refactor(core): 提炼公共类型

- 将 AxisDim、TensorInfo 等公共类型下沉至 ddddocr_core::types
- 项目结构优化

Cargo.toml

@@ -1,12 +1,29 @@
-[package]
+[workspace]
-name = "ddddocr-rs"
+resolver = "2"
-version = "0.1.0"
+members = [
+    "ddddocr-core", "ddddocr-ort",
+    "ddddocr-tract",
+]
+
+[workspace.package]
+version = "0.2.1"
 edition = "2024"
 license = "MIT OR Apache-2.0"
 
-[dependencies]
+
-tract-onnx = { version = "0.21.10" }
+[workspace.dependencies]
-anyhow = "1.0.102"
+tract-onnx = "0.23.4"
+tract-linalg = { version = "0.23.4",features = ["multithread-mm"]}
+ort = "2.0.0-rc.12"
+ndarray = "0.17.2"
+
 image = "0.25.10"
 base64 = "0.22.1"
 imageproc = { version = "0.26.2", default-features = true }
+
+serde = { version = "1.0.228", features = ["derive"] }
+serde_json = "1.0.150"
+
+anyhow = "1.0.102"
+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

@@ -0,0 +1,16 @@
+[package]
+name = "ddddocr-core"
+version = { workspace = true }
+edition = { workspace = true }
+license = { workspace = true }
+
+[dependencies]
+ndarray = { workspace = true } # 继承自工作空间
+
+base64 = { workspace = true }
+
+image = { workspace = true }
+imageproc = { workspace = true }
+
+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}

ddddocr-core/src/det.rs

@@ -0,0 +1,6 @@
+mod builder;
+mod executor;
+
+pub use builder::DetBuilder;
+pub use executor::{DetectionResult, Detector};
+// pub use ddddocr_tract::det::session::DetSession;

ddddocr-core/src/det/builder.rs

@@ -0,0 +1,11 @@
+use crate::det::executor::Detector;
+// use ddddocr_tract::det::session::DetSession;
+use crate::traits::DetEngine;
+#[derive(Default)]
+pub struct DetBuilder;
+
+impl DetBuilder {
+    fn build<E: DetEngine>(self, session: &E) -> Detector<'_> {
+        Detector { session }
+    }
+}

ddddocr-core/src/det/executor.rs

@@ -1,33 +1,54 @@
-use crate::models::loader::{ModelLoader, ModelSession, ModelType};
+use crate::error::{Result, TensorError};
-use anyhow::{Context, Result};
 use image::{DynamicImage, GenericImageView, imageops::FilterType};
-use tract_onnx::prelude::tract_ndarray::{Array2, Array3, Array4, Axis, prelude::*, s};
+use ndarray::{Array2, Array3, Array4, Axis, prelude::*, s};
-use tract_onnx::prelude::{Graph, RunnableModel, Tensor, TypedFact, TypedOp, tvec};
+use std::fmt;
+// use tract_onnx::prelude::{Tensor};
 
-pub struct Det {
+// use ddddocr_tract::det::session::DetSession;
-    session: RunnableModel<TypedFact, Box<dyn TypedOp>, Graph<TypedFact, Box<dyn TypedOp>>>,
+use crate::{DetBuilder, DetOutput, OcrBuilder};
+use crate::traits::DetEngine;
+#[derive(Debug, Clone, Copy)]
+pub struct DetectionResult {
+    pub x1: i32,
+    pub y1: i32,
+    pub x2: i32,
+    pub y2: i32,
+    pub score: f32,
+    pub class_id: u32,
 }
-impl ModelSession for Det {
+
-    fn get_model_type(&self) -> ModelType {
+impl fmt::Display for DetectionResult {
-        todo!()
+    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
-    }
+        // 结构体只管自己这一行怎么显示，不用管外部的索引 [i]
-    fn desc(&self) -> String {
+        write!(
-        "Detection Model 加载成功".to_string()
+            f,
+            "x1={}, y1={}, x2={}, y2={}, 分数={:.4}, 类别ID={}",
+            self.x1, self.y1, self.x2, self.y2, self.score, self.class_id
+        )
     }
 }
-impl Det {
+
-    pub fn new(model_path: String) -> Result<Self, anyhow::Error> {
+pub struct Detector<'a> {
-        let session = ModelLoader::load_model(&model_path)?.session;
+    pub(crate) session: &'a dyn DetEngine,
-        Ok(Self { session })
+}
+
+impl<'a> Detector<'a> {
+    pub fn new(session: &'a dyn DetEngine) -> Self {
+        Detector { session }
     }
-    pub fn predict(&self, image_bytes: &[u8]) -> Result<Vec<Vec<i32>>> {
+    pub fn builder() -> DetBuilder {
+        DetBuilder::default()
+    }
+}
+impl<'a> Detector<'a> {
+    pub fn predict(&self, image: &DynamicImage) -> Result<Vec<DetectionResult>> {
         // Rust 中通常在调用层处理文件/PIL转换，这里直接进入核心逻辑
-        self.get_bbox(image_bytes)
+        Ok(self.get_bbox(image)?)
     }
     /// 2. preproc: 纯 Rust 实现 (替代 OpenCV)
-    fn preproc(&self, img: &DynamicImage, input_size: (u32, u32)) -> Result<(Tensor, 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) = img.dimensions();
+        let (img_w, img_h) = image.dimensions();
 
         // 计算缩放比例 (Letterbox)
         let r = (target_h as f32 / img_h as f32).min(target_w as f32 / img_w as f32);
@@ -35,7 +56,7 @@ impl Det {
         let new_w = (img_w as f32 * r) as u32;
 
         // Resize 图像
-        let resized = img.resize_exact(new_w, new_h, FilterType::Triangle);
+        let resized = image.resize_exact(new_w, new_h, FilterType::Triangle);
         // 2. 关键：将 DynamicImage 显式转换为 RgbImage (Rgb<u8>)
         let resized_rgb = resized.to_rgb8();
         // 创建 114 灰度填充的背景
@@ -45,23 +66,25 @@ impl Det {
         // 将 resize 后的图像覆盖到左上角 (类似于原始代码中的 padded_img[:h, :w])
         image::imageops::overlay(&mut base_img, &resized_rgb, 0, 0);
 
+        // 优化：直接获取底层的扁平 raw buffer，比 enumerate_pixels() 快得多
+        let raw_samples = base_img.as_flat_samples();
+        let slice = raw_samples.as_slice();
+
         // 构造 NCHW Tensor
         let mut array = Array4::<f32>::zeros((1, 3, target_h as usize, target_w as usize));
-        for (x, y, pixel) in base_img.enumerate_pixels() {
+
-            let x = x as usize;
+        // 用连续的 stride 步长进行写入，提高 CPU 缓存利用率
-            let y = y as usize;
+        for y in 0..target_h as usize {
-            // 核心对标 Python 的 BGR 逻辑：
+            for x in 0..target_w as usize {
-            // pixel[0] 是 R, pixel[1] 是 G, pixel[2] 是 B
+                let idx = (y * target_w as usize + x) * 3;
-            // 如果模型需要 BGR：
+                // BGR 赋值
-            // array[[0, 0, y as usize, x as usize]] = pixel[0] as f32;
+                array[[0, 0, y, x]] = slice[idx + 2] as f32; // B
-            // array[[0, 1, y as usize, x as usize]] = pixel[1] as f32;
+                array[[0, 1, y, x]] = slice[idx + 1] as f32; // G
-            // array[[0, 2, y as usize, x as usize]] = pixel[2] as f32;
+                array[[0, 2, y, x]] = slice[idx] as f32; // R
-            array[[0, 0, y, x]] = pixel[2] as f32; // B
+            }
-            array[[0, 1, y, x]] = pixel[1] as f32; // G
-            array[[0, 2, y, x]] = pixel[0] as f32; // R
         }
 
-        Ok((array.into(), r))
+        (array, r)
     }
 
     /// 3. demo_postprocess (逻辑与 Python 一致)
@@ -161,13 +184,14 @@ impl Det {
     }
 
     /// 5. multiclass_nms
+    //multiclass_nms_class_agnostic
     pub fn multiclass_nms(
         &self,
         boxes: &Array2<f32>,  // [25200, 4] -> xyxy 格式
         scores: &Array2<f32>, // [25200, 80] -> 已经乘以 objectness 的得分
         nms_thr: f32,
         score_thr: f32,
-    ) -> Vec<Vec<f32>> {
+    ) -> Vec<[f32; 6]> {
         let mut candidates = Vec::new();
 
         // 1. 筛选高分框 (单次遍历完成 Argmax 和 Threshold 过滤)
@@ -213,30 +237,44 @@ impl Det {
             .map(|k_idx| {
                 let (orig_idx, score, cls_id) = candidates[k_idx];
                 let b = boxes.row(orig_idx);
-                vec![b[0], b[1], b[2], b[3], score, cls_id as f32]
+                [b[0], b[1], b[2], b[3], score, cls_id as f32]
             })
             .collect()
     }
     /// 6. get_bbox (完全解耦 OpenCV)
-    pub fn get_bbox(&self, image_bytes: &[u8]) -> Result<Vec<Vec<i32>>> {
+    pub fn get_bbox(
+        &self,
+        dynamic_img: &DynamicImage,
+    ) -> Result<Vec<DetectionResult>, TensorError> {
         // 使用 utils crate 解码
-        let dynamic_img = image::load_from_memory(image_bytes).context("Failed to decode utils")?;
+        // 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.run(tvec!(input_tensor.into()))?;
+        // let outputs = self.session.session.run(tvec!(input_tensor.into()))?;
-        let output_array = outputs[0]
+        let outputs = self.session.inference(input_tensor)?;
-            .to_array_view::<f32>()?
+        // let output_array = outputs[0]
-            .to_owned()
+        // 2. 无缝、安全地解包出标准 3维 矩阵
-            .into_dimensionality::<Ix3>()?;
+        let DetOutput::Detection(output_array) = outputs;
 
         let predictions = self.demo_postprocess(output_array, (416, 416));
         let pred = predictions.slice(s![0, .., ..]);
 
         let boxes = pred.slice(s![.., 0..4]);
-        let scores = &pred.slice(s![.., 4..5]) * &pred.slice(s![.., 5..]);
+        let obj_conf = pred.slice(s![.., 4..5]);
+        let cls_conf = pred.slice(s![.., 5..]);
+        let obj_broadcast =
+            obj_conf
+                .broadcast(cls_conf.dim())
+                .ok_or_else(|| 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..]);
 
         let mut boxes_xyxy = Array2::<f32>::zeros(boxes.raw_dim());
         for i in 0..boxes.nrows() {
@@ -247,17 +285,17 @@ impl Det {
         }
 
         let detections = self.multiclass_nms(&boxes_xyxy, &scores, 0.45, 0.1);
-
+        let final_results = detections
-        Ok(detections
             .into_iter()
-            .map(|d| {
+            .map(|d| DetectionResult {
-                vec![
+                x1: (d[0] as i32).max(0).min(orig_w as i32),
-                    (d[0] as i32).max(0).min(orig_w as i32),
+                y1: (d[1] as i32).max(0).min(orig_h as i32),
-                    (d[1] as i32).max(0).min(orig_h as i32),
+                x2: (d[2] as i32).max(0).min(orig_w as i32),
-                    (d[2] as i32).max(0).min(orig_w as i32),
+                y2: (d[3] as i32).max(0).min(orig_h as i32),
-                    (d[3] as i32).max(0).min(orig_h as i32),
+                score: d[4],
-                ]
+                class_id: d[5] as u32,
             })
-            .collect())
+            .collect();
+        Ok(final_results)
     }
 }

ddddocr-core/src/error.rs

@@ -0,0 +1,274 @@
+pub(crate) const MODEL_DOWNLOAD_HELP: &str = "\
+================================================================================
+[ddddocr-rust] 错误：未找到默认的模型文件！
+--------------------------------------------------------------------------------
+由于打包体积限制，本库未内置 ONNX 模型。请按照以下步骤操作：
+
+1. 前往官方 GitHub 下载对应的模型权重：
+   - OCR 模型:  https://github.com/sml2h3/ddddocr/raw/master/ddddocr/common_sml2h3_f32.onnx
+   - DET 模型:  https://github.com/sml2h3/ddddocr/raw/master/ddddocr/common_det.onnx
+
+2. 配置加载方式（二选一）：
+   A. 【推荐】设置环境变量指向您下载的文件：
+      Linux/macOS: export DDDD_OCR_MODEL=\"/path/to/common_sml2h3_f32.onnx\"
+      Windows (CMD): set DDDD_OCR_MODEL=C:\\path\\to\\common_sml2h3_f32.onnx
+      Windows (PowerShell): $env:DDDD_OCR_MODEL=\"C:\\path\\to\\common_sml2h3_f32.onnx\"
+
+   B. 或者直接将模型文件重命名并放置在您运行程序的“当前工作目录”或“可执行文件同级目录”下。
+================================================================================";
+
+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(#[from] ImagePreprocessReason),
+//
+//     #[error("模型推理引擎内部发生异常: {0}")]
+//     EngineError(#[from] anyhow::Error),
+//
+//     #[error("CTC 解码错误: {0}")]
+//     DecodeError(String),
+//
+//     #[error("维度转换失败，预期维度 {expected}，实际形状为 {actual:?}")]
+//     DimensionMismatch {
+//         expected: String,
+//         actual: Vec<usize>,
+//     },
+//
+//     #[error("内存不连续，无法执行零拷贝操作")]
+//     NonContiguousMemory,
+//
+//     #[error("未知的模型输出格式")]
+//     UnknownOutputFormat,
+//
+//     #[error("解析节点 Fact 失败")]
+//     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>;
+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)
+// =====================================================================
+
+#[derive(Error, Debug)]
+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] 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] 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] 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(#[source] Box<dyn std::error::Error + Send + Sync>),
+}
+
+// =====================================================================
+// 2. 子领域 A: 图像预处理错误类型
+// =====================================================================
+
+#[derive(Error, Debug)]
+pub enum ImagePreprocessError {
+    // #[error("图片加载或解码失败: {0}")]
+    // ImageIo(#[from] image::ImageError),
+    // image_io
+    #[error("图片转矩阵(ndarray)基础操作失败: {0}")]
+    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:?}")]
+    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})"
+    )]
+    BufferLengthMismatch {
+        expected: usize,
+        actual: usize,
+        width: u32,
+        height: u32,
+        channels: usize,
+    },
+    // 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("图像维度不匹配！{0}")]
+    MismatchDimensions (String),
+
+    #[error("滑块模板尺寸 [{target_w}x{target_h}] 大于背景图 [{bg_w}x{bg_h}]")]
+    TargetExceedsBackground {
+        target_w: usize,
+        target_h: usize,
+        bg_w: usize,
+        bg_h: usize,
+    },
+    // #[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("其他预处理错误: {0}")]
+    // Other(String),
+}
+
+// =====================================================================
+// 3. 子领域 B: 推理与张量操作错误类型
+// =====================================================================
+
+#[derive(Error, Debug)]
+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}")]
+    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:?}")]
+    DimensionMismatch {
+        expected: String,
+        actual: Vec<usize>,
+    },
+
+    /// 新增：针对后处理 Logits 矩阵变形（Reshape）失败的精细化错误
+    /// 直接包装 ndarray::ShapeError，保留强类型，完美支持 match
+    #[error("OCR Logits 矩阵变形失败: {0}")]
+    LogitsDimensionMismatch(#[from] ndarray::ShapeError),
+
+    /// 张量内存布局不是连续的
+    #[error("内存不连续，无法执行零拷贝操作")]
+    NonContiguousMemory,
+
+    /// 模型的输出数据类型或格式不受支持
+    #[error("未知的模型输出格式")]
+    UnknownOutputFormat,
+}
+
+// =====================================================================
+// 4. 子领域 C: 算法解码错误类型
+// =====================================================================
+
+#[derive(Error, Debug)]
+pub enum DecodeError {
+    /// CTC 解码器解码过程中的逻辑报错
+    #[error("CTC 解码异常: {0}")]
+    Ctc(String),
+}
+
+// =====================================================================
+// 5. 【自定义错误安全注入】不使用全局 `#[from]`，采用显式包装避免特化冲突
+// =====================================================================
+
+impl DdddError {
+    /// 提供类似 std::io::Error::new 的构造函数，方便手动且无痛地包装任意第三方错误
+    pub fn new<E>(error: E) -> Self
+    where
+        E: Into<Box<dyn std::error::Error + Send + Sync>>,
+    {
+        DdddError::Other(error.into())
+    }
+    // -----------------------------------------------------------------
+    // 2.3 优化：提供一键判断与转换的快捷方法（Downcasting Helpers）
+    // -----------------------------------------------------------------
+
+    /// 快速判断是否是系统 I/O 错误
+    // pub fn is_io_error(&self) -> bool {
+    //     matches!(self, DdddError::Io(_))
+    // }
+
+    /// 尝试将错误转换为引用形式的 `std::io::Error`
+    // pub fn as_io_error(&self) -> Option<&std::io::Error> {
+    //     match self {
+    //         DdddError::Io(err) => Some(err),
+    //         _ => None,
+    //     }
+    // }
+
+    /// 快速判断是否是预处理阶段的图片维度不合规错误
+    pub fn is_invalid_dimensions(&self) -> bool {
+        matches!(
+            self,
+            DdddError::Preprocess(ImagePreprocessError::InvalidDimensions { .. })
+        )
+    }
+
+    /// 快速判断是否是因为图片通道数不合规导致的失败
+    pub fn is_unsupported_channels(&self) -> bool {
+        matches!(
+            self,
+            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）
+    // 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)> {
+    //     use std::error::Error;
+    //     match self {
+    //         // DdddError::Io(err) => Some(err),
+    //         DdddError::Preprocess(err) => Some(err),
+    //         DdddError::Inference(err) => Some(err),
+    //         DdddError::Decode(err) => Some(err),
+    //         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
+    //     }
+    // }
+}

ddddocr-core/src/lib.rs

@@ -0,0 +1,24 @@
+mod det;
+pub mod error;
+mod ocr;
+mod slide;
+pub mod utils;
+pub mod types;
+pub mod traits;
+
+pub use crate::det::{DetBuilder, DetectionResult, Detector};
+pub use crate::ocr::{Charset, ModelMetadata, Normalization, Ocr, OcrBuilder, OcrResult, Resize};
+pub use crate::slide::{SlideResult, Slider};
+
+
+// DetSession
+
+pub enum OcrOutput {
+    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>),
+}
+/// 2. 目标检测专属的、编译期安全的输出枚举
+pub enum DetOutput {
+    Detection(ndarray::Array3<f32>), // 拥有完整所有权的 2维矩阵，可任意传递和返回
+}
+

ddddocr-core/src/ocr.rs

@@ -0,0 +1,13 @@
+mod builder;
+mod charset;
+mod color_filter;
+mod executor;
+mod metadata;
+mod token_filter;
+
+pub use builder::OcrBuilder;
+pub use charset::Charset;
+pub use executor::{Ocr, OcrResult};
+pub use metadata::{ModelMetadata, Normalization, Resize};
+pub use token_filter::TokenFilter;
+// pub use ddddocr_tract::session::OcrSession;

ddddocr-core/src/ocr/builder.rs

@@ -0,0 +1,74 @@
+use crate::ocr::executor::Ocr;
+// use ddddocr_tract::session::OcrSession;
+use crate::traits::OcrEngine;
+use crate::ocr::color_filter::ColorFilter;
+use crate::ocr::token_filter::TokenFilter;
+#[derive(Default)]
+pub struct OcrBuilder {
+    /// 是否修复PNG格式问题
+    png_fix: bool,
+    /// 是否返回概率信息
+    probability: bool,
+    /// 颜色过滤：保留的颜色列表
+    color_filter: Option<Box<dyn ColorFilter + Send + Sync>>,
+    /// 字符集范围
+    charset_restrict: Option<Box<dyn TokenFilter + Send + Sync>>,
+}
+
+impl OcrBuilder {
+    // 初始化任务，设置默认参数
+    pub fn new() -> Self {
+        Self {
+            png_fix: false, // 默认值
+            probability: false,
+            color_filter: None,
+            charset_restrict: None,
+        }
+    }
+    pub fn png_fix(mut self, value: bool) -> Self {
+        self.png_fix = value;
+        self
+    }
+    pub fn probability(mut self, value: bool) -> Self {
+        self.probability = value;
+        self
+    }
+
+    pub fn color_filter<T>(mut self, filter: T) -> Self
+    where
+        T: ColorFilter + Send + Sync + 'static,
+    {
+        self.color_filter = Some(Box::new(filter));
+        self
+    }
+
+    pub fn charset_restrict<T>(mut self, restrict: T) -> Self
+    where
+        T: TokenFilter + Send + Sync + 'static,
+    {
+        self.charset_restrict = Some(Box::new(restrict));
+        self
+    }
+    pub fn runner<E: OcrEngine>(self, runtime: &E) -> Ocr<'_> {
+        // 1. 原地解析颜色过滤器
+        let final_color_ranges = match &self.color_filter {
+            Some(filter) => filter.collect_to_vec(),
+            None => Ok(None),
+        };
+        // 2. 原地解析字符集过滤
+        let tokens = &runtime.metadata().charset.tokens;
+        let final_charset_indices = match &self.charset_restrict {
+            Some(restrict) => restrict.apply_to_charset(tokens),
+            None => None,
+        };
+
+        // Ocr::new(session, self)
+        Ocr {
+            runtime,
+            png_fix: self.png_fix, // 原地解构出来
+            probability: self.probability,
+            final_color_ranges,
+            final_charset_indices,
+        }
+    }
+}

ddddocr-core/src/ocr/charset.rs

@@ -0,0 +1,66 @@
+use std::borrow::Cow;
+use std::collections::HashMap;
+
+// ==========================================
+// 3. 字符集核心结构体 (重命名为 Charset)
+// ==========================================
+#[derive(Debug, Clone)]
+pub struct Charset {
+    // 使用 Cow 统一静态切片和动态读取的 Vec<String>，内部实现真正的零拷贝
+    pub tokens: Vec<Cow<'static, str>>,
+    // 反向查找表，保证字符转索引为 O(1)
+    pub char_to_idx: HashMap<Cow<'static, str>, usize>,
+    // 当前处于激活状态的有效索引缓存 (用于 CTC 解码前的过滤加速)
+    // pub valid_indices: HashSet<usize>,
+}
+
+impl Charset {
+    // 内部底层统一收拢构造
+    pub fn new(tokens: Vec<Cow<'static, str>>) -> Self {
+        let mut char_to_idx = HashMap::with_capacity(tokens.len());
+        for (idx, token) in tokens.iter().enumerate() {
+            char_to_idx.entry(token.clone()).or_insert(idx);
+            // 如果字符集有重复，保留第一个遇到的索引 (符合 Python .index 逻辑)
+            // char_to_idx.entry(token.to_string()).or_insert(idx);
+        }
+
+        Self {
+            tokens,
+            char_to_idx,
+        }
+    }
+
+    // --- 业务策略方法 ---
+
+    /// 将字符转为索引，不存在返回 -1 (保持与原 Python 库行为一致)
+    pub fn char_to_index(&self, char_str: &str) -> i32 {
+        if let Some(&idx) = self.char_to_idx.get(char_str) {
+            idx as i32
+        } else {
+            -1
+        }
+    }
+
+    /// 将索引转为字符引用，零拷贝。若越界返回 None
+    pub fn index_to_char_ref(&self, index: usize) -> Option<&str> {
+        self.tokens.get(index).map(|cow| cow.as_ref())
+    }
+
+    pub fn is_valid_char(&self, char_str: &str) -> bool {
+        self.char_to_idx.get(char_str).is_some()
+    }
+    pub fn size(&self) -> usize {
+        self.tokens.len()
+    }
+}
+
+// ==========================================
+// 4. 标准 Display 接口实现 (对应 __str__)
+// ==========================================
+impl std::fmt::Display for Charset {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        write!(f, "Charset [Total Size: {}", self.size(),)
+    }
+}
+
+

ddddocr-core/src/ocr/color_filter.rs

@@ -0,0 +1,301 @@
+use crate::error::{ImagePreprocessError, Result};
+use crate::utils::image_processor::rgb_to_opencv_hsv;
+use image::{DynamicImage, ImageBuffer, Rgb};
+use std::str::FromStr;
+
+/// 核心区间判定辅助函数
+#[inline(always)]
+fn is_pixel_matched(ranges: &[HsvRange], h: u8, s: u8, v: u8) -> bool {
+    ranges.iter().any(|range| {
+        h >= range.lower.0
+            && h <= range.upper.0
+            && s >= range.lower.1
+            && s <= range.upper.1
+            && v >= range.lower.2
+            && v <= range.upper.2
+    })
+}
+pub fn apply_to_image(
+    image: &DynamicImage,
+    hsv_ranges: &[HsvRange],
+) -> 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) {
+        let r = chunk[0];
+        let g = chunk[1];
+        let b = chunk[2];
+
+        // 像素级转换为 OpenCV 标准的 HSV
+        let (h, s, v) = rgb_to_opencv_hsv(r, g, b);
+
+        // 模拟 Python 的多范围 mask bitwise_or 并在 mask == 0 处刷白
+        // 如果该像素没有命中任何一个配置的颜色区间，立刻原地刷白 [255, 255, 255]
+        if !is_pixel_matched(hsv_ranges, h, s, v) {
+            chunk[0] = 255;
+            chunk[1] = 255;
+            chunk[2] = 255;
+        }
+    }
+
+    // 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(|| ImagePreprocessError::BufferLengthMismatch {
+            expected: expected_len,
+            actual: actual_len,
+            width,
+            height,
+            channels: 3,
+        })?;
+    Ok(DynamicImage::ImageRgb8(filtered_buffer))
+}
+
+#[derive(Debug, Clone, Copy, PartialEq, Eq, PartialOrd, Ord)]
+pub struct HsvRange {
+    pub lower: (u8, u8, u8), // (H, S, V)
+    pub upper: (u8, u8, u8), // (H, S, V)
+}
+
+impl HsvRange {
+    pub const fn new(lower: (u8, u8, u8), upper: (u8, u8, u8)) -> Self {
+        Self { lower, upper }
+    }
+}
+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<(), 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(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(ImagePreprocessError::InvalidHsvRange(
+                "HSV范围下界不能大于上界".to_string(),
+            ));
+        }
+
+        Ok(())
+    }
+}
+#[derive(Debug, Clone, PartialEq, Eq)]
+pub enum ColorPreset {
+    Red,
+    Blue,
+    Green,
+    Yellow,
+    Orange,
+    Purple,
+    Cyan,
+    Black,
+    White,
+    Gray,
+    Custom(Vec<HsvRange>),
+}
+
+impl ColorPreset {
+    /// 纯裸数据定义，没有任何结构体包装，干净利落
+    /// 返回值：(范围数量, 范围数组)
+    /// 完美的零成本抽象：利用常量提升将数据直接打入只读数据段 (.rodata)
+    pub fn matches(&self) -> &[HsvRange] {
+        match self {
+            ColorPreset::Red => &[
+                HsvRange {
+                    lower: (0, 50, 50),
+                    upper: (10, 255, 255),
+                },
+                HsvRange {
+                    lower: (170, 50, 50),
+                    upper: (180, 255, 255),
+                },
+            ],
+            ColorPreset::Blue => &[HsvRange {
+                lower: (100, 50, 50),
+                upper: (130, 255, 255),
+            }],
+            ColorPreset::Green => &[HsvRange {
+                lower: (40, 50, 50),
+                upper: (80, 255, 255),
+            }],
+            ColorPreset::Yellow => &[HsvRange {
+                lower: (20, 50, 50),
+                upper: (40, 255, 255),
+            }],
+            ColorPreset::Orange => &[HsvRange {
+                lower: (10, 50, 50),
+                upper: (20, 255, 255),
+            }],
+            ColorPreset::Purple => &[HsvRange {
+                lower: (130, 50, 50),
+                upper: (170, 255, 255),
+            }],
+            ColorPreset::Cyan => &[HsvRange {
+                lower: (80, 50, 50),
+                upper: (100, 255, 255),
+            }],
+            ColorPreset::Black => &[HsvRange {
+                lower: (0, 0, 0),
+                upper: (180, 255, 50),
+            }],
+            ColorPreset::White => &[HsvRange {
+                lower: (0, 0, 200),
+                upper: (180, 30, 255),
+            }],
+            ColorPreset::Gray => &[HsvRange {
+                lower: (0, 0, 50),
+                upper: (180, 30, 200),
+            }],
+            ColorPreset::Custom(ranges) => ranges,
+        }
+    }
+    /// 校验逻辑：在这里实现完美的“责任分离”
+    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) => {
+                // 2. 只有 Custom 变体需要接受严格的参数政审
+                for r in ranges {
+                    r.validate()?;
+                }
+                Ok(())
+            }
+            _ => Ok(()),
+        }
+    }
+}
+
+impl FromStr for ColorPreset {
+    type Err = ImagePreprocessError;
+    fn from_str(s: &str) -> Result<Self, Self::Err> {
+        match s.to_lowercase().as_str() {
+            "red" => Ok(ColorPreset::Red),
+            "blue" => Ok(ColorPreset::Blue),
+            "green" => Ok(ColorPreset::Green),
+            "yellow" => Ok(ColorPreset::Yellow),
+            "orange" => Ok(ColorPreset::Orange),
+            "purple" => Ok(ColorPreset::Purple),
+            "cyan" => Ok(ColorPreset::Cyan),
+            "black" => Ok(ColorPreset::Black),
+            "white" => Ok(ColorPreset::White),
+            "gray" => Ok(ColorPreset::Gray),
+            // _ => Err(format!("不支持的颜色预设: {}", s)),
+            _ => Err(ImagePreprocessError::UnknownColorPreset(s.to_string())),
+        }
+    }
+}
+
+// =====================================================================
+// 3. 颜色约束特征（Trait）与组合子设计模式
+// =====================================================================
+
+pub struct PixelCtx {
+    pub hsv: (u8, u8, u8),
+}
+
+/// 统一的颜色约束接口
+pub trait ColorFilter {
+    /// 将自身的有效约束平铺追加到统一的目标容器中
+    fn append_ranges(&self, target: &mut Vec<HsvRange>);
+    /// 预估范围数量，借助原生内置的 len() 实现 O(1) 完美控容
+    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<(), 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>>, ImagePreprocessError> {
+        // 1. 触发自检
+        self.validate_self()?;
+
+        let total_capacity = self.estimated_count();
+        if total_capacity == 0 {
+            return Ok(None);
+        }
+
+        // 2. 永远一击必中分配精准内存，不需要再考虑追加和扩容！
+        let mut v = Vec::with_capacity(total_capacity.max(16));
+
+        // 2. 倒入数据
+        self.append_ranges(&mut v);
+
+        // 3. 原地完成排序与去重
+        v.sort_unstable();
+        v.dedup();
+
+        Ok(Some(v))
+    }
+}
+
+impl ColorFilter for ColorPreset {
+    fn append_ranges(&self, target: &mut Vec<HsvRange>) {
+        // 直接利用我们第一步写好的 matches() 拿到切片，整块高速拷贝倒入目标容器
+        target.extend_from_slice(self.matches());
+    }
+    fn estimated_count(&self) -> usize {
+        // 直接获取切片长度
+        self.matches().len()
+    }
+    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()
+    }
+}
+
+/// 多路颜色“或”逻辑组合子（并集网络）
+pub struct MultiOrColorRestrict<'a> {
+    pub filters: Vec<&'a dyn ColorFilter>,
+}
+
+impl<'a> ColorFilter for MultiOrColorRestrict<'a> {
+    fn append_ranges(&self, target: &mut Vec<HsvRange>) {
+        // 管道递延：依次指挥内部每一个子过滤器把数据倒进目标容器
+        for f in &self.filters {
+            f.append_ranges(target);
+        }
+    }
+    fn estimated_count(&self) -> usize {
+        // 数量累加：$O(1)$ 地把所有子过滤器的预估容量加起来
+        self.filters.iter().map(|f| f.estimated_count()).sum()
+    }
+
+    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()?;
+        }
+        Ok(())
+    }
+}
+
+// =====================================================================
+// 4. 声明式宏：一语定乾坤
+// =====================================================================
+
+#[macro_export]
+macro_rules! color_any_of {
+    ($only:expr) => {
+        &$only as &dyn $crate::ColorFilter
+    };
+    ($($filter:expr),+ $(,)?) => {
+        &$crate::MultiOrColorRestrict {
+            filters: vec![ $( &$filter as &dyn $crate::ColorFilter ),+ ]
+        }
+    };
+}

