03-convolutions.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "03-convolutions.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOcYkMZcy1WeVsxU87S4ZvO",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/changsin/DeepLearning-101/blob/master/03-convolutions.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "egb7kjYb4pF0"
      },
      "source": [
        "This notebook is from [David Foster](https://github.com/davidADSP/GDL_code/blob/master/02_02_deep_learning_convolutions.ipynb) to visualize the effect of convolution.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pgg_vHXa4IlE"
      },
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.ndimage import correlate\n",
        "import numpy as np\n",
        "from skimage import data\n",
        "from skimage.color import rgb2gray\n",
        "from skimage.transform import rescale,resize"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RWGgfm_d54UD"
      },
      "source": [
        "# The original image"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T5QxuYyg4LBQ",
        "outputId": "6b04fd7b-af5b-4ca8-a123-68ffeb294512",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "im = rgb2gray(data.coffee())\n",
        "im = resize(im, (64,64))\n",
        "print(im.shape)\n",
        "\n",
        "plt.axis('off')\n",
        "plt.imshow(im, cmap = 'gray');"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(64, 64)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m01VkmfD5Hbc"
      },
      "source": [
        "# Horizontal edge filter¶"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MOrA8zH24PFm",
        "outputId": "f3c112de-943e-4629-d9f0-7bd1e01af638",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        }
      },
      "source": [
        "filter1 = np.array([\n",
        "    [ 1,  1,  1],\n",
        "    [ 0,  0,  0],\n",
        "    [-1, -1, -1]\n",
        "])\n",
        "\n",
        "new_image = np.zeros(im.shape)\n",
        "\n",
        "im_pad = np.pad(im, 1, 'constant')\n",
        "\n",
        "for i in range(im.shape[0]):\n",
        "    for j in range(im.shape[1]):\n",
        "        try:\n",
        "            new_image[i,j] = \\\n",
        "            im_pad[i-1,j-1] * filter1[0,0] + \\\n",
        "            im_pad[i-1,j] * filter1[0,1] + \\\n",
        "            im_pad[i-1,j+1] * filter1[0,2] + \\\n",
        "            im_pad[i,j-1] * filter1[1,0] + \\\n",
        "            im_pad[i,j] * filter1[1,1] + \\\n",
        "            im_pad[i,j+1] * filter1[1,2] +\\\n",
        "            im_pad[i+1,j-1] * filter1[2,0] + \\\n",
