GANs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Objective: generate video from text description\n",
    "\n",
    "- Download dataset UCF-101 https://www.crcv.ucf.edu/data/UCF101.php. It's an action recognition dataset, with input videos and corresponding action classes.\n",
    "\n",
    "- Download MoCoGAN code (conditional GAN model for video generation, with categorical condition): https://github.com/DLHacks/mocogan\n",
    "\n",
    "- Train MoCoGAN on UCF-101: the resulting model will be able to generate videos from action classes\n",
    "\n",
    "- Download S2VT pre-trained model (video-to-text model): https://vsubhashini.github.io/s2vt.html\n",
    "\n",
    "  - Note: S2VT uses the Caffe library. Install the library (you don't need to train the model).\n",
    "\n",
    "- Process each video in UCF-101 and get the corresponding text description. Create a dataset with input=text description and output=action class.\n",
    "\n",
    "- Train an LSTM classifier (similar to the one used in class for sentiment analysis) to classify text descriptions into actions. Report the performance.\n",
    "\n",
    "- The final model works by: getting an input text description by the user, converting it into an action class with the LSTM model, and using the action class as a condition to MoCoGAN.\n",
    "\n",
    "Note: the model will not be precise in generating video details, since conditioning is based on the class only. \n",
    "For example:\n",
    "\n",
    "\"I'm running in the park\" -> action: running\n",
    "\n",
    "\"My dog is running on the beach\" -> action: running\n",
    "\n",
    "Output videos will not actually take into account the context, but only the action."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import torch\n",
    "import numpy as np\n",
    "from glob import glob\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "import skvideo.io"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/carlo/Documenti/Text2VideoGAN/mocogan/resized_data/Haircut/v_Haircut_g20_c05.mp4'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dir = \"/home/carlo/Documents/Cognitive Computing/Text2VideoGAN/mocogan/resized_data\"\n",
    "image_dir_name = \"*\"\n",
    "image_paths = glob(os.path.join(data_dir, image_dir_name, \"*\"))\n",
    "\n",
    "if (len(image_paths) < 1):\n",
    "    data_dir = \"/home/carlo/Documenti/Text2VideoGAN/mocogan/resized_data\"\n",
    "    image_paths = glob(os.path.join(data_dir, image_dir_name, \"*\"))\n",
    "    \n",
    "image_paths[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "original_video = skvideo.io.vread(image_paths[0], 96, 96)\n",
    "video =  original_video.transpose(3, 0, 1, 2) / 255.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trying to display a video changed by transposing it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "dirToSave = \"./\"\n",
    "fileName = \"transposedVideo.mp4\"\n",
    "#filepath = os.path.join(dirToSave, fileName)\n",
    "\n",
    "toSaveVideo = original_video.astype(np.uint8)\n",
    "skvideo.io.vwrite(fileName, toSaveVideo)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import the necessary dependencies to train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imageio: 0.063938s, Skvideo: 0.070394s\n",
      "(201, 96, 96, 3) (201, 96, 96, 3)\n",
      "Imageio: 79.99903747322416, Skvideo: 79.99903747322416\n"
     ]
    }
   ],
   "source": [
    "import imageio\n",
    "import pylab\n",
    "import math\n",
    "import numpy as np\n",
    "import skvideo.io\n",
    "from glob import glob\n",
    "filenames = glob(\"/home/carlo/Documenti/Text2VideoGAN/mocogan/resized_data/*/*\")\n",
    "\n",
    "from datetime import datetime\n",
    "\n",
    "def readImageio(filename, frame= 0,show= False):\n",
    "\n",