ddddocr-core/src/ocr/executor.rs

@@ -0,0 +1,552 @@
+use crate::ocr::metadata::Resize;
+
+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 image::DynamicImage;
+use std::borrow::Cow;
+use std::fmt;
+// use tract_onnx::prelude::tract_ndarray::{ Ix2, s};
+// use tract_onnx::prelude::{DatumType, Tensor, tract_ndarray};
+// ！！！【核心纠正】：彻底弃用 tract_ndarray，全线转用标准 ndarray
+use ndarray::ArrayView2;
+// pub enum ModelOutput {
+//     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::{OcrBuilder, OcrOutput};
+use crate::traits::OcrEngine;
+use tracing::{ warn};
+#[derive(Debug, Clone)]
+pub enum OcrResult {
+    /// 纯文本分支（对应 probability = false）
+    Text(String),
+    /// 包含全量概率的分支（对应 probability = true）
+    Probability {
+        text: String,
+        /// 满额概率矩阵 [Steps, Classes]
+        probabilities: Vec<Vec<f32>>,
+        /// 全局平均置信度
+        confidence: f64,
+    },
+    /// 不支持的模型或未知输出
+    Unsupported { message: String },
+}
+impl OcrResult {
+    /// 消费自身，直接提取最终文本
+    pub fn into_text(self) -> String {
+        match self {
+            OcrResult::Text(text) => text,
+            OcrResult::Probability { text, .. } => text,
+            OcrResult::Unsupported { message } => {
+                // 作为库，这里可以返回空，或者直接携带错误信息，取决于你的设计
+                format!("Error: {}", message)
+            }
+        }
+    }
+}
+impl fmt::Display for OcrResult {
+    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
+        match self {
+            OcrResult::Text(text) => {
+                // 纯文本分支，直接输出文本内容
+                write!(f, "{}", text)
+            }
+            OcrResult::Probability {
+                text,
+                probabilities,
+                confidence,
+            } => {
+                // 概率分支，友好地展示文本以及百分比形式的置信度
+                // 1. 基本信息
+                write!(f, "{} (置信度: {:.2}%)", text, confidence * 100.0)?;
+
+                // 2. 概率矩阵流式安全打印
+                write!(f, " [概率矩阵预览: ")?;
+
+                let max_steps_to_show = 10;
+                let take_steps = probabilities.iter().take(max_steps_to_show);
+
+                for (i, step_probs) in take_steps.enumerate() {
+                    if i > 0 {
+                        write!(f, ", ")?;
+                    }
+
+                    // 为了防止单行内部数据过长，单行也做一下截断保护（比如每行最多显示前 3 个概率）
+                    let max_classes_to_show = 3;
+                    write!(f, "[")?;
+                    for (j, prob) in step_probs.iter().take(max_classes_to_show).enumerate() {
+                        if j > 0 {
+                            write!(f, ", ")?;
+                        }
+                        write!(f, "{:.4}", prob)?;
+                    }
+                    if step_probs.len() > max_classes_to_show {
+                        write!(f, ", ..")?;
+                    }
+                    write!(f, "]")?;
+                }
+
+                // 如果总 Step 数量超过 10，末尾追加 .. 表示截断
+                if probabilities.len() > max_steps_to_show {
+                    write!(f, ", ..")?;
+                }
+                write!(f, "]")
+            }
+            OcrResult::Unsupported { message } => {
+                // 错误分支，直观输出异常原因
+                write!(f, "未识别成功: {}", message)
+            }
+        }
+    }
+}
+
+pub struct Ocr<'a> {
+    pub(crate) runtime: &'a dyn OcrEngine,
+    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>>, ImagePreprocessError>,
+
+    /// 字符集范围
+    pub(crate) final_charset_indices: Option<Vec<usize>>,
+}
+
+impl<'a> Ocr<'a> {
+    // 初始化任务，设置默认参数
+
+    pub fn new(runtime: &'a dyn OcrEngine) -> Self {
+        Ocr {
+            runtime,
+            png_fix: false, // 默认值
+            probability: false,
+            final_color_ranges: Ok(None),
+            final_charset_indices: None,
+        }
+    }
+    pub fn builder() -> OcrBuilder {
+        OcrBuilder::default()
+    }
+
+}
+impl<'a> Ocr<'a> {
+    pub fn predict(&self, image: &DynamicImage) -> Result<OcrResult> {
+        println!("当前颜色过滤器状态: {:?}", self.final_color_ranges);
+
+        // =====================================================================
+        // 管道节点 1: 颜色过滤流水线
+        // 使用 Cow (Copy-On-Write) 智能指针。
+        // 如果未开启过滤，img_cow 内部只是持有原图的【只读借用】，发生【零内存分配】！
+        // =====================================================================
+        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(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
+                Cow::Borrowed(image)
+            }
+            Ok(Some(ranges)) => {
+                // 只有真正需要过滤时，才在内部提取像素并生成清洗后的 Owned 新图
+                let filtered_img = apply_to_image(image, ranges)?;
+                Cow::Owned(filtered_img)
+            }
+        };
+        let tensor = self.preprocess_image(&img_cow)?;
+
+        let raw_tensor = self.runtime.inference(tensor)?;
+
+        // 3. 后处理分流：直接返回 OcrResult
+        // let ocr_output = match raw_tensor.datum_type() {
+        //     DatumType::I64 => self.process_i64_tensor(raw_tensor)?,
+        //     DatumType::F32 => self.process_f32_tensor(raw_tensor)?,
+        //     _ => OcrResult::Unsupported {
+        //         message: format!("不支持的模型输出数据类型: {:?}", raw_tensor.datum_type()),
+        //     },
+        // };
+
+        // 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)?;
+        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) -> Result<ndarray::Array4<f32>,ImagePreprocessError> {
+        // 1. 获取模型元数据配置
+        let meta = self.runtime.metadata();
+        let norm = &meta.normalization; // 获取归一化器
+
+        // A. 修复 PNG 透明背景 (内部逻辑你之前已实现)
+        let current_img = if self.png_fix && img.color().has_alpha() {
+            // 只有满足条件才去触发分配，生成新图
+            Cow::Owned(png_rgba_white_preprocess(img))
+        } else {
+            // 正常情况下，仅仅是再次安全借用，无开销
+            Cow::Borrowed(img)
+        };
+
+        // 3. 管道节点 2: 根据 Resize 策略计算目标宽高并进行缩放
+        let (target_w, target_h) = match meta.resize {
+            Resize::Fixed(w, h) => (w, h),
+            Resize::DynamicWidth(h) => {
+                // 高度固定，宽度根据原始比例动态计算：W_target = W_orig * (H_target / H_orig)
+                let w =
+                    (current_img.width() as f32 * (h as f32 / current_img.height() as f32)) as u32;
+                (w, h)
+            }
+            Resize::Square(size) => {
+                // 单字识别模型，直接缩放为正方形
+                (size, size)
+            }
+        };
+        // 执行缩放
+        let resized_img = resize_image(&current_img, target_w, target_h);
+
+        // 4. 管道节点 3: 颜色通道转换（单通道灰度 vs 三通道 RGB）与 4D 张量填充
+        let array4 = match meta.channel {
+            // --- 情况 A: 单通道（灰度图），对应 Python 的 len(shape) == 2 展开 ---
+            1 => {
+                let gray_img = convert_to_grayscale(&resized_img);
+
+                let array = ndarray::Array4::from_shape_fn(
+                    (1, 1, target_h as usize, target_w as usize),
+                    |(_, _, y, x)| {
+                        let pixel = gray_img.get_pixel(x as u32, y as u32)[0] as f32;
+                        // pixel / 255.0 // 严格对齐 Python 归一化 [0.0, 1.0]
+                        // (pixel / 255.0 - 0.5) / 0.5
+                        norm.normalize(pixel)
+                    },
+                );
+                array
+            }
+
+            // --- 情况 B: 三通道（RGB），对应 Python 的 transpose(2, 0, 1) 的 CHW 布局 ---
+            3 => {
+                let rgb_img = resized_img.to_rgb8();
+
+                let array = ndarray::Array4::from_shape_fn(
+                    (1, 3, target_h as usize, target_w as usize),
+                    |(_, c, y, x)| {
+                        let pixel = rgb_img.get_pixel(x as u32, y as u32)[c] as f32;
+                        // pixel / 255.0 // 严格对齐 Python 归一化 [0.0, 1.0]
+                        // (pixel / 255.0 - 0.5) / 0.5
+                        norm.normalize(pixel)
+                    },
+                );
+                // Tensor::from(array)
+                array
+            }
+
+            // _ => 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)
+
+        // let h = 64u32;
+        // let w = (current_img.width() as f32 * (h as f32 / current_img.height() as f32)) as u32;
+        // let gray_img = convert_to_grayscale(&current_img);
+        // let resized = resize_image(&gray_img, w, h);
+        // // resized.save("debug_preprocessed.png").unwrap();
+        // // 1. 预处理：转灰度 -> Resize -> 归一化
+        // // let resized = img.resize_exact(w, h, FilterType::Lanczos3).to_luma8();
+        //
+        // // 使用 tract_ndarray 构造，避免版本冲突
+        // let array =
+        //     tract_ndarray::Array4::from_shape_fn((1, 1, h as usize, w as usize), |(_, _, y, x)| {
+        //         let pixel = resized.get_pixel(x as u32, y as u32)[0] as f32;
+        //         (pixel / 255.0 - 0.5) / 0.5
+        //     });
+        //
+        // let tensor = Tensor::from(array);
+        //
+        // Ok(tensor)
+    }
+
+    // 这段代码未来直接放入 ddddocr-core
+    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(|| TensorError::NonContiguousMemory)?;
+                let final_text = self.ctc_decode_to_string(slice);
+
+                if self.probability {
+                    Ok(OcrResult::Probability {
+                        text: final_text,
+                        probabilities: vec![],
+                        confidence: 1.0,
+                    })
+                } else {
+                    Ok(OcrResult::Text(final_text))
+                }
+            }
+            OcrOutput::Logits(matrix_view) => {
+                // 对应你原来的 process_f32_tensor
+                // 注意：此时的 matrix_view 已经是干净的标准的 ndarray::Array2<f32>，且保证是 [Steps, Classes] 2D 形状
+                if self.probability {
+                    let (probabilities_list, confidence, predicted_indices) =
+                        self.compute_f32_full_probability(matrix_view.view());
+                    let final_text = self.ctc_decode_to_string(&predicted_indices);
+                    Ok(OcrResult::Probability {
+                        text: final_text,
+                        probabilities: probabilities_list,
+                        confidence: confidence as f64,
+                    })
+                } else {
+                    let predicted_indices: Vec<i64> = matrix_view
+                        .outer_iter()
+                        .map(|row| {
+                            row.iter()
+                                .enumerate()
+                                .max_by(|(_, a), (_, b)| a.total_cmp(b))
+                                .map(|(idx, _)| idx as i64)
+                                .unwrap_or(0)
+                        })
+                        .collect();
+
+                    let final_text = self.ctc_decode_to_string(&predicted_indices);
+                    Ok(OcrResult::Text(final_text))
+                }
+            }
+        }
+    }
+}
+impl<'a> Ocr<'a> {
+    fn is_valid_indices(&self, idx: usize) -> bool {
+        if idx >= self.runtime.metadata().charset.size() {
+            return false;
+        }
+
+        match &self.final_charset_indices {
+            Some(v) => v.binary_search(&idx).is_ok(),
+            None => true,
+        }
+    }
+    /// 【按需延迟打印】：当用户真的需要“知道当前有哪些限制字符”时，一秒反查并打印
+    /// 这里的 &str 完美借用了自 tokens，依然是彻底的零拷贝！
+    pub fn valid_tokens(&self) -> Vec<&str> {
+        let charset = &self.runtime.metadata().charset;
+        let tokens = &charset.tokens;
+        match &self.final_charset_indices {
+            Some(indices) => indices
+                .iter()
+                .filter_map(|&idx| tokens.get(idx).map(|cow| cow.as_ref()))
+                .collect(),
+            // 如果是 None，现场映射出全量 Token 视图给外部
+            None => tokens.iter().map(|cow| cow.as_ref()).collect(),
+        }
+    }
+    pub fn valid_size(&self) -> usize {
+        match &self.final_charset_indices {
+            Some(indices) => indices.len(),
+            None => self.runtime.metadata().charset.tokens.len(),
+        }
+    }
+    /// 变体 B 核心处理器：单次遍历 2D 视图，融合计算 Softmax、Argmax、置信度并输出概率大包
+    fn compute_f32_full_probability(
+        &self,
+        matrix_view: ArrayView2<f32>,
+    ) -> (Vec<Vec<f32>>, f32, Vec<i64>) {
+        let steps = matrix_view.nrows();
+        let classes = matrix_view.ncols();
+
+        // 1. 预分配满额概率矩阵内存
+        let mut prob_matrix = ndarray::Array2::<f32>::zeros((steps, classes));
+        let mut predicted_indices = Vec::with_capacity(steps);
+        let mut confidence_sum = 0.0f32;
+
+        // 2. 融合单次遍历
+        for (step_idx, row) in matrix_view.outer_iter().enumerate() {
+            // 寻找当前 Step 的最大值和最大值索引 (Argmax)
+            let (row_max_idx, max_logit) = row
+                .iter()
+                .enumerate()
+                .max_by(|(_, a), (_, b)| a.total_cmp(b))
+                .map(|(idx, &val)| (idx, val))
+                .unwrap_or((0, 0.0));
+
+            predicted_indices.push(row_max_idx as i64);
+
+            // 计算单行 exp 溢出防范和
+            let mut exp_sum = 0.0f32;
+            for &val in row.iter() {
+                exp_sum += (val - max_logit).exp();
+            }
+
+            // 归一化 Softmax 顺序写入
+            for (class_idx, &val) in row.iter().enumerate() {
+                prob_matrix[[step_idx, class_idx]] = (val - max_logit).exp() / exp_sum;
+            }
+
+            // 当前 Step 最大概率在线累加
+            confidence_sum += 1.0f32 / exp_sum;
+        }
+
+        // 3. 统计全局平均置信度
+        let confidence = if steps > 0 {
+            confidence_sum / steps as f32
+        } else {
+            1.0
+        };
+
+        // 4. 将矩阵转化为标准安全序列化格式 [Steps, Classes]
+        let probabilities_list: Vec<Vec<f32>> =
+            prob_matrix.outer_iter().map(|row| row.to_vec()).collect();
+
+        (probabilities_list, confidence, predicted_indices)
+    }
+    /// 变体 A 专属提取器：直接从 I64 Tensor 零拷贝提取 CTC 文本与初始概率包
+    // fn process_i64_tensor(&self, raw_tensor: Tensor) -> anyhow::Result<OcrResult> {
+    //     // 1. 拿到底层的动态维度只读视图
+    //     let view = raw_tensor.to_array_view::<i64>()?;
+    //
+    //     // 2. 索要底层连续的只读切片引用
+    //     let slice = view
+    //         .as_slice()
+    //         .ok_or_else(|| anyhow::anyhow!("I64 模型输出内存不连续，无法执行零拷贝解码"))?;
+    //
+    //     // 3. 直接喂给 CTC 解码器（无任何物理克隆开销）
+    //     let final_text = self.ctc_decode_to_string(slice);
+    //
+    //     // 4. 组装返回
+    //     if self.probability {
+    //         Ok(OcrResult::Probability {
+    //             text: final_text,
+    //             probabilities: vec![], // I64 模型物理上丢失了全量 Logits 分值网，降级处理
+    //             confidence: 1.0,       // 判定即百分之百置信
+    //         })
+    //     } else {
+    //         Ok(OcrResult::Text(final_text))
+    //     }
+    // }
+    // /// 变体二（F32）的总体管线：负责降维，并分流文本和概率
+    // fn process_f32_tensor(&self, raw_tensor: Tensor) -> anyhow::Result<OcrResult> {
+    //     let shape = raw_tensor.shape();
+    //     println!("模型输出shape数据: {:?}", shape);
+    //     let view = raw_tensor.to_array_view::<f32>()?;
+    //
+    //     // 1. 极其纯粹的、无拷贝的多维 Shape 压扁清洗
+    //     let (steps, classes, data_dyn_view) = match shape.len() {
+    //         3 => {
+    //             if shape[1] == 1 {
+    //                 // 形状: [Steps, 1, Classes] -> 你的原有逻辑
+    //                 (shape[0], shape[2], view.into_dyn())
+    //             } else if shape[0] == 1 {
+    //                 // 形状: [1, Steps, Classes] -> 另一种常见导出格式
+    //                 (shape[1], shape[2], view.into_dyn())
+    //             } else {
+    //                 // 默认取第一个 batch: [Batch, Steps, Classes]
+    //                 // 使用 slice 对应 Python 的 output[0, :, :]
+    //                 let sliced = view.slice(s![0, .., ..]);
+    //                 (shape[1], shape[2], sliced.into_dyn())
+    //             }
+    //         }
+    //         // 形状: [Steps, Classes] -> 已经剥离了 Batch 维度
+    //         2 => (shape[0], shape[1], view.into_dyn()),
+    //         // 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], view.into_dyn()),
+    //         _ => return Err(anyhow::anyhow!("不支持的输出维度: {:?}", shape)),
+    //     };
+    //     let matrix_cow = data_dyn_view
+    //         .to_shape(Ix2(steps, classes))
+    //         .map_err(|e| anyhow::anyhow!("转换为2D静态矩阵失败: {:?}", e))?;
+    //
+    //     let matrix_view: ArrayView2<f32> = matrix_cow.view();
+    //
+    //     // 2. 根据业务参数明确分流
+    //     if self.probability {
+    //         // 走向 B1：调用刚刚拆分出来的“全量概率计算器”
+    //         let (probabilities_list, confidence, predicted_indices) =
+    //             self.compute_f32_full_probability(matrix_view);
+    //         // 5. 执行 CTC 解码
+    //         let final_text = self.ctc_decode_to_string(&predicted_indices);
+    //
+    //         Ok(OcrResult::Probability {
+    //             text: final_text,
+    //             probabilities: probabilities_list,
+    //             confidence: confidence as f64,
+    //         })
+    //     } else {
+    //         // 走向 B2：极速免 Softmax 提取纯文本（代码保持原地提取，简单短小不需要再拆）
+    //         let predicted_indices: Vec<i64> = matrix_view
+    //             .outer_iter()
+    //             .map(|row| {
+    //                 row.iter()
+    //                     .enumerate()
+    //                     .max_by(|(_, a), (_, b)| a.total_cmp(b))
+    //                     .map(|(idx, _)| idx as i64)
+    //                     .unwrap_or(0)
+    //             })
+    //             .collect();
+    //
+    //         let final_text = self.ctc_decode_to_string(&predicted_indices);
+    //         Ok(OcrResult::Text(final_text))
+    //     }
+    // }
+    /// 获取有效字符索引列表 (用于外部验证或过滤)
+    fn ctc_decode_to_string(&self, predicted_indices: &[i64]) -> String {
+        println!("indices模型输出原始数据: {:?}", predicted_indices);
+        let charset = &self.runtime.metadata().charset;
+        let tokens = &charset.tokens;
+        // let valid_indices = &charset.valid_indices;
+
+        // 对应 _ctc_decode_indices 的逻辑：去重、去 blank (0)
+        let mut res = String::new();
+        let mut prev_idx: i64 = -1;
+
+        for &idx in predicted_indices {
+            // 1. CTC 去重：如果是连续重复的，直接跳过
+            if idx == prev_idx {
+                continue;
+            }
+            // 【关键核心】只要不是连续重复，立刻更新 prev_idx 状态，绝对不能被后续的过滤短路！
+            prev_idx = idx;
+
+            // 2. CTC 过滤 Blank (0)
+            if idx == 0 {
+                continue;
+            }
+            // 3. 类型安全转换
+            let u_idx = match usize::try_from(idx) {
+                Ok(u) => u,
+                Err(_) => continue,
+            };
+
+            // 史诗级加速点：如果是 None，说明没限制，根本不进入分支，直接放行！
+            // 只有当有具体限制（Some）时，才去跑 4-5 次 CPU 寄存器级别的二分查找
+            if let Some(ref indices) = self.final_charset_indices {
+                if indices.binary_search(&u_idx).is_err() {
+                    continue;
+                }
+            }
+
+            // 5. 字符映射
+            if let Some(char_str) = tokens.get(u_idx) {
+                res.push_str(char_str);
+            }
+            else {
+                warn!("警告: 预测索引 {} 超出字符集范围", u_idx);
+            }
+        }
+        res
+    }
+}

ddddocr-core/src/ocr/metadata.rs

@@ -0,0 +1,86 @@
+// =====================================================================
+// 1. 辅助定义的枚举与结构体
+// =====================================================================
+
+use crate::ocr::Charset;
+use std::borrow::Cow;
+
+#[derive(Debug, Clone, Copy)]
+pub enum Normalization {
+    /// 映射到 [0.0, 1.0] -> pixel / 255.0
+    ZeroToOne,
+    /// 映射到 [-1.0, 1.0] -> (pixel / 255.0 - 0.5) / 0.5
+    MinusOneToOne,
+}
+
+impl Normalization {
+    /// 统一归一化计算逻辑
+    #[inline(always)]
+    pub fn normalize(&self, pixel: f32) -> f32 {
+        match self {
+            Normalization::ZeroToOne => pixel / 255.0,
+            Normalization::MinusOneToOne => (pixel / 255.0 - 0.5) / 0.5,
+        }
+    }
+}
+
+/// 图像缩放策略枚举
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+pub enum Resize {
+    /// 固定宽高，例如 (64, 64)
+    Fixed(u32, u32),
+    /// 高度固定，宽度根据原始比例动态计算（对应 Python 的 [-1, H]）
+    DynamicWidth(u32),
+    /// 单字识别的正方形切图（对应 Python 的 word 为 True 且 [-1, H]）
+    Square(u32),
+}
+
+#[derive(Debug, Clone)]
+pub struct ModelMetadata {
+    /// 字符集管理器
+    pub charset: Charset,
+    /// 是否为单字识别模型
+    pub word: bool,
+    /// 预处理的缩放策略
+    pub resize: Resize,
+    /// 图像通道数 (1 或 3)
+    pub channel: u8,
+    /// 新增：传递给核心业务使用的归一化配置
+    pub normalization: Normalization,
+}
+
+impl ModelMetadata {
+    pub fn new(
+        charset: Charset,
+        word: bool,
+        resize: Resize,
+        channel: u8,
+        normalization: Normalization,
+    ) -> Self {
+        Self {
+            charset,
+            word,
+            resize,
+            channel,
+            normalization,
+        }
+    }
+    // --- 优雅的工厂模式构造器 ---
+    /// 通用的静态切片转换构造器
+    pub fn from_static_slice(
+        slice: &[&'static str],
+        word: bool,
+        resize: Resize,
+        channel: u8,
+        normalization: Normalization,
+    ) -> Self {
+        let tokens: Vec<Cow<'static, str>> = slice.iter().map(|&s| Cow::Borrowed(s)).collect();
+        Self {
+            charset: Charset::new(tokens),
+            word,
+            resize,
+            channel,
+            normalization,
+        }
+    }
+}

ddddocr-core/src/ocr/token_filter.rs

@@ -0,0 +1,146 @@
+use std::borrow::Cow;
+
+/// 字符集范围限制枚举
+pub struct ValidationCtx<'a> {
+    pub text: &'a str,   // 当前 Token 的文本内容
+    pub token_id: usize, // 当前 Token 的 ID 索引
+}
+
+/// 统一的约束接口
+pub trait TokenFilter {
+    fn matches(&self, ctx: &ValidationCtx) -> bool;
+    /// 预估容量提示，帮助精准开辟 Vec 内存
+    fn estimated_capacity(&self) -> usize {
+        128
+    }
+    /// 【新引入的架构级核心方法】
+    /// 统一接管全量字符集的密集遍历、CTC Blank放行、去重、排序及空交集退化兜底
+    fn apply_to_charset(&self, tokens: &[Cow<str>]) -> Option<Vec<usize>> {
+        let mut has_any_match = false;
+        let estimated_capacity = self.estimated_capacity();
+
+        // 1. 精准开辟内存，完美利用容量提示，避免动态乱涨
+        let mut temp_indices = Vec::with_capacity(estimated_capacity.max(16));
+
+        // 2. 高性能原地单次流式迭代
+        for (idx, token) in tokens.iter().enumerate() {
+            let token_str = token.as_ref();
+
+            // 规则 A: CTC Blank 空字符串或 0 号索引无条件放行
+            if token_str.is_empty() || idx == 0 {
+                temp_indices.push(idx);
+                continue; // 关键：直接跳过，防止后续 matches 匹配成功导致重复 push 产生 Bug
+            }
+
+            // 规则 B: 组装无拷贝上下文
+            let ctx = ValidationCtx {
+                text: token_str,
+                token_id: idx,
+            };
+
+            // 规则 C: 路由到各自具体实现的特异性匹配中（如 Digit 判定、TopN 判定、组合子判定等）
+            if self.matches(&ctx) {
+                temp_indices.push(idx);
+                has_any_match = true;
+            }
+        }
+
+        // 3. 终极防御：如果整个模型字符集除了 Blank，一个都没对上，直接退化为 None（全量识别）
+        if !has_any_match {
+            println!("警告：当前限制策略与模型字符集完全没有交集！已自动恢复全量识别。");
+            None
+        } else {
+            // 4. 排序并去重，为 Ocr 引擎后续进行极其高频的『二分查找』筑起绝对安全的底层保障
+            temp_indices.sort_unstable();
+            temp_indices.dedup();
+            Some(temp_indices)
+        }
+    }
+}
+
+#[derive(Debug, Clone, PartialEq, Eq)]
+pub enum CharRestrict {
+    Digit,
+    Lowercase,
+    Uppercase,
+    CustomList(Vec<String>),
+}
+
+impl TokenFilter for CharRestrict {
+    fn matches(&self, ctx: &ValidationCtx) -> bool {
+        match self {
+            Self::Digit => ctx.text.len() == 1 && ctx.text.as_bytes()[0].is_ascii_digit(),
+            Self::Lowercase => ctx.text.len() == 1 && ctx.text.as_bytes()[0].is_ascii_lowercase(),
+            Self::Uppercase => ctx.text.len() == 1 && ctx.text.as_bytes()[0].is_ascii_uppercase(),
+            Self::CustomList(vec) => vec.iter().any(|t| t == ctx.text),
+        }
+    }
+    fn estimated_capacity(&self) -> usize {
+        match self {
+            Self::Digit => 16,
+            Self::Lowercase | Self::Uppercase => 32,
+            Self::CustomList(vec) => vec.len() + 1,
+        }
+    }
+}
+
+#[derive(Debug, Clone, PartialEq, Eq)]
+pub enum IdRestrict {
+    TopN(usize),
+    IdRange(std::ops::Range<usize>),
+    IdList(Vec<usize>),
+}
+
+impl TokenFilter for IdRestrict {
+    fn matches(&self, ctx: &ValidationCtx) -> bool {
+        match self {
+            Self::TopN(n) => ctx.token_id < *n,
+            Self::IdRange(range) => range.contains(&ctx.token_id),
+            Self::IdList(vec) => vec.contains(&ctx.token_id),
+        }
+    }
+    fn estimated_capacity(&self) -> usize {
+        match self {
+            Self::TopN(n) => *n + 1,
+            // 2. IdRange：标准标准库 Range 的长度
+            // 注意：因为范围可能是 1000..2000，它的 len() 返回的是 usize
+            Self::IdRange(range) => range.len() + 1,
+            // 3. IdList：Vec 里的元素个数
+            Self::IdList(vec) => vec.len() + 1,
+        }
+    }
+}
+
+/// 多路“或”逻辑组合子（支持 N 个规则无缝并集）
+pub struct MultiOrRestrict<'a> {
+    pub filters: Vec<&'a dyn TokenFilter>,
+}
+
+impl<'a> TokenFilter for MultiOrRestrict<'a> {
+    fn matches(&self, ctx: &ValidationCtx) -> bool {
+        // 核心高阶函数：只要有一个过滤器命中，该 Token 即可放行
+        self.filters.iter().any(|f| f.matches(ctx))
+    }
+
+    fn estimated_capacity(&self) -> usize {
+        // 将所有过滤器的预估容量累加，作为最终容量参考
+        self.filters.iter().map(|f| f.estimated_capacity()).sum()
+    }
+}
+// =====================================================================
+// 声明式宏：替代 `+` 运算符，解决组合扩展痛苦
+// =====================================================================
+#[macro_export]
+macro_rules! any_of {
+    // 场景 A：如果用户只传了一个规则，免去构建 Vec 的开销，直接返回其引用
+    ($only:expr) => {
+        &$only as &dyn $crate::TokenFilter
+    };
+
+    // 场景 B：如果用户传入了多个规则，自动织成一张静态组合网
+    ($($filter:expr),+ $(,)?) => {
+        &$crate::MultiOrRestrict {
+            filters: vec![ $( &$filter as &dyn $crate::TokenFilter ),+ ]
+        }
+    };
+}

ddddocr-core/src/slide.rs

@@ -1,46 +1,54 @@
-use crate::utils::cv_ops;
+use crate::error::{ImagePreprocessError, Result};
-use crate::utils::cv_ops::{abs_diff, min_max_loc, ndarray_to_luma8, rgb_to_gray};
+use crate::utils::image_convert::{ColorMode, image_to_ndarray};
-use crate::utils::image_io::image_to_ndarray;
+use crate::utils::image_processor;
-use anyhow::{Context, Result, anyhow};
+use crate::utils::image_processor::{abs_diff, min_max_loc, ndarray_to_luma8, rgb_to_gray};
-use image::{DynamicImage, GenericImageView};
+use image::DynamicImage;
-use image::{ImageBuffer, Luma};
+use image::Luma;
 use imageproc::contrast::{ThresholdType, threshold};
 use imageproc::distance_transform::Norm;
 use imageproc::edges::canny;
 use imageproc::morphology::{close, open};
 use imageproc::region_labelling::{Connectivity, connected_components};
 use imageproc::template_matching::{MatchTemplateMethod, match_template};
-use std::cmp::{max, min};
+use ndarray::{ArrayView2, ArrayView3};
-use tract_onnx::prelude::tract_ndarray::{Array2, Array3, ArrayView2, ArrayView3, Axis, s};
+use std::fmt;
-
+#[derive(Debug)]
 pub struct SlideResult {
     pub target: [i32; 2],
     pub target_x: i32,
     pub target_y: i32,
     pub confidence: f64,
 }
+impl fmt::Display for SlideResult {
+    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
+        writeln!(f, "滑块匹配测试结果:")?;
+        writeln!(f, "检测坐标: [x: {}, y: {}]", self.target_x, self.target_y)?;
+        // 注意：这里保留 4 位小数，如果想让外部控制，也可以直接写 {:.4}
+        write!(f, "置信度: {:.4}", self.confidence)?;
+        Ok(())
+    }
+}
 
-pub struct Slide;
+pub struct Slider;
 
-impl Slide {
+impl Slider {
     pub fn new() -> Self {
         Self
     }
-
+    /// 对应 Python: slide_match 滑块匹配接口
-    /// 对应 Python: slide_match
     pub fn slide_match(
         &self,
         target_image: &DynamicImage,
         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)
-            .map_err(|e| anyhow!("滑块匹配失败: {}", e))
+            .map_err(Into::into)
     }
-    /// 对应 Python: slide_comparison
+    /// 对应 Python: slide_comparison 差异比较接口
     /// 用于比较带坑位的图片与原始背景图，定位差异点
     pub fn slide_comparison(
         &self,
@@ -48,35 +56,37 @@ impl Slide {
         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())
-            .map_err(|e| anyhow!("滑块比较执行失败: {}", e))
+            .map_err(Into::into)
     }
     /// 对应 Python: _perform_slide_comparison
     pub fn perform_slide_comparison(
         &self,
         target: ArrayView3<u8>,
         background: ArrayView3<u8>,
-    ) -> Result<SlideResult> {
+    ) -> Result<SlideResult, ImagePreprocessError> {
-        let (h, w, _) = target.dim();
-
-        // 1. 计算图像差异并灰度化 (对应 cv2.absdiff + cv2.cvtColor)
-        // 使用 OpenCV 标准权重公式：0.299R + 0.587G + 0.114B
-        // let mut diff_buffer = ImageBuffer::new(w as u32, h as u32);
-        // for y in 0..h {
-        //     for x in 0..w {
-        //         let r_diff = (target[[y, x, 0]] as i16 - background[[y, x, 0]] as i16).abs() as f32;
-        //         let g_diff = (target[[y, x, 1]] as i16 - background[[y, x, 1]] as i16).abs() as f32;
-        //         let b_diff = (target[[y, x, 2]] as i16 - background[[y, x, 2]] as i16).abs() as f32;
-        //
-        //         let gray_diff = (0.299 * r_diff + 0.587 * g_diff + 0.114 * b_diff) as u8;
-        //         diff_buffer.put_pixel(x as u32, y as u32, Luma([gray_diff]));
-        //     }
-        // }
         // 1. 计算差异数组 (复用 cv2::absdiff)
+        let (th, tw, tc) = target.dim();
+        let (bh, bw, bc) = background.dim();
+
+        // 1. 比较模式下的严格尺寸校验
+        if th != bh || tw != bw || tc != bc {
+            return Err(ImagePreprocessError::MismatchDimensions(format!(
+                "比较模式要求两张图分辨率与通道数完全一致！Target: [{}x{}x{}], Background: [{}x{}x{}]",
+                tw, th, tc, bw, bh, bc
+            )));
+        }
+        if th == 0 || tw == 0 {
+            return Err(ImagePreprocessError::InvalidDimensions {
+                expected: "输入图像尺寸不能为0".to_string(),
+                actual: vec![th, tw],
+            });
+        }
+
         let diff_array = abs_diff(&target, &background);
 
         // 2. 转换为灰度数组 (复用你的 cv2.cvtColor)
@@ -100,11 +110,12 @@ impl Slide {
 
         // // 统计每个标签出现的频率（即面积）
         // 4. 寻找最大连通区域 (对应 findContours + max area)
-        if let Some(max_label) = cv_ops::find_contours_and_max(&labelled) {
+        if let Some(max_label) = image_processor::find_contours_and_max(&labelled) {
             // 5. 计算最大区域的边界框 (对应 cv2.boundingRect)
-            let (x, y, w, h) = cv_ops::bounding_rect(&labelled, max_label);
+            let (x, y, w, h) = image_processor::bounding_rect(&labelled, max_label);
             // 6. 计算中心点 (调用之前封装的 calculate_center)
-            let (center_x, center_y) = cv_ops::calculate_center((x, y), w as usize, h as usize);
+            let (center_x, center_y) =
+                image_processor::calculate_center((x, y), w as usize, h as usize);
 
             Ok(SlideResult {
                 target: [center_x, center_y],
@@ -129,18 +140,44 @@ impl Slide {
         target: ArrayView3<u8>,
         background: ArrayView3<u8>,
         simple_target: bool, // 增加这个参数
-    ) -> Result<SlideResult> {
+    ) -> Result<SlideResult, ImagePreprocessError> {
+        let (th, tw, tc) = target.dim();
+        let (bh, bw, bc) = background.dim();
+
+        // 1. 严格的鲁棒性校验（防止底层的 imageproc 算子崩溃）
+        if th == 0 || tw == 0 || bh == 0 || bw == 0 {
+            return Err(ImagePreprocessError::InvalidDimensions {
+                expected: "输入图像的宽度或高度不能为0".to_string(),
+                actual: vec![th, tw, tc],
+            });
+        }
+        if th > bh || tw > bw {
+            return Err(ImagePreprocessError::TargetExceedsBackground {
+                // "尺寸不匹配：滑块模板(target)尺寸 [{}x{}] 不能大于背景图(background) [{}x{}]",
+                target_w: tw,
+                target_h: th,
+                bg_w: bw,
+                bg_h: bh,
+            });
+        }
+        if tc != bc {
+            return Err(ImagePreprocessError::MismatchDimensions(format!(
+                "目标图与背景图的通道数不一致 (target: {}, bg: {})",
+                tc, bc
+            )));
+        }
+
         // 1. 统一灰度化
         let target_gray = rgb_to_gray(target);
         let background_gray = rgb_to_gray(background);
 
         if simple_target {
             // 2a. 简单模式：直接在灰度图上匹配
-            self.simple_template_match(target_gray.view(), background_gray.view())
+            Ok(self.simple_template_match(target_gray.view(), background_gray.view()))
         } else {
             // 2b. 复杂模式：先提取边缘，再匹配
 
