scale_crop_images.ipynb

{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# Prepare Images for Deep Learning\n\nGet your images with faces in them that should be roughly square already and with the face in the middle approx.\n\nThen run the script below."
  },
  {
   "metadata": {
    "trusted": true,
    "collapsed": false
   },
   "cell_type": "code",
   "source": "import os\nimport subprocess\n\ndirectory_in_str = '/Users/paulg/Documents/developer/fastai/test/nad'\ndirectory = os.fsencode(directory_in_str)\n\nfor file in os.listdir(directory):\n     filename = os.fsdecode(file)\n     if filename.lower().endswith(\".jpg\"):\n        input_file = os.path.join(directory_in_str, filename)\n        print(\"processing:{}\".format(input_file))\n        output_file = \"{}/{}\".format(directory_in_str,filename.split('.')[0])\n        resize_command = \"ffmpeg -i {} -vf scale=224:-1 {}_r.jpg\".format(input_file,output_file)\n        crop_command = \"ffmpeg -i {}_r.jpg -vf crop=224:224:ih/2-112:iw-2-112 {}_c.jpg\".format(output_file,output_file)\n        cleanup_orig = \"rm {}.jpg\".format(output_file)\n        cleanup_resized = \"rm {}_r.jpg\".format(output_file)\n        #print(resize_command)\n        #print(crop_command)\n        subprocess.run(resize_command.split(' '))\n        subprocess.run(crop_command.split(' '))\n        subprocess.run(cleanup_orig.split(' '))\n        subprocess.run(cleanup_resized.split(' '))",
   "execution_count": null,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true,
    "collapsed": false
   },
   "cell_type": "code",
   "source": "",
   "execution_count": null,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true,
    "collapsed": true
   },
   "cell_type": "code",
   "source": "",
   "execution_count": null,
   "outputs": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "conda-env-nlp-py",
   "display_name": "Python [conda env:nlp]",
   "language": "python"
  },
  "language_info": {
   "name": "python",
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "pygments_lexer": "ipython3",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "nbconvert_exporter": "python",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}