refactor: 优化图像输入源设计，重构为零污染的 TryFromImage 特征

- 移除原有的 ImageInput 枚举，避免运行时匹配与所有权限制
- 引入自定义 TryFromImage 特征，专用于将不同来源安全转换为 ImageSource
- 优化错误处理新增 InvalidBase64Header 错误信息
- 迁移 load_image_from_input 到 image_helper.rs 为后续剥离到业务层做准备

ddddocr-core/src/error.rs

@@ -21,8 +21,12 @@ use thiserror::Error;
 
 #[derive(Error, Debug)]
 pub enum DdddError {
+    // 【新增】专门处理文件读取、路径不存在等原生 I/O 错误
+    #[error("系统网络或文件 I/O 异常: {0}")]
+    Io(#[from] std::io::Error),
+
     #[error("图像预处理失败: {0}")]
-    PreprocessError(String),
+    PreprocessError(#[from] ImagePreprocessReason),
 
     #[error("模型推理引擎内部发生异常: {0}")]
     EngineError(#[from] anyhow::Error),
@@ -46,5 +50,27 @@ pub enum DdddError {
     InternalError(String),
 }
 
+/// 专门服务于预处理的子错误枚举，保留全部底层上下文
+#[derive(Error, Debug)]
+pub enum ImagePreprocessReason {
+    #[error("图片加载或文件 I/O 失败: {0}")]
+    ImageIo(#[from] image::ImageError),
+
+    #[error("图片转矩阵矩阵(ndarray)失败: {0}")]
+    NdarrayError(#[from] ndarray::ShapeError),
+
+    #[error("Base64 解码失败: {0}")]
+    Base64(#[from] base64::DecodeError),
+
+    #[error("Base64 头部格式不正确，缺少 ';base64,' 分隔符")]
+    InvalidBase64Header,
+
+    #[error("不支持的通道数: {0}")]
+    UnsupportedChannels(usize),
+
+    #[error("其他预处理错误: {0}")]
+    Custom(String),
+}
+
 /// 统一用我们自己的 DdddError 包装 Result
 pub type Result<T> = std::result::Result<T, DdddError>;

ddddocr-core/src/slide.rs

@@ -1,4 +1,4 @@
-use crate::utils::image_io::image_to_ndarray;
+use crate::utils::image_io::{image_to_ndarray,ColorMode};
 use crate::utils::image_processor;
 use crate::utils::image_processor::{abs_diff, min_max_loc, ndarray_to_luma8, rgb_to_gray};
 use anyhow::{Result, anyhow};
@@ -42,8 +42,8 @@ impl Slider {
         background_image: &DynamicImage,
         simple_target: bool,
     ) -> Result<SlideResult> {
-        let target_array = image_to_ndarray(target_image);
+        let target_array = image_to_ndarray(target_image,ColorMode::RGB)?;
-        let background_array = image_to_ndarray(background_image);
+        let background_array = image_to_ndarray(background_image,ColorMode::RGB)?;
 
         self.perform_slide_match(target_array.view(), background_array.view(), simple_target)
     }
@@ -55,8 +55,8 @@ impl Slider {
         background_image: &DynamicImage,
     ) -> Result<SlideResult> {
         // 1. 转换为 ndarray (HWC RGB)
-        let target_array = image_to_ndarray(target_image);
+        let target_array = image_to_ndarray(target_image,ColorMode::RGB)?;
-        let background_array = image_to_ndarray(background_image);
+        let background_array = image_to_ndarray(background_image,ColorMode::RGB)?;
 
