1. Load and Visualize Data.ipynb

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    "# Facial Keypoint Detection\n",
    "  \n",
    "This project will be all about defining and training a convolutional neural network to perform facial keypoint detection, and using computer vision techniques to transform images of faces.  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",
    "\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",
    "---"
   ]
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   "source": [
    "## Load and Visualize Data\n",
    "\n",
    "The first step in working with any dataset is to become familiar with your data; you'll need to load in the images of faces and their keypoints and visualize them! 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",
    "---"
   ]
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   "source": [
    "First, before we do anything, we have to load in our image data. This data is stored in a zip file and in the below cell, we access it by it's URL and unzip the data in a `/data/` directory that is separate from the workspace home directory."
   ]
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      "--2018-05-26 16:41:10--  https://s3.amazonaws.com/video.udacity-data.com/topher/2018/May/5aea1b91_train-test-data/train-test-data.zip\n",
      "Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.216.97.85\n",
      "Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.216.97.85|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 338613624 (323M) [application/zip]\n",
      "Saving to: ‘/data/train-test-data.zip’\n",
      "\n",
      "train-test-data.zip 100%[===================>] 322.93M  27.0MB/s    in 8.9s    \n",
      "\n",
      "2018-05-26 16:41:19 (36.3 MB/s) - ‘/data/train-test-data.zip’ saved [338613624/338613624]\n",
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      "  inflating: /data/training_frames_keypoints.csv  \n"
     ]
    }
   ],
   "source": [
    "# -- DO NOT CHANGE THIS CELL -- #\n",
    "!mkdir /data\n",
    "!wget -P /data/ https://s3.amazonaws.com/video.udacity-data.com/topher/2018/May/5aea1b91_train-test-data/train-test-data.zip\n",
    "!unzip -n /data/train-test-data.zip -d /data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": "markdown",
   "metadata": {},
   "source": [
    "Then, let's load in our training data and display some stats about that dat ato make sure it's been loaded in correctly!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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_frame = pd.read_csv('/data/training_frames_keypoints.csv')\n",
    "\n",
    "n = 0\n",
    "image_name = key_pts_frame.iloc[n, 0]\n",
    "key_pts = key_pts_frame.iloc[n, 1:].as_matrix()\n",
    "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": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of images:  3462\n"
     ]
    }
   ],
   "source": [
    "# print out some stats about the data\n",
    "number_of_images = key_pts_frame.shape[0]\n",
    "print('Number of images: ', number_of_images)"
   ]
  },
  {
   "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": 35,
   "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": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f97a42bb400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "# 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 = random.randint(0, number_of_images-1)\n",
    "image_name = key_pts_frame.iloc[n, 0]\n",
    "key_pts = key_pts_frame.iloc[n, 1:].as_matrix()\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()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset class and Transformations\n",
    "\n",
    "To prepare our data for training, we'll be using PyTorch's Dataset class. Much of this this code is a modified version of what can be found in the [PyTorch data loading tutorial](http://pytorch.org/tutorials/beginner/data_loading_tutorial.html).\n",
    "\n",
    "#### Dataset class\n",
    "\n",
    "``torch.utils.data.Dataset`` is an abstract class representing a\n",
    "dataset. This class will allow us to load batches of image/keypoint data, and uniformly apply transformations to our data, such as rescaling and normalizing images for training a neural network.\n",
    "\n",
    "\n",
    "Your custom dataset should inherit ``Dataset`` and override the following\n",
    "methods:\n",
    "\n",
    "-  ``__len__`` so that ``len(dataset)`` returns the size of the dataset.\n",
