transforms.py

from __future__ import division
import torch
import math
import random
from PIL import Image, ImageOps
try:
    import accimage
except ImportError:
    accimage = None
import numpy as np
import numbers
import types
import collections


def _is_pil_image(img):
    if accimage is not None:
        return isinstance(img, (Image.Image, accimage.Image))
    else:
        return isinstance(img, Image.Image)


def _is_tensor_image(img):
    return torch.is_tensor(img) and img.ndimension() == 3


def _is_numpy_image(img):
    return isinstance(img, np.ndarray) and (img.ndim in {2, 3})


def to_tensor(pic):
    assert _is_pil_image(pic) or _is_numpy_image(pic), 'pic should be PIL Image or ndarray'

    if isinstance(pic, np.ndarray):
        # handle numpy array
        img = torch.from_numpy(pic.transpose((2, 0, 1)))
        # backward compatibility
        return img.float().div(255)

    if accimage is not None and isinstance(pic, accimage.Image):
        nppic = np.zeros([pic.channels, pic.height, pic.width], dtype=np.float32)
        pic.copyto(nppic)
        return torch.from_numpy(nppic)

    # handle PIL Image
    if pic.mode == 'I':
        img = torch.from_numpy(np.array(pic, np.int32, copy=False))
    elif pic.mode == 'I;16':
        img = torch.from_numpy(np.array(pic, np.int16, copy=False))
    else:
        img = torch.ByteTensor(torch.ByteStorage.from_buffer(pic.tobytes()))
    # PIL image mode: 1, L, P, I, F, RGB, YCbCr, RGBA, CMYK
    if pic.mode == 'YCbCr':
        nchannel = 3
    elif pic.mode == 'I;16':
        nchannel = 1
    else:
        nchannel = len(pic.mode)
    img = img.view(pic.size[1], pic.size[0], nchannel)
    # put it from HWC to CHW format
    # yikes, this transpose takes 80% of the loading time/CPU
    img = img.transpose(0, 1).transpose(0, 2).contiguous()
    if isinstance(img, torch.ByteTensor):
        return img.float().div(255)
    else:
        return img


def to_pilimage(pic):
    assert _is_numpy_image(pic) or _is_tensor_image(pic), 'pic should be Tensor or ndarray'

    npimg = pic
    mode = None
    if isinstance(pic, torch.FloatTensor):
        pic = pic.mul(255).byte()
    if torch.is_tensor(pic):
        npimg = np.transpose(pic.numpy(), (1, 2, 0))
    assert isinstance(npimg, np.ndarray)
    if npimg.shape[2] == 1:
        npimg = npimg[:, :, 0]

        if npimg.dtype == np.uint8:
            mode = 'L'
        if npimg.dtype == np.int16:
            mode = 'I;16'
        if npimg.dtype == np.int32:
            mode = 'I'
        elif npimg.dtype == np.float32:
            mode = 'F'
    else:
        if npimg.dtype == np.uint8:
            mode = 'RGB'
    assert mode is not None, '{} is not supported'.format(npimg.dtype)
    return Image.fromarray(npimg, mode=mode)


def normalize(tensor, mean, std):
    assert _is_tensor_image(tensor)
    # TODO: make efficient
    for t, m, s in zip(tensor, mean, std):
        t.sub_(m).div_(s)
    return tensor


def scale(img, size, interpolation=Image.BILINEAR):
    assert _is_pil_image(img), 'img should be PIL Image'
    assert isinstance(size, int) or (isinstance(size, collections.Iterable) and len(size) == 2)
    if isinstance(size, int):
        w, h = img.size
        if (w <= h and w == size) or (h <= w and h == size):
            return img
        if w < h:
            ow = size
            oh = int(size * h / w)
            return img.resize((ow, oh), interpolation)
        else:
            oh = size
            ow = int(size * w / h)
            return img.resize((ow, oh), interpolation)
    else:
        return img.resize(size, interpolation)


def pad(img, padding, fill=0):
    assert _is_pil_image(img), 'img should be PIL Image'
    assert isinstance(padding, (numbers.Number, tuple))
    assert isinstance(fill, (numbers.Number, str, tuple))
    if isinstance(padding, collections.Sequence) and len(padding) not in [2, 4]:
        raise ValueError("Padding must be an int or a 2, or 4 element tuple, not a " +
                         "{} element tuple".format(len(padding)))

    return ImageOps.expand(img, border=padding, fill=fill)


def crop(img, x, y, w, h):
    assert _is_pil_image(img), 'img should be PIL Image'
    return img.crop((x, y, x + w, y + h))


def scaled_crop(img, x, y, w, h, size, interpolation=Image.BILINEAR):
    assert _is_pil_image(img), 'img should be PIL Image'
    img = crop(img, x, y, w, h)
    img = scale(img, size, interpolation)


def hflip(img):
    assert _is_pil_image(img), 'img should be PIL Image'
    return img.transpose(Image.FLIP_LEFT_RIGHT)


class Compose(object):
    """Composes several transforms together.

