image_folder.py

import torch.utils.data as Data
from torchvision.datasets.folder import default_loader
from torchvision import transforms
from os.path import join as ospj
from PIL import Image
import os
def has_file_allowed_extension(filename, extensions):
    """Checks if a file is an allowed extension.

    Args:
        filename (string): path to a file

    Returns:
        bool: True if the filename ends with a known image extension
    """
    filename_lower = filename.lower()
    return any(filename_lower.endswith(ext) for ext in 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, extensions):
    images = []
    dir = os.path.expanduser(dir)
    for target in sorted(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 sorted(fnames):
                if has_file_allowed_extension(fname, extensions):
                    path = os.path.join(root, fname)
                    item = (path, class_to_idx[target])
                    images.append(item)

    return images
#getting one image of a folder.
def make_dataset_one(dir, class_to_idx, extensions, reverse=False):
    images = []
    dir = os.path.expanduser(dir)
    for target in sorted(os.listdir(dir)):
        d = os.path.join(dir, target)
        if not os.path.isdir(d):
            continue

        for root, _, fnames in sorted(os.walk(d)):
            index = 0
            for fname in sorted(fnames, reverse=reverse):
                index += 1
                if has_file_allowed_extension(fname, extensions) and index == 36:
                    path = os.path.join(root, fname)
                    item = (path, class_to_idx[target])
                    images.append(item)
                    break

    return images


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:
        img = Image.open(f)
        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)

class customData(Data.Dataset):
    def __init__(self, root, transform = None, target_transform = None, loader = default_loader, rotate = 0, pad = 0):
        classes, class_to_idx = find_classes(root)
        IMG_EXTENSIONS = ['.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif']
        imgs = make_dataset(root, class_to_idx, IMG_EXTENSIONS)
        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.rotate = rotate
        self.pad = pad
    def __getitem__(self, index):
            """
            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)
            img = transforms.functional.rotate(img,self.rotate)
            if self.pad > 0:
                # 10
                img = transforms.functional.resize(img,(512,512),interpolation=3)
                # 10
                img = transforms.functional.pad(img,(self.pad,0,0,0),padding_mode='reflect')
                # For(+P, 0), img = transforms.functional.pad(img, (self.pad, 0, 0, 0), padding_mode='reflect')
                # For(+P, +P), img = transforms.functional.pad(img, (self.pad, self.pad, 0, 0), padding_mode='reflect')
                # For(-P, -P), img = transforms.functional.pad(img, (0, 0, self.pad, self.pad), padding_mode='reflect')
                # For(+P, -P), img = transforms.functional.pad(img, (self.pad, 0, 0, self.pad), padding_mode='reflect')
                # For(-P, +P), img = transforms.functional.pad(img, (0, self.pad, self.pad, 0), padding_mode='reflect')
                img = transforms.functional.five_crop(img,(512,512))[0]
            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)

class customData_one(Data.Dataset):
    def __init__(self, root, transform = None, target_transform = None, loader = default_loader, rotate = 0, pad = 0, reverse=False):
        classes, class_to_idx = find_classes(root)
        IMG_EXTENSIONS = ['.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif']
        imgs = make_dataset_one(root, class_to_idx, IMG_EXTENSIONS, reverse)
        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.rotate = rotate
        self.pad = pad
    def __getitem__(self, index):
            """
            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)
            img = transforms.functional.rotate(img,self.rotate)
            if self.pad > 0:
                img = transforms.functional.resize(img,(256,256),interpolation=3)
                img = transforms.functional.pad(img,(self.pad,0,0,0))
                img = transforms.functional.five_crop(img,(256,256))[0]
            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)