        "            im_pad[i+1,j] * filter1[2,1] + \\\n",
        "            im_pad[i+1,j+1] * filter1[2,2] \n",
        "        except:\n",
        "            pass\n",
        "\n",
        "plt.axis('off')\n",
        "plt.imshow(new_image, cmap='Greys');"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A-dPR3lS5QJr"
      },
      "source": [
        "# Vertical edge filter"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ewQTUb14Skm",
        "outputId": "33477405-f96c-44ce-91f1-0090a0878622",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        }
      },
      "source": [
        "filter2 = np.array([\n",
        "    [ -1,  0,  1],\n",
        "    [ -1,  0,  1],\n",
        "    [ -1,  0,  1]\n",
        "])\n",
        "\n",
        "new_image = np.zeros(im.shape)\n",
        "\n",
        "im_pad = np.pad(im,1, 'constant')\n",
        "\n",
        "for i in range(im.shape[0]):\n",
        "    for j in range(im.shape[1]):\n",
        "        try:\n",
        "            new_image[i,j] = \\\n",
        "            im_pad[i-1,j-1] * filter2[0,0] + \\\n",
        "            im_pad[i-1,j] * filter2[0,1] + \\\n",
        "            im_pad[i-1,j+1] * filter2[0,2] + \\\n",
        "            im_pad[i,j-1] * filter2[1,0] + \\\n",
        "            im_pad[i,j] * filter2[1,1] + \\\n",
        "            im_pad[i,j+1] * filter2[1,2] +\\\n",
        "            im_pad[i+1,j-1] * filter2[2,0] + \\\n",
        "            im_pad[i+1,j] * filter2[2,1] + \\\n",
        "            im_pad[i+1,j+1] * filter2[2,2] \n",
        "        except:\n",
        "            pass\n",
        "\n",
        "plt.axis('off')\n",
        "plt.imshow(new_image, cmap='Greys');"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g9IpTSYt5i9G"
      },
      "source": [
        "# Horizontal edge filter with stride 2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IHLz2SUB4YXX",
        "outputId": "e7002526-cc99-4277-c522-be364d985069",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        }
      },
      "source": [
        "filter1 = np.array([\n",
        "    [ 1,  1,  1],\n",
        "    [ 0,  0,  0],\n",
        "    [-1, -1, -1]\n",
        "])\n",
        "\n",
        "stride = 2\n",
        "\n",
        "new_image = np.zeros((int(im.shape[0] / stride), int(im.shape[1] / stride)))\n",
        "\n",
        "im_pad = np.pad(im,1, 'constant')\n",
        "\n",
        "for i in range(0,im.shape[0],stride):\n",
        "    for j in range(0,im.shape[1],stride):\n",
        "        try:\n",
        "            new_image[int(i/stride),int(j/stride)] = \\\n",