         // 2. 执行比较逻辑 (对应 _perform_slide_comparison)
         self.perform_slide_comparison(target_array.view(), background_array.view())

ddddocr-core/src/utils.rs

@@ -1,5 +1,8 @@
 pub mod image_io;
 pub mod image_processor;
 mod tensor_transform;
+mod image_helper;
+
 // 对外统一暴露干净的 API 语义层
 pub use tensor_transform::normalize_ocr_logits;
+pub use image_io::{ColorMode};

ddddocr-core/src/utils/image_helper.rs

@@ -0,0 +1,102 @@
+use crate::error::{DdddError, ImagePreprocessReason, Result};
+use crate::utils::image_io::{base64_to_image, ndarray_to_pil_image};
+use image::DynamicImage;
+use ndarray::ArrayViewD;
+use std::path::Path;
+use std::path::PathBuf;
+
+pub struct Base64<'a>(pub &'a str);
+/// 专属图像输入源转换器
+pub struct ImageSource {
+    inner: DynamicImage,
+}
+// 1. 将 into_inner 优化为 into_image，符合 Rust 官方命名规范
+impl ImageSource {
+    /// 消耗当前包装器，获取最终的 DynamicImage
+    pub fn into_image(self) -> DynamicImage {
+        self.inner
+    }
+}
+
+pub trait TryFromImage<T>: Sized {
+    // 唯一的转换入口，通过目标类型来调用
+    fn try_from_image(value: T) -> Result<Self>;
+}
+
+// 1. 本身是 DynamicImage
+impl TryFromImage<DynamicImage> for ImageSource {
+    fn try_from_image(img: DynamicImage) -> Result<Self> {
+        Ok(Self { inner: img })
+    }
+}
+// 2.2 路径类型 A: &str (最常用)
+impl<'a> TryFromImage<&'a str> for ImageSource {
+    fn try_from_image(path_or_b64: &'a str) -> Result<Self> {
+        // 1. 嗅探：如果包含 Base64 特征
+        if path_or_b64.starts_with("data:image/") && path_or_b64.contains(";base64,") {
+            // 提取出真正的 base64 数据部分
+            let (_, clean_b64) = path_or_b64.split_once(";base64,").ok_or_else(|| {
+                // 返回一个明确的、可读性极佳的格式错误
+                DdddError::PreprocessError(ImagePreprocessReason::InvalidBase64Header)
+            })?;
+            // 转换为 Base64 包装器，并复用其 TryFromImage 实现
+            Self::try_from_image(Base64(clean_b64))
+        } else {
+            // 2. 否则，老老实实当作本地路径打开
+            let img = image::open(path_or_b64).map_err(ImagePreprocessReason::from)?;
+            Ok(Self { inner: img })
+        }
+    }
+}
+
+// 2.3 路径类型 B: &Path (标准借用)
+impl<'a> TryFromImage<&'a Path> for ImageSource {
+    fn try_from_image(path: &'a Path) -> Result<Self> {
+        let img = image::open(path).map_err(ImagePreprocessReason::from)?;
+        Ok(Self { inner: img })
+    }
+}
+// 2.4 路径类型 C: PathBuf / String (拥有所有权，透传给借用)
+impl TryFromImage<PathBuf> for ImageSource {
+    fn try_from_image(path: PathBuf) -> Result<Self> {
+        Self::try_from_image(path.as_path())
+    }
+}
+
+impl TryFromImage<String> for ImageSource {
+    fn try_from_image(path_or_b64: String) -> Result<Self> {
+        Self::try_from_image(path_or_b64.as_str())
+    }
+}
+// 2. 支持带有生命周期的借用：直接支持 &[u8]（不强绑生命周期到 ImageSource 结构体上！）
+impl<'a> TryFromImage<&'a [u8]> for ImageSource {
+    fn try_from_image(bytes: &'a [u8]) -> Result<Self> {
+        let img = image::load_from_memory(bytes).map_err(ImagePreprocessReason::from)?;
+        Ok(Self { inner: img })
+    }
+}
+
+// 4. 完美支持 ndarray 的借用 ArrayViewD
+impl<'a> TryFromImage<ArrayViewD<'a, u8>> for ImageSource {
+    fn try_from_image(array: ArrayViewD<'a, u8>) -> Result<Self> {
+        let img = ndarray_to_pil_image(array)?;
+        Ok(Self { inner: img })
+    }
+}
+
+impl<'a> TryFromImage<Base64<'a>> for ImageSource {
+    fn try_from_image(b64_str: Base64<'a>) -> Result<Self> {
+        let img = base64_to_image(b64_str.0)?;
+        Ok(Self { inner: img })
+    }
+}
+
+/// 模拟 Python 的 load_image_from_input
+#[allow(dead_code)]
+pub fn load_image_from_input<I>(input: I) -> Result<DynamicImage>
+where
+    ImageSource: TryFromImage<I>,
+{
+    let img = ImageSource::try_from_image(input)?.into_image();
+    Ok(img)
+}

