refactor(error): 规范化分层错误类型并优化异常捕捉

- 新增 tracing 记录异常，移除不必要的 Result
- 重构 错误处理架构

Cargo.toml

@@ -21,3 +21,4 @@ serde = { version = "1.0.228", features = ["derive"] }
 serde_json = "1.0.150"
 ndarray = "0.16.1"
 thiserror = "1.0" # 刚好可以开始接入你需要的标准库错误处理
+tracing = "0.1.44" # 埋入日志打点(后续需要继续优化，现在只是简单尝试)

ddddocr-core/Cargo.toml

@@ -13,5 +13,5 @@ serde = { workspace = true }
 serde_json = "1.0.150"
 ndarray = { workspace = true } # 继承自工作空间
 thiserror = { workspace = true } # 刚好可以开始接入你需要的标准库错误处理
-
+tracing={workspace = true}
 #serde = { workspace = true, features = ["derive"] }

ddddocr-core/src/det/executor.rs

@@ -1,7 +1,7 @@
-use anyhow::{Context, Result};
+use crate::error::{Result, TensorError};
 use image::{imageops::FilterType, DynamicImage, GenericImageView};
-use std::fmt;
 use ndarray::{prelude::*, s, Array2, Array3, Array4, Axis};
+use std::fmt;
 // use tract_onnx::prelude::{Tensor};
 
 
@@ -51,7 +51,7 @@ impl<'a> Detector<'a> {
         self.get_bbox(image)
     }
     /// 2. preproc: 纯 Rust 实现 (替代 OpenCV)
-    fn preproc(&self, image: &DynamicImage, input_size: (u32, u32)) -> Result<(Array4<f32>, f32)> {
+    fn preproc(&self, image: &DynamicImage, input_size: (u32, u32)) -> (Array4<f32>, f32) {
         let (target_h, target_w) = input_size;
         let (img_w, img_h) = image.dimensions();
 
@@ -89,7 +89,7 @@ impl<'a> Detector<'a> {
             }
         }
 
-        Ok((array, r))
+        (array, r)
     }
 
     /// 3. demo_postprocess (逻辑与 Python 一致)
@@ -252,7 +252,7 @@ impl<'a> Detector<'a> {
         // let dynamic_img = image::load_from_memory(image_bytes).context("Failed to decode utils")?;
         let (orig_w, orig_h) = dynamic_img.dimensions();
 
-        let (input_tensor, ratio) = self.preproc(dynamic_img, (416, 416))?;
+        let (input_tensor, ratio) = self.preproc(dynamic_img, (416, 416));
 
         // tract 推理
         // let outputs = self.session.session.run(tvec!(input_tensor.into()))?;
@@ -268,8 +268,13 @@ impl<'a> Detector<'a> {
         let obj_conf = pred.slice(s![.., 4..5]);
         let cls_conf = pred.slice(s![.., 5..]);
         let obj_broadcast = obj_conf
-            .broadcast(cls_conf.dim())
+            .broadcast(cls_conf.dim()).ok_or_else(|| {
-            .context("ndarray broadcasting failed for scores calculation")?;
+            TensorError::DimensionMismatch {
+                expected: format!("可广播至 cls_conf 形状 {:?}", cls_conf.shape()),
+                actual: obj_conf.shape().to_vec(),
+            }
+        })?;
+            // .context("ndarray broadcasting failed for scores calculation")?;
         let scores = &obj_broadcast * &cls_conf;
         // let scores = &pred.slice(s![.., 4..5]) * &pred.slice(s![.., 5..]);
 

ddddocr-core/src/error.rs

@@ -73,7 +73,8 @@ use thiserror::Error;
 // }
 
 /// 统一用我们自己的 DdddError 包装 Result
-pub type Result<T> = std::result::Result<T, DdddError>;
+// pub type Result<T> = std::result::Result<T, DdddError>;
+pub type Result<T, E = DdddError> = std::result::Result<T, E>;
 // =====================================================================
 // 1. 顶层全局 Error 分流器 (去 anyhow 化，完全基于标准库/自定义类型)
 // =====================================================================
@@ -83,27 +84,26 @@ pub enum DdddError {
     // /// 系统文件、网络等原生 I/O 异常 (高优先级自动转换)
     // #[error("系统网络或文件 I/O 异常: {0}")]
     // Io(#[from] std::io::Error),
-
     /// 图像预处理阶段发生异常
     #[error("图像预处理失败: {0}")]
-    Preprocess(#[from] ImagePreprocessReason),
+    Preprocess(#[from] ImagePreprocessError),
 