    "    reader = imageio.get_reader(filename,  'avbin')\n",
    "    \n",
    "    #nframes = math.floor(vid.get_meta_data()['fps'] * vid.get_meta_data()['duration'])\n",
    "    #shape = vid.get_meta_data()['size']\n",
    "    #n_channels = 3\n",
    "\n",
    "    #for i, im in enumerate(vid):\n",
    "        #print(i, type(im), type(np.asarray(im)), np.asarray(im).shape)\n",
    "\n",
    "    image = reader.get_data(frame)\n",
    "        \n",
    "    if show:\n",
    "        fig = pylab.figure()\n",
    "        fig.suptitle('image', fontsize=20)\n",
    "        pylab.imshow(image)\n",
    "        pylab.show()\n",
    "        \n",
    "    return np.asarray(image)\n",
    "\n",
    "def printMetaData(filename):\n",
    "    \n",
    "    meta = {}\n",
    "    with imageio.get_reader(filename, 'ffmpeg') as reader:\n",
    "        meta = reader.get_meta_data()\n",
    "    \n",
    "    print(meta)\n",
    "    \n",
    "    return meta\n",
    "\n",
    "def composeVideoImageio(filename):\n",
    "    \n",
    "    #reader = imageio.get_reader(filename,  'ffmpeg')\n",
    "    \n",
    "    with imageio.get_reader(filename, 'ffmpeg') as reader:\n",
    "    \n",
    "        #nframes = math.floor(reader.get_meta_data()['fps'] * reader.get_meta_data()['duration'])\n",
    "        \n",
    "        '''print(reader.get_meta_data()['fps'], reader.get_meta_data()['duration'],\n",
    "            reader.get_meta_data()['fps'] * reader.get_meta_data()['duration'],\n",
    "            math.ceil(reader.get_meta_data()['fps'] * reader.get_meta_data()['duration']),\n",
    "            int(reader.get_meta_data()['fps'] * reader.get_meta_data()['duration']))'''\n",
    "        \n",
    "        shape = reader.get_meta_data()['size']\n",
    "\n",
    "        #videodata = np.zeros((nframes, shape[0], shape[1], 3), dtype= np.uint8)\n",
    "\n",
    "        frames = []\n",
    "        \n",
    "        for idx, img in enumerate(reader):\n",
    "            try:\n",
    "                frames.append(img)\n",
    "            \n",
    "            except Error as err:\n",
    "                print(Error)\n",
    "            #try:\n",
    "            #    videodata[idx, :, :, :] = img\n",
    "            #except IndexError as _:\n",
    "            #    videodata.resize((nframes + 1, shape[0], shape[1], 3))\n",
    "            #    videodata[idx, :, :, :] = img\n",
    "            \n",
    "    if not reader.closed:\n",
    "        reader.close()\n",
    "        \n",
    "    videodata = np.array(frames, dtype= np.uint8)\n",
    "    \n",
    "    #del reader\n",
    "            \n",
    "    return videodata\n",
    "        \n",
    "\n",
    "def measurePerformance(method, args):\n",
    "    start = datetime.now()\n",
    "    \n",
    "    res = method(args)\n",
    "    \n",
    "    end = datetime.now()\n",
    "    \n",
    "    return (end - start ).total_seconds(), res\n",
    "\n",
    "imageio.plugins.avbin.download()\n",
    "\n",
    "try:\n",
    "    seconds1, res = measurePerformance( composeVideoImageio,filenames[0])\n",
    "    seconds2, res_ = measurePerformance( skvideo.io.vread, filenames[0])\n",
    "except IndexError as _:\n",
    "    filenames = glob(\"/home/carlo/Documents/Cognitive Computing/Text2VideoGAN/mocogan/resized_data/*/*\")\n",
    "    seconds1, res = measurePerformance( composeVideoImageio, filenames[0])\n",
    "    seconds2, res_ = measurePerformance( skvideo.io.vread, filenames[0])\n",
    "    \n",
    "print(f\"Imageio: {seconds1}s, Skvideo: {seconds2}s\")\n",
    "\n",
    "print(res_.shape, res.shape)\n",
    "\n",
    "\"\"\"for arrs in (res - res_):\n",
    "    for arr in arrs:\n",
    "        for els in arr:\n",
    "            for el in els:\n",
    "                assert el == 0.0\"\"\"\n",
