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Golang Image Processing: Learn How to Add Filter Effects

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2023-08-19 11:35:00881browse

Golang Image Processing: Learn How to Add Filter Effects

Golang Image Processing: Learn how to add filter effects

Introduction: Image processing is one of the common requirements in program development, and using Golang for image processing is also An efficient and easy-to-use method. This article will introduce how to use Golang to add filter effects to images, with code examples.

1. Environment preparation

Before starting, make sure that the Golang development environment has been installed correctly and the dependent libraries related to image processing have been installed.

  1. Install Golang: Visit the official website https://golang.org/, download and install Golang according to the operating system.
  2. Install image processing dependent libraries: There is a powerful image processing library in Golang called "image", which has many built-in methods for processing images. You can use the following command to install:

go get -u github.com/golang/image

2. Add filter effects

Now, we can start Learn how to add filter effects to images. Below we will introduce two common filter effects: grayscale filter and blur filter.

  1. Grayscale filter

Grayscale filter converts the image to black and white. The following is a code example for using Golang to implement a grayscale filter effect:

package main

import (
    "image"
    "image/color"
    "image/jpeg"
    "image/png"
    "log"
    "os"
)

func main() {
    // 打开图像文件
    file, err := os.Open("input.jpg")
    if err != nil {
        log.Fatal(err)
    }
    defer file.Close()

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

    // 创建灰度图像
    grayImg := image.NewGray(img.Bounds())

    // 将每个像素点转换为灰度色彩
    for x := 0; x < img.Bounds().Max.X; x++ {
        for y := 0; y < img.Bounds().Max.Y; y++ {
            gray := color.GrayModel.Convert(img.At(x, y)).(color.Gray)
            grayImg.Set(x, y, gray)
        }
    }

    // 创建输出图像文件
    outFile, err := os.Create("output_gray.jpg")
    if err != nil {
        log.Fatal(err)
    }
    defer outFile.Close()

    // 编码并保存图像文件
    err = jpeg.Encode(outFile, grayImg, &jpeg.Options{Quality: 100})
    if err != nil {
        log.Fatal(err)
    }
}

Save the above code as gray_filter.go and name it input.jpg## Place the # image in the same directory and run the following command:

go run gray_filter.go

After successful operation, you will get an image named # in the same directory. The image of ##output_gray.jpg

is an image with a grayscale filter effect added.

Blur filter
  1. The blur filter can make the image look softer and blurry. The following is a code example for using Golang to implement the blur filter effect:
package main

import (
    "image"
    "image/jpeg"
    "image/png"
    "log"
    "os"

    "github.com/anthonynsimon/bild/blur"
)

func main() {
    // 打开图像文件
    file, err := os.Open("input.jpg")
    if err != nil {
        log.Fatal(err)
    }
    defer file.Close()

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

    // 创建模糊图像
    blurredImg := blur.Gaussian(img, 10.0)

    // 创建输出图像文件
    outFile, err := os.Create("output_blur.jpg")
    if err != nil {
        log.Fatal(err)
    }
    defer outFile.Close()

    // 编码并保存图像文件
    err = jpeg.Encode(outFile, blurredImg, &jpeg.Options{Quality: 100})
    if err != nil {
        log.Fatal(err)
    }
}

Save the above code as

blur_filter.go

and name it input.jpg Place the image in the same directory and run the following command: go run blur_filter.go

After successful operation, you will get an image named ## in the same directory The image of #output_blur.jpg

is the image with the blur filter effect added.

Summary:

By learning the implementation codes of the above two filter effects, I believe readers can already understand how to use Golang for image processing and adding filter effects. Golang provides a wealth of image processing methods and libraries, and developers can use these functions to perform more interesting and practical image processing tasks. Hope this article can help you!

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