    "-  ``__getitem__`` to support the indexing such that ``dataset[i]`` can\n",
    "   be used to get the i-th sample of image/keypoint data.\n",
    "\n",
    "Let's create a dataset class for our face keypoints dataset. We will\n",
    "read the CSV file in ``__init__`` but leave the reading of images to\n",
    "``__getitem__``. This is memory efficient because all the images are not\n",
    "stored in the memory at once but read as required.\n",
    "\n",
    "A sample of our dataset will be a dictionary\n",
    "``{'image': image, 'keypoints': key_pts}``. Our dataset will take an\n",
    "optional argument ``transform`` so that any required processing can be\n",
    "applied on the sample. We will see the usefulness of ``transform`` in the\n",
    "next section.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "class FacialKeypointsDataset(Dataset):\n",
    "    \"\"\"Face Landmarks dataset.\"\"\"\n",
    "\n",
    "    def __init__(self, csv_file, root_dir, transform=None):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            csv_file (string): Path to the csv file with annotations.\n",
    "            root_dir (string): Directory with all the images.\n",
    "            transform (callable, optional): Optional transform to be applied\n",
    "                on a sample.\n",
    "        \"\"\"\n",
    "        self.key_pts_frame = pd.read_csv(csv_file)\n",
    "        self.root_dir = root_dir\n",
    "        self.transform = transform\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.key_pts_frame)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        image_name = os.path.join(self.root_dir,\n",
    "                                self.key_pts_frame.iloc[idx, 0])\n",
    "        \n",
    "        image = mpimg.imread(image_name)\n",
    "        \n",
    "        # if image has an alpha color channel, get rid of it\n",
    "        if(image.shape[2] == 4):\n",
    "            image = image[:,:,0:3]\n",
    "        \n",
    "        key_pts = self.key_pts_frame.iloc[idx, 1:].as_matrix()\n",
    "        key_pts = key_pts.astype('float').reshape(-1, 2)\n",
    "        sample = {'image': image, 'keypoints': key_pts}\n",
    "\n",
    "        if self.transform:\n",
    "            sample = self.transform(sample)\n",
    "\n",
    "        return sample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've defined this class, let's instantiate the dataset and display some images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Construct the dataset\n",
    "face_dataset = FacialKeypointsDataset(csv_file='/data/training_frames_keypoints.csv',\n",
    "                                      root_dir='/data/training/')\n",
    "\n",
    "# print some stats about the dataset\n",
    "print('Length of dataset: ', len(face_dataset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Display a few of the images from the dataset\n",
    "num_to_display = 3\n",
    "\n",
    "for i in range(num_to_display):\n",
    "    \n",
    "    # define the size of images\n",
    "    fig = plt.figure(figsize=(20,10))\n",
    "    \n",
    "    # randomly select a sample\n",
    "    rand_i = np.random.randint(0, len(face_dataset))\n",
    "    sample = face_dataset[rand_i]\n",
    "\n",
    "    # print the shape of the image and keypoints\n",
    "    print(i, sample['image'].shape, sample['keypoints'].shape)\n",
    "\n",
    "    ax = plt.subplot(1, num_to_display, i + 1)\n",
    "    ax.set_title('Sample #{}'.format(i))\n",
    "    \n",
    "    # Using the same display function, defined earlier\n",
    "    show_keypoints(sample['image'], sample['keypoints'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transforms\n",
    "\n",
    "Now, the images above are not of the same size, and neural networks often expect images that are standardized; a fixed size, with a normalized range for color ranges and coordinates, and (for PyTorch) converted from numpy lists and arrays to Tensors.\n",
    "\n",
    "Therefore, we will need to write some pre-processing code.\n",
    "Let's create four transforms:\n",
    "\n",
    "-  ``Normalize``: to convert a color image to grayscale values with a range of [0,1] and normalize the keypoints to be in a range of about [-1, 1]\n",
    "-  ``Rescale``: to rescale an image to a desired size.\n",
    "-  ``RandomCrop``: to crop an image randomly.\n",
