PIL.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://repo.dev.wixpress.com/artifactory/api/pypi/pypi-local/simple, https://pypi.python.org/simple\n",
      "Collecting pillow\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/0c/43/b52847e473ac6cbd10a749b63018b2bb08b55c6e1a1923872361443906de/Pillow-7.0.0-cp37-cp37m-macosx_10_6_intel.whl (3.9MB)\n",
      "\u001b[K     |████████████████████████████████| 3.9MB 575kB/s eta 0:00:01\n",
      "\u001b[?25hInstalling collected packages: pillow\n",
      "Successfully installed pillow-7.0.0\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install pillow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x11401ad10>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "%matplotlib inline\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "from matplotlib.pyplot import imshow\n",
    "\n",
    "w, h = 512, 512\n",
    "data = np.zeros((h, w, 3), dtype=np.uint8)\n",
    "data[0:256, 0:256] = [255, 0, 0]\n",
    "data[330:350, 300:302] = [255,100,100]\n",
    "\n",
    "img = Image.fromarray(data, 'RGB')\n",
    "img.save('my.png')\n",
    "\n",
    "pil_im = Image.open('my.png')\n",
    "imshow(np.asarray(pil_im))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
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}