vid_to_images_and_validation_split.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Image to Frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import io\n",
    "import base64\n",
    "from IPython.display import HTML\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "import cv2\n",
    "import numpy as np\n",
    "import os\n",
    "# video = io.open('test.avi', 'r+b').read()\n",
    "FOLDER = \"data/\"\n",
    "# cv2.imshow(video)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "IOError",
     "evalue": "[Errno 2] No such file or directory: 'data/validation1.txt'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIOError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-9f61cf073d19>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mcontent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadlines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mtrain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\" \"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcontent\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFOLDER\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\"validation1.txt\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0mcontent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadlines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mvalidation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\" \"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcontent\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIOError\u001b[0m: [Errno 2] No such file or directory: 'data/validation1.txt'"
     ]
    }
   ],
   "source": [
    "with open(FOLDER+\"train1.txt\") as f:\n",
    "    content = f.readlines()\n",
    "train = [x.strip().split(\" \") for x in content] \n",
    "with open(FOLDER+\"validation1.txt\") as f:\n",
    "    content = f.readlines()\n",
    "validation = [x.strip().split(\" \") for x in content] \n",
    "\n",
    "with open(FOLDER+\"trainlist01.txt\") as f:\n",
    "    content = f.readlines()\n",
    "all_data = [x.strip().split(\" \") for x in content] \n",
    "\n",
    "with open(FOLDER+\"train_test/sample.txt\") as f:\n",
    "    content = f.readlines()\n",
    "sample = [x.strip().split(\" \") for x in content] \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'sample' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-13-d7a99e882551>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m(\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'sample' is not defined"
     ]
    }
   ],
   "source": [
    "(sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "False\n",
      "False\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "filelist = []\n",
    "for video,label in all_data:\n",
    "    category = video.split(\"/\")[0]\n",
    "    filename = video.split(\"/\")[1].split(\".avi\")[0]    \n",
    "    directory_name = FOLDER + \"UCF101_images/\" + category \n",
    "\n",
    "    if not os.path.exists(directory_name):\n",
    "        os.makedirs(directory_name)\n",
    "\n",
    "    video_file = FOLDER + \"UCF101/\" + video.split(\"/\")[1]\n",
    "    cap = cv2.VideoCapture(video_file)\n",
    "    counter = 0\n",
    "    while(cap.isOpened()):\n",
    "        frameId = cap.get(1) #current frame number\n",
    "        ret, frame = cap.read()\n",
    "        if (ret != True):\n",
    "            break\n",
    "        if (frameId % 6) == 0:\n",
    "            f_ = directory_name + \"/\" + filename + \"_\" + str(counter) + \".jpg\"\n",
    "            cv2.imwrite(f_, frame)\n",
    "            counter += 1\n",
    "    cap.release()\n",
    "    filelist.append([category + \"/\" + filename, counter, label])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'filelist' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-5c9bd082f1cb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavetxt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'data/all_images1.txt'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mfilelist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfmt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'%s'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'%s'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'%s'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdelimiter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m' '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'filelist' is not defined"
     ]
    }
   ],
   "source": [
    "np.savetxt('data/all_images1.txt', (filelist), fmt=['%s', '%s', '%s'], delimiter=' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "counter = {i:0 for i in range(101)}\n",
    "train_counter = {i:0 for i in range(100)}\n",
    "val_counter = {i:0 for i in range(100)}\n",
    "\n",
    "with open(\"data/all_images1.txt\") as f:\n",
    "    for line in f:\n",
    "        label = int(line.split(\" \")[2])\n",
    "        counter[label-1] += 1        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainlist = []\n",
    "validlist = []\n",
    "with open(\"data/all_images1.txt\") as f:\n",
    "    for line in f:\n",
    "        label = int(line.split(\" \")[2])\n",
    "        if label != 37:\n",
    "            if label > 37:\n",
    "#                 print(label)\n",
    "                label -= 1\n",
    "            filename = line.split(\" \")[0] + \" \" + line.split(\" \")[1]\n",
    "            train_or_val = np.random.rand()\n",
    "            if train_or_val < 0.21:\n",
    "                val_counter[label-1] += 1            \n",
    "                validlist.append([filename, label-1])\n",
    "            else:\n",
    "                train_counter[label-1] += 1\n",
    "                trainlist.append([filename, label-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt('data/train1.txt', (trainlist), fmt=['%s', '%s'], delimiter=' ')\n",
    "np.savetxt('data/val1.txt', (validlist), fmt=['%s', '%s'], delimiter=' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<matplotlib.axes._subplots.AxesSubplot at 0x7fd2ae12a5d0>,\n",
       " <Container object of 100 artists>)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd2ae4259d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1,2,1),plt.bar(train_counter.keys(), train_counter.values(), 1, color='g')\n",
    "plt.subplot(1,2,2),plt.bar(val_counter.keys(), val_counter.values(), 1, color='b')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "1.0\n",
      "1.0\n",
      "1.0\n",
      "1.0\n",
      "1.0\n",
      "1.0\n",
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      "1.0\n",
      "1.0\n",
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      "1.0\n",
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      "1.0\n",
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      "1.0\n",
      "1.0\n",
      "1.0\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "for i in train_counter:\n",
    "    if i < 36:\n",
    "        j = i - 1\n",
    "        print (train_counter[i]+val_counter[i])/float(counter[i])\n",
    "    if i > 35:\n",
    "        j = i + 1\n",
    "        print (train_counter[i]+val_counter[i])/float(counter[j])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_counter[35]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}