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