face_process.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install imageio-ffmpeg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from skimage import img_as_float\n",
    "from imageio import get_reader, get_writer\n",
    "import skimage.transform as sktransform\n",
    "from numpy import asarray, array\n",
    "from numpy.linalg import inv\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image, ImageFilter\n",
    "import cv2\n",
    "\n",
    "\n",
    "from mpl_point_clicker import clicker\n",
    "\n",
    "from IPython.display import display, Image\n",
    "import ipywidgets as widgets\n",
    "import threading\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert RBG to YIQ\n",
    "def rgb2ntsc(src):\n",
    "    [rows,cols]=src.shape[:2]\n",
    "    dst=np.zeros((rows,cols,3),dtype=np.float64)\n",
    "    T = np.array([[0.114, 0.587, 0.298], [-0.321, -0.275, 0.596], [0.311, -0.528, 0.212]])\n",
    "    for i in range(rows):\n",
    "        for j in range(cols):\n",
    "            dst[i, j]=np.dot(T,src[i,j])\n",
    "    return dst\n",
    "\n",
    "#convert YIQ to RBG\n",
    "def ntsc2rbg(src):\n",
    "    [rows, cols] = src.shape[:2]\n",
    "    dst=np.zeros((rows,cols,3),dtype=np.float64)\n",
    "    T = np.array([[1, -1.108, 1.705], [1, -0.272, -0.647], [1, 0.956, 0.620]])\n",
    "    for i in range(rows):\n",
    "        for j in range(cols):\n",
    "            dst[i, j]=np.dot(T,src[i,j])\n",
    "    return dst\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def load_video(filename):\n",
    "    reader = get_reader(filename)\n",
    "    orig_vid = []\n",
    "    for i, im in enumerate(reader):\n",
    "        orig_vid.append(im)\n",
    "    return asarray(orig_vid)\n",
    "\n",
    "def write_video(video, fps, name):\n",
    "    writer = get_writer(name, fps=fps)\n",
    "    for i in range(video.shape[0]):\n",
    "        writer.append_data(video[i])\n",
    "    writer.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Build Gaussian Pyramid\n",
    "def build_gaussian_pyramid(src,level=3):\n",
    "    s=src.copy()\n",
    "    pyramid=[s]\n",
    "    for i in range(level):\n",
    "        s=cv2.pyrDown(s)\n",
    "        pyramid.append(s)\n",
    "    return pyramid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Build Laplacian Pyramid\n",
    "def build_laplacian_pyramid(src,levels=3):\n",
    "    \n",
    "    pyramid=[]\n",
    "    gaussianPyramid = build_gaussian_pyramid(src, levels)\n",
    "        \n",
    "    for i in range(levels,0,-1):\n",
    "        GE=cv2.pyrUp(gaussianPyramid[i])\n",
    "        L=cv2.subtract(gaussianPyramid[i-1],GE)\n",
    "        pyramid.append(L)\n",
    "    return pyramid\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Show users the frame and ask them to choose ROI\n",
    "def ROI_choice(video):\n",
    "    # display a frame of the video, ask user to input ROI by mouse click\n",
    "    # return the yiq version of the ROI\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "video_file = \"after.mp4\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "video = load_video(video_file)\n",
    "face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_frames, h, w, num_channels = video.shape\n",
    "#num_frames = 500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "288"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "ystart = int(klicker.get_positions()[\"event\"][0][1])\n",
    "yend = int(klicker.get_positions()[\"event\"][2][1])\n",
    "xstart = int(klicker.get_positions()[\"event\"][0][0])\n",
    "xend=int(klicker.get_positions()[\"event\"][1][0])\n",
    "\n",
    "h_crop = yend-ystart\n",
    "w_crop = xend-xstart\n",
    "\n",
    "#convert video to yiq\n",
    "video_yiq = np.zeros((num_frames,h_crop,w_crop,num_channels))\n",
    "\n",
    "for frame in range(num_frames):\n",
    "    video_yiq[frame] = rgb2ntsc(video[frame][ystart:yend,xstart:xend])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Construct gaussian levels\n",
    "pyramid=[]\n",
    "levels = 3\n",
    "l0 = []\n",
    "l1 =[]\n",
    "l2=[]\n",
    "l3=[]\n",
    "for frames in range(num_frames):\n",
    "    \n",
