fast hog calculation #1
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WeiChungChang
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Sep 3, 2020
enable anistropic window shape
WeiChungChang
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Sep 3, 2020
…it-image#4446) * Implemeted multichannel support for denoise_tv_bregman (#1) Compare issue scikit-image#4427 in upstream * Implemented unittest * Refactored code * wrapped around long lines * Indention, empty line and whitespace lint issues * Update skimage/restoration/_denoise.py Co-Authored-By: Mark Harfouche <mark.harfouche@gmail.com> * Update skimage/restoration/_denoise.py Co-Authored-By: Mark Harfouche <mark.harfouche@gmail.com> * For the multichannel flag only 2D images + channel dimension is supported * over-indented * Update skimage/restoration/_denoise.py Co-Authored-By: Mark Harfouche <mark.harfouche@gmail.com> * Update skimage/restoration/_denoise.py * Apply suggestions from code review Allocate channel output array prior to loop. * Apply suggestions from code review Only allocate `channel_out` if `multichannel` is `True` * Added check for multichannel bregman Co-authored-by: Mark Harfouche <mark.harfouche@gmail.com>
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Description
strange logic to slow down Hog execution speed
Checklist
Current Hog takes use of 3 loops, ex: for 9 bins, 128 * 128 cells.
However, 2 loops should be fine:
then divide orientation by number_of_orientations_per_180 and put result into corresponding bin.
Ex, if number_of_orientations_per_180 is 20 (divide 180 degree by 9 bins so each bin's range is 20 degree);
if current pixel has orientation = 45, it locates at (45/20) = 2nd bin.
if current pixel has orientation = 18, it locates at (18/20) = 0th bin.
and so on.
The experiment shows this way can speed up the calculation about 10% ~ 20% of whole flow.
Since the execution time is dominated by (1) calculation bin distribution for each cell, and (2) normalization,
it implies for (1) it speed up 20~40%(may by more).
Here provided a test patch and attached a file to measure the difference.
It shows the difference is small enough to be ignored.
Please consider to rework current logic to provide better performance.