On 9/18/2011 2:30 PM, Eric Firing wrote: > On 09/18/2011 09:30 AM, Christoph Gohlke wrote: >> Hello, >> >> matplotlib uses int(x*255) or np.array(x*255, np.uint8) to quantize >> normalized floating point numbers x in the range [0.0 to 1.0] to >> integers in the range [0 to 255]. This way only 1.0 is mapped to 255, >> not for example 0.999. Is this really intended or would not the largest >> floating point number below 256.0 be a better scale factor than 255? The >> exact factor depends on the floating point precision (~255.999992 for >> np.float32, ~255.93 for np.float16). >> >> Christoph > > Christoph, > > It's a reasonable question; but do you have use cases in mind where it > actually makes a difference? > > The simple scaling with truncation is used in many places, both in the > python and the c++ code. > > Eric >
Hi Eric, visually it will be hardly noticeable in most cases. However, I'd expect the histogram of normalized intensity data to be the same as the histogram of a linear grayscale image of that data (neglecting gamma correction, image scaling/interpolation for now). Consider this code for example: import numpy as np a = np.random.rand(1024*1024) a[0], a[-1] = 0.0, 1.0 h0 = np.histogram(a, bins=256, range=(0, 1))[0] h1 = np.bincount(np.uint8(a * 255)) h2 = np.bincount(np.uint8(a * 255.9999999999999)) print (h0 - h1) print (h0 - h2) Christoph ------------------------------------------------------------------------------ BlackBerry® DevCon Americas, Oct. 18-20, San Francisco, CA Learn about the latest advances in developing for the BlackBerry® mobile platform with sessions, labs & more. See new tools and technologies. Register for BlackBerry® DevCon today! http://p.sf.net/sfu/rim-devcon-copy1 _______________________________________________ Matplotlib-devel mailing list Matplotlib-devel@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-devel
