Sobel filter a simple image array.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1117120d0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111613210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import signal\n",
    "\n",
    "#random sample image\n",
    "A = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
    "                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
    "                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
    "                   [0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n",
    "                   [0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n",
    "                   [0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n",
    "                   [0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n",
    "                   [0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n",
    "                   [0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n",
    "                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
    "                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
    "                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])\n",
    "\n",
    "#kernels \n",
    "kernel_horizontal = np.array([[-1, 0, 1], \n",
    "                              [-2, 0, 2], \n",
    "                              [-1, 0, 1]], dtype = np.float)\n",
    "\n",
    "kernel_vertical   = np.array([[1, 2, 1], \n",
    "                              [0, 0, 0], \n",
    "                              [-1, -2, -1]], dtype = np.float)\n",
    "\n",
    "Gx = signal.convolve2d(A, kernel_horizontal, boundary='symm', mode='same')\n",
    "Gy = signal.convolve2d(A, kernel_vertical, boundary='symm', mode='same')\n",
    "\n",
    "plt.subplot(131)\n",
    "plt.title(\"Original\")\n",
    "plt.imshow(A, cmap=\"gray\")\n",
    "plt.subplot(132)\n",
    "plt.title(\"Gx\")\n",
    "plt.imshow(Gx, cmap=\"gray\")\n",
    "plt.subplot(133)\n",
    "plt.title(\"Gy\")\n",
    "plt.imshow(Gy, cmap=\"gray\")\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
      "  0.         0.         0.         0.         0.         0.        ]\n",
      " [0.         0.         1.41421356 3.16227766 4.         4.\n",
      "  4.         4.         3.16227766 1.41421356 0.         0.        ]\n",
      " [0.         0.         3.16227766 4.24264069 4.         4.\n",
      "  4.         4.         4.24264069 3.16227766 0.         0.        ]\n",
      " [0.         0.         4.         4.         0.         0.\n",
      "  0.         0.         4.         4.         0.         0.        ]\n",
      " [0.         0.         4.         4.         0.         0.\n",
      "  0.         0.         4.         4.         0.         0.        ]\n",
      " [0.         0.         4.         4.         0.         0.\n",
      "  0.         0.         4.         4.         0.         0.        ]\n",
      " [0.         0.         4.         4.         0.         0.\n",
      "  0.         0.         4.         4.         0.         0.        ]\n",
      " [0.         0.         3.16227766 4.24264069 4.         4.\n",
      "  4.         4.         4.24264069 3.16227766 0.         0.        ]\n",
      " [0.         0.         1.41421356 3.16227766 4.         4.\n",
      "  4.         4.         3.16227766 1.41421356 0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.        ]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x111b53850>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1117f1e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gradient_magnitude = np.sqrt(Gx * Gx + Gy * Gy)\n",
    "print gradient_magnitude\n",
    "plt.subplot(121)\n",
    "plt.title(\"Original image\")\n",
    "plt.imshow(A, cmap=\"gray\")\n",
    "plt.subplot(122)\n",
    "plt.title(\"Gradient magnitude\")\n",
    "plt.imshow(gradient_magnitude, cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}