-            self.edge_based_match(target_gray.view(), background_gray.view())
+            Ok(self.edge_based_match(target_gray.view(), background_gray.view()))
         }
     }
     /// 对应 Python: _simple_template_match
@@ -150,11 +187,8 @@ impl Slide {
         &self,
         target: ArrayView2<u8>,
         background: ArrayView2<u8>,
-    ) -> Result<SlideResult> {
+    ) -> SlideResult {
         // 1. 将 ndarray 转换为 imageproc 需要的 ImageBuffer (无拷贝或轻量转换)
-
-        // let (bh, bw) = background.dim();
-
         // 转换逻辑 (假设你已经有方法转回 ImageBuffer)
         let t_buf = ndarray_to_luma8(target);
         let b_buf = ndarray_to_luma8(background);
@@ -174,16 +208,17 @@ impl Slide {
         // 4. 计算中心点 (与 Python 逻辑完全一致)
         let (th, tw) = target.dim();
 
-        let (center_x, center_y) = cv_ops::calculate_center(max_loc, tw as usize, th as usize);
+        let (center_x, center_y) =
+            image_processor::calculate_center(max_loc, tw as usize, th as usize);
         // println!("Rust Target Width (tw): {}", tw);
         // println!("Rust Best Max Loc X: {}", max_loc.0);
         // println!("Rust Final Center X: {}", center_x);
-        Ok(SlideResult {
+        SlideResult {
             target: [center_x, center_y],
             target_x: center_x,
             target_y: center_y,
             confidence: max_val as f64,
-        })
+        }
     }
 
     /// 对应 Python: _edge_based_match
@@ -192,7 +227,7 @@ impl Slide {
         &self,
         target: ArrayView2<u8>,
         background: ArrayView2<u8>,
-    ) -> Result<SlideResult> {
+    ) -> SlideResult {
         // 1. 将 ndarray 转换为 ImageBuffer
         // 注意：Canny 和 match_template 需要 ImageBuffer 格式
         let t_buf = ndarray_to_luma8(target);
@@ -219,18 +254,19 @@ impl Slide {
         // 5. 计算中心位置 (对齐 Python 逻辑)
         // target_w, target_h 来自输入数组的维度
         let (th, tw) = target.dim();
-        let (center_x, center_y) = cv_ops::calculate_center(max_loc, tw as usize, th as usize);
+        let (center_x, center_y) =
+            image_processor::calculate_center(max_loc, tw as usize, th as usize);
 
         // 打印调试信息，方便与 Python 对比
         // println!("Edge Match: max_val: {}, max_loc: {:?}", max_val, max_loc);
         println!("-Rust Target Width (tw): {}", tw);
         println!("-Rust Best Max Loc X: {}", max_loc.0);
         println!("-Rust Final Center X: {}", center_x);
-        Ok(SlideResult {
+        SlideResult {
             target: [center_x, center_y],
             target_x: center_x,
             target_y: center_y,
             confidence: max_val as f64,
-        })
+        }
     }
 }

ddddocr-core/src/traits.rs

@@ -0,0 +1,42 @@
+use crate::error::TensorError;
+use crate::types::{ModelInfo, TensorInfo};
+use crate::{DetOutput, ModelMetadata, OcrOutput};
+use std::path::Path;
+
+
+pub trait Info {
+    fn input_info(&self) -> crate::error::Result<Vec<TensorInfo>>;
+    fn output_info(&self) -> crate::error::Result<Vec<TensorInfo>>;
+    fn model_info(&self) -> crate::error::Result<ModelInfo>;
+}
+
+/// 核心层定义的统一推理引擎接口。
+/// 未来的 ddddocr-tract 和 ddddocr-ort 都必须实现这个 Trait
+pub trait InferenceEngine {
+    /// 关联类型：具体的 Session 需要声明自己到底产出什么枚举
+    type Output;
+    fn inference(
+        &self,
+        input_array: ndarray::Array4<f32>,
+    ) -> crate::error::Result<Self::Output, TensorError>;
+}
+
+pub trait OcrEngine: InferenceEngine<Output = OcrOutput> + Info {
+    fn metadata(&self) -> &ModelMetadata;
+}
+
+pub trait DetEngine: InferenceEngine<Output = DetOutput> {}
+
+pub trait Loader {
+    type Session;
+    type Error;
+    fn build_for_path<P: AsRef<Path>>(
+        &self,
+        model_path: P,
+    ) -> crate::error::Result<Self::Session, Self::Error>;
+
+    fn build_from_bytes(
+        &self,
+        model_bytes: &[u8],
+    ) -> crate::error::Result<Self::Session, Self::Error>;
+}

ddddocr-core/src/types.rs

@@ -0,0 +1,46 @@
+#[derive(Debug,Clone)]
+pub enum TensorType{
+    F32,
+    I64,
+    Other
+}
+/// 明确命名为 AxisDim，代表模型某一个轴的维度特征
+#[derive(Clone, PartialEq, Eq)]
+pub enum AxisDim {
+    /// 静态固定维度（如通道数固定为 1，高度固定为 64）
+    Static(usize),
+    /// 动态符号维度（如宽度是动态的 "image_width"）
+    Dynamic(String),
+}
+
+impl AxisDim {
+    /// 便捷方法：判断是否为动态维度
+    pub fn is_dynamic(&self) -> bool {
+        matches!(self, AxisDim::Dynamic(_))
+    }
+}
+/// 自定义 Debug 格式化输出，彻底融化套娃外壳，保证日志干净漂亮
+impl std::fmt::Debug for AxisDim {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        match self {
+            AxisDim::Static(size) => write!(f, "{}", size),
+            AxisDim::Dynamic(expr) => write!(f, "Dynamic(\"{}\")", expr),
+        }
+    }
+}
+/// 模拟 Python 的 input_info 和 output_info 结构
+#[derive(Debug, Clone)]
+pub struct TensorInfo {
+    pub name: String,
+    pub shape: Vec<AxisDim>, // 既包含 Fixed 静态维度，也包含 Dynamic 动态符号
+    pub tensor_type: TensorType, // 对应 Python 的 type
+}
+
+/// 最终返回的模型完整信息
+#[derive(Debug, Clone)]
+pub struct ModelInfo {
+    pub inputs: Vec<TensorInfo>,
+    pub outputs: Vec<TensorInfo>,
+    /// 硬件执行提供者（采用 Option 兼容不同底层的推理引擎）
+    pub providers: Option<Vec<String>>,
+}

ddddocr-core/src/utils.rs

@@ -0,0 +1,8 @@
+pub mod image_convert;
+mod image_helper;
+pub mod image_processor;
+mod tensor_transform;
+
+// 对外统一暴露干净的 API 语义层
+pub use image_convert::ColorMode;
+pub use tensor_transform::normalize_ocr_logits;

ddddocr-core/src/utils/image_convert.rs

@@ -0,0 +1,174 @@
+use crate::error::{ImagePreprocessError, Result};
+use image::{DynamicImage, GenericImageView, ImageBuffer, Luma, Rgb, Rgba};
+use ndarray::{Array3, ArrayViewD};
+
+#[derive(Debug)]
+pub enum ColorMode {
+    RGB,
+    RGBA,
+    L,
+}
+
+/// 封装数组转图像的逻辑，
+// 对应 Python 版 _numpy_to_pil_image
+pub fn ndarray_to_hwc_image(array: ArrayViewD<u8>) -> Result<DynamicImage,ImagePreprocessError> {
+    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 (raw_data, _offset) = standard.to_owned().into_raw_vec_and_offset();
+
+    let color_mode = match dim {
+        // 对应 Python: len(array.shape) == 2 (灰度图 H, W)
+        2 => ColorMode::L,
+
+        // 对应 Python: len(array.shape) == 3 (H, W, C)
+        3 => {
+            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 => ColorMode::L,
+                // 对应 Python: array.shape[2] == 3 (RGB H, W, 3)
+                3 => ColorMode::RGB,
+                // 对应 Python: array.shape[2] == 4 (RGBA H, W, 4)
+                4 => ColorMode::RGBA,
+                _ => {
+                    return Err(ImagePreprocessError::UnsupportedChannels(c));
+                }
+            }
+        }
+        _ => {
+            return Err(ImagePreprocessError::InvalidDimensions {
+                expected: "2D (H,W) 或 3D (H,W,C)".to_string(),
+                actual: shape.to_vec(),
+            });
+        }
+    };
+    from_ndarray(array, color_mode)
+}
+
+/// 处理PNG图片的RGBA透明背景，将透明部分设置为白色背景
+// 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() {
+        return DynamicImage::ImageRgb8(img.to_rgb8());
+    }
+
+    let (width, height) = img.dimensions();
+
+    // 2. 创建一个新的 RGB 图像缓冲，默认填充为白色 (255, 255, 255)
+    let mut background = ImageBuffer::from_pixel(width, height, Rgb([255u8, 255u8, 255u8]));
+
+    // 3. 获取原图的 RGBA 视图
+    let rgba_img = img.to_rgba8();
+
+    // 4. 遍历像素并手动进行 Alpha 混合
+    // 对应 Python 的 utils.paste(img, ..., mask=img)
+    // 使用 enumerate_pixels_mut 同时获取坐标和背景像素的可变引用，减少查找开销
+    for (x, y, bg_pixel) in background.enumerate_pixels_mut() {
+        // 安全性说明：x, y 源自 background 尺寸，与 rgba_img 一致，get_pixel 是安全的
+        let src_pixel = rgba_img.get_pixel(x, y);
+        let alpha_u8 = src_pixel[3];
+
+        match alpha_u8 {
+            // 情况 A：完全不透明，直接覆盖背景色
+            255 => {
+                bg_pixel.0 = [src_pixel[0], src_pixel[1], src_pixel[2]];
+            }
+            // 情况 B：完全透明，保持背景色（白色），无需操作
+            0 => {
+                continue;
+            }
+            // 情况 C：半透明，进行 Alpha 混合计算
+            _ => {
+                let alpha = alpha_u8 as f32 / 255.0;
+                let inv_alpha = 1.0 - alpha;
+
+                bg_pixel[0] = (src_pixel[0] as f32 * alpha + 255.0 * inv_alpha).round() as u8;
+                bg_pixel[1] = (src_pixel[1] as f32 * alpha + 255.0 * inv_alpha).round() as u8;
+                bg_pixel[2] = (src_pixel[2] as f32 * alpha + 255.0 * inv_alpha).round() as u8;
+            }
+        }
+    }
+
+    DynamicImage::ImageRgb8(background)
+}
+/// 将 DynamicImage 转换为 array 数组
+pub fn image_to_ndarray(image: &DynamicImage, mode: ColorMode) -> Result<Array3<u8>,ImagePreprocessError> {
+    // 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::L => (1, image.to_luma8().into_raw()),
+        ColorMode::RGB => (3, image.to_rgb8().into_raw()),
+        ColorMode::RGBA => (4, image.to_rgba8().into_raw()),
+    };
+
+    let array = Array3::from_shape_vec((height as usize, width as usize, channels), raw)
+        .map_err(ImagePreprocessError::from)?;
+    Ok(array)
+}
+/// 将 array 数组转换为 DynamicImage
+pub fn ndarray_to_image(array: ArrayViewD<u8>, mode: ColorMode) -> Result<DynamicImage,ImagePreprocessError> {
+    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(ImagePreprocessError::InvalidDimensions {
+            expected: "至少为 2D array [H, W]".to_string(),
+            actual: shape.to_vec(),
+        })?;
+    }
+    from_ndarray(array, mode)
+}
+
+fn from_ndarray(array: ArrayViewD<u8>, mode: ColorMode) -> Result<DynamicImage,ImagePreprocessError> {
+    let shape = array.shape();
+
+    // 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;
+
+    // 1. 确保数据在内存中是连续的 (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();
+
+    // 获取当前模式对应的通道数
+    let channels = match mode {
+        ColorMode::L => 1,
+        ColorMode::RGB => 3,
+        ColorMode::RGBA => 4,
+    };
+
+    let expected_len = (width * height) as usize * channels;
+
+    // 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 = || {
+        ImagePreprocessError::BufferLengthMismatch {
+            expected: expected_len,
+            actual: raw_len,
+            width,
+            height,
+            channels,
+        }
+    };
+
+    // 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)
+            .ok_or_else(make_err),
+        ColorMode::RGB => ImageBuffer::<Rgb<u8>, _>::from_raw(width, height, raw_data)
+            .map(DynamicImage::ImageRgb8)
+            .ok_or_else(make_err),
+        ColorMode::RGBA => ImageBuffer::<Rgba<u8>, _>::from_raw(width, height, raw_data)
+            .map(DynamicImage::ImageRgba8)
+            .ok_or_else(make_err),
+    }
+}

ddddocr-core/src/utils/image_helper.rs

@@ -0,0 +1,152 @@
+use crate::error::{DdddError, Result};
+use crate::utils::image_convert::ndarray_to_hwc_image;
+use base64::{engine::general_purpose, Engine as _};
+use image::DynamicImage;
+use ndarray::ArrayViewD;
+use std::fmt;
+use std::fmt::{Debug, Formatter};
+use std::fs;
+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 })
+    }
+}
+#[derive(Debug)]
+enum Base64ProcessError {
+    InvalidBase64Header,
+}
+impl fmt::Display for Base64ProcessError {
+    fn fmt(&self, f: &mut Formatter<'_>) -> fmt::Result {
+        match self {
+            Base64ProcessError::InvalidBase64Header => {
+                write!(f, "Base64 头部格式不正确，缺少 ';base64,' 分隔符")
+            }
+        }
+    }
+}
+
+impl std::error::Error for Base64ProcessError {}
+// 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::new(Base64ProcessError::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(DdddError::new)?;
+            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(DdddError::new)?;
+        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(DdddError::new)?;
+        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_hwc_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)
+}
+
+/// 将base64编码的图片转换为 DynamicImage
+pub 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..]
+    } else {
+        &b64_str
+    };
+
+    let bytes = general_purpose::STANDARD
+        .decode(clean_b64.trim())
+        // .map_err(|e| DdddError::new(e))?;
+        .map_err(DdddError::new)?;
+
+    let img = image::load_from_memory(&bytes).map_err(DdddError::new)?;
+    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
+pub fn img_to_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).map_err(DdddError::new)?;
+
+    // 2. 进行 Base64 编码
+    // 使用 STANDARD 引擎对齐 Python 的 base64.b64encode
+    let b64_string = general_purpose::STANDARD.encode(image_data);
+
+    Ok(b64_string)
+}

ddddocr-core/src/utils/image_processor.rs

@@ -0,0 +1,196 @@
+use image::{imageops::FilterType, DynamicImage, GrayImage, ImageBuffer, Luma};
+
+use ndarray::{azip, Array2, Array3, ArrayView2, ArrayView3};
+use std::cmp::{max, min};
+
+// 模拟openCV
+/// 1. 计算两个数组的绝对差值 (对应 cv2.absdiff)
+pub fn abs_diff(a: &ArrayView3<u8>, b: &ArrayView3<u8>) -> Array3<u8> {
+    // 利用 ndarray 的 map_collect，生成差值的绝对值数组
+    // 或者直接使用 zip_mut_with 处理以减少内存分配
+    let mut diff = Array3::zeros(a.dim());
+    azip!((res in &mut diff, &va in a, &vb in b) {
+        *res = (va as i16 - vb as i16).abs() as u8;
+    });
+    diff
+}
+
+/// RGB 到灰度转换
+pub fn rgb_to_gray(rgb: ArrayView3<u8>) -> Array2<u8> {
+    let (h, w, _) = rgb.dim();
+    Array2::from_shape_fn((h, w), |(y, x)| {
+        let r = rgb[[y, x, 0]] as f32;
+        let g = rgb[[y, x, 1]] as f32;
+        let b = rgb[[y, x, 2]] as f32;
+        // 完全忽略 a，只按权重计算
+        (0.299 * r + 0.587 * g + 0.114 * b) as u8
+    })
+}
+
+/// 寻找匹配结果图中的最大值及其坐标 (模拟 cv2.minMaxLoc 的一部分)
+pub fn min_max_loc(result_map: &ImageBuffer<Luma<f32>, Vec<f32>>) -> (f32, (u32, u32)) {
+    // 4. 找到最佳匹配位置 (对齐 cv2.minMaxLoc)
+    let mut max_val: f32 = -1.0;
+    let mut max_loc = (0, 0);
+
+    // 遍历匹配得分图
+    for (x, y, score) in result_map.enumerate_pixels() {
+        let s = score.0[0];
+
+        // 可以在此处加入你之前验证过的起始位过滤
+        // if x < 15 { continue; }
+
+        if s > max_val {
+            max_val = s;
+            max_loc = (x, y);
+        }
+    }
+    (max_val, max_loc)
+}
+
+/// 1. 模拟 findContours 并获取最大面积区域的 Label
+/// 返回 Option<u32>，如果找不到任何区域则返回 None
+pub fn find_contours_and_max(labelled: &ImageBuffer<Luma<u32>, Vec<u32>>) -> Option<u32> {
+    // 统计每个标签出现的频率（即面积）
+    let mut max_label = 0;
+    let mut max_area = 0;
+    let mut areas = std::collections::HashMap::new();
+
+    for pixel in labelled.pixels() {
+        let label = pixel.0[0];
+        if label == 0 {
+            continue;
+        } // 跳过背景
+        let count = areas.entry(label).or_insert(0);
+        *count += 1;
+        if *count > max_area {
+            max_area = *count;
+            max_label = label;
+        }
+    }
+    if max_label == 0 {
+        None
+    } else {
+        Some(max_label)
+    }
+}
+/// 根据目标连通域标签，计算其在图像中的外接矩形边界框（对应 `cv2.boundingRect`）
+///
+/// 返回格式: `(min_x, min_y, width, height)`
+pub fn bounding_rect(
+    labelled: &ImageBuffer<Luma<u32>, Vec<u32>>,
+    max_label: u32,
+) -> (u32, u32, u32, u32) {
+    // 5. 计算最大区域的边界框 (对应 cv2.boundingRect)
+    let mut min_x = labelled.width();
+    let mut max_x = 0;
+    let mut min_y = labelled.height();
+    let mut max_y = 0;
+
+    for (x, y, pixel) in labelled.enumerate_pixels() {
+        if pixel.0[0] == max_label {
+            min_x = min(min_x, x);
+            max_x = max(max_x, x);
+            min_y = min(min_y, y);
+            max_y = max(max_y, y);
+        }
+    }
+
+    let w = max_x - min_x;
+    let h = max_y - min_y;
+    (min_x, min_y, w, h)
+}
+
+/// 根据左上角坐标与矩形长宽，计算其中央核心点坐标
+#[inline]
+pub fn calculate_center(top_left: (u32, u32), width: usize, height: usize) -> (i32, i32) {
+    let center_x = top_left.0 as i32 + (width as i32 / 2);
+    let center_y = top_left.1 as i32 + (height as i32 / 2);
+    (center_x, center_y)
+}
+
+/// 高性能转换：将 `ndarray` 2D 灰度视图规整为 `image::ImageBuffer` 格式
+///
+/// 放弃低效的逐像素显式嵌套循环，采用原生内存池直接构造，减少寻址开销
+pub fn ndarray_to_luma8(array: ArrayView2<u8>) -> ImageBuffer<Luma<u8>, Vec<u8>> {
+    let (height, width) = array.dim();
+    // 技巧：直接将已有的规整连续内存打平转换，或用 from_raw 包装
+    // 此处保留安全的一步转换，但用更内聚的迭代器或切片拷贝进行速度优化
+    let mut buffer = ImageBuffer::new(width as u32, height as u32);
+    for y in 0..height {
+        for x in 0..width {
+            buffer.put_pixel(x as u32, y as u32, Luma([array[[y, x]]]));
+        }
+    }
+    buffer
+}
+// =====================================================================
+// 5. 核心高性能图像转换算法 (纯 Rust 编写)
+// =====================================================================
+
+#[inline(always)]
+pub fn rgb_to_opencv_hsv(r: u8, g: u8, b: u8) -> (u8, u8, u8) {
+    // 1. 规避高昂的除法，直接转为 f32 进行比对
+    let r_f = r as f32;
+    let g_f = g as f32;
+    let b_f = b as f32;
+
+    let max = r_f.max(g_f).max(b_f);
+    let min = r_f.min(g_f).min(b_f);
+    let delta = max - min;
+
+    // 2. 计算 H (色调) - 移除负数取余陷阱，改用平铺分支
+    let h = if delta == 0.0 {
+        0.0
+    } else if max == r_f {
+        let mut diff = (g_f - b_f) / delta;
+        if diff < 0.0 {
+            diff += 6.0; // 规避 Rust f32 % 负数的行为
+        }
+        60.0 * diff
+    } else if max == g_f {
+        60.0 * (((b_f - r_f) / delta) + 2.0)
+    } else {
+        60.0 * (((r_f - g_f) / delta) + 4.0)
+    };
+
+    // OpenCV 的 H 量化：H / 2
+    // 注意：OpenCV 底层使用截断还是四舍五入与特定版本有关，
+    // 标准的 cvtColor 内部实现通常是： h * (180.0 / 360.0) -> h * 0.5
+    // 这里使用强转(截断)，若单测对齐发现差1，可改为 (h * 0.5 + 0.5) 或 round()
+    let h_opencv = (h * 0.5) as u8;
+
+    // 3. 计算 S (饱和度)
+    // OpenCV 公式: S = max == 0 ? 0 : 255 * delta / max
+    let s_opencv = if max == 0.0 {
+        0
+    } else {
+        ((255.0 * delta) / max) as u8
+    };
+
+    // 4. 计算 V (明度)
+    let v_opencv = max as 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 {
+    // Rust utils 库的 to_luma8 会根据标准的亮度公式进行转换
+    image.to_luma8()
+}
+
+/// 对应 Python 的 resize_image
+/// 调整图像尺寸。当前版本仅实现 keep_aspect_ratio=false
+pub fn resize_image(
+    image: &DynamicImage,
+    target_width: u32,
+    target_height: u32,
+    // resample 参数我们直接使用 FilterType，Lanczos3 是最接近 Python LANCZOS 的
+) -> DynamicImage {
+    // image::imageops::resize 的最高层封装
+    // FilterType::Lanczos3 与 Python Pillow 的 Image.LANCZOS 算法完全对齐，缩放质量最高
+    image.resize_exact(target_width, target_height, FilterType::Lanczos3)
+}
+

ddddocr-core/src/utils/tensor_transform.rs

@@ -0,0 +1,51 @@
+use crate::OcrOutput;
+use crate::error::{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,TensorError> {
+    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(
+                TensorError::DimensionMismatch {
+                    expected: "1D, 2D, or 3D OCR Logits".to_string(),
+                    actual: shape.to_vec(),
+                },
+            );
+        }
+    };
+
+    // 转换为标准的 2D 静态矩阵 [Steps, Classes]
+    let matrix_cow = data_dyn_view
+        .to_shape(ndarray::Ix2(steps, classes))
+        .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() {
+                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
+                TensorError::LogitsDimensionMismatch(shape_err)
+            }
+        })?
+        .to_owned();
+
+    Ok(OcrOutput::Logits(matrix_cow))
+}

ddddocr-ort/Cargo.toml

@@ -0,0 +1,23 @@
+[package]
+name = "ddddocr-ort"
+version.workspace = true
+edition.workspace = true
+license.workspace = true
+
+[dependencies]
+ddddocr-core = { path = "../ddddocr-core" } # 引入兄弟库
+ort = { workspace = true ,features = ["cuda"]}
+ndarray = { workspace = true } # 继承自工作空间
+
+image = { workspace = true }
+
+serde = { workspace = true }
+serde_json = { workspace = true }
+
+anyhow = { workspace = true }
+thiserror = { workspace = true } # 刚好可以开始接入你需要的标准库错误处理
+
+[features]
+# 1. 声明 cuda feature
+# 2. 当开启 ddddocr-ort 的 cuda feature 时，自动开启底层 ort 库的 cuda 支持（如果 ort 库支持的话）
+cuda = ["ort/cuda"]

ddddocr-ort/src/det.rs

@@ -0,0 +1 @@
+pub mod session;

ddddocr-ort/src/det/session.rs

@@ -0,0 +1,65 @@
+use crate::types::Session;
+use ddddocr_core::DetOutput;
+use ddddocr_core::error::{Result, TensorError};
+use ddddocr_core::traits::{DetEngine, InferenceEngine};
+use ndarray::Ix3;
+use ort::inputs;
+use ort::value::TensorRef;
+// use tract_onnx::prelude::{tvec, IntoTensor, Tensor};
+
+#[derive(Debug)]
+pub struct DetRuntime {
+    pub session: Session,
+}
+
+impl DetRuntime {
+    pub fn new(session: Session) -> Self {
+        Self { session }
+    }
+}
+
+impl InferenceEngine for DetRuntime {
+    type Output = DetOutput; // 明确绑定 OCR 小枚举
+    fn inference(&self, input_array: ndarray::Array4<f32>) -> Result<Self::Output, TensorError> {
+        // tract 的 run 会返回一个 Vec<TValue>，我们通常只需要第一个输出
+        // let result = self.ocr.run(tvec!(tensor.into()))?;
+        let mut session_guard = self
+            .session
+            .lock()
+            .map_err(|_| TensorError::Engine("获取 Session 锁失败 (Poisoned)".to_string()))?;
+
+        let result = session_guard
+            .run(inputs![TensorRef::from_array_view(&input_array).map_err(
+                |e| TensorError::Engine(format!("构建输入失败: {e}"))
+            )?])
+            .map_err(|e| TensorError::Engine(format!("执行模型推理失败: {e}")))?;
+        // .context("执行模型推理失败")?;
+        println!("模型输出原始数据: {:?}", result);
+
+        // Ok(result.swap_remove(0).into_tensor())
+        let raw_value = &result[0];
+
+        // raw_tensor.into_plain_array()?
+        let (shape_ref, slice) = raw_value.try_extract_tensor::<f32>().map_err(|_| {
+            TensorError::Engine("Tract 实体张量无法转换为 ndarray::ArrayD".to_string())
+        })?;
+        // 提前利用克隆(Clone)备份好当前未转维度前的真实 shape (Vec<usize>)
+        let shape_vec: Vec<usize> = shape_ref.to_vec().iter().map(|v| *v as usize).collect();
+        let shape_vec_slice = shape_vec.as_slice();
+
+        let view = ndarray::ArrayViewD::from_shape(shape_vec_slice, slice)
+            .map_err(|_| TensorError::Engine("构建 ndarray ArrayViewD 失败".to_string()))?;
+
+        let array3 = view.to_owned().into_dimensionality::<Ix3>().map_err(|_| {
+            TensorError::DimensionMismatch {
+                expected: "3D 检测矩阵 [Batch, Box_Count, Box_Attributes]".to_string(),
+                actual: shape_vec, // 优雅降维失败时动态捕获
+            }
+        })?;
+        Ok(DetOutput::Detection(array3))
+
+        // 在引擎内部消化掉 DatumType 强耦合
+    }
+}
+
+impl DetEngine for DetRuntime {}

ddddocr-ort/src/lib.rs

@@ -0,0 +1,9 @@
+mod det;
+pub mod loader;
+mod ocr;
+mod types;
+
+
+pub use ddddocr_core::{SlideResult, Slider,OcrBuilder};
+pub use det::session::DetRuntime;
+pub use ocr::session::OcrRuntime;

ddddocr-ort/src/loader.rs

@@ -0,0 +1,7 @@
+mod error;
+mod metadata;
+mod model;
+
+pub use error::{Error, ParseError, Result};
+pub use metadata::{ModelMetadataDto, NormalizationDto, Metadata};
+pub use model::ModelLoader;

ddddocr-ort/src/loader/error.rs

@@ -0,0 +1,66 @@
+use ort::Error as OrtError;
+pub type Result<T> = std::result::Result<T, Error>;
+#[derive(thiserror::Error, Debug)]
+pub enum Error {
+    #[error("builder构建失败")]
+    Build(#[from] BuildError),
+    /// 解析 ONNX 模型/路径失败（如文件损坏、算子不支持、路径非法）
+    #[error("解析 ONNX 模型结构失败: {0}")]
+    ModelParse(#[from] ParseError),
+
+    /// 模型计算图优化失败（如常量折叠、形状推导失败）
+    #[error("优化 Tract 模型图失败: {0}")]
+    OptimizationFailed(#[source] OrtError),
+
+    /// 构建可执行 Session 失败（如输入输出 Tensor 类型/形状未确定）
+    #[error("构建可运行 Tract 实例失败: {0}")]
+    RunnableBuildFailed(#[source] OrtError),
+
+    /// JSON 反序列化失败（自动透传 serde_json 报错）
+    #[error("模型 Metadata JSON 解析失败: {0}")]
+    JsonParse(#[from] serde_json::Error),
+
+    /// 字节流非合法 UTF-8 编码（自动透传 Utf8Error）
+    #[error("Metadata 字节流不是合法的 UTF-8 编码: {0}")]
+    InvalidUtf8(#[from] std::str::Utf8Error),
+
+    #[error("模型元数据解析失败: {0}")]
+    MetadataParse(String),
+
+    /// 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("{0}: {1}")]
+    Other(String, #[source] Box<dyn std::error::Error + Send + Sync>),
+}
+
+
+
+impl Error {
+    /// 方便将任何第三方 Error 包装为 Error::Other
+    pub fn new<E>(msg: impl Into<String>, err: E) -> Self
+    where
+        E: Into<Box<dyn std::error::Error + Send + Sync>>,
+    {
+        Self::Other(msg.into(), err.into())
+    }
+}
+#[derive(thiserror::Error, Debug)]
+pub enum ParseError {
+    /// 策略 A：从文件路径加载失败（附带路径上下文信息，方便排查是找不到文件还是格式不对）
+    #[error("从路径 '{0}' 加载 ONNX 模型失败: {1}")]
+    Path(String, #[source] OrtError),
+
+    /// 策略 B：从内存字节流加载失败（如 include_bytes! 传入的字节流损坏）
+    #[error("从内存字节流解析 ONNX 模型失败: {0}")]
+    Bytes(#[source] OrtError),
+}
+#[derive(thiserror::Error, Debug)]
+pub enum BuildError{
+    #[error("builder构建失败")]
+    BuildFailed(#[from] OrtError),
+    #[error("builder构建失败")]
+    Threads(String),
+    #[error("builder构建失败")]
+    EnabledCudaFailed(String),
+    #[error("builder构建失败")]
+    NotEnabledCuda(String)
+}

ddddocr-ort/src/loader/metadata.rs

@@ -0,0 +1,93 @@
+use crate::loader::error::{Error, Result};
+
+use ddddocr_core::ModelMetadata;
+use ddddocr_core::Resize;
+use ddddocr_core::{Charset, Normalization};
+use serde::Deserialize;
+use std::borrow::Cow;
+
+#[derive(Deserialize)]
+#[serde(rename_all = "snake_case")] // 支持 json 中写 "zero_to_one" 或 "minus_one_to_one"
+pub enum NormalizationDto {
+    /// 映射到 [0.0, 1.0] -> pixel / 255.0
+    ZeroToOne,
+    /// 映射到 [-1.0, 1.0] -> (pixel / 255.0 - 0.5) / 0.5
+    MinusOneToOne,
+}
+
+impl From<NormalizationDto> for Normalization {
+    fn from(dto: NormalizationDto) -> Self {
+        match dto {
+            NormalizationDto::ZeroToOne => Normalization::ZeroToOne,
+            NormalizationDto::MinusOneToOne => Normalization::MinusOneToOne,
+        }
+    }
+}
+
+/// 仅用于反序列化 JSON 的中间临时结构体（DTO）
+#[derive(Deserialize)]
+pub struct ModelMetadataDto {
+    charset: Vec<String>,
+    word: bool,
+    #[serde(alias = "image")]
+    resize: Vec<i32>,
+    channel: u8,
+    /// 新增：允许在配置文件中指定归一化策略。
+    /// 使用 serde(default) 可以在不配置时提供一个默认值（比如默认 ZeroToOne）
+    #[serde(default = "default_normalization")]
+    normalization: NormalizationDto,
+}
+fn default_normalization() -> NormalizationDto {
+    NormalizationDto::ZeroToOne
+}
+
+/// Tract 专属扩展trait 或 工具函数
+pub trait Metadata: Sized {
+    fn from_json_str(json_str: &str) -> Result<Self>;
+    /// 机制 2：从内存字节流加载（极大地方便 include_bytes! 或网络下载）
+    fn from_json_bytes(bytes: &[u8]) -> Result<Self> {
+        let json_str = std::str::from_utf8(bytes)?;
+        Self::from_json_str(json_str)
+    }
+}
+impl Metadata for ModelMetadata {
+    // --- 优雅的工厂模式构造器 ---
+    fn from_json_str(json_str: &str) -> Result<ModelMetadata> {
+        let dto: ModelMetadataDto = serde_json::from_str(json_str)?;
+
+        // 1. 将 DTO 的字符串数组转化为强类型的 Charset
+        let tokens: Vec<Cow<'static, str>> =
+            dto.charset.into_iter().map(|s| Cow::Owned(s)).collect();
+        let charset = Charset::new(tokens);
+
+        // 2. 解析 resize 策略（重现 Python 的复杂条件判断）
+        if dto.resize.len() != 2 {
+            return Err(Error::MetadataParse(
+                "'resize (or image)' 字段必须是包含两个元素的数组，例如 [-1, 64]".to_string(),
+            ));
+        }
+        let r0 = dto.resize[0];
+        let r1 = dto.resize[1];
+
+        let resize = if r0 == -1 {
+            if dto.word {
+                // 如果 word 为 true，且包含 -1，Python 里是 resize 为 (r1, r1) 的正方形
+                Resize::Square(r1 as u32)
+            } else {
+                // 如果 word 为 false，且包含 -1，Python 里是高度固定为 r1，宽度按原图比例缩放
+                Resize::DynamicWidth(r1 as u32)
+            }
+        } else {
+            // 正常的固定宽高
+            Resize::Fixed(r0 as u32, r1 as u32)
+        };
+
+        Ok(ModelMetadata::new(
+            charset,
+            dto.word,
+            resize,
+            dto.channel,
+            dto.normalization.into(),
+        ))
+    }
+}

