4. Fun with Keypoints.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Facial Filters\n",
    "\n",
    "Using your trained facial keypoint detector, you can now do things like add filters to a person's face, automatically. In this optional notebook, you can play around with adding sunglasses to detected face's in an image by using the keypoints detected around a person's eyes. Checkout the `images/` directory to see what pther .png's have been provided for you to try, too!\n",
    "\n",
    "<img src=\"images/face_filter_ex.png\" width=60% height=60%/>\n",
    "\n",
    "Let's start this process by looking at a sunglasses .png that we'll be working with!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import necessary resources\n",
    "import matplotlib.image as mpimg\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image shape:  (1123, 3064, 4)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f64c3203e48>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load in sunglasses image with cv2 and IMREAD_UNCHANGED\n",
    "sunglasses = cv2.imread('images/sunglasses.png', cv2.IMREAD_UNCHANGED)\n",
    "\n",
    "# plot our image\n",
    "plt.imshow(sunglasses)\n",
    "\n",
    "# print out its dimensions\n",
    "print('Image shape: ', sunglasses.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The 4th dimension\n",
    "\n",
    "You'll note that this image actually has *4 color channels*, not just 3 as your avg RGB image does. This is due to the flag we set `cv2.IMREAD_UNCHANGED`, which tells this to read in another color channel.\n",
    "\n",
    "#### Alpha channel\n",
    "It has the usual red, blue, and green channels any color image has, and the 4th channel respresents the **transparency level of each pixel** in the image; this is often called the **alpha** channel. Here's how the transparency channel works: the lower the value, the more transparent, or see-through, the pixel will become.  The lower bound (completely transparent) is zero here, so any pixels set to 0 will not be seen; these look like white background pixels in the image above, but they are actually totally transparent. \n",
    "\n",
    "This transparent channel allows us to place this rectangular image of sunglasses on an image of a face and still see the face area that is techically covered by the transparentbackground of the sunglasses image!\n",
    "\n",
    "Let's check out the alpha channel of our sunglasses image in the next Python cell. Because many of the pixels in the background of the image have an alpha value of 0, we'll need to explicitly print out non-zero values if we want to see them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The alpha channel looks like this (black pixels = transparent): \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f64c30ade48>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f64c316c1d0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# print out the sunglasses transparency (alpha) channel\n",
    "alpha_channel = sunglasses[:,:,3]\n",
    "print ('The alpha channel looks like this (black pixels = transparent): ')\n",
    "plt.imshow(alpha_channel, cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The non-zero values of the alpha channel are: \n",
      "(array([  17,   17,   17, ..., 1109, 1109, 1109]), array([ 687,  688,  689, ..., 2376, 2377, 2378]))\n"
     ]
    }
   ],
   "source": [
    "# just to double check that there are indeed non-zero values\n",
    "# let's find and print out every value greater than zero\n",
    "values = np.where(alpha_channel != 0)\n",
    "print ('The non-zero values of the alpha channel are: ')\n",
    "print (values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Overlaying images\n",
    "\n",
    "This means that when we place this sunglasses image on top of another image, we can use the transparency channel as a filter:\n",
    "\n",
    "* If the pixels are non-transparent (alpha_channel > 0), overlay them on the new image\n",
    "\n",
    "#### Keypoint locations\n",
    "\n",
    "In doing this, it's helpful to understand which keypoint belongs to the eyes, mouth, etc., so in the image below we also print the index of each facial keypoint directly on the image so you can tell which keypoints are for the eyes, eyebrows, etc.,\n",
    "\n",
    "<img src=\"images/landmarks_numbered.jpg\" width=50% height=50%/>\n",
    "\n",
    "It may be useful to use keypoints that correspond to the edges of the face to define the width of the sunglasses, and the locations of the eyes to define the placement.\n",
    "\n",