    "\n",
    "skmean = res_.mean()\n",
    "imgmean = res.mean()\n",
    "\n",
    "print(f\"Imageio: {imgmean}, Skvideo: {skmean}\")\n",
    "\n",
    "assert np.isclose(skmean - imgmean, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[ 1.2339, -0.2296,  1.1954,  0.0766,  1.8319, -1.5679]],\n",
      "       grad_fn=<EmbeddingBackward>) tensor([[ 0.5597, -1.0115, -1.2876,  1.6354, -0.6230,  2.2848]],\n",
      "       grad_fn=<EmbeddingBackward>) tensor([[ 0.2361,  0.3149, -0.5461, -0.3276, -0.0337,  1.5490]],\n",
      "       grad_fn=<EmbeddingBackward>) tensor([[ 1.2339, -0.2296,  1.1954,  0.0766,  1.8319, -1.5679]],\n",
      "       grad_fn=<EmbeddingBackward>)\n",
      "Parameter containing:\n",
      "tensor([[ 4.7921e-02, -5.3535e-01, -2.2795e+00, -1.5989e+00, -1.9442e+00,\n",
      "          1.1909e-02],\n",
      "        [ 1.1355e+00,  7.3753e-01,  8.8629e-01, -1.5201e+00,  1.3649e+00,\n",
      "         -8.9513e-01],\n",
      "        [-1.6694e+00, -9.3621e-01,  4.1999e-02,  5.9370e-01,  4.8361e-01,\n",
      "          1.3135e-01],\n",
      "        [-5.7331e-01,  4.8860e-01, -8.3437e-02, -4.7477e-01,  1.4459e-01,\n",
      "         -1.8485e+00],\n",
      "        [ 3.9649e-01,  2.0868e-01, -1.2799e-01, -4.7812e-01, -4.4858e-02,\n",
      "         -1.6832e+00],\n",
      "        [-4.4351e-01, -1.9429e-01,  1.2704e-02, -6.7517e-01, -3.4294e-01,\n",
      "         -1.1846e-01],\n",
      "        [-5.5559e-01, -6.3953e-01, -1.2473e+00,  4.6382e-01, -1.1224e+00,\n",
      "         -2.1098e-01],\n",
      "        [ 2.1467e-01, -1.7015e+00, -4.7285e-01,  1.2097e+00, -1.1954e-01,\n",
      "          3.1347e-01],\n",
      "        [ 1.7667e+00, -5.0664e-01, -9.9568e-01,  1.0326e+00, -8.7681e-01,\n",
      "         -1.1139e-01],\n",
      "        [-1.5793e+00, -5.9459e-01,  1.4956e+00,  4.1618e-01,  1.8148e+00,\n",
      "          3.1916e+00],\n",
      "        [ 1.0054e+00,  7.0111e-03, -6.7797e-01, -1.0170e+00, -7.3626e-01,\n",
      "          4.9141e-01],\n",
      "        [ 9.1649e-01,  7.0361e-01, -3.5267e-01,  1.7482e+00, -9.3636e-01,\n",
      "         -1.4279e+00],\n",
      "        [ 8.5159e-01, -7.4573e-01,  1.1645e+00, -8.0566e-01, -7.4473e-01,\n",
      "          5.2043e-01],\n",
      "        [ 9.3238e-01,  4.4349e-01,  8.3669e-02, -1.6019e+00,  8.8518e-01,\n",
      "          1.6901e+00],\n",
      "        [-9.2355e-01,  1.4238e+00, -3.0989e-01, -1.0363e+00, -2.3370e-01,\n",
      "          5.0751e-01],\n",
      "        [-6.0209e-01, -6.3458e-01,  8.3452e-01, -6.2637e-01, -6.5697e-01,\n",
      "         -1.6526e+00],\n",
      "        [ 1.6889e+00,  1.5802e+00,  8.1423e-01, -1.0524e+00, -3.9105e-01,\n",
      "         -8.3068e-01],\n",
      "        [-9.9521e-01,  3.1890e-03, -2.4335e-01,  2.5198e-01, -3.1606e-01,\n",
      "         -1.8287e+00],\n",
      "        [ 1.2599e+00,  1.6005e-01, -1.2526e+00, -1.9691e-01, -8.4391e-02,\n",
      "          4.9401e-01],\n",
      "        [-1.3253e+00,  2.5550e+00, -1.1781e+00,  3.7704e-01,  4.0413e-01,\n",
      "         -1.4700e+00],\n",
      "        [-1.1586e-01, -3.2810e-01, -1.1518e+00, -1.6579e+00,  9.9198e-01,\n",
      "          2.5335e+00],\n",
      "        [-2.0576e-02, -1.3416e+00, -9.2426e-01,  3.7125e-01,  1.2546e+00,\n",
      "          9.9507e-02],\n",
      "        [ 1.8624e+00, -2.2654e+00, -8.2187e-01, -6.2184e-01, -1.0481e+00,\n",
      "         -4.5742e-01],\n",
      "        [ 3.2803e-01, -1.9821e+00, -5.1751e-01, -1.3517e-01,  2.7322e-01,\n",
      "         -6.8738e-01],\n",
      "        [ 1.1400e-02,  1.3818e-01, -8.3427e-01, -8.2247e-01, -1.6219e+00,\n",
      "          1.9533e+00],\n",
      "        [ 4.3627e-01, -4.2597e-01,  1.2566e-01,  1.7816e+00, -3.9779e-01,\n",
      "          9.6173e-01],\n",
