transform_utils.py

import torch
import random
import numbers
import numpy as np
from PIL import Image
from torchvision import transforms
from torchvision.transforms import RandomCrop, RandomResizedCrop

def _is_tensor_video_clip(clip):
    if not torch.is_tensor(clip):
        raise TypeError("clip should be Tensor. Got %s" % type(clip))

    if not clip.ndimension() == 4:
        raise ValueError("clip should be 4D. Got %dD" % clip.dim())
    return True


def center_crop_arr(pil_image, image_size):
    """
    Center cropping implementation from ADM.
    https://github.com/openai/guided-diffusion/blob/8fb3ad9197f16bbc40620447b2742e13458d2831/guided_diffusion/image_datasets.py#L126
    """
    while min(*pil_image.size) >= 2 * image_size:
        pil_image = pil_image.resize(
            tuple(x // 2 for x in pil_image.size), resample=Image.BOX
        )

    scale = image_size / min(*pil_image.size)
    pil_image = pil_image.resize(
        tuple(round(x * scale) for x in pil_image.size), resample=Image.BICUBIC
    )

    arr = np.array(pil_image)
    crop_y = (arr.shape[0] - image_size) // 2
    crop_x = (arr.shape[1] - image_size) // 2
    return Image.fromarray(arr[crop_y: crop_y + image_size, crop_x: crop_x + image_size])


def crop(clip, i, j, h, w):
    """
    Args:
        clip (torch.tensor): Video clip to be cropped. Size is (T, C, H, W)
    """
    if len(clip.size()) != 4:
        raise ValueError("clip should be a 4D tensor")
    return clip[..., i : i + h, j : j + w]


def resize(clip, target_size, interpolation_mode):
    if len(target_size) != 2:
        raise ValueError(f"target size should be tuple (height, width), instead got {target_size}")
    return torch.nn.functional.interpolate(clip, size=target_size, mode=interpolation_mode, align_corners=False)

def resize_scale(clip, target_size, interpolation_mode):
    if len(target_size) != 2:
        raise ValueError(f"target size should be tuple (height, width), instead got {target_size}")
    H, W = clip.size(-2), clip.size(-1)
    scale_ = target_size[0] / min(H, W)
    return torch.nn.functional.interpolate(clip, scale_factor=scale_, mode=interpolation_mode, align_corners=False)


def resized_crop(clip, i, j, h, w, size, interpolation_mode="bilinear"):
    """
    Do spatial cropping and resizing to the video clip
    Args:
        clip (torch.tensor): Video clip to be cropped. Size is (T, C, H, W)
        i (int): i in (i,j) i.e coordinates of the upper left corner.
        j (int): j in (i,j) i.e coordinates of the upper left corner.
        h (int): Height of the cropped region.
        w (int): Width of the cropped region.
        size (tuple(int, int)): height and width of resized clip
    Returns:
        clip (torch.tensor): Resized and cropped clip. Size is (T, C, H, W)
    """
    if not _is_tensor_video_clip(clip):
        raise ValueError("clip should be a 4D torch.tensor")
    clip = crop(clip, i, j, h, w)
    clip = resize(clip, size, interpolation_mode)
    return clip


def center_crop(clip, crop_size):
    if not _is_tensor_video_clip(clip):
        raise ValueError("clip should be a 4D torch.tensor")
    h, w = clip.size(-2), clip.size(-1)
    th, tw = crop_size
    if h < th or w < tw:
        raise ValueError("height and width must be no smaller than crop_size")

    i = int(round((h - th) / 2.0))
    j = int(round((w - tw) / 2.0))
    return crop(clip, i, j, th, tw)


def center_crop_using_short_edge(clip):
    if not _is_tensor_video_clip(clip):
        raise ValueError("clip should be a 4D torch.tensor")
    h, w = clip.size(-2), clip.size(-1)
    if h < w:
        th, tw = h, h
        i = 0
        j = int(round((w - tw) / 2.0))
    else:
        th, tw = w, w
        i = int(round((h - th) / 2.0))
        j = 0
    return crop(clip, i, j, th, tw)


def random_shift_crop(clip):
    '''
    Slide along the long edge, with the short edge as crop size
    '''
    if not _is_tensor_video_clip(clip):
        raise ValueError("clip should be a 4D torch.tensor")
    h, w = clip.size(-2), clip.size(-1)
    
    if h <= w:
        long_edge = w
        short_edge = h
    else:
        long_edge = h
        short_edge =w

    th, tw = short_edge, short_edge

    i = torch.randint(0, h - th + 1, size=(1,)).item()
    j = torch.randint(0, w - tw + 1, size=(1,)).item()
    return crop(clip, i, j, th, tw)


