Image classification model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project title:Indian Human Image Classification Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: ipython-autotime in /home/pavank/anaconda3/lib/python3.8/site-packages (0.3.1)\n",
      "Requirement already satisfied: ipython in /home/pavank/anaconda3/lib/python3.8/site-packages (from ipython-autotime) (7.19.0)\n",
      "Requirement already satisfied: setuptools>=18.5 in /home/pavank/anaconda3/lib/python3.8/site-packages (from ipython->ipython-autotime) (50.3.1.post20201107)\n",
      "Requirement already satisfied: pickleshare in /home/pavank/anaconda3/lib/python3.8/site-packages (from ipython->ipython-autotime) (0.7.5)\n",
      "Requirement already satisfied: traitlets>=4.2 in /home/pavank/anaconda3/lib/python3.8/site-packages (from ipython->ipython-autotime) (5.0.5)\n",
      "Requirement already satisfied: backcall in /home/pavank/anaconda3/lib/python3.8/site-packages (from ipython->ipython-autotime) (0.2.0)\n",
      "Requirement already satisfied: decorator in /home/pavank/anaconda3/lib/python3.8/site-packages (from ipython->ipython-autotime) (4.4.2)\n",
      "Requirement already satisfied: pygments in /home/pavank/anaconda3/lib/python3.8/site-packages (from ipython->ipython-autotime) (2.7.2)\n",
      "Requirement already satisfied: jedi>=0.10 in /home/pavank/anaconda3/lib/python3.8/site-packages (from ipython->ipython-autotime) (0.17.1)\n",
      "Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in /home/pavank/anaconda3/lib/python3.8/site-packages (from ipython->ipython-autotime) (3.0.8)\n",
      "Requirement already satisfied: pexpect>4.3; sys_platform != \"win32\" in /home/pavank/anaconda3/lib/python3.8/site-packages (from ipython->ipython-autotime) (4.8.0)\n",
      "Requirement already satisfied: ipython-genutils in /home/pavank/anaconda3/lib/python3.8/site-packages (from traitlets>=4.2->ipython->ipython-autotime) (0.2.0)\n",
      "Requirement already satisfied: parso<0.8.0,>=0.7.0 in /home/pavank/anaconda3/lib/python3.8/site-packages (from jedi>=0.10->ipython->ipython-autotime) (0.7.0)\n",
      "Requirement already satisfied: wcwidth in /home/pavank/anaconda3/lib/python3.8/site-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython->ipython-autotime) (0.2.5)\n",
      "Requirement already satisfied: ptyprocess>=0.5 in /home/pavank/anaconda3/lib/python3.8/site-packages (from pexpect>4.3; sys_platform != \"win32\"->ipython->ipython-autotime) (0.6.0)\n",
      "The autotime extension is already loaded. To reload it, use:\n",
      "  %reload_ext autotime\n",
      "time: 2.19 s (started: 2021-04-19 13:44:17 +05:30)\n"
     ]
    }
   ],
   "source": [
    "#Importing libraries\n",
    "!pip install ipython-autotime\n",
    "%load_ext autotime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 14.8 s (started: 2021-04-19 13:44:21 +05:30)\n"
     ]
    }
   ],
   "source": [
    "#1.Resize\n",
    "#2.flatten\n",
    "import os\n",
    "import random\n",
    "import ntpath\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from skimage.io import imread\n",
    "from skimage.transform import resize\n",
    "target=[]\n",
    "images=[]\n",
    "flat_data=[]\n",
    "DATADIR='./Images dataset/cropped'\n",
    "CATEGORIES=[]\n",
    "for entry in os.scandir(DATADIR):\n",
    "    if entry.is_dir():\n",
    "        CATEGORIES.append(ntpath.basename(entry.path))\n",
    "for category in CATEGORIES:\n",
    "  class_num=CATEGORIES.index(category)#label encoding the values\n",
    "  path=os.path.join(DATADIR,category)#create path to use all the images\n",
    "  for img in os.listdir(path):\n",
    "    img_array=imread(os.path.join(path,img))\n",
    "    #print(img_array.shape)\n",
    "    #plt.imshow(img_array)\n",
    "    img_resized=resize(img_array,(100,100,3))#resize\n",
    "    flat_data.append(img_resized.flatten())#flatten\n",
    "    images.append(img_resized)\n",
    "    target.append(class_num)\n",
    "\n",
    "flat_data=np.array(flat_data)\n",
    "images=np.array(images)\n",
    "target=np.array(target)\n",
    "random.shuffle(flat_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.33535382, 0.25588324, 0.21562833, ..., 0.93360873, 0.44209804,\n",
       "       0.5208048 ])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 2.64 ms (started: 2021-04-19 13:44:38 +05:30)\n"
     ]
    }
   ],
   "source": [
    "flat_data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1072"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.72 ms (started: 2021-04-19 13:44:39 +05:30)\n"
     ]
    }
   ],
   "source": [
    "len(target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 2 artists>"
      ]