     /// 推理引擎与张量操作阶段发生异常
     #[error("推理与模型输入/输出张量异常: {0}")]
-    Inference(#[from] TensorErrorReason),
+    Inference(#[from] TensorError),
 
     /// 算法后处理解码阶段发生异常
     #[error("后处理解码错误: {0}")]
-    Decode(#[from] DecodeReason),
+    Decode(#[from] DecodeError),
 
     /// 框架内部不可恢复的逻辑断言错误（例如解析节点 Fact 失败）
     #[error("内部严重逻辑错误: {0}")]
     Internal(String),
 
-    /// 【流派一核心】接替 anyhow::Error 的用户自定义扩展错误
+    /// 【流派核心】接替 anyhow::Error 的用户自定义扩展错误
     /// 承载任何第三方扩展、解密、特定预处理插件在执行时产生的自定义错误
     #[error("用户自定义扩展错误: {0}")]
-    Other(Box<dyn std::error::Error + Send + Sync>),
+    Other(#[source]Box<dyn std::error::Error + Send + Sync>),
 }
 
 // =====================================================================
@@ -111,7 +111,7 @@ pub enum DdddError {
 // =====================================================================
 
 #[derive(Error, Debug)]
-pub enum ImagePreprocessReason {
+pub enum ImagePreprocessError {
     // #[error("图片加载或解码失败: {0}")]
     // ImageIo(#[from] image::ImageError),
     // image_io
@@ -119,14 +119,16 @@ pub enum ImagePreprocessReason {
     Ndarray(#[from] ndarray::ShapeError),
     // image_io
     #[error("图像矩阵维度不合规！预期: {expected}，实际图像形状: {actual:?}")]
-    InvalidImageDimensions {
+    InvalidDimensions {
         expected: String,
         actual: Vec<usize>,
     },
 
     // image_io
     /// 从 ndarray 原始数据构建图像缓冲区时，缓冲区长度与分辨率/通道数不匹配
-    #[error("图像缓冲区长度不匹配！预期大小: {expected}，实际大小: {actual} (分辨率: {width}x{height}, 通道数: {channels})")]
+    #[error(
+        "图像缓冲区长度不匹配！预期大小: {expected}，实际大小: {actual} (分辨率: {width}x{height}, 通道数: {channels})"
+    )]
     BufferLengthMismatch {
         expected: usize,
         actual: usize,
@@ -137,7 +139,18 @@ pub enum ImagePreprocessReason {
     // image_io
     #[error("不支持的图像通道数: {0} (仅支持单通道灰度L、3通道RGB、4通道RGBA)")]
     UnsupportedChannels(usize),
+    // ================= 新增：针对 HSV 和 Preset 的强类型错误 =================
+    /// HSV 颜色区间非法 (例如 H > 180 或 lower > upper)
+    #[error("HSV 颜色区间参数非法: {0}")]
+    InvalidHsvRange (String ),
 
+    /// 不支持或未知的颜色预设名称
+    #[error("不支持的颜色预设名称: {0}")]
+    UnknownColorPreset(String),
+
+    /// 颜色过滤器/预处理规则配置非法导致失败
+    #[error("颜色过滤器配置无效或初始化失败: {0}")]
+    FilterConfigInvalid(String),
     // #[error("Base64 解码失败: {0}")]
     // Base64(#[from] base64::DecodeError),
     //
@@ -153,14 +166,14 @@ pub enum ImagePreprocessReason {
 // =====================================================================
 
 #[derive(Error, Debug)]
-pub enum TensorErrorReason {
+pub enum TensorError {
     /// 替换原有的 anyhow::Error，明确将 Tract/ONNX 引擎底层报错序列化为干净的 String
     #[error("推理引擎内部发生异常: {0}")]
-    EngineError(String),
+    Engine(String),
 
     /// 模型张量维度不匹配 (原有的顶层 DimensionMismatch 被优雅地归入本模块)
     #[error("模型张量维度不匹配！预期: {expected}，实际 Tensor 形状: {actual:?}")]
-    TensorDimensionMismatch {
+    DimensionMismatch {
         expected: String,
         actual: Vec<usize>,
     },
@@ -184,10 +197,10 @@ pub enum TensorErrorReason {
 // =====================================================================
 