def to_tensor(clip):
    """
    Convert tensor data type from uint8 to float, divide value by 255.0 and
    permute the dimensions of clip tensor
    Args:
        clip (torch.tensor, dtype=torch.uint8): Size is (T, C, H, W)
    Return:
        clip (torch.tensor, dtype=torch.float): Size is (T, C, H, W)
    """
    _is_tensor_video_clip(clip)
    if not clip.dtype == torch.uint8:
        raise TypeError("clip tensor should have data type uint8. Got %s" % str(clip.dtype))
    # return clip.float().permute(3, 0, 1, 2) / 255.0
    return clip.float() / 255.0


def normalize(clip, mean, std, inplace=False):
    """
    Args:
        clip (torch.tensor): Video clip to be normalized. Size is (T, C, H, W)
        mean (tuple): pixel RGB mean. Size is (3)
        std (tuple): pixel standard deviation. Size is (3)
    Returns:
        normalized clip (torch.tensor): Size is (T, C, H, W)
    """
    if not _is_tensor_video_clip(clip):
        raise ValueError("clip should be a 4D torch.tensor")
    if not inplace:
        clip = clip.clone()
    mean = torch.as_tensor(mean, dtype=clip.dtype, device=clip.device)
    # print(mean)
    std = torch.as_tensor(std, dtype=clip.dtype, device=clip.device)
    clip.sub_(mean[:, None, None, None]).div_(std[:, None, None, None])
    return clip


def hflip(clip):
    """
    Args:
        clip (torch.tensor): Video clip to be normalized. Size is (T, C, H, W)
    Returns:
        flipped clip (torch.tensor): Size is (T, C, H, W)
    """
    if not _is_tensor_video_clip(clip):
        raise ValueError("clip should be a 4D torch.tensor")
    return clip.flip(-1)


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

    def __call__(self, clip):
        """
        Args:
            clip (torch.tensor): Video clip to be cropped. Size is (T, C, H, W)
        Returns:
            torch.tensor: randomly cropped video clip.
                size is (T, C, OH, OW)
        """
        i, j, h, w = self.get_params(clip)
        return crop(clip, i, j, h, w)
    
    def get_params(self, clip):
        h, w = clip.shape[-2:]
        th, tw = self.size

        if h < th or w < tw:
            raise ValueError(f"Required crop size {(th, tw)} is larger than input image size {(h, w)}")

        if w == tw and h == th:
            return 0, 0, h, w

        i = torch.randint(0, h - th + 1, size=(1,)).item()
        j = torch.randint(0, w - tw + 1, size=(1,)).item()

        return i, j, th, tw

    def __repr__(self) -> str:
        return f"{self.__class__.__name__}(size={self.size})"
    
class CenterCropResizeVideo:
    '''
    First use the short side for cropping length, 
    center crop video, then resize to the specified size
    '''
    def __init__(
        self,
        size,
        interpolation_mode="bilinear",
    ):
        if isinstance(size, tuple):
            if len(size) != 2:
                raise ValueError(f"size should be tuple (height, width), instead got {size}")
            self.size = size
        else:
            self.size = (size, size)

        self.interpolation_mode = interpolation_mode
       

    def __call__(self, clip):
        """
        Args:
            clip (torch.tensor): Video clip to be cropped. Size is (T, C, H, W)
        Returns:
            torch.tensor: scale resized / center cropped video clip.
                size is (T, C, crop_size, crop_size)
        """
        clip_center_crop = center_crop_using_short_edge(clip)
        clip_center_crop_resize = resize(clip_center_crop, target_size=self.size, interpolation_mode=self.interpolation_mode)
        return clip_center_crop_resize

    def __repr__(self) -> str:
        return f"{self.__class__.__name__}(size={self.size}, interpolation_mode={self.interpolation_mode}"

class UCFCenterCropVideo:
    '''
    First scale to the specified size in equal proportion to the short edge, 
    then center cropping
    '''
    def __init__(
        self,
        size,
        interpolation_mode="bilinear",
    ):
        if isinstance(size, tuple):
            if len(size) != 2:
                raise ValueError(f"size should be tuple (height, width), instead got {size}")
            self.size = size
        else:
            self.size = (size, size)

        self.interpolation_mode = interpolation_mode
       

    def __call__(self, clip):
        """
        Args:
            clip (torch.tensor): Video clip to be cropped. Size is (T, C, H, W)
        Returns:
            torch.tensor: scale resized / center cropped video clip.
                size is (T, C, crop_size, crop_size)
        """
        clip_resize = resize_scale(clip=clip, target_size=self.size, interpolation_mode=self.interpolation_mode)
        clip_center_crop = center_crop(clip_resize, self.size)
        return clip_center_crop

    def __repr__(self) -> str:
        return f"{self.__class__.__name__}(size={self.size}, interpolation_mode={self.interpolation_mode}"
    
class KineticsRandomCropResizeVideo:
    '''
    Slide along the long edge, with the short edge as crop size. And resie to the desired size.
    '''
    def __init__(
            self,
            size,
            interpolation_mode="bilinear",
         ):
        if isinstance(size, tuple):
                if len(size) != 2:
                    raise ValueError(f"size should be tuple (height, width), instead got {size}")
                self.size = size
        else:
            self.size = (size, size)