ddddocr-core/src/utils/image_io.rs

@@ -1,43 +1,18 @@
-use anyhow::{Context, Result, anyhow, bail};
+use crate::error::{DdddError, ImagePreprocessReason, Result};
 use base64::{Engine as _, engine::general_purpose};
-use image::{DynamicImage, GenericImageView, ImageBuffer, ImageFormat, Luma, Rgb, RgbImage, Rgba};
+use image::{DynamicImage, GenericImageView, ImageBuffer, Luma, Rgb, Rgba};
+use ndarray::{Array3, ArrayD, ArrayViewD};
 use std::fs;
 use std::path::{Path, PathBuf};
-use ndarray::{Array3, ArrayD, ArrayViewD};
+
 #[derive(Debug)]
 pub enum ColorMode {
     RGB,
     RGBA,
     L,
 }
-/// 定义支持的输入类型枚举
+/// 将base64编码的图片转换为 DynamicImage
-pub enum ImageInput {
+pub fn base64_to_image(b64_str: &str) -> Result<DynamicImage> {
-    Bytes(Vec<u8>),
-    Array(ArrayD<u8>), // 对应 numpy 数组
-    Path(PathBuf),
-    Base64(String),
-    DynamicImage(DynamicImage),
-}
-/// 模拟 Python 的 load_image_from_input
-#[allow(dead_code)]
-pub fn load_image_from_input(img_input: ImageInput) -> Result<DynamicImage> {
-    match img_input {
-        // 2. 处理字节流 (Bytes)
-        ImageInput::Bytes(bytes) => {
-            image::load_from_memory(&bytes).context("Failed to load utils from bytes")
-        }
-        // 1. 已经是 DynamicImage
-        ImageInput::DynamicImage(i) => Ok(i),
-        // 5. 处理 ndarray (Numpy-like)
-        // 假设输入是 HWC 格式的 Array3<u8>
-        ImageInput::Array(a) => numpy_to_pil_image(a.view()),
-        // 4. 处理 Base64 字符串
-        ImageInput::Base64(b) => base64_to_image(&b),
-        // 3. 处理文件路径 (Path)
-        ImageInput::Path(p) => image::open(p).context("Failed to open utils from path"),
-    }
-}
-fn base64_to_image(b64_str: &str) -> Result<DynamicImage> {
     // 过滤掉可能存在的 base64 前缀，例如 "data:utils/png;base64,"
     let clean_b64 = if let Some(pos) = b64_str.find(",") {
         &b64_str[pos + 1..]
@@ -47,18 +22,18 @@ fn base64_to_image(b64_str: &str) -> Result<DynamicImage> {
 
     let bytes = general_purpose::STANDARD
         .decode(clean_b64.trim())
-        .map_err(|e| anyhow!("Base64 decode error: {}", e))?;
+        .map_err(ImagePreprocessReason::from)?;
 
-    image::load_from_memory(&bytes).context("Failed to load utils from decoded base64")
+    let img = image::load_from_memory(&bytes).map_err(ImagePreprocessReason::from)?;
+    Ok(img)
 }
 
 /// 读取图片文件并转换为 base64 编码字符串
-/// 对应 Python 版 get_img_base64
+// 对应 Python 版 get_img_base64
-pub fn get_img_base64<P: AsRef<Path>>(image_path: P) -> Result<String> {
+pub fn img_base64<P: AsRef<Path>>(image_path: P) -> Result<String> {
     // 1. 读取文件原始字节流
     // 使用 AsRef<Path> 泛型可以让函数同时支持 String, &str, PathBuf 等类型
-    let image_data = fs::read(&image_path)
+    let image_data = fs::read(&image_path)?;
-        .with_context(|| format!("Failed to read utils file: {:?}", image_path.as_ref()))?;
 
