🐛 修复onnx环境

internal/application/classifier/classifier.go

@@ -1,225 +0,0 @@
-package classifier
-
-import (
-	"bufio"
-	"fmt"
-	_ "image/jpeg"
-	_ "image/png"
-	"likedream-goframe/internal/framework/logger"
-	"os"
-	"path/filepath"
-	"runtime"
-	"strings"
-
-	ort "github.com/yalue/onnxruntime_go"
-	"go.uber.org/zap"
-)
-
-type ImageClassifier struct {
-	session      *ort.AdvancedSession
-	inputShape   []int64
-	classes      []string
-	inputTensor  *ort.Tensor[float32]
-	outputTensor *ort.Tensor[float32]
-}
-
-// NewImageClassifier 创建新的图片分类器
-func NewImageClassifier(modelPath, classesPath string) (*ImageClassifier, error) {
-	// 初始化ONNX Runtime
-	exePath := strings.ToLower(os.Args[0])
-	// 获取绝对路径
-	absPath, err := filepath.Abs(exePath)
-	dir := filepath.Dir(absPath)
-	systemOS := runtime.GOOS
-	switch systemOS {
-	case "windows":
-		dir = filepath.Join(dir, "onnxruntime.dll")
-		break
-	case "darwin":
-		dir = filepath.Join(dir, "onnxruntime.dylib")
-		break
-	case "linux":
-		dir = filepath.Join(dir, "onnxruntime.so")
-		break
-	}
-	ort.SetSharedLibraryPath(dir)
-	err = ort.InitializeEnvironment()
-	if err != nil {
-		logger.Logger.Error("初始化onnx：", zap.String("绝对路径", absPath), zap.String("父文件夹", dir))
-		return nil, fmt.Errorf("初始化ONNX Runtime失败: %v", err)
-	}
-
-	// 加载类别名称
-	classes, err := loadClasses(classesPath)
-	if err != nil {
-		return nil, fmt.Errorf("加载类别失败: %v", err)
-	}
-
-	// 创建session选项
-	options, err := ort.NewSessionOptions()
-	if err != nil {
-		return nil, fmt.Errorf("创建session选项失败: %v", err)
-	}
-	defer options.Destroy()
-
-	// 创建输入输出tensor
-	inputShape := ort.NewShape(1, 3, 224, 224)
-	outputShape := ort.NewShape(1, int64(len(classes)))
-
-	inputTensor, err := ort.NewEmptyTensor[float32](inputShape)
-	if err != nil {
-		return nil, fmt.Errorf("创建输入tensor失败: %v", err)
-	}
-
-	outputTensor, err := ort.NewEmptyTensor[float32](outputShape)
-	if err != nil {
-		inputTensor.Destroy()
-		return nil, fmt.Errorf("创建输出tensor失败: %v", err)
-	}
-
-	// 加载模型
-	session, err := ort.NewAdvancedSession(modelPath,
-		[]string{"input"}, []string{"output"},
-		[]ort.ArbitraryTensor{inputTensor}, []ort.ArbitraryTensor{outputTensor},
-		options)
-	if err != nil {
-		inputTensor.Destroy()
-		outputTensor.Destroy()
-		return nil, fmt.Errorf("加载模型失败: %v", err)
-	}
-
-	return &ImageClassifier{
-		session:      session,
-		inputShape:   []int64{1, 3, 224, 224},
-		classes:      classes,
-		inputTensor:  inputTensor,
-		outputTensor: outputTensor,
-	}, nil
-}
-
