GUI Face Recognizer.ipynb

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  {
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   "source": [
    "import tkinter as tk\n",
    "from tkinter import messagebox\n",
    "import cv2\n",
    "import os\n",
    "from PIL import Image\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "546ec426",
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   "source": [
    "window = tk.Tk()\n",
    "window.title(\"Face Recognition System\")\n",
    "window.config(background='skyblue')\n",
    "\n",
    "l1 = tk.Label(window,text=\"Name\",font=(\"Algerian\",20),bg='sky blue')\n",
    "l1.grid(column=0, row=0)\n",
    "t1=tk.Entry(window,width=50,bd=5)\n",
    "t1.grid(column=1, row=0)\n",
    "\n",
    "l2 = tk.Label(window,text=\"Age\",font=(\"Algerian\",20),bg='sky blue')\n",
    "l2.grid(column=0, row=1)\n",
    "t2=tk.Entry(window,width=50,bd=5)\n",
    "t2.grid(column=1, row=1)\n",
    "\n",
    "#TRAIN THE CLASSIFIER\n",
    "def train_classifier():\n",
    "    data_dir=\"C:/Users/Lenovo/Downloads/faace Recognition/data\"\n",
    "    path = [os.path.join(data_dir,f) for f in os.listdir(data_dir)]\n",
    "    faces = []\n",
    "    ids = []\n",
    "    \n",
    "    for image in path:\n",
    "        img = Image.open(image).convert('L')\n",
    "        imageNp = np.array(img, 'uint8')\n",
    "        id = int(os.path.split(image)[1].split(\".\")[1])\n",
    "        \n",
    "        faces.append(imageNp)\n",
    "        ids.append(id)\n",
    "        \n",
    "    ids = np.array(ids)\n",
    "    \n",
    "    #train the classifier \n",
    "    clf = cv2.face.LBPHFaceRecognizer_create()\n",
    "    clf.train(faces,ids)\n",
    "    clf.write(\"classifier.xml\") \n",
    "    messagebox.shoeinfo('Result','Training dataset completed')\n",
    "b1=tk.Button(window,text=\"Training\",font=(\"algerian\",20),bg='light blue',fg='black',command= train_classifier)\n",
    "b1.grid(column=0,row=4)\n",
    "\n",
    "#DETECT THE FACE\n",
    "def detect_face():\n",
    "    def draw_boundary(img,classifier,scaleFactor,minNeighbors,color,text,clf):\n",
    "        gray_image = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
    "        features = classifier.detectMultiScale(gray_image,scaleFactor,minNeighbors)\n",
    "        names = ['Dhairya','Kirti']\n",
    "        coords = []\n",
    "\n",
    "        for(x,y,w,h) in features:\n",
    "            cv2.rectangle(img,(x,y),(x+w,y+h),color,2)\n",
    "            id,confidence = clf.predict(gray_image[y:y+h,x:x+w])\n",
    "            \n",
    "            if (confidence < 100):\n",
    "                id = names[id]\n",
    "                confidence = \"  {0}%\".format(round(100 - confidence))\n",
    "            else:\n",
    "                id = \"unknown\"\n",
    "                confidence = \"  {0}%\".format(round(100 - confidence))\n",
    "        \n",
    "           \n",
    "            cv2.putText(\n",
    "                    img, \n",
    "                    str(id), \n",
    "                    (x+5,y-5), \n",
    "                    font, \n",
    "                    1, \n",
    "                    (255,255,255), \n",
    "                    2\n",
    "                   )\n",
    "            cv2.putText(\n",
    "                    img, \n",
    "                    str(confidence), \n",
    "                    (x+5,y+h-5), \n",
    "                    font, \n",
    "                    1, \n",
    "                    (255,255,0), \n",
    "                    1\n",
    "                   )  \n",
    "            \n",
    "\n",
    "          #  if confidence>60:\n",
