TASK FAC RECOG FILE 1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TASK :- FACIAL RECOGNITION USING TRANSFER LEARNING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Face not found\n",
      "Collecting Samples Complete\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "# Load HAAR face classifier\n",
    "face_classifier = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\n",
    "\n",
    "# Load functions\n",
    "def face_extractor(img):\n",
    "    # Function detects faces and returns the cropped face\n",
    "    # If no face detected, it returns the input image\n",
    "    \n",
    "    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
    "    faces = face_classifier.detectMultiScale(gray, 1.3, 5)\n",
    "    \n",
    "    if faces is ():\n",
    "        return None\n",
    "    \n",
    "    # Crop all faces found\n",
    "    for (x,y,w,h) in faces:\n",
    "        cropped_face = img[y:y+h, x:x+w]\n",
    "\n",
    "    return cropped_face\n",
    "\n",
    "# Initialize Webcam\n",
    "cap = cv2.VideoCapture(0)\n",
    "count = 0\n",
    "\n",
    "# Collect 100 samples of your face from webcam input\n",
    "while True:\n",
    "\n",
    "    ret, frame = cap.read()\n",
    "    if face_extractor(frame) is not None:\n",
    "        count += 1\n",
    "        face = cv2.resize(face_extractor(frame), (200, 200))\n",
    "        face = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "        # Save file in specified directory with unique name\n",
    "        file_name_path = '' + str(count) + '.jpg'\n",
    "        cv2.imwrite(file_name_path, face)\n",
    "\n",
    "        # Put count on images and display live count\n",
    "        cv2.putText(face, str(count), (50, 50), cv2.FONT_HERSHEY_COMPLEX, 1, (0,255,0), 2)\n",
    "        cv2.imshow('Face Cropper', face)\n",
    "        \n",
    "    else:\n",
    "        print(\"Face not found\")\n",
    "        pass\n",
    "\n",
    "    if cv2.waitKey(1) == 13 or count == 100: #13 is the Enter Key\n",
    "        break\n",
    "        \n",
    "cap.release()\n",
    "cv2.destroyAllWindows()      \n",
    "print(\"Collecting Samples Complete\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.2.0\n",
      "Model trained sucessefully\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "from os import listdir\n",
    "from os.path import isfile, join\n",
    "print(cv2.__version__)\n",
    "# Get the training data we previously made\n",
    "data_path = 'd://faces//'\n",
    "# a=listdir('d:/faces')\n",
    "# print(a)\n",
    "# \"\"\"\n",
    "onlyfiles = [f for f in listdir(data_path) if isfile(join(data_path, f))]\n",
    "\n",
    "# Create arrays for training data and labels\n",
    "Training_Data, Labels = [], []\n",
    "\n",
    "# Open training images in our datapath\n",
    "# Create a numpy array for training data\n",
    "for i, files in enumerate(onlyfiles):\n",
    "    image_path = data_path + onlyfiles[i]\n",
    "    images = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)\n",
    "    Training_Data.append(np.asarray(images, dtype=np.uint8))\n",
    "    Labels.append(i)\n",
    "# \n",
    "# Create a numpy array for both training data and labels\n",
    "Labels = np.asarray(Labels, dtype=np.int32)\n",
    "model=cv2.face_LBPHFaceRecognizer.create()\n",
    "# Initialize facial recognizer\n",
    "# model = cv2.face_LBPHFaceRecognizer.create()\n",
    "# model=cv2.f\n",
    "# NOTE: For OpenCV 3.0 use cv2.face.createLBPHFaceRecognizer()\n",
    "\n",
    "# Let's train our model \n",
    "model.train(np.asarray(Training_Data), np.asarray(Labels))\n",
    "print(\"Model trained sucessefully\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(89, 84.68085012931421)\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import webbrowser\n",
    "\n",
    "\n",
    "face_classifier = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\n",
    "\n",
    "def face_detector(img, size=0.5):\n",
    "    \n",
    "    # Convert image to grayscale\n",
    "    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
    "    faces = face_classifier.detectMultiScale(gray, 1.3, 5)\n",
    "    if faces is ():\n",
    "        return img, []\n",
    "    \n",
    "    for (x,y,w,h) in faces:\n",
    "        cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,255),2)\n",
    "        roi = img[y:y+h, x:x+w]\n",
    "        roi = cv2.resize(roi, (200, 200))\n",
    "    return img, roi\n",
    "\n",
    "\n",
    "# Open Webcam\n",
    "cap = cv2.VideoCapture(0)\n",
    "\n",
    "while True:\n",
    "\n",
    "    ret, frame = cap.read()\n",
    "    \n",
    "    image, face = face_detector(frame)\n",
    "    \n",
    "    try:\n",
    "        face = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "        # Pass face to prediction model\n",
    "        # \"results\" comprises of a tuple containing the label and the confidence value\n",
    "        results = model.predict(face)\n",
    "        print(results)\n",
    "        if results[1] < 500:\n",
    "            confidence = int( 100 * (1 - (results[1])/400) )\n",
    "            display_string = str(confidence) + '% Confident it is User'\n",
    "            \n",
    "        cv2.putText(image, display_string, (100, 120), cv2.FONT_HERSHEY_COMPLEX, 1, (255,120,150), 2)\n",
    "        \n",
    "        if confidence > 75:\n",
    "            cv2.putText(image, \"Hey GAJANAND SINGH\", (250, 450), cv2.FONT_HERSHEY_COMPLEX, 1, (0,255,0), 2)\n",
    "            cv2.imshow('Face Recognition', image )\n",
    "            webbrowser.open('')\n",
    "            break\n",
    "        else:\n",
    "            cv2.putText(image, \"Locked\", (250, 450), cv2.FONT_HERSHEY_COMPLEX, 1, (0,0,255), 2)\n",
    "            cv2.imshow('Face Recognition', image )\n",
    "\n",
    "    except:\n",
    "        cv2.putText(image, \"No Face Found\", (220, 120) , cv2.FONT_HERSHEY_COMPLEX, 1, (0,0,255), 2)\n",
    "        cv2.putText(image, \"Locked\", (250, 450), cv2.FONT_HERSHEY_COMPLEX, 1, (0,0,255), 2)\n",
    "        cv2.imshow('Face Recognition', image )\n",
    "        pass\n",
    "        \n",
    "    if cv2.waitKey(1) == 13: #13 is the Enter Key\n",
    "        break\n",
    "        \n",
    "cap.release()\n",
    "cv2.destroyAllWindows()   "
   ]
  },
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