updated documentation for some util transformation methods

cvdatasets/utils/transforms.py

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