imageio vs pillow execution time

imageio vs pillow.ipynb

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   "cell_type": "code",
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     "name": "stderr",
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     "text": [
      "INFO:root:Test\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import time\n",
    "import logging\n",
    "import tensorflow as tf\n",
    "\n",
    "from hudl_beatrix.dataset import BrainWashDataset\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "%matplotlib inline\n",
    "\n",
    "logging.info('Test')\n",
    "logger = logging.getLogger()\n",
    "logger.setLevel(logging.INFO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_imageio = BrainWashDataset(image_method='imageio')\n",
    "dataset_pillow_jpeg = BrainWashDataset(image_method='pillow', encoding='JPEG')\n",
    "dataset_pillow_png = BrainWashDataset(image_method='pillow', encoding='PNG')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR:root:Directory already exists, no data will be converted! Did you mean to set the overwrite flag to True?\n",
      "ERROR:root:Directory already exists, no data will be converted! Did you mean to set the overwrite flag to True?\n",
      "ERROR:root:Directory already exists, no data will be converted! Did you mean to set the overwrite flag to True?\n"
     ]
    }
   ],
   "source": [
    "dataset_imageio.convert(dataset_path='./brainwash_tf_records_imageio')\n",
    "dataset_pillow_jpeg.convert(dataset_path='./brainwash_tf_records_pillow_jpeg')\n",
    "dataset_pillow_png.convert(dataset_path='./brainwash_tf_records_pillow_png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dir = './brainwash_tf_records_imageio/train'\n",
    "records_imageio = [os.path.join(train_dir, record) for record in os.listdir(train_dir)]\n",
    "train_dir = './brainwash_tf_records_pillow_jpeg/train'\n",
    "records_pillow_jpeg = [os.path.join(train_dir, record) for record in os.listdir(train_dir)]\n",
    "train_dir = './brainwash_tf_records_pillow_png/train'\n",
    "records_pillow_png = [os.path.join(train_dir, record) for record in os.listdir(train_dir)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Imageio time : 12.12564206123352 sec\n",
      "INFO:root:Pillow jpeg time : 12.177225112915039 sec\n",
      "INFO:root:Pillow png time : 12.364830732345581 sec\n"
     ]
    }
   ],
   "source": [
    "n_threads = 8\n",
    "batch_size = 1\n",
    "min_after_dequeue = 8\n",
    "\n",
    "input_fn_imageio = dataset_imageio.input_fn(records_imageio, \n",
    "                                           batch_size=batch_size,\n",
    "                                           n_threads=n_threads,\n",
    "                                           min_after_dequeue=min_after_dequeue)\n",
    "input_fn_pillow_jpeg = dataset_pillow_jpeg.input_fn(records_pillow_jpeg, \n",
    "                                           batch_size=batch_size,\n",
    "                                           n_threads=n_threads,\n",
    "                                           min_after_dequeue=min_after_dequeue)\n",
    "input_fn_pillow_png = dataset_pillow_png.input_fn(records_pillow_png, \n",
    "                                           batch_size=batch_size,\n",
    "                                           n_threads=n_threads,\n",
    "                                           min_after_dequeue=min_after_dequeue)\n",
    "\n",
    "record_imageio = input_fn_imageio()\n",
    "record_pillow_jpeg = input_fn_pillow_jpeg()\n",
    "record_pillow_png = input_fn_pillow_png()\n",
    "\n",
    "imageio_time = 0\n",
    "pillow_jpeg_time = 0\n",
    "pillow_png_time = 0\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "\n",
    "    coord = tf.train.Coordinator()\n",
    "    threads = tf.train.start_queue_runners(coord=coord)\n",
    "\n",
    "    for i in range(len(records_imageio)):\n",
    "        start_time = time.time()\n",
    "        img, bx = sess.run(record_imageio)\n",
    "        imageio_time += time.time() - start_time\n",
    "        \n",
    "        start_time = time.time()\n",
    "        img, bx = sess.run(record_pillow_jpeg)\n",
    "        pillow_jpeg_time += time.time() - start_time\n",
    "        \n",
    "        start_time = time.time()\n",
    "        img, bx = sess.run(record_pillow_png)\n",
    "        pillow_png_time += time.time() - start_time\n",
    "\n",
    "    coord.request_stop()\n",
    "    coord.join(threads)\n",
    "\n",
    "logger.info('Imageio time : {} sec'.format(imageio_time))\n",
    "logger.info('Pillow jpeg time : {} sec'.format(pillow_jpeg_time))\n",
    "logger.info('Pillow png time : {} sec'.format(pillow_png_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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