      "        [-6.3184e-02,  3.3409e+00,  4.8294e-02,  1.6726e-02,  6.3920e-02,\n",
      "         -3.3804e-01],\n",
      "        [ 3.9014e-01, -3.7194e-01,  1.0081e+00,  9.8498e-01, -6.7156e-02,\n",
      "          1.6418e-02],\n",
      "        [ 9.4275e-02,  1.7033e-01, -1.2393e+00,  1.9370e-01,  3.3404e-01,\n",
      "         -1.4939e-01],\n",
      "        [-1.4634e+00,  6.1922e-02,  1.4089e+00, -1.0034e+00,  1.0889e+00,\n",
      "          1.5735e+00],\n",
      "        [-1.8016e+00,  1.4209e+00, -1.1279e+00, -3.8099e-01, -1.2906e-01,\n",
      "          1.5693e-01],\n",
      "        [ 1.9496e+00,  1.0815e+00, -3.5654e-01,  8.0332e-01, -9.2811e-01,\n",
      "         -2.3214e-01],\n",
      "        [ 1.2694e+00,  4.8570e-01,  3.8258e-01,  3.3582e-01,  2.0282e+00,\n",
      "         -5.5723e-01],\n",
      "        [ 7.9621e-01, -2.8662e-01, -1.3586e+00, -1.8572e+00, -1.9810e-01,\n",
      "          9.3474e-01],\n",
      "        [-3.6974e-01,  4.0661e-02,  8.4024e-01,  1.5485e+00,  7.8898e-01,\n",
      "          2.9630e-01],\n",
      "        [-1.4349e-02, -2.8698e-01, -4.7554e-01,  2.3135e-01, -7.6139e-01,\n",
      "         -5.3099e-01],\n",
      "        [-1.4620e+00,  1.0925e-01,  5.6479e-01,  2.9814e-01,  7.9565e-01,\n",
      "         -7.4123e-01],\n",
      "        [-1.3598e+00, -9.2982e-01,  1.8592e+00,  1.9110e+00, -7.9322e-01,\n",
      "         -2.9283e+00],\n",
      "        [-5.9661e-01, -1.6170e-01, -1.4614e+00,  3.4390e-01,  5.5083e-01,\n",
      "         -4.8650e-01],\n",
      "        [ 7.6364e-01, -2.1022e+00, -2.2394e-01, -7.8799e-01, -1.3063e-01,\n",
      "         -6.1368e-01],\n",
      "        [ 1.8729e+00,  4.2106e-01,  1.4177e+00,  9.9861e-02,  7.5875e-01,\n",
      "          7.0227e-01],\n",
      "        [-2.0581e+00,  5.2487e-01,  3.5066e-01,  1.0131e+00,  1.5769e+00,\n",
      "         -2.9335e-01],\n",
      "        [ 2.4710e-01,  2.5785e-01, -1.2975e+00,  8.7543e-02,  6.3289e-02,\n",
      "          2.5262e-01],\n",
      "        [ 2.7748e-01, -6.9398e-01,  1.6478e+00,  4.4078e-01, -7.7775e-01,\n",
      "          1.0792e+00],\n",
      "        [-5.2072e-01, -9.5399e-01, -1.5451e-01, -1.8636e+00,  6.7889e-01,\n",
      "          4.0355e-01],\n",
      "        [-2.4297e-01,  1.2434e-01, -1.3726e+00, -1.2339e+00, -1.0685e-01,\n",
      "          7.9103e-02],\n",
      "        [-1.0655e-02, -1.5412e+00,  8.7079e-01,  3.8524e-01, -1.3615e+00,\n",
      "         -2.1217e-01],\n",
      "        [-5.5724e-01, -9.7544e-01,  1.6879e+00, -8.4099e-01, -2.5124e-01,\n",
      "         -1.7856e-01],\n",
      "        [ 1.8771e-01, -4.3643e-01,  1.0606e+00,  1.5807e-01, -5.7976e-01,\n",
      "         -1.0476e-01],\n",
      "        [ 8.0099e-01,  6.3450e-01, -1.0888e+00, -5.7999e-01,  5.5782e-01,\n",
      "         -2.1127e+00],\n",
      "        [-1.6069e+00,  1.6684e+00, -1.0624e+00, -5.3669e-01,  2.4503e-01,\n",
      "         -1.1812e-01],\n",
      "        [ 1.7646e-01,  7.7572e-01, -3.1646e-01,  1.5419e+00, -3.2815e-01,\n",
      "         -6.5410e-01],\n",
      "        [ 2.7614e-01, -2.7611e-01,  8.5824e-01,  5.8342e-01, -6.6938e-02,\n",
      "          5.3837e-01],\n",
      "        [ 4.6403e-02,  4.3600e-01, -1.2051e+00, -1.4948e-02,  4.6624e-01,\n",
      "          9.0622e-01],\n",
      "        [ 1.9799e-01,  2.9315e+00,  5.4904e-01,  4.4086e-01, -2.5809e-01,\n",
      "          2.1195e+00],\n",
      "        [-2.1622e+00, -4.9737e-01,  6.2943e-01,  8.0724e-01, -1.7558e-01,\n",
      "         -5.0930e-01],\n",
      "        [-3.3184e-01,  4.1831e-01,  8.5678e-02, -3.6547e-01,  1.4368e+00,\n",
      "          1.8843e-01],\n",
      "        [ 1.1589e+00,  3.9204e-01, -1.1266e+00, -2.2137e-01,  6.0045e-01,\n",
      "          3.6372e-01],\n",
