Let's talk about how to compare colors in Golang

Golang is an open source programming language developed by Google and is widely used in web development, cloud computing, big data processing and other fields. In Golang, processing images is a very common task, and processing colors in images is also an important job. This article will introduce how to compare colors in Golang.

1. Representation of color

In Golang, the commonly used representation methods of color are RGB value and hex value. The RGB (Red, Green, Blue) value refers to the value of the three primary colors, usually expressed as three integers (0~255):

type RGB struct {
    R, G, B uint8
}

hex value is the color value expressed in hexadecimal, usually expressed It is a six-digit string (such as "#FFFFFF" means white):

type Hex struct {
    R, G, B uint8
}

In addition, there is also a color representation method as HSV (Hue, Saturation, Value) value, which is a relatively intuitive Color representation method, but will not be introduced too much in this article.

2. Color comparison

Comparing the similarity of two colors can usually be achieved by calculating their distance. In Golang, we can use Euclidean distance or Manhattan distance to calculate the distance between colors.

Euclidean distance refers to the straight-line distance between two points:

func euclideanDistance(c1, c2 RGB) float64 {
    r := float64(c1.R) - float64(c2.R)
    g := float64(c1.G) - float64(c2.G)
    b := float64(c1.B) - float64(c2.B)
    return math.Sqrt(r*r + g*g + b*b)
}

Manhattan distance refers to the sum of the horizontal and vertical distances between two points:

func manhattanDistance(c1, c2 RGB) float64 {
    r := math.Abs(float64(c1.R) - float64(c2.R))
    g := math.Abs(float64(c1.G) - float64(c2.G))
    b := math.Abs(float64(c1.B) - float64(c2.B))
    return r + g + b
}

Of course, we can also apply the above function to the color representation of hex value:

func euclideanDistance(c1, c2 Hex) float64 {
    r1, g1, b1 := hexToRGB(c1)
    r2, g2, b2 := hexToRGB(c2)
    r := float64(r1) - float64(r2)
    g := float64(g1) - float64(g2)
    b := float64(b1) - float64(b2)
    return math.Sqrt(r*r + g*g + b*b)
}

func manhattanDistance(c1, c2 Hex) float64 {
    r1, g1, b1 := hexToRGB(c1)
    r2, g2, b2 := hexToRGB(c2)
    r := math.Abs(float64(r1) - float64(r2))
    g := math.Abs(float64(g1) - float64(g2))
    b := math.Abs(float64(b1) - float64(b2))
    return r + g + b
}

func hexToRGB(c Hex) (uint8, uint8, uint8) {
    return c.R, c.G, c.B
}

3. Color contrast application

Color contrast is often used in image processing Scenarios such as color replacement and color analysis. For example, we can use the color replacement function to replace a certain color with another color:

func replaceColor(img image.Image, oldColor, newColor RGB, threshold float64) image.Image {
    bounds := img.Bounds()
    out := image.NewRGBA(bounds)
    for x := bounds.Min.X; x < bounds.Max.X; x++ {
        for y := bounds.Min.Y; y < bounds.Max.Y; y++ {
            pixel := img.At(x, y)
            c := RGBModel.Convert(pixel).(RGB)
            distance := euclideanDistance(c, oldColor)
            if distance <= threshold {
                out.Set(x, y, newColor)
            } else {
                out.Set(x, y, pixel)
            }
        }
    }
    return out
}

We can also use the color analysis function to find pixels of a specific color in a picture and count their number ：

func getColorCount(img image.Image, color RGB, threshold float64) int {
    bounds := img.Bounds()
    count := 0
    for x := bounds.Min.X; x < bounds.Max.X; x++ {
        for y := bounds.Min.Y; y < bounds.Max.Y; y++ {
            pixel := img.At(x, y)
            c := RGBModel.Convert(pixel).(RGB)
            distance := euclideanDistance(c, color)
            if distance <= threshold {
                count++
            }
        }
    }
    return count
}

4. Summary

This article introduces how to compare colors in Golang and how to apply the color contrast function for image processing. Color contrast is an important technology in image processing, and mastering it is of great significance to improving the efficiency and accuracy of image processing.

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