emnist.ipynb

{
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 "nbformat_minor": 2,
 "metadata": {
  "language_info": {
   "name": "python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "version": "3.7.6-final"
  },
  "orig_nbformat": 2,
  "file_extension": ".py",
  "mimetype": "text/x-python",
  "name": "python",
  "npconvert_exporter": "python",
  "pygments_lexer": "ipython3",
  "version": 3,
  "kernelspec": {
   "name": "python37664bit7bcce433135f493d8d948432b28ff3f6",
   "display_name": "Python 3.7.6 64-bit"
  }
 },
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "from sklearn import datasets\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "import time\n",
    "from emnist import list_datasets\n",
    "from emnist import extract_training_samples,extract_test_samples\n",
    "import emnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading letters dataset\n",
    "letters, _ = extract_test_samples('letters')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "(20800, 28, 28)"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "letters.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Get_All_Desc(images):\n",
    "    '''\n",
    "        this function returns a list of dictionnaries containing all images in letters dataset + their descriptor\n",
    "    '''\n",
    "    images_descriptors = []\n",
    "    image = []\n",
    "\n",
    "    for i in range(len(images)):\n",
    "        image = images[i]\n",
    "        image_and_desc = { 'image':image,'desc':image.flatten()} #change descriptor here!\n",
    "        images_descriptors.append(image_and_desc)\n",
    "\n",
    "    return images_descriptors\n",
    "\n",
    "def Get_Distances(images_descriptors,query_image):\n",
    "    ''' \n",
    "        this function returns a list of dictionnaries containing all images in letters dataset + their descriptor + the distance between the query image descriptor         & the descriptor of each image in the dataset of letters.\n",
    "    '''\n",
    "    query_desc = query_image.flatten() #change descriptor here!\n",
    "    for i in range(len(images_descriptors)):\n",
    "        image = images_descriptors[i]\n",
    "        image['distance'] = np.corrcoef(query_desc,image['desc'])[0][1]\n",
    "\n",
    "def Show_Results(result,size):\n",
    "    '''\n",
    "        this function plots the top(size) similar/close images to the query image.\n",
    "    '''\n",
    "    print(len(result))\n",
    "    plt.figure(figsize=(15,15))\n",
    "    size = size if size < len(result) else len(result)\n",
    "\n",
    "    for i in range(size):\n",
    "        image_and_desc = result[i]\n",
    "        plt.subplot(5,10,i+1)\n",
    "        plt.xticks([image_and_desc['distance']])\n",
    "        plt.yticks([])\n",
    "        plt.grid(False)\n",
    "        plt.imshow(image_and_desc['image'], cmap=plt.cm.binary)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "Execution Time :  4.922882700000628\n"
    }
   ],
   "source": [
    "initial = time.perf_counter() #store statrting time\n",
    "\n",
    "images_descriptors = Get_All_Desc(letters) #get images and descriptors\n",
    "distances = Get_Distances(images_descriptors,letters[2300]) #getting the images & distances between images & query image\n",
    "result = list(filter(lambda x:x['distance'] > 0.6,images_descriptors)) # filtring images to get only those with correlation higher than 0.6\n",
    "\n",
    "print(\"Execution Time : \",time.perf_counter() - initial) # printing execution time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "473\n"
    },
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 1080x1080 with 20 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Show_Results(result,20)"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# END"
   ]
  }
 ]
}