[scikit-image] regarding the parameter of clip_limit for applying contrast limited adaptive historgram equalization

[wine lover]

Dear All,

The following is an example given in opencv regarding applying Contrast
Limited Adaptive Histogram Equalization (CLAHE)

*import numpy as np*
*import cv2*
*img = cv2.imread('tsukuba_l.png',0)*
*clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))*
*cl1 = clahe.apply(img)*

Here the parameter clipLimit =2.0

In Skimage, CLAHE  is perfored using *exposure.equalize_adapthist*

For instance, in this example,
http://scikit-image.org/docs/dev/auto_examples/plot_equalize.html

*img_adapteq = exposure.equalize_adapthist(img, clip_limit=0.03)*

My question is that how to setup the clip_limit value in skimage for a
corresponding case in opencv


For instance, in an example implemented using opencv, clipLimit is setup as
2.0; if I want to convert this implementation using skimage
which value should I assign to clip_limit?

According to the document  looks like clip_limit between 0 and 1.
*clip_limit : float, optional*
*Clipping limit, normalized between 0 and 1 (higher values give more
contrast).*

while opencv does not have this limitation for clipLimit

Thanks,
Yuanyuan
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