        "            im_pad[i-1,j-1] * filter1[0,0] + \\\n",
        "            im_pad[i-1,j] * filter1[0,1] + \\\n",
        "            im_pad[i-1,j+1] * filter1[0,2] + \\\n",
        "            im_pad[i,j-1] * filter1[1,0] + \\\n",
        "            im_pad[i,j] * filter1[1,1] + \\\n",
        "            im_pad[i,j+1] * filter1[1,2] +\\\n",
        "            im_pad[i+1,j-1] * filter1[2,0] + \\\n",
        "            im_pad[i+1,j] * filter1[2,1] + \\\n",
        "            im_pad[i+1,j+1] * filter1[2,2] \n",
        "        except:\n",
        "            pass\n",
        "\n",
        "plt.axis('off')\n",
        "plt.imshow(new_image, cmap='Greys');"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ajP2So2v5bis"
      },
      "source": [
        "# Vertical edge filter with stride 2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4bldsjYV4idF",
        "outputId": "784a4781-3cc0-4776-8864-d15456307611",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        }
      },
      "source": [
        "filter2 = np.array([\n",
        "    [ -1,  0,  1],\n",
        "    [ -1,  0,  1],\n",
        "    [ -1,  0,  1]\n",
        "])\n",
        "\n",
        "stride = 2\n",
        "\n",
        "new_image = np.zeros((int(im.shape[0] / stride), int(im.shape[1] / stride)))\n",
        "\n",
        "im_pad = np.pad(im,1, 'constant')\n",
        "\n",
        "for i in range(0,im.shape[0],stride):\n",
        "    for j in range(0,im.shape[1],stride):\n",
        "        try:\n",
        "            new_image[int(i/stride),int(j/stride)] = \\\n",
        "            im_pad[i-1,j-1] * filter2[0,0] + \\\n",
        "            im_pad[i-1,j] * filter2[0,1] + \\\n",
        "            im_pad[i-1,j+1] * filter2[0,2] + \\\n",
        "            im_pad[i,j-1] * filter2[1,0] + \\\n",
        "            im_pad[i,j] * filter2[1,1] + \\\n",
        "            im_pad[i,j+1] * filter2[1,2] +\\\n",
        "            im_pad[i+1,j-1] * filter2[2,0] + \\\n",
        "            im_pad[i+1,j] * filter2[2,1] + \\\n",
        "            im_pad[i+1,j+1] * filter2[2,2] \n",
        "        except:\n",
        "            pass\n",
        "\n",
        "plt.axis('off')\n",
