InternVideo/InternVideo-Next/functional.py at main · OpenGVLab/InternVideo · GitHub

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import numbers

import cv2

import numpy as np

import PIL

import torch

def _is_tensor_clip(clip):

return torch.is_tensor(clip) and clip.ndimension() == 4

def crop_clip(clip, min_h, min_w, h, w):

if isinstance(clip[0], np.ndarray):

cropped = [img[min_h:min_h + h, min_w:min_w + w, :] for img in clip]

elif isinstance(clip[0], PIL.Image.Image):

cropped = [

img.crop((min_w, min_h, min_w + w, min_h + h)) for img in clip

]

else:

raise TypeError('Expected numpy.ndarray or PIL.Image' +

'but got list of {0}'.format(type(clip[0])))

return cropped

def resize_clip(clip, size, interpolation='bilinear'):

if isinstance(clip[0], np.ndarray):

if isinstance(size, numbers.Number):

im_h, im_w, im_c = clip[0].shape

# Min spatial dim already matches minimal size

if (im_w <= im_h and im_w == size) or (im_h <= im_w

and im_h == size):

return clip

new_h, new_w = get_resize_sizes(im_h, im_w, size)

size = (new_w, new_h)

else:

size = size[0], size[1]

if interpolation == 'bilinear':

np_inter = cv2.INTER_LINEAR

else:

np_inter = cv2.INTER_NEAREST

scaled = [

cv2.resize(img, size, interpolation=np_inter) for img in clip

]

elif isinstance(clip[0], PIL.Image.Image):

if isinstance(size, numbers.Number):

im_w, im_h = clip[0].size

# Min spatial dim already matches minimal size

if (im_w <= im_h and im_w == size) or (im_h <= im_w

and im_h == size):

return clip

new_h, new_w = get_resize_sizes(im_h, im_w, size)

size = (new_w, new_h)

else:

size = size[1], size[0]

if interpolation == 'bilinear':

pil_inter = PIL.Image.BILINEAR

else:

pil_inter = PIL.Image.NEAREST

scaled = [img.resize(size, pil_inter) for img in clip]

else:

raise TypeError('Expected numpy.ndarray or PIL.Image' +

'but got list of {0}'.format(type(clip[0])))

return scaled

def get_resize_sizes(im_h, im_w, size):

if im_w < im_h:

ow = size

oh = int(size * im_h / im_w)

else:

oh = size

ow = int(size * im_w / im_h)

return oh, ow

def normalize(clip, mean, std, inplace=False):

if not _is_tensor_clip(clip):

raise TypeError('tensor is not a torch clip.')

if not inplace:

clip = clip.clone()

dtype = clip.dtype

mean = torch.as_tensor(mean, dtype=dtype, device=clip.device)

std = torch.as_tensor(std, dtype=dtype, device=clip.device)

clip.sub_(mean[:, None, None, None]).div_(std[:, None, None, None])

return clip

InternVideo/InternVideo-Next/functional.py at main · OpenGVLab/InternVideo · GitHub

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