     // 2. 进行 Base64 编码
     // 使用 STANDARD 引擎对齐 Python 的 base64.b64encode
@@ -67,55 +42,50 @@ pub fn get_img_base64<P: AsRef<Path>>(image_path: P) -> Result<String> {
     Ok(b64_string)
 }
 
-/// 封装数组转图像的逻辑，对齐 Python 版 _numpy_to_pil_image
+/// 封装数组转图像的逻辑，
-fn numpy_to_pil_image(array: ArrayViewD<u8>) -> Result<DynamicImage> {
+// 对应 Python 版 _numpy_to_pil_image
+pub(crate) fn ndarray_to_pil_image(array: ArrayViewD<u8>) -> Result<DynamicImage> {
     let shape = array.shape();
     let dim = shape.len();
 
     // 1. 确保数据在内存中是连续的 (C order / Standard Layout)
     // 如果 arr 是经过切片或转置的，这一步会进行必要的内存拷贝
-    let standard = array.as_standard_layout();
+    // let standard = array.as_standard_layout();
-    let (raw_data, _offset) = standard.to_owned().into_raw_vec_and_offset();
+    // let (raw_data, _offset) = standard.to_owned().into_raw_vec_and_offset();
 
-    match dim {
+    let color_mode = match dim {
         // 对应 Python: len(array.shape) == 2 (灰度图 H, W)
-        2 => {
+        2 => ColorMode::L,
-            let (h, w) = (shape[0], shape[1]);
-            ImageBuffer::<Luma<u8>, _>::from_raw(w as u32, h as u32, raw_data)
-                .map(DynamicImage::ImageLuma8)
-                .ok_or_else(|| anyhow!("Failed to create Luma utils from 2D array"))
-        }
 
         // 对应 Python: len(array.shape) == 3 (H, W, C)
         3 => {
-            let (h, w, c) = (shape[0], shape[1], shape[2]);
+            let (_h, _w, c) = (shape[0], shape[1], shape[2]);
             match c {
                 // 对应 Python: array.shape[2] == 1 (单通道 H, W, 1)
-                1 => ImageBuffer::<Luma<u8>, _>::from_raw(w as u32, h as u32, raw_data)
+                1 => ColorMode::L,
-                    .map(DynamicImage::ImageLuma8),
-
                 // 对应 Python: array.shape[2] == 3 (RGB H, W, 3)
-                3 => ImageBuffer::<Rgb<u8>, _>::from_raw(w as u32, h as u32, raw_data)
+                3 => ColorMode::RGB,
-                    .map(DynamicImage::ImageRgb8),
-
                 // 对应 Python: array.shape[2] == 4 (RGBA H, W, 4)
-                4 => ImageBuffer::<Rgba<u8>, _>::from_raw(w as u32, h as u32, raw_data)
+                4 => ColorMode::RGBA,
-                    .map(DynamicImage::ImageRgba8),
-
                 _ => {
-                    return Err(anyhow!("不支持的通道数: {}", c));
+                    return Err(DdddError::PreprocessError(
+                        ImagePreprocessReason::UnsupportedChannels(c),
+                    ));
                 }
             }
-            .ok_or_else(|| anyhow!("转换彩色图失败"))
         }
-
+        _ => {
-        _ => Err(anyhow!("不支持的数组维度: {}，仅支持 2D 或 3D", dim)),
+            return Err(DdddError::DimensionMismatch {
-    }
+                expected: "2D (H,W) 或 3D (H,W,C)".to_string(),
+                actual: shape.to_vec(),
+            });
+        }
+    };
+    from_ndarray(array, color_mode)
 }
 
