Color range conversion
Changes the range and distribution of values for all pixels
Algorithms: 
adaptive histogram
Improves contrast by stretching and equalizing the histogramin a sliding window over the image
 bins:
Number of bins in computed histogram (default 256)
 adaptive kernel size:
Size of the sliding window. Must evenly divide both image width and height.
 sigma:
Clipping limit (normalized between 0 and 1). Higher values give more contrast. (default 1.0)
gamma correction
Applies the correction: Vout = scale Vin^gamma
Processes each channel separately
 scale:
Constant scale factor applied after gamma correction
 gamma:
Gamma factor applied to image.
<1 increases intensities of midtones,
>1 decreases intensities of midtones
histogram equalization
Improves contrast by stretching and equalizing the histogram
 bins:
Number of bins in computed histogram (default 256)
log correction
Applies the correction: Vout = scale log(1 + Vin)
Processes each channel separately
 scale:
Constant scale factor applied after gamma correction
 inverse:
Perform inverse logcorrection instead (default false):
Vout = scale (2^Vin  1)
sigmoid
Performs Sigmoid correction on input image. Also known as contrast adjustment.
Vout = 1/(1+exp(gain*(cutoffVin)))
Processes each channel separately
 inverse:
Perform negative sigmoid correction instead (default false)
 cutoff:
Shifts the characteristic curve for the sigmoid horizontally(default 0.5)
 gain:
Gain of sigmoid, affects rise time of curve (default 10.0)

Inputs:  source : image

Outputs:  result : image

Ports:
Inputs:
source:  image
source image to filter

Outputs:
result:  image
result after filtering

Configuration:
 algorithm
 (no description)
 gamma
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 inverse
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 adaptive kernel size
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 cutoff
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 scale
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 gain
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 sigma
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 bins
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Some of the docstrings for this module have been
extracted from the scikitimage library
and are covered by their respective licenses.

class
node_colors.
ColorRangeConversion
[source]