Golang's method to implement stroke extraction and image repair in pictures

Golang's method of realizing stroke extraction and image repair of pictures

Introduction:
With the development of digital image processing technology, people's demand for image processing has also increased. Higher and higher. Among them, stroke extraction and image restoration are two important tasks in image processing. This article will implement these two functions through Golang language and give corresponding code examples.

1. Stroke extraction
Stroke extraction refers to extracting the stroke outline in the original image from the image. This has wide applications in image editing, expression recognition and other fields. The following is a simple Golang sample code for stroke extraction:

package main

import (
    "fmt"
    "image"
    "image/color"
    "image/png"
    "os"
)

func main() {
    file, _ := os.Open("input.png") // 读取输入图像
    defer file.Close()

    img, _ := png.Decode(file) // 解码图像

    bounds := img.Bounds() // 获取图像边界

    // 创建一个新的灰度图像，以便于绘制笔画轮廓
    grayImg := image.NewGray(bounds)

    // 遍历图像像素，提取笔画轮廓
    for x := bounds.Min.X; x < bounds.Max.X; x++ {
        for y := bounds.Min.Y; y < bounds.Max.Y; y++ {
            r, g, b, _ := img.At(x, y).RGBA()

            grayValue := (r*299 + g*587 + b*114 + 500) / 1000
            grayColor := color.Gray{uint8(grayValue)}

            grayImg.Set(x, y, grayColor)
        }
    }

    outFile, _ := os.Create("output.png") // 创建输出图像文件
    defer outFile.Close()

    png.Encode(outFile, grayImg) // 编码并保存输出图像
    fmt.Println("笔画提取完成！")
}

In the above code, input.png is used as the input image, and the input image is decoded into an image.ImageObject. Then, create a new grayscale image based on the boundaries of the input image.

Next, by traversing each pixel of the image, calculate the gray value corresponding to each pixel, and use the gray value to create a gray color object. Finally, set the grayscale color object into the new grayscale image.

Finally, encode the resulting grayscale image into a PNG file and save it as output.png.

2. Image Repair
Image repair refers to the repair of damaged or defective images to restore the image to its original state. Image restoration is often used to restore old photos, complete missing image content, etc. The following is a simple Golang sample code for image repair:

package main

import (
    "fmt"
    "image"
    "image/color"
    "image/png"
    "os"
)

func main() {
    file, _ := os.Open("input.png") // 读取输入图像
    defer file.Close()

    img, _ := png.Decode(file) // 解码图像

    bounds := img.Bounds() // 获取图像边界

    // 创建一个新的RGBA图像，以便于修复图像
    repairImg := image.NewRGBA(bounds)

    // 遍历图像像素，修复图像
    for x := bounds.Min.X; x < bounds.Max.X; x++ {
        for y := bounds.Min.Y; y < bounds.Max.Y; y++ {
            r, g, b, a := img.At(x, y).RGBA()

            if a == 0 { // 如果该像素的透明度为0，则修复该像素的RGB值为255
                r = 65535
                g = 65535
                b = 65535
            }

            rgbaColor := color.RGBA{uint8(r >> 8), uint8(g >> 8), uint8(b >> 8), uint8(a >> 8)}
            repairImg.SetRGBA(x, y, rgbaColor)
        }
    }

    outFile, _ := os.Create("output.png") // 创建输出图像文件
    defer outFile.Close()

    png.Encode(outFile, repairImg) // 编码并保存输出图像
    fmt.Println("图像修复完成！")
}

In the above code, input.png is used as the input image, and the input image is also decoded into a image.Image Object. Then, create a new RGBA image based on the boundaries of the input image.

Next, iterate through each pixel of the image and check the transparency (a value) of that pixel. If the transparency of the pixel is 0, it means that the pixel is damaged or defective, so the RGB value of the pixel is repaired to 255.

Finally, encode the repaired image into a PNG file and save it as output.png.

Conclusion:
This article uses the Golang language as a tool to implement the stroke extraction and image repair methods of pictures. Through code examples, we can clearly understand the implementation process of these two functions. These methods are widely used in the field of image processing and can help us better process image data. We hope that readers can have a deeper understanding and application of stroke extraction and image restoration through the introduction and sample code of this article.

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