AlbumentationsImageFolder.py

"""The torchvision ImageFolder with a PIL loader (to use albumentations)"""
import os
import torch
import torch.utils.data as data
from typing import Any, Callable, cast, Dict, List, Optional, Tuple
import cv2
import numpy as np

from PIL import Image


class StandardTransform(object):
    def __init__(self, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None) -> None:
        self.transform = transform
        self.target_transform = target_transform

    def __call__(self, input: Any, target: Any) -> Tuple[Any, Any]:
        if self.transform is not None:
            input = self.transform(input)
        if self.target_transform is not None:
            target = self.target_transform(target)
        return input, target

    def _format_transform_repr(self, transform: Callable, head: str) -> List[str]:
        lines = transform.__repr__().splitlines()
        return (["{}{}".format(head, lines[0])] +
                ["{}{}".format(" " * len(head), line) for line in lines[1:]])

    def __repr__(self) -> str:
        body = [self.__class__.__name__]
        if self.transform is not None:
            body += self._format_transform_repr(self.transform,
                                                "Transform: ")
        if self.target_transform is not None:
            body += self._format_transform_repr(self.target_transform,
                                                "Target transform: ")

        return '\n'.join(body)


class VisionDataset(data.Dataset):
    _repr_indent = 4

    def __init__(
            self,
            root: str,
            transforms: Optional[Callable] = None,
            transform: Optional[Callable] = None,
            target_transform: Optional[Callable] = None,
    ) -> None:
        if isinstance(root, torch._six.string_classes):
            root = os.path.expanduser(root)
        self.root = root

        has_transforms = transforms is not None
        has_separate_transform = transform is not None or target_transform is not None
        if has_transforms and has_separate_transform:
            raise ValueError("Only transforms or transform/target_transform can "
                             "be passed as argument")

        # for backwards-compatibility
        self.transform = transform
        self.target_transform = target_transform

        if has_separate_transform:
            transforms = StandardTransform(transform, target_transform)
        self.transforms = transforms

    def __getitem__(self, index: int) -> Any:
        raise NotImplementedError

    def __len__(self) -> int:
        raise NotImplementedError

    def __repr__(self) -> str:
        head = "Dataset " + self.__class__.__name__
        body = ["Number of datapoints: {}".format(self.__len__())]
        if self.root is not None:
            body.append("Root location: {}".format(self.root))
        body += self.extra_repr().splitlines()
        if hasattr(self, "transforms") and self.transforms is not None:
            body += [repr(self.transforms)]
        lines = [head] + [" " * self._repr_indent + line for line in body]
        return '\n'.join(lines)

    def _format_transform_repr(self, transform: Callable, head: str) -> List[str]:
        lines = transform.__repr__().splitlines()
        return (["{}{}".format(head, lines[0])] +
                ["{}{}".format(" " * len(head), line) for line in lines[1:]])

    def extra_repr(self) -> str:
        return ""



def has_file_allowed_extension(filename: str, extensions: Tuple[str, ...]) -> bool:
    """Checks if a file is an allowed extension.

    Args:
        filename (string): path to a file
        extensions (tuple of strings): extensions to consider (lowercase)

    Returns:
        bool: True if the filename ends with one of given extensions
    """
    return filename.lower().endswith(extensions)


def is_image_file(filename: str) -> bool:
    """Checks if a file is an allowed image extension.

    Args:
        filename (string): path to a file

    Returns:
        bool: True if the filename ends with a known image extension
    """
    return has_file_allowed_extension(filename, IMG_EXTENSIONS)


def make_dataset(
    directory: str,
    class_to_idx: Dict[str, int],
    extensions: Optional[Tuple[str, ...]] = None,
    is_valid_file: Optional[Callable[[str], bool]] = None,
) -> List[Tuple[str, int]]:
    instances = []
    directory = os.path.expanduser(directory)
    both_none = extensions is None and is_valid_file is None
    both_something = extensions is not None and is_valid_file is not None
    if both_none or both_something:
        raise ValueError("Both extensions and is_valid_file cannot be None or not None at the same time")
    if extensions is not None:
        def is_valid_file(x: str) -> bool:
            return has_file_allowed_extension(x, cast(Tuple[str, ...], extensions))
    is_valid_file = cast(Callable[[str], bool], is_valid_file)
    for target_class in sorted(class_to_idx.keys()):
        class_index = class_to_idx[target_class]
        target_dir = os.path.join(directory, target_class)
        if not os.path.isdir(target_dir):
            continue
        for root, _, fnames in sorted(os.walk(target_dir, followlinks=True)):
            for fname in sorted(fnames):
                path = os.path.join(root, fname)
                if is_valid_file(path):
                    item = path, class_index
                    instances.append(item)
    return instances


class DatasetFolder(VisionDataset):
    """A generic data loader where the samples are arranged in this way: ::

        root/class_x/xxx.ext
        root/class_x/xxy.ext
        root/class_x/xxz.ext

        root/class_y/123.ext
        root/class_y/nsdf3.ext
        root/class_y/asd932_.ext

    Args:
        root (string): Root directory path.
        loader (callable): A function to load a sample given its path.
        extensions (tuple[string]): A list of allowed extensions.
            both extensions and is_valid_file should not be passed.
        transform (callable, optional): A function/transform that takes in
            a sample and returns a transformed version.
            E.g, ``transforms.RandomCrop`` for images.
        target_transform (callable, optional): A function/transform that takes
            in the target and transforms it.
        is_valid_file (callable, optional): A function that takes path of a file
            and check if the file is a valid file (used to check of corrupt files)
            both extensions and is_valid_file should not be passed.

