validation_folders.py

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import configparser 
from builtins import open
from builtins import str
# etc., as needed

#from future import standard_library
import torch.utils.data as data
import numpy as np
from imageio import imread
from path import Path


def crawl_folders(folders_list):
        imgs = []
        depth = []
        for folder in folders_list:
            current_imgs = sorted(folder.files('*.jpg'))
            current_depth = []
            for img in current_imgs:
                d = img.dirname()/(img.name[:-4] + '.npy')
                assert(d.isfile()), "depth file {} not found".format(str(d))
                depth.append(d)
            imgs.extend(current_imgs)
            depth.extend(current_depth)
        return imgs, depth


def load_as_float(path):
    return imread(path).astype(np.float32)


class ValidationSet(data.Dataset):
    """A sequence data loader where the files are arranged in this way:
        root/scene_1/0000000.jpg
        root/scene_1/0000000.npy
        root/scene_1/0000001.jpg
        root/scene_1/0000001.npy
        ..
        root/scene_2/0000000.jpg
        root/scene_2/0000000.npy
        .

        transform functions must take in a list a images and a numpy array which can be None
    """

    def __init__(self, root, transform=None):
        self.root = Path(root)
        scene_list_path = self.root/'val_left.txt'
        self.scenes = [self.root/folder[:-1] for folder in open(scene_list_path)]
        self.imgs, self.depth = crawl_folders(self.scenes)
        self.transform = transform

    def __getitem__(self, index):
        img = load_as_float(self.imgs[index])
        depth = np.load(self.depth[index]).astype(np.float32)
        if self.transform is not None:
            img, _ = self.transform([img], None)
            img = img[0]
        return img, depth            ###---the depth file is the .npy file correspoding to the .jpg with the same name

    def __len__(self):
        return len(self.imgs)