ddddocr-ort/src/loader/model.rs

@@ -0,0 +1,114 @@
+use crate::loader::Error;
+use crate::loader::error::{BuildError, ParseError, Result};
+use crate::types::Session;
+use ddddocr_core::traits::Loader;
+use ort::session::Session as OrtSession;
+use ort::session::builder::SessionBuilder;
+use std::sync::{Arc, Mutex};
+
+// pub struct OrtModelLoader;
+//
+// impl OrtModelLoader {
+//     /// 获取针对 ORT 后端的链式构建器
+//     pub fn builder() -> OrtModelBuilder {
+//         OrtModelBuilder::default()
+//     }
+// }
+/// ORT 专用的链式构建器
+#[derive(Debug, Clone)]
+pub struct ModelLoader {
+    use_gpu: bool,
+    device_id: i32,
+    intra_threads: Option<usize>,
+}
+impl Default for ModelLoader {
+    fn default() -> Self {
+        Self {
+            use_gpu: false,
+            device_id: 0,
+            intra_threads: None,
+        }
+    }
+}
+impl ModelLoader {
+    /// 开启或关闭 GPU 加速
+    pub fn use_gpu(mut self, enable: bool) -> Self {
+        self.use_gpu = enable;
+        self
+    }
+
+    /// 指定 GPU 设备 ID
+    pub fn device_id(mut self, id: i32) -> Self {
+        self.device_id = id;
+        self
+    }
+    pub fn num_threads(mut self, threads: usize) -> Self {
+        self.intra_threads = Some(threads);
+        self
+    }
+
+    /// 内部辅助方法：根据当前的配置构建 ORT 底层的 SessionBuilder
+    fn create_session_builder(&self) -> Result<SessionBuilder> {
+        let mut builder = OrtSession::builder().map_err(|e| BuildError::BuildFailed(e))?;
+        // 如果用户显式设置了线程数，则配置给 ORT
+        if let Some(threads) = self.intra_threads {
+            builder = builder
+                .with_intra_threads(threads)
+                .map_err(|e| BuildError::Threads(format!("设置线程数失败: {e}")))?;
+        }
+
+        if self.use_gpu {
+            // 根据 ort 库版本配置 CUDA 执行提供者 (Execution Provider)
+            #[cfg(feature = "cuda")]
+            {
+                use ort::ep::CUDAExecutionProvider;
+                let cuda_ep = CUDAExecutionProvider::default().with_device_id(self.device_id);
+                builder = builder
+                    .with_execution_providers([cuda_ep.build()])
+                    .map_err(|e| {
+                        BuildError::EnabledCudaFailed(format!("配置 CUDA 硬件加速失败: {e}"))
+                    })?
+            }
+            #[cfg(not(feature = "cuda"))]
+            {
+                // 如果用户明确开启了 GPU，但 Feature 没编译进去，明确抛错提醒
+                return Err(BuildError::NotEnabledCuda(
+                    "未启用 CUDA 支持：请在 Cargo.toml 中为 ddddocr-ort 开启 `cuda` feature"
+                        .to_string(),
+                ))?;
+            }
+        }
+
+        Ok(builder)
+    }
+}
+
+impl Loader for ModelLoader {
+    type Session = Session;
+    type Error = Error;
+
+    fn build_for_path<P>(&self, model_path: P) -> Result<Session>
+    where
+        P: AsRef<std::path::Path>,
+    {
+        let path_ref = model_path.as_ref();
+        let mut builder = self.create_session_builder()?;
+        // Session::builder() 会返回 Result<SessionBuilder, ort::Error>
+
+        let session = builder
+            .commit_from_file(path_ref)
+            .map_err(|e| ParseError::Path(path_ref.display().to_string(), e))?;
+
+        Ok(Arc::new(Mutex::new(session))) // 这里的session需要包装下
+    }
+    /// 策略 B：从内存字节流加载模型（配合 include_bytes! 使用）
+    fn build_from_bytes(&self, model_bytes: &[u8]) -> Result<Session> {
+        let mut builder = self.create_session_builder()?;
+
+        let session = builder
+            .commit_from_memory(model_bytes)
+            .map_err(ParseError::Bytes)?;
+
+        Ok(Arc::new(Mutex::new(session)))
+    }
+}

ddddocr-ort/src/ocr.rs

@@ -0,0 +1 @@
+pub mod session;

ddddocr-ort/src/ocr/session.rs

@@ -0,0 +1,150 @@
+use crate::types::Session;
+use ddddocr_core::ModelMetadata;
+use ddddocr_core::OcrOutput;
+use ddddocr_core::error::{DdddError, Result, TensorError};
+use ddddocr_core::traits::{InferenceEngine, Info, OcrEngine};
+use ddddocr_core::types::{AxisDim, ModelInfo, TensorInfo, TensorType};
+use ddddocr_core::utils::normalize_ocr_logits;
+use ort::inputs;
+use ort::value::{TensorElementType, TensorRef};
+use std::sync::Mutex;
+// 引入核心层的统一错误类型
+/// 明确命名为 AxisDim，代表模型某一个轴的维度特征
+// #[derive(Clone, PartialEq, Eq)]
+// pub enum AxisDim {
+//     /// 静态固定维度（如通道数固定为 1，高度固定为 64）
+//     Static(usize),
+//     /// 动态符号维度（如宽度是动态的 "image_width"）
+//     Dynamic(String),
+// }
+
+// impl AxisDim {
+//     /// 便捷方法：判断是否为动态维度
+//     pub fn is_dynamic(&self) -> bool {
+//         matches!(self, AxisDim::Dynamic(_))
+//     }
+// }
+// /// 自定义 Debug 格式化输出，彻底融化套娃外壳，保证日志干净漂亮
+// impl std::fmt::Debug for AxisDim {
+//     fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+//         match self {
+//             AxisDim::Static(size) => write!(f, "{}", size),
+//             AxisDim::Dynamic(expr) => write!(f, "Dynamic(\"{}\")", expr),
+//         }
+//     }
+// }
+/// 模拟 Python 的 input_info 和 output_info 结构
+// #[derive(Debug, Clone)]
+// pub struct TensorInfo {
+//     pub name: String,
+//     pub shape: Vec<AxisDim>, // 既包含 Fixed 静态维度，也包含 Dynamic 动态符号
+//     pub data_type: TensorElementType, // 对应 Python 的 type
+// }
+//
+// /// 最终返回的模型完整信息
+// #[derive(Debug, Clone)]
+// pub struct ModelInfo {
+//     pub inputs: Vec<TensorInfo>,
+//     pub outputs: Vec<TensorInfo>,
+//     /// 硬件执行提供者（采用 Option 兼容不同底层的推理引擎）
+//     pub providers: Option<Vec<String>>,
+// }
+pub struct OcrRuntime {
+    pub session: Session,
+    pub metadata: ModelMetadata,
+}
+impl OcrRuntime {
+    pub fn new(session: Session, metadata: ModelMetadata) -> Self {
+        Self { session, metadata }
+    }
+}
+impl OcrEngine for OcrRuntime {
+    fn metadata(&self) -> &ModelMetadata {
+        &self.metadata
+    }
+}
+impl InferenceEngine for OcrRuntime {
+    type Output = OcrOutput;
+    /// 对应 Python 的 _inference
+    fn inference(&self, input_array: ndarray::Array4<f32>) -> Result<Self::Output, TensorError> {
+        // tract 的 run 会返回一个 Vec<TValue>，我们通常只需要第一个输出
+        // let result = self.ocr.run(tvec!(tensor.into()))?;
+        // let tensor = Tensor::from(input_array);
+        let mut session_guard = self
+            .session
+            .lock()
+            .map_err(|_| TensorError::Engine("获取 Session 锁失败 (Poisoned)".to_string()))?;
+
+        let result = session_guard
+            .run(inputs![TensorRef::from_array_view(&input_array).map_err(
+                |e| TensorError::Engine(format!("构建输入失败: {e}"))
+            )?])
+            .map_err(|e| TensorError::Engine(format!("执行模型推理失败: {e}")))?;
+        // .context("执行模型推理失败")?;
+        println!("模型输出原始数据: {:?}", result);
+        // Ok(result.swap_remove(0).into_tensor())
+        let raw_value = &result[0];
+
+        match raw_value.dtype().tensor_type().unwrap() {
+            TensorElementType::Int64 => {
+                let (array_d, slice) = raw_value
+                    .try_extract_tensor::<i64>()
+                    .map_err(|_| TensorError::Engine("Tract 无法获取 i64 内存视图".to_string()))?;
+                // .context("Tract 无法获取 i64 内存视图")?;
+
+                //  提前提取真实维度
+                let actual_shape = array_d
+                    .to_vec()
+                    .iter()
+                    .map(|v| *v as usize)
+                    .collect::<Vec<usize>>();
+
+                let view = ndarray::ArrayViewD::from_shape(actual_shape.as_slice(), slice)
+                    .map_err(|_| TensorError::Engine("构建 ndarray ArrayViewD 失败".to_string()))?;
+                // 转成标准的 Array1 传给 core
+                let array1 = view
+                    .to_owned()
+                    .into_dimensionality::<ndarray::Ix1>()
+                    .map_err(|_| TensorError::DimensionMismatch {
+                        expected: "1D 字符索引静态矩阵".to_string(),
+                        actual: actual_shape,
+                    })?;
+                Ok(OcrOutput::Indices(array1))
+            }
+            TensorElementType::Float32 => {
+                let shape = raw_value.shape();
+                println!("模型输出shape数据: {:?}", shape);
+                // raw_tensor.to_plain_array_view()
+                let (shape_ref, slice) = raw_value
+                    .try_extract_tensor::<f32>()
+                    .map_err(|_| TensorError::Engine("Tract 无法获取 f32 内存视图".to_string()))?;
+                // 1. 极其纯粹的、无拷贝的多维 Shape 压扁清洗
+                let shape_vec: Vec<usize> =
+                    shape_ref.to_vec().iter().map(|v| *v as usize).collect();
+                let shape_vec_slice = shape_vec.as_slice();
+
+                let view = ndarray::ArrayViewD::from_shape(shape_vec_slice, slice)
+                    .map_err(|_| TensorError::Engine("构建 ndarray ArrayViewD 失败".to_string()))?;
+
+                normalize_ocr_logits(view, shape_vec_slice)
+            }
+            _ => Err(
+                // anyhow::anyhow!("不支持的模型输出数据类型: {:?}",raw_tensor.datum_type())
+                TensorError::UnknownOutputFormat,
+            ),
+        }
+    }
+}
+impl Info for OcrRuntime {
+    fn input_info(&self) -> Result<Vec<TensorInfo>> {
+        todo!()
+    }
+
+    fn output_info(&self) -> Result<Vec<TensorInfo>> {
+        todo!()
+    }
+
+    fn model_info(&self) -> Result<ModelInfo> {
+        todo!()
+    }
+}

ddddocr-ort/src/types.rs

@@ -0,0 +1,4 @@
+use ort::session::Session as OrtSession;
+use std::sync::{Arc, Mutex};
+
+pub type Session = Arc<Mutex<OrtSession>>;

ddddocr-ort/tests/char_slice.rs

@@ -1,3 +1,10 @@
+use std::borrow::Cow;
+use std::fs::File;
+use std::path::Path;
+use anyhow::anyhow;
+use ddddocr_core::Charset;
+use ddddocr_core::{Normalization, Resize};
+
 pub const CHARSET_BETA: &[&str] = &[
     "", "笤", "谴", "膀", "荔", "佰", "电", "臁", "矍", "同", "奇", "芄", "吠", "6", "曛", "荇",
     "砥", "蹅", "晃", "厄", "殣", "ｃ", "辱", "钋", "杻", "價", "眙", "鴿", "⒄", "裙", "训", "涛",
@@ -514,6 +521,80 @@ pub const CHARSET_BETA: &[&str] = &[
     "谬", "溝", "言", "哽", "婿", "猿", "跗", "獴", "俜", "呙", "弗", "凿", "窭", "铌", "友", "唉",
     "怫", "荘",
 ];
-pub fn get_default_charset() -> Vec<String> {
+
-    CHARSET_BETA.iter().map(|&s| s.to_string()).collect()
+pub const CHARSET_OLD: &[&str] = &["", "笤", "谴", "膀", "荔"];
-}
+
+
+
+
+// pub fn from_builtin_old() -> Self {
+//     Self::from_static_slice(
+//         CHARSET_OLD,
+//         false,
+//         Resize::DynamicWidth(64),
+//         1,
+//         Normalization::ZeroToOne,
+//     )
+// }
+// 
+// /// 从预设的 Beta 版字符集创建
+// pub fn from_builtin_beta() -> Self {
+//     Self::from_static_slice(
+//         CHARSET_BETA,
+//         false,
+//         Resize::DynamicWidth(64),
+//         1,
+//         Normalization::MinusOneToOne,
+//     )
+// }
+
+
+// /// 从外部外部 JSON 文件动态加载字符集(在后续优化中移除)
+// pub fn from_json_file<P: AsRef<Path>>(path: P) -> anyhow::Result<Self> {
+//     let path = path.as_ref();
+//     if !path.exists() {
+//         return Err(anyhow!("模型元数据配置文件不存在: {:?}", path));
+//     }
+// 
+//     let mut file = File::open(path)?;
+//     let mut content = String::new();
+//     file.read_to_string(&mut content)?;
+// 
+//     let dto: ModelMetadataDto = serde_json::from_str(&content)
+//         .map_err(|e| anyhow!("JSON 反序列化失败，请检查字段是否完整: {}", e))?;
+// 
+//     // 1. 将 DTO 的字符串数组转化为强类型的 Charset
+//     let tokens: Vec<Cow<'static, str>> =
+//         dto.charset.into_iter().map(|s| Cow::Owned(s)).collect();
+//     let charset = Charset::new(tokens);
+// 
+//     // 2. 解析 resize 策略（重现 Python 的复杂条件判断）
+//     if dto.resize.len() != 2 {
+//         return Err(anyhow!(
+//                 "'resize (or image)' 字段必须是包含两个元素的数组，例如 [-1, 64]"
+//             ));
+//     }
+//     let r0 = dto.resize[0];
+//     let r1 = dto.resize[1];
+// 
+//     let resize = if r0 == -1 {
+//         if dto.word {
+//             // 如果 word 为 true，且包含 -1，Python 里是 resize 为 (r1, r1) 的正方形
+//             Resize::Square(r1 as u32)
+//         } else {
+//             // 如果 word 为 false，且包含 -1，Python 里是高度固定为 r1，宽度按原图比例缩放
+//             Resize::DynamicWidth(r1 as u32)
+//         }
+//     } else {
+//         // 正常的固定宽高
+//         Resize::Fixed(r0 as u32, r1 as u32)
+//     };
+// 
+//     Ok(Self {
+//         charset,
+//         word: dto.word,
+//         resize,
+//         channel: dto.channel,
+//         normalization: dto.normalization,
+//     })
+// }

ddddocr-ort/tests/ocr_test.rs

@@ -0,0 +1,253 @@
+use anyhow::Context;
+use ddddocr_core::{DetectionResult,  Ocr};
+use ddddocr_core::traits::Loader;
+use ddddocr_core::{Detector, ModelMetadata, Normalization, Slider};
+// 假设你的包名是这个
+use ddddocr_ort::{DetRuntime, OcrBuilder, OcrRuntime};
+use image::{DynamicImage, ImageBuffer, Luma, Rgb};
+use std::fs;
+use std::path::Path;
+
+mod char_slice;
+use char_slice::CHARSET_BETA;
+use ddddocr_core::Resize;
+
+use ddddocr_ort::loader::ModelLoader as OrtModelLoader;
+
+fn load_image<P: AsRef<Path>>(path: P) -> anyhow::Result<image::DynamicImage> {
+    // 1. 先将泛型转为具体的 &Path 引用
+    let path_ref = path.as_ref();
+
+    // 2. 调用 open 时传入引用（utils::open 支持 AsRef<Path>）
+    image::open(path_ref).map_err(|e| {
+        // 3. 此时 path_ref 依然有效，可以安全地在闭包中使用
+        anyhow::anyhow!("无法加载图片 {:?}: {}", path_ref, e)
+    })
+}
+/// 将检测结果绘制在图像上并保存
+fn save_debug_image(
+    dynamic_img: &DynamicImage, // 【优化点 1】直接传入解码好的引用，拒绝重复解码
+    bboxes: &[DetectionResult], // 【修改点 1】类型改为自定义结构体切片
+    output_path: &str,
+) -> anyhow::Result<()> {
+    // 删除了原本的 let dynamic_img = image::load_from_memory(image_bytes)?;
+    let mut img = dynamic_img.to_rgb8();
+    let (width, height) = img.dimensions();
+    let red = Rgb([255u8, 0, 0]);
+
+    for bbox in bboxes {
+        // 【修改点 2】将原来的索引 bbox[0].. 改为结构体字段访问 .x1, .y1 ..
+        let x1 = bbox.x1.max(0).min(width as i32 - 1) as u32;
+        let y1 = bbox.y1.max(0).min(height as i32 - 1) as u32;
+        let x2 = bbox.x2.max(0).min(width as i32 - 1) as u32;
+        let y2 = bbox.y2.max(0).min(height as i32 - 1) as u32;
+
+        // 绘制横向线条
+        for x in x1..=x2 {
+            img.put_pixel(x, y1, red);
+            img.put_pixel(x, y2, red);
+            if y1 + 1 < height {
+                img.put_pixel(x, y1 + 1, red);
+            }
+            if y2.saturating_sub(1) > 0 {
+                img.put_pixel(x, y2 - 1, red);
+            }
+        }
+        // 绘制纵向线条
+        for y in y1..=y2 {
+            img.put_pixel(x1, y, red);
+            img.put_pixel(x2, y, red);
+            if x1 + 1 < width {
+                img.put_pixel(x1 + 1, y, red);
+            }
+            if x2.saturating_sub(1) > 0 {
+                img.put_pixel(x2 - 1, y, red);
+            }
+        }
+    }
+
+    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() {
+    let model = OrtModelLoader::default().use_gpu(false)
+        .build_for_path("D:\\CNWei\\CNW\\Rust\\ddddocr-rs\\models\\common_sml2h3_f32.onnx")
+        // .build_for_path("D:\\CNWei\\CNW\\Rust\\ddddocr-rs\\models\\common_old.onnx")
+        .expect("模型加载失败");
+    let metadata = ModelMetadata::from_static_slice(
+        CHARSET_BETA,
+        false,
+        Resize::DynamicWidth(64),
+        1,
+        Normalization::MinusOneToOne,
+    );
+    // 1. 初始化模型
+    let ocr = OcrRuntime::new(model, metadata);
+    // 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");
+
+    // 3. 执行识别
+    // let result = Ocr::new(&ocr)
+    //     .predict(&img)
+    //     .expect("识别过程出错")
+    //     .into_text();
+    // let result = OcrBuilder::new()
+    //     .build(&ocr)
+    //     .predict(&img)
+    //     .expect("识别过程出错")
+    //     .into_text();
+    let res=Ocr::builder().runner(&ocr).predict(&img).expect("s").into_text();
+
+    // println!("识别结果: {}", result);
+    println!("识别结果: {}", res);
+    // assert!(!result.is_empty());
+    assert!(!res.is_empty());
+}
+#[test]
+fn test_det_load() -> anyhow::Result<()> {
+    let det_model = OrtModelLoader::default()
+        .build_for_path("D:\\CNWei\\CNW\\Rust\\ddddocr-rs\\models\\common_det.onnx")
+        .expect("模型加载失败");
+    let det = DetRuntime::new(det_model);
+    let image_path = "D:/CNWei/CNW/Rust/ddddocr-rs/samples/det1.png";
+    let image_bytes =
+        fs::read(image_path).map_err(|e| anyhow::anyhow!("无法读取图片 {}: {}", image_path, e))?;
+
+    println!("图片读取成功，字节大小: {}", image_bytes.len());
+
+    // 【修改点 1】将字节流解码为统一的 DynamicImage
+    let img = image::load_from_memory(&image_bytes)
+        .map_err(|e| anyhow::anyhow!("图片解码失败: {}", e))?;
+
+    // 【修改点 2】传入统一的 &DynamicImage 引用
+    let bboxes = Detector::new(&det).predict(&img)?;
+    // println!("{:?}", det);
+    println!("检测到的目标数量: {}", bboxes.len());
+
+    if bboxes.is_empty() {
+        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",
+        )?;
+
+        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
+            println!("目标 [{}]: {}", i, bbox);
+        }
+    }
+    Ok(())
+}
+
+#[test]
+fn test_real_slide_match() {
+    let engine = Slider::new();
+
+    // 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 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
+    let start = std::time::Instant::now();
+    let result = engine
+        .slide_match(&target_img, &bg_img, false)
+        .expect("Slide match 执行失败");
+    let duration = start.elapsed();
+
+    // 3. 打印结果
+    println!("-------------------------------------------");
+    println!("{}", result);
+    println!("耗时: {:?}", duration);
+    println!("-------------------------------------------");
+
+    // 验证基本逻辑：坐标不应为 0 (除非匹配失败)
+    assert_eq!(result.target_x, 237);
+    assert_eq!(result.target_y, 77);
+    assert!(result.confidence > 0.0);
+}
+
+#[test]
+fn test_real_slide_comparison() {
+    let engine = Slider::new();
+
+    // 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 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
+    let start = std::time::Instant::now();
+    let result = engine
+        .slide_comparison(&target_img, &bg_img)
+        .expect("Slide match 执行失败");
+    let duration = start.elapsed();
+
+    // 3. 打印结果
+    println!("-------------------------------------------");
+    println!("滑块匹配测试结果:");
+    println!("检测坐标: [x: {}, y: {}]", result.target_x, result.target_y);
+    println!("置信度: {:.4}", result.confidence);
+    println!("耗时: {:?}", duration);
+    println!("-------------------------------------------");
+
+    // 验证基本逻辑：坐标不应为 0 (除非匹配失败)
+    assert_eq!(result.target_x, 171);
+    assert_eq!(result.target_y, 90);
+    assert!(result.confidence > 0.0);
+}
+#[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 = OrtModelLoader::default()
+        .build_for_path(
+            // "D:\\CNWei\\CNW\\Rust\\ddddocr-rs\\models\\common_sml2h3_f32.onnx",
+            "D:\\CNWei\\CNW\\Rust\\ddddocr-rs\\models\\common_huashi666_i64.onnx",
+        )
+        .expect("建立测试模型图失败");
+}

ddddocr-tract/Cargo.toml

@@ -0,0 +1,26 @@
+[package]
+name = "ddddocr-tract"
+version = { workspace = true }
+edition = { workspace = true }
+license = { workspace = true }
+
+[dependencies]
+
+ddddocr-core = { path = "../ddddocr-core" } # 引入兄弟库
+tract-onnx = { workspace = true }
+tract-linalg = {workspace = true }
+ndarray = { workspace = true } # 继承自工作空间
+
+image = { workspace = true }
+
+serde = { workspace = true }
+serde_json = { workspace = true }
+
+anyhow = { workspace = true }
+thiserror = { workspace = true } # 刚好可以开始接入你需要的标准库错误处理
+
+
+
+[features]
+default = []
+embed-models = [] # 这是一个留给有特殊需求、且自己下载了模型放入 models/ 目录的人的后门

ddddocr-tract/src/det.rs

@@ -0,0 +1 @@
+pub mod session;

ddddocr-tract/src/det/session.rs

@@ -0,0 +1,52 @@
+use crate::types::Session;
+use ddddocr_core::error::{Result, TensorError};
+use ddddocr_core::{ DetOutput};
+use ddddocr_core::traits::{DetEngine, InferenceEngine};
+use ndarray::Ix3;
+use tract_onnx::prelude::*;
+#[derive(Debug)]
+pub struct DetRuntime {
+    pub session: Session,
+}
+
+impl DetRuntime {
+    pub fn new(session: Session) -> Self {
+        Self { session }
+    }
+
+}
+
+impl InferenceEngine for DetRuntime {
+    type Output = DetOutput; // 明确绑定 OCR 小枚举
+    fn inference(&self, input_array: ndarray::Array4<f32>) -> Result<Self::Output, TensorError> {
+        // tract 的 run 会返回一个 Vec<TValue>，我们通常只需要第一个输出
+        // let result = self.ocr.run(tvec!(tensor.into()))?;
+        let tensor = Tensor::from(input_array);
+
+        let mut result = self
+            .session
+            .run(tvec!(tensor.into()))
+            .map_err(|_| TensorError::Engine("执行模型推理失败".to_string()))?;
+        println!("模型输出原始数据: {:?}", result);
+        // Ok(result.swap_remove(0).into_tensor())
+        let raw_tensor = result.swap_remove(0).into_tensor();
+        // raw_tensor.into_plain_array()?
+        let array_d = raw_tensor.into_plain_array::<f32>().map_err(|_| {
+            TensorError::Engine("Tract 实体张量无法转换为 ndarray::ArrayD".to_string())
+        })?;
+        // 提前利用克隆(Clone)备份好当前未转维度前的真实 shape (Vec<usize>)
+        let actual_shape = array_d.shape().to_vec();
+
+        let array3 = array_d.into_dimensionality::<Ix3>().map_err(|_| {
+            TensorError::DimensionMismatch {
+                expected: "3D 检测矩阵 [Batch, Box_Count, Box_Attributes]".to_string(),
+                actual: actual_shape, // 优雅降维失败时动态捕获
+            }
+        })?;
+        Ok(DetOutput::Detection(array3))
+
+        // 在引擎内部消化掉 DatumType 强耦合
+    }
+}
+
+impl DetEngine for DetRuntime {}

ddddocr-tract/src/error.rs

@@ -0,0 +1,8 @@
+use thiserror::Error;
+
+#[derive(Error, Debug)]
+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}")]
+    Engine(String),
+}

ddddocr-tract/src/lib.rs

@@ -0,0 +1,11 @@
+mod det;
+mod error;
+pub mod loader;
+mod ocr;
+mod types;
+
+pub use ddddocr_core::{
+    DetectionResult, Detector, ModelMetadata, Normalization, Ocr, OcrBuilder, SlideResult, Slider,
+};
+pub use det::session::DetRuntime;
+pub use ocr::session::OcrRuntime;

ddddocr-tract/src/loader.rs

@@ -0,0 +1,7 @@
+mod error;
+mod metadata;
+mod model;
+
+pub use error::{Error, ParseError, Result};
+pub use metadata::{ModelMetadataDto, NormalizationDto, Metadata};
+pub use model::ModelLoader;

ddddocr-tract/src/loader/error.rs

@@ -0,0 +1,50 @@
+use tract_onnx::prelude::TractError;
+pub type Result<T> = std::result::Result<T, Error>;
+#[derive(thiserror::Error, Debug)]
+pub enum Error {
+    /// 解析 ONNX 模型/路径失败（如文件损坏、算子不支持、路径非法）
+    #[error("解析 ONNX 模型结构失败: {0}")]
+    ModelParse(#[from] ParseError),
+
+    /// 模型计算图优化失败（如常量折叠、形状推导失败）
+    #[error("优化 Tract 模型图失败: {0}")]
+    OptimizationFailed(#[source] TractError),
+
+    /// 构建可执行 Session 失败（如输入输出 Tensor 类型/形状未确定）
+    #[error("构建可运行 Tract 实例失败: {0}")]
+    RunnableBuildFailed(#[source] TractError),
+
+    /// JSON 反序列化失败（自动透传 serde_json 报错）
+    #[error("模型 Metadata JSON 解析失败: {0}")]
+    JsonParse(#[from] serde_json::Error),
+
+    /// 字节流非合法 UTF-8 编码（自动透传 Utf8Error）
+    #[error("Metadata 字节流不是合法的 UTF-8 编码: {0}")]
+    InvalidUtf8(#[from] std::str::Utf8Error),
+
+    #[error("模型元数据解析失败: {0}")]
+    MetadataParse(String),
+
+    /// 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("{0}: {1}")]
+    Other(String, #[source] Box<dyn std::error::Error + Send + Sync>),
+}
+impl Error {
+    /// 方便将任何第三方 Error 包装为 Error::Other
+    pub fn new<E>(msg: impl Into<String>, err: E) -> Self
+    where
+        E: Into<Box<dyn std::error::Error + Send + Sync>>,
+    {
+        Self::Other(msg.into(), err.into())
+    }
+}
+#[derive(thiserror::Error,Debug)]
+pub enum ParseError{
+    /// 策略 A：从文件路径加载失败（附带路径上下文信息，方便排查是找不到文件还是格式不对）
+    #[error("从路径 '{0}' 加载 ONNX 模型失败: {1}")]
+    Path(String, #[source] TractError),
+
+    /// 策略 B：从内存字节流加载失败（如 include_bytes! 传入的字节流损坏）
+    #[error("从内存字节流解析 ONNX 模型失败: {0}")]
+    Bytes(#[source] TractError),
+}

ddddocr-tract/src/loader/metadata.rs

@@ -0,0 +1,93 @@
+use crate::loader::error::{Error, Result};
+
+use ddddocr_core::ModelMetadata;
+use ddddocr_core::Resize;
+use ddddocr_core::{Charset, Normalization};
+use serde::Deserialize;
+use std::borrow::Cow;
+
+#[derive(Deserialize)]
+#[serde(rename_all = "snake_case")] // 支持 json 中写 "zero_to_one" 或 "minus_one_to_one"
+pub enum NormalizationDto {
+    /// 映射到 [0.0, 1.0] -> pixel / 255.0
+    ZeroToOne,
+    /// 映射到 [-1.0, 1.0] -> (pixel / 255.0 - 0.5) / 0.5
+    MinusOneToOne,
+}
+
+impl From<NormalizationDto> for Normalization {
+    fn from(dto: NormalizationDto) -> Self {
+        match dto {
+            NormalizationDto::ZeroToOne => Normalization::ZeroToOne,
+            NormalizationDto::MinusOneToOne => Normalization::MinusOneToOne,
+        }
+    }
+}
+
+/// 仅用于反序列化 JSON 的中间临时结构体（DTO）
+#[derive(Deserialize)]
+pub struct ModelMetadataDto {
+    charset: Vec<String>,
+    word: bool,
+    #[serde(alias = "image")]
+    resize: Vec<i32>,
+    channel: u8,
+    /// 新增：允许在配置文件中指定归一化策略。
+    /// 使用 serde(default) 可以在不配置时提供一个默认值（比如默认 ZeroToOne）
+    #[serde(default = "default_normalization")]
+    normalization: NormalizationDto,
+}
+fn default_normalization() -> NormalizationDto {
+    NormalizationDto::ZeroToOne
+}
+
+/// Tract 专属扩展trait 或 工具函数
+pub trait Metadata: Sized {
+    fn from_json_str(json_str: &str) -> Result<Self>;
+    /// 机制 2：从内存字节流加载（极大地方便 include_bytes! 或网络下载）
+    fn from_json_bytes(bytes: &[u8]) -> Result<Self> {
+        let json_str = std::str::from_utf8(bytes)?;
+        Self::from_json_str(json_str)
+    }
+}
+impl Metadata for ModelMetadata {
+    // --- 优雅的工厂模式构造器 ---
+    fn from_json_str(json_str: &str) -> Result<ModelMetadata> {
+        let dto: ModelMetadataDto = serde_json::from_str(json_str)?;
+
+        // 1. 将 DTO 的字符串数组转化为强类型的 Charset
+        let tokens: Vec<Cow<'static, str>> =
+            dto.charset.into_iter().map(|s| Cow::Owned(s)).collect();
+        let charset = Charset::new(tokens);
+
+        // 2. 解析 resize 策略（重现 Python 的复杂条件判断）
+        if dto.resize.len() != 2 {
+            return Err(Error::MetadataParse(
+                "'resize (or image)' 字段必须是包含两个元素的数组，例如 [-1, 64]".to_string(),
+            ));
+        }
+        let r0 = dto.resize[0];
+        let r1 = dto.resize[1];
+
+        let resize = if r0 == -1 {
+            if dto.word {
+                // 如果 word 为 true，且包含 -1，Python 里是 resize 为 (r1, r1) 的正方形
+                Resize::Square(r1 as u32)
+            } else {
+                // 如果 word 为 false，且包含 -1，Python 里是高度固定为 r1，宽度按原图比例缩放
+                Resize::DynamicWidth(r1 as u32)
+            }
+        } else {
+            // 正常的固定宽高
+            Resize::Fixed(r0 as u32, r1 as u32)
+        };
+
+        Ok(ModelMetadata::new(
+            charset,
+            dto.word,
+            resize,
+            dto.channel,
+            dto.normalization.into(),
+        ))
+    }
+}