 #[derive(Error, Debug)]
-pub enum DecodeReason {
+pub enum DecodeError {
     /// CTC 解码器解码过程中的逻辑报错
     #[error("CTC 解码异常: {0}")]
-    CtcDecodeError(String),
+    Ctc(String),
 }
 
 // =====================================================================
@@ -223,7 +236,7 @@ impl DdddError {
     pub fn is_invalid_dimensions(&self) -> bool {
         matches!(
             self,
-            DdddError::Preprocess(ImagePreprocessReason::InvalidImageDimensions { .. })
+            DdddError::Preprocess(ImagePreprocessError::InvalidDimensions { .. })
         )
     }
 
@@ -231,22 +244,21 @@ impl DdddError {
     pub fn is_unsupported_channels(&self) -> bool {
         matches!(
             self,
-            DdddError::Preprocess(ImagePreprocessReason::UnsupportedChannels(_))
+            DdddError::Preprocess(ImagePreprocessError::UnsupportedChannels(_))
         )
     }
 
-    /// 提取出底层最原始的那个错误（无论是 IO、预处理、推理、还是第三方扩展错误）
+    // 提取出底层最原始的那个错误（无论是 IO、预处理、推理、还是第三方扩展错误）
-    /// 方便外层统一打印更深层的 `source` 链条
+    // 方便外层统一打印更深层的 `source` 链条
-    pub fn source_error(&self) -> Option<&(dyn std::error::Error + 'static)> {
+    // pub fn source_error(&self) -> Option<&(dyn std::error::Error + 'static)> {
-        use std::error::Error;
+    //     use std::error::Error;
-        match self {
+    //     match self {
-            // DdddError::Io(err) => Some(err),
+    //         // DdddError::Io(err) => Some(err),
-            DdddError::Preprocess(err) => Some(err),
+    //         DdddError::Preprocess(err) => Some(err),
-            DdddError::Inference(err) => Some(err),
+    //         DdddError::Inference(err) => Some(err),
-            DdddError::Decode(err) => Some(err),
+    //         DdddError::Decode(err) => Some(err),
-            DdddError::Other(err) => Some(err.as_ref()),
+    //         DdddError::Other(err) => Some(err.as_ref()),
-            DdddError::Internal(_) => None, // Internal 内部目前只有 String，没有底层的 Error source
+    //         DdddError::Internal(_) => None, // Internal 内部目前只有 String，没有底层的 Error source
-        }
+    //     }
-    }
+    // }
-
 }

ddddocr-core/src/ocr/color_filter.rs

@@ -1,5 +1,5 @@
+use crate::error::{ImagePreprocessError, Result};
 use crate::utils::image_processor::rgb_to_opencv_hsv;
-use anyhow::anyhow;
 use image::{DynamicImage, ImageBuffer, Rgb};
 use std::str::FromStr;
 
@@ -18,12 +18,13 @@ fn is_pixel_matched(ranges: &[HsvRange], h: u8, s: u8, v: u8) -> bool {
 pub fn apply_to_image(
     image: &DynamicImage,
     hsv_ranges: &[HsvRange],
-) -> anyhow::Result<DynamicImage> {
+) -> Result<DynamicImage, ImagePreprocessError> {
     // 1. 统一转换为连续内存的 RGB8 缓冲区 (对应 Python 的 Image 到 RGB/BGR 数组转换)
     let rgb_img = image.to_rgb8();
     let (width, height) = rgb_img.dimensions();
     let mut raw_pixels = rgb_img.into_raw();
-
+    let actual_len = raw_pixels.len();
+    let expected_len = (width as usize) * (height as usize) * 3;
     // 2. 密集计算核心：原地流式迭代修改
     // 每次取出 3 个 u8 字节，分别代表 [R, G, B]，无多余掩膜矩阵内存分配
     for chunk in raw_pixels.chunks_exact_mut(3) {
@@ -45,8 +46,14 @@ pub fn apply_to_image(
 
