26-Finding_contours.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">یافتن contour در تصویر</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">در OpenCV، یافتن خطوط مانند یافتن شی سفید از پس زمینه سیاه است. بنابراین به یاد داشته باشید، شیء یافت شده باید سفید و پس زمینه باید سیاه باشد.\n",
    "<br/>\n",
    "خطوط یک ابزار مفید برای تجزیه و تحلیل شکل و تشخیص و تشخیص اشیا هستند.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "``contours, hierarchy = cv2.findContours(image, mode, method)``"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">\n",
    "آرگومانهای این تابع به ترتیب اول تصویر منبع، دوم حالت بازیابی کانتور، سوم روش تقریب کانتور است.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "image = cv2.imread('images/shapes.png', 0)\n",
    "ret, thresh = cv2.threshold(image, 127, 255, 0)\n",
    "contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(24, 1, 2)\n",
      "(134, 1, 2)\n",
      "(146, 1, 2)\n",
      "(17, 1, 2)\n",
      "(117, 1, 2)\n",
      "(4, 1, 2)\n"
     ]
    }
   ],
   "source": [
    "for contour in contours:\n",
    "    print(contour.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">ترسیم خروجی با روش رسم چند ضلعی در</div>\n",
    "\n",
    "[07-Drawing-and-annotating.ipynb](https://nbviewer.org/github/Alireza-Akhavan/class.vision/blob/master/07-Drawing-and-annotating.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x160b28b4cd0>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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++SR33nknZ599Np988olnx5Gq/yh+IiL+B2EYaURGRgYPPPAAAwcOZPDgwRw5cqQxu1urYSbvty6SYaQhgUCA9evXc8EFF/DDH/7Qs+NEMul3JxH5p4h8KiKbROQXbnm+iKwQka3ua17QNpaf2jASnBdeeIFNmzYxcOBA7w4SwYBcB6CPu5wLfIaTg/ohYJpbPg140F1ucH5qEuBBMJMpHTV27FjdvXu3dunSpTH7aVRu6r2q+pG7XAp8ipMOdjQwz11tHnC1u2z5qQ0jSdi5cyc5OTmePQDZoDEYEekM/Bfwb6C9qu4Fx4SAdu5qlp/aMJKENWvW8OqrrzJmzBiys7Njvv+IDUZEcoAXgCmqWtf1xYjyU1tuasNIDH7/+99z7rnn0qJFi5jvOyKDEZGmOObyjKoucYv3VaWQdV8L3fKI8lOr5aY2jISguLiYwsJCTyYFj+QqkgBPAJ+q6h+DPloKTHSXJwIvB5VbfmrDSBK+/vprVq1axeTJk2P/IGoEV5EG4HRxNgDrXI0AWgNvAVvd1/ygbWbgXD3aAgyP4Bi+j6abTOmsiy66SA8ePKj9+vWLZvuwV5HsTl7DMGjWrBmPPvoob7zxBs8991xDNw97J68ZjGEYAGRlZVFRUUEUnhDWYOxhR8MwADh27FjM92nPIhmG4RlmMIZheIYZjGEYnmEGYxiGZ5jBGIbhGWYwhmF4hhmMYRieYQZjGIZnmMEYhuEZZjCGYXiGGYxhGJ5hBmMYhmeYwRiG4RlmMIZheIYZjGEYnmEGYxiGZ5jBGIbhGY3JTT1TRL4UkXWuRgRtY7mpDcOIaMrM74D/VtWPRCQXWCsiK9zP/qSqfwheWUTOAcYBPYFTgTdFpLuqVsYy8GSiDU7uFoBdwF7/QjGMuFKvwbhpYatSxJaKSFVu6nBU56YGdopIVW7q1TGIN6nIxEkYNRm40C1bBzwJzMZxbiO1yczMpFOnTjjpxZwcRGVlZT5HFT8ak5sa4C4R2SAic0Qkzy2z3NQ4J/ZmHCO5MKi8N/AH4Bc4BmSkLi1atOC2227jk08+YcuWLWzZsoU//OEP5Obm+h1a/KgvKVpQcrQcYC1wrfu+Pc7fSAZwPzDHLZ8N3BC03RPAj0LsbzLwoSvfE0/