2.imread(self.path,0)\n",
    "        boudary = self.object_location\n",
    "        if np.sum(boudary) != 0:\n",
    "            image = image[boudary[0][0]:boudary[0][1],boudary[1][0]:boudary[1][1]]\n",
    "        if image[0,0] > 80:\n",
    "            ret, img = cv2.threshold(image, self.thresh,255, cv2.THRESH_BINARY_INV)\n",
    "        else:\n",
    "            ret, img = cv2.threshold(image, self.thresh,255, cv2.THRESH_BINARY)\n",
    "        return img\n",
    "    \n",
    "    def _start_point(self):\n",
    "        if self.mor == 'closing':\n",
    "            img = self._closing()\n",
    "        if self.mor == 'opening':\n",
    "            img = self._opening()\n",
    "        if self.mor == 'None':\n",
    "            img = self._preprocessing()\n",
    "        for i, row in enumerate(img):\n",
    "            for j, value in enumerate(row):\n",
    "                if value == 255:\n",
    "                    start_point = (i, j)\n",
    "                    break\n",
    "            else:\n",
    "                continue\n",
    "            break\n",
    "        return start_point, img\n",
    "    def _closing(self):\n",
    "        kernel = np.ones((self.kernel_size,self.kernel_size),np.uint8)\n",
    "        img = self._preprocessing()\n",
    "        closing = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)\n",
    "        return closing\n",
    "    def _opening(self):\n",
    "        kernel = np.ones((self.kernel_size,self.kernel_size),np.uint8)\n",
    "        img = self._preprocessing()\n",
    "        opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)\n",
    "        return opening\n",
    "    def _get_chain_code(self):\n",
    "        '''\n",
    "        Get chain code by direction\n",
    "        '''\n",
    "        start_point, img = self._start_point()\n",
    "        if self.direction == 4:\n",
    "            dicrections = [   0,\n",
    "                           1,    2,\n",
    "                              3    ]\n",
    "            num_necess = 3\n",
    "        elif self.direction == 8:\n",
    "            directions = [ 0,  1,  2,\n",
    "                           7,      3,\n",
    "                           6,  5,  4]\n",
    "            num_necess = 5\n",
    "        dir2idx = dict(zip(directions, range(len(directions))))\n",
    "        move = self.length\n",
    "        # Move the current point follow the direction\n",
    "        if self.direction == 8:\n",
    "            # Columns\n",
    "            change_x =   [-move,  0, move, \n",
    "                          -move,     move,\n",
    "                          -move,  0, move]\n",
    "\n",
    "            # Rows\n",
    "            change_y =   [-move,-move,-move,\n",
    "                           0,             0,\n",
    "                           move, move, move]\n",
    "        elif self.direction == 4:\n",
    "            # Columns\n",
    "            change_x = [       0,    \n",
    "                        -move,    move,\n",
    "                               0       ]\n",
    "            # Rows\n",
    "            change_y = [     -move,\n",
    "                         0,        0,\n",
    "                              move    ]\n",
    "\n",
    "        # Border for drawing the boundary when finish the job\n",
    "        border = []\n",
    "        chain = []\n",
    "        curr_point = start_point\n",
    "        '''\n",
    "        This find the first shape, the direction we choose to move our chain code\n",
    "        '''\n",
    "        for direction in directions:\n",
    "            idx = dir2idx[direction]\n",
    "            # Move to each directions to find the true way\n",
    "            new_point = (start_point[0]+change_y[idx], start_point[1]+change_x[idx])\n",
    "            if img[new_point] != 0:\n",
    "                border.append(new_point)\n",
    "                chain.append(direction)\n",
    "                curr_point = new_point\n",
    "                break\n",
    "        count = 0\n",
    "        while(count < 30):\n",
    "            while curr_point != start_point:\n",
    "                # Make sure the direction always be the first opposite with the current direction\n",
    "                b_direction = (direction + num_necess) % self.direction\n",