      "        [ 1.6542e-02, -4.9496e-01,  8.4204e-01,  5.9342e-02, -9.1190e-01,\n",
      "          5.9688e-02],\n",
      "        [-2.7451e-01,  9.1356e-01,  2.9368e-02, -8.3542e-01, -7.3498e-01,\n",
      "         -1.9161e-01],\n",
      "        [ 1.9998e-01,  9.2969e-01,  9.6591e-02,  1.1790e+00, -3.8885e-01,\n",
      "          7.0834e-01],\n",
      "        [-8.4633e-01, -1.1049e+00, -1.8785e-01,  3.5870e-01, -2.0841e+00,\n",
      "          5.8819e-01],\n",
      "        [-1.4613e+00, -3.6191e-01,  3.0518e-01,  8.5562e-01,  4.4218e-01,\n",
      "         -3.4717e-01],\n",
      "        [ 2.6783e+00, -3.5062e-01,  3.0338e-01,  9.6212e-01,  1.3107e-01,\n",
      "         -1.1431e-01],\n",
      "        [-9.6109e-01, -9.1438e-01, -4.0137e-01, -9.9311e-01, -5.2384e-01,\n",
      "          1.1146e+00],\n",
      "        [-2.3293e-02,  1.8235e-01,  2.2883e-01,  8.7322e-01, -5.7649e-01,\n",
      "          2.7338e-01],\n",
      "        [-1.2650e-01,  5.3183e-01,  3.4769e-03,  7.0230e-01,  6.3055e-01,\n",
      "          5.0696e-02],\n",
      "        [-1.2325e+00,  2.2603e-01,  5.3451e-01,  2.4231e-01,  2.1288e+00,\n",
      "          8.6962e-01],\n",
      "        [-5.8593e-02,  1.6663e+00,  9.1331e-01,  1.7703e-01,  1.1194e-01,\n",
      "          2.4295e+00],\n",
      "        [ 2.7171e-01, -2.0872e+00, -3.5826e-02,  1.0243e+00, -3.0501e-01,\n",
      "         -1.3585e+00],\n",
      "        [-6.6318e-01, -1.3496e-01, -4.9216e-01,  1.8886e-01,  1.4120e+00,\n",
      "         -4.0115e-01],\n",
      "        [ 7.3823e-02,  6.9671e-01, -1.0127e+00, -1.6006e+00,  1.5411e-01,\n",
      "         -1.0280e+00],\n",
      "        [ 5.9002e-02, -4.0705e-01, -1.1413e+00,  5.5675e-01,  1.4599e-01,\n",
      "         -1.8996e+00],\n",
      "        [ 1.7919e-01, -8.2827e-01, -7.8892e-01, -3.2625e-01,  1.1352e-01,\n",
      "          1.6632e-01],\n",
      "        [ 2.2105e+00,  5.2253e-01,  1.0876e+00, -2.5372e+00, -1.9936e-01,\n",
      "         -7.6635e-01],\n",
      "        [-3.0533e-01,  4.9317e-01, -1.8736e-01, -2.2308e-01,  5.4190e-01,\n",
      "          2.1420e+00],\n",
      "        [ 7.0512e-01, -3.6120e-01,  1.4794e-02,  1.2006e+00, -1.3138e+00,\n",
      "         -9.6664e-01],\n",
      "        [-7.5235e-01, -1.3141e+00,  1.1277e+00,  7.9413e-01,  2.7915e-01,\n",
      "         -6.1421e-01],\n",
      "        [ 2.1634e+00, -1.1273e-01, -1.2247e-01,  1.0726e+00, -6.0447e-01,\n",
      "         -3.0789e-01],\n",
      "        [-9.0610e-01,  4.9898e-01, -1.4233e+00, -1.5829e+00, -6.9894e-01,\n",
      "         -2.5254e-01],\n",
      "        [ 9.3710e-01,  7.1586e-01, -2.0043e-01,  1.2018e+00, -8.1812e-02,\n",
      "          9.3178e-02],\n",
      "        [ 1.0688e+00, -7.7456e-01, -1.8704e+00,  9.5122e-01, -5.6406e-01,\n",
      "          5.0593e-01],\n",
      "        [ 4.4531e-01, -8.0681e-01,  1.9154e+00, -4.8334e-01,  2.2280e-01,\n",
      "          1.2156e+00],\n",
      "        [-9.2201e-01, -1.8662e+00,  1.8058e+00, -3.2336e-01, -1.0318e+00,\n",
      "          3.4489e-01],\n",
      "        [ 6.4916e-01, -2.3577e+00, -6.2588e-01,  1.9835e+00,  1.5947e-01,\n",
      "          1.2721e+00],\n",
      "        [-1.4934e+00,  2.4152e-01, -3.0896e-01,  1.9275e+00, -1.4613e-01,\n",
      "         -1.7778e+00],\n",
      "        [-3.9816e-01,  3.0896e-01,  2.9753e-01,  4.0280e-02,  4.0512e-01,\n",
      "         -3.6926e-01],\n",
      "        [ 3.1969e-01,  6.3643e-01,  3.0140e-01, -1.9990e+00, -4.3080e-01,\n",
      "          7.4299e-01],\n",
      "        [-5.9145e-01,  8.5224e-01, -1.4338e+00, -7.7158e-01, -1.0293e+00,\n",
      "          1.0157e+00],\n",
      "        [-2.3590e-01,  1.5434e+00, -8.8343e-01,  1.9690e-02, -2.3106e-01,\n",
      "          6.8153e-01],\n",
      "        [-2.6069e-01, -1.6074e+00,  3.6159e-01,  6.3596e-01,  5.1593e-01,\n",
