Pillow Draw Text

pillowdrawtext.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x10665dcf8>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWEAAAFiCAYAAAAna2l5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUkGGsCQVZAhLUkH/HzBq6KBBgGPuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10635b748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from matplotlib.pyplot import imshow\n",
    "\n",
    "from PIL import Image\n",
    "from PIL import ImageFont\n",
    "from PIL import ImageDraw\n",
    "import PIL.ImageOps\n",
    "\n",
    "font = ImageFont.truetype(\"FiraSans-Regular.ttf\", 60)\n",
    "\n",
    "digit = \"1\"\n",
    "img = Image.new('RGBA', (64, 64), 255)\n",
    "draw = ImageDraw.Draw(img)\n",
    "w, h = font.getsize(digit)\n",
    "draw.text(((64 - w) / 2, (64 - h) / 2 - 7), digit, font=font)\n",
    "img.save(digit + '.png')\n",
    "\n",
    "imshow(img)"
   ]
  }
 ],
 "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}