notebooks/VideoStats.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook the EmoReact dataset is explored and preprocessed such that only the faces are kept for later processing. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from os import listdir, mkdir\n",
    "import skvideo.io\n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a file listing, counting all the videos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "videopath = \"/mnt/disk1/documents/data/EmoReact_V_1.0/Data/Test\"\n",
    "processed_path = '/mnt/disk1/documents/data/emoreact/test/'\n",
    "files = listdir(videopath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "faceCascade=cv2.CascadeClassifier('/home/pieter/projects/engagement-l2tor/data/haarcascade_frontalface_alt.xml')\n",
    "faceCascade.empty()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "SWING_COPTERS91_2\n",
      "337\n",
      "SPAM86_2\n",
      "338\n",
      "SUSHI74_2\n",
      "339\n",
      "MAKEUP_APPS27_2\n",
      "340\n",
      "SWING_COPTERS47_2\n",
      "341\n",
      "SPAM56_2\n",
      "342\n",
      "OLDCOMPUTERS93_2\n",
      "343\n",
      "SUSHI84_2\n",
      "344\n",
      "SWING_COPTERS93_2\n",
      "345\n",
      "HARLEMSHAKE100_2\n",
      "346\n",
      "BULLYING134_2\n",
      "347\n",
      "NYAN_CAT84_2\n",
      "348\n",
      "CAN_YOUR_PET15_2\n",
      "349\n",
      "SWING_COPTERS24_2\n",
      "350\n",
      "KIMCHI9_2\n",
      "351\n",
      "ANNOYING_ORANGE89_2\n",
      "352\n",
      "Gay_Marriage131_2\n",
      "353\n",
      "TOFU87_2\n",
      "354\n",
      "SQUID22_2\n",
      "355\n",
      "Gay_Marriage142_2\n",
      "356\n",
      "VEGEMITE79_2\n",
      "357\n",
      "MAKEUP_APPS37_2\n",
      "358\n",
      "MAKEUP_APPS81_2\n",
      "359\n",
      "CAN_YOUR_PET22_2\n",
      "360\n",
      "SWING_COPTERS153_2\n",
      "361\n",
      "iPOD83_2\n",
      "362\n",
      "DUBSTEP95_2\n",
      "363\n",
      "WALKMANS118_2\n",
      "364\n",
      "80'sTOYS104_2\n",
      "365\n",
      "SPAM84_2\n",
      "366\n",
      "SPAM38_2\n"
     ]
    }
   ],
   "source": [
    "for c, file in enumerate(files):\n",
    "    print(c)\n",
    "    video_file = skvideo.io.vreader(videopath + \"/\" + file)\n",
    "    video_face = []\n",
    "    for frame in video_file:\n",
    "        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
    "        faces = faceCascade.detectMultiScale(gray, 1.3, 5)\n",
    "        # Add found faces to new video\n",
    "        if len(faces) != 0:\n",
    "            for (x, y, w, h) in faces:\n",
    "                #cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)\n",
    "                video_face.append(frame[y:y+h, x:x+w])\n",
    "    folder_name = file[:-4]\n",
    "    print(folder_name)\n",
    "    path = processed_path + folder_name\n",
    "    mkdir(path)\n",
    "    #now we loop over the faces and put the separate frames inside the right folder\n",
    "    for i, face in enumerate(video_face):\n",
    "        file_name = path + \"/\" + str(i) + \".png\"\n",
    "        #write the face to the file\n",
    "        cv2.imwrite(file_name, cv2.cvtColor(face, cv2.COLOR_RGB2BGR))\n",
    "        \n",
    "    "
   ]
  }
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