-/// 对应 Python 的 png_rgba_black_preprocess
+/// 处理PNG图片的RGBA透明背景，将透明部分设置为白色背景
-/// 将带有透明通道的图片转换为白色背景的 RGB 图片
+// 对应 Python 的 png_rgba_black_preprocess
-
 pub fn png_rgba_white_preprocess(img: &DynamicImage) -> DynamicImage {
     // 1. 检查是否包含透明通道，如果没有，直接克隆并返回
     if !img.color().has_alpha() {
@@ -161,104 +131,71 @@ pub fn png_rgba_white_preprocess(img: &DynamicImage) -> DynamicImage {
 
     DynamicImage::ImageRgb8(background)
 }
-pub fn image_to_numpy(image: &DynamicImage, mode: ColorMode) -> Result<Array3<u8>> {
+/// 将 DynamicImage 转换为 array 数组
+pub fn image_to_ndarray(image: &DynamicImage, mode: ColorMode) -> Result<Array3<u8>> {
     // 1. 模式转换 (对应 utils.convert(target_mode)),此函数在时保留看后续优化是否需要替代image_to_ndarray
     // Rust utils 库通过 to_rgb8, to_luma8 等方法实现转换
     let (width, height) = image.dimensions();
 
     let (channels, raw) = match mode {
-        ColorMode::RGB => (3, image.to_rgb8().into_raw()),
         ColorMode::L => (1, image.to_luma8().into_raw()),
+        ColorMode::RGB => (3, image.to_rgb8().into_raw()),
         ColorMode::RGBA => (4, image.to_rgba8().into_raw()),
     };
 
-    Array3::from_shape_vec((height as usize, width as usize, channels), raw)
+    let array = Array3::from_shape_vec((height as usize, width as usize, channels), raw)
-        .map_err(|e| anyhow!("Failed to build ndarray: {}", e))
+        .map_err(ImagePreprocessReason::from)?;
+    Ok(array)
+}
+/// 将 array 数组转换为 DynamicImage
+pub fn ndarray_to_image(array: ArrayViewD<u8>, mode: ColorMode) -> Result<DynamicImage> {
+    let shape = array.shape();
+
+    // 基础边界检查：至少要有 H 和 W 两个维度
+    if shape.len() < 2 {
+        return Err(DdddError::DimensionMismatch {
+            expected: "At least 2D array [H, W]".to_string(),
+            actual: shape.to_vec(),
+        });
+    }
+    from_ndarray(array, mode)
 }
 
-pub fn numpy_to_image(array: ArrayViewD<u8>, mode: ColorMode) -> Result<DynamicImage> {
+fn from_ndarray(array: ArrayViewD<u8>, mode: ColorMode) -> Result<DynamicImage> {
     let shape = array.shape();
-    // 1. 基础维度检查 (必须是 H, W, C 三维数组)
-    if shape.len() != 3 {
-        bail!("Expected a 3D array (H, W, C), but got {}D", shape.len());
-    }
 
