Face Recognition with GUI.ipynb

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    "from tkinter import Tk, Label, Button, Entry, StringVar, messagebox\n",
    "\n",
    "import cv2\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# Load HAAR face classifier\n",
    "face_classifier = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\n",
    "\n",
    "def face_extractor(img):\n",
    "    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "    faces = face_classifier.detectMultiScale(gray, 1.3, 5)\n",
    "\n",
    "    if len(faces) == 0:\n",
    "        return None\n",
    "\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",
    "class FaceRecognitionApp:\n",
    "    def __init__(self, master):\n",
    "        self.master = master\n",
    "        master.title(\"Face Recognition App\")\n",
    "\n",
    "        self.label = Label(master, text=\"Enter Your Name:\")\n",
    "        self.label.grid(row=0, column=0, padx=10, pady=10)\n",
    "\n",
    "        self.name_entry = Entry(master)\n",
    "        self.name_entry.grid(row=0, column=1, padx=10, pady=10)\n",
    "\n",
    "        self.collect_button = Button(master, text=\"Collect Samples\", command=self.collect_samples)\n",
    "        self.collect_button.grid(row=1, column=0, columnspan=2, pady=10)\n",
    "\n",
    "        self.train_button = Button(master, text=\"Train Model\", command=self.train_model)\n",
    "        self.train_button.grid(row=2, column=0, columnspan=2, pady=10)\n",
    "\n",
    "        self.recognize_button = Button(master, text=\"Recognize Faces\", command=self.recognize_faces)\n",
    "        self.recognize_button.grid(row=3, column=0, columnspan=2, pady=10)\n",
    "\n",
    "        self.status_var = StringVar()\n",
    "        self.status_label = Label(master, textvariable=self.status_var, fg=\"blue\")\n",
    "        self.status_label.grid(row=4, column=0, columnspan=2, pady=10)\n",
    "\n",
    "    def collect_samples(self):\n",
    "        name = self.name_entry.get().strip()\n",
    "        if not name:\n",
    "            messagebox.showerror(\"Input Error\", \"Please enter your name.\")\n",
    "        else:\n",
    "            self.status_var.set(\"Collecting samples for \" + name + \"...\")\n",
    "            collect_face_samples(name)\n",
    "            self.status_var.set(\"Samples collected for \" + name + \".\")\n",
    "\n",
    "    def train_model(self):\n",
    "        self.status_var.set(\"Training the model, please wait...\")\n",
    "        self.model = train_face_recognition()\n",
    "        self.status_var.set(\"Model trained successfully!\")\n",
    "\n",
    "    def recognize_faces(self):\n",
    "        name = self.name_entry.get().strip()\n",
    "        if not name:\n",
    "            messagebox.showerror(\"Input Error\", \"Please enter your name.\")\n",
    "        else:\n",
    "            self.status_var.set(\"Recognizing faces, please wait...\")\n",
    "            recognize_face(self.model, name)\n",
    "            self.status_var.set(\"Recognition complete for \" + name + \".\")\n",
    "            \n",
    "def collect_face_samples(name, data_path='D://Projects//Face-Detection-using-OpenCV//image_samples'):\n",
    "    cap = cv2.VideoCapture(0)\n",
    "    count = 0\n",
    "\n",
    "    while True:\n",
    "        ret, frame = cap.read()\n",
    "        detected_face = face_extractor(frame)\n",
    "\n",
    "        if detected_face is not None:\n",
    "            count += 1\n",
    "            face = cv2.resize(detected_face, (200, 200))\n",
    "            face = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "            file_name_path = os.path.join(data_path, f'{name}_image{count}.jpg')\n",
    "            cv2.imwrite(file_name_path, face)\n",
    "\n",
    "            cv2.putText(face, str(count), (50, 50), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 2)\n",
    "            cv2.imshow('Face Cropper', face)\n",
    "        else:\n",
    "            print(\"Face not found\")\n",
    "            pass\n",
    "\n",
    "        if cv2.waitKey(1) == 13 or count == 100:\n",
    "            break\n",
    "\n",
    "    cap.release()\n",
    "    cv2.destroyAllWindows()\n",
    "    print(\"Collecting Samples Complete\")\n",
    "\n",
    "def train_face_recognition(data_path='D://Projects//Face-Detection-using-OpenCV//image_samples'):\n",
    "    onlyfiles = [f for f in os.listdir(data_path) if os.path.isfile(os.path.join(data_path, f))]\n",
    "\n",
    "    Training_Data, Labels = [], []\n",
    "\n",
    "    for i, file in enumerate(onlyfiles):\n",
    "        image_path = os.path.join(data_path, file)\n",
    "        print(\"Loading image:\", image_path)\n",
    "        images = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)\n",
    "        \n",
    "        if images is not None:\n",
    "            Training_Data.append(np.asarray(images, dtype=np.uint8))\n",
    "            Labels.append(i)\n",
    "            print(\"Image loaded successfully.\")\n",
    "        else:\n",
    "            print(\"Error loading image:\", image_path)\n",
    "\n",
    "    print(\"Number of training images loaded:\", len(Training_Data))\n",
    "\n",
    "    Labels = np.asarray(Labels, dtype=np.int32)\n",
    "    model = cv2.face.LBPHFaceRecognizer_create() \n",
    "\n",
    "    model.train(np.asarray(Training_Data), np.asarray(Labels))\n",
    "    print(\"Model trained successfully\")\n",
    "\n",
    "    return model\n",
    "\n",
    "def recognize_face(model, name):\n",
    "    cap = cv2.VideoCapture(0)\n",
    "\n",
    "    while True:\n",
    "        ret, frame = cap.read()\n",
    "        image, face = face_detector(frame)\n",
    "\n",
    "        try:\n",
    "            face = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "            results = model.predict(face)\n",
    "            print(results)\n",
    "            if results[1] < 500:\n",
    "                confidence = int(100 * (1 - (results[1])/400))\n",
    "                display_string = f'{confidence}% Confident it is {name}'\n",
    "\n",
    "            cv2.putText(image, display_string, (100, 120), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 120, 150), 2)\n",
    "\n",
    "            if confidence > 80:\n",
    "                cv2.putText(image, f'Hey {name}', (250, 450), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 2)\n",
    "                cv2.imshow('Face Recognition', image )\n",
    "            else:\n",
    "                cv2.putText(image, \"I don't know\", (250, 450), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 0, 255), 2)\n",
    "                cv2.imshow('Face Recognition', image )\n",
    "\n",
    "        except Exception as e:\n",
    "            print(f\"Error Recognising Face: {e}\")\n",
    "            cv2.putText(image, \"Error Recognising Face\", (130, 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:\n",
    "            break\n",
    "\n",
    "    cap.release()\n",
    "    cv2.destroyAllWindows()\n",
    "\n",
    "def face_detector(img, size=0.5):\n",
    "    # Convert image to grayscale\n",
    "    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "    faces = face_classifier.detectMultiScale(gray, 1.3, 5)\n",
    "    \n",
    "    if len(faces) == 0:\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",
    "        \n",
    "    return img, roi \n",
    "\n",
    "root = Tk()\n",
    "app = FaceRecognitionApp(root)\n",
    "root.mainloop()\n"
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