    Args:
        transforms (list of ``Transform`` objects): list of transforms to compose.

    Example:
        >>> transforms.Compose([
        >>>     transforms.CenterCrop(10),
        >>>     transforms.ToTensor(),
        >>> ])
    """

    def __init__(self, transforms):
        self.transforms = transforms

    def __call__(self, img):
        for t in self.transforms:
            img = t(img)
        return img


class ToTensor(object):
    """Convert a ``PIL.Image`` or ``numpy.ndarray`` to tensor.

    Converts a PIL.Image or numpy.ndarray (H x W x C) in the range
    [0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0].
    """

    def __call__(self, pic):
        """
        Args:
            pic (PIL.Image or numpy.ndarray): Image to be converted to tensor.

        Returns:
            Tensor: Converted image.
        """
        return to_tensor(pic)


class ToPILImage(object):
    """Convert a tensor to PIL Image.

    Converts a torch.*Tensor of shape C x H x W or a numpy ndarray of shape
    H x W x C to a PIL.Image while preserving the value range.
    """

    def __call__(self, pic):
        """
        Args:
            pic (Tensor or numpy.ndarray): Image to be converted to PIL.Image.

        Returns:
            PIL.Image: Image converted to PIL.Image.

        """
        return to_pilimage(pic)


class Normalize(object):
    """Normalize an tensor image with mean and standard deviation.

    Given mean: (R, G, B) and std: (R, G, B),
    will normalize each channel of the torch.*Tensor, i.e.
    channel = (channel - mean) / std

    Args:
        mean (sequence): Sequence of means for R, G, B channels respecitvely.
        std (sequence): Sequence of standard deviations for R, G, B channels
            respecitvely.
    """

    def __init__(self, mean, std):
        self.mean = mean
        self.std = std

    def __call__(self, tensor):
        """
        Args:
            tensor (Tensor): Tensor image of size (C, H, W) to be normalized.

        Returns:
            Tensor: Normalized image.
        """
        return normalize(tensor, self.mean, self.std)


class Scale(object):
    """Rescale the input PIL.Image to the given size.

    Args:
        size (sequence or int): Desired output size. If size is a sequence like
            (w, h), output size will be matched to this. If size is an int,
            smaller edge of the image will be matched to this number.
            i.e, if height > width, then image will be rescaled to
            (size * height / width, size)
        interpolation (int, optional): Desired interpolation. Default is
            ``PIL.Image.BILINEAR``
    """

    def __init__(self, size, interpolation=Image.BILINEAR):
        assert isinstance(size, int) or (isinstance(size, collections.Iterable) and len(size) == 2)
        self.size = size
        self.interpolation = interpolation

    def __call__(self, img):
        """
        Args:
            img (PIL.Image): Image to be scaled.

        Returns:
            PIL.Image: Rescaled image.
        """
        return scale(img, self.size, self.interpolation)


class CenterCrop(object):
    """Crops the given PIL.Image at the center.

    Args:
        size (sequence or int): Desired output size of the crop. If size is an
            int instead of sequence like (h, w), a square crop (size, size) is
            made.
    """

    def __init__(self, size):
        if isinstance(size, numbers.Number):
            self.size = (int(size), int(size))
        else:
            self.size = size

    def get_params(self, img):
        w, h = img.size
        th, tw = self.size
        x1 = int(round((w - tw) / 2.))
        y1 = int(round((h - th) / 2.))
        return x1, y1, tw, th

    def __call__(self, img):
        """
        Args:
            img (PIL.Image): Image to be cropped.

        Returns:
            PIL.Image: Cropped image.
        """
        x1, y1, tw, th = self.get_params(img)
        return crop(img, x1, y1, tw, th)


class Pad(object):
    """Pad the given PIL.Image on all sides with the given "pad" value.