    "Next, we'll load in an example image. Below, you've been given an image and set of keypoints from the provided training set of data, but you can use your own CNN model to generate keypoints for *any* image of a face (as in Notebook 3) and go through the same overlay process!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2019-12-26 04:35:12--  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)... 52.216.108.85\n",
      "Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.216.108.85|: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  66.5MB/s    in 4.9s    \n",
      "\n",
      "2019-12-26 04:35:17 (66.1 MB/s) - ‘/data/train-test-data.zip’ saved [338613624/338613624]\n",
      "\n",
      "Archive:  /data/train-test-data.zip\n",
      "   creating: /data/test/\n",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  inflating: /data/training_frames_keypoints.csv  \r\n"
     ]
    }
   ],
   "source": [
    "# load in the data if you have not already!\n",
    "# otherwise, you may comment out this cell\n",
    "# -- 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": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of images:  3462\n"
     ]
    }
   ],
   "source": [
    "# load in training data\n",
    "key_pts_frame = pd.read_csv('/data/training_frames_keypoints.csv')\n",
    "\n",
    "# print out some stats about the data\n",
    "print('Number of images: ', key_pts_frame.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# helper function to display keypoints\n",
    "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": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image name:  Yuri_Fedotov_40.jpg\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:5: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f64c25edc50>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# a selected image\n",
    "n = 165\n",
    "image_name = key_pts_frame.iloc[n, 0]\n",
    "image = mpimg.imread(os.path.join('/data/training/', image_name))\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",
    "\n",
    "plt.figure(figsize=(5, 5))\n",
    "show_keypoints(image, key_pts)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, you'll see an example of placing sunglasses on the person in the loaded image.\n",
    "\n",
    "Note that the keypoints are numbered off-by-one in the numbered image above, and so `key_pts[0,:]` corresponds to the first point (1) in the labelled image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f64c256b278>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f64c3155f28>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display sunglasses on top of the image in the appropriate place\n",
    "\n",
    "# copy of the face image for overlay\n",
    "image_copy = np.copy(image)\n",
    "\n",
    "# top-left location for sunglasses to go\n",
    "# 17 = edge of left eyebrow\n",
    "x = int(key_pts[17, 0])\n",
    "y = int(key_pts[17, 1])\n",
    "\n",
    "# height and width of sunglasses\n",
    "# h = length of nose\n",
    "h = int(abs(key_pts[27,1] - key_pts[34,1]))\n",
    "# w = left to right eyebrow edges\n",
    "w = int(abs(key_pts[17,0] - key_pts[26,0]))\n",
    "\n",
    "# read in sunglasses\n",
    "sunglasses = cv2.imread('images/sunglasses.png', cv2.IMREAD_UNCHANGED)\n",
    "# resize sunglasses\n",
    "new_sunglasses =  cv2.resize(sunglasses, (w, h), interpolation = cv2.INTER_CUBIC)\n",
    "\n",
    "# get region of interest on the face to change\n",
    "roi_color = image_copy[y:y+h,x:x+w]\n",
    "\n",
    "# find all non-transparent pts\n",
    "ind = np.argwhere(new_sunglasses[:,:,3] > 0)\n",
    "\n",
    "# for each non-transparent point, replace the original image pixel with that of the new_sunglasses\n",
    "for i in range(3):\n",
    "    roi_color[ind[:,0],ind[:,1],i] = new_sunglasses[ind[:,0],ind[:,1],i]    \n",
    "# set the area of the image to the changed region with sunglasses\n",
    "image_copy[y:y+h,x:x+w] = roi_color\n",
    "\n",
    "\n",
    "# display the result!\n",
    "plt.imshow(image_copy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Further steps\n",
    "\n",
    "Look in the `images/` directory to see other available .png's for overlay! Also, you may notice that the overlay of the sunglasses is not entirely perfect; you're encouraged to play around with the scale of the width and height of the glasses and investigate how to perform [image rotation](https://docs.opencv.org/3.0-beta/doc/py_tutorials/py_imgproc/py_geometric_transformations/py_geometric_transformations.html) in OpenCV so as to match an overlay with any facial pose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}