ddddocr-tract/src/loader/model.rs

@@ -0,0 +1,127 @@
+use crate::loader::error::{Error, ParseError, Result};
+use crate::types::Session;
+use ddddocr_core::traits::Loader;
+use std::io::Cursor;
+use tract_linalg::multithread::{Executor, set_default_executor};
+use tract_onnx::onnx;
+use tract_onnx::prelude::*;
+
+/// Tract 专用的链式构建器
+#[derive(Debug, Clone, Default)]
+pub struct ModelLoader {
+    num_threads: Option<usize>,
+}
+
+impl ModelLoader {
+    /// 可选扩展：设置 CPU 线程数（不提供任何 GPU 相关的 API）
+    pub fn num_threads(mut self, threads: usize) -> Self {
+        self.num_threads = Some(threads);
+        self
+    }
+    fn setup_tract_threads(&self) {
+        // 💡 1. 如果设置了线程数，可以通过 tract 的 multithread 配置应用给 model
+        if let Some(threads) = self.num_threads {
+            // 在 Tract 中可以通过 set_num_threads 或设置底层环境控制并发
+            // （注：Tract 0.20+ 版本支持全局/局部线程控制）
+            let executor = if threads <= 1 {
+                Executor::SingleThread
+            } else {
+                Executor::multithread(threads)
+            };
+            set_default_executor(executor);
+        }
+    }
+}
+
+impl Loader for ModelLoader {
+    type Session = Session;
+    type Error = Error;
+    fn build_for_path<P>(&self, model_path: P) -> Result<Session>
+    where
+        P: AsRef<std::path::Path>,
+    {
+        self.setup_tract_threads();
+        let path_ref = model_path.as_ref();
+
+        let session = onnx()
+            .model_for_path(path_ref)
+            .map_err(|e| ParseError::Path(path_ref.display().to_string(), e))?
+            // .with_context(|| "加载 ONNX 模型失败，请检查路径是否正确")?
+            .into_optimized()
+            .map_err(Error::OptimizationFailed)?
+            // .with_context(|| "优化 Tract 模型图失败")?
+            .into_runnable()
+            .map_err(Error::RunnableBuildFailed)?;
+        // .with_context(|| "构建可运行 Tract 实例失败")?;
+        Ok(session)
+    }
+    /// 策略 B：从内存字节流加载模型（配合 include_bytes! 使用）
+    fn build_from_bytes(&self, model_bytes: &[u8]) -> Result<Session> {
+        self.setup_tract_threads();
+        // 使用 std::io::Cursor 将 &[u8] 包装为可读的流（实现 std::io::Read）
+        let mut cursor = Cursor::new(model_bytes);
+
+        let session = onnx()
+            .model_for_read(&mut cursor)
+            .map_err(ParseError::Bytes)?
+            // .with_context(|| "从内存字节流解析 ONNX 模型失败")?
+            .into_optimized()
+            .map_err(Error::OptimizationFailed)?
+            // .with_context(|| "优化 Tract 模型图失败")?
+            .into_runnable()
+            .map_err(Error::RunnableBuildFailed)?;
+        // .with_context(|| "构建可运行 Tract 实例失败")?;
+
+        Ok(session)
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    /// 辅助函数：动态构建一个简单的 ONNX/Tract 内存模型图用于测试
+    fn create_test_model() -> std::result::Result<Session, anyhow::Error> {
+        let mut rect = TypedModel::default();
+
+        // 0.21.10 最稳妥的静态 Fact 构建
+        let input_fact = TypedFact::dt_shape(DatumType::F32, &[1, 3, 224, 224]);
+
+        let input_node = rect
+            .add_source("input", input_fact)
+            .map_err(|e| anyhow::anyhow!("{:?}", e))?;
+
+        rect.set_input_outlets(&[input_node.into()])
+            .map_err(|e| anyhow::anyhow!("{:?}", e))?;
+
+        let typed = rect
+            .into_optimized()
+            .map_err(|e| anyhow::anyhow!("{:?}", e))?;
+        let runnable = typed
+            .into_runnable()
+            .map_err(|e| anyhow::anyhow!("{:?}", e))?;
+        Ok(runnable)
+    }
+
+    // #[test]
+    // fn test_input_output_shapes_and_type() {
+    //     let session = create_test_model().expect("建立测试模型图失败");
+    //
+    //     println!("{:?}", ModelLoader::model_info(&session).unwrap());
+    //     // 1. 测试输入维度解析
+    // }
+    //
+    // #[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 session = create_test_model().expect("建立测试模型图失败");
+    //
+    //     let dims: Vec<TDim> = vec![TDim::from(1), TDim::from(3), TDim::from(224)];
+    //     // 方案二的精髓：我们直接利用已导出的 ShapeFact 来纯手工验证边界逻辑！
+    //     // 1. 验证纯静态维度是否被正确还原
+    //     let static_shape = ShapeFact::from_dims(dims);
+    //
+    //     let res = ModelLoader
+    //         ::resolve_shape(&static_shape);
+    // }
+}

ddddocr-tract/src/ocr.rs

@@ -0,0 +1 @@
+pub mod session;

ddddocr-tract/src/ocr/session.rs

@@ -0,0 +1,153 @@
+use crate::types::Session;
+use ddddocr_core::ModelMetadata;
+use ddddocr_core::OcrOutput;
+use ddddocr_core::error::{DdddError, Result, TensorError};
+use ddddocr_core::traits::{InferenceEngine, Info, OcrEngine};
+use ddddocr_core::types::{AxisDim, ModelInfo, TensorInfo, TensorType};
+use ddddocr_core::utils::normalize_ocr_logits;
+use tract_onnx::prelude::{DatumType, OutletId, ShapeFact, TypedModel};
+use tract_onnx::prelude::{IntoTensor, Tensor, tvec};
+
+pub struct OcrRuntime {
+    pub session: Session,
+    pub metadata: ModelMetadata,
+}
+impl OcrRuntime {
+    pub fn new(session: Session, metadata: ModelMetadata) -> Self {
+        Self { session, metadata }
+    }
+    /// 获取模型输入的节点信息列表
+
+    /// 提取出来的公共转换逻辑：将一组 OutletId 解析为 TensorInfo 列表
+    fn resolve_tensors(&self, model: &TypedModel, outlets: &[OutletId]) -> Result<Vec<TensorInfo>> {
+        outlets
+            .iter()
+            .map(|&outlet_id| {
+                let fact = model.outlet_fact(outlet_id).map_err(DdddError::new)?;
+                //     .map_err(|e| {
+                //     DdddError::InternalError(format!("解析节点 Fact 失败: {:?}", e))
+                // })?;
+
+                let shape = self.resolve_shape(&fact.shape);
+                let node_name = model.node(outlet_id.node).name.clone();
+                let tensor_type = match fact.datum_type {
+                    DatumType::F32 => TensorType::F32,
+                    DatumType::I64 => TensorType::I64,
+                    _ => TensorType::Other,
+                };
+
+                Ok(TensorInfo {
+                    name: node_name,
+                    shape,
+                    tensor_type,
+                })
+            })
+            .collect() // 函数式声明：自动传播第一处发生的错误
+    }
+
+    /// 安全还原 Tract 维度至 Vec<AxisDim>
+    fn resolve_shape(&self, shape_fact: &ShapeFact) -> Vec<AxisDim> {
+        let tract_shape = shape_fact.to_tvec();
+
+        let resolved = tract_shape
+            .iter()
+            .map(|dim| {
+                // 防御性编程：必须同时满足能够转换为 i64 且 大于等于 0
+                if let Ok(size) = dim.to_i64() {
+                    if size >= 0 {
+                        AxisDim::Static(size as usize)
+                    } else {
+                        // 如果 ONNX 导出时某些动态维度被标记为了 -1，安全地作为动态符号捕获
+                        AxisDim::Dynamic(dim.to_string())
+                    }
+                } else {
+                    AxisDim::Dynamic(dim.to_string())
+                }
+            })
+            .collect();
+
+        resolved
+    }
+}
+impl OcrEngine for OcrRuntime {
+    fn metadata(&self) -> &ModelMetadata {
+        &self.metadata
+    }
+}
+impl InferenceEngine for OcrRuntime {
+    type Output = OcrOutput;
+    /// 对应 Python 的 _inference
+    fn inference(&self, input_array: ndarray::Array4<f32>) -> Result<Self::Output, TensorError> {
+        // tract 的 run 会返回一个 Vec<TValue>，我们通常只需要第一个输出
+        // let result = self.ocr.run(tvec!(tensor.into()))?;
+        let tensor = Tensor::from(input_array);
+
+        let mut result = self
+            .session
+            .run(tvec!(tensor.into()))
+            .map_err(|_| TensorError::Engine("执行模型推理失败".to_string()))?;
+        // .context("执行模型推理失败")?;
+        println!("模型输出原始数据: {:?}", result);
+        // Ok(result.swap_remove(0).into_tensor())
+        let raw_tensor = result.swap_remove(0).into_tensor();
+        // 在引擎内部消化掉 DatumType 强耦合
+        match raw_tensor.datum_type() {
+            DatumType::I64 => {
+                let array_d = raw_tensor
+                    .into_plain_array::<i64>()
+                    .map_err(|_| TensorError::Engine("Tract 无法获取 i64 内存视图".to_string()))?;
+                // .context("Tract 无法获取 i64 内存视图")?;
+                // 🌟 提前提取真实维度
+                let actual_shape = array_d.shape().to_vec();
+                // 转成标准的 Array1 传给 core
+                let array1 = array_d
+                    .to_owned()
+                    .into_dimensionality::<ndarray::Ix1>()
+                    .map_err(|_| TensorError::DimensionMismatch {
+                        expected: "1D 字符索引静态矩阵".to_string(),
+                        actual: actual_shape,
+                    })?;
+                Ok(OcrOutput::Indices(array1))
+            }
+            DatumType::F32 => {
+                let shape = raw_tensor.shape();
+                println!("模型输出shape数据: {:?}", shape);
+                // raw_tensor.to_plain_array_view()
+                let view = raw_tensor
+                    .to_plain_array_view::<f32>()
+                    .map_err(|_| TensorError::Engine("Tract 无法获取 f32 内存视图".to_string()))?;
+                // 1. 极其纯粹的、无拷贝的多维 Shape 压扁清洗
+                normalize_ocr_logits(view, shape)
+            }
+            _ => Err(
+                // anyhow::anyhow!("不支持的模型输出数据类型: {:?}",raw_tensor.datum_type())
+                TensorError::UnknownOutputFormat,
+            ),
+        }
+    }
+}
+impl Info for OcrRuntime {
+    fn input_info(&self) -> Result<Vec<TensorInfo>> {
+        let model = self.session.model();
+        let outlets = model.input_outlets().map_err(DdddError::new)?;
+        self.resolve_tensors(model, outlets)
+    }
+
+    /// 获取模型输出的节点信息列表
+    fn output_info(&self) -> Result<Vec<TensorInfo>> {
+        let model = self.session.model();
+        let outlets = model.output_outlets().map_err(DdddError::new)?;
+        self.resolve_tensors(model, outlets)
+    }
+
+    /// 获取模型详细元数据信息（对标 Python ddddocr 的 get_model_info）
+    /// 完美包容 [1, 1, 64, image_width] 这样的变长图像模型
+    /// 获取模型详细元数据信息（代码更紧凑、优雅）
+    fn model_info(&self) -> Result<ModelInfo> {
+        Ok(ModelInfo {
+            inputs: self.input_info()?,
+            outputs: self.output_info()?,
+            providers: None,
+        })
+    }
+}

ddddocr-tract/src/types.rs

@@ -0,0 +1,4 @@
+use std::sync::Arc;
+use tract_onnx::prelude::TypedRunnableModel;
+
+pub type Session = Arc<TypedRunnableModel>;

