fix: use letterbox preprocessing for YOLO (same as ultralytics)

- Keep aspect ratio with gray padding instead of stretching
- Add ImageNet normalization (mean/std)
- Proper coordinate restoration with offset/scale
- This matches original Python ultralytics preprocessing

internal/captcha/handler.go

@@ -873,8 +873,8 @@ func (h *Handler) detectYOLO(modelName, imageBase64 string) (*DetectionResult, e
 	origW := img.Bounds().Dx()
 	origH := img.Bounds().Dy()
 
-	// 预处理：Resize 到 640x640，归一化
-	input, err := preprocessYOLO(img)
+	// 预处理：Letterbox resize 到 640x640，保持纵横比
+	input, letterboxInfo, err := preprocessYOLO(img)
 	if err != nil {
 		return nil, err
 	}
@@ -899,7 +899,7 @@ func (h *Handler) detectYOLO(modelName, imageBase64 string) (*DetectionResult, e
 	if origW < 300 || origH < 150 {
 		confThresh = 0.05 // 小图使用更低阈值
 	}
-	detections := postprocessYOLO(output, origW, origH, confThresh, 0.45)
+	detections := postprocessYOLO(output, letterboxInfo, confThresh, 0.45)
 
 	result := &DetectionResult{
 		Detections: make([]Detection, len(detections)),
@@ -925,28 +925,84 @@ type YOLODetection struct {
 	ClassName     string
 }
 
+// LetterboxInfo 保存 letterbox 预处理的参数，用于后处理坐标还原
+type LetterboxInfo struct {
+	OrigW, OrigH int     // 原图尺寸
+	Scale        float64 // 缩放比例
+	OffsetX      int     // X 方向偏移
+	OffsetY      int     // Y 方向偏移
+}
+
 // preprocessYOLO 预处理图片为 YOLO 输入
-func preprocessYOLO(img image.Image) ([]float32, error) {
-	// Resize 到 640x640
-	resized := imaging.Resize(img, 640, 640, imaging.Lanczos)
+// 使用 Letterbox 方式保持纵横比（与 ultralytics 一致）
+func preprocessYOLO(img image.Image) ([]float32, LetterboxInfo, error) {
+	bounds := img.Bounds()
+	srcW, srcH := bounds.Dx(), bounds.Dy()
+	targetSize := 640
 
-	pixels := make([]float32, 3*640*640)
-	for y := 0; y < 640; y++ {
-		for x := 0; x < 640; x++ {
-			c := resized.At(x, y)
+	// Letterbox: 保持纵横比缩放，灰色填充
+	scale := minFloat(float64(targetSize)/float64(srcW), float64(targetSize)/float64(srcH))
+	newW := int(float64(srcW) * scale)
+	newH := int(float64(srcH) * scale)
+
+	// 确保至少 1 像素
+	if newW <= 0 {
+		newW = 1
+	}
+	if newH <= 0 {
+		newH = 1
+	}
+
+	// 缩放图片
+	resized := imaging.Resize(img, newW, newH, imaging.Lanczos)
+
+	// 创建灰色背景画布 (128, 128, 128)
+	canvas := image.NewRGBA(image.Rect(0, 0, targetSize, targetSize))
+	gray := color.RGBA{128, 128, 128, 255}
+	for x := 0; x < targetSize; x++ {
+		for y := 0; y < targetSize; y++ {
+			canvas.Set(x, y, gray)
+		}
+	}
+
+	// 居中粘贴缩放后的图片
+	offsetX := (targetSize - newW) / 2
+	offsetY := (targetSize - newH) / 2
+	for x := 0; x < newW; x++ {
+		for y := 0; y < newH; y++ {
+			canvas.Set(offsetX+x, offsetY+y, resized.At(x, y))
+		}
+	}
+
+	// YOLO 标准化参数 (ImageNet)
+	mean := [3]float32{0.485, 0.456, 0.406}
+	std := [3]float32{0.229, 0.224, 0.225}
+
+	pixels := make([]float32, 3*targetSize*targetSize)
+	for y := 0; y < targetSize; y++ {
+		for x := 0; x < targetSize; x++ {
+			c := canvas.At(x, y)
 			r, g, b, _ := c.RGBA()
-			// 归一化到 [0, 1]
-			pixels[0*640*640+y*640+x] = float32(r) / 65535.0
-			pixels[1*640*640+y*640+x] = float32(g) / 65535.0
-			pixels[2*640*640+y*640+x] = float32(b) / 65535.0
+			// 归一化到 [0, 1]，然后标准化
+			pixels[0*targetSize*targetSize+y*targetSize+x] = (float32(r)/65535.0 - mean[0]) / std[0]
+			pixels[1*targetSize*targetSize+y*targetSize+x] = (float32(g)/65535.0 - mean[1]) / std[1]
+			pixels[2*targetSize*targetSize+y*targetSize+x] = (float32(b)/65535.0 - mean[2]) / std[2]
 		}
 	}
 
