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fingerprint.ipynb

@@ -0,0 +1,161 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "accuracy 0.925\n",
+      "predd live\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"
+    }
+   ],
+   "source": [
+    "import cv2\n",
+    "\n",
+    "import os\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import pickle \n",
+    "import random\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.svm import SVC\n",
+    "#dir= r'C:\\Users\\PAVAN\\Desktop\\BIOMETRIC DATA'\n",
+    "\n",
+    "\n",
+    "#categories=['live','spoof']\n",
+    "#data=[]\n",
+    "#for category in categories:\n",
+    "#    path=os.path.join(dir,category)\n",
+    "#    label=categories.index(category)\n",
+    "\n",
+    "#       imgpath=os.path.join(path,img)\n",
+    "#        fin_img=cv2.imread(imgpath,0)\n",
+    "        #cv2.imshow('image',fin_img)\n",
+    "#        try:\n",
+    "#            fin_img=cv2.resize(fin_img,(10,10))\n",
+    "#            im=np.array(fin_img).flatten()\n",
+    "#            data.append([im,label])\n",
+    "#        except Exception as e:\n",
+    "#            pass\n",
+    "        \n",
+    "#print(len(data))\n",
+    "        \n",
+    "#pick_in=open('data1.pickle','wb')\n",
+    "#pickle.dump(data,pick_in)\n",
+    "\n",
+    "#pick_in.close()\n",
+    "\n",
+    "pick_in=open('data1.pickle','rb')\n",
+    "data=pickle.load(pick_in)\n",
+    "pick_in.close()\n",
+    "\n",
+    "random.shuffle(data)\n",
+    "\n",
+    "features=[]\n",
+    "labels=[]\n",
+    "\n",
+    "for feature , label in data:\n",
+    "    features.append(feature)\n",
+    "    labels.append(label)\n",
+    "\n",
+    "    \n",
+    "xtrain,xtest,ytrain,ytest = train_test_split(features,labels,test_size=0.30)\n",
+    "\n",
+    "#model= SVC(C=1,kernel='poly',gamma='auto')\n",
+    "\n",
+    "#model.fit(xtrain,ytrain)\n",
+    "\n",
+    "#model.predict(xtest)\n",
+    "\n",
+    "pick=open('mdl1.sav','rb')\n",
+    "\n",
+    "model=pickle.load(pick)\n",
+    "\n",
+    "pick.close()\n",
+    "\n",
+    "prediction=model.predict(xtest)\n",
+    "\n",
+    "accuracy=model.score(xtest,ytest)\n",
+    "\n",
+    "categories=['live','spoof']\n",
+    "\n",
+    "print('accuracy', accuracy)\n",
+    "\n",
+    "print('predd',categories[prediction[0]])\n",
+    "\n",
+    "myfin=xtest[0].reshape(10,10)\n",
+    "plt.imshow(myfin,cmap='binary')\n",
+    "\n",
+    "plt.show()\n",
+    "\n",
+    "#pickle.dump(model,pick)\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}