| | import random |
| |
|
| | import torch |
| |
|
| |
|
| | def short_size_scale(images, size): |
| | h, w = images.shape[-2:] |
| | short, long = (h, w) if h < w else (w, h) |
| |
|
| | scale = size / short |
| | long_target = int(scale * long) |
| |
|
| | target_size = (size, long_target) if h < w else (long_target, size) |
| |
|
| | return torch.nn.functional.interpolate( |
| | input=images, size=target_size, mode="bilinear", antialias=True |
| | ) |
| |
|
| |
|
| | def random_short_side_scale(images, size_min, size_max): |
| | size = random.randint(size_min, size_max) |
| | return short_size_scale(images, size) |
| |
|
| |
|
| | def random_crop(images, height, width): |
| | image_h, image_w = images.shape[-2:] |
| | h_start = random.randint(0, image_h - height) |
| | w_start = random.randint(0, image_w - width) |
| | return images[:, :, h_start : h_start + height, w_start : w_start + width] |
| |
|
| |
|
| | def center_crop(images, height, width): |
| | |
| | image_h, image_w = images.shape[-2:] |
| | h_start = (image_h - height) // 2 |
| | w_start = (image_w - width) // 2 |
| | return images[:, :, h_start : h_start + height, w_start : w_start + width] |
| |
|
| | def offset_crop(image, left=0, right=0, top=200, bottom=0): |
| |
|
| | n, c, h, w = image.shape |
| | left = min(left, w-1) |
| | right = min(right, w - left - 1) |
| | top = min(top, h - 1) |
| | bottom = min(bottom, h - top - 1) |
| | image = image[:, :, top:h-bottom, left:w-right] |
| |
|
| | return image |