    "-  ``ToTensor``: to convert numpy images to torch images.\n",
    "\n",
    "\n",
    "We will write them as callable classes instead of simple functions so\n",
    "that parameters of the transform need not be passed everytime it's\n",
    "called. For this, we just need to implement ``__call__`` method and \n",
    "(if we require parameters to be passed in), the ``__init__`` method. \n",
    "We can then use a transform like this:\n",
    "\n",
    "    tx = Transform(params)\n",
    "    transformed_sample = tx(sample)\n",
    "\n",
    "Observe below how these transforms are generally applied to both the image and its keypoints.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from torchvision import transforms, utils\n",
    "# tranforms\n",
    "\n",
    "class Normalize(object):\n",
    "    \"\"\"Convert a color image to grayscale and normalize the color range to [0,1].\"\"\"        \n",
    "\n",
    "    def __call__(self, sample):\n",
    "        image, key_pts = sample['image'], sample['keypoints']\n",
    "        \n",
    "        image_copy = np.copy(image)\n",
    "        key_pts_copy = np.copy(key_pts)\n",
    "\n",
    "        # convert image to grayscale\n",
    "        image_copy = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)\n",
    "        \n",
    "        # scale color range from [0, 255] to [0, 1]\n",
    "        image_copy=  image_copy/255.0\n",
    "        \n",
    "        # scale keypoints to be centered around 0 with a range of [-1, 1]\n",
    "        # mean = 100, sqrt = 50, so, pts should be (pts - 100)/50\n",
    "        key_pts_copy = (key_pts_copy - 100)/50.0\n",
    "\n",
    "\n",
    "        return {'image': image_copy, 'keypoints': key_pts_copy}\n",
    "\n",
    "\n",
    "class Rescale(object):\n",
    "    \"\"\"Rescale the image in a sample to a given size.\n",
    "\n",
    "    Args:\n",
    "        output_size (tuple or int): Desired output size. If tuple, output is\n",
    "            matched to output_size. If int, smaller of image edges is matched\n",
    "            to output_size keeping aspect ratio the same.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, output_size):\n",
    "        assert isinstance(output_size, (int, tuple))\n",
    "        self.output_size = output_size\n",
    "\n",
    "    def __call__(self, sample):\n",
    "        image, key_pts = sample['image'], sample['keypoints']\n",
    "\n",
    "        h, w = image.shape[:2]\n",
    "        if isinstance(self.output_size, int):\n",
    "            if h > w:\n",
    "                new_h, new_w = self.output_size * h / w, self.output_size\n",
    "            else:\n",
    "                new_h, new_w = self.output_size, self.output_size * w / h\n",
    "        else:\n",
    "            new_h, new_w = self.output_size\n",
    "\n",
    "        new_h, new_w = int(new_h), int(new_w)\n",
    "\n",
    "        img = cv2.resize(image, (new_w, new_h))\n",
    "        \n",
    "        # scale the pts, too\n",
    "        key_pts = key_pts * [new_w / w, new_h / h]\n",
    "\n",
    "        return {'image': img, 'keypoints': key_pts}\n",
    "\n",
    "\n",
    "class RandomCrop(object):\n",
    "    \"\"\"Crop randomly the image in a sample.\n",
    "\n",
    "    Args:\n",
    "        output_size (tuple or int): Desired output size. If int, square crop\n",
    "            is made.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, output_size):\n",
    "        assert isinstance(output_size, (int, tuple))\n",
    "        if isinstance(output_size, int):\n",
    "            self.output_size = (output_size, output_size)\n",
    "        else:\n",
    "            assert len(output_size) == 2\n",
    "            self.output_size = output_size\n",
    "\n",
    "    def __call__(self, sample):\n",
    "        image, key_pts = sample['image'], sample['keypoints']\n",
    "\n",
    "        h, w = image.shape[:2]\n",
    "        new_h, new_w = self.output_size\n",
    "\n",
    "        top = np.random.randint(0, h - new_h)\n",
    "        left = np.random.randint(0, w - new_w)\n",
    "\n",
    "        image = image[top: top + new_h,\n",
    "                      left: left + new_w]\n",
    "\n",
    "        key_pts = key_pts - [left, top]\n",
    "\n",
    "        return {'image': image, 'keypoints': key_pts}\n",
    "\n",
    "\n",
    "class ToTensor(object):\n",
