VGGFaceRecognition.ipynb

{
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: keras_applications in c:\\users\\ahmad\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (1.0.8)\n",
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     ]
    }
   ],
   "source": [
    "!pip install keras_vggface\n",
    "!pip install keras_applications"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "26a6c8ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras_vggface import VGGFace\n",
    "from keras_vggface import utils\n",
    "from keras_vggface.utils import preprocess_input\n",
    "from mtcnn import MTCNN\n",
    "from ipyfilechooser import FileChooser\n",
    "import cv2 as cv\n",
    "import numpy as np\n",
    "from numpy import expand_dims"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "31ed1325",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: keras-vggfaceNote: you may need to restart the kernel to use updated packages.\n",
      "\n",
      "Version: 0.5\n",
      "Summary: VGGFace implementation with Keras framework\n",
      "Home-page: https://github.com/rcmalli/keras-vggface\n",
      "Author: Refik Can MALLI\n",
      "Author-email: mallir@itu.edu.tr\n",
      "License: MIT\n",
      "Location: c:\\users\\ahmad\\appdata\\local\\programs\\python\\python310\\lib\\site-packages\n",
      "Requires: h5py, keras, numpy, pillow, pyyaml, scipy, six, tensorflow\n",
      "Required-by: \n"
     ]
    }
   ],
   "source": [
    "pip show keras-vggface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c377189b",
   "metadata": {},
   "outputs": [],
   "source": [
    "vggface = VGGFace(model='senet50')\n",
    "detector_obj = MTCNN()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d74e8e6b",
   "metadata": {},
   "source": [
    "### Upload Image "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1a7900c3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "674af0b1fb5d41e1b28e0efe5a8dd860",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FileChooser(path='C:\\Users\\Ahmad\\Desktop\\Machine_Learning_Books\\projects\\FaceRecognitionVGG', filename='', tit…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fc = FileChooser()\n",
    "display(fc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3d9be8c6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ahmad\\Desktop\\Machine_Learning_Books\\projects\\FaceRecognitionVGG\\ahmad.jpg\n"
     ]
    }
   ],
   "source": [
    "print(fc.selected)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4e183ba",
   "metadata": {},
   "source": [
    "### Manipulalting Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "327fcd17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 62ms/step\n",
      "1/1 [==============================] - 0s 44ms/step\n",
      "1/1 [==============================] - 0s 37ms/step\n",
      "1/1 [==============================] - 0s 31ms/step\n",
      "1/1 [==============================] - 0s 29ms/step\n",
      "1/1 [==============================] - 0s 29ms/step\n",
      "1/1 [==============================] - 0s 28ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 33ms/step\n",
      "1/1 [==============================] - 0s 27ms/step\n",
      "1/1 [==============================] - 0s 30ms/step\n",
      "5/5 [==============================] - 0s 8ms/step\n",
      "1/1 [==============================] - 0s 35ms/step\n"
     ]
    },
    {
     "data": {
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       "         [ -91.432556, -103.80819 , -131.001   ]]]], dtype=float32)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def extract_face(address):\n",
    "    img = cv.imread(address)\n",
    "\n",
    "    rgb_img = cv.cvtColor(img , cv.COLOR_BGR2RGB)\n",
    "\n",
    "    face = detector_obj.detect_faces(rgb_img)[0]\n",
    "    x , y ,w, h = face['box']\n",
    "\n",
    "    actual_face = rgb_img[y:y+h , x:x+w] # This crop only section of image that contain person Face\n",
    "    actual_face = cv.resize(actual_face , (224,224))\n",
    "    actual_face = actual_face/255.0\n",
    "\n",
    "    return np.asarray(actual_face)\n",
    "\n",
    "face = extract_face(fc.selected)\n",
    "face = face.astype('float32')\n",
    "samples = expand_dims(face, axis=0)\n",
    "samples = preprocess_input(samples, version=2)\n",
    "\n",
    "samples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f1e738e",
   "metadata": {},
   "source": [
    "### Face Identification With VGGFace2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "bb6d6a57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inputs: [<KerasTensor: shape=(None, 224, 224, 3) dtype=float32 (created by layer 'input_1')>]\n",
      "Outputs: [<KerasTensor: shape=(None, 8631) dtype=float32 (created by layer 'classifier')>]\n"
     ]
    }
   ],
   "source": [
    "# summarize input and output shape\n",
    "print('Inputs: %s' % vggface.inputs)\n",
    "print('Outputs: %s' % vggface.outputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1928a9dc",
   "metadata": {},
   "source": [
    "#### Predicting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1b895f8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 193ms/step\n",
      "[[4.6287125e-05 1.2256233e-04 8.3936560e-05 ... 1.2859362e-04\n",
      "  1.7007625e-04 1.0346011e-04]]\n",
      "[[[\"b' Phylicia_Rash\\\\xc4\\\\x81d'\", 0.00058736745], [\"b' Roger_Donaldson'\", 0.0005233481], [\"b' Cris_Vianna'\", 0.00046721328], [\"b' Richa_Soni'\", 0.00046343866], [\"b' Shabana_Azmi'\", 0.00044361933]]]\n",
      "b' Phylicia_Rash\\xc4\\x81d': 0.059%\n",
      "b' Roger_Donaldson': 0.052%\n",
      "b' Cris_Vianna': 0.047%\n",
      "b' Richa_Soni': 0.046%\n",
      "b' Shabana_Azmi': 0.044%\n"
     ]
    }
   ],
   "source": [
    "predict = vggface.predict(samples)\n",
    "print(predict)\n",
    "\n",
    "output = utils.decode_predictions(predict)\n",
    "print(output)\n",
    "\n",
    "for result in output[0]:\n",
    "    print('%s: %.3f%%' % (result[0], result[1]*100))"
   ]
  }
 ],
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