01 - Load and Visualize Data.ipynb

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   "source": [
    "# Facial Keypoint Detection\n",
    "  \n",
    "The first step in any challenge like this will be to load and visualize the data you'll be working with. \n",
    "\n",
    "Let's take a look at some examples of images and corresponding facial keypoints.\n",
    "<img src='images/key_pts_example.png' width=50% height=50%/>\n",
    "\n",
    "Facial keypoints (also called facial landmarks) are the small magenta dots shown on each of the faces in the image above. In each training and test image, there is a single face and **68 keypoints, with coordinates (x, y), for that face**.  These keypoints mark important areas of the face: the eyes, corners of the mouth, the nose, etc. These keypoints are relevant for a variety of tasks, such as face filters, emotion recognition, pose recognition, and so on. Here they are, numbered, and you can see that specific ranges of points match different portions of the face.\n",
    "\n",
    "<img src='images/landmarks_numbered.jpg' width=30% height=30%/>\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and Visualize Data\n",
    "\n",
    "This set of image data has been extracted from the [YouTube Faces Dataset](https://www.cs.tau.ac.il/~wolf/ytfaces/), which includes videos of people in YouTube videos. These videos have been fed through some processing steps and turned into sets of image frames containing one face and the associated keypoints.\n",
    "\n",
    "#### Training and Testing Data\n",
    "\n",
    "This facial keypoints dataset consists of 5770 color images. All of these images are separated into either a training or a test set of data.\n",
    "\n",
    "* 3462 of these images are training images, for you to use as you create a model to predict keypoints.\n",
    "* 2308 are test images, which will be used to test the accuracy of your model.\n",
    "\n",
    "The information about the images and keypoints in this dataset are summarized in CSV files, which we can read in using `pandas`. Let's read the training CSV and get the annotations in an (N, 2) array where N is the number of keypoints and 2 is the dimension of the keypoint coordinates (x, y).\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the required libraries\n",
    "import glob\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "key_pts_frame = pd.read_csv('data/training_frames_keypoints.csv')\n",
    "key_pts_frame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "key_pts_frame.shape"
   ]
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   "cell_type": "code",
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   "metadata": {},
   "outputs": [
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       "       77.0, 122.0], dtype=object)"
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = 0\n",
    "image_name = key_pts_frame.iloc[n, 0]\n",
    "key_pts = key_pts_frame.iloc[n, 1:].values\n",
    "key_pts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(136,)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "key_pts.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image name:  Luis_Fonsi_21.jpg\n",
      "Landmarks shape:  (68, 2)\n",
      "First 4 key pts: [[ 45.  98.]\n",
      " [ 47. 106.]\n",
      " [ 49. 110.]\n",
      " [ 53. 119.]]\n"
     ]
    }
   ],
   "source": [
    "key_pts = key_pts.astype('float').reshape(-1, 2)\n",
    "\n",
    "print('Image name: ', image_name)\n",
    "print('Landmarks shape: ', key_pts.shape)\n",
    "print('First 4 key pts: {}'.format(key_pts[:4]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of images:  3462\n"
     ]
    }
   ],
   "source": [
    "# print out some stats about the data\n",
    "print('Number of images: ', key_pts_frame.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Look at some images\n",
    "\n",
    "Below, is a function `show_keypoints` that takes in an image and keypoints and displays them.  As you look at this data, **note that these images are not all of the same size**, and neither are the faces! To eventually train a neural network on these images, we'll need to standardize their shape."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_keypoints(image, key_pts):\n",
    "    \"\"\"Show image with keypoints\"\"\"\n",
    "    plt.imshow(image)\n",
    "    plt.scatter(key_pts[:, 0], key_pts[:, 1], s=20, marker='.', c='m')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display a few different types of images by changing the index n\n",
    "\n",
    "# select an image by index in our data frame\n",
    "n = 0\n",
    "image_name = key_pts_frame.iloc[n, 0]\n",
    "key_pts = key_pts_frame.iloc[n, 1:].values\n",
    "key_pts = key_pts.astype('float').reshape(-1, 2)\n",
    "\n",
    "plt.figure(figsize=(5, 5))\n",
    "show_keypoints(mpimg.imread(os.path.join('data/training/', image_name)), key_pts)\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}