01 - 03 - Convolutional kernels and pooling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import math\n",
    "import shutil\n",
    "import requests\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Utility\n",
    "Resize and crop image to be square"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def open_and_resize(file, resize=(500,500)):\n",
    "    if file.startswith('http'):\n",
    "        response = requests.get(file)\n",
    "        image = Image.open(BytesIO(response.content)).convert('RGB')\n",
    "    else:\n",
    "        image = Image.open(file).convert('RGB')\n",
    "    w, h = image.size\n",
    "    left = int((w - h) / 2 if w > h else 0)\n",
    "    upper = int((h - w) / 2 if h > w else 0)\n",
    "    right = int(w - ((w - h) / 2) if w > h else w)\n",
    "    lower = int(h - ((h - w) / 2) if h > w else h)\n",
    "    image = image.crop((left, upper, right, lower))\n",
    "    image.thumbnail(resize)\n",
    "    return image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolution\n",
    "Implements filtering on a single image with selected pad and stride"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def conv2d(X, W, pad=1, stride=1):\n",
    "    # filter\\kernel size\n",
    "    f, f, _ = W.shape\n",
    "    n_C = 1\n",
    "    \n",
    "    # new output volume\n",
    "    n_H = int(np.floor(X.shape[0] - f + 2 * pad) / (stride * 1.)) + 1\n",
    "    n_W = int(np.floor(X.shape[1] - f + 2 * pad) / (stride * 1.)) + 1\n",
    "    \n",
    "    Z = np.zeros((n_H, n_W, n_C))\n",
    "    \n",
    "    # padding\n",
    "    x = np.pad(X, ((pad, pad), (pad, pad), (0, 0)), 'constant', constant_values=(0, 0))\n",
    "    for h in range(n_H):\n",
    "        for w in range(n_W):\n",
    "            for c in range(n_C):\n",
    "                vert_start = h * stride\n",
    "                vert_end = vert_start + f\n",
    "                horiz_start = w * stride\n",
    "                horiz_end = horiz_start + f\n",
    "\n",
    "                Z[h, w, c] = np.sum(W[:,:,c] * x[vert_start:vert_end,horiz_start:horiz_end,:])\n",
    "    return Z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Max Pooling\n",
    "Implements max pooling with the given pool size and stride."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def max_pooling2d(X, pool_size=2, stride=2):\n",
    "    # new output volume\n",
    "    n_H = int(np.floor(X.shape[0] - pool_size) / (stride * 1.)) + 1\n",
    "    n_W = int(np.floor(X.shape[1] - pool_size) / (stride * 1.)) + 1\n",
    "    n_C = X.shape[2]\n",
    "    \n",
    "    Z = np.zeros((n_H, n_W, n_C))\n",
    "    for h in range(n_H):\n",
    "        for w in range(n_W):\n",
    "            for c in range(n_C):\n",
    "                vert_start = h * stride\n",
    "                vert_end = vert_start + pool_size\n",
    "                horiz_start = w * stride\n",
    "                horiz_end = horiz_start + pool_size\n",
    "\n",
    "                Z[h, w, c] = np.amax(X[vert_start:vert_end,horiz_start:horiz_end,c])\n",
    "    \n",
    "    return Z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Filter\\Kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "w = np.zeros((3, 3, 3))\n",
    "#t = np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]) / 3\n",
    "t = np.array([[0, 0, 0], [0, 3., 0], [0, 0, 0]]) / 3\n",
    "w[:,:,0] = t\n",
    "w[:,:,1] = t\n",
    "w[:,:,2] = t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example Filter Application\n",
    "Run filter on selected image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x120158470>"
      ]
     },
     "execution_count": 38,
     "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": [
    "image = np.array(open_and_resize(\"model.png\", resize=(200,200)))\n",
    "plt.imshow(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 200, 1)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x120230898>"
      ]
     },
     "execution_count": 40,
     "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": [
    "n = conv2d(image, w)\n",
    "plt.imshow(Image.fromarray(n[:,:,0]).convert(\"L\"), cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x12031b9e8>"
      ]
     },
     "execution_count": 41,
     "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": [
    "x = max_pooling2d(n, pool_size=5)\n",
    "plt.imshow(Image.fromarray(x[:,:,0]).convert(\"L\"), cmap='gray')"
   ]
  },
  {
   "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": "3.6.6"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}