        self.interpolation_mode = interpolation_mode

    def __call__(self, clip):
        clip_random_crop = random_shift_crop(clip)
        clip_resize = resize(clip_random_crop, self.size, self.interpolation_mode)
        return clip_resize


class CenterCropVideo:
    def __init__(
        self,
        size,
        interpolation_mode="bilinear",
    ):
        if isinstance(size, tuple):
            if len(size) != 2:
                raise ValueError(f"size should be tuple (height, width), instead got {size}")
            self.size = size
        else:
            self.size = (size, size)

        self.interpolation_mode = interpolation_mode
       

    def __call__(self, clip):
        """
        Args:
            clip (torch.tensor): Video clip to be cropped. Size is (T, C, H, W)
        Returns:
            torch.tensor: center cropped video clip.
                size is (T, C, crop_size, crop_size)
        """
        clip_center_crop = center_crop(clip, self.size)
        return clip_center_crop

    def __repr__(self) -> str:
        return f"{self.__class__.__name__}(size={self.size}, interpolation_mode={self.interpolation_mode}"
    

class NormalizeVideo:
    """
    Normalize the video clip by mean subtraction and division by standard deviation
    Args:
        mean (3-tuple): pixel RGB mean
        std (3-tuple): pixel RGB standard deviation
        inplace (boolean): whether do in-place normalization
    """

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

    def __call__(self, clip):
        """
        Args:
            clip (torch.tensor): video clip must be normalized. Size is (C, T, H, W)
        """
        return normalize(clip, self.mean, self.std, self.inplace)

    def __repr__(self) -> str:
        return f"{self.__class__.__name__}(mean={self.mean}, std={self.std}, inplace={self.inplace})"


class ToTensorVideo:
    """
    Convert tensor data type from uint8 to float, divide value by 255.0 and
    permute the dimensions of clip tensor
    """

    def __init__(self):
        pass

    def __call__(self, clip):
        """
        Args:
            clip (torch.tensor, dtype=torch.uint8): Size is (T, C, H, W)
        Return:
            clip (torch.tensor, dtype=torch.float): Size is (T, C, H, W)
        """
        return to_tensor(clip)

    def __repr__(self) -> str:
        return self.__class__.__name__


class RandomHorizontalFlipVideo:
    """
    Flip the video clip along the horizontal direction with a given probability
    Args:
        p (float): probability of the clip being flipped. Default value is 0.5
    """

    def __init__(self, p=0.5):
        self.p = p

    def __call__(self, clip):
        """
        Args:
            clip (torch.tensor): Size is (T, C, H, W)
        Return:
            clip (torch.tensor): Size is (T, C, H, W)
        """
        if random.random() < self.p:
            clip = hflip(clip)
        return clip

    def __repr__(self) -> str:
        return f"{self.__class__.__name__}(p={self.p})"
    
#  ------------------------------------------------------------
#  ---------------------  Sampling  ---------------------------
#  ------------------------------------------------------------
class TemporalRandomCrop(object):
	"""Temporally crop the given frame indices at a random location.

	Args:
		size (int): Desired length of frames will be seen in the model.
	"""

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

	def __call__(self, total_frames):
		rand_end = max(0, total_frames - self.size - 1)
		begin_index = random.randint(0, rand_end)
		end_index = min(begin_index + self.size, total_frames)
		return begin_index, end_index
    
def build_image_transform(image_size, mean, std):
    transform = transforms.Compose([
        transforms.Lambda(lambda pil_image: center_crop_arr(pil_image, image_size)),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize(mean=mean, std=std, inplace=True)
    ])
    def transform_image_batch(batch):
        images = [transform(img.convert("RGB")) for img in batch['image']]
        return {'data': images, 'labels': batch['label']}
    return transform_image_batch

def build_video_transform(image_size=256, num_frames=16, frame_interval=3, mean=None, std=None):
    mean, std = [0.5, 0.5, 0.5], [0.5, 0.5, 0.5]
    transform = transforms.Compose([
        RandomHorizontalFlipVideo(),
        UCFCenterCropVideo(image_size),
        ToTensorVideo(),
        transforms.Normalize(mean=mean, std=std, inplace=True)
    ])
    temporal_sample = TemporalRandomCrop(num_frames*frame_interval)
    def transform_video_batch(batch):
        videos = batch['video']
        sampled_videos = []
        for video in videos:
            start_frame_ind, end_frame_ind = temporal_sample(len(video))
            frame_indice = np.linspace(start_frame_ind, end_frame_ind-1, num_frames, dtype=int)
            video = torch.tensor(np.array([video[i].asnumpy() for i in frame_indice])).permute(0, 3, 1, 2)
            video = transform(video)
            sampled_videos.append(video)
        total_videos = torch.stack(sampled_videos)
        return {'data': total_videos, 'labels': batch['label']}
    return transform_video_batch