Golang image processing: learn how to perform edge extraction and shape detection on images

Golang Image Processing: Learn how to perform edge extraction and shape detection on images

Introduction:
In the field of image processing, edge extraction and shape detection are important one of the technologies. By extracting the edges of the image, the contour information of the object can be obtained, which can then be used for shape detection and recognition. This article will introduce how to use Golang for edge extraction and shape detection of images, and provide relevant code examples for readers' reference.

1. Install and configure the Golang environment
Before we begin, we need to install and configure the Golang development environment so that we can run and compile Go code smoothly. Readers can visit the Golang official website (https://golang.org) and install and configure according to the official guidance.

2. Import image processing library
Go language provides some excellent image processing libraries, such as GoCV and Pigo. This article will use the GoCV library for image edge extraction and shape detection operations. Readers can download and install the GoCV library through the command go get -u -d gocv.io/x/gocv.

3. Image edge extraction
Image edge extraction refers to extracting the edge information of objects from the image. Here we use the Canny algorithm for edge detection, which has been implemented in the GoCV library. Here is a simple sample code:

package main

import (
    "fmt"
    "gocv.io/x/gocv"
)

func main() {
    img := gocv.IMRead("input.jpg", gocv.IMReadColor)
    defer img.Close()

    gray := gocv.NewMat()
    defer gray.Close()

    gocv.CvtColor(img, &gray, gocv.ColorBGRToGray)

    edges := gocv.NewMat()
    defer edges.Close()

    gocv.Canny(gray, &edges, 75, 200)

    window := gocv.NewWindow("Canny")
    defer window.Close()

    window.SetWindowTitle("Canny")
    window.IMShow(edges)
    window.WaitKey(0)
}

In the above code, we first read an image from a file using the IMRead function and then convert it to a grayscale image. Next, we use the Canny algorithm for edge detection and display the results in a new window.

4. Shape Detection
Shape detection refers to detecting specific shapes from images, such as circles, rectangles, etc. In the GoCV library, we can use the FindContours function to implement shape detection. The following is a simple sample code:

package main

import (
    "fmt"
    "gocv.io/x/gocv"
)

func main() {
    img := gocv.IMRead("input.jpg", gocv.IMReadGrayScale)
    defer img.Close()

    blur := gocv.NewMat()
    defer blur.Close()

    gocv.GaussianBlur(img, &blur, image.Pt(7, 7), 0, 0, gocv.BorderDefault)

    thresh := gocv.NewMat()
    defer thresh.Close()

    gocv.Threshold(blur, &thresh, 127, 255, gocv.ThresholdBinary)

    contours := gocv.FindContours(thresh, gocv.RetrievalExternal, gocv.ChainApproxSimple)

    for _, contour := range contours {
        area := gocv.ContourArea(contour)
        fmt.Println("Contour area:", area)
    }
}

In the above code, we first use the IMRead function to read a grayscale image from the file, and then perform Gaussian blur on the image. Next, we use binarization technology (Threshold) to convert the image into a binary image. Finally, we use the FindContours function to find the contours in the image and calculate the area of ​​each contour.

Conclusion:
Through the introduction and sample code of this article, readers can learn how to use Golang for image edge extraction and shape detection operations. By mastering these technologies, readers can apply them to various scenarios in the field of image processing, such as object recognition, image segmentation, etc. Hope this article is helpful to readers.

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