Golang's method of image segmentation and content recognition

Golang’s method of achieving image segmentation and content recognition

With the advancement of artificial intelligence and computer vision technology, image segmentation and content recognition play a role in various fields plays an increasingly important role. This article will introduce how to use Golang to achieve image segmentation and content recognition, and come with code examples.

Before we start, we need to install several necessary Go packages. First, we need to install "github.com/otiai10/gosseract/v2", which is a Golang library for text recognition. Secondly, we also need to install "gonum.org/v1/gonum/mat", which is a Golang library for matrix operations. You can use the following command to install:

go get github.com/otiai10/gosseract/v2
go get -u gonum.org/v1/gonum/...

Next, we will use the following steps to achieve image segmentation and content recognition.

Step 1: Read the image and perform grayscale processing

First, we need to read the image from the file and convert it into a grayscale image. The code example is as follows:

package main

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

func main() {
    file, err := os.Open("image.jpg")
    if err != nil {
        fmt.Println("图片读取失败：", err)
        return
    }
    defer file.Close()

    img, err := jpeg.Decode(file)
    if err != nil {
        fmt.Println("图片解码失败：", err)
        return
    }

    gray := image.NewGray(img.Bounds())
    for x := gray.Bounds().Min.X; x < gray.Bounds().Max.X; x++ {
        for y := gray.Bounds().Min.Y; y < gray.Bounds().Max.Y; y++ {
            r, g, b, _ := img.At(x, y).RGBA()
            grayColor := color.Gray{(r + g + b) / 3}
            gray.Set(x, y, grayColor)
        }
    }
}

In this code, we first open and read an image named "image.jpg". Then, we decode the picture into an image object through the "jpeg.Decode" function. Next, we created a new grayscale image object "gray" and used a double loop to convert the original image to grayscale.

Step 2: Segment the image

After obtaining the grayscale image, we can use some image processing algorithms to segment the image. Here we use the OTSU algorithm for threshold segmentation. The code example is as follows:

package main

import (
    "fmt"
    "image"
    "image/color"
    "image/jpeg"
    "math"
    "os"
)

func main() {
    // ...

    // 分割图片
    bounds := gray.Bounds()
    threshold := otsu(gray) // OTSU算法获取阈值
    binary := image.NewGray(bounds)
    for x := bounds.Min.X; x < bounds.Max.X; x++ {
        for y := bounds.Min.Y; y < bounds.Max.Y; y++ {
            if gray.GrayAt(x, y).Y > threshold {
                binary.Set(x, y, color.Gray{255})
            } else {
                binary.Set(x, y, color.Gray{0})
            }
        }
    }
}

// OTSU算法计算阈值
func otsu(img *image.Gray) uint32 {
    var hist [256]int
    bounds := img.Bounds()
    for x := bounds.Min.X; x < bounds.Max.X; x++ {
        for y := bounds.Min.Y; y < bounds.Max.Y; y++ {
            hist[img.GrayAt(x, y).Y]++
        }
    }

    total := bounds.Max.X * bounds.Max.Y
    var sum float64
    for i := 0; i < 256; i++ {
        sum += float64(i) * float64(hist[i])
    }
    var sumB float64
    wB := 0
    wF := 0
    var varMax float64
    threshold := 0

    for t := 0; t < 256; t++ {
        wB += hist[t]
        if wB == 0 {
            continue
        }
        wF = total - wB
        if wF == 0 {
            break
        }
        sumB += float64(t) * float64(hist[t])

        mB := sumB / float64(wB)
        mF := (sum - sumB) / float64(wF)

        var between float64 = float64(wB) * float64(wF) * (mB - mF) * (mB - mF)
        if between >= varMax {
            threshold = t
            varMax = between
        }
    }

    return uint32(threshold)
}

In this code, we define a function named "otsu" to calculate the threshold of the OTSU algorithm. We then use this function in the "main" function to get the threshold. Next, we create a new binary image "binary" and threshold segment the grayscale image using a double loop.

Step 3: Content identification

After segmenting the image, we can use the "gosseract" library to identify the content of each area. The code example is as follows:

package main

import (
    "fmt"
    "image"
    "image/color"
    "image/jpeg"
    "os"
    "strings"

    "github.com/otiai10/gosseract/v2"
)

func main() {
    // ...

    client := gosseract.NewClient()
    defer client.Close()

    texts := make([]string, 0)
    bounds := binary.Bounds()
    for x := bounds.Min.X; x < bounds.Max.X; x++ {
        for y := bounds.Min.Y; y < bounds.Max.Y; y++ {
            if binary.GrayAt(x, y).Y == 255 {
                continue
            }
            sx := x
            sy := y
            ex := x
            ey := y
            for ; ex < bounds.Max.X && binary.GrayAt(ex, y).Y == 0; ex++ {
            }
            for ; ey < bounds.Max.Y && binary.GrayAt(x, ey).Y == 0; ey++ {
            }
            rect := image.Rect(sx, sy, ex, ey)
            subImg := binary.SubImage(rect)

            pix := subImg.Bounds().Max.X * subImg.Bounds().Max.Y
            blackNum := 0
            for i := subImg.Bounds().Min.X; i < subImg.Bounds().Max.X; i++ {
                for j := subImg.Bounds().Min.Y; j < subImg.Bounds().Max.Y; j++ {
                    if subImg.At(i, j) == color.Gray{255} {
                        blackNum++
                    }
                }
            }
            if float64(blackNum)/float64(pix) < 0.1 { // 去除噪音
                continue
            }

            output, _ := client.ImageToText(subImg)
            output = strings.ReplaceAll(output, "
", "")
            output = strings.ReplaceAll(output, " ", "")
            texts = append(texts, output)
        }
    }

    fmt.Println(texts)
}

In this code, we use the "NewClient" and "Close" functions in the "gosseract" library to create and close the recognition client. We then use a double loop to iterate over the segmented binary images. For non-white areas, we get the coordinate range of the area and convert it into a sub-image. Next, we calculate the proportion of black pixels in the sub-image to remove noise. Finally, we convert the subimage to text via the "ImageToText" function and save the result in the "texts" array.

Through the above steps, we have completed the method of using Golang to achieve image segmentation and content recognition. You can modify and optimize the code according to your own needs to adapt to different scenarios and needs. I hope this article can provide some help for you to understand and apply image segmentation and content recognition technology.

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