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Golang image operation: How to perform color balance and color conversion of pictures
Introduction: In the field of image processing, color balance and color conversion are one of the commonly used operations. This article will introduce how to use Go language to perform color balance and color conversion of pictures, and provide corresponding code examples.
1. Color balance
Color balance refers to adjusting the intensity of each color channel in the image to make the overall color of the image more uniform and natural. Commonly used color balance algorithms include brightness balance, white balance and histogram equalization.
package main import ( "image" "image/color" "image/png" "os" ) func brightnessBalance(img image.Image) image.Image { width := img.Bounds().Dx() height := img.Bounds().Dy() balanceImg := image.NewRGBA(img.Bounds()) for y := 0; y < height; y++ { for x := 0; x < width; x++ { r, g, b, a := img.At(x, y).RGBA() r = r * 2 g = g * 2 b = b * 2 balanceImg.Set(x, y, color.RGBA{uint8(r), uint8(g), uint8(b), uint8(a)}) } } return balanceImg } func main() { file, err := os.Open("input.png") if err != nil { panic(err) } defer file.Close() img, _, err := image.Decode(file) if err != nil { panic(err) } balanceImg := brightnessBalance(img) outputFile, err := os.Create("output.png") if err != nil { panic(err) } defer outputFile.Close() err = png.Encode(outputFile, balanceImg) if err != nil { panic(err) } }
In the above code, we loop through each pixel of the image and combine the red, green and blue channels of each pixel. The value is multiplied by 2 to achieve an increase in overall brightness. By loading the original image and saving the processed image, we can get a color-balanced image.
package main import ( "image" "image/color" "image/png" "math" "os" ) func whiteBalance(img image.Image) image.Image { width := img.Bounds().Dx() height := img.Bounds().Dy() whiteBalanceImg := image.NewRGBA(img.Bounds()) var sumR, sumG, sumB float64 for y := 0; y < height; y++ { for x := 0; x < width; x++ { r, g, b, a := img.At(x, y).RGBA() sumR += math.Log(float64(r)) sumG += math.Log(float64(g)) sumB += math.Log(float64(b)) } } avgR := math.Exp(sumR / (float64(width * height))) avgG := math.Exp(sumG / (float64(width * height))) avgB := math.Exp(sumB / (float64(width * height))) for y := 0; y < height; y++ { for x := 0; x < width; x++ { r, g, b, a := img.At(x, y).RGBA() r = uint32(math.Log(float64(r)) * avgR / float64(r)) g = uint32(math.Log(float64(g)) * avgG / float64(g)) b = uint32(math.Log(float64(b)) * avgB / float64(b)) whiteBalanceImg.Set(x, y, color.RGBA{uint8(r), uint8(g), uint8(b), uint8(a)}) } } return whiteBalanceImg } func main() { file, err := os.Open("input.png") if err != nil { panic(err) } defer file.Close() img, _, err := image.Decode(file) if err != nil { panic(err) } whiteBalanceImg := whiteBalance(img) outputFile, err := os.Create("output.png") if err != nil { panic(err) } defer outputFile.Close() err = png.Encode(outputFile, whiteBalanceImg) if err != nil { panic(err) } }
In the above code, we calculate the average of the logarithmic values of all pixels in the image and divide the logarithmic value of each pixel White balance is achieved by multiplying the average and then performing an exponential operation. Similarly, by loading the original image and saving the processed image, we can get the white balanced image.
2. Color conversion
Color conversion refers to converting colors in one color space into colors in another color space. Commonly used color conversions include RGB to HSV and RGB to YUV.
The following is a simple sample code to convert RGB colors to HSV colors:
package main import ( "fmt" "image/color" ) func rgbToHsv(r, g, b uint8) (uint16, uint8, uint8) { var h, s, v uint16 max := uint16(r) if uint16(g) > max { max = uint16(g) } if uint16(b) > max { max = uint16(b) } min := uint16(r) if uint16(g) < min { min = uint16(g) } if uint16(b) < min { min = uint16(b) } v = max delta := max - min if max != 0 { s = uint8(delta) * 255 / uint8(max) } else { s = 0 } if delta != 0 { if max == uint16(r) { h = (uint16(g) - uint16(b)) * 60 / delta if uint16(g) < uint16(b) { h += 360 } } else if max == uint16(g) { h = (2 + (uint16(b)-uint16(r))/delta) * 60 } else { h = (4 + (uint16(r)-uint16(g))/delta) * 60 } } else { h = 0 } return h, s, uint8(v) } func main() { r := uint8(255) g := uint8(0) b := uint8(0) h, s, v := rgbToHsv(r, g, b) fmt.Printf("RGB(%d, %d, %d) -> HSV(%d, %d, %d) ", r, g, b, h, s, v) }
In the above code, we pass a series of Calculate and calculate the value of the corresponding HSV color component. We output a pure red RGB color by setting the value of the RGB component to the maximum value of red, and calculate the corresponding HSV color.
package main import ( "fmt" "image/color" ) func rgbToYuv(r, g, b uint8) (uint8, uint8, uint8) { y := uint8(float32(r)*0.299 + float32(g)*0.587 + float32(b)*0.114) u := uint8((-float32(r)*0.14713 - float32(g)*0.28886 + float32(b)*0.436 + 128) / 2) v := uint8((float32(r)*0.615 + float32(g)*0.51499 - float32(b)*0.10001 + 128) / 2) return y, u, v } func main() { r := uint8(255) g := uint8(0) b := uint8(0) y, u, v := rgbToYuv(r, g, b) fmt.Printf("RGB(%d, %d, %d) -> YUV(%d, %d, %d) ", r, g, b, y, u, v) }
In the above code, we calculate the corresponding values through a series of calculations based on the values of the RGB color components. The value of the YUV color component. Similarly, we output a pure red RGB color by setting the value of the RGB component to the maximum value of red, and calculate the corresponding YUV color.
Conclusion: This article introduces the method of color balance and color conversion of images using Go language, and provides corresponding code examples. I hope readers will have a deeper understanding of Golang image operations through this article and be able to apply it to actual projects.
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