-// loadClasses 从文件加载类别名称
-func loadClasses(path string) ([]string, error) {
-	file, err := os.Open(path)
-	if err != nil {
-		return nil, err
-	}
-	defer file.Close()
-
-	var classes []string
-	scanner := bufio.NewScanner(file)
-	for scanner.Scan() {
-		classes = append(classes, scanner.Text())
-	}
-
-	if err := scanner.Err(); err != nil {
-		return nil, err
-	}
-
-	return classes, nil
-}
-
-// Predict 预测图片类别
-func (ic *ImageClassifier) Predict(imgPath string) (string, float32, error) {
-	// 加载并预处理图片
-	inputData, err := ic.preprocessImage(imgPath)
-	if err != nil {
-		return "", 0, fmt.Errorf("预处理图片失败: %v", err)
-	}
-
-	// 设置输入数据
-	copy(ic.inputTensor.GetData(), inputData)
-
-	// 运行推理
-	err = ic.session.Run()
-	if err != nil {
-		return "", 0, fmt.Errorf("推理失败: %v", err)
-	}
-
-	// 获取输出数据
-	outputData := ic.outputTensor.GetData()
-
-	// 找到最大概率的类别
-	maxIdx := 0
-	maxProb := outputData[0]
-	for i, prob := range outputData {
-		if prob > maxProb {
-			maxProb = prob
-			maxIdx = i
-		}
-	}
-
-	// 应用softmax获取概率
-	expSum := float32(0)
-	for _, v := range outputData {
-		expSum += exp(v)
-	}
-	probability := exp(outputData[maxIdx]) / expSum
-
-	return ic.classes[maxIdx], probability, nil
-}
-
-// PredictFromBytes 从字节数据预测图片类别
-func (ic *ImageClassifier) PredictFromBytes(imgData []byte) (string, float32, error) {
-	// 预处理图片
-	inputData, err := ic.preprocessImageBytes(imgData)
-	if err != nil {
-		return "", 0, fmt.Errorf("预处理图片失败: %v", err)
-	}
-
-	// 设置输入数据
-	copy(ic.inputTensor.GetData(), inputData)
-
-	// 运行推理
-	err = ic.session.Run()
-	if err != nil {
-		return "", 0, fmt.Errorf("推理失败: %v", err)
-	}
-
-	// 获取输出数据
-	outputData := ic.outputTensor.GetData()
-
-	// 找到最大概率的类别
-	maxIdx := 0
-	maxProb := outputData[0]
-	for i, prob := range outputData {
-		if prob > maxProb {
-			maxProb = prob
-			maxIdx = i
-		}
-	}
-
-	// 应用softmax获取概率
-	expSum := float32(0)
-	for _, v := range outputData {
-		expSum += exp(v)
-	}
-	probability := exp(outputData[maxIdx]) / expSum
-
-	return ic.classes[maxIdx], probability, nil
-}
-
-// Close 关闭分类器
-func (ic *ImageClassifier) Close() error {
-	if ic.inputTensor != nil {
-		ic.inputTensor.Destroy()
-	}
-	if ic.outputTensor != nil {
-		ic.outputTensor.Destroy()
-	}
-	if ic.session != nil {
-		ic.session.Destroy()
-	}
-	return nil
-}
-
-// exp 计算e的x次方（简单实现）
-func exp(x float32) float32 {
-	// 使用泰勒级数近似
-	result := float32(1.0)
-	term := float32(1.0)
-	for i := 1; i < 20; i++ {
-		term *= x / float32(i)
-		result += term
-	}
-	return result
-}