    "    \"\"\"Convert ndarrays in sample to Tensors.\"\"\"\n",
    "\n",
    "    def __call__(self, sample):\n",
    "        image, key_pts = sample['image'], sample['keypoints']\n",
    "         \n",
    "        # if image has no grayscale color channel, add one\n",
    "        if(len(image.shape) == 2):\n",
    "            # add that third color dim\n",
    "            image = image.reshape(image.shape[0], image.shape[1], 1)\n",
    "            \n",
    "        # swap color axis because\n",
    "        # numpy image: H x W x C\n",
    "        # torch image: C X H X W\n",
    "        image = image.transpose((2, 0, 1))\n",
    "        \n",
    "        return {'image': torch.from_numpy(image),\n",
    "                'keypoints': torch.from_numpy(key_pts)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test out the transforms\n",
    "\n",
    "Let's test these transforms out to make sure they behave as expected. As you look at each transform, note that, in this case, **order does matter**. For example, you cannot crop a image using a value smaller than the original image (and the orginal images vary in size!), but, if you first rescale the original image, you can then crop it to any size smaller than the rescaled size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# test out some of these transforms\n",
    "rescale = Rescale(100)\n",
    "crop = RandomCrop(50)\n",
    "composed = transforms.Compose([Rescale(250),\n",
    "                               RandomCrop(224)])\n",
    "\n",
    "# apply the transforms to a sample image\n",
    "test_num = 500\n",
    "sample = face_dataset[test_num]\n",
    "\n",
    "fig = plt.figure()\n",
    "for i, tx in enumerate([rescale, crop, composed]):\n",
    "    transformed_sample = tx(sample)\n",
    "\n",
    "    ax = plt.subplot(1, 3, i + 1)\n",
    "    plt.tight_layout()\n",
    "    ax.set_title(type(tx).__name__)\n",
    "    show_keypoints(transformed_sample['image'], transformed_sample['keypoints'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create the transformed dataset\n",
    "\n",
    "Apply the transforms in order to get grayscale images of the same shape. Verify that your transform works by printing out the shape of the resulting data (printing out a few examples should show you a consistent tensor size)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# define the data tranform\n",
    "# order matters! i.e. rescaling should come before a smaller crop\n",
    "data_transform = transforms.Compose([Rescale(250),\n",
    "                                     RandomCrop(224),\n",
    "                                     Normalize(),\n",
    "                                     ToTensor()])\n",
    "\n",
    "# create the transformed dataset\n",
    "transformed_dataset = FacialKeypointsDataset(csv_file='/data/training_frames_keypoints.csv',\n",
    "                                             root_dir='/data/training/',\n",
    "                                             transform=data_transform)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# print some stats about the transformed data\n",
    "print('Number of images: ', len(transformed_dataset))\n",
    "\n",
    "# make sure the sample tensors are the expected size\n",
    "for i in range(5):\n",
    "    sample = transformed_dataset[i]\n",
    "    print(i, sample['image'].size(), sample['keypoints'].size())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Iteration and Batching\n",
    "\n",
    "Right now, we are iterating over this data using a ``for`` loop, but we are missing out on a lot of PyTorch's dataset capabilities, specifically the abilities to:\n",
    "\n",
    "-  Batch the data\n",
    "-  Shuffle the data\n",
    "-  Load the data in parallel using ``multiprocessing`` workers.\n",
    "\n",
    "``torch.utils.data.DataLoader`` is an iterator which provides all these\n",
    "features, and we'll see this in use in the *next* notebook, Notebook 2, when we load data in batches to train a neural network!\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ready to Train!\n",
    "\n",
    "Now that you've seen how to load and transform our data, you're ready to build a neural network to train on this data.\n",
    "\n",
    "In the next notebook, you'll be tasked with creating a CNN for facial keypoint detection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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