Image data types and what they mean

Image data types and what they mean

In skimage, images are simply numpy arrays, which support a variety of data types [1], i.e. “dtypes”. To avoid distorting image intensities (see Rescaling intensity values), we assume that images use the following dtype ranges:

Note that float images should be restricted to the range -1 to 1 even though the data type itself can exceed this range; all integer dtypes, on the other hand, have pixel intensities that can span the entire data type range. With a few exceptions, 64-bit (u)int images are not supported.

Functions in skimage are designed so that they accept any of these dtypes, but, for efficiency, may return an image of a different dtype (see Output types). If you need a particular dtype, skimage provides utility functions that convert dtypes and properly rescale image intensities (see Input types). You should never use astype on an image, because it violates these assumptions about the dtype range:

>>> from skimage import img_as_float
>>> image = np.arange(0, 50, 10, dtype=np.uint8)
>>> print(image.astype(np.float)) # These float values are out of range.
[  0.  10.  20.  30.  40.]
>>> print(img_as_float(image))
[ 0.          0.03921569  0.07843137  0.11764706  0.15686275]