+    // 映射：ndarray 的 shape 默认是 [Height, Width, (Channels)]
+    // image 库的 from_raw 接收 (width, height)
     let height = shape[0] as u32;
     let width = shape[1] as u32;
-    let channels = shape[2];
+
-    // 2. 检查通道数是否与模式匹配
+    // 1. 确保数据在内存中是连续的 (C order)
-    let expected_channels = match mode {
-        ColorMode::L => 1,
-        ColorMode::RGB => 3,
-        ColorMode::RGBA => 4,
-    };
-    if channels != expected_channels {
-        bail!(
-            "Mode {:?} expects {} channels, but array has {}",
-            mode,
-            expected_channels,
-            channels
-        );
-    }
-    // 确保数据连续性 (C-order)
     let standard = array.as_standard_layout();
     let (raw_data, _) = standard.to_owned().into_raw_vec_and_offset();
-
+    let raw_len = raw_data.len();
+    // 2. 重新解释内存并构建 ImageBuffer
     match mode {
         ColorMode::L => ImageBuffer::<Luma<u8>, _>::from_raw(width, height, raw_data)
-            .map(DynamicImage::ImageLuma8),
+            .map(DynamicImage::ImageLuma8).ok_or_else(|| {
+            DdddError::PreprocessError(ImagePreprocessReason::Custom(format!(
+                "Failed to construct Luma image: buffer size {} does not match expected {} ({}x{}x1)",
+                raw_len, width * height * 1, width, height
+            )))
+        }),
         ColorMode::RGB => ImageBuffer::<Rgb<u8>, _>::from_raw(width, height, raw_data)
-            .map(DynamicImage::ImageRgb8),
+            .map(DynamicImage::ImageRgb8).ok_or_else(|| {
+            DdddError::PreprocessError(ImagePreprocessReason::Custom(format!(
+                "Failed to construct RGB image: buffer size {} does not match expected {} ({}x{}x3)",
+                raw_len, width * height * 3, width, height
+            )))
+        }),
         ColorMode::RGBA => ImageBuffer::<Rgba<u8>, _>::from_raw(width, height, raw_data)
-            .map(DynamicImage::ImageRgba8),
+            .map(DynamicImage::ImageRgba8).ok_or_else(|| {
+            DdddError::PreprocessError(ImagePreprocessReason::Custom(format!(
+                "Failed to construct RGBA image: buffer size {} does not match expected {} ({}x{}x4)",
+                raw_len, width * height * 4, width, height
+            )))
+        }),
     }
-    .ok_or_else(|| anyhow!("Failed to construct ImageBuffer. Buffer size might be incorrect."))
-}
-pub fn image_to_ndarray(img: &DynamicImage) -> Array3<u8> {
-    let (width, height) = img.dimensions();
-
-    // 1. 强制转为 RGB8 (丢弃 Alpha 通道，与 Python 的 target_mode='RGB' 对齐)
-    let rgb_img = img.to_rgb8();
-
-    // 2. 获取原始像素数据
-    let raw_data = rgb_img.into_raw();
-
-    // 3. 构造数组 (通道数改为 3)
-    Array3::from_shape_vec((height as usize, width as usize, 3), raw_data)
-        .expect("Failed to construct ndarray from utils") // 建议显式报错，而不是返回全黑图
 }
 
-#[allow(dead_code)]
-fn save_rust_result(result: &ImageBuffer<Luma<f32>, Vec<f32>>, filename: &str) {
-    let (width, height) = result.dimensions();
-
-    // 1. 寻找最值进行归一化
-    let mut max_val = f32::MIN;
-    let mut min_val = f32::MAX;
-    for p in result.pixels() {
-        if p.0[0] > max_val {
-            max_val = p.0[0];
-        }
-        if p.0[0] < min_val {
-            min_val = p.0[0];
-        }
-    }
-
-    // 2. 创建 8 位灰度图
-    let mut out_buf = ImageBuffer::new(width, height);
-    for y in 0..height {
-        for x in 0..width {
-            let val = result.get_pixel(x, y).0[0];
-            let normalized = if max_val > min_val {
-                ((val - min_val) / (max_val - min_val) * 255.0) as u8
-            } else {
-                0u8
-            };
-            out_buf.put_pixel(x, y, Luma([normalized]));
-        }
-    }
-
-    // 3. 保存
-    DynamicImage::ImageLuma8(out_buf).save(filename).unwrap();
-    println!("Rust 结果热力图已保存至: {}", filename);
-}

ddddocr-core/src/utils/image_processor.rs

@@ -1,6 +1,6 @@
-use image::{DynamicImage, GrayImage, ImageBuffer, Luma, imageops::FilterType};
+use image::{imageops::FilterType, DynamicImage, GrayImage, ImageBuffer, Luma};
 
-use ndarray::{Array2, Array3, ArrayView2, ArrayView3, azip};
+use ndarray::{azip, Array2, Array3, ArrayView2, ArrayView3};
 use std::cmp::{max, min};
 
 // 模拟openCV
@@ -174,8 +174,6 @@ pub fn rgb_to_opencv_hsv(r: u8, g: u8, b: u8) -> (u8, u8, u8) {
     (h_opencv, s_opencv, v_opencv)
 }
 
