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@@ -1,10 +1,15 @@
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import numpy as np
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import random
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+import typing as T
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from PIL import Image
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from functools import partial
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-def dimensions(im):
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+def dimensions(im: T.Union[Image.Image, np.ndarray]) -> T.Tuple[int, int, int]:
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+ """ Generic method that return the height, width and number of
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+ channels for a given input. The input can be a numpy array
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+ or a PIL.Image instance.
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+ """
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if isinstance(im, np.ndarray):
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if im.ndim != 3:
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import pdb; pdb.set_trace()
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@@ -21,6 +26,21 @@ def dimensions(im):
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raise ValueError("Unknown image instance ({})!".format(type(im)))
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def rescale(im, coords, rescale_size, center_cropped=True, no_offset=False):
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+ """ Rescales coordinates for a given image.
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+ The assumption is that the (pixel) coordinates were estimated
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+ based on a different image resolution (rescale_size). This is
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+ typically the input size of the CNN that was used for the
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+ estimation of the coordinates (e.g. 224 or 448 for ResNets,
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+ 299 or 427 for InceptionV3, or 227 for AlexNet).
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+
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+ The parameter "center_cropped" indicates whether the input image
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+ was center cropped before resize or resized without preserving
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+ the original aspect ratio.
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+
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+ If you want to rescale the width and height (e.g. of a bounding
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+ box) then you should set "no_offset=True"!
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+ """
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+
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h, w, c = dimensions(im)
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if np.all(coords == -1):
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