    "    gaussianPyramid = build_gaussian_pyramid(video_yiq[frames], levels)\n",
    "    \n",
    "    l0.append(video[frames][ystart:yend,xstart:xend])\n",
    "    l1.append(gaussianPyramid[0])\n",
    "    l2.append(gaussianPyramid[1])\n",
    "    l3.append(gaussianPyramid[2])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(127, 328, 3)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# l1 is the original in ntsc color scheme\n",
    "# l0 is the original in rgb color scheme\n",
    "l1[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "px_0 = np.zeros(num_frames)\n",
    "px_1 = np.zeros(num_frames)\n",
    "px_2 =np.zeros(num_frames)\n",
    "px_3 =np.zeros(num_frames)\n",
    "\n",
    "\n",
    "for frame in range(num_frames):\n",
    "    \n",
    "    px_0[frame] = np.average(l0[frame])\n",
    "    px_1[frame] = np.average(l1[frame])\n",
    "    px_2[frame] = np.average(l2[frame])\n",
    "    px_3[frame] = np.average(l3[frame])\n",
    "    \n",
    "    \n",
    "    #px_R[frame] = video[frame][600:601,450:451][0][0][0]\n",
    "   # px_G[frame] = video[frame][600:601,450:451][0][0][1]\n",
    "    #px_B[frame] = video[frame][600:601,450:451][0][0][2]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x16838a83648>]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.plot(px_1,'g')\n",
    "plt.plot(px_2,'k')\n",
    "plt.plot(px_3,'b')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Detrend and normalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import sparse\n",
    "\n",
    "def advance_detrend(signal, lam = 10):\n",
    "    T = len(signal)\n",
    "    I  = np.identity(T)\n",
    "\n",
    "    filt = [1,-2,1]* np.ones((1,T-2),dtype=np.int).T\n",
    "    D2 = sparse.spdiags(filt.T, (range(0,3)),T-2,T)\n",
    "\n",
    "    # detrend_sig = (I - np.linalg.inv(I + lam**2*D2.T*D2))@signal\n",
    "    trend = (np.linalg.inv(I + lam**2*D2.T*D2))@signal\n",
    "    detrend_sig = np.squeeze(np.array(signal - trend))\n",
    "    trend = np.squeeze(np.array(trend))\n",
    "    return detrend_sig, trend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# normalization section\n",
    "def normalizeToUnitVariance(sig):\n",
    "    normalized = (sig - np.mean(sig))/np.std(sig)\n",
    "    return normalized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Smart\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:7: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  import sys\n"
     ]
    }
   ],
   "source": [
    "sig,trend = advance_detrend(px_1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x16838ae64c8>]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "truncated_sig = sig[:int(0.8*len(sig))] #take 80% of the data\n",
    "plt.plot(truncated_sig) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convert pixel data to pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df1 = pd.DataFrame(truncated_sig,columns=['orig'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>orig</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.153760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.538295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.096141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.386423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.062956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>0.587053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>0.299325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>0.166123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>-0.077864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>-0.477920</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>230 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         orig\n",
       "0   -0.153760\n",
       "1    0.538295\n",
       "2   -0.096141\n",
       "3    0.386423\n",
       "4    0.062956\n",
       "..        ...\n",
       "225  0.587053\n",
       "226  0.299325\n",
       "227  0.166123\n",
       "228 -0.077864\n",
       "229 -0.477920\n",
       "\n",
       "[230 rows x 1 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.to_pickle(\"after.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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