Face Verification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow \n",
    "from tensorflow.keras.models import load_model\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tensorflow.keras.models.load_model(\"Face_verification_model.hdf5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_3 (InputLayer)            [(None, 105, 105, 1) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_4 (InputLayer)            [(None, 105, 105, 1) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "sequential_1 (Sequential)       (None, 4096)         10636096    input_3[0][0]                    \n",
      "                                                                 input_4[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "lambda_1 (Lambda)               (None, 4096)         0           sequential_1[1][0]               \n",
      "                                                                 sequential_1[2][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 1)            4097        lambda_1[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 10,640,193\n",
      "Trainable params: 10,640,193\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Nayeem\n",
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      "Nayeem\n",
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    }
   ],
   "source": [
    "cam = cv2.VideoCapture(0)\n",
    "\n",
    "facedata =os.getcwd()+\"//\"+\"haarcascade_frontalface_alt.xml\"\n",
    "faceCascade = cv2.CascadeClassifier(facedata)\n",
    "    \n",
    "classes = ['Nayeem', 'Sai']\n",
    "\n",
    "while cam.isOpened():\n",
    "    \n",
    "    _, frame = cam.read()\n",
    "    \n",
    "    #frame = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
    "    \n",
    "    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "    faces = faceCascade.detectMultiScale(gray, scaleFactor=1.1)\n",
    "\n",
    "    for (x, y, w, h) in faces:\n",
    "        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2, 5)\n",
    "        face_crop = frame[y:y + h, x:x + w]\n",
    "        face_crop = cv2.resize(face_crop, (105, 105))\n",
    "        face_crop = cv2.cvtColor(face_crop, cv2.COLOR_BGR2GRAY)\n",
    "        face_crop = face_crop.astype('float32')\n",
    "        face_crop = np.asarray(face_crop)\n",
    "        face_crop = face_crop.reshape(-1, face_crop.shape[0], face_crop.shape[1], 1)\n",
    "        print(face_crop.shape)\n",
    "        value = model.predict([face_crop, face_crop])\n",
    "        \n",
    "        val = np.argmax(value)\n",
    "        real_value = value[0][val]\n",
    "        print(real_value)\n",
    "        #real_values.append(real_value)\n",
    "        result = classes[val]\n",
    "        \n",
    "        print(result)\n",
    "            \n",
    "    cv2.imshow(\"image\", frame)\n",
    "    if cv2.waitKey(1) & 0xFF == ord('q'):\n",
    "        break\n",
    "\n",
    "cv2.destroyAllWindows()"
   ]
  },
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