Reader.py

import torch.utils.data as data

from PIL import Image
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True

import numpy as np
import os
import os.path

IMG_EXTENSIONS = [
    '.jpg', '.JPG', '.jpeg', '.JPEG',
    '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP',
]

def is_image_file(filename):
    return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)

def find_classes(data_dict):
    classes = [c for c in sorted(data_dict)]
    classes.sort()
    class_to_idx = {classes[i]: i for i in range(len(classes))}
    return classes, class_to_idx

def make_dataset(dict, class_to_idx):
    images = []
    idx_to_class = {}
    intervals = []
    i0,i1 = 0,0
    
    for catg in sorted(dict):
        for fdir in dict[catg]:
            if is_image_file(fdir):
                idx_to_class[i1] = class_to_idx[catg]
                images.append((fdir, class_to_idx[catg]))
                i1 += 1
        intervals.append((i0,i1))
        i0 = i1

    return images, intervals, idx_to_class

def pil_loader(path):
    # open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835)
    with open(path, 'rb') as f:
        with Image.open(f) as img:
            return img.convert('RGB')


def accimage_loader(path):
    import accimage
    try:
        return accimage.Image(path)
    except IOError:
        # Potentially a decoding problem, fall back to PIL.Image
        return pil_loader(path)


def default_loader(path):
    from torchvision import get_image_backend
    if get_image_backend() == 'accimage':
        return accimage_loader(path)
    else:
        return pil_loader(path)

def label_transform():
    pass

def folderReader(path):
    pass

class ImageReader(data.Dataset):

    def __init__(self, data_dict, transform=None, target_transform=None,
                 loader=default_loader):
        
        classes, class_to_idx = find_classes(data_dict)
        imgs, intervals, idx_to_class = make_dataset(data_dict, class_to_idx)
        
        if len(imgs) == 0:
            raise(RuntimeError("Found 0 images!"))

        self.imgs = imgs
        self.classes = classes
        self.class_to_idx = class_to_idx# cat->1
        self.intervals = intervals
        self.idx_to_class = idx_to_class# i(img idx)->2(class)
        self.transform = transform
        self.target_transform = target_transform
        self.loader = loader

    def __getitem__(self, index):
        """
        Args:
            index (int): Index
        Returns:
            tuple: (image, target) where target is class_index of the target class.
        """
        path, target = self.imgs[index]
        img = self.loader(path)
        if self.transform is not None:
            img = self.transform(img)
        if self.target_transform is not None:
            target = self.target_transform(target)

        return img, target

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