     // 3. 将扁平字节数组重新打包回 DynamicImage 容器
     let filtered_buffer = ImageBuffer::<Rgb<u8>, Vec<u8>>::from_raw(width, height, raw_pixels)
-        .ok_or_else(|| anyhow!("图像缓冲重新组装失败，维度与数据大小不匹配"))?;
+        // .ok_or_else(|| anyhow!("图像缓冲重新组装失败，维度与数据大小不匹配"))?;
-
+        .ok_or_else(|| ImagePreprocessError::BufferLengthMismatch {
+            expected: expected_len,
+            actual: actual_len,
+            width,
+            height,
+            channels: 3,
+        })?;
     Ok(DynamicImage::ImageRgb8(filtered_buffer))
 }
 
@@ -64,16 +71,22 @@ impl HsvRange {
 impl HsvRange {
     /// 验证当前 HSV 范围是否合法
     /// 对应 Python 逻辑：H 在 0-180，S/V 在 0-255，且下界 <= 上界
-    pub fn validate(&self) -> Result<(), String> {
+    pub fn validate(&self) -> Result<(), ImagePreprocessError> {
         // 1. 校验 H 通道边界 (OpenCV 中 H 范围是 0-180)
         if self.lower.0 > 180 || self.upper.0 > 180 {
-            return Err("H通道值必须在 0-180 范围内".to_string());
+            // return Err("H通道值必须在 0-180 范围内".to_string());
+            return Err(ImagePreprocessError::InvalidHsvRange(
+                "H通道值必须在 0-180 范围内".to_string(),
+            ));
         }
 
         // 2. 校验下界不能大于上界
         if self.lower.0 > self.upper.0 || self.lower.1 > self.upper.1 || self.lower.2 > self.upper.2
         {
-            return Err("HSV范围下界不能大于上界".to_string());
+            // return Err("HSV范围下界不能大于上界".to_string());
+            return Err(ImagePreprocessError::InvalidHsvRange(
+                "HSV范围下界不能大于上界".to_string(),
+            ));
         }
 
         Ok(())
@@ -150,7 +163,7 @@ impl ColorPreset {
         }
     }
     /// 校验逻辑：在这里实现完美的“责任分离”
-    pub fn validate(&self) -> Result<(), String> {
+    pub fn validate(&self) -> Result<(), ImagePreprocessError> {
         match self {
             // 1. 快捷变体：完全绕过，根本不校验，0 运行时开销放行！
             ColorPreset::Custom(ranges) => {
@@ -166,7 +179,7 @@ impl ColorPreset {
 }
 
 impl FromStr for ColorPreset {
-    type Err = String;
+    type Err = ImagePreprocessError;
     fn from_str(s: &str) -> Result<Self, Self::Err> {
         match s.to_lowercase().as_str() {
             "red" => Ok(ColorPreset::Red),
@@ -179,7 +192,8 @@ impl FromStr for ColorPreset {
             "black" => Ok(ColorPreset::Black),
             "white" => Ok(ColorPreset::White),
             "gray" => Ok(ColorPreset::Gray),
-            _ => Err(format!("不支持的颜色预设: {}", s)),
+            // _ => Err(format!("不支持的颜色预设: {}", s)),
+            _ => Err(ImagePreprocessError::UnknownColorPreset(s.to_string())),
         }
     }
 }
@@ -200,12 +214,12 @@ pub trait ColorFilter {
     fn estimated_count(&self) -> usize;
     /// 将自身的有效约束平铺追加到统一目标容器中
     /// 验证当前过滤器是否合法，默认直接放行（Ok(())）
-    fn validate_self(&self) -> Result<(), String> {
+    fn validate_self(&self) -> Result<(), ImagePreprocessError> {
         Ok(())
     }
     /// 【新扩展的架构方法】将自身安全的合并到已有的普通容器中，并完成去重和排序
     /// 完美的责任分离：Builder 不再需要关心怎么分配内存、怎么排序去重
-    fn collect_to_vec(&self) -> Result<Option<Vec<HsvRange>>, String> {
+    fn collect_to_vec(&self) -> Result<Option<Vec<HsvRange>>, ImagePreprocessError> {
         // 1. 触发自检
         self.validate_self()?;
 
@@ -237,7 +251,7 @@ impl ColorFilter for ColorPreset {
         // 直接获取切片长度
         self.matches().len()
     }
-    fn validate_self(&self) -> Result<(), String> {
+    fn validate_self(&self) -> Result<(), ImagePreprocessError> {
         // 直接调用我们在第一步中为 ColorPreset 实现的精细化分流校验
         // 快捷变体在这里会直接返回 Ok(()), 只有 Custom 才会去真正校验
         self.validate()
@@ -261,7 +275,7 @@ impl<'a> ColorFilter for MultiOrColorRestrict<'a> {
         self.filters.iter().map(|f| f.estimated_count()).sum()
     }
 
