folder.py

from operator import itemgetter

import torch
import torch.utils.data as data

from PIL import Image
from PIL import ImageFilter
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(dir):
    classes = [d for d in os.listdir(dir)
               if os.path.isdir(os.path.join(dir, d))]
    classes.sort()
    class_to_idx = {classes[i]: i for i in range(len(classes))}
    return classes, class_to_idx


def make_dataset(dir, class_to_idx):
    images = []
    for target in os.listdir(dir):
        d = os.path.join(dir, target)
        if not os.path.isdir(d):
            continue

        for root, _, fnames in sorted(os.walk(d)):
            for fname in fnames:
                if is_image_file(fname):
                    path = os.path.join(root, fname)
                    item = (path, class_to_idx[target])
                    images.append(item)
    images.sort(key=lambda t: os.path.split(t[0])[1])
    return images


def default_loader(path):
    return Image.open(path).convert('RGB')


class ImageFolder(data.Dataset):

    def __init__(self, root, transform=None, target_transform=None,
                 loader=default_loader, out_name=False, out_image_size=False):
        classes, class_to_idx = find_classes(root)
        imgs = make_dataset(root, class_to_idx)
        if len(imgs) == 0:
            raise (RuntimeError(
                "Found 0 images in subfolders of: " + root + "\n"
                "Supported image extensions are: " + ",".join(IMG_EXTENSIONS)))

        self.root = root
        self.imgs = imgs
        self.classes = classes
        self.class_to_idx = class_to_idx
        self.transform = transform
        self.target_transform = target_transform
        self.loader = loader
        self.out_name = out_name
        self.out_image_size = out_image_size

    def __getitem__(self, index):
        path, target = self.imgs[index]
        img = self.loader(path)
        img_size = torch.LongTensor(img.size)
        # img = img.filter(ImageFilter.GaussianBlur(7))
        if self.transform is not None:
            img = self.transform(img)
        if self.target_transform is not None:
            target = self.target_transform(target)

        output = [img, target]
        if self.out_name:
            output.append(os.path.split(path)[1])
        if self.out_image_size:
            output.append(img_size)

        return tuple(output)

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