     Attributes:
        classes (list): List of the class names sorted alphabetically.
        class_to_idx (dict): Dict with items (class_name, class_index).
        samples (list): List of (sample path, class_index) tuples
        targets (list): The class_index value for each image in the dataset
    """

    def __init__(
            self,
            root: str,
            loader: Callable[[str], Any],
            extensions: Optional[Tuple[str, ...]] = None,
            transform: Optional[Callable] = None,
            target_transform: Optional[Callable] = None,
            is_valid_file: Optional[Callable[[str], bool]] = None,
    ) -> None:
        super(DatasetFolder, self).__init__(root, transform=transform,
                                            target_transform=target_transform)
        classes, class_to_idx = self._find_classes(self.root)
        samples = make_dataset(self.root, class_to_idx, extensions, is_valid_file)
        if len(samples) == 0:
            msg = "Found 0 files in subfolders of: {}\n".format(self.root)
            if extensions is not None:
                msg += "Supported extensions are: {}".format(",".join(extensions))
            raise RuntimeError(msg)

        self.loader = loader
        self.extensions = extensions

        self.classes = classes
        self.class_to_idx = class_to_idx
        self.samples = samples
        self.targets = [s[1] for s in samples]

    def _find_classes(self, dir: str) -> Tuple[List[str], Dict[str, int]]:
        """
        Finds the class folders in a dataset.

        Args:
            dir (string): Root directory path.

        Returns:
            tuple: (classes, class_to_idx) where classes are relative to (dir), and class_to_idx is a dictionary.

        Ensures:
            No class is a subdirectory of another.
        """
        classes = [d.name for d in os.scandir(dir) if d.is_dir()]
        classes.sort()
        class_to_idx = {cls_name: i for i, cls_name in enumerate(classes)}
        return classes, class_to_idx

    def __getitem__(self, index: int) -> Tuple[Any, Any]:
        """
        Args:
            index (int): Index

        Returns:
            tuple: (sample, target) where target is class_index of the target class.
        """
        path, target = self.samples[index]
        sample = self.loader(path)
        # if self.transform is not None:
        #     sample = self.transform(sample)
        # if self.target_transform is not None:
        #     target = self.target_transform(target)
        # if self.transform is not None:
        #     sample = np.array(sample)
        #     augmented = self.transform(image=sample)
        #     sample = Image.fromarray(augmented['image'])
        # if self.target_transform is not None:
        #     target = np.array(target)
        #     augmented = self.transform(image=target)
        #     target = Image.fromarray(augmented['image'])
        if self.transform is not None:
            sample = np.array(sample)
            try:
                sample = self.transform(image=sample)
            except OverflowError:
                print("Error in SunFlare")
                sample = self.transform(image=sample)
            sample = sample['image']
            # sample = self.transform(sample)
        if self.target_transform is not None:
            target = np.array(target)
            target = self.target_transform(image=target)
            target = target['image']
            # target = self.target_transform(target)
        return sample, target


    def __len__(self) -> int:
        return len(self.samples)



IMG_EXTENSIONS = ('.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', '.tiff', '.webp')


def cv_loader(path: str):
    image = cv2.imread(path)
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    return image

def pil_loader(path: str) -> Image.Image:
    # 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')


# TODO: specify the return type
def accimage_loader(path: str) -> Any:
    return pil_loader(path)


def default_loader(path: str) -> Any:
    return pil_loader(path)


class ImageFolder(DatasetFolder):
    """A generic data loader where the images are arranged in this way: ::

        root/dog/xxx.png
        root/dog/xxy.png
        root/dog/xxz.png

        root/cat/123.png
        root/cat/nsdf3.png
        root/cat/asd932_.png

    Args:
        root (string): Root directory path.
        transform (callable, optional): A function/transform that  takes in an PIL image
            and returns a transformed version. E.g, ``transforms.RandomCrop``
        target_transform (callable, optional): A function/transform that takes in the
            target and transforms it.
        loader (callable, optional): A function to load an image given its path.
        is_valid_file (callable, optional): A function that takes path of an Image file
            and check if the file is a valid file (used to check of corrupt files)

     Attributes:
        classes (list): List of the class names sorted alphabetically.
        class_to_idx (dict): Dict with items (class_name, class_index).
        imgs (list): List of (image path, class_index) tuples
    """

    def __init__(
            self,
            root: str,
            transform: Optional[Callable] = None,
            target_transform: Optional[Callable] = None,
            loader: Callable[[str], Any] = default_loader,
            is_valid_file: Optional[Callable[[str], bool]] = None,
    ):
        super(ImageFolder, self).__init__(root, loader, IMG_EXTENSIONS if is_valid_file is None else None,
                                          transform=transform,
                                          target_transform=target_transform,
                                          is_valid_file=is_valid_file)
        self.imgs = self.samples