Golang implements image filter effects and image reconstruction methods

Golang’s method of realizing filter effects and image reconstruction of images

Introduction:
With the advancement of computer technology, image processing has become a very important important task. Among them, image filter effects and image reconstruction are two important aspects in the field of image processing. This article will introduce how to use Golang to achieve these two tasks and give corresponding code examples.

1. Image filter effect
The image filter effect is achieved by modifying the pixels of the image. Common filter effects include grayscale, edge detection, blur, etc. The following takes the grayscale filter as an example to introduce how to implement it using Golang.

Code example:

package main

import (

"image"
"image/color"
"image/jpeg"
"log"
"os"

)

func main() {

// 读取图片
file, err := os.Open("input.jpg")
if err != nil {
    log.Fatal(err)
}
defer file.Close()

img, err := jpeg.Decode(file)
if err != nil {
    log.Fatal(err)
}

// 灰度化处理
grayImg := image.NewGray(img.Bounds())
for x := 0; x < img.Bounds().Dx(); x++ {
    for y := 0; y < img.Bounds().Dy(); y++ {
        c := img.At(x, y)
        gray := color.GrayModel.Convert(c).(color.Gray)
        grayImg.Set(x, y, gray)
    }
}

// 保存处理后的图片
outFile, err := os.Create("output.jpg")
if err != nil {
    log.Fatal(err)
}
defer outFile.Close()

jpeg.Encode(outFile, grayImg, nil)

}

In the above code, the image is first read through the Decode function in the jpeg package. Then create a new grayscale image grayImg, use a double loop to traverse all pixels, convert each pixel in the original image into a grayscale value, and set it to the new grayscale image. Finally, the processed image is saved in a file using the Encode function in the jpeg package.

2. Image reconstruction
Image reconstruction refers to restoring the lossy compressed image to the original image. In Golang, the interpolation method of pixel values ​​can be used to achieve image reconstruction. The following takes nearest neighbor interpolation as an example to introduce how to implement it using Golang.

Code example:

package main

import (

"image"
"image/color"
"image/jpeg"
"log"
"os"

)

func main() {

// 读取压缩后的图片
file, err := os.Open("compressed.jpg")
if err != nil {
    log.Fatal(err)
}
defer file.Close()

img, err := jpeg.Decode(file)
if err != nil {
    log.Fatal(err)
}

// 图像重建
width := img.Bounds().Dx()
height := img.Bounds().Dy()

reconstructed := image.NewRGBA(image.Rect(0, 0, width*2, height*2))
for x := 0; x < width*2; x++ {
    for y := 0; y < height*2; y++ {
        originX := x / 2
        originY := y / 2
        c := img.At(originX, originY)
        reconstructed.Set(x, y, c)
    }
}

// 保存重建后的图片
outFile, err := os.Create("reconstructed.jpg")
if err != nil {
    log.Fatal(err)
}
defer outFile.Close()

jpeg.Encode(outFile, reconstructed, nil)

}

In the above code, the compressed image is first read through the Decode function in the jpeg package. Then create a new reconstructed image according to the size of the compressed image reconstructed, traverse all pixels through a double loop, and set the value of each pixel in the original image to the new image. Finally, the reconstructed image is saved in a file using the Encode function from the jpeg package.

Summary:
This article introduces how to use Golang to achieve image filter effects and image reconstruction. Through the above code examples, we can see that Golang has certain advantages in image processing and can produce good results in practical applications. I hope this article will be helpful to readers in their learning and application of Golang image processing.

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