    Args:
        padding (int or tuple): Padding on each border. If a single int is provided this
            is used to pad all borders. If tuple of length 2 is provided this is the padding
            on left/right and top/bottom respectively. If a tuple of length 4 is provided
            this is the padding for the left, top, right and bottom borders
            respectively.
        fill: Pixel fill value. Default is 0. If a tuple of
            length 3, it is used to fill R, G, B channels respectively.
    """

    def __init__(self, padding, fill=0):
        assert isinstance(padding, (numbers.Number, tuple))
        assert isinstance(fill, (numbers.Number, str, tuple))
        if isinstance(padding, collections.Sequence) and len(padding) not in [2, 4]:
            raise ValueError("Padding must be an int or a 2, or 4 element tuple, not a " +
                             "{} element tuple".format(len(padding)))

        self.padding = padding
        self.fill = fill

    def __call__(self, img):
        """
        Args:
            img (PIL.Image): Image to be padded.

        Returns:
            PIL.Image: Padded image.
        """
        return pad(img, self.padding, self.fill)


class Lambda(object):
    """Apply a user-defined lambda as a transform.

    Args:
        lambd (function): Lambda/function to be used for transform.
    """

    def __init__(self, lambd):
        assert isinstance(lambd, types.LambdaType)
        self.lambd = lambd

    def __call__(self, img):
        return self.lambd(img)


class RandomCrop(object):
    """Crop the given PIL.Image at a random location.

    Args:
        size (sequence or int): Desired output size of the crop. If size is an
            int instead of sequence like (h, w), a square crop (size, size) is
            made.
        padding (int or sequence, optional): Optional padding on each border
            of the image. Default is 0, i.e no padding. If a sequence of length
            4 is provided, it is used to pad left, top, right, bottom borders
            respectively.
    """

    def __init__(self, size, padding=0):
        if isinstance(size, numbers.Number):
            self.size = (int(size), int(size))
        else:
            self.size = size
        self.padding = padding

    def get_params(self, img):
        w, h = img.size
        th, tw = self.size
        if w == tw and h == th:
            return img

        x1 = random.randint(0, w - tw)
        y1 = random.randint(0, h - th)
        return x1, y1, tw, th

    def __call__(self, img):
        """
        Args:
            img (PIL.Image): Image to be cropped.

        Returns:
            PIL.Image: Cropped image.
        """
        if self.padding > 0:
            img = pad(img, self.padding)

        x1, y1, tw, th = self.get_params(img)

        return crop(img, x1, y1, tw, th)


class RandomHorizontalFlip(object):
    """Horizontally flip the given PIL.Image randomly with a probability of 0.5."""

    def __call__(self, img):
        """
        Args:
            img (PIL.Image): Image to be flipped.

        Returns:
            PIL.Image: Randomly flipped image.
        """
        if random.random() < 0.5:
            return hflip(img)
        return img


class RandomSizedCrop(object):
    """Crop the given PIL.Image to random size and aspect ratio.

    A crop of random size of (0.08 to 1.0) of the original size and a random
    aspect ratio of 3/4 to 4/3 of the original aspect ratio is made. This crop
    is finally resized to given size.
    This is popularly used to train the Inception networks.

    Args:
        size: size of the smaller edge
        interpolation: Default: PIL.Image.BILINEAR
    """

    def __init__(self, size, interpolation=Image.BILINEAR):
        self.size = size
        self.interpolation = interpolation

    def get_params(self, img):
        for attempt in range(10):
            area = img.size[0] * img.size[1]
            target_area = random.uniform(0.08, 1.0) * area
            aspect_ratio = random.uniform(3. / 4, 4. / 3)

            w = int(round(math.sqrt(target_area * aspect_ratio)))
            h = int(round(math.sqrt(target_area / aspect_ratio)))

            if random.random() < 0.5:
                w, h = h, w

            if w <= img.size[0] and h <= img.size[1]:
                x = random.randint(0, img.size[0] - w)
                y = random.randint(0, img.size[1] - h)
                return x, y, w, h

        # Fallback
        w = min(img.size[0], img.shape[1])
        x = (img.shape[0] - w) // 2
        y = (img.shape[1] - w) // 2
        return x, y, w, w

    def __call__(self, img):
        x, y, w, h = self.get_params(img)
        return scaled_crop(img, x, y, w, h, self.size, self.interpolation)