datasets.py

import os

import cv2
import numpy as np
from torch.utils.data import DataLoader
from torch.utils.data import Dataset as BaseDataset

class DRACDataset(BaseDataset):
    """DRAC Dataset. Read images, apply augmentation and preprocessing transformations.

    Args:
        images_dir (str): path to images folder
        masks_dir (str): path to segmentation masks folder
        augmentation (albumentations.Compose): data transfromation pipeline
            (e.g. flip, scale, etc.)
        preprocessing (albumentations.Compose): data preprocessing
            (e.g. noralization, shape manipulation, etc.)

    """


    def __init__(
            self,
            images_dir,
            masks_dir,
            augmentation=None,
            preprocessing=None,
    ):
        self.ids = os.listdir(images_dir)
        self.images_fps = [os.path.join(images_dir, image_id) for image_id in self.ids]
        self.masks_fps = [os.path.join(masks_dir, image_id) for image_id in self.ids]

        # convert str names to class values on masks
        self.class_values = [255]

        self.augmentation = augmentation
        self.preprocessing = preprocessing

    def __getitem__(self, i):

        # read data
        image = cv2.imread(self.images_fps[i],cv2.IMREAD_GRAYSCALE)
        image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
        # print(image.shape)

        mask = cv2.imread(self.masks_fps[i], 0)

        # extract certain classes from mask (e.g. cars)
        masks = [(mask == v) for v in self.class_values]
        mask = np.stack(masks, axis=-1).astype('float')

        # apply augmentations
        if self.augmentation:
            sample = self.augmentation(image=image, mask=mask)
            image, mask = sample['image'], sample['mask']

        # apply preprocessing
        if self.preprocessing:
            sample = self.preprocessing(image=image, mask=mask)
            image, mask = sample['image'], sample['mask']
        if self.augmentation == None and self.preprocessing == None:
            return image, mask, os.path.split(self.images_fps[i])[-1]
        return image, mask

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