-	return pixels, nil
+	info := LetterboxInfo{
+		OrigW:   srcW,
+		OrigH:   srcH,
+		Scale:   scale,
+		OffsetX: offsetX,
+		OffsetY: offsetY,
+	}
+
+	return pixels, info, nil
 }
 
 // postprocessYOLO YOLO11 后处理
-func postprocessYOLO(output []float32, origW, origH int, confThresh, nmsThresh float64) []YOLODetection {
+func postprocessYOLO(output []float32, info LetterboxInfo, confThresh, nmsThresh float64) []YOLODetection {
 	// YOLO11 ONNX 输出格式: [1, 5, 8400] 或 [1, 84, 8400]
 	// 布局: [batch, features, boxes]
 	// 对于单类检测 [1, 5, 8400]:
@@ -972,10 +1028,6 @@ func postprocessYOLO(output []float32, origW, origH int, confThresh, nmsThresh f
 	// 类别名称
 	classNames := []string{"icon"}
 
-	// 坐标缩放比例：从 640x640 到原图
-	scaleX := float64(origW) / 640.0
-	scaleY := float64(origH) / 640.0
-
 	detections := []YOLODetection{}
 	maxConf := 0.0
 
@@ -994,17 +1046,20 @@ func postprocessYOLO(output []float32, origW, origH int, confThresh, nmsThresh f
 			continue
 		}
 
-		// 获取边界框 (x, y, w, h)
+		// 获取边界框 (x, y, w, h) - 640x640 坐标系
 		cx := float64(output[0*numBoxes + i])
 		cy := float64(output[1*numBoxes + i])
 		w := float64(output[2*numBoxes + i])
 		h := float64(output[3*numBoxes + i])
 
-		// 缩放坐标到原图尺寸
-		cx *= scaleX
-		cy *= scaleY
-		w *= scaleX
-		h *= scaleY
+		// Letterbox 坐标还原：
+		// 1. 减去 offset（灰色填充区域）
+		// 2. 除以 scale（还原到原图尺寸）
+		// 注意：offset 和 scale 对应的是 letterbox 参数
+		cx = (cx - float64(info.OffsetX)) / info.Scale
+		cy = (cy - float64(info.OffsetY)) / info.Scale
+		w = w / info.Scale
+		h = h / info.Scale
 
 		// 转换为 x1, y1, x2, y2
 		x1 := cx - w/2
@@ -1012,6 +1067,12 @@ func postprocessYOLO(output []float32, origW, origH int, confThresh, nmsThresh f
 		x2 := cx + w/2
 		y2 := cy + h/2
 
+		// 边界检查
+		x1 = max(0, x1)
+		y1 = max(0, y1)
+		x2 = min(float64(info.OrigW), x2)
+		y2 = min(float64(info.OrigH), y2)
+
 		className := "object"
 		if 0 < len(classNames) {
 			className = classNames[0]