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import torch | |
import random | |
import numbers | |
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 | |
if __name__ == '__main__': | |
from torchvision import transforms | |
import torchvision.io as io | |
import numpy as np | |
from torchvision.utils import save_image | |
import os | |
vframes, aframes, info = io.read_video( | |
filename='./v_Archery_g01_c03.avi', | |
pts_unit='sec', | |
output_format='TCHW' | |
) | |
trans = transforms.Compose([ | |
ToTensorVideo(), | |
RandomHorizontalFlipVideo(), | |
UCFCenterCropVideo(512), | |
# NormalizeVideo(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True), | |
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True) | |
]) | |
target_video_len = 32 | |
frame_interval = 1 | |
total_frames = len(vframes) | |
print(total_frames) | |
temporal_sample = TemporalRandomCrop(target_video_len * frame_interval) | |
# Sampling video frames | |
start_frame_ind, end_frame_ind = temporal_sample(total_frames) | |
# print(start_frame_ind) | |
# print(end_frame_ind) | |
assert end_frame_ind - start_frame_ind >= target_video_len | |
frame_indice = np.linspace(start_frame_ind, end_frame_ind - 1, target_video_len, dtype=int) | |
print(frame_indice) | |
select_vframes = vframes[frame_indice] | |
print(select_vframes.shape) | |
print(select_vframes.dtype) | |
select_vframes_trans = trans(select_vframes) | |
print(select_vframes_trans.shape) | |
print(select_vframes_trans.dtype) | |
select_vframes_trans_int = ((select_vframes_trans * 0.5 + 0.5) * 255).to(dtype=torch.uint8) | |
print(select_vframes_trans_int.dtype) | |
print(select_vframes_trans_int.permute(0, 2, 3, 1).shape) | |
io.write_video('./test.avi', select_vframes_trans_int.permute(0, 2, 3, 1), fps=8) | |
for i in range(target_video_len): | |
save_image(select_vframes_trans[i], os.path.join('./test000', '%04d.png' % i), normalize=True, value_range=(-1, 1)) |