morphology.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1655af190>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img_matrix = np.array([[1,1,1,1,1,1,1,1,1,1,1],\n",
    "              [1,1,1,1,1,1,1,1,1,1,1],\n",
    "              [1,1,1,1,1,1,1,1,1,1,1],\n",
    "              [1,1,1,1,1,0,0,1,1,1,1],\n",
    "              [1,1,1,1,1,0,0,1,1,1,1],\n",
    "              [1,1,1,1,1,1,1,1,1,1,1],\n",
    "              [1,1,1,1,1,1,1,1,1,1,1],\n",
    "              [1,1,1,1,1,1,1,1,1,1,1],\n",
    "              ]).astype('uint8')\n",
    "structuring_kernel = np.array([[1,1,1],\n",
    "                               [1,1,1],\n",
    "                               [1,1,1]]).astype('uint8')\n",
    "inverted_img = np.invert(img_matrix)\n",
    "plt.imshow(inverted_img, cmap = 'gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x16569c3a0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_img = cv2.dilate(inverted_img, structuring_kernel)\n",
    "plt.imshow(final_img, cmap = 'gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x16570d430>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_img = cv2.erode(inverted_img, structuring_kernel)\n",
    "plt.imshow(final_img, cmap = 'gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## And if we invert the image, we could get away with only one operation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x16576c1c0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_img = np.invert(cv2.erode(img_matrix, structuring_kernel))\n",
    "plt.imshow(final_img, cmap = 'gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Image Skeletonization:\n",
    "(Assumption: Image is already binarized)\n",
    "### Algorithm:\n",
    "1. Compute Opening of Original Image\n",
    "2. Subract opened image from original image (img_temp)\n",
    "3. Erode the original Image, Compute it's union with temp\n",
    "4. Repeat 2-4 until the image is completely eroded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x165c18e80>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = cv2.imread(\"images/ninja.png\", 0)\n",
    "img_binarized = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)[1]\n",
    "plt.imshow(img_binarized, cmap = 'gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating Empty Skeleton:\n",
    "blank_canvas = np.zeros(img_binarized.shape, dtype = 'uint8')\n",
    "#Creating a Kernel:\n",
    "element = cv2.getStructuringElement(cv2.MORPH_CROSS, (3,3))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "while True:\n",
    "    open = cv2.morphologyEx(img_binarized, cv2.MORPH_OPEN, element)\n",
    "    temp = cv2.subtract(img_binarized, open)\n",
    "    eroded = cv2.erode(img_binarized, element)\n",
    "    blank_canvas = cv2.bitwise_or(blank_canvas, temp)\n",
    "    img_binarized = eroded.copy()\n",
    "    if cv2.countNonZero(img_binarized)==0:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x165c81fd0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(blank_canvas, cmap = 'gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Limitations of this code:\n",
    "1. Your image should be a black canvas with white pixels as image of interest, not the opposite."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "463a0e88ca0eab714d8c4b0ec170b469cf96d6c4c8f6fd8de5cffeb6b0e77319"
  },
  "kernelspec": {
   "display_name": "Python 3.8.9 ('env')",
   "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": "3.8.9"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}