    "                if self.direction > 4:\n",
    "                    b_direction += 1\n",
    "                dirs_1 = range(b_direction, 8)\n",
    "                dirs_2 = range(0, b_direction)\n",
    "                dirs = []\n",
    "                dirs.extend(dirs_1)\n",
    "                dirs.extend(dirs_2)\n",
    "                for direction in dirs:\n",
    "                    idx = dir2idx[direction]\n",
    "                    new_point = (curr_point[0]+change_y[idx], curr_point[1]+change_x[idx])\n",
    "                    if img[new_point] != 0:\n",
    "                        border.append(new_point)\n",
    "                        chain.append(direction)\n",
    "                        curr_point = new_point\n",
    "                        break\n",
    "                count += 1\n",
    "            # if self.length == 1:\n",
    "            #     move+=1\n",
    "            # else:\n",
    "            #     break\n",
    "        return border, chain, img, move\n",
    "    def _different(self):\n",
    "        '''\n",
    "        Return the different from chain code\n",
    "        '''\n",
    "        directions = self.direction\n",
    "        _,chain_code,_,_ = self._get_chain_code()\n",
    "        result = []\n",
    "        dir = list(range(directions))\n",
    "        len_code = len(chain_code)\n",
    "        for i in range(len_code):\n",
    "            step = [dir.index(chain_code[i]), dir.index(chain_code[i-1])]\n",
    "            if i ==0:\n",
    "                step = [dir.index(chain_code[len_code-1]), dir.index(chain_code[0])]\n",
    "            temp = (dir.index(chain_code[i])-dir.index(chain_code[i-1]))\n",
    "            if temp < 0:\n",
    "                minimum = min(step)\n",
    "                maximum = max(step)\n",
    "                temp = len(dir[:minimum])+len(dir[maximum:])\n",
    "            result.append(temp)\n",
    "        return result        \n",
    "    def result(self):\n",
    "        '''\n",
    "        This function give the final result, return the boundary point of object\n",
    "        '''\n",
    "        border, chain, img, length = self._get_chain_code()\n",
    "        diff = self._different()\n",
    "        shape = [diff[len(diff)-1]] + diff[:(len(diff)-1)]\n",
    "        chain_code = (''.join([str(i) for i in chain]))\n",
    "        differ = (''.join([str(i) for i in diff]))\n",
    "        shape_no = (''.join([str(i) for i in chain_code]))\n",
    "        return border, chain_code, differ, shape_no, img, length\n",
    "    def show(self):\n",
    "        '''\n",
    "        Show the final solution\n",
    "        '''\n",
    "        #temp\n",
    "        border, chain_code, differ, shape_no, img, length = self.result()\n",
    "        print(\"Our chain code is {}:\".format(chain_code))\n",
    "        print(\"Different {}:\".format(differ))\n",
    "        print(\"Shape number {}:\".format(shape_no))\n",
    "        print(\"Length of shape is: {}\".format(length))\n",
    "        mlt.imshow(img, cmap='Greys')\n",
    "        mlt.plot([i[1] for i in border], [i[0] for i in border])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Our chain code is 33333333333333333333355657677766777777765555771077777776555555543354333355433554354335543576555557765777765577776557777657777765577777777777777777111133333332113333212333213333211333332132111110710777771111107107711111077777771111111333321333333333333333211:\n",
      "Different 20000000000000000000020172710070100000077000202770000007700000077027700020770207727702077227700002077200077020007702000772000077020000000000000000200020000007702000771100772000770200007727700007727700002000077277020000770000002000000200077200000000000000770:\n",
      "Shape number 33333333333333333333355657677766777777765555771077777776555555543354333355433554354335543576555557765777765577776557777657777765577777777777777777111133333332113333212333213333211333332132111110710777771111107107711111077777771111111333321333333333333333211:\n",
      "Length of shape is: 3\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# from assignment import FreemanChainCode\n",
    "# from stringmatching import *\n",
    "five = FreemanChainCode(image_path = 'five.jpg', shape_length = 3)\n",
    "five.show()"
   ]
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}