How to use Golang to enhance borders and edges of images

How to use Golang to enhance borders and edges of images

Overview:
In the field of image processing, border and edge enhancement is a commonly used technology that can Effectively improve the visual effects of images and improve the accuracy of image recognition. This article will introduce how to use Golang language to perform border and edge enhancement operations on images, and provide corresponding code examples.

Note: This article assumes that you have installed and configured the Golang development environment in your local environment.

1. Import dependency packages
   First, we need to import the following dependency packages for image processing operations:

import (
    "image"
    "image/color"
    "image/draw"
)

2. Load images
   Connect Next, we need to load the image file to be processed. Here we take a picture in JPEG format as an example, and load the picture file through the image/jpeg package:

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

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

3. Add a border
   Now, we can add borders to the image Border added. We can customize the size and color of the border, as well as the distance of the border from the edge of the image.

borderWidth := 10
borderColor := color.RGBA{255, 0, 0, 255} // 红色边框
borderRect := image.Rect(0, 0, img.Bounds().Dx()+borderWidth*2, img.Bounds().Dy()+borderWidth*2)

borderImg := image.NewRGBA(borderRect)
draw.Draw(borderImg, borderImg.Bounds(), &image.Uniform{borderColor}, image.ZP, draw.Src)
draw.Draw(borderImg, img.Bounds().Add(image.Point{borderWidth, borderWidth}), img, image.ZP, draw.Src)

outputFile, err := os.Create("output_with_border.jpg")
if err != nil {
    log.Fatal(err)
}
defer outputFile.Close()

jpeg.Encode(outputFile, borderImg, &jpeg.Options{Quality: 100})

In this code, we first create a new image object based on the size of the original image and the specified border size. Then use the draw.Draw function to draw the color of the border into the new image and draw the original image within the border.

Finally, we use the jpeg.Encode function to save the image with the border added to a file.

4. Edge enhancement
   In addition to adding borders, we can also enhance the edges of the image to highlight the outline of the objects in the image.

radius := 1.0 // 边缘增强半径
threshold := 50.0 // 边缘增强阈值

enhancedImg := image.NewRGBA(img.Bounds())
for y := img.Bounds().Min.Y; y < img.Bounds().Max.Y; y++ {
    for x := img.Bounds().Min.X; x < img.Bounds().Max.X; x++ {
        r, g, b, _ := img.At(x, y).RGBA()
        neighbors := [9]color.Color{
            img.At(x-1, y+1), img.At(x, y+1), img.At(x+1, y+1),
            img.At(x-1, y), img.At(x, y), img.At(x+1, y),
            img.At(x-1, y-1), img.At(x, y-1), img.At(x+1, y-1),
        }
        var totalDiff float64
        for _, neighbor := range neighbors {
            nr, ng, nb, _ := neighbor.RGBA()
            totalDiff += diff(r, nr) + diff(g, ng) + diff(b, nb)
        }
        if totalDiff/9 > threshold {
            enhancedImg.Set(x, y, color.Black)
        } else {
            enhancedImg.Set(x, y, color.White)
        }
    }
}

outputFile, err = os.Create("output_with_enhanced_edges.jpg")
if err != nil {
    log.Fatal(err)
}
defer outputFile.Close()

jpeg.Encode(outputFile, enhancedImg, &jpeg.Options{Quality: 100})

In this code, we iterate through each pixel of the image and obtain the surrounding pixel values. Then the difference between each pixel and surrounding pixels is calculated and these difference values ​​are accumulated. If the accumulated value is greater than the specified threshold, it means that this pixel is located at the edge of the image, and we set it to black; otherwise, it is set to white. Finally, save the enhanced edges to a file.

Summary:
Through the above sample code, we learned how to use Golang to perform border and edge enhancement operations on images. These technologies can be applied to fields such as image processing, computer vision, and machine learning to improve the quality and accuracy of images. Hope this article helps you!

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