    "               # if id==1:\n",
    "                    #cv2.putText(img,\"Dhairya\",(x,y-5),cv2.FONT_HERSHEY_SIMPLEX,0.8,color,1,cv2.LINE_AA)\n",
    "               # if id==2:\n",
    "                    #cv2.putText(img,\"Kirti\",(x,y-5),cv2.FONT_HERSHEY_SIMPLEX,0.8,color,1,cv2.LINE_AA)\n",
    "           ## else:\n",
    "               # cv2.putText(img,\"UNKNOWN\",(x,y-5),cv2.FONT_HERSHEY_SIMPLEX,0.8,(0,0,255),1,cv2.LINE_AA)\n",
    "\n",
    "            #coords=[x,y,w,h]\n",
    "        #return coords \"\"\"\n",
    " \n",
    "    def recognize(img,clf,faceCascade):\n",
    "        coords = draw_boundary(img,faceCascade,1.1,10,(255,255,255),\"Face\",clf)\n",
    "        return img\n",
    "    faceCascade =  cv2.CascadeClassifier(\"haarcascade_frontalface_default.xml\")\n",
    "    clf= cv2.face.LBPHFaceRecognizer_create()\n",
    "    clf.read(\"classifier.xml\")\n",
    "\n",
    "    video_capture = cv2.VideoCapture(0)\n",
    "\n",
    "    while True:\n",
    "        ret,img = video_capture.read()\n",
    "        img = recognize(img,clf,faceCascade)\n",
    "        cv2.imshow(\"face detection\",img)\n",
    "\n",
    "        if cv2.waitKey(1)==13:\n",
    "            break\n",
    "\n",
    "    video_capture.release()\n",
    "    cv2.destroyAllWindows()\n",
    "b2=tk.Button(window,text=\"Detect\",font=(\"algerian\",20),bg='light blue',fg='black',command=detect_face)\n",
    "b2.grid(column=1,row=4)\n",
    "\n",
    "#GENERATING DATASET \n",
    "def generate_dataset():\n",
    "    if(t1.get()==\"\" or t2.get()==\"\"):\n",
    "        messagebox.showinfo('Result','Please Provide Complete Details')\n",
    "    else:\n",
    "        face_classifier = cv2.CascadeClassifier(\"haarcascade_frontalface_default.xml\")\n",
    "        def face_cropped(img):\n",
    "            gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "            faces = face_classifier.detectMultiScale(gray,1.3,5)\n",
    "            #scaling factor = 1.3\n",
    "            #Minimum Neighbor = 5\n",
    "            \n",
    "            if faces is ():\n",
    "                return None\n",
    "            for(x,y,w,h) in faces:\n",
    "                cropped_face = img[y:y+h,x:x+w]\n",
    "                return cropped_face\n",
    "    \n",
    "    cap = cv2.VideoCapture(0)\n",
    "    id = 1\n",
    "    img_id = 0\n",
    "    \n",
    "    while True:\n",
    "        ret,frame = cap.read()\n",
    "        if face_cropped(frame) is not None:\n",
    "            img_id+=1\n",
    "            face = cv2.resize(face_cropped(frame),(200,200))\n",
    "            face = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)\n",
    "            file_path = \"data/user.\"+str(id)+\".\"+str(img_id)+\".jpg\"\n",
    "            cv2.imwrite(file_path,face)\n",
    "            cv2.putText(face, str(img_id),(50,50),cv2.FONT_HERSHEY_COMPLEX,1 ,(0,255,0),2)\n",
    "            #(50,50) IS THE ORIGIN POINT FROM WHERE TEXT IS TO BE WRITTEN\n",
    "            #FONT SCALE =1\n",
    "            #THICKNESS=2\n",
    "            cv2.imshow(\"Cropped Face\", face)\n",
    "            if cv2.waitKey(1)==13 or int(img_id)==200:\n",
    "                break\n",
    "    cap.release()\n",
    "    cv2.desstroyAllWindows()\n",
    "    messagebox.showinfo('Results','Generating dataset completed!')\n",
    "    \n",
    "\n",
    "b3=tk.Button(window,text=\"Generate Dataset\",font=(\"algerian\",20),bg='light blue',fg='black',command=generate_dataset)\n",
    "b3.grid(column=2,row=4)\n",
    "\n",
    "window.geometry(\"900x400\")\n",
    "window.mainloop()"
   ]
  },
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