3-contour-detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = cv2.imread(\"data/contour.jpg\", 0)\n",
    "\n",
    "plt.figure(figsize=(5,5)), plt.title(\"Original\"), plt.imshow(img, cmap=\"gray\"), plt.axis(\"off\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find Contour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "contours, hierarch = cv2.findContours(image=img, mode=cv2.RETR_CCOMP, method=cv2.CHAIN_APPROX_SIMPLE)\n",
    "\n",
    "external_contour = np.zeros(img.shape)\n",
    "internal_contour = np.zeros(img.shape)\n",
    "\n",
    "for i in range(len(contours)):\n",
    "    \n",
    "    # external\n",
    "    if hierarch[0][i][3] == -1:\n",
    "        cv2.drawContours(image=external_contour, contours=contours, contourIdx=i, color=255, thickness = -1)\n",
    "        \n",
    "    # internal\n",
    "    else:\n",
    "        cv2.drawContours(image=internal_contour, contours=contours, contourIdx=i, color=255, thickness = -1)\n",
    "        \n",
    "plt.figure(figsize=(5,5)), plt.title(\"External Contours\"), plt.imshow(external_contour, cmap=\"gray\"), plt.axis(\"off\"); \n",
    "\n",
    "plt.figure(figsize=(5,5)), plt.title(\"Internal Contours\"), plt.imshow(internal_contour, cmap=\"gray\"), plt.axis(\"off\"); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}