        "plt.imshow(new_image, cmap='Greys');"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOcAAADnCAYAAADl9EEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAPd0lEQVR4nO2dS49W1baGJ4oKClWAIAIBQS4qiOES8H6JJsZEe5pIy44dWsa+sWGM/gB/gIl2bGnbRGMwMaIRMQG8BaS4FMVNpChLvKK7exrrfTzMvfc5I+Z5mvPN/L71rW+9WckYc4wx46+//moiUo8r/r8vQESG0ZwiRdGcIkXRnCJF0ZwiRZlJ4muvvRZDuVdckX09Y8aMy76Qnj1/R4pE//LLL3HPn3/+GbVrr702anT9V111VdQSk5OTURsdHY3a77//HjX6z/7444/B9SuvvDLuoXvVu68Huvf0XbSPshhpH+2h63juuecGP9A3p0hRNKdIUTSnSFE0p0hRNKdIUTSnSFEwlfLzzz9H7eqrr75s7f/6kP3U1NRlrbfGKYB58+ZFLaUiWmttzpw5UTt79uzgOoXee9MlPamD/3Ta4+/47bffBtdnz54d91y6dOmyP681fh5nzZoVtXT/yS89qULfnCJF0ZwiRdGcIkXRnCJF0ZwiRdGcIkXBVAqFminlkPZRmJ+gUDmFr8+dOze4Pnfu3LhnwYIF//sL+x9QxcrRo0ejdujQocH1FStWxD1U5TIyMhI1+t033HDD4PrJkyfjHqqcof+M0grXXHPN4DqldChdQt81c2Z+/HsqXeg3U+ox4ZtTpCiaU6QomlOkKJpTpCiaU6QoGK2l6FNPtJagPRSRPXXqVNSuv/76wXWKWtLvIm1sbCxq+/fvj9q2bdsG15csWRL3XLx4MWo//vhj1M6fPx+1FIFcv3593HP8+PGoHTx4MGoUte+JGtOzk6K/f6dRcUG6/9Tb6ddff41awjenSFE0p0hRNKdIUTSnSFE0p0hRNKdIUboPvvf0RKHPoxEJExMTUaOD6illQgeer7vuuqhROP+9996L2rPPPhu1dP0nTpyIeyjM3zsW4siRI4PrqcdRa6098cQTUfvhhx+i9tNPP0UtpSko/UIpLvqv6T5S6iP1M6J7T9+V8M0pUhTNKVIUzSlSFM0pUhTNKVIUzSlSFEyl/Keh8DSlS2icAVWYpL4tFF6fnp6O2u7du6P2yCOPRG3dunVR27dv3+A6VQT1jkigdES6jxcuXIh7du3aFbWHHnooau+++27UUkqN0hTUv4nSd5TSoe9Lozfof6H/M+GbU6QomlOkKJpTpCiaU6QomlOkKJpTpCjdVSkUlk9Npk6fPh330BgBSqVQyDt9JjUMoyqMNDqhtdZeeOGFqB0+fDhqCaq0oN9Mowl6JpXTNG9q4nX33XdHrafBFzVyo8+jZ7i3YiXdf5pu3oNvTpGiaE6RomhOkaJoTpGiaE6RomhOkaJgKoVO2VPYeHx8fHCdmnFRuoRC5WkeSmu56oA+j+aQ0DWSRhOgUzi/pyqitf75NuleUSqC0izvv/9+1DZu3Bi1dP2U4ur9zXQfe1Iw9F02+BL5B6E5RYqiOUWKojlFiqI5RYqC0VqKMB07dixqqafLokWL4h6Kkt54441Ro8PcSaMJxGfOnInaqlWrokYHztOh8tby+AeKJNK9Io1GXqToJPUQmj9/ftSOHj0atQceeCBqdMA9QROq6X70RmvT9/WMcCB8c4oURXOKFEVzihRFc4oURXOKFEVzihQFUynHjx+PGvX82bp16+A6pT1oojSFvHs+k1JEqf9Ra3zIng5fUwrm/Pnzg+s0GZqun+4jFTKk/k6UppiamooapRV6xif0FiRQ+ogOqtMU8PSZ9AxQOi3hm1OkKJpTpCiaU6QomlOkKJpTpCiaU6QomEr55ptvovbkk09GrSdsTGHoEydORG316tVRS2kRmmhM6RIaJ0GVLvR9qXqD0hQ0zZvSA8uWLYta6u/0xRdfdF0HpTDoOt54443B9bVr18Y9VDlD1SWUgumZek3pIxr9kPDNKVIUzSlSFM0pUhTNKVIUzSlSFM0pUhSM79JIAAqjU7OrxMTERNQovUGNk2gMQoIqSN55552opYnMrfFoglSVQmF+qpigURPnzp2L2rp16wbXaQxC74gEqkpJn7lmzZq4hyqT6PmgdAmlA6enpwfX6XdRdU/CN6dIUTSnSFE0p0hRNKdIUTSnSFE0p0hRMJVC4V8K56dGUjQ/g1Izt9xyS9QOHz4ctXSNlOqhtA2lIlJ4vTWumkgpEwrlU+VJz4yP1vJ/QykMmpezfv36qNH9SN+3Z8+euGfTpk1Ro0qRnmnTreX/hiZ9U5VRwjenSFE0p0hRNKdIUTSnSFE0p0hRNKdIUTCVQmF5SqWksDztoZH0vamDnuoY+q7HHnssaq+88krUnnrqqagdOHDgsq+DqoVoHgpViqR9dA9pnstLL70Utd27d0ctVZHQ76KqFKrSmTVrVtRoPk9KSVH6i74r4ZtTpCiaU6QomlOkKJpTpCiaU6Qo3Qffjxw5ErV0mJsOt1Okiw4vU+SyJ1pLEc3HH388ai+++GLU7rrrrqilnkXUU4mukaAIZOK7776L2vbt26NG//W+ffuiliZzL1++PO6ha9ywYUPU6Pmg6Gq6/6kfVGutzZ8/P2oJ35wiRdGcIkXRnCJF0ZwiRdGcIkXRnCJFwVTK2NhY1B5++OGopdEEadJ0azmE3hpPSaa0QjoUTyF0OnBO/YV27twZtZdffjlqzz///OA6TX+mfjR0j+l3p31p4nVrrT3zzDNR++ijj7quI400oOejl56RCwSlHmn0Q8I3p0hRNKdIUTSnSFE0p0hRNKdIUTSnSFEwlfL9999HbeHChVFLKQyqLqFW9lShQaS+MzSRedu2bVGj9v2rV6+O2o4dO6L29ttvD65v2bIl