-    fn validate_self(&self) -> Result<(), String> {
+    fn validate_self(&self) -> Result<(), ImagePreprocessError> {
         // 递归政审：只要其中一个子过滤器校验失败（比如某个 Custom 变体非法），立刻熔断
         for f in &self.filters {
             f.validate_self()?;

ddddocr-core/src/ocr/executor.rs

@@ -4,7 +4,6 @@ use crate::ocr::color_filter::{HsvRange, apply_to_image};
 // use ddddocr_tract::session::{ModelOutput, OcrSession};
 use crate::utils::image_convert::png_rgba_white_preprocess;
 use crate::utils::image_processor::{convert_to_grayscale, resize_image};
-use anyhow::Result;
 use image::DynamicImage;
 use serde::Serialize;
 use std::borrow::Cow;
@@ -17,7 +16,9 @@ use ndarray::ArrayView2;
 //     Indices(ndarray::Array1<i64>), // 拥有完整所有权的 1维数组，可任意传递和返回
 //     Logits(ndarray::Array2<f32>),  // 拥有完整所有权的 2维矩阵，可任意传递和返回
 // }
+use crate::error::{ImagePreprocessError, Result, TensorError};
 use crate::{OcrEngine, OcrOutput};
+use tracing::{ warn};
 #[derive(Debug, Clone, Serialize)]
 pub enum OcrResult {
     /// 纯文本分支（对应 probability = false）
@@ -107,7 +108,7 @@ pub struct Ocr<'a> {
     pub(crate) png_fix: bool,
     pub(crate) probability: bool,
     /// 颜色过滤：保留的颜色列表
-    pub(crate) final_color_ranges: Result<Option<Vec<HsvRange>>, String>,
+    pub(crate) final_color_ranges: Result<Option<Vec<HsvRange>>, ImagePreprocessError>,
 
     /// 字符集范围
     pub(crate) final_charset_indices: Option<Vec<usize>>,
@@ -127,7 +128,7 @@ impl<'a> Ocr<'a> {
     }
 }
 impl<'a> Ocr<'a> {
-    pub fn predict(&self, image: &DynamicImage) -> anyhow::Result<OcrResult> {
+    pub fn predict(&self, image: &DynamicImage) -> Result<OcrResult> {
         println!("当前颜色过滤器状态: {:?}", self.final_color_ranges);
 
         // =====================================================================
@@ -137,10 +138,13 @@ impl<'a> Ocr<'a> {
         // =====================================================================
         let img_cow = match &self.final_color_ranges {
             Err(err_msg) => {
-                return Err(anyhow::anyhow!(
+                // return Err(anyhow::anyhow!(
-                    "颜色过滤器初始化失败，全链路短路: {}",
+                //     "颜色过滤器初始化失败，全链路短路: {}",
-                    err_msg
+                //     err_msg
-                ));
+                // ));
+                return Err(ImagePreprocessError::FilterConfigInvalid(
+                    err_msg.to_string(),
+                ))?;
             }
             Ok(None) => {
                 // 核心优化点：直接借用原图，不发生任何克隆
@@ -168,12 +172,12 @@ impl<'a> Ocr<'a> {
         // let raw_indices = self.ocr.extract_indices_from_tensor(&raw_tensor)?;
         // // 步骤 2: 将索引切片 `&[i64]` 传给解码器进行 CTC 去重和字符映射
         // let final_text = self.ctc_decode_to_string(&raw_indices);
-        let ocr_output = self.process_model_output(raw_tensor);
+        let ocr_output = self.process_model_output(raw_tensor)?;
-        ocr_output
+        Ok(ocr_output)
     }
     /// 对应 Python 的 _preprocess_image
     /// 负责：透明背景修复 -> 灰度化 -> 按比例 Resize -> 归一化 -> 4维张量转换
-    fn preprocess_image(&self, img: &DynamicImage) -> anyhow::Result<ndarray::Array4<f32>> {
+    fn preprocess_image(&self, img: &DynamicImage) -> Result<ndarray::Array4<f32>,ImagePreprocessError> {
         // 1. 获取模型元数据配置
         let meta = self.session.metadata();
         let norm = &meta.normalization; // 获取归一化器
@@ -239,7 +243,12 @@ impl<'a> Ocr<'a> {
                 array
             }
 
