BUG: scale to minmax range

[valhassan]

Describe the bug
The original min and max ranges are inferred from the array in this function. The correct case is to provide the range as parameters to avoid floating point errors
"Expected: (0, 1)
Actual: (-4.768371586472142e-09, 1.0000000190734863)"

geo-deep-learning/utils/utils.py

Lines 202 to 220 in c4d4b75

	def minmax_scale(img, scale_range=(0, 1), orig_range=(0, 255)):
	"""
	Scale data values from original range to specified range
	:param img: (numpy array) Image to be scaled
	:param scale_range: Desired range of transformed data (0, 1) or (-1, 1).
	:param orig_range: Original range of input data.
	:return: (numpy array) Scaled image
	"""
	if img.min() < orig_range[0] or img.max() > orig_range[1]:
	raise ValueError(f"Actual original range exceeds expected original range.\n"
	f"Expected: {orig_range}\n"
	f"Actual: ({img.min()}, {img.max()})")
	o_r = (orig_range[1] - orig_range[0])
	s_r = (scale_range[1] - scale_range[0])
	if isinstance(img, (np.ndarray, torch.Tensor)):
	scale_img = (s_r * (img - orig_range[0]) / o_r) + scale_range[0]
	else:
	raise TypeError(f"Expected a numpy array or torch tensor, got {type(img)}")
	return scale_img

To Reproduce
Steps to reproduce the behavior:

1. Test the function with an array of high-precision floating points that exceed the expected range (0, 1)

Expected behavior
The function should scale based on the provided parameters and shift from dynamic to static scaling.

Screenshots
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Additional context
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