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Documentation
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Index ¶
- Constants
- func QuantizeWu(input pixels, maxColor int) pixels
- func QuantizeWuContext(ctx context.Context, input pixels, maxColor int) (pixels, error)
- func QuantizeWuWithContext(ctx context.Context, input pixels, maxColor int) (pixels, error)deprecated
- type QuantizedMap
- func QuantizeCelebi(input pixels, maxColor int) QuantizedMap
- func QuantizeCelebiContext(ctx context.Context, input pixels, maxColor int) (QuantizedMap, error)
- func QuantizeCelebiWithContext(ctx context.Context, input pixels, maxColor int) (QuantizedMap, error)deprecated
- func QuantizeMap(input pixels) QuantizedMap
- func QuantizeWsMeans(input pixels, startingClusters []color.Lab, maxColors int) QuantizedMap
- func QuantizeWsMeansContext(ctx context.Context, input pixels, startingClusters []color.Lab, maxColors int) (QuantizedMap, error)
- func QuantizeWsMeansWithContext(ctx context.Context, input pixels, startingClusters []color.Lab, maxColors int) (QuantizedMap, error)deprecated
Constants ¶
const ( MaxIterations int = 10 MinMovementDistance float64 = 3.0 )
Variables ¶
This section is empty.
Functions ¶
func QuantizeWu ¶
func QuantizeWu(input pixels, maxColor int) pixels
QuantizeWu is an image quantizer that divides the image's pixels into clusters by recursively cutting an RGB cube, based on the weight of pixels in each area of the cube.
The algorithm was described by Xiaolin Wu in Graphic Gems II, published in 1991.
func QuantizeWuContext ¶
QuantizeWuContext is QuantizeWu with context.Context support.
func QuantizeWuWithContext
deprecated
Types ¶
type QuantizedMap ¶
func QuantizeCelebi ¶
func QuantizeCelebi(input pixels, maxColor int) QuantizedMap
QuantizeCelebi is an image quantizer that improves on the quality of a standard K-Means algorithm by setting the K-Means initial state to the output of a Wu quantizer, instead of random centroids. Improves on speed by several optimizations, as implemented in Wsmeans, or Weighted Square Means, K-Means with those optimizations.
This algorithm was designed by M. Emre Celebi, and was found in their 2011 paper, Improving the Performance of K-Means for Color Quantization. https://arxiv.org/abs/1101.0395
func QuantizeCelebiContext ¶
func QuantizeCelebiContext(ctx context.Context, input pixels, maxColor int) (QuantizedMap, error)
QuantizeCelebiContext is QuantizeCelebi with context.Context support. Returns ctx.Err() if context is Done.
func QuantizeCelebiWithContext
deprecated
func QuantizeCelebiWithContext(ctx context.Context, input pixels, maxColor int) (QuantizedMap, error)
QuantizeCelebiWithContext is QuantizeCelebi with context.Context support.
Deprecated: Use QuantizeCelebiContext
func QuantizeMap ¶
func QuantizeMap(input pixels) QuantizedMap
QuantizeMap takes a slice of []color.Color and returns Quantized
func QuantizeWsMeans ¶
func QuantizeWsMeans( input pixels, startingClusters []color.Lab, maxColors int, ) QuantizedMap
QuantizeWsMeans is an image quantizer that improves on the speed of a standard K-Means algorithm by implementing several optimizations, including deduping identical pixels and a triangle inequality rule that reduces the number of comparisons needed to identify which cluster a point should be moved to.
Wsmeans stands for Weighted Square Means.
This algorithm was designed by M. Emre Celebi, and was found in their 2011 paper, Improving the Performance of K-Means for Color Quantization. https://arxiv.org/abs/1101.0395
func QuantizeWsMeansContext ¶
func QuantizeWsMeansContext( ctx context.Context, input pixels, startingClusters []color.Lab, maxColors int, ) (QuantizedMap, error)
QuantizeWsMeansContext is QuantizeWsMeans with context.Context support.
Source Files