Image_Resize_TF_vs_PIL

Image_Resize_TF_vs_PIL.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from multiprocessing import Pool, cpu_count\n",
    "\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from PIL import Image\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _resize_tf(_):\n",
    "    x = Image.open(\"dog.jpg\")\n",
    "    x = tf.constant(np.array(x))\n",
    "    x = x[tf.newaxis, ...]\n",
    "    x = tf.image.resize(x, [224, 224], method=tf.image.ResizeMethod.BICUBIC)[\n",
    "        0, ...\n",
    "    ].numpy()\n",
    "\n",
    "\n",
    "def _resize_PIL(_):\n",
    "    x = Image.open(\"dog.jpg\")\n",
    "    x = x.resize((224, 224), Image.BICUBIC)\n",
    "    x = np.array(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1000/1000 [00:02<00:00, 384.79it/s]\n"
     ]
    }
   ],
   "source": [
    "with Pool(cpu_count()) as pool:\n",
    "    X = list(\n",
    "        tqdm(\n",
    "            pool.imap(\n",
    "                _resize_PIL,\n",
    "                range(N),\n",
    "            ),\n",
    "            total=N,\n",
    "            smoothing=0,\n",
    "        )\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1000/1000 [00:38<00:00, 25.74it/s]\n"
     ]
    }
   ],
   "source": [
    "for i in tqdm(range(N), smoothing=0):\n",
    "    _resize_tf(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1000/1000 [00:45<00:00, 21.89it/s]\n"
     ]
    }
   ],
   "source": [
    "for i in tqdm(range(N), smoothing=0):\n",
    "    _resize_PIL(i)"
   ]
  }
 ],
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