notebooks/hrvs_dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "\n",
    "sys.path.append('../')\n",
    "from dataloader.MiddleburyLoader import myImageFloder\n",
    "from notebooks.notebook_utils import visualize_sample, download_sample, list_s3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_list = list_s3('hdsm/hrvs/carla-highres/trainingF/')['CommonPrefixes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def walk_through_dataset(dataset_list):\n",
    "    for example in dataset_list:\n",
    "        example = example['Prefix']\n",
    "        example = 's3://autogpe-datasets/{}'.format(example)\n",
    "        folders = example.split('/')[-2:]\n",
    "        folder = '/'.join(folders)\n",
    "        out_folder = '../data/hrvs/carla-highres/trainingF/{}'.format(folder)\n",
    "        download_sample(example, out_folder)\n",
    "        \n",
    "        all_left_img = [os.path.join(out_folder, 'im0.png')]\n",
    "        all_right_img = [os.path.join(out_folder, 'im1.png')]\n",
    "        all_left_disp = [os.path.join(out_folder, 'disp0GT.pfm')]\n",
    "        all_right_disp = [os.path.join(out_folder, 'disp1GT.pfm')]\n",
    "        scale_factor = 1.0\n",
    "        loader_carla = myImageFloder(all_left_img, all_right_img, all_left_disp, right_disparity=all_right_disp,\n",
    "                                     rand_scale=[0.225, 0.6 * scale_factor], rand_bright=[0.8, 1.2], order=2)\n",
    "        \n",
    "        for (img_l, img_r, disp_l) in loader_carla:\n",
    "            visualize_sample(img_l, img_r, disp_l)\n",
    "            yield"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_iter = walk_through_dataset(train_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "next(train_iter)"
   ]
  }
 ],
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