ddddocr-tract/tests/char_slice.rs

@@ -0,0 +1,600 @@
+use std::borrow::Cow;
+use std::fs::File;
+use std::path::Path;
+use anyhow::anyhow;
+use ddddocr_core::Charset;
+use ddddocr_core::{Normalization, Resize};
+
+pub const CHARSET_BETA: &[&str] = &[
+    "", "笤", "谴", "膀", "荔", "佰", "电", "臁", "矍", "同", "奇", "芄", "吠", "6", "曛", "荇",
+    "砥", "蹅", "晃", "厄", "殣", "ｃ", "辱", "钋", "杻", "價", "眙", "鴿", "⒄", "裙", "训", "涛",
+    "酉", "挞", "忙", "怍", "︰", "镍", "檐", "眯", "茓", "辖", "淩", "啟", "蜀", "芟", "裟", "楝",
+    "彘", "嶪", "费", "亞", "滁", "榉", "朝", "f", "倻", "裎", "谧", "崂", "卑", "助", "触", "氐",
+    "锟", "铢", "膪", "脐", "渲", "荫", "佾", "琯", "钣", "珰", "翦", "膻", "娥", "浥", "淄", "猸",
+    "内", "消", "粞", "反", "苪", "冽", "酵", "玩", "父", "存", "屯", "殷", "俐", "篱", "俛", "塮",
+    "苕", "耲", "输", "壖", "溶", "琤", "氏", "真", "黩", "瑄", "阶", "茔", "眩", "浙", "痄", "噔",
+    "烤", "楯", "²", "铆", "裈", "偬", "盏", "祐", "伯", "庙", "獯", "榀", "裒", "综", "蒸", "架",
+    "蜱", "鹖", "涴", "肌", "廖", "祾", "蔗", "破", "!", "鑫", "瓷", "Ｈ", "宛", "倪", "贝", "酝",
+    "倞", "榼", "菊", "帕", "胍", "淌", "抨", "倕", "味", "独", "à", "庐", "蹲", "肸", "洹", "騳",
+    "绖", "觉", "蝙", "铜", "選", "郚", "奄", "手", "篦", "忮", "潺", "歁", "湖", "貔", "缚", "癫",
+    "捣", "翎", "勇", "徒", "杪", "捃", "纴", "郞", "蛱", "浓", "講", "薇", "汊", "彻", "琖", "觇",
+    "驩", "野", "闺", "彩", "膊", "簸", "瑭", "龏", "栓", "攸", "堕", "鹿", "檠", "锽", "晟", "煊",
+    "衖", "p", "L", "侞", "吹", "岵", "捡", "邃", "曩", "泼", "娌", "磙", "鞮", "號", "苤", "骁",
+    "感", "氙", "榜", "菟", "蠲", "∶", "焌", "漯", "胪", "以", "剜", "=", "衰", "剔", "疏", "韩",
+    "邋", "探", "搌", "握", "舵", "腰", "咐", "郎", "鈺", "赴", "斩", "铥", "棂", "褫", "秾", "城",
+    "葬", "缺", "甓", "沙", "鴨", "恧", "吩", "膈", "俗", "引", "濑", "坛", "蛘", "谍", "飘", "鎚",
+    "貋", "袅", "圃", "肽", "祖", "瑢", "鄣", "卡", "恸", "饷", "撖", "阖", "碌", "墉", "⇋", "抗",
+    "僇", "撩", "狷", "静", "荽", "憯", "虻", "滹", "簧", "Y", "汾", "嫁", "蚴", "岈", "榔", "邶",
+    "挛", "火", "w", "旌", "線", "3", "跏", "F", "楸", "瞬", "證", "現", "符", "鲀", "窸", "朗",
+    "m", "劉", "襄", "鸻", "敖", "憩", "濛", "胶", "雏", "禽", "缜", "鐀", "澄", "泉", "懈", "鹟",
+    "牙", "叟", "镖", "膨", "硷", "钏", "嵌", "冲", "橪", "厨", "溘", "妻", "贺", "耀", "潲", "瞳",
+    "惺", "涑", "鄄", "舡", "战", "钹", "盍", "窀", "凳", "锋", "（", "绤", "翻", "》", "嶷", "戛",
+    "照", "设", "两", "霹", "風", "格", "栖", "椹", "№", "蔌", "达", "悠", "旒", "函", "抔", "逆",
+    "疙", "玫", "箢", "恩", "樘", "远", "考", "荭", "殒", "靡", "蝾", "舅", "䀲", "偾", "灿", "埴",
+    "瀍", "特", "诸", "搦", "恒", "妊", "课", "劂", "殊", "艋", "柚", "硕", "捅", "钍", "芘", "脖",
+    "襁", "募", "卅", "疆", "嫖", "黹", "臀", "豇", "瘗", "憎", "嗯", "Θ", "跺", "喧", "捺", "爪",
+    "鲠", "縠", "屋", "撑", "者", "娶", "喝", "墼", "丶", "茚", "髅", "瓢", "农", "橼", "攰", "折",
+    "诋", "镒", "赂", "捲", "耑", "沐", "窜", "亰", "煌", "阕", "羯", "纤", "滇", "值", "琥", "渴",
+    "祗", "鳌", "蛞", "庄", "鹇", "訄", "犷", "弋", "琐", "佗", "氛", "揄", "旺", "聱", "榨", "湿",
+    "蟥", "湎", "敕", "轷", "耿", "三", "運", "瑶", "困", "勁", "蚱", "泸", "螫", "斜", "蟠", "轨",
+    "镞", "霆", "嶙", "烷", "瘩", "敦", "塾", "僚", "澙", "關", "酩", "殴", "—", "例", "筷", "乘",
+    "颗", "核", "孬", "舣", "糇", "劾", "黧", "镌", "罅", "X", "仅", "哐", "蟊", "呻", "呕", "粟",
+    "配", "伊", "槊", "昌", "宰", "盘", "肫", "鳥", "圾", "恬", "辘", "绿", "時", "丐", "扃", "敓",
+    "摄", "陕", "滿", "鹆", "嗬", "龅", "渣", "釣", "萦", "督", "孑", "∧", "疥", "噱", "蝎", "君",
+    "笮", "泌", "镔", "称", "柘", "鬣", "罨", "潍", "垣", "顔", "褶", "礓", "△", "骐", "湍", "獾",
+    "羖", "戾", "预", "祭", "鹨", "凤", "茕", "珏", "蛊", "毛", "枰", "鄭", "娲", "丕", "蜜", "纟",
+    "蛹", "粮", "嚰", "嚓", "螨", "裰", "G", "集", "删", "郜", "舻", "嵛", "鵰", "腊", "峄", "脸",
+    "鲺", "坢", "寞", "撰", "顸", "枋", "荠", "夡", "豆", "馆", "赭", "傎", "淼", "镪", "許", "礌",
+    "带", "訂", "饧", "锜", "及", "漾", "編", "究", "仍", "糜", "喳", "嗝", "醦", "堅", "企", "烔",
+    "图", "垲", "枥", "畿", "踱", "槟", "◆", "酽", "溢", "酥", "谚", "缨", "死", "镑", "干", "用",
+    "紊", "坑", "副", "枭", "琮", "鸨", "獻", "弈", "伺", "醍", "氓", "宁", "臻", "贾", "啐", "玉",
+    "咀", "孀", "烆", "嘱", "频", "蜥", "楡", "瞩", "委", "锺", "赀", "睬", "旅", "刿", "韪", "抹",
+    "鞘", "x", "钝", "倌", "奖", "蟭", "灌", "肼", "曰", "啊", "属", "唐", "彦", "煦", "鄘", "坦",
+    "鬻", "告", "单", "菽", "匏", "浚", "仺", "怜", "拦", "鸡", "拄", "乍", "燠", "暧", "竭", "⒂",
+    "济", "Ⓡ", "趑", "舴", "体", "拒", "罂", "说", "猎", "闫", "鄗", "妲", "鑙", "葶", "匍", "等",
+    "略", "盲", "唔", "钰", "渠", "镦", "葫", "蹒", "姘", "婵", "夯", "实", "何", "株", "锌", "礁",
+    "桺", "捉", "鮕", "莓", "轶", "辉", "溋", "视", "嗒", "猞", "猁", "杓", "怡", "咴", "巷", "仂",
+    "婉", "睛", "？", "淡", "/", "郛", "绨", "较", "毁", "沓", "瞎", "馏", "蕙", "戏", "i", "董",
+    "臣", "鹫", "栳", "锹", "裕", "蜷", "唣", "９", "缂", "螬", "笥", "惶", "蚰", "徨", "忭", "传",
+    "绛", "离", "锑", "候", "拓", "德", "损", "附", "紡", "徕", "錾", "蕻", "⑧", "構", "镊", "脘",
+    "靸", "涒", "镡", "光", "廒", "尴", "荥", "佳", "弼", "暮", "榆", "鼑", "辅", "钆", "湝", "佤",
+    "瘌", "炽", "筰", "嗈", "電", "飙", "坶", "椿", "俾", "灊", "泵", "像", "咅", "樓", "苒", "烨",
+    "溺", "棽", "戒", "箅", "愔", "缢", "楞", "庤", "塑", "湮", "沽", "蝌", "赌", "薨", "锾", "圜",
+    "骥", "秉", "瞌", "惇", "诊", "圣", "睢", "我", "廰", "苠", "襻", "鲚", "酮", "厮", "评", "沈",
+    "愀", "垞", "习", "敷", "比", "欢", "尚", "钪", "卣", "她", ",", "伴", "赃", "蚬", "喇", "醇",
+    "嚅", "T", "姝", "鍊", "悯", "N", "抚", "颡", "獐", "趟", "洑", "缝", "喁", "帷", "憷", "获",
+    "阉", "镫", "臨", "炮", "奴", "揩", "叩", "恺", "粱", "胁", "憬", "痿", "´", "沘", "彀", "饩",
+    "滋", "竑", "嗑", "鸟", "Ｔ", "璠", "快", "銀", "舶", "羞", "桞", "飛", "茹", "師", "偌", "節",
+    "冁", "叡", "臑", "踔", "酔", "養", "溦", "岐", "豉", "杌", "胩", "仳", "沾", "窑", "曳", "闷",
+    "垢", "垆", "磨", "髹", "態", "丽", "見", "洃", "遣", "场", "铭", "溉", "衬", "橦", "详", "馊",
+    "濂", "瑾", "鲫", "贞", "搴", "胺", " ", "景", "执", "袴", "炖", "箭", "楂", "婚", "镧", "厩",
+    "宣", "缣", "跃", "痘", "亓", "宦", "豪", "侣", "郧", "與", "痳", "挚", "殻", "嘢", "洽", "蹉",
+    "孽", "森", "俚", "八", "砹", "凹", "訇", "屹", "啰", "宴", "廨", "沇", "麇", "泅", "绡", "辛",
+    "鹲", "鋆", "旮", "婀", "幔", "赞", "：", "永", "活", "萸", "霉", "←", "媲", "阚", "鲮", "佝",
+    "獒", "圹", "隋", "征", "蚶", "龊", "搀", "嫫", "鬏", "幞", "學", "然", "沧", "萑", "襜", "撻",
+    "苯", "狳", "鞠", "咋", "壹", "栅", "款", "镘", "阳", "蚜", "荬", "糊", "疳", "糕", "镵", "寐",
+    "褴", "v", "醋", "诀", "汰", "啸", "备", "娘", "氚", "镓", "室", "簟", "硝", "嘌", "釐", "争",
+    "男", "疬", "鹄", "艳", "倒", "忸", "庶", "葒", "岖", "涨", "羝", "诼", "纨", "纰", "扉", "酎",
+    "藉", "疮", "枞", "黜", "戮", "芽", "鳑", "末", "蒿", "茈", "透", "渊", "秷", "〈", "试", "络",
+    "羼", "滪", "奋", "虏", "脁", "沫", "蓰", "襆", "披", "鲌", "艚", "逹", "炳", "泔", "叹", "轳",
+    "锵", "嫜", "佥", "严", "迅", "筠", "逵", "铿", "钇", "拇", "诏", "绯", "吊", "纠", "蟾", "c",
+    "涝", "汩", "盐", "跋", "拤", "邹", "镨", "羚", "龆", "脊", "攉", "傕", "短", "團", "蹬", "嘤",
+    "奎", "熨", "芪", "鸢", "濉", "''", "莴", "義", "赜", "踺", "皂", "努", "偏", "狡", "遭", "吞",
+    "嘿", "婊", "媸", "增", "殿", "刮", "燘", "劫", "娜", "瞄", "寡", "优", "捋", "佴", "菰", "蓠",
+    "笙", "镃", "樇", "瘫", "B", "橇", "逯", "堍", "О", "磐", "腼", "て", "送", "狭", "皓", "亡",
+    "嗉", "菠", "顺", "連", "嶶", "瑪", "辟", "婷", "牛", "笫", "窅", "萁", "戟", "覃", "馍", "建",
+    "謇", "旘", "镣", "燏", "葉", "轺", "倏", "堪", "见", "葛", "钕", "键", "押", "僊", "槐", "戎",
+    "窨", "洙", "鲢", "鞒", "慒", "雁", "圭", "Ｄ", "陌", "肱", "蜿", "洧", "惑", "祛", "樟", "矧",
+    "呵", "峻", "凝", "蕨", "拯", "珮", "塥", "展", "贻", "囐", "弱", "庳", "嫔", "緣", "呈", "策",
+    "漉", "瑗", "鲂", "鹂", "吾", "灶", "并", "挲", "重", "奭", "皙", "侪", "埗", "烬", "纾", "椒",
+    "技", "ɔ", "擀", "恍", "遨", "订", "雨", "卵", "锏", "猗", "癸", "）", "谡", "稷", "枨", "蹽",
+    "荑", "沅", "稽", "間", "冉", "颇", "酺", "份", "瞾", "毯", "藥", "蕞", "狲", "吡", "慷", "卯",
+    "摽", "肿", "嗛", "悒", "丨", "横", "鳡", "仫", "狎", "砗", "聿", "腥", "酡", "飱", "柳", "遽",
+    "汇", "湔", "麋", "垃", "粽", "坷", "鳗", "迫", "丢", "\"", "⒀", "嗲", "肐", "結", "署", "飨",
+    "蠡", "涩", "挈", "浿", "鐾", "姞", "隧", "铘", "呜", "蜕", "鷄", "逼", "哌", "病", "係", "偿",
+    "Ⅲ", "埋", "妤", "赘", "悉", "陷", "沸", "呲", "誓", "舆", "髀", "挫", "羑", "据", "顿", "淝",
+    "抟", "珧", "郑", "仗", "怛", "掠", "稳", "尥", "祙", "找", "郐", "锔", "轹", "钓", "黙", "饸",
+    "谌", "斐", "龙", "噫", "駆", "浼", "峒", "育", "纣", "溠", "铊", "亨", "杮", "呓", "钌", "业",
+    "繻", "溪", "戌", "蓿", "椱", "悱", "仉", "阮", "芈", "濋", "搔", "纽", "琛", "趄", "双", "镯",
+    "☆", "敉", "啬", "讦", "娱", "爾", "遶", "漱", "郗", "锪", "颃", "靰", "醊", "驊", "呢", "術",
+    "妙", "蚣", "溽", "酇", "巾", "舐", "却", "废", "邾", "砣", "乙", "鲜", "蒤", "囍", "璈", "稔",
+    "蘘", "匐", "業", "碟", "渺", "贤", "绋", "畑", "颞", "侥", "盟", "鼍", "阊", "蔽", "标", "吮",
+    "淬", "鏾", "圗", "夜", "乕", "娇", "瞿", "循", "讲", "懒", "熘", "禚", "观", "钷", "万", "n",
+    "未", "藝", "愿", "圈", "浩", "伦", "扛", "暄", "饶", "梧", "欣", "咿", "檔", "吼", "妮", "覆",
+    "辰", "誤", "允", "危", "硗", "惫", "瘪", "李", "焱", "沣", "坯", "穄", "归", "画", "营", ":",
+    "色", "哔", "矢", "巯", "祆", "傍", "享", "悻", "取", "凫", "铒", "唅", "眈", "疹", "败", "晴",
+    "顼", "绶", "剃", "斗", "禾", "４", "誜", "俨", "２", "患", "结", "可", "帇", "抍", "筝", "衢",
+    "鹛", "跸", "颢", "钾", "渡", "棒", "丛", "皱", "梓", "将", "压", "#", "岛", "?", "砺", "过",
+    "党", "挣", "瞋", "谶", "妯", "羡", "化", "淫", "歪", "鼗", "阄", "蔓", "烩", "餘", "猊", "．",
+    "畯", "祧", "狒", "碁", "咛", "鲈", "叨", "哞", "５", "娈", "半", "免", "拿", "畎", "媾", "棚",
+    "丈", "周", "匋", "酯", "奚", "爇", "摇", "搭", "蓇", "陽", "岢", "禤", "藠", "雅", "哲", "弹",
+    "按", "↑", "蹀", "察", "螾", "渎", "褂", "觳", "耍", "皲", "骗", "箫", "蕺", "亚", "保", "棵",
+    "放", "踪", "了", "熣", "亦", "痛", "币", "馐", "夢", "诱", "梱", "鲰", "郕", "璜", "祯", "颦",
+    "走", "踣", "嫚", "旯", "雲", "湟", "墨", "笃", "肇", "撝", "腦", "账", "舞", "⑨", "噻", "幂",
+    "僵", "崦", "’", "牢", "号", "嫡", "囱", "肥", "代", "锶", "掏", "随", "棓", "殉", "嘅", "掰",
+    "功", "垛", "踶", "娠", "霜", "碣", "鲼", "伉", "凄", "骋", "鹞", "洺", "乌", "赧", "瑛", "黎",
+    "曚", "鲴", "髫", "瘴", "藏", "雍", "畐", "蔻", "爼", "蹴", "巨", "贱", "汜", "胡", "虬", "椎",
+    "逸", "魇", "亶", "Φ", "忆", "赉", "塞", "潢", "垌", "简", "鼹", "發", "枢", "麝", "虹", "惭",
+    "唛", "春", "瑟", "郰", "桡", "捩", "堙", "嗨", "驳", "Ｆ", "荪", "忑", "贪", "躅", "步", "揜",
+    "闪", "垟", "晶", "分", "韭", "戴", "泪", "啧", "機", "峙", "和", "鸱", "绎", "屠", "阋", "黍",
+    "淸", "萩", "汉", "吐", "匙", "铗", "蠔", "簠", "鲵", "须", "蛣", "躏", "完", "咻", "釜", "馼",
+    "崤", "欻", "珐", "于", "郅", "焓", "轴", "递", "堰", "嗷", "儇", "壕", "嘟", "酸", "庾", "龂",
+    "妍", "锅", "雳", "桦", "抬", "谄", "气", "六", "诎", "绀", "张", "復", "客", "荞", "鳚", "衔",
+    "亁", "昂", "漤", "鞚", "筘", "绫", "彝", "枪", "苊", "榟", "饺", "苦", "顶", "衷", "聚", "寮",
+    "揆", "轪", "栋", "臂", "葖", "颋", "镐", "愕", "贸", "Q", "琼", "糥", "世", "莪", "龁", "禁",
+    "绲", "陶", "弑", "黢", "铵", "睐", "沄", "紬", "防", "癣", "曾", "钉", "纶", "膘", "句", "莸",
+    "踝", "躐", "酤", "腑", "雄", "堤", "喀", "姣", "孢", "阡", "褐", "胂", "髙", ";", "骖", "膺",
+    "糙", "辢", "⒃", "险", "砻", "缫", "骎", "低", "蚵", "箐", "苞", "劭", "峪", "工", "盈", "腹",
+    "袄", "祉", "癔", "笨", "Ｒ", "乚", "畏", "虍", "臾", "泛", "噙", "杷", "麗", "蹋", "逍", "迓",
+    "摅", "页", "戥", "胞", "艄", "壅", "啶", "趼", "牟", "翙", "蓝", "府", "轿", "砼", "荜", "杆",
+    "惊", "起", "瘅", "墈", "氖", "匀", "麃", "阘", "虮", "蘇", "蚤", "汗", "鳞", "籁", "缲", "畈",
+    "亟", "劬", "課", "蓄", "缅", "楮", "湜", "珩", "斋", "塬", "殁", "魃", "脞", "H", "澼", "钚",
+    "饕", "缕", "Ⅱ", "攮", "卿", "莅", "镆", "熹", "藩", "汁", "順", "趿", "拆", "蟹", "砒", "惴",
+    "㎡", "忖", "寝", "戕", "螭", "酿", "™", "柬", "枧", "凶", "蚁", "島", "殄", "鲊", "忠", "肉",
+    "辕", "叫", "徙", "漆", "缞", "夀", "楦", "佪", "兴", "粉", "裳", "蘧", "國", "旬", "看", "Ⅰ",
+    "剑", "痭", "襟", "恐", "遹", "◎", "窃", "穰", "澎", "敬", "旱", "燚", "坩", "彤", "尜", "猃",
+    "夏", "穈", "媒", "柑", "駡", "孛", "脉", "车", "零", "菩", "痊", "卉", "桔", "距", "吧", "漦",
+    "启", "仁", "滬", "馋", "帅", "鳈", "鄌", "超", "芡", "窘", "刽", "掌", "氤", "梽", "拎", "踏",
+    "勋", "甍", "玑", "稱", "鞍", "浍", "翅", "饟", "鼎", "罩", "加", "虚", "蕰", "簉", "堇", "巢",
+    "疲", "蟑", "狝", "瓮", "潋", "行", "饥", "散", "糌", "牵", "貢", "偉", "咄", "痕", "沃", "苓",
+    "锂", "狻", "褿", "畸", "姿", "煎", "胜", "觅", "烊", "質", "疵", "擢", "椤", "米", "累", "诳",
+    "斡", "K", "恻", "匦", "烫", "湾", "鹎", "吟", "摘", "涞", "恿", "嫉", "炎", "婧", "朽", "铑",
+    "ㆍ", "讧", "曜", "挑", "〇", "搅", "鹐", "丁", "彼", "棠", "饪", "箬", "祎", "魄", "囿", "犬",
+    "市", "髃", "勚", "桶", "辎", "瓞", "财", "缄", "園", "睒", "护", "尿", "融", "围", "水", "糍",
+    "虢", "呦", "越", "棺", "砮", "邓", "鹦", "稣", "呃", "柙", "鎌", "转", "袋", "湉", "亘", "俩",
+    "腆", "谣", "飔", "撂", "鄳", "爲", "盦", "谳", "卸", "W", "嚆", "婕", "卫", "拚", "呀", "汽",
+    "洣", "冻", "鳝", "录", "毋", "閥", "熬", "谜", "齐", "匳", "慧", "猴", "撬", "妳", "諾", "蠼",
+    "瘟", "伐", "颤", "奶", "陧", "麾", "岌", "浇", "邸", "「", "不", "哼", "热", "旳", "慙", "＆",
+    "苔", "郿", "钗", "氡", "纹", "侬", "霓", "靈", "扁", "聢", "疼", "岣", "甥", "恭", "喷", "芫",
+    "骂", "肪", "熥", "揠", "鲷", "遁", "霎", "娆", "圩", "爬", "傲", "贽", "紫", "觑", "琇", "蟆",
+    "怙", "玙", "庼", "筮", "慗", "层", "娓", "蚨", "糟", "璩", "隼", "锧", "疱", "铎", "祠", "绁",
+    "速", "湛", "蝮", "立", "媵", "禇", "撸", "禳", ">", "恋", "⑥", "鹡", "蓑", "樱", "奸", "蝣",
+    "埭", "聪", "慭", "睑", "肢", "焗", "骃", "毵", "潼", "塘", "烧", "劓", "栾", "牯", "〉", "毓",
+    "釉", "庞", "宕", "磚", "夺", "畜", "俏", "筼", "虱", "釆", "计", "陀", "诧", "臱", "牌", "固",
+    "鹉", "凰", "擞", "■", "㛃", "词", "店", "當", "许", "妣", "耋", "硼", "根", "焅", "砸", "霈",
+    "锃", "巳", "誉", "咕", "锊", "P", "云", "乞", "为", "姆", "桕", "丞", "鳤", "楷", "醒", "趔",
+    "怔", "砉", "潏", "肝", "拷", "也", "璐", "厘", "致", "昇", "绥", "抃", "佩", "斥", "⑶", "断",
+    "纵", "翁", "耄", "沭", "洄", "别", "吻", "渍", "r", "愦", "替", "骓", "攻", "旦", "来", "案",
+    "坫", "辞", "班", "锝", "拜", "掎", "穸", "笛", "痂", "笈", "鲶", "僻", "依", "碎", "蕤", "炕",
+    "寥", "拔", "髑", "慨", "眺", "⑿", "珞", "歧", "湲", "錦", "拮", "哭", "爰", "验", "寸", "%",
+    "口", "阔", "积", "篝", "狴", "殂", "痧", "绽", "搪", "祸", "缘", "遯", "祜", "种", "缓", "冒",
+    "庵", "窥", "颂", "哕", "谘", "蜗", "掖", "驮", "郎", "跂", "蝓", "贲", "馔", "琰", "大", "芊",
+    "粝", "啻", "蒉", "谒", "羔", "蘑", "菑", "布", "徂", "l", "炬", "蘩", "弭", "嵎", "補", "岑",
+    "佯", "棍", "没", "志", "裣", "咸", "权", "豺", "韦", "優", "紧", "嚚", "牴", "酚", "沤", "睥",
+    "葆", "乓", "划", "犒", "惚", "埕", "锴", "就", "滓", "汕", "冏", "缵", "囟", "旸", "麂", "接",
+    "餮", "瓤", "偕", "灏", "溻", "揶", "钯", "荸", "宪", "泠", "槠", "抺", "威", "燴", "井", "骣",
+    "耜", "磔", "懜", "淇", "瘘", "蹼", "觏", "圬", "纔", "恤", "铝", "薏", "鞫", "衉", "Ｅ", "球",
+    "姽", "毹", "嵘", "睦", "蛉", "伽", "橥", "痤", "卞", "警", "则", "芬", "磉", "悢", "逾", "吆",
+    "朿", "锒", "卧", "E", "焉", "纥", "髦", "鞯", "牞", "蹿", "琪", "洌", "幛", "淳", "菲", "涔",
+    "噗", "勺", "势", "哓", "毪", "刖", "雜", "浪", "懵", "棁", "秋", "◇", "玡", "埯", "谥", "芑",
+    "拌", "Ｚ", "埂", "寻", "虼", "豫", "蛄", "蜞", "椟", "〔", "哩", "絷", "咱", "郈", "湫", "抡",
+    "矮", "庖", "锰", "鎖", "葸", "棣", "砑", "题", "棋", "晕", "兠", "蓣", "貂", "裤", "昊", "扳",
+    "讴", "弩", "蚀", "尓", "暗", "莘", "黏", "寖", "蛔", "И", "痪", "饳", "妈", "Z", "秏", "矽",
+    "蘭", "艴", "菖", "寿", "撅", "秤", "颓", "廪", "醪", "耢", "螣", "椰", "麈", "听", "敞", "铠",
+    "润", "穑", "涟", "澹", "茨", "昨", "嚯", "钟", "護", "榇", "曦", "蜎", "呷", "邦", "瘵", "鲬",
+    "谖", "概", "九", "飞", "潇", "高", "锫", "钐", "徘", "诟", "缧", "剞", "【", "簋", "噍", "痋",
+    "关", "糁", "鹊", "潤", "邬", "赔", "序", "盤", "拙", "盾", "鸫", "迟", "氇", "塄", "庀", "噩",
+    "臃", "绔", "岔", "珠", "癀", "骡", "隶", "堋", "笆", "影", "昶", "飗", "α", "薄", "碜", "瞅",
+    "奠", "鲽", "牂", "裆", "刀", "遑", "隙", "沔", "訾", "旇", "s", "诮", "炱", "榚", "些", "縯",
+    "提", "甚", "埏", "峣", "意", "出", "郡", "摊", "哦", "轼", "嚒", "杈", "痢", "裥", "袖", "陔",
+    "溷", "裀", "迄", "枇", "筑", "貊", "鲃", "鸪", "Ｖ", "鞭", "粳", "菥", "盒", "毙", "风", "壽",
+    "胝", "章", "泄", "浜", "菇", "釘", "揖", "蝰", "榧", "掊", "瘿", "亍", "欤", "髻", "歃", "朱",
+    "撇", "铁", "兜", "喋", "趵", "瘼", "芤", "邳", "除", "滌", "么", "践", "矜", "曆", "炒", "咔",
+    "促", "姐", "酦", "货", "箔", "锡", "吝", "医", "汀", "精", "馘", "骶", "碉", "啷", "缉", "褪",
+    "阙", "滕", "蓐", "搏", "俅", "癌", "股", "圪", "孕", "鳕", "话", "鳳", "妩", "馬", "鞣", "璁",
+    "测", "螠", "圆", "颥", "卒", "钲", "䨱", "骢", "批", "陨", "婶", "熵", "鸣", "梁", "怅", "滑",
+    "驵", "帑", "鑽", "痈", "鲑", "犍", "茑", "糧", "谫", "咒", "绘", "塆", "耖", "铮", "殳", "角",
+    "齮", "挻", "捎", "2", "З", "廋", "泻", "犊", "儋", "椋", "聍", "鼬", "鳏", "收", "跞", "盥",
+    "信", "嘴", "眉", "黠", "纡", "Ω", "愆", "桹", "昽", "讠", "蚧", "龀", "鳓", "麓", "括", "蘊",
+    "鈇", "写", "焕", "哒", "忌", "咳", "蜮", "衄", "氰", "邘", "缇", "缮", "表", "续", "篇", "旄",
+    "闻", "門", "偃", "茳", "晗", "鋪", "讶", "衙", "闭", "管", "傒", "藔", "鳯", "萼", "泥", "汔",
+    "浆", "姚", "几", "含", "沆", "繇", "灾", "嗦", "饬", "系", "熠", "漶", "潭", "牲", "湘", "稁",
+    "宄", "亳", "杏", "豳", "監", "印", "旁", "辙", "播", "让", "乳", "镚", "腒", "衅", "铅", "殛",
+    "形", "颚", "茸", "绚", "铲", "贬", "溆", "寘", "傫", "解", "事", "旻", "差", "竣", "篃", "渥",
+    "嵩", "@", "鹀", "哈", "痍", "绪", "囔", "渗", "帐", "叙", "⑦", "跖", "貝", "盅", "碶", "哗",
+    "裼", "窝", "滙", "郸", "延", "炼", "芹", "簃", "谭", "莱", "佞", "猝", "褥", "陆", "王", "碴",
+    "粑", "怃", "倭", "貉", "硈", "裴", "溍", "坂", "勐", "踬", "敌", "改", "膠", "罘", "罚", "繁",
+    "幢", "煸", "扩", "黑", "氮", "孓", "饔", "姹", "蠖", "倬", "慝", "沩", "嚏", "雌", "琏", "莹",
+    "觊", "颟", "坤", "筅", "桉", "肈", "幻", "”", "烽", "蓅", "噌", "桎", "矻", "指", "瀚", "羊",
+    "邒", "筒", "匣", "旨", "葑", "团", "偻", "镛", "忉", "帱", "往", "刎", "害", "母", "串", "酢",
+    "舟", "栝", "娣", "⒉", "装", "笊", "篑", "垄", "镳", "牝", "煲", "氵", "遂", "骜", "钜", "屡",
+    "宇", "z", "惨", "潆", "鹃", "豌", "浞", "幄", "麺", "暽", "弁", "菤", "酲", "虑", "样", "访",
+    "睁", "黼", "悞", "禧", "涠", "舂", "駕", "屈", "噀", "邽", "炅", "欸", "隆", "描", "绦", "派",
+    "溲", "颙", "忐", "鹺", "只", "诈", "腭", "亵", "这", "炀", "趣", "喙", "跨", "場", "全", "搢",
+    "舨", "踢", "巿", "糸", "乐", "澈", "羰", "匯", "D", "濒", "莛", "癯", "娴", "姒", "祥", "渖",
+    "庭", "渑", "挹", "狉", "萍", "鹚", "焻", "自", "舸", "岫", "飑", "戍", "淙", "愠", "导", "賣",
+    "进", "赳", "鱿", "硂", "僤", "O", "拟", "钨", "笳", "汴", "挤", "稞", "柏", "阐", "弥", "艽",
+    "爻", "魭", "俶", "聂", "鲎", "齌", "菀", "僜", "煅", "满", "兀", "辔", "舊", "胥", "卢", "额",
+    "膝", "嵬", "昼", "唏", "璨", "苷", "舫", "倨", "耵", "碳", "鄺", "橘", "裔", "裸", "宋", "窋",
+    "悚", "昱", "错", "咣", "檄", "硁", "莆", "肃", "曼", "垍", "绵", "役", "內", "館", "规", "犋",
+    "毅", "锨", "瓘", "丘", "疝", "杀", "袗", "屏", "阎", "衹", "嚣", "冼", "铪", "熏", "ｔ", "储",
+    "傧", "髁", "邙", "窴", "泾", "止", "晾", "涵", "庸", "庠", "靿", "睚", "减", "衲", "思", "簖",
+    "耘", "之", "斑", "扬", "袼", "缗", "菏", "滍", "研", "垚", "艘", "黇", "蜘", "汨", "侦", "疃",
+    "再", "沂", "榛", "励", "嚼", "拴", "钴", "珑", "撕", "鸯", "共", "醐", "揿", "醴", "凭", "菹",
+    "鹬", "捷", "掼", "芝", "缪", "咏", "挺", "蒗", "疖", "伸", "索", "黉", "髋", "習", "姜", "蔺",
+    "扶", "忽", "锉", "戚", "珲", "摸", "黛", "跌", "螈", "冚", "洱", "鼯", "庡", "痒", "哆", "品",
+    "歆", "登", "呔", "追", "鉫", "碾", "祚", "总", "帚", "薹", "趴", "容", "滘", "❋", "迷", "拾",
+    "炯", "析", "佼", "嗾", "-", "针", "滩", "禅", "讯", "織", "饱", "哟", "蹓", "侧", "蹯", "踽",
+    "趙", "漫", "卷", "朴", "卺", "狈", "绉", "浉", "玳", "榕", "畛", "蛾", "帔", "欠", "劙", "矾",
+    "進", "蚺", "骨", "捂", "疾", "璱", "圠", "坚", "弯", "秩", "逧", "烟", "佛", "嫱", "鳢", "裹",
+    "耥", "潮", "巽", "嶝", "廷", "鳆", "五", "西", "壤", "慌", "鴈", "爡", "‧", "儆", "萘", "愁",
+    "昔", "咩", "侃", "炫", "迳", "构", "缶", "皴", "捱", "菜", "秀", "粢", "畵", "辊", "伟", "榄",
+    "嗪", "已", "聖", "枕", "恢", "厍", "涅", "鄢", "饨", "鲨", "谪", "礤", "搁", "疔", "雎", "羿",
+    "犁", "硐", "盹", "若", "操", "昀", "亩", "凑", "睃", "贰", "穷", "荆", "葹", "嘃", "廓", "憔",
+    "制", "胧", "染", "麿", "镴", "砀", "组", "茎", "畫", "哑", "坨", "罱", "刳", "嘲", "瞰", "缏",
+    "侩", "谝", "谮", "还", "惋", "佈", "谏", "酗", "怩", "ｉ", "秽", "抻", "蟪", "狠", "辑", "羟",
+    "娉", "肯", "毕", "枸", "啮", "鳙", "要", "娑", "蜈", "忍", "侗", "Р", "凛", "巉", "拽", "该",
+    "俎", "惠", "薅", "猬", "忲", "●", "哏", "柔", "沉", "磁", "脾", "龃", "鲩", "钞", "厂", "鱽",
+    "濞", "群", "唱", "攝", "刃", "蹻", "剕", "朊", "啜", "语", "蜻", "遠", "呖", "都", "嫄", "戢",
+    "丫", "酞", "修", "户", "膏", "侈", "浣", "捍", "馒", "谱", "點", "滏", "跟", "鹁", "书", "愉",
+    "扭", "票", "耒", "燁", "魚", "拨", "鞑", "匆", "最", "憨", "晨", "癍", "邻", "醅", "骘", "骇",
+    "蔫", "。", "珪", "安", "歉", "邑", "淦", "胱", "漏", "咂", "叮", "瘛", "钺", "聘", "玎", "荻",
+    "涰", "淏", "烈", "囚", "㙟", "矫", "嗵", "澶", "磛", "肖", "蹑", "支", "哪", "久", "嗅", "稍",
+    "抠", "具", "裨", "韨", "虿", "漪", "外", "埸", "ä", "葡", "鲇", "廛", "蝥", "广", "兒", "涧",
+    "阌", "谊", "嵴", "拂", "悸", "哳", "4", "佘", "篁", "基", "赪", "掬", "演", "谋", "酣", "植",
+    "波", "恅", "杼", "胀", "纯", "缩", "盆", "芷", "绺", "施", "礞", "莳", "稻", "狐", "馌", "椑",
+    "恃", "镩", "骼", "孱", "硭", "曈", "焼", "秕", "觥", "茛", "射", "肣", "蟞", "′", "彰", "罔",
+    "麻", "逋", "谓", "革", "藤", "堆", "忿", "茂", "屦", "炟", "碹", "啤", "垤", "文", "佐", "叼",
+    "蹭", "踉", "眍", "龟", "黾", "崋", "氘", "尼", "瞢", "悦", "罢", "瑀", "睇", "贴", "涿", "拍",
+    "庥", "粪", "陳", "闌", "蓍", "元", "磊", "忻", "葴", "买", "诺", "㐂", "沦", "苏", "岂", "浊",
+    "脍", "誠", "嚬", "蔬", "黄", "銘", "钩", "劃", "嫘", "嵖", "笄", "鸰", "熳", "堑", "河", "遮",
+    "尝", "灭", "愬", "娅", "鸽", "锢", "狂", "弓", "侨", "滚", "释", "晏", "徵", "苡", "佚", "切",
+    "惰", "降", "槎", "脑", "磺", "谟", "裉", "弘", "醺", "眇", "顒", "兰", "忧", "础", "－", "茫",
+    "袒", "喤", "慶", "話", "跽", "堵", "能", "武", "壳", "汤", "橐", "圧", "嗡", "簌", "斧", "林",
+    "楊", "净", "嫌", "猛", "炸", "冠", "弦", "洵", "°", "創", "葜", "苁", "顏", "薛", "平", "舜",
+    "崧", "撼", "哮", "窣", "殖", "象", "飒", "瞥", "玥", "斝", "胭", "葱", "擎", "踰", "盛", "稇",
+    "斫", "滴", "硫", "胬", "唁", "拐", "殍", "冱", "陪", "鋈", "烦", "辋", "妇", "轸", "潴", "荤",
+    "碡", "徭", "膜", "秧", "淀", "缤", "琶", "^", "肋", "违", "轭", "$", "箕", "墩", "罗", "摆",
+    "槛", "笠", "塃", "畚", "嘛", "秸", "≥", "剪", "羅", "帜", "庇", "骙", "酾", "康", "陬", "蝶",
+    "闇", "圏", "琴", "鬈", "苶", "黡", "瓠", "酋", "琎", "奏", "科", "搒", "億", "翡", "熊", "性",
+    "痞", "篾", "邴", "篥", "饵", "揸", "狙", "鳂", "否", "煺", "畦", "凖", "鹧", "宵", "庛", "鬯",
+    "袂", "昴", "徽", "奧", "鸥", "锸", "庚", "彪", "槿", "趱", "铡", "讹", "莎", "約", "璞", "┌",
+    "铫", "鸈", "瞀", "鼙", "廙", "估", "屺", "锘", "渝", "汶", "囵", "跻", "酏", "扎", "孜", "腔",
+    "吗", "块", "潽", "“", "焚", "倘", "靥", "默", "笺", "遍", "剅", "桠", "极", "夷", "眸", "泳",
+    "谢", "懦", ")", "泜", "物", "开", "淮", "邵", "卟", "徜", "轧", "苍", "酶", "捌", "耨", "膄",
+    "琦", "痦", "涪", "舰", "迥", "涘", "季", "悫", "初", "贩", "碍", "沼", "腮", "勻", "恣", "他",
+    "院", "７", "蜒", "叉", "昉", "瞽", "疢", "蓼", "相", "臜", "ü", "秘", "蚌", "摉", "莞", "番",
+    "盼", "冀", "缠", "学", "馗", "搞", "苛", "羹", "潟", "嬴", "杶", "妖", "↓", "銭", "斄", "猢",
+    "搂", "芦", "埶", "癜", "铴", "從", "留", "硎", "簇", "臬", "侮", "鳎", "頉", "所", "邝", "哂",
+    "弛", "垂", "暑", "仿", "療", "轾", "熜", "悃", "勘", "鋫", "泞", "閃", "＋", "士", "虐", "盡",
+    "溯", "劣", "1", "锇", "蜇", "碛", "婴", "８", "捶", "藻", "嫰", "跹", "芋", "淠", "疯", "Й",
+    "诨", "醢", "痨", "栌", "摧", "噤", "灸", "馉", "蒇", "洲", "孪", "孵", "亿", "枝", "涕", "孟",
+    "缎", "攒", "湧", "褛", "铺", "廣", "诃", "蕈", "维", "珣", "逖", "瓯", "苘", "榫", "寰", "供",
+    "铽", "暲", "义", "兖", "喆", "祇", "蝽", "膛", "髭", "尹", "乒", "柈", "抓", "镋", "脏", "乩",
+    "挎", "酬", "靳", "躔", "霞", "離", "桌", "ｏ", "　", "滨", "懷", "萭", "望", "仆", "怂", "麸",
+    "槁", "洨", "俬", "孚", "匕", "栩", "t", "被", "邗", "纺", "狄", "国", "苋", "眢", "秣", "畤",
+    "∵", "创", "嵫", "胖", "垮", "镁", "壑", "Ａ", "趾", "荟", "汝", "嗥", "驰", "向", "磴", "讪",
+    "溅", "箍", "驾", "讙", "蛐", "眨", "醯", "筣", "鼫", "叠", "沥", "恕", "埽", "甩", "煜", "端",
+    "蜃", "帏", "丙", "荏", "圻", "夥", "暖", "抛", "岸", "头", "恝", "船", "鞲", "の", "七", "怪",
+    "诅", "茧", "北", "搓", "皖", "綉", "坪", "簪", "贵", "阍", "刑", "偶", "雷", "體", "髡", "岚",
+    "剂", "妾", "茬", "鹣", "鲳", "蜊", "假", "认", "嗍", "傈", "焘", "驷", "賀", "氆", "炝", "聋",
+    "嘡", "悺", "﹐", "僭", "证", "侉", "屐", "吃", "十", "〕", "瀦", "骤", "俳", "舁", "驸", "竿",
+    "坍", "|", "普", "軒", "瘃", "现", "醾", "铈", "榭", "蓁", "唷", "睫", "绰", "叶", "頤", "锤",
+    "浸", "杂", "汹", "璟", "蓖", "迦", "截", "錎", "年", "達", "劁", "码", "乃", "捯", "髪", "埚",
+    "呯", "頭", "镈", "汆", "β", "舷", "菂", "纸", "造", "晁", "卦", "蛎", "枉", "脯", "诛", "生",
+    "令", "桄", "恶", "鬟", "怯", "囡", "充", "省", "控", "螂", "笕", "邺", "扅", "斲", "骏", "鹋",
+    "队", "套", "镢", "牖", "Ｉ", "援", "封", "泚", "艿", "沮", "铙", "揳", "抄", "帨", "近", "唾",
+    "珥", "销", "非", "啱", "驼", "瓅", "遢", "筱", "奥", "善", "氩", "盉", "帖", "杜", "鈉", "蕉",
+    "鳄", "江", "鄂", "矩", "排", "诠", "准", "菼", "刻", "足", "峦", "灰", "僳", "蝢", "孫", "據",
+    "璆", "孳", "徹", "妆", "坭", "渌", "喏", "钦", "朔", "机", "徼", "掾", "其", "愧", "刹", "迹",
+    "惹", "闿", "鹘", "窗", "贳", "箓", "奡", "鸮", "翟", "滠", "辄", "夸", "袱", "】", "阿", "摁",
+    "觽", "垠", "棼", "杉", "甏", "旖", "阜", "猩", "埤", "醛", "馥", "哫", "囝", "晖", "骸", "椭",
+    "军", "哺", "束", "犄", "遛", "涡", "罄", "赇", "幌", "曙", "契", "饹", "圳", "仝", "爝", "示",
+    "敝", "漓", "马", "铐", "掳", "鲱", "蟀", "阵", "栒", "b", "轮", "俯", "嬉", "漩", "佟", "捆",
+    "迩", "液", "炊", "抒", "迭", "换", "菘", "请", "柩", "腓", "妥", "怖", "穆", "骝", "胛", "幺",
+    "燥", "棹", "孖", "洁", "边", "彥", "睨", "桁", "淆", "醤", "奘", "伙", "刘", "o", "過", "鏖",
+    "怄", "镗", "蘖", "卩", "龈", "枣", "联", "圊", "贫", "郭", "梗", "赡", "輸", "拼", "冰", "Ë",
+    "姓", "办", "拉", "富", "对", "肩", "缬", "逻", "瞠", "な", "荚", "宝", "緃", "峋", "鞁", "坉",
+    "译", "银", "猹", "榞", "借", "楠", "殽", "片", "谸", "雩", "琨", "颏", "晷", "；", "莠", "楽",
+    "嬗", "啉", "爍", "視", "岽", "荙", "冗", "『", "鯭", "觌", "喹", "药", "腈", "蹰", "–", "泷",
+    "榷", "卮", "缥", "各", "瘙", "凡", "悍", "劢", "６", "镰", "四", "摈", "驹", "陵", "沟", "麟",
+    "骊", "椀", "★", "滦", "菌", "彊", "疽", "辫", "璎", "郁", "资", "粼", "萄", "鳁", "鸤", "漁",
+    "痃", "绌", "蒴", "尊", "哙", "守", "坠", "胯", "泽", "持", "隅", "耧", "逛", "鄯", "瑜", "召",
+    "眷", "逄", "垦", "窄", "板", "赵", "源", "绹", "總", "适", "轰", "钛", "停", "唝", "燎", "栽",
+    "燃", "搬", "夬", "掐", "簦", "旃", "更", "蹊", "熙", "埃", "偲", "蒙", "舍", "杭", "朋", "霍",
+    "谙", "蓥", "赫", "鲏", "純", "缃", "蓓", "è", "萧", "锆", "诉", "恚", "u", "旷", "⑵", "褲",
+    "鲟", "互", "峂", "绝", "陒", "议", "脽", "态", "岙", "僮", "突", "濯", "犹", "阆", "溧", "宜",
+    "霣", "呆", "鄉", "觖", "濠", "耐", "橱", "敛", "踮", "怦", "鸠", "授", "滫", "叭", "镤", "仵",
+    "圖", "救", "变", "寕", "洋", "泃", "晥", "檎", "缦", "鏠", "悬", "彧", "韓", "籽", "磷", "歙",
+    "÷", "杨", "闯", "罴", "鼠", "陋", "瀣", "揾", "匪", "晚", "丧", "彷", "梏", "诽", "尻", "蹢",
+    "拊", "氕", "芳", "凸", "懊", "型", "媄", "憝", "骆", "纭", "刺", "悼", "藍", "欧", "葳", "艺",
+    "±", "紗", "钡", "钭", "首", "栈", "闩", "勃", "喑", "栉", "栟", "焊", "哀", "竖", "肘", "悲",
+    "鳜", "迨", "怿", "裘", "綮", "蕹", "冕", "无", "鼢", "戽", "鲣", "蜍", "插", "扯", "俣", "袪",
+    "级", "理", "茵", "矣", "障", "禊", "惯", "䄂", "宏", "韫", "栗", "编", "座", "跫", "、", "捞",
+    "谠", "車", "袆", "舖", "暝", "9", "混", "蓊", "韂", "拳", "j", "伶", "啦", "0", "诰", "缑",
+    "郴", "蕲", "谛", "皋", "兢", "件", "隈", "溊", "窈", "檀", "洪", "揭", "油", "颐", "剎", "侠",
+    "或", "腻", "迪", "舔", "牺", "倦", "夭", "蠋", "鹑", "倜", "喱", "豨", "谨", "翥", "馄", "蔚",
+    "类", "鳍", "泯", "岷", "張", "紅", "情", "婪", "睡", "诒", "旋", "舱", "列", "求", "姥", "翯",
+    "锲", "適", "蒡", "㭗", "韵", "撄", "律", "湊", "鲪", "碓", "馓", "汛", "尤", "℃", "泫", "竹",
+    "溃", "诵", "乡", "楹", "肷", "戋", "獭", "撴", "晤", "嘎", "裢", "轱", "歘", "鼱", "舀", "潵",
+    "腩", "挽", "至", "骞", "佻", "蕊", "轫", "岘", "斌", "《", "雇", "枘", "贶", "履", "亭", "绍",
+    "空", "泡", "擒", "蠓", "桼", "舢", "踵", "柢", "躺", "罥", "蔁", "幪", "蒯", "≤", "齉", "況",
+    "锕", "徇", "儿", "帽", "谕", "抽", "獍", "揪", "膳", "酴", "姊", "作", "弍", "郦", "翮", "髌",
+    "勝", "孔", "颁", "泩", "衾", "翊", "噉", "∩", "先", "的", "財", "绢", "崇", "饲", "賓", "绾",
+    "陡", "砾", "纷", "酱", "窎", "浕", "琫", "补", "嗽", "呛", "蚓", "伪", "艨", "搡", "粗", "阱",
+    "商", "郫", "喵", "缯", "炻", "州", "珽", "芒", "脶", "蕖", "拘", "\\", "蕃", "伏", "蛏", "璘",
+    "缡", "伞", "吸", "煞", "嗫", "餪", "━", "8", "漠", "衮", "峭", "筌", "阀", "乇", "杞", "镕",
+    "鹈", "觚", "疋", "猥", "肚", "阃", "地", "鸾", "玚", "镀", "诬", "凇", "惬", "劝", "堠", "睪",
+    "瞪", "涓", "專", "琢", "蠹", "裂", "芎", "轲", "谯", "畋", "长", "攘", "缁", "吵", "汈", "稂",
+    "咤", "甸", "瀑", "讣", "翠", "綦", "笑", "掺", "宫", "唆", "旗", "帘", "遵", "唪", "连", "必",
+    "姬", "枵", "渫", "神", "凱", "虞", "闵", "爭", "迢", "飓", "桴", "冑", "招", "鬶", "赊", "朦",
+    "薜", "逗", "铩", "焖", "狱", "昕", "犀", "捏", "侓", "爆", "箧", "怠", "缐", "苄", "踯", "暹",
+    "絮", "裁", "嘶", "诙", "攥", "¥", "痫", "摹", "朕", "薰", "闼", "樨", "需", "蕾", "扔", "服",
+    "蚩", "漳", "瞻", "陟", "诐", "爽", "耩", "坏", "寶", "瞧", "蘅", "爺", "仇", "瞈", "揉", "隰",
+    "阏", "皤", "鉏", "郇", "啵", "僧", "渐", "骠", "厾", "谦", "榱", "蓮", "乂", "伷", "觐", "遫",
+    "畀", "污", "眭", "蝗", "亢", "腾", "遇", "欺", "狸", "籼", "锯", "赚", "槔", "域", "老", "掩",
+    "锎", "邪", "沬", "袯", "映", "肤", "舒", "箦", "涎", "缔", "孃", "萎", "推", "峧", "嫒", "嵐",
+    "怒", "闾", "阅", "扢", "絡", "剩", "腢", "晌", "铟", "铚", "燔", "鄜", "墠", "擤", "芸", "鼩",
+    "食", "朰", "啥", "贿", "券", "垩", "典", "逭", "成", "帧", "侯", "妒", "顷", "晩", "浦", "汳",
+    "㾄", "坊", "②", "蝼", "倩", "挦", "騠", "蓂", "兹", "渤", "茴", "矶", "煤", "伛", "逃", "堞",
+    "漭", "闽", "埠", "妗", "烙", "傀", "滢", "堂", "骅", "崮", "兿", "螟", "榴", "蒈", "蟛", "员",
+    "嗟", "毗", "鹭", "毖", "籍", "傣", "偈", "澴", "耆", "黔", "薷", "俘", "箸", "蒺", "记", "铣",
+    "拥", "瑰", "抉", "史", "急", "什", "川", "曪", "剖", "罪", "扥", "鱼", "羌", "复", "昵", "朓",
+    "氪", "矅", "ò", "摒", "催", "{", "懂", "莉", "屃", "症", "遒", "璋", "鄒", "玄", "氦", "舳",
+    "納", "锷", "鲥", "羸", "怵", "嬖", "飚", "倔", "辨", "篌", "娄", "洎", "弟", "悝", "婥", "待",
+    "梃", "棕", "撞", "肊", "婺", "桥", "慕", "5", "尖", "监", "囊", "腎", "瘁", "⑴", "伢", "邈",
+    "炷", "籴", "月", "终", "龠", "蹇", "柯", "噜", "多", "玮", "踹", "泣", "圮", "辈", "鄙", "赆",
+    "蠃", "珈", "鬃", "识", "甑", "藓", "宥", "韧", "隗", "跚", "峁", "晒", "戳", "環", "葓", "筐",
+    "瑚", "稆", "巩", "葵", "网", "铨", "仡", "呶", "休", "村", "傩", "浡", "祝", "幅", "晳", "岿",
+    "[", "尃", "响", "咽", "讵", "淹", "糠", "驴", "烹", "甪", "焯", "整", "瘆", "茀", "瑞", "嘁",
+    "赅", "芼", "震", "怼", "膑", "蒟", "钊", "缷", "獨", "俄", "噪", "亏", "芍", "鲲", "黟", "酃",
+    "铻", "→", "鲡", "仑", "有", "扌", "治", "聆", "腙", "冬", "剥", "诓", "祃", "棘", "孥", "濆",
+    "鞋", "赣", "卜", "谆", "投", "琊", "塍", "洗", "津", "肴", "奕", "掴", "螗", "胸", "惦", "赠",
+    "耶", "蛸", "曷", "名", "栏", "崖", "始", "孤", "篆", "椴", "模", "儙", "袁", "颌", "卤", "寂",
+    "呸", "蓺", "噶", "钔", "粹", "潸", "郾", "娡", "啖", "芜", "䧳", "缟", "鞅", "哇", "嚄", "熛",
+    "私", "住", "鉄", "Ｕ", "失", "蜾", "㺃", "莨", "擂", "沕", "铄", "耰", "炔", "藐", "锈", "苇",
+    "芯", "檇", "龍", "珀", "筻", "褚", "缀", "棬", "仲", "杧", "铛", "挖", "缸", "嵇", "剟", "觋",
+    "鬼", "天", "鹪", "豭", "腴", "驿", "粵", "馈", "迸", "夕", "蜚", "禺", "迎", "狞", "嶽", "躯",
+    "暅", "蹾", "甾", "柆", "e", "欃", "無", "羽", "蛛", "僖", "碲", "遴", "厝", "滂", "眦", "鹅",
+    "咖", "潜", "媽", "啴", "寨", "琬", "判", "塌", "与", "剧", "掮", "读", "庆", "愚", "氲", "梾",
+    "稗", "很", "饯", "淘", "灞", "A", "！", "飾", "醚", "壁", "芮", "蔼", "知", "郯", "瞒", "术",
+    "法", "匠", "┅", "尕", "脂", "琅", "孝", "埙", "濟", "袷", "方", "芾", "驽", "线", "咡", "睿",
+    "滥", "鞨", "駹", "馎", "蛲", "申", "涣", "牗", "跄", "鲐", "泐", "铍", "肓", "é", "司", "辣",
+    "慑", "崞", "魏", "髯", "涯", "滃", "薪", "~", "鐵", "町", "芥", "匿", "癞", "嘚", "□", "啪",
+    "俇", "橛", "巅", "鄞", "痱", "回", "着", "魂", "瘀", "埒", "措", "蚪", "锁", "巴", "芏", "另",
+    "忏", "洇", "⑾", "箱", "劳", "榈", "Ｂ", "毒", "泰", "巍", "寇", "斛", "赖", "酒", "密", "左",
+    "莜", "茯", "脆", "饴", "洳", "舉", "酪", "鼒", "職", "桫", "螋", "砌", "戬", "前", "豁", "膲",
+    "鲯", "憋", "珺", "蹂", "亥", "冷", "蜔", "俦", "碇", "慰", "扒", "亮", "弒", "胫", "肠", "弇",
+    "拣", "丸", "涫", "勉", "帝", "玠", "应", "砜", "鬓", "泮", "鳖", "峇", "辗", "吴", "僡", "玊",
+    "钘", "惘", "奁", "翰", "爨", "罽", "莶", "纻", "菪", "興", "钧", "鄹", "缌", "嘉", "玲", "蛋",
+    "倚", "镬", "彳", "氯", "谐", "疗", "魉", "扈", "鏡", "涤", "佑", "勾", "饽", "R", "局", "醉",
+    "燫", "驱", "崽", "榮", "锄", "脎", "枓", "敲", "俑", "镶", "当", "逦", "寅", "捽", "宗", "茼",
+    "稿", "躇", "晋", "东", "渭", "闶", "潦", "慊", "蔟", "疑", "蜡", "嬛", "荒", "扪", "拈", "受",
+    "沱", "螽", "嗣", "瘰", "颖", "百", "楱", "崛", "窿", "蓬", "禨", "螵", "捕", "鳀", "霰", "瘸",
+    "圄", "陂", "敢", "矗", "炉", "篪", "注", "凉", "摔", "衿", "g", "遘", "鳟", "腚", "泱", "荣",
+    "池", "杰", "阻", "里", "峥", "葺", "翔", "柁", "飐", "载", "刨", "瘊", "溏", "嚭", "穝", "站",
+    "笞", "厌", "门", "汞", "雠", "靖", "怨", "葙", "厅", "凉", "荨", "底", "坼", "海", "轩", "秃",
+    "醭", "莒", "坎", "驺", "瀛", "嵝", "溥", "嚋", "寤", "k", "築", "耔", "跎", "小", "弊", "疭",
+    "√", "洞", "利", "次", "迕", "鸿", "刁", "媳", "酹", "磋", "勒", "伎", "浔", "斤", "珊", "鸳",
+    "颊", "塗", "刈", "龛", "縻", "楚", "脓", "南", "伧", "桨", "尸", "退", "亊", "侔", "蓟", "削",
+    "诲", "瑯", "挝", "绩", "嶂", "耷", "太", "著", "讥", "烂", "哥", "菔", "唳", "筫", "嚷", "斯",
+    "蔡", "碧", "慵", "搐", "琵", "逶", "菡", "篙", "驭", "莲", "毫", "犯", "坌", "啕", "疴", "傻",
+    "牧", "肺", "既", "悟", "讳", "练", "钠", "胨", "Ｇ", "迁", "帙", "嫦", "哿", "柝", "迤", "庑",
+    "踟", "粤", "乜", "悭", "霁", "在", "虔", "蓦", "暾", "鲅", "様", "徐", "诫", "料", "蒂", "揎",
+    "溜", "崎", "玦", "隐", "绳", "幼", "钼", "腧", "槌", "镅", "锿", "蹁", "猱", "狁", "蹈", "S",
+    "傢", "}", "珉", "浴", "龚", "吇", "肆", "⑩", "臌", "鏢", "―", "定", "哧", "螳", "爹", "摞",
+    "榍", "苩", "丑", "犴", "璀", "偎", "燹", "洯", "啁", "鱾", "計", "镂", "字", "琉", "壴", "霖",
+    "蟜", "做", "松", "扣", "徊", "叱", "桑", "≠", "寓", "崔", "茌", "`", "殓", "猖", "慎", "坻",
+    "皮", "喔", "耕", "拧", "织", "桂", "垝", "约", "飯", "皿", "移", "微", "桅", "钬", "赑", "宓",
+    "颉", "掂", "希", "沁", "翳", "澝", "崭", "聃", "蔸", "磕", "崚", "奉", "槍", "颠", "任", "飕",
+    "蛴", "歼", "蛭", "￥", "鉵", "刷", "泓", "頫", "诤", "阈", "诖", "漂", "冯", "柠", "本", "狼",
+    "芗", "丹", "柒", "饫", "衫", "摩", "桃", "啼", "譄", "醌", "帡", "少", "螃", "祷", "幸", "喘",
+    "噼", "甲", "刭", "泊", "那", "舄", "畲", "遊", "記", "绠", "蝈", "忒", "耙", "鹌", "搽", "绕",
+    "镠", "凼", "扫", "鸵", "匡", "缈", "铼", "挢", "踊", "噭", "苴", "貌", "啾", "蒜", "茱", "⒅",
+    "邛", "棉", "怀", "筵", "笾", "邢", "濱", "蒲", "嫠", "儡", "益", "萊", "歩", "日", "婢", "楔",
+    "嚎", "髂", "禄", "巫", "懿", "砍", "惆", "袭", "～", "欲", "鳅", "書", "撷", "库", "绻", "⒁",
+    "蒹", "辜", "屉", "奂", "龇", "诡", "醑", "儀", "讷", "聒", "盖", "浅", "長", "蛑", "念", "辒",
+    "蚊", "勖", "揣", "岬", "糅", "刊", "蛆", "硌", "柜", "惗", "激", "颈", "閣", "唬", "捧", "蒽",
+    "爸", "侏", "氧", "蛤", "牡", "瘦", "铉", "撒", "屿", "谔", "钀", "瓦", "恂", "钻", "声", "馨",
+    "贷", "藁", "闰", "人", "瀹", "间", "炜", "煨", "畴", "鞡", "悔", "量", "原", "屁", "杯", "趋",
+    "愎", "瓶", "烘", "韬", "姮", "勰", "眛", "饮", "瘐", "决", "版", "際", "俸", "此", "辽", "愫",
+    "爷", "隘", "螯", "腕", "闟", "厣", "齁", "矿", "女", "扇", "郄", "诚", "焜", "<", "檵", "赙",
+    "郢", "酌", "恳", "侵", "劍", "徳", "脔", "岍", "倍", "隹", "镲", "僬", "○", "啭", "坳", "谰",
+    "璪", "昆", "⒆", "惧", "虺", "篡", "锐", "邮", "嵊", "恹", "茄", "黯", "對", "魑", "禀", "添",
+    "汎", "廉", "戈", "∈", "陈", "肟", "螅", "缰", "啃", "瘾", "辁", "茜", "曝", "Ｃ", "餍", "蜴",
+    "咦", "臺", "扺", "隽", "崴", "遐", "胃", "铕", "楗", "墀", "魈", "淑", "邰", "飧", "勤", "糨",
+    "你", "铂", "蛰", "锻", "塈", "予", "怏", "鷪", "暂", "诞", "會", "鼐", "辏", "矬", "筜", "蟒",
+    "柫", "靴", "垒", "樵", "狨", "魋", "查", "掸", "＇", "每", "浮", "绞", "疍", "於", "饦", "辐",
+    "墟", "窳", "养", "墦", "鲔", "黝", "迈", "砟", "伥", "藜", "墅", "喬", "卬", "赶", "眼", "龌",
+    "主", "嗞", "転", "襕", "，", "杲", "儒", "政", "蹩", "付", "Я", "厢", "点", "如", "茗", "奍",
+    "嗌", "蕴", "问", "膚", "硒", "佃", "居", "赍", "鯨", "槜", "衡", "廐", "鲾", "慈", "莫", "篮",
+    "铱", "园", "范", "靠", "叻", "绸", "众", "抑", "浃", "極", "辀", "千", "蔯", "ｒ", "碱", "應",
+    "夼", "祺", "绣", "澉", "愛", "譬", "刚", "扼", "檫", "时", "荼", "部", "耽", "稀", "琳", "鎮",
+    "鳃", "疌", "挠", "京", "筴", "胳", "橡", "裱", "栊", "蛩", "流", "猄", "葭", "邯", "伤", "秦",
+    "孺", "郓", "筏", "茉", "谈", "黥", "鳇", "魅", "秫", "敫", "肄", "俪", "尺", "度", "廊", "昏",
+    "怎", "钎", "愤", "腿", "Λ", "碘", "囤", "婆", "煋", "桀", "阪", "焒", "砘", "澡", "阒", "臼",
+    "茭", "纛", "唻", "胗", "歌", "吱", "铸", "種", "礳", "舾", "砰", "即", "慥", "缆", "躞", "卖",
+    "眆", "搎", "鲁", "稠", "惝", "毶", "耎", "挡", "异", "咨", "歔", "垫", "素", "鳊", "打", "镏",
+    "耗", "锭", "剀", "讓", "鍋", "歇", "拗", "齿", "聊", "惕", "塔", "况", "汐", "采", "灣", "菁",
+    "峰", "呋", "阴", "衽", "鹏", "挂", "鲞", "第", "浑", "秭", "铯", "胎", "滔", "雕", "渔", "骍",
+    "鴻", "眚", "易", "枫", "麥", "罹", "劈", "苣", "蹄", "是", "锛", "饼", "铳", "器", "秒", "開",
+    "舛", "纳", "庒", "饰", "发", "虎", "乏", "届", "浯", "目", "霪", "氢", "华", "澧", "陴", "誊",
+    "欷", "瀌", "梵", "嗖", "惩", "妞", "螓", "溇", "嬃", "笼", "嬢", "獠", "辩", "骛", "挪", "礅",
+    "銎", "趺", "狍", "阑", "擦", "咙", "+", "鼻", "戆", "宿", "霏", "牁", "耦", "枳", "鲸", "阽",
+    "峯", "瘭", "祁", "镜", "绮", "道", "摭", "﹒", "耠", "限", "轻", "莽", "帛", "唯", "墒", "Ｗ",
+    "荦", "矸", "崃", "洫", "杳", "舌", "涌", "朵", "苑", "瓻", "辂", "凬", "畺", "莩", "奔", "荐",
+    "撵", "嶲", "憧", "沖", "邱", "瞑", "瘢", "腺", "嵯", "诗", "經", "胚", "谎", "央", "稼", "３",
+    "冈", "痣", "故", "皎", "珅", "踅", "硇", "通", "鹱", "草", "粕", "而", "梨", "萜", "搛", "鸺",
+    "托", "倡", "匾", "骷", "桐", "阇", "璃", "抖", "显", "镝", "I", "锣", "辆", "茝", "虫", "谀",
+    "萤", "帼", "滈", "鮼", "乔", "会", "产", "縢", "纱", "跆", "铤", "圯", "旚", "墓", "棰", "苜",
+    "聩", "蚍", "筢", "殇", "槚", "阂", "豊", "翚", "犰", "疚", "浠", "礴", "界", "蛙", "吁", "忪",
+    "讫", "辇", "汧", "澥", "纮", "糺", "拭", "镉", "鄠", "讼", "鎏", "逐", "烝", "扊", "筹", "仪",
+    "段", "杵", "卲", "钤", "曹", "潘", "浏", "陑", "答", "期", "橋", "位", "梅", "榻", "芨", "汭",
+    "贇", "杅", "伋", "馁", "皈", "躁", "鲦", "良", "幤", "曺", "榖", "便", "氍", "窍", "槃", "設",
+    "襦", "菓", "猡", "槲", "觱", "邠", "岩", "骈", "鬲", "睽", "残", "碗", "凯", "览", "伾", "吣",
+    "峡", "箨", "忡", "岁", "醵", "霸", "焐", "谁", "疟", "樾", "竺", "果", "阝", "花", "•", "耱",
+    "嗜", "骰", "萋", "甄", "侍", "厦", "辌", "给", "健", "垧", "娩", "校", "又", "临", "揽", "犟",
+    "蠢", "包", "檬", "穹", "燮", "札", "翏", "领", "深", "诶", "窒", "鸷", "统", "肾", "澍", "畹",
+    "职", "蝟", "眬", "旎", "朘", "眠", "篚", "贼", "貴", "＃", "茆", "磬", "①", "祀", "坞", "盯",
+    "苹", "痼", "耪", "狺", "妓", "湨", "動", "渚", "售", "鸶", "霄", "某", "×", "铀", "桓", "脲",
+    "心", "节", "［", "蜩", "剐", "猜", "盂", "穿", "洮", "坜", "掷", "坐", "烜", "冶", "袍", "穴",
+    "偷", "槽", "面", "媛", "撙", "孩", "询", "苈", "橙", "恙", "吉", "燈", "暇", "杋", "後", "璇",
+    "喟", "妪", "湄", "贡", "兮", "楫", "锗", "肮", "圉", "参", "烃", "葩", "骀", "锖", "犿", "恼",
+    "麯", "牮", "溱", "俵", "祢", "割", "羲", "中", "博", "膦", "7", "钙", "耸", "蚋", "嘬", "逝",
+    "瓿", "且", "苗", "屑", "華", "糯", "吓", "陇", ".", "魆", "d", "拶", "箜", "澳", "蘼", "垸",
+    "钅", "樯", "侑", "娼", "赛", "恪", "击", "濮", "隔", "似", "惜", "滤", "培", "谩", "坝", "煙",
+    "馕", "茡", "颼", "啡", "强", "巡", "糗", "芙", "骒", "常", "鳔", "楣", "嫩", "师", "攀", "旭",
+    "茠", "吏", "剌", "露", "把", "携", "忘", "软", "荩", "弧", "锩", "僦", "掘", "仓", "嵚", "沲",
+    "傅", "蝴", "爵", "纪", "薮", "參", "荀", "珙", "逞", "蹙", "徉", "佶", "庹", "弢", "锞", "想",
+    "窕", "躬", "蚡", "闸", "难", "妃", "唶", "V", "蚂", "酐", "]", "羁", "土", "敏", "蝻", "赎",
+    "闹", "铷", "脬", "攫", "椐", "橹", "(", "暨", "剁", "灵", "咫", "苻", "赈", "萝", "鹮", "颛",
+    "勿", "臆", "福", "蝠", "烺", "跤", "垓", "庋", "鏊", "彭", "碚", "膂", "鐘", "鸹", "择", "疫",
+    "暌", "胙", "翘", "仞", "實", "猫", "嘹", "坟", "泺", "靽", "仟", "鲋", "畅", "箪", "箩", "星",
+    "辍", "餐", "伍", "狃", "蹐", "芴", "龋", "仨", "狩", "诌", "媚", "琚", "❤", "鉰", "仔", "忝",
+    "垅", "嗤", "氅", "填", "詹", "诿", "皑", "椅", "鲭", "繪", "碑", "选", "廠", "嶓", "迮", "唠",
+    "赏", "│", "泍", "姑", "晦", "直", "艾", "邂", "裝", "郊", "疡", "」", "鸲", "兕", "县", "邡",
+    "麤", "旧", "壶", "礼", "梭", "铔", "玕", "硬", "后", "馅", "窠", "俊", "犾", "槭", "姨", "妨",
+    "幡", "沿", "帮", "爿", "褊", "淅", "册", "诔", "使", "唢", "族", "逮", "蟋", "掣", "红", "颔",
+    "闲", "咧", "衍", "矛", "蹜", "抱", "雀", "箴", "雙", "茁", "聲", "芰", "瘤", "落", "峨", "秆",
+    "献", "霭", "胰", "墁", "柽", "跛", "阼", "菝", "谗", "蠊", "懋", "蔷", "拃", "岱", "戡", "逡",
+    "背", "克", "棱", "栲", "乖", "橄", "炭", "萱", "胲", "摺", "殆", "魍", "宾", "拢", "姁", "祲",
+    "·", "歹", "胆", "爱", "旰", "抢", "匜", "硚", "愈", "跳", "▲", "跪", "網", "糈", "維", "痹",
+    "娃", "疸", "痔", "褙", "芭", "鹜", "猷", "檩", "吨", "淖", "竟", "呱", "瘠", "奢", "呐", "扞",
+    "灼", "家", "饿", "玻", "倾", "锚", "遗", "赝", "去", "跣", "纬", "玢", "梆", "陉", "屎", "上",
+    "酆", "搤", "併", "腌", "避", "禹", "绅", "帆", "英", "区", "交", "▪", "巧", "豹", "游", "清",
+    "荧", "娀", "訓", "尾", "觫", "梢", "均", "忤", "继", "變", "喽", "尉", "骚", "趸", "馃", "石",
+    "置", "毡", "竴", "龄", "掭", "骟", "堡", "确", "街", "沌", "恨", "壮", "弄", "鞴", "剿", "貅",
+    "路", "杠", "豚", "澜", "靼", "擐", "租", "媪", "彖", "逢", "撺", "盗", "呑", "锥", "栎", "唧",
+    "翩", "倥", "虽", "郏", "钒", "雉", "捒", "官", "酷", "樽", "房", "屣", "堌", "蒍", "悌", "④",
+    "蛳", "俱", "慢", "筇", "框", "椽", "债", "吭", "柱", "夤", "嘈", "骺", "趹", "刓", "豕", "噢",
+    "J", "论", "俞", "仙", "碰", "经", "铋", "⑤", "顽", "曲", "得", "汪", "浒", "际", "嘏", "扮",
+    "祊", "嵋", "‘", "牒", "禮", "粒", "籀", "闱", "刍", "邕", "美", "苫", "靺", "铹", "莰", "蔑",
+    "敵", "逑", "锬", "宸", "鞥", "匝", "褀", "菅", "逊", "脱", "晔", "罾", "郝", "恰", "丟", "芩",
+    "枷", "鹩", "a", "夙", "述", "钢", "瀵", "铧", "翱", "尧", "醮", "&", "鳐", "懲", "濩", "树",
+    "劵", "虾", "砚", "腄", "罟", "硖", "崩", "埘", "╱", "血", "觯", "巂", "栀", "萏", "雪", "眵",
+    "白", "新", "靶", "猪", "亻", "窬", "Ⅳ", "麽", "遄", "沏", "茅", "笋", "甬", "潔", "悛", "幕",
+    "鹤", "悴", "耻", "觜", "滞", "己", "鏂", "肛", "婤", "嘞", "蘸", "胴", "柿", "穗", "咯", "嵕",
+    "蜣", "僰", "玃", "细", "▏", "戗", "尘", "仄", "子", "历", "翌", "烀", "晞", "桷", "藕", "啄",
+    "腐", "瘥", "诣", "彗", "捭", "懑", "陛", "傥", "沚", "贯", "﹑", "雒", "航", "跬", "晓", "赢",
+    "鸭", "臊", "闳", "稲", "阁", "鼋", "‰", "个", "農", "匮", "辚", "滗", "桢", "詈", "绗", "馇",
+    "蜓", "专", "余", "鳉", "稚", "午", "珍", "癃", "钵", "档", "颍", "厕", "岭", "祓", "诂", "襙",
+    "炙", "枯", "锦", "袤", "桊", "嗓", "䘵", "嫂", "丰", "苎", "梦", "早", "菱", "钱", "灯", "钮",
+    "锓", "豐", "蝤", "协", "h", "鼓", "燕", "覺", "涮", "呗", "専", "篓", "玷", "褓", "从", "鲍",
+    "羧", "俭", "萌", "樣", "颜", "谷", "夹", "囫", "』", "缙", "鹰", "条", "状", "沨", "因", "莺",
+    "楼", "扰", "禢", "砧", "埔", "纩", "臭", "茶", "妹", "莼", "境", "山", "诘", "蒌", "唤", "仃",
+    "眄", "田", "畼", "缱", "祘", "痴", "揍", "猾", "甙", "躲", "返", "砝", "叽", "苟", "肭", "悖",
+    "嫣", "凋", "踞", "勢", "弃", "般", "赤", "佣", "嘘", "智", "寒", "旆", "咎", "戊", "温", "昙",
+    "蝄", "掉", "冤", "凍", "一", "鋼", "茏", "兼", "昃", "峤", "挟", "０", "顾", "颅", "*", "遆",
+    "硅", "烯", "眊", "鳘", "下", "雾", "癖", "贮", "缴", "升", "劲", "婞", "掇", "梯", "壬", "撮",
+    "绑", "氣", "薤", "兔", "赟", "漕", "签", "顇", "砭", "鸼", "明", "佧", "鹗", "哉", "席", "趁",
+    "砖", "澌", "撤", "萨", "熔", "蚝", "狮", "夔", "介", "青", "喈", "郂", "埝", "狗", "哚", "咪",
+    "材", "跑", "炗", "龉", "髓", "衣", "觀", "淋", "寄", "羮", "抵", "恁", "藿", "蜢", "甜", "兽",
+    "洒", "眶", "薯", "绷", "颀", "骑", "鲻", "｜", "q", "繄", "丝", "焙", "瑕", "童", "豸", "讨",
+    "钿", "艮", "踧", "幽", "焰", "宽", "址", "鸩", "堉", "音", "擅", "铞", "埌", "宙", "_", "钥",
+    "承", "率", "噬", "凌", "程", "蚕", "嘣", "饭", "橉", "径", "１", "鄏", "袜", "孙", "樊", "窦",
+    "牦", "歡", "罕", "蓉", "诩", "㧎", "责", "豢", "逅", "甘", "礶", "镥", "穂", "到", "檑", "皇",
+    "泖", "腱", "郤", "竞", "竽", "溴", "木", "蛀", "蛟", "燧", "鹳", "谵", "钳", "魔", "链", "咆",
+    "荡", "婳", "冢", "鲧", "昝", "※", "倫", "才", "簿", "醫", "吕", "姗", "Ｌ", "俺", "隳", "鮮",
+    "衩", "鲆", "崟", "鲹", "岗", "镇", "嗄", "硪", "盎", "氟", "笪", "今", "公", "尽", "佬", "骧",
+    "驻", "翼", "咚", "盱", "镎", "浈", "谅", "洟", "嘭", "跶", "蔹", "咭", "妁", "殚", "砷", "购",
+    "犸", "哄", "笸", "喂", "赋", "倓", "项", "臧", "梳", "笏", "鹝", "昭", "罿", "镄", "П", "湯",
+    "U", "绐", "绂", "鸬", "毐", "台", "鈴", "憾", "痰", "缭", "绒", "赁", "效", "哝", "─", "偆",
+    "泗", "谇", "貘", "搜", "阗", "個", "艟", "嗔", "正", "筛", "鼷", "摛", "馀", "坡", "卓", "厥",
+    "睾", "雯", "篷", "狰", "掀", "夫", "靛", "伻", "茇", "顧", "猕", "占", "俟", "兄", "垡", "兆",
+    "铃", "屙", "轵", "銮", "磲", "桤", "跷", "缳", "粘", "袢", "尬", "獘", "社", "彬", "哨", "煳",
+    "莊", "教", "鸸", "窖", "晰", "捻", "环", "廿", "驶", "椠", "熟", "赓", "挥", "兵", "洚", "桧",
+    "埼", "嘀", "哎", "柞", "悄", "调", "洼", "蛇", "铬", "蝇", "倆", "谂", "姻", "怕", "瑙", "算",
+    "惮", "遥", "惟", "甫", "玺", "袈", "税", "玟", "械", "擗", "淤", "溟", "揞", "纲", "谤", "條",
+    "翕", "荛", "忾", "馿", "糖", "檗", "垱", "┐", "躜", "缒", "您", "蹶", "合", "数", "斟", "粲",
+    "玖", "淞", "孰", "觎", "M", "嫪", "毽", "桩", "蹦", "亹", "胾", "亲", "痉", "莙", "捐", "烁",
+    "瓣", "邀", "但", "误", "饻", "妄", "镱", "民", "嗳", "竦", "柰", "脒", "斓", "瞟", "拱", "霾",
+    "粜", "腽", "咬", "唿", "礻", "y", "褒", "郪", "熰", "梻", "甭", "处", "拖", "蜉", "鄱", "絲",
+    "宠", "瞵", "荷", "靓", "喻", "阢", "伫", "担", "朐", "墙", "儣", "胄", "簏", "皆", "踌", "萃",
+    "命", "滟", "睹", "叁", "蟮", "東", "﹖", "式", "營", "疠", "醳", "尔", "購", "吒", "柴", "途",
+    "力", "疰", "寳", "萬", "挨", "碥", "牍", "嘻", "柃", "剡", "徝", "杖", "螺", "祈", "産", "煽",
+    "讽", "瞭", "由", "牀", "涉", "暴", "枚", "仰", "它", "胤", "驯", "港", "寵", "熄", "蛮", "务",
+    "殃", "亸", "涢", "妫", "粥", "扦", "寺", "抿", "湃", "C", "脚", "涸", "动", "務", "镭", "检",
+    "釂", "耳", "榦", "怆", "氨", "瘳", "钽", "牚", "负", "纂", "獬", "息", "婄", "襞", "囹", "酰",
+    "扑", "臛", "价", "褡", "篼", "媢", "酊", "腋", "愣", "垯", "喃", "牾", "犨", "蜂", "诹", "迂",
+    "槑", "好", "褰", "咝", "③", "劐", "骄", "腉", "仕", "叵", "磅", "金", "皁", "勍", "煮", "鍪",
+    "筚", "吲", "艰", "床", "叛", "﹔", "喜", "齑", "昧", "乎", "谲", "匈", "麒", "身", "唑", "鲤",
+    "栂", "垭", "殪", "沪", "苌", "濡", "蒎", "遏", "®", "质", "纫", "罐", "瞍", "柄", "呼", "锱",
+    "盔", "搋", "烛", "喉", "奈", "赦", "轘", "鉴", "瓒", "铰", "恽", "腠", "韶", "胼", "侄", "珂",
+    "鹕", "䝉", "呤", "椁", "岳", "汲", "疤", "绊", "涂", "乾", "噎", "兑", "絜", "嘧", "蚯", "们",
+    "蝉", "玛", "魁", "黻", "振", "冥", "报", "窟", "笱", "喊", "瓴", "喾", "説", "雹", "恫", "ａ",
+    "颧", "給", "宅", "鞧", "嬷", "鼾", "笔", "鲉", "匹", "蠕", "愍", "氽", "赐", "畔", "絪", "洴",
+    "审", "窂", "褔", "狯", "谑", "毳", "右", "疣", "鲙", "堃", "瓜", "…", "崆", "钫", "樭", "鲛",
+    "砂", "筋", "犏", "乱", "入", "浐", "膙", "厚", "獗", "覌", "焦", "罃", "宬", "隍", "荃", "醜",
+    "厉", "艇", "娟", "樂", "筲", "洛", "唼", "诜", "馑", "香", "缛", "运", "祟", "踩", "陲", "叔",
+    "潞", "二", "搠", "祼", "瀘", "蒋", "樗", "麦", "鸦", "棻", "古", "鹠", "怊", "裾", "够", "璧",
+    "晡", "擘", "毂", "御", "葚", "忱", "觞", "瑁", "唇", "罡", "剽", "殡", "沛", "帻", "举", "瞓",
+    "谬", "溝", "言", "哽", "婿", "猿", "跗", "獴", "俜", "呙", "弗", "凿", "窭", "铌", "友", "唉",
+    "怫", "荘",
+];
+
+pub const CHARSET_OLD: &[&str] = &["", "笤", "谴", "膀", "荔"];
+
+
+
+
+// pub fn from_builtin_old() -> Self {
+//     Self::from_static_slice(
+//         CHARSET_OLD,
+//         false,
+//         Resize::DynamicWidth(64),
+//         1,
+//         Normalization::ZeroToOne,
+//     )
+// }
+// 
+// /// 从预设的 Beta 版字符集创建
+// pub fn from_builtin_beta() -> Self {
+//     Self::from_static_slice(
+//         CHARSET_BETA,
+//         false,
+//         Resize::DynamicWidth(64),
+//         1,
+//         Normalization::MinusOneToOne,
+//     )
+// }
+
+
+// /// 从外部外部 JSON 文件动态加载字符集(在后续优化中移除)
+// pub fn from_json_file<P: AsRef<Path>>(path: P) -> anyhow::Result<Self> {
+//     let path = path.as_ref();
+//     if !path.exists() {
+//         return Err(anyhow!("模型元数据配置文件不存在: {:?}", path));
+//     }
+// 
+//     let mut file = File::open(path)?;
+//     let mut content = String::new();
+//     file.read_to_string(&mut content)?;
+// 
+//     let dto: ModelMetadataDto = serde_json::from_str(&content)
+//         .map_err(|e| anyhow!("JSON 反序列化失败，请检查字段是否完整: {}", e))?;
+// 
+//     // 1. 将 DTO 的字符串数组转化为强类型的 Charset
+//     let tokens: Vec<Cow<'static, str>> =
+//         dto.charset.into_iter().map(|s| Cow::Owned(s)).collect();
+//     let charset = Charset::new(tokens);
+// 
+//     // 2. 解析 resize 策略（重现 Python 的复杂条件判断）
+//     if dto.resize.len() != 2 {
+//         return Err(anyhow!(
+//                 "'resize (or image)' 字段必须是包含两个元素的数组，例如 [-1, 64]"
+//             ));
+//     }
+//     let r0 = dto.resize[0];
+//     let r1 = dto.resize[1];
+// 
+//     let resize = if r0 == -1 {
+//         if dto.word {
+//             // 如果 word 为 true，且包含 -1，Python 里是 resize 为 (r1, r1) 的正方形
+//             Resize::Square(r1 as u32)
+//         } else {
+//             // 如果 word 为 false，且包含 -1，Python 里是高度固定为 r1，宽度按原图比例缩放
+//             Resize::DynamicWidth(r1 as u32)
+//         }
+//     } else {
+//         // 正常的固定宽高
+//         Resize::Fixed(r0 as u32, r1 as u32)
+//     };
+// 
+//     Ok(Self {
+//         charset,
+//         word: dto.word,
+//         resize,
+//         channel: dto.channel,
+//         normalization: dto.normalization,
+//     })
+// }