FWpNAj4JqGJWD3pMAcZpir4yMDB03bpyuWbNGy8rKNJiKigrdtWuX/uxnP9Pc3FzfY42RwiZei9RcmgLLgXvCfN4Z2OguTwemB322HLgonTI73gpaSi1Tufhi1YEDa5QdxTGZjASI2RQb9ejRQx944AH99ttvtS4CgYA+//zz2r9/f99jjoGiNxhAgKeAP9cq7xC0fDfOuAs4g7vrgWbAGcAOIDMdDCYD9GZqmYuI6oQJqt98o3rkiOrtt6tmZFR/XoZjMk0SIH5T9GrZsqX+8Y9/1F27dtVpLLU5cOCAPvvss9q+fXvf69AINcpgwuWmng/8xy1fSk3DSbvc1FXmUqNblJGhOn68amnp8V9UebnqHXeoZmZWr3cMM5lkVcuWLXXIkCH6yiuvaCAQaJC5VBEIBPT999/XW2+9VU8++WTf6xSFGtdF8loJcIIaJQG9Cac1olUScczl8OETf1Hl5aq33easw3GTudvdl9/1MUXwnYtoXl6evvTSS1peXh6VsdTm2LFjum7dOu3atatmZGT4XscGyAzGS90MepggcwHVceNqtlxqc+SIYzJB25SBTkmA+pjqVocOHXTKlCm6e/duraysjIG11GTfvn06a9Ys7dWrl+91jVBmMF5pEOghaplL1ZhLfRw+fEJL5ihOS8YGfhNPGRkZ2r9/f121alUj7CNydu7cqSNHjtSWLVv6Xvd6ZAbjhfJBXyXIWESclsvBg5H/iqpaMkFjMvtBOyVA/UzHddZZZ+mSJUti1h2KlEAgoG+99ZYOHTrU93NQh8xgYq080GWgAYIM5uqr6+4WhaO8XHXy5OqrSwHQd0E7JEA90105OTk6ZcoU3bRpU9SDuLGgqKhI77//fm3btq2KiO/npZbMYGKpkOZy0kmqy5ZF/wsqK1Pt1at6fwHQd0BPSYD6pqNERM8//3zdunWrHjt2LPrvNYZUVlZqcXGxTpgwIdFMxgwmVmoN+gq1zOXkk1UXL1ZtzH+4QED19ddV27Y9vl/QlZjJxFsXXHCBPvHEE7pv377ov08POXTokC5fvlwHDRqUKEZjBhML5YP+nSBjAdVWrVSffz52v57XX1dt08ZMxgfl5eXpmDFjtKioKHbfp4eUlZXp1KlTtWPHjn6fOzOYRv/4cAZ0a7RccnNVX3ihcS2X2gQCqitWqLZuXX0c6y7FRxMnTvR1nCUaAoGAFhQU+H3uwhqMzWgXAXnAM8BwnOcmAMjNhSefhKuvBpFwmzYcEbj8cli4EFq3doqAS4DFwCmxO5IRAonldxkHEj1eM5h6yMd5JmIYQeZy0kkwd65jLhkenMIqk1mwANq0cYqAi4FncR5jN2LPjh07KCkp8TuMBlFRUcH69ev9DiM84Zo28RQJ0DwOpXzQpYQY0F20KLbdorpYtuyEMZl/gLZPgPOTivr444/j873GiNLSUm3btq3f5826SA0lD2fmuVHUarkUFMB118W2W1QXw4bB/PnV3SWAy4CFWEvGC+bOnet3CA3i+eef5+DBg36HEZ5wzhNPkQD/uYJ1MiEuRefkOFeL/BgEDARUly8/YeD3bawlE2t17NhRN27cGP/vOApKSkoSZT4Za8FEykk4Yy41BnRzcpwxl2uvjV/LJRgRGDzYGZPJz3eKcObReA5oF/+IUpY9e/awY8eOqn98Cc3Bgwf56KOP/A6jbsI5TzyF/w5crVGg3xHUcmnVyhlz8eCp2QYTCKi+9lqNMZkA6FtYSyaWGjBggCdPSceau+66y/dz5cpaMJEwDGcC4erJuFu1gscfhzFjvLla1FBEYPhwmDevxiXsQcA1vgaWWqxdu5alS5f6HUadHDlyhM8++8zvMOolAf5qEoMhOPOCtg0uHDkSxo71p1tUF8OHw9NPV3eXAH4PXEtQt86ImrKyMjZv3kwgEPA7lLCsXr2aN954w+8w6idc0yaewucm3hWgRQR1i0B1wADVvXs9bOA2kkBAdebMGjEfAv0RNiteLNSuXTs92JBpN+LMpZde6vs5CpJ1kcJxBc7Na22CCwcMgEWL4JQEvm9WBH7+cxhRnbGXXGAO1l2KBUeOHEnYG9g+//xzvvrqK7/DiIxwzqPHWxfZwAc4mQI2Af/rlucDK4Ct7mte0DbTgW04k34PjeAYvjjvmaCF1Gq5/PCHql99Fa9/RI1n/37VK6+sUYcDoL39/6+W9Lr++uv16NGjfn/DNQgEAvrQQw/5fm5qqdFpS3Lc5aY4WR37Aw8B09zyacCD7vI51Exbsp0ETVvyMLXudRk4MLnMpYqiItURI2qYzBz/f3RJryZNmuj27dv9/nZrUFFRoV26dPH93NRS9F0kt16H3bdNXSlODup5bvk84Gp3uTo3taruxGnJ9KvvOH7Qk1qDog8+CB06+BRNI2jTBh5+uMaVrp4+hpMqVFZW8vLLL/sdRg1WrVpFcXGx32FETERjMCKSKSLrgEJghar+G2ivqnsB3Neq+72SJjd1AMcpq3n7bUjgKwdhUYV33nFeXZKwFgmHqvLkk08mzHhHeXk5BQUFHDp0yO9QIiYig1HVSlXtDXQE+olIrzpWD3WlVE9YSWSyiHwoIh9GFmrseZBagf361/DnPyeXyajCnDkwZUoNg3nQx5BSiQ0bNrBr1y6/wwDg6NGjLF++3O8wGkSDriKp