internal/application/classifier/preprocess.go

@@ -1,120 +0,0 @@
-package classifier
-
-import (
-	"bytes"
-	"image"
-	"os"
-)
-
-// preprocessImage 预处理图片文件
-func (ic *ImageClassifier) preprocessImage(imgPath string) ([]float32, error) {
-	file, err := os.Open(imgPath)
-	if err != nil {
-		return nil, err
-	}
-	defer file.Close()
-
-	img, _, err := image.Decode(file)
-	if err != nil {
-		return nil, err
-	}
-
-	return ic.processImage(img)
-}
-
-// preprocessImageBytes 预处理图片字节数据
-func (ic *ImageClassifier) preprocessImageBytes(imgData []byte) ([]float32, error) {
-	img, _, err := image.Decode(bytes.NewReader(imgData))
-	if err != nil {
-		return nil, err
-	}
-
-	return ic.processImage(img)
-}
-
-// processImage 处理图片为模型输入格式
-func (ic *ImageClassifier) processImage(img image.Image) ([]float32, error) {
-	// 调整图片大小到224x224
-	resized := resizeImage(img, 224, 224)
-
-	// 转换为float32数组，并进行归一化
-	// ImageNet的均值和标准差
-	mean := []float32{0.485, 0.456, 0.406}
-	std := []float32{0.229, 0.224, 0.225}
-
-	data := make([]float32, 3*224*224)
-
-	for y := 0; y < 224; y++ {
-		for x := 0; x < 224; x++ {
-			r, g, b, _ := resized.At(x, y).RGBA()
-
-			// 转换为0-1范围
-			rNorm := float32(r) / 65535.0
-			gNorm := float32(g) / 65535.0
-			bNorm := float32(b) / 65535.0
-
-			// 应用归一化
-			idx := y*224 + x
-			data[idx] = (rNorm - mean[0]) / std[0]           // R通道
-			data[224*224+idx] = (gNorm - mean[1]) / std[1]   // G通道
-			data[2*224*224+idx] = (bNorm - mean[2]) / std[2] // B通道
-		}
-	}
-
-	return data, nil
-}
-
-// resizeImage 调整图片大小
-func resizeImage(img image.Image, width, height int) image.Image {
-	bounds := img.Bounds()
-	srcW := bounds.Dx()
-	srcH := bounds.Dy()
-
-	dst := image.NewRGBA(image.Rect(0, 0, width, height))
-
-	for y := 0; y < height; y++ {
-		for x := 0; x < width; x++ {
-			// 简单的最近邻插值
-			srcX := x * srcW / width
-			srcY := y * srcH / height
-
-			if srcX >= srcW {
-				srcX = srcW - 1
-			}
-			if srcY >= srcH {
-				srcY = srcH - 1
-			}
-
-			dst.Set(x, y, img.At(srcX, srcY))
-		}
-	}
-
-	return dst
-}
-
-// centerCrop 中心裁剪图片
-func centerCrop(img image.Image, size int) image.Image {
-	bounds := img.Bounds()
-	width := bounds.Dx()
-	height := bounds.Dy()
-
-	var x0, y0 int
-	if width > height {
-		x0 = (width - height) / 2
-		y0 = 0
-		width = height
-	} else {
-		x0 = 0
-		y0 = (height - width) / 2
-		height = width
-	}
-
-	cropped := image.NewRGBA(image.Rect(0, 0, width, height))
-	for y := 0; y < height; y++ {
-		for x := 0; x < width; x++ {
-			cropped.Set(x, y, img.At(x0+x, y0+y))
-		}
-	}
-
-	return resizeImage(cropped, size, size)
-}

internal/application/classifier/resolver.go

@@ -1,17 +0,0 @@
-package classifier
-
-import "likedream-goframe/internal/utils"
-
-var imageClassifier, _ = NewImageClassifier("static/model.onnx", "static/classes.txt")
-
-// 图片数据 & 类别名称
-// return 是否匹配
-func ClassifyImage(imgData []byte, className string) bool {
-	if imageClassifier == nil {
-		c, err := NewImageClassifier("static/model.onnx", "static/classes.txt")
-		utils.OkOrPanic(err)
-		imageClassifier = c
-	}
-	name, _, _ := imageClassifier.PredictFromBytes(imgData)
-	return className == name
-}

internal/application/recognizer/init.go

@@ -43,15 +43,15 @@ func getOnnxRuntimeName() (onnxRuntimeFileName string, onnxRuntimeFileBytes []by
 	switch systemOS {
 	case "windows":
 		onnxRuntimeFileName = "onnxruntime.dll"
-		onnxRuntimeFileBytes, _ = fs.ReadFile("static/" + onnxRuntimeFileName)
+		onnxRuntimeFileBytes, _ = fs.ReadFile("static/onnxruntime.dll")
 		return
 	case "darwin":
 		onnxRuntimeFileName = "onnxruntime.dylib"
-		onnxRuntimeFileBytes, _ = fs.ReadFile("static/" + onnxRuntimeFileName)
+		onnxRuntimeFileBytes, _ = fs.ReadFile("static/libonnxruntime.1.23.2.dylib")
 		return
 	case "linux":
 		onnxRuntimeFileName = "onnxruntime.so"
-		onnxRuntimeFileBytes, _ = fs.ReadFile("static/" + onnxRuntimeFileName)
+		onnxRuntimeFileBytes, _ = fs.ReadFile("static/libonnxruntime.so.1.23.2")
 		return
 	}
 	logger.Logger.Panic("暂不支持的操作系统：", zap.String("系统名称", systemOS))