      "          8.4036e-02],\n",
      "        [ 1.2145e+00,  5.6750e-01,  1.4991e-01,  3.1823e-01,  3.8974e-01,\n",
      "          1.9740e+00],\n",
      "        [-2.2954e-01,  3.6408e-01, -4.5382e-03, -1.4571e+00, -9.4309e-01,\n",
      "         -1.1140e+00],\n",
      "        [-9.7877e-02, -4.1674e-01,  7.8354e-01, -7.2312e-01, -2.9974e-01,\n",
      "          1.4920e+00],\n",
      "        [-1.6309e+00, -6.1615e-01,  2.2902e-01, -1.2545e+00, -1.6427e+00,\n",
      "          6.7987e-01],\n",
      "        [-2.9644e-02,  9.5474e-01,  4.9930e-01,  1.5697e+00,  9.1334e-01,\n",
      "          5.4719e-01],\n",
      "        [-1.2842e+00, -7.6221e-01, -8.5346e-01,  1.0181e+00, -5.9600e-01,\n",
      "         -6.8772e-01],\n",
      "        [ 4.2939e-01, -1.3649e+00, -1.6404e+00,  2.6826e-01, -8.8393e-01,\n",
      "         -1.1754e+00],\n",
      "        [ 3.1160e-01,  8.4375e-01, -8.2511e-01, -1.1162e+00,  1.4417e-02,\n",
      "          8.4923e-01],\n",
      "        [-1.2592e+00, -1.4931e-01, -3.0317e-01,  7.0439e-01, -1.2083e+00,\n",
      "          4.7715e-02],\n",
      "        [ 2.1235e+00,  5.2039e-01,  4.5986e-01,  1.8901e+00,  7.5116e-01,\n",
      "          1.0486e+00]], requires_grad=True)\n",
      "tensor([[ 0.4828,  2.2835,  0.3935,  ...,  0.7676,  0.1457, -0.4843],\n",
      "        [ 0.7901,  0.3978,  0.7853,  ...,  0.2192, -2.0975,  1.3082],\n",
      "        [-1.0382,  1.7707, -1.0668,  ...,  1.0275,  0.2455, -1.4117],\n",
      "        ...,\n",
      "        [ 2.8677, -1.3424,  0.2222,  ...,  0.6161,  0.2172,  2.3854],\n",
      "        [-1.8312,  0.2942,  0.5228,  ...,  0.5401,  0.6846,  0.6920],\n",
      "        [-0.2533, -2.1772, -0.5176,  ..., -1.1744,  0.8571,  0.9046]])\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "\n",
    "emb = nn.Embedding(101, 6)\n",
    "t = torch.LongTensor([0])\n",
    "t1 = torch.LongTensor([1])\n",
    "t2 = torch.LongTensor([100])\n",
    "t3 = torch.LongTensor([0])\n",
    "\n",
    "print( emb(t), emb(t1), emb(t2), emb(t3) )\n",
    "\n",
    "class Model(nn.Module):\n",
    "    \n",
    "    def __init__(self, embEls, embSize):\n",
    "        super().__init__()\n",
    "        self.embedding = nn.Embedding(embEls, embSize)\n",
    "        \n",
    "model = Model(101, 6)\n",
    "print(*model.parameters())\n",
    "\n",
    "print(torch.randn(128,60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def showImg(frame, title = \"Image\"):\n",
    "    fig = pylab.figure()\n",
    "    fig.suptitle(title, fontsize=20)\n",
    "    pylab.imshow(frame)\n",
    "    pylab.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/carlo/anaconda3/lib/python3.6/site-packages/dask/config.py:168: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.\n",
      "  data = yaml.load(f.read()) or {}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "from torch_videovision.videotransforms.video_transforms import ColorJitter\n",
    "\n",
    "jitter = ColorJitter(brightness=0.1, contrast=0.9, saturation=1, hue=-0.5)\n",
    "\n",
    "jitteredRes_ = jitter(res_)\n",
    "\n",
    "fig = pylab.figure()\n",
    "fig.suptitle('Ffmpeg', fontsize=20)\n",
    "pylab.imshow(res[2])\n",
    "fig = pylab.figure()\n",
    "fig.suptitle('NormalImage', fontsize=20)\n",
    "pylab.imshow(res_[1])\n",
    "fig = pylab.figure()\n",
    "fig.suptitle('JitteredImage', fontsize=20)\n",
    "pylab.imshow(jitteredRes_[0])\n",
    "pylab.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test all the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "r (96, 96, 3) g (96, 3) b (3,) b [0 0 0]\n",
      "[154059934 151167965 139346592] (3,)\n",
      "Check Completed.\n"
     ]
    }
   ],
   "source": [
    "print(\"-\"*50)\n",
    "\n",
    "total_num_frames = 0\n",
