Golang图像处理：学习如何进行图片的密度聚类和图像分析

import (
    "fmt"
    "image"
    "image/color"
    "image/jpeg"
    "os"
    "github.com/mjibson/go-dsp/fft"
)

func readImage(filename string) (image.Image, error) {
    file, err := os.Open(filename)
    if err != nil {
        return nil, err
    }
    defer file.Close()

    img, err := jpeg.Decode(file)
    if err != nil {
        return nil, err
    }

    grayImg := image.NewGray(img.Bounds())
    for x := 0; x < img.Bounds().Dx(); x++ {
        for y := 0; y < img.Bounds().Dy(); y++ {
            grayImg.Set(x, y, img.At(x, y))
        }
    }

    return grayImg, nil
}

func densityClustering(img image.Image, epsilon float64, minPts int) {
    width := img.Bounds().Dx()
    height := img.Bounds().Dy()
    data := make([][]int, width)
    visited := make([][]bool, width)

    for x := 0; x < width; x++ {
        data[x] = make([]int, height)
        visited[x] = make([]bool, height)
    }

    // 遍历每个像素点，计算其灰度值
    for x := 0; x < width; x++ {
        for y := 0; y < height; y++ {
            r, _, _, _ := img.At(x, y).RGBA()
            gray := int(r)

            data[x][y] = gray
        }
    }

    // 进行密度聚类
    for x := 0; x < width; x++ {
        for y := 0; y < height; y++ {
            if !visited[x][y] {
                visited[x][y] = true
                neighbors := getNeighbors(x, y, data, epsilon)

                if len(neighbors) < minPts {
                    // 噪声点
                    continue
                }

                // 新簇
                cluster := make([]image.Point, 0)
                cluster = append(cluster, image.Point{x, y})

                for len(neighbors) > 0 {
                    current := neighbors[0]
                    neighbors = neighbors[1:]

                    cx, cy := current.X, current.Y
                    if !visited[cx][cy] {
                        visited[cx][cy] = true
                        n := getNeighbors(cx, cy, data, epsilon)

                        if len(n) >= minPts {
                            neighbors = append(neighbors, n...)
                        }
                    }

                    // 将当前点加入簇
                    cluster = append(cluster, current)
                }

                fmt.Println(cluster)
            }
        }
    }
}

func getNeighbors(x, y int, data [][]int, epsilon float64) []image.Point {
    neighbors := make([]image.Point, 0)

    for dx := -1; dx <= 1; dx++ {
        for dy := -1; dy <= 1; dy++ {
            nx := x + dx
            ny := y + dy

            if nx >= 0 && ny >= 0 && nx < len(data) && ny < len(data[nx]) {
                if abs(float64(data[x][y]-data[nx][ny])) <= epsilon {
                    neighbors = append(neighbors, image.Point{nx, ny})
                }
            }
        }
    }

    return neighbors
}

func abs(x float64) float64 {
    if x < 0 {
        return -x
    }
    return x
}

import (
    "fmt"
    "github.com/mjibson/go-dsp/fft"
)

func fourierTransform(img image.Image) {
    width := img.Bounds().Dx()
    height := img.Bounds().Dy()
    data := make([][]float64, width)

    for x := 0; x < width; x++ {
        data[x] = make([]float64, height)
    }

    // 遍历每个像素点，计算其灰度值
    for x := 0; x < width; x++ {
        for y := 0; y < height; y++ {
            r, _, _, _ := img.At(x, y).RGBA()
            gray := float64(r)

            data[x][y] = gray
        }
    }

    // 进行傅里叶变换
    fftImg := make([][]complex128, width)
    for x := 0; x < width; x++ {
        fftImg[x] = make([]complex128, height)
    }
    for x := 0; x < width; x++ {
        temp := make([]complex128, height)
        for y := 0; y < height; y++ {
            temp[y] = complex(data[x][y], 0)
        }
        fft.FFT(temp)
        fftImg[x] = temp
    }

    fmt.Println(fftImg)
}