Process of Harris Corner Detection Algorithm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Process of Harris Corner Detection Algorithm\n",
    "1. Color to grayscale\n",
    "2. Spatial derivative calculation\n",
    "3. Structure tensor setup\n",
    "4. Harris response calculation\n",
    "5. Non-maximum suppression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1 : Color to grayscale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from skimage.io import imread\n",
    "from skimage.color import rgb2gray\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import ndimage as ndi\n",
    "from skimage.feature import peak_local_max\n",
    "from skimage.feature import corner_harris, corner_peaks\n",
    "\n",
    "img = imread('resources/box.jpg')\n",
    "imggray = rgb2gray(img)\n",
    "\n",
    "plt.imshow(imggray, cmap=\"gray\")\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 2 : Spatial derivative calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import signal as sig\n",
    "import numpy as np\n",
    "\n",
    "def gradient_x(imggray):\n",
    "    ##Sobel operator kernels.\n",
    "    kernel_x = np.array([[-1, 0, 1],[-2, 0, 2],[-1, 0, 1]])\n",
    "    return sig.convolve2d(imggray, kernel_x, mode='same')\n",
    "def gradient_y(imggray):\n",
    "    kernel_y = np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]])\n",
    "    return sig.convolve2d(imggray, kernel_y, mode='same')\n",
    "\n",
    "I_x = gradient_x(imggray)\n",
    "I_y = gradient_y(imggray)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 3 : Structure tensor setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "Ixx = ndi.gaussian_filter(I_x**2, sigma=1)\n",
    "Ixy = ndi.gaussian_filter(I_y*I_x, sigma=1)\n",
    "Iyy = ndi.gaussian_filter(I_y**2, sigma=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 4: Harris response calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "k = 0.05\n",
    "\n",
    "# determinant\n",
    "detA = Ixx * Iyy - Ixy ** 2\n",
    "# trace\n",
    "traceA = Ixx + Iyy\n",
    "    \n",
    "harris_response = detA - k * traceA ** 2\n",
    "\n",
    "# height, width = imggray.shape\n",
    "# harris_response = []\n",
    "# window_size = 6\n",
    "# offset = int(window_size/2)\n",
    "# for y in range(offset, height-offset):\n",
    "#     for x in range(offset, width-offset):\n",
    "#         Sxx = np.sum(Ixx[y-offset:y+1+offset, x-offset:x+1+offset])\n",
    "#         Syy = np.sum(Iyy[y-offset:y+1+offset, x-offset:x+1+offset])\n",
    "#         Sxy = np.sum(Ixy[y-offset:y+1+offset, x-offset:x+1+offset])\n",
    "        \n",
    "#         #Find determinant and trace, use to get corner response\n",
    "#         det = (Sxx * Syy) - (Sxy**2)\n",
    "#         trace = Sxx + Syy\n",
    "#         r = det - k*(trace**2)\n",
    "        \n",
    "#         harris_response.append(r)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 5: Non-maximum suppression\n",
    "1. Edge : r < 0\n",
    "2. Corner : r > 0\n",
    "3. Flat: r = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img_copy_for_corners = np.copy(img)\n",
    "img_copy_for_edges = np.copy(img)\n",
    "\n",
    "for rowindex, response in enumerate(harris_response):\n",
    "    for colindex, r in enumerate(response):\n",
    "        if r > 0:\n",
    "            # this is a corner\n",
    "            img_copy_for_corners[rowindex, colindex] = [255,0,0]\n",
    "        elif r < 0:\n",
    "            # this is an edge\n",
    "            img_copy_for_edges[rowindex, colindex] = [0,255,0]\n",
    "    \n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(10,10))\n",
    "ax[0].set_title(\"corners found\")\n",
    "ax[0].imshow(img_copy_for_corners)\n",
    "ax[1].set_title(\"edges found\")\n",
    "ax[1].imshow(img_copy_for_edges)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grouping the corners into one point\n",
    "You can see there are multiple corners points found in the image, we need to find the local maxima of the corner points. there is a method in skimage which does a neat job in finding the peaks "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10ba44630>]"
      ]
     },
     "execution_count": 6,
     "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": [
    "corners = corner_peaks(harris_response)\n",
    "fig, ax = plt.subplots()\n",
    "ax.imshow(img, interpolation='nearest', cmap=plt.cm.gray)\n",
    "ax.plot(corners[:, 1], corners[:, 0], '.r', markersize=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10c492470>]"
      ]
     },
     "execution_count": 8,
     "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": [
    "#Harris corner detection using skimage library\n",
    "from skimage.feature import corner_harris, corner_peaks\n",
    "coords = corner_peaks(skimage_harris_response)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.imshow(img, interpolation='nearest', cmap=plt.cm.gray)\n",
    "ax.plot(coords[:, 1], coords[:, 0], '.r', markersize=3)"
   ]
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}