7rnpppuiRqkgqhTZvHnz4PrKlSvjnrfeeitqNOmbekklqBfQyMhI1CiF0dtDaHR0dHCdUkQ9FVK+OUWKojlFiqI5RYqiOUWKojlFiqI5RYqCqZSZM7NMYxBS+Lp36jKlMOgaT506Nbi+du3auIeqEXrC4a21dtttt0UtpaTGx8fjnjNnzkRt8eLFUVu6dGnUUgrm9ddfj3uIm2++OWqUSqGGbT2fR/8nNd2iVEp65i5evBj30LOf8M0pUhTNKVIUzSlSFM0pUhTNKVIUzSlSFEylUAMkmmqcKhKoKoVSIjS5mCoLUliemladO3cualTVcfz48ahRRUWqcKB7T2F5SkWkieOttbZ3797Bdao+omuk+SU96RKC7i81hyOomVt6Ruh+9Myp8c0pUhTNKVIUzSlSFM0pUhTNKVIUjNZSa386RH3hwoXB9TTxujU+VE4Tg+lQeYquUtSYDlFTRDn1K2qN+9ik3kk0ZqK3EIAOZqdD4HSAfc+ePVGjHkJ0r1IkmiK81DeJIrk04oGKC9LICHpO0x7CN6dIUTSnSFE0p0hRNKdIUTSnSFE0p0hRMJVCbe6JlDqYPXt23EM9hOhQ+T333BO1lLqhNAWF5ekQNaU3jh07FrU0quHkyZNxD2mUcqCUTkqz0AFwSmPRCA06BJ4Oj9PvonQJPXNUUEHfl56Dnj2Eb06RomhOkaJoTpGiaE6RomhOkaJoTpGiYCqFpk2PjY1FLY0EoN43dGqfJmxTFUaqtJiamuq6DqpmoWnZVN2Tqj7o/lLqgML51G8pXT9VZ1Aa6/Tp01Gj/lMplULVQpQuoRQXpZbo+yYnJwfX6Vm0h5DIPwjNKVIUzSlSFM0pUhTNKVIUzSlSFEylUCMmClGnxkkUuqZqkEWLFkUtNRNrjasOEpSmoHA4affff3/UDh06NLhO94qukTQaF7Br167B9Z07d8Y9NKph+fLlUfvqq6+itmzZssF1ehapURdV8KRRGK3xs5MqqKhJHfkl4ZtTpCiaU6QomlOkKJpTpCiaU6QomlOkKJhKoWqQJUuWRC1VYVA4nJpubdq0KWoHDx6MWqowocoNqpwhaF9KD7TW2vj4+OA6VUVQdQxB1TjpnqxZsybuSWmg1nhWCt3/VNlBDbIopfP1119HjZqX9TwH1KSuZ5q3b06RomhOkaJoTpGiaE6RomhOkaJgtJZ6s1D0LPWqoSgjHQzeuHFj1N58882o3XfffYPrdKicIsp0cDz1K2qNf3eahkz9fuhwO0ER4BUrVgyuf/rpp3HP008/HbUPP/wwaj2R/t4+Qb3jGOi/Ts8IFT842VrkH4TmFCmK5hQpiuYUKYrmFCmK5hQpSncPoZ728gS1/d+wYUPUKC2Serr0ts2nVASlB+i3pTQApVLoUDYdsKZ0z2effTa4/uqrr8Y9X375ZdRoBAXdx/TbUqqntdYOHDgQNfqv586dG7We66ffZQ8hkX8QmlOkKJpTpCiaU6QomlOkKJpTpCiYSpmeno4ahaFTxQqd9KcW/ffee2/UNm/eHLUPPvhgcH3Hjh1xD01kJmgkAIXR0z3p7RNEaRZKKzz66KOD699++23cQxOqqeKDUg6pemPOnDlxz8TERNRWrlwZNbrGnoos8gT140r45hQpiuYUKYrmFCmK5hQpiuYUKYrmFCkKplKovXzPqIbUzKo1TkVQhQCNakhpgE8++STuufPOO6N29uzZqFEVSU91T+9YCIKuY/v27YPrY2NjcQ9VBPVex9atWwfXP/7447iHUkQXL16M2oIFC6JGk9ZTKojSX5QKSvjmFCmK5hQpiuYUKYrmFCmK5hQpiuYUKQqmUiYnJ6M2MjIStRRS7mns1BpXRtx6661Ru/322wfX9+7dG/fs27cvalu2bIkahexJS2mFnknIrfF9pFB/mr9CFTX0f1Iqgv6zVOly4sSJuGfevHlRo2ne1DSMZgGl30appdHR0aglfHOKFEVzihRFc4oURXOKFEVzihRFc4oUBVMpdNqfwvKp0RE1hKIKAaqMoJH0q1atGly/dOlS3LN///6o0bj3Bx98MGo0KyXNc6FmaHQdvfNt0mdSSocqiShNQSmYlObqTdv0Qr87/WeUXqRmYgnfnCJF0ZwiRdGcIkXRnCJF0ZwiRcFoLUHRp3T4OkW5WuMePARNNU4H1U+dOhX3UPSXRjV8/vnnUaPocOqPtHTp0riHDrfTgW3al+4/HdimA+d0YP7gwYNRS88VRaHp/i5evDhq9Jl0H9M+6rnVMwneN6dIUTSnSFE0p0hRNKdIUTSnSFE0p0hRMJVCqQ+a4pv6vVBYvuegcWucwki9au64446uz6P0BqWWKK2QwvLUd4gOvlNKiv6zpFHxAx3op1QE9fVJ0D2kg+8LFy6MWm9KKk29psP5PT2hfHOKFEVzihRFc4oURXOKFEVzihRFc4oUZcZ/Y4KyiPz7+OYUKYrmFCmK5hQpiuYUKYrmFCmK5hQpyr8AIlHUn6Ui8SQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kxKwcwBr4mVi"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}