Hi Victor, The biggest problem is that you’re getting bitten by datatypes. Please read the following document:
http://scikit-image.org/docs/dev/user_guide/data_types.html Specifically, rank_mean is a uint8 image, so only contains integers between 0 and 255. naive_convolve is a float image, with continuous values between 0 and 255. Try this: In [17]: naive_convolve_float = naive_convolve / 255 In [18]: rank_mean_float = rank_mean / 255 In [19]: plt.imshow(np.abs(naive_convolve_float - rank_mean_float), cmap='magma') Out[19]: <matplotlib.image.AxesImage at 0x10d098fd0> Result: There is a smaller difference also. Internally, filters.rank uses a fancy rolling histogram algorithm with integer data values. This means that the result of the rank_mean is only approximately accurate, essentially to within integer rounding (good enough for most real-world uses), while the convolve2d code gives you an exact value (to within floating point error). Hope this helps! Juan. On 2 Nov 2017, 7:54 PM +1100, Poughon Victor <victor.poug...@cnes.fr>, wrote: > Hello, > > I looks like skimage.filters.rank.mean and scipy.signal.convolve2d don't > output exactly the same images. When doing: > > image = data.coins() > K = np.ones((11, 11)) > > rank_mean = rank.mean(image, selem=K) > naive_convolve = convolve2d(image, K, mode="same") / K.sum() > > All output pixel are different, with an absolute difference varying randomly > between 0 and 1. Of course there's also a massive difference at the border, > but that's expected because convolve2d treats image boundaries differently. > But even in the center of the image all pixels are different. I've made a > test script with an illustrated output image, you can check it out in this > gist: > > https://gist.github.com/vpoughon/b4afc76ce5dc681fda9d0550d41359d3 > > Am I doing something wrong? > > Thanks, > > Victor Poughon > > > > > _______________________________________________ > scikit-image mailing list > scikit-image@python.org > https://mail.python.org/mailman/listinfo/scikit-image
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