6jfASpx70vaJSAcA97XQXW0P0Clos47ACf8CVLVAVfuqat8o4o4Ja4ACoLKqoLwcZsyAv/wFvvvOr7AiJxBwHmGYMgWOHgUcw3wG+IevgaUWs2fPrhor9JU5c+Zw+PDh+ldMJMINzujxAdi2wMnucnPgHeBKYBY1B3kfcpeTKjd1M9DHQCsIGuxt2lT18ce9GqeLHfv2qZ5ySnXclaDPgub4P+iXUmrdurWuXr3a16/62LFjeuONN/p+LsKoUVeRzgM+xslBvRH4H7e8NfAWzmXqt4D8oG2SKjd1FuhfqfUM0gUXqH75Zdx+QA2muFh1yJATzKWl/z+2lNT8+fN9nU5z48aN2rRpU9/PQxjZnLz1KRvHZDRYAwYk5mXr4mLVYcNqxLoVa7l4qV69eum3337r21c+YcIE389BHbI7eeujHHiE4wNJAPzrXzBuHOzd609QoSguhp/+FF5/vbroAPBLIMl650lFYWEhX3zxhS/HPnDgADt27PDl2I0mnPPEU/jvwNUaTIi7e3/wg8RoyezfrzpyZI3YvsF5/sjv85YOmjp1qlZUVMT1Kw8EArpw4ULf616PwrZgRJ0/cF9xk0clDJfjTElZ4/mkiy+GxYv9uxGvpARuuAGWLasuOgTcBCzxJ6K0Iysrix//+MdkZ2fH9biLFy+mqKgorsdsIGs13NXgcM4TT+G/A5+gywnRkrnkEn9aMvv3qw4dqipSHctB0KuxJ6dNCSEb5I1GQ0iAhyGLi094zugA6LWYuZgSRmYw0WoYYUwmHnPFFBerjhp1grlclwDnxWQKkhlMYzSYEBNSeT3wG2JA9yA2oGtKSJnBNFYhZ7275BJvWjIlJarDh9c4VtVsdX6fB5MphMxgYqEhhDCZWHeXqloutQZ0bSpMUwLLDCZWCjkmc9llsTOZm26qse8DoGMSoN4mUx0yg4mlhoEWU8tkBg9WbcxNWCFyHn0DOjYB6msy1SN7VCCWvA5MAPYHF65eDS+9FN1cMqqwYgX85CfOowDAd8BtwKJGR2sYPuJ36yUZWzBVGozThdEqRZO/OhBQfeONkHmn8xKgjiZTBLIukhfKBH2RWl2l3FzHZCKlVlJ7BV0FekoC1M9kilBmMF4pH3QptUzm5JNVn3uu/pbMsmU1xlwU9B+YuZiSTmYwXiof9BVqpUBp1Ur1hRdUQyVRDwSclkutJPYrMXMxJaXMYLxWHugyaplMbq7qkiU1TSYQUH3zzRPM5V+YuZiSVmYw8VAe6GuEMJkXXnCMxczFlJoKazBNMGLGAeAG4ClgZFVhaSncfDPs3w/Z2XDPPdWXosGZQf164Ot4B2sY8SCC1kW43NQzgS+Bda5GBG2TFLmpvVJrQozJhNBKrOViSgl5kpt6JjA1xPpJk5vaS+UTYkyG492itzFzMaWMPMlNHY6kyU3tJSXAj4E3gIqg8u+A1Vi3yEgPGpObGuAuEdkgInNEJM8tS5rc1F5TAowFxuDM8bsQmIiTtS6B8hQYhmc0Jjf1Y8CZQG+cv5eH3dWTJjd1PDgELAXGu1qAMxhsGOlA1LmpVXWfazwB4P843g1KmtzUhmF4S70GIyJtReRkd7k5cAWwuSrxvcs1OGllwfmHPU5EmonIGUA3nKtQhmGkGZHcB9MBmCcimTiGtEhVXxGR+SLSG6f7swtndgFUdZOILAI+wRnT/JmqVnoSvWEYCY0lXjMMo7GETbxmE04ZhuEZZjCGYXiGGYxhGJ5hBmMYhmeYwRiG4RlmMIZheIYZjGEYnmEGYxiGZ5jBGIbhGWYwhmF4hhmMYRieYQZjGIZnmMEYhuEZZjCGYXiGGYxhGJ5hBmMYhmckSmbHYuCI+5qqtMHql8xY/cJzergPEmJGOwAR+TCVJwC3+iU3Vr/osC6SYRieYQZjGIZnJJLBFPgdgMdY/ZIbq18UJMwYjGEYqUcitWAMw0gxfDcYERkmIltEZJuITPM7nmgQkTkiUigiG4PK8kVkhYhsdV/zgj6b7tZ3i4gM9SfqyBGRTiLyTxH5VEQ2icgv3PKUqKOIZIvIByKy3q3f/7rlKVG/KkQkU0Q+FpFX3Pfe109VfROQCWwHugBZwHrgHD9jirIeA4E+wMagsoeAae7yNOBBd/kct57NgDPc+mf6XYd66tcB6OMu5wKfufVIiToCAuS4y02BfwP9U6V+QfW8B1gAvBKv36jfLZh+wDZV3aGqx4CFwGifY2owqvo2UFKreDQwz12eB1wdVL5QVb9V1Z3ANpzzkLCo6l5V/chdLgU+BU4jReqoDofdt01dKSlSPwAR6QiMBP4WVOx5/fw2mNOA3Zh/OxoAAAGdSURBVEHv97hlqUB7Vd0Lzh8o0M4tT+o6i0hn4L9w/sunTB3d7sM6oBBYoaopVT/gz8B9QCCozPP6+W0wEqIs1S9rJW2dRSQHeAGYoqqH6lo1RFlC11FVK1W1N9AR6CcivepYPanqJyJXAoWqujbSTUKURVU/vw1mD9Ap6H1H4CufYok1+0SkA4D7WuiWJ2WdRaQpjrk8o6pL3OKUqiOAqn4DrASGkTr1uwS4SkR24QxDDBKRp4lD/fw2mDVANxE5Q0SygHHAUp9jihVLgYnu8kTg5aDycSLSTETOALoBH/gQX8SIiABPAJ+q6h+DPkqJOopIWxE52V1uDlwBbCZF6qeq01W1o6p2xvkb+4eq3kA86pcAI9sjcK5KbAdm+B1PlHV4FtgLVOC4/ySgNfAWsNV9zQ9af4Zb3y3AcL/jj6B+A3CayBuAda5GpEodgfOAj936bQT+xy1PifrVquulHL+K5Hn97E5ewzA8w+8ukmEYKYwZjGEYnmEGYxiGZ5jBGIbhGWYwhmF4hhmMYRieYQZjGIZnmMEYhuEZ/w+xhdhjbLg6JQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# draw first contour\n",
