From 80a9a5aba5f6e629219acb0bd44a35567e00b5c7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= Date: Wed, 14 Nov 2012 08:33:27 +0100 Subject: [PATCH] Apply numpy reference style to rank filter descriptions --- skimage/filter/rank/bilateral_rank.pyx | 13 ++++++++----- skimage/filter/rank/percentile_rank.pyx | 16 ++++++++-------- skimage/filter/rank/rank.pyx | 13 ++++++++----- 3 files changed, 24 insertions(+), 18 deletions(-) diff --git a/skimage/filter/rank/bilateral_rank.pyx b/skimage/filter/rank/bilateral_rank.pyx index 0d3cdd88..dec07c28 100644 --- a/skimage/filter/rank/bilateral_rank.pyx +++ b/skimage/filter/rank/bilateral_rank.pyx @@ -1,11 +1,7 @@ """Approximate bilateral rank filter for local (custom kernel) mean. The local histogram is computed using a sliding window similar to the method -described in: - -.. [1] Reference: Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional - median filtering algorithm", IEEE Transactions on Acoustics, Speech and - Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18. +described in [1]_. Input image can be 8-bit or 16-bit with a value < 4096 (i.e. 12 bit), 8-bit images are casted in 16-bit the number of histogram bins is determined from the @@ -21,6 +17,13 @@ equally). Result image is 16-bit with respect to the input image. +References +---------- + +.. [1] Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional + median filtering algorithm", IEEE Transactions on Acoustics, Speech and + Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18. + """ import numpy as np diff --git a/skimage/filter/rank/percentile_rank.pyx b/skimage/filter/rank/percentile_rank.pyx index 62cc4576..7deae623 100644 --- a/skimage/filter/rank/percentile_rank.pyx +++ b/skimage/filter/rank/percentile_rank.pyx @@ -5,14 +5,7 @@ instead of using [min, max]. It means that isolated bright or dark pixels will not produce halos. The local histogram is computed using a sliding window similar to the method -described in [1]. - -References -========== - -.. [1] Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional - median filtering algorithm", IEEE Transactions on Acoustics, Speech and - Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18. +described in [1]_. Input image can be 8-bit or 16-bit with a value < 4096 (i.e. 12 bit), for 16-bit input images, the number of histogram bins is determined from the maximum value @@ -20,6 +13,13 @@ present in the image. Result image is 8 or 16-bit with respect to the input image. +References +---------- + +.. [1] Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional + median filtering algorithm", IEEE Transactions on Acoustics, Speech and + Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18. + """ import numpy as np diff --git a/skimage/filter/rank/rank.pyx b/skimage/filter/rank/rank.pyx index be078ddd..43f03133 100644 --- a/skimage/filter/rank/rank.pyx +++ b/skimage/filter/rank/rank.pyx @@ -1,9 +1,5 @@ """The local histogram is computed using a sliding window similar to the method -described in: - -.. [1] Reference: Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional - median filtering algorithm", IEEE Transactions on Acoustics, Speech and - Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18. +described in [1]_. Input image can be 8-bit or 16-bit with a value < 4096 (i.e. 12 bit), for 16-bit input images, the number of histogram bins is determined from the maximum value @@ -11,6 +7,13 @@ present in the image. Result image is 8 or 16-bit with respect to the input image. +References +---------- + +.. [1] Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional + median filtering algorithm", IEEE Transactions on Acoustics, Speech and + Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18. + """ import numpy as np