1.3 Extracting faces from images using mtcnn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "c:\\python36\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "c:\\python36\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "c:\\python36\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "c:\\python36\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "c:\\python36\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "c:\\python36\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
      "c:\\python36\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "c:\\python36\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "c:\\python36\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "c:\\python36\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "c:\\python36\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "c:\\python36\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
      "In c:\\python36\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The text.latex.preview rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In c:\\python36\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The mathtext.fallback_to_cm rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In c:\\python36\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: Support for setting the 'mathtext.fallback_to_cm' rcParam is deprecated since 3.3 and will be removed two minor releases later; use 'mathtext.fallback : 'cm' instead.\n",
      "In c:\\python36\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The validate_bool_maybe_none function was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In c:\\python36\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The savefig.jpeg_quality rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In c:\\python36\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The keymap.all_axes rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In c:\\python36\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The animation.avconv_path rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In c:\\python36\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The animation.avconv_args rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n"
     ]
    }
   ],
   "source": [
    "# Load the required libraries\n",
    "import mtcnn\n",
    "from mtcnn import MTCNN\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Rectangle, Circle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From c:\\python36\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:4070: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\python36\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 27 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw each face separately\n",
    "\n",
    "def draw_faces(filename, result_list):\n",
    "    # Load the image\n",
    "    data = plt.imread(filename)\n",
    "    \n",
    "    # Plot each face as a sub-plot\n",
    "    for i in range(len(result_list)):\n",
    "        x1, y1, width, height = result_list[i][\"box\"]\n",
    "        x2, y2 = x1 + width, y1 + height\n",
    "        \n",
    "        # Define sub-plot\n",
    "        plt.subplot(1, len(result_list), i+1)\n",
    "        plt.axis('off')\n",
    "        \n",
    "        # plot face\n",
    "        plt.imshow(data[y1:y2, x1:x2])\n",
    "    # Show the plot\n",
    "    plt.show\n",
    "        \n",
    "        \n",
    "# Load the image data to a file\n",
    "\n",
    "filename = \"test1.jpg\"\n",
    "\n",
    "# Load th image data from a file\n",
    "pixels = plt.imread(filename)\n",
    "\n",
    "# Creat the detector using default weights\n",
    "detector = MTCNN()\n",
    "\n",
    "# detect faces in the image \n",
    "faces = detector.detect_faces(pixels)\n",
    "\n",
    "\n",
    "draw_faces(filename, faces)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}