PerformConvolution01.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ##                                                    Perform Convolution 01"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I have try to implement these example by the help of \"Andrew Ng CNN videos\". Thanx to Andrew Ng :) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "#import numpy as np\n",
    "from scipy import signal \n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f = [\n",
    "        [10,10,10,0,0,0],\n",
    "        [10,10,10,0,0,0],\n",
    "        [10,10,10,0,0,0],\n",
    "        [10,10,10,0,0,0],\n",
    "        [10,10,10,0,0,0],\n",
    "        [10,10,10,0,0,0],\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f77dca86198>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPgAAAD8CAYAAABaQGkdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f78103c2240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#filter or kernel\n",
    "g = [\n",
    "        [3,0,-3],\n",
    "        [10,0,-10],\n",
    "        [3,0,-3]\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f77dc824588>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DQ1ITw0NSk/8HRu8L8XV/OIsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f77dc85dba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(g) #;plt.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f77dc787470>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQoAAAD8CAYAAACPd+p5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niR6vAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f77dc839358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conV = signal.convolve2d(f, g , mode = 'valid')\n",
    "plt.imshow(conV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   0, -160, -160,    0],\n",
       "       [   0, -160, -160,    0],\n",
       "       [   0, -160, -160,    0],\n",
       "       [   0, -160, -160,    0]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets change our function 'f'. let new f is f1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f77dc76cf60>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPgAAAD8CAYAAABaQGkdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhRE4UBiBA4UROFDYfwDL9k0GiDDCzwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f77dc7c8828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f1 = [\n",
    "        [10,10,10,0,0,0],\n",
    "        [10,10,10,0,0,0],\n",
    "        [10,10,10,0,0,0],\n",
    "        [0,0,0,10,10,10],\n",
    "        [0,0,0,10,10,10],\n",
    "        [0,0,0,10,10,10],\n",
    "    ]\n",
    "plt.imshow(f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f77dc6d3898>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DQ1ITw0NSk/8HRu8L8XV/OIsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f77deb0c518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#filter or kernel\n",
    "g1 = [\n",
    "        [3,0,-3],\n",
    "        [10,0,-10],\n",
    "        [3,0,-3]\n",
    "]\n",
    "plt.imshow(g1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[   0 -160 -160    0]\n",
      " [   0 -100 -100    0]\n",
      " [   0  100  100    0]\n",
      " [   0  160  160    0]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f77dc6b7898>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQoAAAD8CAYAAACPd+p5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f77dc7292b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conV1 = signal.convolve2d(f1, g1 , mode = 'valid')\n",
    "print(conV1)\n",
    "plt.imshow(conV1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By changing g or g1 we can try different filters."
   ]
  }
 ],
 "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}