    "\n",
    "for idx, filename in enumerate(filenames):\n",
    "    readVideo = composeVideoImageio(filename)\n",
    "    readVideo_ = skvideo.io.vread(filename)\n",
    "    \n",
    "    \n",
    "    try:\n",
    "        assert readVideo.mean() == readVideo_.mean()\n",
    "        assert readVideo.shape[0] == readVideo_.shape[0]\n",
    "            \n",
    "    except AssertionError as _:\n",
    "        print(f\"Video: {filename} || Mean for Imageio: {readVideo.mean()} || Shape for Imageio: {readVideo.shape}\")\n",
    "        print(f\"Video: {filename} || Mean for Skvideo: {readVideo_.mean()} || Shape for Skvideo: {readVideo_.shape}\")\n",
    "        printMetaData(filename)\n",
    "        showImg(readVideo[readVideo.shape[0] - 1], f\"{filename}-Frame {readVideo.shape[0] - 1}-Imageio\")\n",
    "        showImg(readVideo_[readVideo_.shape[0] - 1], f\"{filename}-Frame {readVideo_.shape[0] - 1}-Skvideo\")\n",
    "        print(\"-\"*50)\n",
    "        \n",
    "    if idx % 100 == 0:\n",
    "        print(f\"Processed {idx + 1} videos\")\n",
    "        \n",
    "print(\"Check Completed.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "\n",
    "def readVideoOpenCV(filename, show= False):\n",
    "    \n",
    "    cap = cv2.VideoCapture(filename)\n",
    "    frames = []\n",
    "    while(True):\n",
    "        # Capture frame-by-frame\n",
    "        ret, frame = cap.read()\n",
    "\n",
    "        if ret != False:\n",
    "            # Our operations on the frame come here\n",
    "            frames.append(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))\n",
    "\n",
    "            if show:\n",
    "                showImg(frame, \"Cv2\")\n",
    "\n",
    "            if cv2.waitKey(1) & 0xFF == ord('q'):\n",
    "                break\n",
    "\n",
    "        else:\n",
    "            break\n",
    "\n",
    "\n",
    "    # When everything done, release the capture\n",
    "    cap.release()\n",
    "    return np.asarray(frames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "video = readVideoOpenCV(image_paths[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41.8 ms ± 1.37 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "74.5 ms ± 498 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "108 ms ± 785 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "## Timing methods\n",
    "\n",
    "%timeit composeVideoImageio(image_paths[1])\n",
    "%timeit skvideo.io.vread(image_paths[1])\n",
    "%timeit readVideoOpenCV(image_paths[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "def removeBackground(video):\n",
    "    \n",
    "    bg_removal = cv2.createBackgroundSubtractorKNN( history=125, dist2Threshold= 600.0, detectShadows = False)\n",
    "    lr = 1/(1000*1000)\n",
    "    newVideo = []\n",
    "    for idx,frame in enumerate(video):\n",
    "        \n",
    "        mask = bg_removal.apply(frame, lr)\n",
    "        mask = cv2.cvtColor(mask, cv2.COLOR_BGR2RGB)\n",
    "        mask[mask > 0.4] = 1\n",
    "        mask[mask <= 0.4] = 0\n",
    "        toShow = frame * mask\n",
    "        #showImg(toShow, f\"Mask-{idx}\")\n",
    "        newVideo.append(toShow)\n",
    "        \n",
    "    return np.array(newVideo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "newVideo = removeBackground(video)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/carlo/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:24: DeprecationWarning: The binary mode of fromstring is deprecated, as it behaves surprisingly on unicode inputs. Use frombuffer instead\n",
      "/home/carlo/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:37: DeprecationWarning: The binary mode of fromstring is deprecated, as it behaves surprisingly on unicode inputs. Use frombuffer instead\n",