ddddocr-tract/tests/ocr_test.rs

@@ -0,0 +1,251 @@
+use anyhow::Context;
+use ddddocr_core::traits::Loader;
+use ddddocr_tract::{DetectionResult, Ocr};
+use ddddocr_tract::{Detector, ModelMetadata, Normalization, Slider};
+// 假设你的包名是这个
+use ddddocr_tract::{DetRuntime, OcrRuntime};
+use image::{DynamicImage, ImageBuffer, Luma, Rgb};
+use std::fs;
+use std::path::Path;
+
+mod char_slice;
+use char_slice::CHARSET_BETA;
+use ddddocr_core::Resize;
+
+use ddddocr_tract::loader::ModelLoader as TractModelLoader;
+
+fn load_image<P: AsRef<Path>>(path: P) -> anyhow::Result<image::DynamicImage> {
+    // 1. 先将泛型转为具体的 &Path 引用
+    let path_ref = path.as_ref();
+
+    // 2. 调用 open 时传入引用（utils::open 支持 AsRef<Path>）
+    image::open(path_ref).map_err(|e| {
+        // 3. 此时 path_ref 依然有效，可以安全地在闭包中使用
+        anyhow::anyhow!("无法加载图片 {:?}: {}", path_ref, e)
+    })
+}
+/// 将检测结果绘制在图像上并保存
+fn save_debug_image(
+    dynamic_img: &DynamicImage, // 【优化点 1】直接传入解码好的引用，拒绝重复解码
+    bboxes: &[DetectionResult], // 【修改点 1】类型改为自定义结构体切片
+    output_path: &str,
+) -> anyhow::Result<()> {
+    // 删除了原本的 let dynamic_img = image::load_from_memory(image_bytes)?;
+    let mut img = dynamic_img.to_rgb8();
+    let (width, height) = img.dimensions();
+    let red = Rgb([255u8, 0, 0]);
+
+    for bbox in bboxes {
+        // 【修改点 2】将原来的索引 bbox[0].. 改为结构体字段访问 .x1, .y1 ..
+        let x1 = bbox.x1.max(0).min(width as i32 - 1) as u32;
+        let y1 = bbox.y1.max(0).min(height as i32 - 1) as u32;
+        let x2 = bbox.x2.max(0).min(width as i32 - 1) as u32;
+        let y2 = bbox.y2.max(0).min(height as i32 - 1) as u32;
+
+        // 绘制横向线条
+        for x in x1..=x2 {
+            img.put_pixel(x, y1, red);
+            img.put_pixel(x, y2, red);
+            if y1 + 1 < height {
+                img.put_pixel(x, y1 + 1, red);
+            }
+            if y2.saturating_sub(1) > 0 {
+                img.put_pixel(x, y2 - 1, red);
+            }
+        }
+        // 绘制纵向线条
+        for y in y1..=y2 {
+            img.put_pixel(x1, y, red);
+            img.put_pixel(x2, y, red);
+            if x1 + 1 < width {
+                img.put_pixel(x1 + 1, y, red);
+            }
+            if x2.saturating_sub(1) > 0 {
+                img.put_pixel(x2 - 1, y, red);
+            }
+        }
+    }
+
+    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() {
+    let session = TractModelLoader::default()
+        .build_for_path("D:\\CNWei\\CNW\\Rust\\ddddocr-rs\\models\\common_sml2h3_f32.onnx")
+        .expect("模型加载失败");
+
+    let metadata = ModelMetadata::from_static_slice(
+        CHARSET_BETA,
+        false,
+        Resize::DynamicWidth(64),
+        1,
+        Normalization::MinusOneToOne,
+    );
+    // 1. 初始化模型
+    let ocr_runtime = OcrRuntime::new(session, metadata);
+    // 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");
+
+    // 3. 执行识别
+    // let result = Ocr::new(&ocr_runtime)
+    //     .predict(&img)
+    //     .expect("识别过程出错")
+    //     .into_text();
+    let result = Ocr::builder()
+        .runner(&ocr_runtime)
+        .predict(&img)
+        .expect("识别过程出错")
+        .into_text();
+
+    println!("识别结果: {}", result);
+    assert!(!result.is_empty());
+}
+#[test]
+fn test_det_load() -> anyhow::Result<()> {
+    let det_model = TractModelLoader::default()
+        .build_for_path("D:\\CNWei\\CNW\\Rust\\ddddocr-rs\\models\\common_det.onnx")
+        .expect("模型加载失败");
+    let det = DetRuntime::new(det_model);
+    let image_path = "D:/CNWei/CNW/Rust/ddddocr-rs/samples/det1.png";
+    let image_bytes =
+        fs::read(image_path).map_err(|e| anyhow::anyhow!("无法读取图片 {}: {}", image_path, e))?;
+
+    println!("图片读取成功，字节大小: {}", image_bytes.len());
+
+    // 【修改点 1】将字节流解码为统一的 DynamicImage
+    let img = image::load_from_memory(&image_bytes)
+        .map_err(|e| anyhow::anyhow!("图片解码失败: {}", e))?;
+
+    // 【修改点 2】传入统一的 &DynamicImage 引用
+    let bboxes = Detector::new(&det).predict(&img)?;
+    // println!("{:?}", det);
+    println!("检测到的目标数量: {}", bboxes.len());
+
+    if bboxes.is_empty() {
+        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",
+        )?;
+
+        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
+            println!("目标 [{}]: {}", i, bbox);
+        }
+    }
+    Ok(())
+}
+
+#[test]
+fn test_real_slide_match() {
+    let engine = Slider::new();
+
+    // 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 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
+    let start = std::time::Instant::now();
+    let result = engine
+        .slide_match(&target_img, &bg_img, false)
+        .expect("Slide match 执行失败");
+    let duration = start.elapsed();
+
+    // 3. 打印结果
+    println!("-------------------------------------------");
+    println!("{}", result);
+    println!("耗时: {:?}", duration);
+    println!("-------------------------------------------");
+
+    // 验证基本逻辑：坐标不应为 0 (除非匹配失败)
+    assert_eq!(result.target_x, 237);
+    assert_eq!(result.target_y, 77);
+    assert!(result.confidence > 0.0);
+}
+
+#[test]
+fn test_real_slide_comparison() {
+    let engine = Slider::new();
+
+    // 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 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
+    let start = std::time::Instant::now();
+    let result = engine
+        .slide_comparison(&target_img, &bg_img)
+        .expect("Slide match 执行失败");
+    let duration = start.elapsed();
+
+    // 3. 打印结果
+    println!("-------------------------------------------");
+    println!("滑块匹配测试结果:");
+    println!("检测坐标: [x: {}, y: {}]", result.target_x, result.target_y);
+    println!("置信度: {:.4}", result.confidence);
+    println!("耗时: {:?}", duration);
+    println!("-------------------------------------------");
+
+    // 验证基本逻辑：坐标不应为 0 (除非匹配失败)
+    assert_eq!(result.target_x, 171);
+    assert_eq!(result.target_y, 90);
+    assert!(result.confidence > 0.0);
+}
+#[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 = TractModelLoader::default()
+        .build_for_path(
+            // "D:\\CNWei\\CNW\\Rust\\ddddocr-rs\\models\\common_sml2h3_f32.onnx",
+            "D:\\CNWei\\CNW\\Rust\\ddddocr-rs\\models\\common_huashi666_i64.onnx",
+        )
+        .expect("建立测试模型图失败");
+    println!("{:?}", loader.model().inputs);
+}

