generate_gif.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import numpy as np\n",
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# folder_to_scan = './results/00001-sgan-ffhq-mars-1gpu/'\n",
    "folder_to_scan = './results/00002-sgan-ffhq-mars-1gpu/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "files = os.listdir(folder_to_scan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_file_types = ['.png']\n",
    "valid_files = []\n",
    "for file in files:\n",
    "    f, fe = os.path.splitext(file)\n",
    "    if fe in valid_file_types:\n",
    "        valid_files.append(file)\n",
    "\n",
    "full_path_valid_files = [folder_to_scan+file for file in valid_files]\n",
    "fakes = [ff for ff in full_path_valid_files if 'fake' in ff]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "fakes.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "im = Image.open(fakes[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {