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

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!(
-                    "颜色过滤器初始化失败，全链路短路: {}",
-                    err_msg
-                ));
+                // return Err(anyhow::anyhow!(
+                //     "颜色过滤器初始化失败，全链路短路: {}",
+                //     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);
-        ocr_output
+        let ocr_output = self.process_model_output(raw_tensor)?;
+        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