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 correctiongamma:Gamma factor applied to image. <1 increases intensities of mid-tones, >1 decreases intensities of mid-toneshistogram 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 correctioninverse:Perform inverse log-correction 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*(cutoff-Vin))) 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) | 
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| Inputs: | source : image 
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| Outputs: | result : image 
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- Input ports:
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| source: | image source image to filter | 
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- Output ports:
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| result: | image result after filtering | 
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- Configuration:
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- 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 scikit-image library
and are covered by their respective licenses.
- 
class node_colors.ColorRangeConversion[source]