-
-
 /// 对应 Python 的 convert_to_grayscale
 /// 将图像转换为灰度图 (L模式)
 pub fn convert_to_grayscale(image: &DynamicImage) -> GrayImage {

ddddocr-core/src/utils/tensor_transform.rs

@@ -6,16 +6,22 @@ pub fn normalize_ocr_logits(array: ndarray::ArrayD<f32>, shape: &[usize]) -> Res
     let (steps, classes, data_dyn_view) = match shape.len() {
         3 => {
             if shape[1] == 1 {
+                // 形状: [Steps, 1, Classes]
                 (shape[0], shape[2], array)
             } else if shape[0] == 1 {
+                // 形状: [1, Steps, Classes]
                 (shape[1], shape[2], array)
             } else {
+                // 默认取第一个 batch: [Batch, Steps, Classes]
                 // 使用 ndarray 的 s! 宏，对应 Python 的 output[0, :, :]
                 let sliced = array.slice_move(s![0, .., ..]);
                 (shape[1], shape[2], sliced.into_dyn())
             }
         }
+        // 形状: [Steps, Classes]
         2 => (shape[0], shape[1], array),
+        // 形状: [Classes] -> 单字符输出（对应 Python 的 ndim == 0 保护逻辑）
+        // 我们把它虚构成一个 [1, Classes] 的 2D 矩阵来复用后面的 argmax 逻辑
         1 => (1, shape[0], array),
         _ => {
             return Err(DdddError::DimensionMismatch {

ddddocr-tract/tests/ocr_test.rs

@@ -1,10 +1,10 @@
+use anyhow::Context;
 use ddddocr_core::det::DetectionResult;
-use ddddocr_core::{DetBuilder,  Detector, ModelMetadata, Ocr,  Slider}; // 假设你的包名是这个
+use ddddocr_core::{DetBuilder, Detector, ModelMetadata, Ocr, Slider}; // 假设你的包名是这个
-use ddddocr_tract::{DetSession,OcrSession};
+use ddddocr_tract::{DetSession, OcrSession};
-use image::{DynamicImage, Rgb};
+use image::{DynamicImage, ImageBuffer, Luma, Rgb};
 use std::fs;
 use std::path::Path;
-use anyhow::Context;
 use tract_onnx::prelude::{ShapeFact, TDim};
 
 mod char_slice;
@@ -67,7 +67,40 @@ fn save_debug_image(
     img.save(output_path)?;
     Ok(())
 }
+#[allow(dead_code)]
+fn save_rust_result(result: &ImageBuffer<Luma<f32>, Vec<f32>>, filename: &str) {
+    let (width, height) = result.dimensions();
 
+    // 1. 寻找最值进行归一化
+    let mut max_val = f32::MIN;
+    let mut min_val = f32::MAX;
+    for p in result.pixels() {
+        if p.0[0] > max_val {
+            max_val = p.0[0];
+        }
+        if p.0[0] < min_val {
+            min_val = p.0[0];
+        }
+    }
+
+    // 2. 创建 8 位灰度图
+    let mut out_buf = ImageBuffer::new(width, height);
+    for y in 0..height {
+        for x in 0..width {
+            let val = result.get_pixel(x, y).0[0];
+            let normalized = if max_val > min_val {
+                ((val - min_val) / (max_val - min_val) * 255.0) as u8
+            } else {
+                0u8
+            };
+            out_buf.put_pixel(x, y, Luma([normalized]));
+        }
+    }
+
+    // 3. 保存
+    DynamicImage::ImageLuma8(out_buf).save(filename).unwrap();
+    println!("Rust 结果热力图已保存至: {}", filename);
+}
 #[test]
 fn test_full_classification() {
     // 1. 初始化模型
@@ -84,7 +117,8 @@ fn test_full_classification() {
     .expect("模型加载失败");
 
