How to use Golang to perform color histogram and binarization processing on images

How to use Golang to perform color histogram and binarization processing on images

With the widespread application of digital image processing, image processing and analysis have also become Hot topics in computer vision. Among them, color histogram and binarization are two common and important image processing methods. This article will introduce how to use Golang to perform color histogram and binarization processing on images, and comes with code examples.

The color histogram is a statistics of the color frequency of pixels in an image. Histogram analysis can be used in applications such as image enhancement, image retrieval, and image classification. Here is a sample code that uses Golang to calculate a color histogram:

package main

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

func main() {
    imgFile, err := os.Open("test.jpg") // 读取图像文件
    if err != nil {
        log.Fatal(err)
    }
    defer imgFile.Close()

    img, _, err := image.Decode(imgFile) // 解码图像
    if err != nil {
        log.Fatal(err)
    }

    bounds := img.Bounds()
    histogram := make(map[color.Color]int) // 创建颜色直方图

    for y := bounds.Min.Y; y < bounds.Max.Y; y++ {
        for x := bounds.Min.X; x < bounds.Max.X; x++ {
            c := img.At(x, y)
            histogram[c]++
        }
    }

    for c, count := range histogram {
        fmt.Printf("颜色: %v，频率: %d
", c, count)
    }
}

The above code first opens and decodes the image file, and then creates a color histogram. By traversing each pixel and counting the frequency of color occurrences, a color histogram of an image is finally obtained. Here we directly use the image.Decode function in the Golang official library image to decode the image. You can choose other image processing libraries according to your needs.

Binarization is the conversion of an image into an image with only two colors, usually black and white. This process can simplify complex images and also extract key information in the image. Here is a sample code that uses Golang to binarize an image:

package main

import (
    "image"
    "image/color"
    "log"
    "os"
)

func main() {
    imgFile, err := os.Open("test.jpg") // 读取图像文件
    if err != nil {
        log.Fatal(err)
    }
    defer imgFile.Close()

    img, _, err := image.Decode(imgFile) // 解码图像
    if err != nil {
        log.Fatal(err)
    }

    bounds := img.Bounds()
    binaryImg := image.NewGray(bounds) // 创建一个新的灰度图像

    for y := bounds.Min.Y; y < bounds.Max.Y; y++ {
        for x := bounds.Min.X; x < bounds.Max.X; x++ {
            c := img.At(x, y)
            gray := color.GrayModel.Convert(c).(color.Gray)
            if gray.Y >= 128 {
                binaryImg.SetGray(x, y, color.White) // 大于等于128的像素点设为白色
            } else {
                binaryImg.SetGray(x, y, color.Black) // 小于128的像素点设为黑色
            }
        }
    }

    binaryFile, err := os.Create("binary.jpg") // 创建输出文件
    if err != nil {
        log.Fatal(err)
    }
    defer binaryFile.Close()

    err = jpeg.Encode(binaryFile, binaryImg, &jpeg.Options{Quality: 100}) // 编码二值化图像
    if err != nil {
        log.Fatal(err)
    }
}

The above code first opens and decodes the image file, and then creates a new grayscale image. By traversing each pixel, the gray value of the pixel is compared with a set threshold, and black or white is selected based on the threshold. Finally, the binarized image is saved to a file. Similarly, the image.Decode function in the Golang official library image is used here to decode the image. You can choose other image processing libraries according to your needs.

Through the above two examples, you can easily use Golang to perform color histogram and binarization processing on images. These image processing methods are widely used in the fields of computer vision and image analysis and can help us better understand and process image data. At the same time, Golang provides a wealth of image processing libraries and functions, which provides great convenience for our development work.

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