      "/home/carlo/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:55: DeprecationWarning: The binary mode of fromstring is deprecated, as it behaves surprisingly on unicode inputs. Use frombuffer instead\n",
      "/home/carlo/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:80: DeprecationWarning: `imresize` is deprecated!\n",
      "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.3.0.\n",
      "Use Pillow instead: ``numpy.array(Image.fromarray(arr).resize())``.\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy.misc\n",
    "\n",
    "import skvideo.datasets\n",
    "\n",
    "try:\n",
    "    xrange\n",
    "except NameError:\n",
    "    xrange = range\n",
    "\n",
    "def getPlots(motionData):\n",
    "    motionMagnitude = np.sqrt(np.sum(motionData**2, axis=2))\n",
    "\n",
    "    fig = plt.figure()\n",
    "    plt.quiver(motionData[::-1, :, 0], motionData[::-1, :, 1])\n",
    "    fig.axes[0].get_xaxis().set_visible(False)\n",
    "    fig.axes[0].get_yaxis().set_visible(False)\n",
    "    plt.tight_layout()\n",
    "    fig.canvas.draw()\n",
    " \n",
    "    # Get the RGBA buffer from the figure\n",
    "    w,h = fig.canvas.get_width_height()\n",
    "    buf = np.fromstring(fig.canvas.tostring_argb(), dtype=np.uint8)\n",
    "    buf.shape = (h, w, 4)\n",
    "    quiver = buf[:, :, 1:]\n",
    "    plt.close()\n",
    "\n",
    "    fig = plt.figure()\n",
    "    plt.imshow(motionMagnitude, cmap=\"Greys_r\")\n",
    "    fig.axes[0].get_xaxis().set_visible(False)\n",
    "    fig.axes[0].get_yaxis().set_visible(False)\n",
    "    plt.tight_layout()\n",
    "    fig.canvas.draw()\n",
    "\n",
    "    w,h = fig.canvas.get_width_height()\n",
    "    buf = np.fromstring(fig.canvas.tostring_argb(), dtype=np.uint8)\n",
    "    buf.shape = (h, w, 4)\n",
    "    magnitude = buf[:, :, 1:]\n",
    "    plt.close()\n",
    "\n",
    "    # histogram it\n",
    "    fig = plt.figure()\n",
    "    hist, bins = np.histogram(motionMagnitude, bins=10, range=(-0.5, 9.5))\n",
    "    center = (bins[1:] + bins[:-1])/2.0\n",
    "    plt.scatter(center, hist)\n",
    "    plt.xlabel(\"Motion magnitude\")\n",
    "    plt.ylabel(\"Count\")\n",
    "    plt.ylim([0, 14000])\n",
    "    plt.grid()\n",
    "    plt.tight_layout()\n",
    "    fig.canvas.draw()\n",
    "\n",
    "    w,h = fig.canvas.get_width_height()\n",
    "    buf = np.fromstring(fig.canvas.tostring_argb(), dtype=np.uint8)\n",
    "    buf.shape = (h, w, 4)\n",
    "    histogram = buf[:, :, 1:]\n",
    "    plt.close()\n",
    "\n",
    "    return quiver, magnitude, histogram\n",
    "\n",
    "\n",
    "filename = skvideo.datasets.bigbuckbunny()\n",
    "\n",
    "videodata = skvideo.io.vread(filename)\n",
    "\n",
    "videometadata = skvideo.io.ffprobe(filename)\n",
    "frame_rate = videometadata['video']['@avg_frame_rate']\n",
    "\n",
    "T, M, N, C = videodata.shape\n",
    "\n",
    "motionData = skvideo.motion.blockMotion(videodata)\n",
    "\n",
    "writer = skvideo.io.FFmpegWriter(\"motion.mp4\", inputdict={\n",
    "    \"-r\": frame_rate\n",
    "})\n",
    "\n",
    "for i in xrange(T-1):\n",
    "    a, b, c = getPlots(motionData[i])\n",
    "    frame = scipy.misc.imresize(videodata[i+1], (a.shape[0], a.shape[1], 3))\n",
    "\n",
    "    outputframe = np.zeros((frame.shape[0]*2, frame.shape[1]*2, 3), dtype=np.uint8)\n",
    "\n",
    "    outputframe[:frame.shape[0], :frame.shape[1]] = frame\n",
    "    outputframe[frame.shape[0]:, :frame.shape[1]] = a\n",
    "    outputframe[:frame.shape[0], frame.shape[1]:] = b\n",
    "    outputframe[frame.shape[0]:, frame.shape[1]:] = c\n",
    "\n",
    "    writer.writeFrame(outputframe)\n",
    "\n",
    "writer.close()"
   ]
  }
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