    "bgr_image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)\n",
    "cv2.polylines(bgr_image, [contours[0]], True, (0,0,255), 5)\n",
    "plt.imshow(bgr_image[...,::-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x160b22d9460>"
      ]
     },
     "execution_count": 45,
     "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": [
    "# draw last contour\n",
    "cv2.polylines(bgr_image, [contours[-1]], True, (0,255,0), 5)\n",
    "plt.imshow(bgr_image[...,::-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">ترسیم با تابع `drawContours` در opencv</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl;text-align:right;font-family:Tahoma\">\n",
    "ترسیم همه‌ی contour ها</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x160b248cc40>"
      ]
     },
     "execution_count": 47,
     "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": [
    "import numpy as np\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "image = cv2.imread('images/shapes.png', 0)\n",
    "ret, thresh = cv2.threshold(image, 127, 255, 0)\n",
    "contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n",
    "\n",
    "bgr_image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)\n",
    "\n",
    "\n",
    "all_contours = cv2.drawContours(bgr_image, contours, -1, (0,255,0), 3)\n",
    "plt.imshow(all_contours)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl;text-align:right;font-family:Tahoma\">\n",
    "رسم یک کانتور با تابع drawContours</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x160b25c0f40>"
      ]
     },
     "execution_count": 49,
     "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": [
    "import numpy as np\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "image = cv2.imread('images/shapes.png', 0)\n",
    "ret, thresh = cv2.threshold(image, 127, 255, 0)\n",
    "contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n",
    "\n",
    "bgr_image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)\n",
    "\n",
    "# as an example, third contour\n",
    "cnt = contours[3]\n",
    "\n",
    "third_contour = cv2.drawContours(bgr_image, [cnt], -1, (0,255,0), 3)\n",
    "plt.imshow(third_contour)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl;text-align:right;font-family:Tahoma\">\n",
    "و یا ...</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x160b2725640>"
