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 0x7f7f13017c88>"
      ]
     },
     "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 0x7f7f12f28be0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bdnsVsa03ukdQeyP6wTeoPyudPTQXeDF1Hd0GnCxpQjpIfHIqyzVJhVyoeeJ5mE9eLq23YcOGws7T0Zw+ugq4H3inpAFJS4AvAydJ6gNOSuMAtwBPAv3At4BPAUTEc8BlwEPpcWkqKwSvNPW75557PM8KQFJh+rHzTBJHH3101mE0rLTXGmrGN77xDc4555ysw8gtJ4Bi+t73vscZZ5yRdRiFkuO2Xte1hpwImpTn+ddpOV4prA6rV6/m9NNPzzqM3Bo/fjw7d+7MOoy98UXnOqnabVTmH8Gy17/bLFy4EEmsWrUq61BypdrOC5AE6uZE0EK1SeHWW2/NOpy2cgLsfh/96EeRxEc+8pGsQ8lMWdq5u4Y64JBDDuGpp57KOoymdfvKYHuX59+LVumSdu6uobx5+umnd9uyKEpDO/bYYwsXs7VXN7aHIq6breZEkJHBjS/rBjhUPA8++GCmMVm+5an9jsaXvvSl3K13eeFEkCNDNdLqo6envitubN68+bXry4zmYdasodrVlVde2fE4qpeJGfy48MILOx5LUfgYgZll5pxzzuHtb387r776Ki+99BL77LMPL7/8MuPGjWPXrl0AvPLKKzzwwAPcfvvtGUdbKP4fgZlZydWVCBq96Fyn/AbYnHUQbfI24Nmsg2iTbq1bt9YLXLciGqleh9TzQXlPBJvryWpFIqnHdSuWbq0XuG5F1Mp6+WCxmVnJORGYmZVc3hPB8qwDaCPXrXi6tV7guhVRy+qV67OGzMys/fK+R2BmZm2W20Qg6VRJmyX1S1qWdTz1kvSUpEckbZDUk8oOkLRWUl96npDKJenqVNeNkmZnG/3uJF0raYek3pqyuusiaXGavk/S4qG+q9OGqdsXJP0yLbsNkubXvPb5VLfNkk6pKc9Ve5U0VdJdkjZJelTSeam88MtthLoVerlJ2k/Sg5IeTvX6p1R+qKR1af7fKGnfVD4ujfen16fVfNaQ9R1WROTuAYwBngAOA/YFHgbenXVcddbhKeBtg8quAJal4WXAV9LwfOB/AAFzgXVZxz8o7g8As4HeRusCHEDlNqYHABPS8ISc1u0LwAVDTPvu1BbHAYemNjomj+0VmATMTsNvAX6e4i/8chuhboVebmnevzkN7wOsS8vi+8CiVP5N4Jw0/Cngm2l4EXDjSPUd6bvzukdwDNAfEU9GxO+BG4AFGcfUCguAlWl4JXBaTfl1UfEAsL+kSVkEOJSI+Akw+B7T9dblFGBtRDwXEc8Da4FT2x/9yIap23AWADdExEsR8b9U7s19DDlsrxGxLSJ+moZ3ApuAyXTBchuhbsMpxHJL8/43aXSf9AjgQ8DqVD54mVWX5WrgREli+PoOK6+JYDKwpWZ8gJEXdB4FcLuk9ZKWprKDI2IbVBozcFAqL2J9661L0er416mL5Npq9wkFrVvqMjiayhZmVy23QXWDgi83SWMkbQB2UEm6TwAvRMSuNEltjK/Fn15/ETiQBuqV10Qw1OUwi3Z60/siYjYwD/i0pA+MMG031LdquLoUqY7XAO8AjgK2Af+SygtXN0lvBm4CPhMRvx5p0iHKila3wi+3iHglIo4CplDZin/XUJOl55bVK6+JYACYWjM+BdiaUSwNiYit6XkH8F9UFur2apdPet6RJi9ifeutS2HqGBHb0wr5KvAtXt+tLlTdJO1D5YfyuxHxg1TcFcttqLp1y3IDiIgXgLupHCPYX1L1ckC1Mb4Wf3r9rVS6OeuuV14TwUPAjHS0fF8qB0LWZBzTqEl6k6S3VIeBk4FeKnWonnWxGLg5Da8BzkpnbswFXqzuvudYvXW5DThZ0oS0y35yKsudQcdn/pLKsoNK3RalszUOBWYAD5LD9pr6ilcAmyLiqzUvFX65DVe3oi83SRMl7Z+G3wD8KZXjH3cBC9Nkg5dZdVkuBO6MytHi4eo7vKyOkI/iCPp8KmcDPAFcmHU8dcZ+GJWj9g8Dj1bjp9J/dwfQl54PiNfPFvi3VNdHgDlZ12FQfVZR2dV+mcrWxpJG6gKcTeXAVT/w8azrNULdrk+xb0wr1aSa6S9MddsMzMtrewX+hEp3wEZgQ3rM74blNkLdCr3cgCOAn6X4e4F/TOWHUfkh7wf+ExiXyvdL4/3p9cP2Vt/hHv5nsZlZyeW1a8jMzDrEicDMrOScCMzMSs6JwMys5JwIzMxKzonAzKzknAjMzErOicDMrOT+H3D9QqD9xqI8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f12ff8c50>"
      ]
     },
     "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": [
      "mkdir: cannot create directory ‘/data’: File exists\n",
      "--2020-05-08 09:25:43--  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)... 52.217.13.190\n",
      "Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.217.13.190|: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  63.9MB/s    in 5.2s    \n",
      "\n",
      "2020-05-08 09:25:49 (61.6 MB/s) - ‘/data/train-test-data.zip’ saved [338613624/338613624]\n",
      "\n",
      "Archive:  /data/train-test-data.zip\n",
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     ]
    }
   ],
   "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": 8,
   "metadata": {},
   "outputs": [
    {
     "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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image name:  Christopher_Walken_01.jpg\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f12e75b00>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# a selected image\n",
    "n = 120\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": null,
   "metadata": {},
   "outputs": [],
   "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": []
  }
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