Skin-Lesion-Segmentation/dataset.py at main · chains229/Skin-Lesion-Segmentation · GitHub

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from torch.utils.data import Dataset
import numpy as np
import os
from PIL import Image


class NPY_datasets(Dataset):
    def __init__(self, path_Data, config, train=True):
        super(NPY_datasets, self)
        if train:
            images_list = os.listdir(path_Data+'train/images/')
            masks_list = os.listdir(path_Data+'train/masks/')
            self.data = []
            for i in range(len(images_list)):
                img_path = path_Data+'train/images/' + images_list[i]
                mask_path = path_Data+'train/masks/' + masks_list[i]
                self.data.append([img_path, mask_path])
            self.transformer = config.train_transformer
        else:
            images_list = os.listdir(path_Data+'val/images/')
            masks_list = os.listdir(path_Data+'val/masks/')
            self.data = []
            for i in range(len(images_list)):
                img_path = path_Data+'val/images/' + images_list[i]
                mask_path = path_Data+'val/masks/' + masks_list[i]
                self.data.append([img_path, mask_path])
            self.transformer = config.test_transformer

    def __getitem__(self, indx):
        img_path, msk_path = self.data[indx]
        img = np.array(Image.open(img_path).convert('RGB'))
        msk = np.expand_dims(np.array(Image.open(msk_path).convert('L')), axis=2) / 255
        img, msk = self.transformer((img, msk))
        return img, msk

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