-            _ => return Err(anyhow::anyhow!("不支持的通道数配置: {}", meta.channel)),
+            // _ => return Err(anyhow::anyhow!("不支持的通道数配置: {}", meta.channel)),
+            _ => {
+                return Err(ImagePreprocessError::UnsupportedChannels(
+                    meta.channel as usize,
+                ));
+            }
         };
         Ok(array4)
         // Ok(tensor)
@@ -265,13 +274,14 @@ impl<'a> Ocr<'a> {
     }
 
     // 这段代码未来直接放入 ddddocr-core
-    fn process_model_output(&self, output: OcrOutput) -> anyhow::Result<OcrResult> {
+    fn process_model_output(&self, output: OcrOutput) -> Result<OcrResult,TensorError> {
         match output {
             OcrOutput::Indices(array1) => {
                 // 对应你原来的 process_i64_tensor
                 let slice = array1
                     .as_slice()
-                    .ok_or_else(|| anyhow::anyhow!("内存不连续，无法执行零拷贝解码"))?;
+                    // .ok_or_else(|| anyhow::anyhow!("内存不连续，无法执行零拷贝解码"))?;
+                    .ok_or_else(|| TensorError::NonContiguousMemory)?;
                 let final_text = self.ctc_decode_to_string(slice);
 
                 if self.probability {
@@ -528,8 +538,9 @@ impl<'a> Ocr<'a> {
             // 5. 字符映射
             if let Some(char_str) = tokens.get(u_idx) {
                 res.push_str(char_str);
-            } else {
+            }
-                eprintln!("警告: 预测索引 {} 超出字符集范围", u_idx);
+            else {
+                warn!("警告: 预测索引 {} 超出字符集范围", u_idx);
             }
         }
         res

ddddocr-core/src/ocr/metadata.rs

@@ -1,4 +1,4 @@
-use anyhow::{anyhow, Result};
+use crate::error::{ Result};
 use serde::Deserialize;
 use std::borrow::Cow;
 use std::collections::HashMap;

ddddocr-core/src/utils/image_convert.rs

@@ -1,4 +1,4 @@
-use crate::error::{DdddError, ImagePreprocessReason, Result};
+use crate::error::{DdddError, ImagePreprocessError, Result};
 use image::{DynamicImage, GenericImageView, ImageBuffer, Luma, Rgb, Rgba};
 use ndarray::{Array3, ArrayViewD};
 
@@ -35,19 +35,16 @@ pub fn ndarray_to_hwc_image(array: ArrayViewD<u8>) -> Result<DynamicImage> {
                 // 对应 Python: array.shape[2] == 4 (RGBA H, W, 4)
                 4 => ColorMode::RGBA,
                 _ => {
-                    return Err(DdddError::Preprocess(
+                    return Err(ImagePreprocessError::UnsupportedChannels(c))?;
-                        ImagePreprocessReason::UnsupportedChannels(c),
-                    ));
                 }
             }
         }
         _ => {
-            return Err(DdddError::Preprocess(
+            return Err(ImagePreprocessError::InvalidDimensions {
-                ImagePreprocessReason::InvalidImageDimensions {
                 expected: "2D (H,W) 或 3D (H,W,C)".to_string(),
                 actual: shape.to_vec(),
-                },
+            }
-            ));
+            .into());
         }
     };
     from_ndarray(array, color_mode)
@@ -113,7 +110,7 @@ pub fn image_to_ndarray(image: &DynamicImage, mode: ColorMode) -> Result<Array3<
     };
 
     let array = Array3::from_shape_vec((height as usize, width as usize, channels), raw)
-        .map_err(ImagePreprocessReason::from)?;
+        .map_err(ImagePreprocessError::from)?;
     Ok(array)
 }
 /// 将 array 数组转换为 DynamicImage
@@ -122,12 +119,10 @@ pub fn ndarray_to_image(array: ArrayViewD<u8>, mode: ColorMode) -> Result<Dynami
 