     },
     "execution_count": 47,
     "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 123 ms (started: 2021-04-19 13:44:40 +05:30)\n"
     ]
    }
   ],
   "source": [
    "unique,count=np.unique(target,return_counts=True)\n",
    "plt.bar(CATEGORIES,count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spliting data into training and testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 471 ms (started: 2021-04-19 13:44:42 +05:30)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test=train_test_split(flat_data,target,test_size=0.2,random_state=0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 656 µs (started: 2021-04-19 13:45:48 +05:30)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "class_svc=SVC(kernel='rbf',random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(random_state=0)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 8.33 s (started: 2021-04-19 13:45:49 +05:30)\n"
     ]
    }
   ],
   "source": [
    "class_svc.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 6.32 s (started: 2021-04-19 13:45:58 +05:30)\n"
     ]
    }
   ],
   "source": [
    "y_pred=class_svc.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1,\n",
       "       1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 2.34 ms (started: 2021-04-19 13:46:06 +05:30)\n"
     ]
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0,\n",
       "       1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0,\n",
       "       1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1,\n",
       "       1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1,\n",
       "       0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0,\n",
       "       0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0,\n",
       "       0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1,\n",
       "       0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 2.19 ms (started: 2021-04-19 13:46:07 +05:30)\n"
     ]
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "67.90697674418604"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 3.01 ms (started: 2021-04-19 13:46:09 +05:30)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score,confusion_matrix\n",
    "accuracy_score(y_pred,y_test)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[132,  14],\n",
       "       [ 55,  14]])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 4.57 ms (started: 2021-04-19 13:47:27 +05:30)\n"
     ]
    }
   ],
   "source": [
    "confusion_matrix(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Saving the model using pickle library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 419 ms (started: 2021-04-19 13:47:28 +05:30)\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "pickle.dump(class_svc,open('humanface_detection.p','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 86.3 ms (started: 2021-04-19 13:47:29 +05:30)\n"
     ]
    }
   ],
   "source": [
    "model=pickle.load(open('humanface_detection.p','rb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing a brand new image using URL or Imagepath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter your path of your downloaded image->https://www.biography.com/.image/t_share/MTczNjEwODI2NTg5MDg3MTI0/michelle-obama-gettyimages-85246899.jpg\n",
      "(1199, 1200, 3)\n",
      "PREDICTION OUTPUT:foreign\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 9.93 s (started: 2021-04-19 14:24:20 +05:30)\n"
     ]
    }
   ],
   "source": [
    "import shutil\n",
    "import os.path\n",
    "from os import path\n",
    "import ntpath\n",
    "import cv2\n",
    "import os\n",
    "flat_data=[]\n",
    "urls=[]\n",
    "flag=0\n",
    "indianfolder=\"./Images dataset/cropped/indian\"\n",
    "otherfolder=\"./Images dataset/cropped/foreign\"\n",
    "url=input('Enter your path of your downloaded image->')\n",
    "file=ntpath.basename(url)\n",
    "img = cv2.imread(url)\n",
    "img=imread(url)\n",
    "img_resized=resize(img,(100,100,3))\n",
    "flat_data.append(img_resized.flatten())\n",
    "flat_data=np.array(flat_data)\n",
    "print(img.shape)\n",
    "plt.imshow(img_resized)\n",
    "y_out=model.predict(flat_data)\n",
    "y_out=CATEGORIES[y_out[0]]\n",
    "print(f'PREDICTION OUTPUT:{y_out}')\n",
    "if(y_out=='Indian faces'):\n",
    "    print(\"Saving image in indian faces folder\")\n",
    "    shutil.move(url,indianfolder)\n",
    "    print(\"Done\")\n",
    "    \n",
    "if(y_out==\"Other country faces\"):\n",
    "    print(\"Saving image in other country faces folder\")\n",
    "    shutil.move(url,otherfolder)\n",
    "    print(\"Done\")\n",
    "flat_data=[]"
   ]
  },
  {
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}