      ]
     },
     "execution_count": 50,
     "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": [
    "import numpy as np\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "image = cv2.imread('images/shapes.png', 0)\n",
    "ret, thresh = cv2.threshold(image, 127, 255, 0)\n",
    "contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n",
    "\n",
    "bgr_image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)\n",
    "\n",
    "\n",
    "third_contour = cv2.drawContours(bgr_image, contours, 3, (0,255,0), 3)\n",
    "plt.imshow(third_contour)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Contour Approximation Method "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cv2.CHAIN_APPROX_NONE:\n",
      "contours[0].shape: (606, 1, 2) so number of points for the contour: 606\n",
      "-----\n",
      "cv2.CHAIN_APPROX_SIMPLE:\n",
      "contours[0].shape: (4, 1, 2) so number of points for the contour: 4\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'CHAIN_APPROX_SIMPLE')"
      ]
     },
     "execution_count": 87,
     "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"
    },
    {
     "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": [
    "import numpy as np\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "image = cv2.imread('images/rectangle.png', 0)\n",
    "ret, thresh = cv2.threshold(image, 127, 255, 0)\n",
    "contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)\n",
    "\n",
    "print(\"cv2.CHAIN_APPROX_NONE:\")\n",
    "print(\"contours[0].shape: {} so number of points for the contour: {}\".format(contours[0].shape, len(contours[0])))\n",
    "\n",
    "bgr_image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)\n",
    "for point in contours[0]:\n",
    "    cv2.circle(bgr_image, (point[0][0], point[0][1]), 2, (0,255,0), -1)\n",
    "plt.imshow(bgr_image[...,::-1]); plt.title('CHAIN_APPROX_NONE')\n",
    "print('-'*5)\n",
    "\n",
    "\n",
    "contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n",
    "print(\"cv2.CHAIN_APPROX_SIMPLE:\")\n",
    "print(\"contours[0].shape: {} so number of points for the contour: {}\".format(contours[0].shape, len(contours[0])))\n",
    "\n",
    "bgr_image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)\n",
    "for point in contours[0]:\n",
    "    cv2.circle(bgr_image, (point[0][0], point[0][1]), 2, (0,255,0), -1)\n",
    "    \n",
    "plt.figure(); plt.imshow(bgr_image[...,::-1]); plt.title('CHAIN_APPROX_SIMPLE')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://docs.opencv.org/4.x/d4/d73/tutorial_py_contours_begin.html"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}