     // 基础边界检查：至少要有 H 和 W 两个维度
     if shape.len() < 2 {
-        return Err(DdddError::Preprocess(
+        return Err(ImagePreprocessError::InvalidDimensions {
-            ImagePreprocessReason::InvalidImageDimensions {
             expected: "至少为 2D array [H, W]".to_string(),
             actual: shape.to_vec(),
-            },
+        })?;
-        ));
     }
     from_ndarray(array, mode)
 }
@@ -156,13 +151,14 @@ fn from_ndarray(array: ArrayViewD<u8>, mode: ColorMode) -> Result<DynamicImage>
 
     // 构造通用错误闭包，避免 match 分支中重复编写冗长的错误对象
     let make_err = || {
-        DdddError::Preprocess(ImagePreprocessReason::BufferLengthMismatch {
+        ImagePreprocessError::BufferLengthMismatch {
             expected: expected_len,
             actual: raw_len,
             width,
             height,
             channels,
-        })
+        }
+        .into()
     };
 
     // 2. 重新解释内存并构建 ImageBuffer

ddddocr-core/src/utils/image_helper.rs

@@ -1,6 +1,6 @@
-use crate::error::{DdddError, ImagePreprocessReason, Result};
+use crate::error::{DdddError, Result};
 use crate::utils::image_convert::ndarray_to_hwc_image;
-use base64::{Engine as _, engine::general_purpose};
+use base64::{engine::general_purpose, Engine as _};
 use image::DynamicImage;
 use ndarray::ArrayViewD;
 use std::fmt;

ddddocr-core/src/utils/tensor_transform.rs

@@ -1,8 +1,8 @@
 use crate::OcrOutput;
-use crate::error::{DdddError, Result, TensorErrorReason};
+use crate::error::{DdddError, Result, TensorError};
 use ndarray::s;
 /// 核心层复用资产：将异构的动态维度矩阵转化为标准 OCR 2D Logits 矩阵
-pub fn normalize_ocr_logits(array: ndarray::ArrayViewD<f32>, shape: &[usize]) -> Result<OcrOutput> {
+pub fn normalize_ocr_logits(array: ndarray::ArrayViewD<f32>, shape: &[usize]) -> Result<OcrOutput,TensorError> {
     let (steps, classes, data_dyn_view) = match shape.len() {
         3 => {
             if shape[1] == 1 {
@@ -24,12 +24,12 @@ pub fn normalize_ocr_logits(array: ndarray::ArrayViewD<f32>, shape: &[usize]) ->
         // 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::Inference(
+            return Err(
-                TensorErrorReason::TensorDimensionMismatch {
+                TensorError::DimensionMismatch {
                     expected: "1D, 2D, or 3D OCR Logits".to_string(),
                     actual: shape.to_vec(),
                 },
-            ));
+            );
         }
     };
 
@@ -39,10 +39,10 @@ pub fn normalize_ocr_logits(array: ndarray::ArrayViewD<f32>, shape: &[usize]) ->
         .map_err(|shape_err| {
             // 如果是因为切片导致不连续且无法进行零拷贝变换，抛出 NonContiguousMemory
             if !data_dyn_view.is_standard_layout() {
-                DdddError::Inference(TensorErrorReason::NonContiguousMemory)
+                TensorError::NonContiguousMemory
             } else {
                 // 否则，说明是纯粹的数据元素数量不对（Shape 不匹配），抛出专属的强类型错误
-                DdddError::Inference(TensorErrorReason::LogitsDimensionMismatch(shape_err))
+                TensorError::LogitsDimensionMismatch(shape_err)
             }
         })?
         .to_owned();

ddddocr-tract/src/ocr/session.rs

@@ -1,6 +1,6 @@
 use crate::loader::ModelLoader;
 use anyhow::Context;
-use ddddocr_core::error::{DdddError, Result, TensorErrorReason};
+use ddddocr_core::error::{DdddError, Result, TensorError};
 use ddddocr_core::utils::normalize_ocr_logits;
 use ddddocr_core::{InferenceEngine, ModelMetadata, OcrEngine, OcrOutput};
 use ndarray::s;
@@ -49,7 +49,7 @@ impl InferenceEngine for OcrSession {
             .session
             .run(tvec!(tensor.into()))
             .map_err(|_| {
-                DdddError::Inference(TensorErrorReason::EngineError(
+                DdddError::Inference(TensorError::EngineError(
                     "执行模型推理失败".to_string(),
                 ))
             })?;