     // 2. 加载测试图片
-    let img = image::open("D:/CNWei/CNW/Rust/ddddocr-rs/samples/code2.png").expect("测试图片不存在");
+    let img =
+        image::open("D:/CNWei/CNW/Rust/ddddocr-rs/samples/code2.png").expect("测试图片不存在");
 
     // 3. 执行识别
     let result = Ocr::new(&ocr)
@@ -117,7 +151,11 @@ fn test_det_load() -> anyhow::Result<()> {
         println!("未检测到任何目标。");
     } else {
         // 如果 save_debug_image 报错，记得去把它的入参类型和内部访问也改为 DetectionResult
-        save_debug_image(&img, &bboxes, "D:/CNWei/CNW/Rust/ddddocr-rs/samples/result.jpg")?;
+        save_debug_image(
+            &img,
+            &bboxes,
+            "D:/CNWei/CNW/Rust/ddddocr-rs/samples/result.jpg",
+        )?;
 
         for (i, bbox) in bboxes.iter().enumerate() {
             // 【修改点 3】将原来的 bbox[0].. 索引访问改为结构体字段访问
@@ -133,8 +171,10 @@ fn test_real_slide_match() {
 
     // 1. 加载你准备好的测试图
     // 假设图片放在项目根目录下的 assets 文件夹
-    let target_img = load_image("D:/CNWei/CNW/Rust/ddddocr-rs/samples/hua.png").expect("请确保 samples/hua.png 存在");
+    let target_img = load_image("D:/CNWei/CNW/Rust/ddddocr-rs/samples/hua.png")
-    let bg_img = load_image("D:/CNWei/CNW/Rust/ddddocr-rs/samples/huatu.png").expect("请确保 samples/huatu.png 存在");
+        .expect("请确保 samples/hua.png 存在");
+    let bg_img = load_image("D:/CNWei/CNW/Rust/ddddocr-rs/samples/huatu.png")
+        .expect("请确保 samples/huatu.png 存在");
 
     // 2. 执行匹配
     // 如果是那种带有明显阴影边缘的复杂滑块，建议 simple_target 传 false
@@ -162,8 +202,10 @@ fn test_real_slide_comparison() {
 
     // 1. 加载你准备好的测试图
     // 假设图片放在项目根目录下的 assets 文件夹
-    let target_img = load_image("D:/CNWei/CNW/Rust/ddddocr-rs/samples/ken.jpg").expect("请确保 samples/ken.jpg 存在");
+    let target_img = load_image("D:/CNWei/CNW/Rust/ddddocr-rs/samples/ken.jpg")
-    let bg_img = load_image("D:/CNWei/CNW/Rust/ddddocr-rs/samples/kenyuan.jpg").expect("请确保 samples/kenyuan.jpg 存在");
+        .expect("请确保 samples/ken.jpg 存在");
+    let bg_img = load_image("D:/CNWei/CNW/Rust/ddddocr-rs/samples/kenyuan.jpg")
+        .expect("请确保 samples/kenyuan.jpg 存在");
 
     // 2. 执行匹配
     // 如果是那种带有明显阴影边缘的复杂滑块，建议 simple_target 传 false
@@ -189,7 +231,10 @@ fn test_real_slide_comparison() {
 #[test]
 fn test_resolve_shape_logic_direct() {
     // 创建一个哑 ModelLoader 实例（session 用不上，因为我们直接测私有方法）
-    let loader = ModelLoader::model_for_path("D:\\CNWei\\CNW\\Rust\\ddddocr-rs\\models\\common_sml2h3_f32.onnx",).expect("建立测试模型图失败");
+    let loader = ModelLoader::model_for_path(
-    let md_info=&loader.model_info().context("信息");
+        "D:\\CNWei\\CNW\\Rust\\ddddocr-rs\\models\\common_sml2h3_f32.onnx",
-    println!("md_info: {:?}",md_info);
+    )
-}
+    .expect("建立测试模型图失败");
+    let md_info = &loader.model_info().context("信息");
+    println!("md_info: {:?}", md_info);
+}