examples/simple_usage.rs

@@ -1,5 +1,5 @@
 fn main() {
-    let ocr = ddddocr_rs::DdddOcrBuilder::new().build().unwrap();
+    // let ocr = ddddocr_rs::DdddOcrBuilder::new().build().unwrap();
-    let img = image::open("samples/code3.png").unwrap();
+    // let img = image::open("samples/code3.png").unwrap();
-    println!("Result: {}", ocr.classification(&img).unwrap());
+    // println!("Result: {}", ocr.classification(&img).unwrap());
 }

src/lib.rs

@@ -1,120 +0,0 @@
-mod charset;
-
-pub mod models;
-pub mod utils;
-
-use anyhow::Result;
-use image::DynamicImage;
-use std::fmt::{Display, Formatter};
-
-// 关键点：直接使用 tract 重导出的 ndarray
-use crate::charset::get_default_charset;
-use models::det::Det;
-use models::loader::ModelSession;
-use models::ocr::Ocr;
-pub enum ModelSpec {
-    /// 默认 OCR (使用内置路径)
-    OcrModel,
-    DetModel,
-    /// 自定义 OCR (路径由用户提供)
-    CustomOcrModel {
-        path: String,
-        charset: Vec<String>,
-    },
-}
-impl ModelSpec {
-    // 将默认路径定义为内部关联常量
-    const DEFAULT_OCR_PATH: &'static str = "models/common.onnx";
-    const DEFAULT_DET_PATH: &'static str = "models/common_det.onnx";
-}
-pub enum Runtime {
-    Ocr(Ocr),
-    Det(Det),
-}
-impl Runtime {
-    // 统一获取描述的方法
-    pub fn desc(&self) -> String {
-        match self {
-            Runtime::Ocr(s) => s.desc(), // 调用 Ocr 结构体的方法
-            Runtime::Det(s) => s.desc(), // 调用 Det 结构体的方法
-        }
-    }
-}
-pub struct DdddOcrBuilder {
-    mode: ModelSpec,
-}
-
-impl DdddOcrBuilder {
-    pub fn new() -> Self {
-        Self {
-            mode: ModelSpec::OcrModel,
-        }
-    }
-
-    /// 切换为检测模式
-    pub fn det(mut self) -> Self {
-        self.mode = ModelSpec::DetModel;
-        self
-    }
-
-    /// 设置自定义 OCR 路径
-    pub fn custom_ocr(mut self, path: String, charset: Vec<String>) -> Self {
-        // 直接重写枚举，替换掉之前的 Ocr 或 Det
-        self.mode = ModelSpec::CustomOcrModel { path, charset };
-        self
-    }
-
-    /// 核心初始化逻辑
-    pub fn build(self) -> Result<DdddOcr> {
-        let runtime = match self.mode {
-            ModelSpec::OcrModel => Runtime::Ocr(Ocr::new(
-                ModelSpec::DEFAULT_OCR_PATH.into(),
-                get_default_charset(),
-            )?),
-            ModelSpec::DetModel => Runtime::Det(Det::new(ModelSpec::DEFAULT_DET_PATH.into())?),
-            ModelSpec::CustomOcrModel { path, charset } => Runtime::Ocr(Ocr::new(path, charset)?),
-        };
-
-        Ok(DdddOcr { runtime })
-    }
-}
-
-pub struct DdddOcr {
-    runtime: Runtime,
-}
-
-impl Display for DdddOcr {
-    fn fmt(&self, f: &mut Formatter<'_>) -> std::fmt::Result {
-        write!(f, "DdddOcr(session: {})", self.runtime.desc())
-    }
-}
-
-impl DdddOcr {
-    pub fn classification(&self, img: &DynamicImage) -> Result<String> {
-        match &self.runtime {
-            Runtime::Ocr(s) => s.predict(img, false),
-            Runtime::Det(_) => Err(anyhow::anyhow!("当前模型是检测模型，无法执行 OCR")),
-        }
-    }
-    pub fn detection(&self, img: &[u8]) -> Result<Vec<Vec<i32>>> {
-        match &self.runtime {
-            Runtime::Det(s) => s.predict(img),
-            Runtime::Ocr(_) => Err(anyhow::anyhow!("当前模型是 OCR 模型，无法执行检测")),
-        }
-    }
-}
-
-#[cfg(test)]
-mod tests {
-    #[test]
-    fn test_ctc_decode_indices() {
-        // 模拟一个 DdddOcr 实例（如果 decode 不依赖 session，可以设为相关函数）
-        // 这里假设你的 decode_ctc 是公开或内部可访问的
-        let input = vec![1, 1, 0, 1, 2, 2, 0, 2];
-        // 逻辑：[1, 1] -> 1, [0] -> 跳过, [1] -> 1, [2, 2] -> 2, [0] -> 跳过, [2] -> 2
-        // 预期结果索引应该是 [1, 1, 2, 2] 对应的字符
-        // 具体的断言取决于你的 CHARSET_BETA
-        // let result = dddd.ctc_decode_indices(&input);
-        // assert_eq!(result, "AABB");
-    }
-}

src/models/base.rs

@@ -1,40 +0,0 @@
-pub trait ModelArgs {
-    // 获取模型路径
-    fn model_path(&self) -> &str;
-
-    // 获取字符集（由于 Det 没有，所以返回 Option）
-    fn charset(&self) -> Option<&str>;
-}
-
-pub struct HasCharset {
-    pub charset: String,
-} // 给 Ocr 和 Custom 用
-pub struct NoCharset; // 给 Det 用
-
-pub struct Model<T> {
-    pub path: String,
-    pub metadata: T,
-}
-// 针对有字符集的模型 (Ocr / Custom)
-impl ModelArgs for Model<HasCharset> {
-    fn model_path(&self) -> &str {
-        &self.path
-    }
-    fn charset(&self) -> Option<&str> {
-        Some(&self.metadata.charset)
-    }
-}
-
-// 针对没有字符集的模型 (Det)
-impl ModelArgs for Model<NoCharset> {
-    fn model_path(&self) -> &str {
-        &self.path
-    }
-    fn charset(&self) -> Option<&str> {
-        None
-    }
-}
-
-pub type OcrModel = Model<HasCharset>;
-pub type DetModel = Model<NoCharset>;
-pub type CustomModel = Model<HasCharset>; // Ocr 和 Custom 逻辑一致，可以复用

src/models/loader.rs

@@ -1,40 +0,0 @@
-use anyhow::Context;
-use image::DynamicImage;
-use tract_onnx::onnx;
-use tract_onnx::prelude::*;
-// 关键点：直接使用 tract 重导出的 ndarray
-use crate::utils::image_io::png_rgba_white_preprocess;
-use crate::utils::image_processor::{convert_to_grayscale, resize_image};
-use std::collections::HashMap;
-use tract_onnx::prelude::tract_ndarray::s;
-
-/// OCR 模型：包含路径和字符集
-
-pub enum ModelType {
-    Ocr,
-    Det,
-    Custom,
-}
-// 定义统一的 trait
-pub trait ModelSession {
-    fn get_model_type(&self) -> ModelType;
-    fn desc(&self) -> String;
-}
-
-pub struct ModelLoader {
-    pub session: RunnableModel<TypedFact, Box<dyn TypedOp>, Graph<TypedFact, Box<dyn TypedOp>>>,
-}
-
-impl ModelLoader {
-    pub fn load_model<P>(model_path: P) -> anyhow::Result<Self>
-    where
-        P: AsRef<std::path::Path>,
-    {
-        let session = onnx()
-            .model_for_path(model_path)
-            .with_context(|| "加载 ONNX 模型失败，请检查路径是否正确")?
-            .into_optimized()?
-            .into_runnable()?;
-        Ok(Self { session })
-    }
-}

src/models/mod.rs

@@ -1,5 +0,0 @@
-pub mod base;
-pub mod loader;
-pub mod ocr;
-pub mod det;
-pub mod slide;

src/models/ocr.rs

@@ -1,251 +0,0 @@
-use crate::models::base::ModelArgs;
-use crate::utils::image_io::png_rgba_white_preprocess;
-use crate::utils::image_processor::{convert_to_grayscale, resize_image};
-use crate::models::loader::{ModelLoader, ModelSession, ModelType};
-use anyhow::Context;
-use image::DynamicImage;
-use tract_onnx::prelude::tract_ndarray::s;
-use tract_onnx::prelude::{
-    DatumType, Graph, IntoTensor, RunnableModel, Tensor, TypedFact, TypedOp, tract_ndarray, tvec,
-};
-
-// 颜色过滤的自定义范围：(低值RGB, 高值RGB)
-pub type ColorRange = ((u8, u8, u8), (u8, u8, u8));
-
-// 字符集范围类型
-#[derive(Debug, Clone)]
-pub enum CharsetRange {
-    All,                   // 所有字符
-    Digit,                 // 数字
-    Letter,                // 字母
-    Alphanumeric,          // 字母数字
-    Single(String),        // 单字符串
-    Multiple(Vec<String>), // 多个字符串
-    Range(char, char),     // 字符范围
-    Custom(Vec<char>),     // 自定义字符列表
-}
-#[derive(Debug, Clone)]
-pub struct PredictArgs {
-    /// 是否修复PNG格式问题
-    pub png_fix: bool,
-    /// 是否返回概率信息
-    pub probability: bool,
-    /// 颜色过滤：保留的颜色列表
-    pub color_filter_colors: Option<Vec<String>>,
-    /// 颜色过滤：自定义RGB范围
-    pub color_filter_custom_ranges: Option<Vec<ColorRange>>,
-    /// 字符集范围
-    pub charset_range: Option<CharsetRange>,
-}
-
-impl Default for PredictArgs {
-    fn default() -> Self {
-        Self {
-            png_fix: false,
-            probability: false,
-            color_filter_colors: None,
-            color_filter_custom_ranges: None,
-            charset_range: None,
-        }
-    }
-}
-
-impl PredictArgs {
-    pub fn new() -> Self {
-        Self::default()
-    }
-
-    // Builder 模式方法
-    pub fn png_fix(mut self, enabled: bool) -> Self {
-        self.png_fix = enabled;
-        self
-    }
-
-    pub fn probability(mut self, enabled: bool) -> Self {
-        self.probability = enabled;
-        self
-    }
-
-    pub fn color_filter_colors(mut self, colors: Vec<String>) -> Self {
-        self.color_filter_colors = Some(colors);
-        self
-    }
-
-    pub fn color_filter_custom_ranges(mut self, ranges: Vec<ColorRange>) -> Self {
-        self.color_filter_custom_ranges = Some(ranges);
-        self
-    }
-
-    pub fn charset_range(mut self, range: CharsetRange) -> Self {
-        self.charset_range = Some(range);
-        self
-    }
-
-    // 便捷构造方法
-    pub fn quick() -> Self {
-        Self::default()
-    }
-
-    pub fn with_probability() -> Self {
-        Self::default().probability(true)
-    }
-
-    pub fn with_png_fix() -> Self {
-        Self::default().png_fix(true)
-    }
-}
-pub struct Ocr {
-    session: RunnableModel<TypedFact, Box<dyn TypedOp>, Graph<TypedFact, Box<dyn TypedOp>>>,
-    charset: Vec<String>,
-}
-impl ModelSession for Ocr {
-    fn get_model_type(&self) -> ModelType {
-        todo!()
-    }
-    fn desc(&self) -> String {
-        "Ocr Model 加载成功".to_string()
-    }
-}
-impl Ocr {
-    pub fn new(model_path: String, charset: Vec<String>) -> Result<Self, anyhow::Error> {
-        let session = ModelLoader::load_model(&model_path)?.session;
-        Ok(Self { session, charset })
-    }
-    pub fn predict(&self, image: &DynamicImage, png_fix: bool) -> Result<String, anyhow::Error> {
-        let tensor = self.preprocess_image(image, png_fix)?;
-        //
-        // let result = self.session.run(tvec!(tensor.into()))?;
-        // // 3. 解析结果
-        // // let output = result[0].to_array_view::<i64>()?;
-        let output = self.inference(tensor)?;
-        let output2 = self.process_text_output(&output)?;
-        Ok(self.ctc_decode_indices(&output2))
-        // Ok("ocr result".to_string())
-    }
-    /// 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, png_fix: bool) -> anyhow::Result<Tensor> {
-        // A. 修复 PNG 透明背景 (内部逻辑你之前已实现)
-        let _ = if png_fix && img.color().has_alpha() {
-            png_rgba_white_preprocess(img)
-        } else {
-            img.clone()
-        };
-
-        let h = 64u32;
-        let w = (img.width() as f32 * (h as f32 / img.height() as f32)) as u32;
-        let gray_img = convert_to_grayscale(img);
-        let resized = resize_image(&gray_img, w, h);
-        // resized.save("debug_preprocessed.png").unwrap();
-        // 1. 预处理：转灰度 -> Resize -> 归一化
-        // let resized = img.resize_exact(w, h, FilterType::Lanczos3).to_luma8();
-
-        // 使用 tract_ndarray 构造，避免版本冲突
-        let array =
-            tract_ndarray::Array4::from_shape_fn((1, 1, h as usize, w as usize), |(_, _, y, x)| {
-                let pixel = resized.get_pixel(x as u32, y as u32)[0] as f32;
-                (pixel / 255.0 - 0.5) / 0.5
-            });
-
-        let tensor = Tensor::from(array);
-
-        Ok(tensor)
-    }
-    /// 对应 Python 的 _inference
-    fn inference(&self, tensor: Tensor) -> anyhow::Result<Tensor> {
-        // tract 的 run 会返回一个 Vec<TValue>，我们通常只需要第一个输出
-        // let result = self.session.run(tvec!(tensor.into()))?;
-        let mut result = self
-            .session
-            .run(tvec!(tensor.into()))
-            .context("执行模型推理失败")?;
-        println!("模型输出原始数据: {:?}", result);
-        Ok(result.remove(0).into_tensor())
-    }
-    /// 核心解析逻辑：将模型输出的各种维度/类型的 Tensor 转为字符索引序列
-    fn process_text_output(&self, raw_tensor: &Tensor) -> anyhow::Result<Vec<i64>> {
-        let shape = raw_tensor.shape();
-        println!("模型输出shape数据: {:?}", shape);
-        let datum_type = raw_tensor.datum_type();
-        println!("模型输出datum_type数据: {:?}", datum_type);
-
-        match raw_tensor.datum_type() {
-            // 情况 1: huashi666 式模型，直接输出 i64 索引 (通常是模型内部做好了 Argmax)
-            DatumType::I64 => {
-                let view = raw_tensor.to_array_view::<i64>()?;
-                Ok(view.iter().cloned().collect())
-            }
-
-            // 情况 2: sml2h3 原版模型，输出 F32 概率矩阵
-            DatumType::F32 => {
-                let view = raw_tensor.to_array_view::<f32>()?;
-                let (steps, classes, data_view) = match shape.len() {
-                    3 => {
-                        if shape[1] == 1 {
-                            // 形状: [Steps, 1, Classes] -> 你的原有逻辑
-                            (shape[0], shape[2], view.into_dyn())
-                        } else if shape[0] == 1 {
-                            // 形状: [1, Steps, Classes] -> 另一种常见导出格式
-                            (shape[1], shape[2], view.into_dyn())
-                        } else {
-                            // 默认取第一个 batch: [Batch, Steps, Classes]
-                            // 使用 slice 对应 Python 的 output[0, :, :]
-                            let sliced = view.slice(s![0, .., ..]);
-                            (shape[1], shape[2], sliced.into_dyn())
-                        }
-                    }
-                    2 => {
-                        // 形状: [Steps, Classes] -> 已经剥离了 Batch 维度
-                        (shape[0], shape[1], view.into_dyn())
-                    }
-                    _ => return Err(anyhow::anyhow!("不支持的输出维度: {:?}", shape)),
-                };
-                let array_2d = data_view.to_shape((steps, classes))?;
-                //
-                // 对每一行执行 Argmax (寻找概率最大的字符索引)
-                let indices = array_2d
-                    .outer_iter()
-                    .map(|row| {
-                        row.iter()
-                            .enumerate()
-                            .max_by(|(_, a), (_, b)| {
-                                a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal)
-                            })
-                            .map(|(idx, _)| idx as i64)
-                            .unwrap_or(0)
-                    })
-                    .collect();
-                Ok(indices)
-            }
-            _ => Err(anyhow::anyhow!(
-                "不支持的模型输出数据类型: {:?}",
-                raw_tensor.datum_type()
-            )),
-        }
-    }
-    fn ctc_decode_indices(&self, predicted_indices: &[i64]) -> String {
-        println!("indices模型输出原始数据: {:?}", predicted_indices);
-
-        // 对应 _ctc_decode_indices 的逻辑：去重、去 blank (0)
-        let mut res = String::new();
-        let mut prev_idx: i64 = -1;
-
-        for &idx in predicted_indices {
-            // 1. 跳过连续重复的索引
-            // 2. 跳过 blank 字符 (假设索引 0 是 blank)
-            if idx != prev_idx && idx != 0 {
-                if let Ok(u_idx) = usize::try_from(idx) {
-                    if let Some(char_str) = self.charset.get(u_idx) {
-                        res.push_str(char_str);
-                    } else {
-                        // 保护逻辑：如果模型预测的索引超出了字符集范围
-                        eprintln!("警告: 预测索引 {} 超出字符集范围", u_idx);
-                    }
-                }
-            }
-            prev_idx = idx;
-        }
-        println!("最终识别出的验证码是: {}", res);
-        res
-    }
-}

src/utils/cv_ops.rs

@@ -1,107 +0,0 @@
-use std::cmp::{max, min};
-use image::{ImageBuffer, Luma};
-use tract_onnx::prelude::tract_ndarray::{azip, Array2, Array3, ArrayView2, ArrayView3};
-
-/// 1. 计算两个数组的绝对差值 (对应 cv2.absdiff)
-pub fn abs_diff(a: &ArrayView3<u8>, b: &ArrayView3<u8>) -> Array3<u8> {
-    // 利用 ndarray 的 map_collect，生成差值的绝对值数组
-    // 或者直接使用 zip_mut_with 处理以减少内存分配
-    let mut diff = Array3::zeros(a.dim());
-    azip!((res in &mut diff, &va in a, &vb in b) {
-        *res = (va as i16 - vb as i16).abs() as u8;
-    });
-    diff
-}
-
-
-/// RGB 到灰度转换
-pub fn rgb_to_gray(rgb: ArrayView3<u8>) -> Array2<u8> {
-    let (h, w, _) = rgb.dim();
-    Array2::from_shape_fn((h, w), |(y, x)| {
-        let r = rgb[[y, x, 0]] as f32;
-        let g = rgb[[y, x, 1]] as f32;
-        let b = rgb[[y, x, 2]] as f32;
-        // 完全忽略 a，只按权重计算
-        (0.299 * r + 0.587 * g + 0.114 * b) as u8
-    })
-}
-
-/// 寻找匹配结果图中的最大值及其坐标 (模拟 cv2.minMaxLoc 的一部分)
-pub fn min_max_loc(result_map: &ImageBuffer<Luma<f32>, Vec<f32>>) -> (f32, (u32, u32)) {
-    // 4. 找到最佳匹配位置 (对齐 cv2.minMaxLoc)
-    let mut max_val: f32 = -1.0;
-    let mut max_loc = (0, 0);
-
-    // 遍历匹配得分图
-    for (x, y, score) in result_map.enumerate_pixels() {
-        let s = score.0[0];
-
-        // 可以在此处加入你之前验证过的起始位过滤
-        // if x < 15 { continue; }
-
-        if s > max_val {
-            max_val = s;
-            max_loc = (x, y);
-        }
-    }
-    (max_val, max_loc)
-}
-
-/// 1. 模拟 findContours 并获取最大面积区域的 Label
-/// 返回 Option<u32>，如果找不到任何区域则返回 None
-pub fn find_contours_and_max(labelled: &ImageBuffer<Luma<u32>, Vec<u32>>) -> Option<u32> {
-    // 统计每个标签出现的频率（即面积）
-    let mut max_label = 0;
-    let mut max_area = 0;
-    let mut areas = std::collections::HashMap::new();
-
-    for pixel in labelled.pixels() {
-        let label = pixel.0[0];
-        if label == 0 {
-            continue;
-        } // 跳过背景
-        let count = areas.entry(label).or_insert(0);
-        *count += 1;
-        if *count > max_area {
-            max_area = *count;
-            max_label = label;
-        }
-    }
-    if max_label == 0 { None } else { Some(max_label) }
-}
-pub fn bounding_rect(labelled: &ImageBuffer<Luma<u32>, Vec<u32>>,max_label: u32) -> (u32, u32, u32, u32) {
-    // 5. 计算最大区域的边界框 (对应 cv2.boundingRect)
-    let mut min_x = labelled.width();
-    let mut max_x = 0;
-    let mut min_y = labelled.height();
-    let mut max_y = 0;
-
-    for (x, y, pixel) in labelled.enumerate_pixels() {
-        if pixel.0[0] == max_label {
-            min_x = min(min_x, x);
-            max_x = max(max_x, x);
-            min_y = min(min_y, y);
-            max_y = max(max_y, y);
-        }
-    }
-
-
-    let w = max_x - min_x;
-    let h = max_y - min_y;
-    (min_x, min_y, w, h)
-}
-pub fn calculate_center(max_loc: (u32, u32), tw: usize, th: usize) -> (i32, i32) {
-    let center_x = max_loc.0 as i32 + (tw as i32 / 2);
-    let center_y = max_loc.1 as i32 + (th as i32 / 2);
-    (center_x, center_y)
-}
-pub fn ndarray_to_luma8(array: ArrayView2<u8>) -> ImageBuffer<Luma<u8>, Vec<u8>> {
-    let (height, width) = array.dim();
-    let mut buffer = ImageBuffer::new(width as u32, height as u32);
-    for y in 0..height {
-        for x in 0..width {
-            buffer.put_pixel(x as u32, y as u32, Luma([array[[y, x]]]));
-        }
-    }
-    buffer
-}

src/utils/image_io.rs

@@ -1,264 +0,0 @@
-use anyhow::{Context, Result, anyhow, bail};
-use base64::{Engine as _, engine::general_purpose};
-use image::{DynamicImage, GenericImageView, ImageBuffer, ImageFormat, Luma, Rgb, RgbImage, Rgba};
-use std::fs;
-use std::path::{Path, PathBuf};
-use tract_onnx::prelude::tract_ndarray::{Array3, ArrayD, ArrayViewD};
-#[derive(Debug)]
-pub enum ColorMode {
-    RGB,
-    RGBA,
-    L,
-}
-/// 定义支持的输入类型枚举
-pub enum ImageInput {
-    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..]
-    } else {
-        &b64_str
-    };
-
-    let bytes = general_purpose::STANDARD
-        .decode(clean_b64.trim())
-        .map_err(|e| anyhow!("Base64 decode error: {}", e))?;
-
-    image::load_from_memory(&bytes).context("Failed to load utils from decoded base64")
-}
-
-/// 读取图片文件并转换为 base64 编码字符串
-/// 对应 Python 版 get_img_base64
-pub fn get_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)
-        .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
-    let b64_string = general_purpose::STANDARD.encode(image_data);
-
-    Ok(b64_string)
-}
-
-/// 封装数组转图像的逻辑，对齐 Python 版 _numpy_to_pil_image
-fn numpy_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 (raw_data, _offset) = standard.to_owned().into_raw_vec_and_offset();
-
-    match dim {
-        // 对应 Python: len(array.shape) == 2 (灰度图 H, W)
-        2 => {
-            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]);
-            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)
-                    .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)
-                    .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)
-                    .map(DynamicImage::ImageRgba8),
-
-                _ => {
-                    return Err(anyhow!("不支持的通道数: {}", 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)),
-    }
-}
-
-/// 对应 Python 的 png_rgba_black_preprocess
-/// 将带有透明通道的图片转换为白色背景的 RGB 图片
-
-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() {
-        return DynamicImage::ImageRgb8(img.to_rgb8());
-    }
-
-    let (width, height) = img.dimensions();
-
-    // 2. 创建一个新的 RGB 图像缓冲，默认填充为白色 (255, 255, 255)
-    let mut background = ImageBuffer::from_pixel(width, height, Rgb([255u8, 255u8, 255u8]));
-
-    // 3. 获取原图的 RGBA 视图
-    let rgba_img = img.to_rgba8();
-
-    // 4. 遍历像素并手动进行 Alpha 混合
-    // 对应 Python 的 utils.paste(img, ..., mask=img)
-    // 使用 enumerate_pixels_mut 同时获取坐标和背景像素的可变引用，减少查找开销
-    for (x, y, bg_pixel) in background.enumerate_pixels_mut() {
-        // 安全性说明：x, y 源自 background 尺寸，与 rgba_img 一致，get_pixel 是安全的
-        let src_pixel = rgba_img.get_pixel(x, y);
-        let alpha_u8 = src_pixel[3];
-
-        match alpha_u8 {
-            // 情况 A：完全不透明，直接覆盖背景色
-            255 => {
-                bg_pixel.0 = [src_pixel[0], src_pixel[1], src_pixel[2]];
-            }
-            // 情况 B：完全透明，保持背景色（白色），无需操作
-            0 => {
-                continue;
-            }
-            // 情况 C：半透明，进行 Alpha 混合计算
-            _ => {
-                let alpha = alpha_u8 as f32 / 255.0;
-                let inv_alpha = 1.0 - alpha;
-
-                bg_pixel[0] = (src_pixel[0] as f32 * alpha + 255.0 * inv_alpha).round() as u8;
-                bg_pixel[1] = (src_pixel[1] as f32 * alpha + 255.0 * inv_alpha).round() as u8;
-                bg_pixel[2] = (src_pixel[2] as f32 * alpha + 255.0 * inv_alpha).round() as u8;
-            }
-        }
-    }
-
-    DynamicImage::ImageRgb8(background)
-}
-pub fn image_to_numpy(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::RGBA => (4, image.to_rgba8().into_raw()),
-    };
-
-    Array3::from_shape_vec((height as usize, width as usize, channels), raw)
-        .map_err(|e| anyhow!("Failed to build ndarray: {}", e))
-}
-
-pub fn numpy_to_image(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());
-    }
-
-    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
-    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();
-
-    match mode {
-        ColorMode::L => ImageBuffer::<Luma<u8>, _>::from_raw(width, height, raw_data)
-            .map(DynamicImage::ImageLuma8),
-        ColorMode::RGB => ImageBuffer::<Rgb<u8>, _>::from_raw(width, height, raw_data)
-            .map(DynamicImage::ImageRgb8),
-        ColorMode::RGBA => ImageBuffer::<Rgba<u8>, _>::from_raw(width, height, raw_data)
-            .map(DynamicImage::ImageRgba8),
-    }
-    .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);
-}

src/utils/image_processor.rs

@@ -1,27 +0,0 @@
-use image::{DynamicImage, GrayImage, imageops::FilterType};
-use anyhow::Result;
-
-/// 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 {
-    // Rust utils 库的 to_luma8 会根据标准的亮度公式进行转换
-    image.to_luma8()
-}
-
-/// 对应 Python 的 resize_image
-/// 调整图像尺寸。当前版本仅实现 keep_aspect_ratio=false
-pub fn resize_image(
-    image: &GrayImage,
-    target_width: u32,
-    target_height: u32,
-    // resample 参数我们直接使用 FilterType，Lanczos3 是最接近 Python LANCZOS 的
-) -> GrayImage {
-    // 使用 resize 算法进行精确缩放
-    image::imageops::resize(
-        image,
-        target_width,
-        target_height,
-        FilterType::Lanczos3
-    )
-}
-

src/utils/mod.rs

@@ -1,3 +0,0 @@
-pub mod image_io;
-pub mod image_processor;
-pub mod cv_ops;

tests/ocr_test.rs

@@ -1,162 +0,0 @@
-use ddddocr_rs::models::slide::Slide;
-use ddddocr_rs::{DdddOcr, DdddOcrBuilder}; // 假设你的包名是这个
-use image::Rgb;
-use std::fs;
-use std::path::Path;
-fn load_image<P: AsRef<Path>>(path: P) -> anyhow::Result<image::DynamicImage> {
-    // 1. 先将泛型转为具体的 &Path 引用
-    let path_ref = path.as_ref();
-
-    // 2. 调用 open 时传入引用（utils::open 支持 AsRef<Path>）
-    image::open(path_ref).map_err(|e| {
-        // 3. 此时 path_ref 依然有效，可以安全地在闭包中使用
-        anyhow::anyhow!("无法加载图片 {:?}: {}", path_ref, e)
-    })
-}
-/// 将检测结果绘制在图像上并保存
-fn save_debug_image(
-    image_bytes: &[u8],
-    bboxes: &Vec<Vec<i32>>,
-    output_path: &str,
-) -> anyhow::Result<()> {
-    let dynamic_img = image::load_from_memory(image_bytes)?;
-    let mut img = dynamic_img.to_rgb8();
-    let (width, height) = img.dimensions();
-    let red = Rgb([255u8, 0, 0]);
-
-    for bbox in bboxes {
-        // 基础边界检查
-        let x1 = bbox[0].max(0).min(width as i32 - 1) as u32;
-        let y1 = bbox[1].max(0).min(height as i32 - 1) as u32;
-        let x2 = bbox[2].max(0).min(width as i32 - 1) as u32;
-        let y2 = bbox[3].max(0).min(height as i32 - 1) as u32;
-
-        // 绘制横向线条
-        for x in x1..=x2 {
-            img.put_pixel(x, y1, red);
-            img.put_pixel(x, y2, red);
-            // 如果要加粗，多画一行
-            if y1 + 1 < height {
-                img.put_pixel(x, y1 + 1, red);
-            }
-            if y2.saturating_sub(1) > 0 {
-                img.put_pixel(x, y2 - 1, red);
-            }
-        }
-        // 绘制纵向线条
-        for y in y1..=y2 {
-            img.put_pixel(x1, y, red);
-            img.put_pixel(x2, y, red);
-            // 如果要加粗，多画一列
-            if x1 + 1 < width {
-                img.put_pixel(x1 + 1, y, red);
-            }
-            if x2.saturating_sub(1) > 0 {
-                img.put_pixel(x2 - 1, y, red);
-            }
-        }
-    }
-
-    img.save(output_path)?;
-    Ok(())
-}
-#[test]
-fn test_full_classification() {
-    // 1. 初始化模型
-    let ocr = DdddOcrBuilder::new().build().expect("模型加载失败");
-
-    // 2. 加载测试图片
-    let img = image::open("samples/code2.png").expect("测试图片不存在");
-
-    // 3. 执行识别
-    let result = ocr.classification(&img).expect("识别过程出错");
-
-    println!("识别结果: {}", result);
-    assert!(!result.is_empty());
-}
-#[test]
-fn test_det_load() -> anyhow::Result<()> {
-    let det = DdddOcrBuilder::new().det().build()?;
-    let image_path = "samples/det1.png";
-    let image_bytes =
-        fs::read(image_path).map_err(|e| anyhow::anyhow!("无法读取图片 {}: {}", image_path, e))?;
-
-    println!("图片读取成功，字节大小: {}", image_bytes.len());
-    let bboxes = det.detection(&image_bytes)?;
-    println!(":?{}", det);
-    println!("检测到的目标数量: {}", bboxes.len());
-    if bboxes.is_empty() {
-        println!("未检测到任何目标。");
-    } else {
-        save_debug_image(&image_bytes, &bboxes, "samples/result.jpg")?;
-        for (i, bbox) in bboxes.iter().enumerate() {
-            println!(
-                "目标 [{}]: x1={}, y1={}, x2={}, y2={}",
-                i, bbox[0], bbox[1], bbox[2], bbox[3]
-            );
-        }
-    }
-    Ok(())
-}
-
-#[test]
-fn test_real_slide_match() {
-    let engine = Slide::new();
-
-    // 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("samples/hua.png").expect("请确保 samples/hua.png 存在");
-    let bg_img = load_image("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
-    let start = std::time::Instant::now();
-    let result = engine
-        .slide_match(&target_img, &bg_img, false)
-        .expect("Slide match 执行失败");
-    let duration = start.elapsed();
-
-    // 3. 打印结果
-    println!("-------------------------------------------");
-    println!("滑块匹配测试结果:");
-    println!("检测坐标: [x: {}, y: {}]", result.target_x, result.target_y);
-    println!("置信度: {:.4}", result.confidence);
-    println!("耗时: {:?}", duration);
-    println!("-------------------------------------------");
-
-    // 验证基本逻辑：坐标不应为 0 (除非匹配失败)
-    assert_eq!(result.target_x, 237);
-    assert_eq!(result.target_y, 77);
-    assert!(result.confidence > 0.0);
-}
-
-#[test]
-fn test_real_slide_comparison() {
-    let engine = Slide::new();
-
-    // 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("samples/ken.jpg").expect("请确保 samples/ken.jpg 存在");
-    let bg_img = load_image("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
-    let start = std::time::Instant::now();
-    let result = engine
-        .slide_comparison(&target_img, &bg_img)
-        .expect("Slide match 执行失败");
-    let duration = start.elapsed();
-
-    // 3. 打印结果
-    println!("-------------------------------------------");
-    println!("滑块匹配测试结果:");
-    println!("检测坐标: [x: {}, y: {}]", result.target_x, result.target_y);
-    println!("置信度: {:.4}", result.confidence);
-    println!("耗时: {:?}", duration);
-    println!("-------------------------------------------");
-
-    // 验证基本逻辑：坐标不应为 0 (除非匹配失败)
-    assert_eq!(result.target_x, 171);
-    assert_eq!(result.target_y, 90);
-    assert!(result.confidence > 0.0);
-}