🐳 add docker file

app/aisvc/rpc/internal/logic/aiservice/caffe_classification_logic.go

@@ -0,0 +1,31 @@
+package aiservicelogic
+
+import (
+	"context"
+
+	"schisandra-album-cloud-microservices/app/aisvc/rpc/internal/svc"
+	"schisandra-album-cloud-microservices/app/aisvc/rpc/pb"
+
+	"github.com/zeromicro/go-zero/core/logx"
+)
+
+type CaffeClassificationLogic struct {
+	ctx    context.Context
+	svcCtx *svc.ServiceContext
+	logx.Logger
+}
+
+func NewCaffeClassificationLogic(ctx context.Context, svcCtx *svc.ServiceContext) *CaffeClassificationLogic {
+	return &CaffeClassificationLogic{
+		ctx:    ctx,
+		svcCtx: svcCtx,
+		Logger: logx.WithContext(ctx),
+	}
+}
+
+// CaffeClassification
+func (l *CaffeClassificationLogic) CaffeClassification(in *pb.CaffeClassificationRequest) (*pb.CaffeClassificationResponse, error) {
+	// todo: add your logic here and delete this line
+
+	return &pb.CaffeClassificationResponse{}, nil
+}

app/aisvc/rpc/internal/logic/aiservice/face_recognition_logic.go

@@ -43,8 +43,12 @@ func NewFaceRecognitionLogic(ctx context.Context, svcCtx *svc.ServiceContext) *F
 
 // FaceRecognition 人脸识别
 func (l *FaceRecognitionLogic) FaceRecognition(in *pb.FaceRecognitionRequest) (*pb.FaceRecognitionResponse, error) {
+	toJPEG, err := l.ConvertImageToJPEG(in.GetFace())
+	if err != nil {
+		return nil, err
+	}
 	// 提取人脸特征
-	faceFeatures, err := l.svcCtx.FaceRecognizer.RecognizeSingle(in.GetFace())
+	faceFeatures, err := l.svcCtx.FaceRecognizer.RecognizeSingle(toJPEG)
 	if err != nil {
 		return nil, err
 	}
@@ -82,7 +86,7 @@ func (l *FaceRecognitionLogic) FaceRecognition(in *pb.FaceRecognitionRequest) (*
 
 	// 人脸分类
 	classify := l.svcCtx.FaceRecognizer.ClassifyThreshold(faceFeatures.Descriptor, 0.6)
-	if classify >= 0 {
+	if classify >= 0 && classify < len(ids) {
 		return &pb.FaceRecognitionResponse{
 			FaceId: int64(ids[classify]),
 		}, nil
@@ -92,6 +96,26 @@ func (l *FaceRecognitionLogic) FaceRecognition(in *pb.FaceRecognitionRequest) (*
 	return l.saveNewFace(in, faceFeatures, hashKey)
 }
 
+// ConvertImageToJPEG 将非 JPEG 格式的图片字节数据转换为 JPEG
+func (l *FaceRecognitionLogic) ConvertImageToJPEG(imageData []byte) ([]byte, error) {
+	// 使用 image.Decode 解码图像数据
+	img, _, err := image.Decode(bytes.NewReader(imageData))
+	if err != nil {
+		return nil, fmt.Errorf("failed to decode image: %v", err)
+	}
+
+	// 创建一个缓冲区来存储 JPEG 格式的数据
+	var jpegBuffer bytes.Buffer
+
+	// 将图片编码为 JPEG 格式
+	err = jpeg.Encode(&jpegBuffer, img, nil)
+	if err != nil {
+		return nil, fmt.Errorf("failed to encode image to JPEG: %v", err)
+	}
+
+	return jpegBuffer.Bytes(), nil
+}
+
 // 保存新的人脸样本到数据库和 Redis
 func (l *FaceRecognitionLogic) saveNewFace(in *pb.FaceRecognitionRequest, faceFeatures *face.Face, hashKey string) (*pb.FaceRecognitionResponse, error) {
 	// 人脸有效性判断 (大小必须大于50)

app/aisvc/rpc/internal/logic/aiservice/tf_classification_logic.go

@@ -0,0 +1,79 @@
+package aiservicelogic
+
+import (
+	"context"
+	"fmt"
+	"gocv.io/x/gocv"
+	"image"
+	"schisandra-album-cloud-microservices/app/aisvc/rpc/internal/svc"
+	"schisandra-album-cloud-microservices/app/aisvc/rpc/pb"
+
+	"github.com/zeromicro/go-zero/core/logx"
+)
+
+type TfClassificationLogic struct {
+	ctx    context.Context
+	svcCtx *svc.ServiceContext
+	logx.Logger
+}
+
+func NewTfClassificationLogic(ctx context.Context, svcCtx *svc.ServiceContext) *TfClassificationLogic {
+	return &TfClassificationLogic{
+		ctx:    ctx,
+		svcCtx: svcCtx,
+		Logger: logx.WithContext(ctx),
+	}
+}
+
+// TfClassification is a server endpoint to classify an image using TensorFlow.
+func (l *TfClassificationLogic) TfClassification(in *pb.TfClassificationRequest) (*pb.TfClassificationResponse, error) {
+	className, source, err := l.ClassifyImage(in.GetImage())
+	if err != nil {
+		return nil, err
+	}
+	return &pb.TfClassificationResponse{
+		Score:     source,
+		ClassName: className,
+	}, nil
+}
+
+// ClassifyImage 从字节数据分类图像，返回分类标签和最大概率值
+func (l *TfClassificationLogic) ClassifyImage(imageBytes []byte) (string, float32, error) {
+
+	// 解码字节数据为图像
+	img, err := gocv.IMDecode(imageBytes, gocv.IMReadColor)
+	if err != nil || img.Empty() {
+		return "", 0, fmt.Errorf("failed to decode image: %v", err)
+	}
+	defer func(img *gocv.Mat) {
+		_ = img.Close()
+	}(&img)
+
+	// 将图像 Mat 转换为 224x224 blob，以便分类器分析
+	blob := gocv.BlobFromImage(img, 1.0, image.Pt(224, 224), gocv.NewScalar(0, 0, 0, 0), true, false)
+
+	// 将 blob 输入分类器
+	l.svcCtx.TfNet.SetInput(blob, "input")
+
+	// 运行网络的正向传递
+	prob := l.svcCtx.TfNet.Forward("softmax2")
+
+	// 将结果重塑为 1x1000 矩阵
+	probMat := prob.Reshape(1, 1)
+
+	// 确定最可能的分类
+	_, maxVal, _, maxLoc := gocv.MinMaxLoc(probMat)
+
+	// 获取分类描述
+	desc := ""
+	if maxLoc.X < 1000 {
+		desc = l.svcCtx.TfDesc[maxLoc.X]
+	}
+
+	// 清理资源
+	_ = blob.Close()
+	_ = prob.Close()
+	_ = probMat.Close()
+
+	return desc, maxVal, nil
+}