Golang go-face：如何进行全面的人脸捕捉

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我正在使用 https://github.com/kagami/go-face 在 golang 中进行人脸识别，我尝试了 go-face 库中给出的示例。在该示例中，它检测图像中有多少张脸，并将脸部图像与其他多个脸部图像进行分类。

现在我只想在循环面部范围时裁剪每个面部。如果有人在捕捉每张面孔时遇到同样的问题，请尝试这个示例答案。

package main

import (
    "fmt"
    "log"
    "path/filepath"

    "github.com/Kagami/go-face"
)

// Path to directory with models and test images. Here it's assumed it
// points to the <https://github.com/Kagami/go-face-testdata> clone.
const dataDir = "testdata"

var (
    modelsDir = filepath.Join(dataDir, "models")
    imagesDir = filepath.Join(dataDir, "images")
)

// This example shows the basic usage of the package: create an
// recognizer, recognize faces, classify them using few known ones.
func main() {
    // Init the recognizer.
    rec, err := face.NewRecognizer(modelsDir)
    if err != nil {
        log.Fatalf("Can't init face recognizer: %v", err)
    }
    // Free the resources when you're finished.
    defer rec.Close()

    // Test image with 10 faces.
    testImagePristin := filepath.Join(imagesDir, "pristin.jpg")
    // Recognize faces on that image.
    faces, err := rec.RecognizeFile(testImagePristin)
    if err != nil {
        log.Fatalf("Can't recognize: %v", err)
    }
    if len(faces) != 10 {
        log.Fatalf("Wrong number of faces")
    }

    // Fill known samples. In the real world you would use a lot of images
    // for each person to get better classification results but in our
    // example we just get them from one big image.
    var samples []face.Descriptor
    var cats []int32
    for i, f := range faces {
        samples = append(samples, f.Descriptor)
        // Each face is unique on that image so goes to its own category.
        cats = append(cats, int32(i))
    }
    // Name the categories, i.e. people on the image.
    labels := []string{
        "Sungyeon", "Yehana", "Roa", "Eunwoo", "Xiyeon",
        "Kyulkyung", "Nayoung", "Rena", "Kyla", "Yuha",
    }
    // Pass samples to the recognizer.
    rec.SetSamples(samples, cats)

    // Now let's try to classify some not yet known image.
    testImageNayoung := filepath.Join(imagesDir, "nayoung.jpg")
    nayoungFace, err := rec.RecognizeSingleFile(testImageNayoung)
    if err != nil {
        log.Fatalf("Can't recognize: %v", err)
    }
    if nayoungFace == nil {
        log.Fatalf("Not a single face on the image")
    }
    catID := rec.Classify(nayoungFace.Descriptor)
    if catID < 0 {
        log.Fatalf("Can't classify")
    }
    // Finally print the classified label. It should be "Nayoung".
    fmt.Println(labels[catID])
}

正确答案

将使用face.rectangle点来捕捉每个面

package main

import (
    "fmt"
    "log"
    "path/filepath"

    "github.com/Kagami/go-face"
)

// Path to directory with models and test images. Here it's assumed it
// points to the <https://github.com/Kagami/go-face-testdata> clone.
const dataDir = "testdata"

var (
    modelsDir = filepath.Join(dataDir, "models")
    imagesDir = filepath.Join(dataDir, "images")
)

// This example shows the basic usage of the package: create an
// recognizer, recognize faces, classify them using few known ones.
func main() {
    // Init the recognizer.
    rec, err := face.NewRecognizer(modelsDir)
    if err != nil {
        log.Fatalf("Can't init face recognizer: %v", err)
    }
    // Free the resources when you're finished.
    defer rec.Close()

    // Test image with 10 faces.
    testImagePristin := filepath.Join(imagesDir, "pristin.jpg")
    // Recognize faces on that image.
    faces, err := rec.RecognizeFile(testImagePristin)
    if err != nil {
        log.Fatalf("Can't recognize: %v", err)
    }
    if len(faces) != 10 {
        log.Fatalf("Wrong number of faces")
    }

    // Fill known samples. In the real world you would use a lot of images
    // for each person to get better classification results but in our
    // example we just get them from one big image.
    var samples []face.Descriptor
    var cats []int32
    for i, f := range faces {
   // Croping each face and save as image for reference
    err := saveFace(f.Rectangle.Min.X, f.Rectangle.Min.Y, f.Rectangle.Max.X, 
        f.Rectangle.Max.Y, int(i))
    

         if err != nil {
            fmt.Println(err)
        }

        samples = append(samples, f.Descriptor)
        // Each face is unique on that image so goes to its own category.
        cats = append(cats, int32(i))
    }
    // Name the categories, i.e. people on the image.
    labels := []string{
        "Sungyeon", "Yehana", "Roa", "Eunwoo", "Xiyeon",
        "Kyulkyung", "Nayoung", "Rena", "Kyla", "Yuha",
    }
    // Pass samples to the recognizer.
    rec.SetSamples(samples, cats)

    // Now let's try to classify some not yet known image.
    testImageNayoung := filepath.Join(imagesDir, "nayoung.jpg")
    nayoungFace, err := rec.RecognizeSingleFile(testImageNayoung)
    if err != nil {
        log.Fatalf("Can't recognize: %v", err)
    }
    if nayoungFace == nil {
        log.Fatalf("Not a single face on the image")
    }
    catID := rec.Classify(nayoungFace.Descriptor)
    if catID < 0 {
        log.Fatalf("Can't classify")
    }
    // Finally print the classified label. It should be "Nayoung".
    fmt.Println(labels[catID])
}

func saveFace(top int, bottom int, right int, left int, fid int) error {
    testImagePristin := filepath.Join(imagesDir, "pristin.jpg")

    img, err := readImage(testImagePristin)
    if err != nil {
        return err
    }
    img, err = cropImage(img, image.Rect(top, bottom, right, left))
    if err != nil {
        return err
    }
    facePath := fmt.Sprintf("images/%d.png", fid)
    return writeImage(img, facePath)

}

// readImage reads a image file from disk.
func readImage(name string) (image.Image, error) {
    testImagePristin := filepath.Join(imagesDir, "pristin.jpg")

    fd, err := os.Open(testImagePristin)
    if err != nil {
        return nil, err
    }
    defer fd.Close()

    // image.Decode requires that you import the right image package. We've
    // decode jpeg files then we would need to import "image/jpeg".
    img, _, err := image.Decode(fd)
    if err != nil {
        return nil, err
    }

    return img, nil
}

// cropImage takes an image and crops it to the specified rectangle.
func cropImage(img image.Image, crop image.Rectangle) (image.Image, error) {
    type subImager interface {
        SubImage(r image.Rectangle) image.Image
        //Newfunc() int
    }

    // method called SubImage. If it does, then we can use SubImage to crop the
    // image.
    simg, ok := img.(subImager)
    if !ok {
        return nil, fmt.Errorf("image does not support cropping")
    }

    return simg.SubImage(crop), nil
}

// writeImage writes an Image back to the disk.
func writeImage(img image.Image, name string) error {
    fd, err := os.Create(name)
    if err != nil {
        return err
    }
    defer fd.Close()

    return png.Encode(fd, img)
}

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