CatDog_CNN_ImageClassification.ipynb

CatDog_ImagePreprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cat and Dog Datasets Load & Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Necessesary Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import random\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Files Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATADIR = \"Datasets\"\n",
    "CATEGORIES = [\"Dog\", \"Cat\"]\n",
    "IMG_SIZE = 75"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = []\n",
    "\n",
    "for category in CATEGORIES:\n",
    "    path = os.path.join(DATADIR, category)\n",
    "    class_num = CATEGORIES.index(category)\n",
    "    for img in os.listdir(path):\n",
    "        try:\n",
    "            img_array = cv2.imread(os.path.join(path, img), cv2.IMREAD_GRAYSCALE)\n",
    "            new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))\n",
    "            train.append([new_array, class_num])\n",
    "        except Exception as e:\n",
    "            pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24946"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "len(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "random.shuffle(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24946, 2)"
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "train = np.array(train)\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = []\n",
    "y = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "for feature, label in train:\n",
    "    X.append(feature)\n",
    "    y.append(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array(X)\n",
    "y = np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.reshape(-1, IMG_SIZE, IMG_SIZE, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24946, 75, 75, 1)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle_out = open(\"X.pickle\", \"wb\")\n",
    "pickle.dump(X, pickle_out)\n",
    "pickle_out.close()\n",
    "\n",
    "pickle_out = open(\"y.pickle\", \"wb\")\n",
    "pickle.dump(y, pickle_out)\n",
    "pickle_out.close()"
   ]
  }
 ],
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