README
¶
Dominantcolor
Find dominant color in images
import "github.com/cenkalti/dominantcolor"
Package dominantcolor provides a function for finding a color that represents the calculated dominant color in the image. This uses a KMean clustering algorithm to find clusters of pixel colors in RGB space.
The algorithm is ported from Chromium source code:
https://src.chromium.org/svn/trunk/src/ui/gfx/color_analysis.h https://src.chromium.org/svn/trunk/src/ui/gfx/color_analysis.cc
See more at: http://godoc.org/github.com/cenkalti/dominantcolor
####Example
package main
import (
"fmt"
"github.com/cenkalti/dominantcolor"
"image"
_ "image/jpeg"
_ "image/png"
"os"
)
func FindDomiantColor(fileInput string) (string, error) {
f, err := os.Open(fileInput)
defer f.Close()
if err != nil {
fmt.Println("File not found:", fileInput)
return "", err
}
img, _, err := image.Decode(f)
if err != nil {
return "", err
}
return dominantcolor.Hex(dominantcolor.Find(img)), nil
}
func main() {
fmt.Println(FindDomiantColor("aa.png"))
}
####Output:
#CA5527
Documentation
¶
Overview ¶
Package dominantcolor provides a function for finding a color that represents the calculated dominant color in the image. This uses a KMean clustering algorithm to find clusters of pixel colors in RGB space.
The algorithm is ported from Chromium source code:
https://src.chromium.org/svn/trunk/src/ui/gfx/color_analysis.h https://src.chromium.org/svn/trunk/src/ui/gfx/color_analysis.cc
RGB KMean Algorithm (N clusters, M iterations):
1. Pick N starting colors by randomly sampling the pixels. If you see a color you already saw keep sampling. After a certain number of tries just remove the cluster and continue with N = N-1 clusters (for an image with just one color this should devolve to N=1). These colors are the centers of your N clusters.
2. For each pixel in the image find the cluster that it is closest to in RGB space. Add that pixel's color to that cluster (we keep a sum and a count of all of the pixels added to the space, so just add it to the sum and increment count).
3. Calculate the new cluster centroids by getting the average color of all of the pixels in each cluster (dividing the sum by the count).
4. See if the new centroids are the same as the old centroids.
a) If this is the case for all N clusters than we have converged and can move on.
b) If any centroid moved, repeat step 2 with the new centroids for up to M iterations.
5. Once the clusters have converged or M iterations have been tried, sort the clusters by weight (where weight is the number of pixels that make up this cluster).
6. Going through the sorted list of clusters, pick the first cluster with the largest weight that's centroid falls between |lower_bound| and |upper_bound|. Return that color. If no color fulfills that requirement return the color with the largest weight regardless of whether or not it fulfills the equation above.
Index ¶
Constants ¶
This section is empty.
Variables ¶
This section is empty.
Functions ¶
This section is empty.
Types ¶
type DominantColor ¶
type DominantColor struct {
SampleImageSize uint
NumberOfClusters int
UniqueColorSearchRetries int
ConvergenceIterations int
MaximumBrightnessThreshold uint16
MaximumDarknessThreshold uint16
}
func New ¶
func New(sampleImageSize uint, numberOfClusters, uniqueColorSearchRetries, convergenceIterations int, maximumBrightnessThreshold, maximumDarknessThreshold uint16) *DominantColor
New creates a new instance of DominantColor
func NewDefault ¶
func NewDefault() *DominantColor
NewDefault creates a new instance of DominantColor with default settings
Source Files