feat(ocr,det,slide): 重构项目结构

- 优化 规范化模型目录
- 重构 Ocr，Detector，Slide 拆分规范化

Cargo.toml

@@ -12,3 +12,9 @@ base64 = "0.22.1"
 imageproc = { version = "0.26.2", default-features = true }
 serde = { version = "1.0.228", features = ["derive"] }
 serde_json = "1.0.150"
+ndarray="0.16.1"
+
+
+[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

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/algo/mod.rs

@@ -0,0 +1,3 @@
+mod slide;
+
+pub use slide::{SlideResult, Slider};

src/algo/slide.rs

@@ -1,32 +1,40 @@
 use crate::utils::cv_ops;
 use crate::utils::cv_ops::{abs_diff, min_max_loc, ndarray_to_luma8, rgb_to_gray};
 use crate::utils::image_io::image_to_ndarray;
-use anyhow::{Context, Result, anyhow};
+use anyhow::{Result, anyhow};
-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 std::fmt;
-use tract_onnx::prelude::tract_ndarray::{Array2, Array3, ArrayView2, ArrayView3, Axis, s};
+use tract_onnx::prelude::tract_ndarray::{ArrayView2, ArrayView3};
-
+#[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 {
-pub struct Slide;
+    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
-
+        writeln!(f, "滑块匹配测试结果:")?;
-impl Slide {
+        writeln!(f, "检测坐标: [x: {}, y: {}]", self.target_x, self.target_y)?;
-    pub fn new() -> Self {
+        // 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}
-        Self
+        write!(f, "置信度: {:.4}", self.confidence)?;
+        Ok(())
     }
+}
 
+pub struct Slider;
+
+impl Slider {
+    pub fn new() -> Result<Self, anyhow::Error> {
+        Ok(Self)
+    }
     /// 对应 Python: slide_match 滑块匹配接口
     pub fn slide_match(
         &self,
@@ -59,23 +67,8 @@ impl Slide {
         target: ArrayView3<u8>,
         background: ArrayView3<u8>,
     ) -> Result<SlideResult> {
-        // 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();
 
@@ -194,9 +187,6 @@ impl Slide {
         background: ArrayView2<u8>,
     ) -> Result<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);

src/error.rs

@@ -0,0 +1,18 @@
+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。
+================================================================================";

src/lib.rs

@@ -1,184 +1,9 @@
-mod charset;
+mod algo;
-
+mod error;
-mod model_metadata;
 pub mod models;
 pub mod utils;
 
-use anyhow::{Result, anyhow};
+pub use crate::algo::{SlideResult, Slider};
-use image::DynamicImage;
+pub use crate::models::det::{DetBuilder, DetSession, DetectionResult, Detector};
-use std::fmt::{Display, Formatter};
+pub use crate::models::ocr::{Ocr, OcrBuilder, OcrResult, OcrSession};
-
+pub use models::ocr::metadata::ModelMetadata;
-// 关键点：直接使用 tract 重导出的 ndarray
-use crate::charset::CharRestrict;
-use crate::model_metadata::ModelMetadata;
-use crate::models::det::DetectionResult;
-use crate::utils::color_filter::{ColorPreset, HsvRange};
-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,
-        model_metadata: ModelMetadata,
-    },
-}
-impl ModelSpec {
-    // 将默认路径定义为内部关联常量
-    const DEFAULT_OCR_PATH: &'static str = "models/common_sml2h3_f32.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, model_metadata: ModelMetadata) -> Self {
-        // 直接重写枚举，替换掉之前的 Ocr 或 Det
-        self.mode = ModelSpec::CustomOcrModel {
-            path,
-            model_metadata,
-        };
-        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(),
-                ModelMetadata::from_builtin_beta(),
-            )?),
-            ModelSpec::DetModel => Runtime::Det(Det::new(ModelSpec::DEFAULT_DET_PATH.into())?),
-            ModelSpec::CustomOcrModel {
-                path,
-                model_metadata,
-            } => Runtime::Ocr(Ocr::new(path, model_metadata)?),
-        };
-
-        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).run(),
-            // Runtime::Ocr(s) => s.predictor().probability(false).predict(img),
-            // Runtime::Ocr(s) => {
-            //     let predictor = s.predictor();
-            //     let restricted = predictor.charset_restrict(&CharRestrict::Lowercase);
-            //     let a = restricted.valid_tokens();
-            //     println!("{:?}", a);
-            //     Ok("".to_string())
-            // }
-            Runtime::Ocr(s) => {
-                let res = s.predictor().probability(true).predict(img)?;
-                println!("{}", res);
-                Ok(res.to_string())
-            }
-            // Runtime::Ocr(s) => s.predictor().charset_restrict(&CharRestrict::Digit).predict(img),
-            // Runtime::Ocr(s) => s.predictor().color_filter(&ColorPreset::Custom(vec![
-            //     // 错误：下界 (82, 221, 14) 没问题
-            //     //       但上界的 H 通道写成了 240，超过了 180 的法定上限！
-            //     HsvRange::new((82, 221, 14), (240, 203, 82)),
-            // ])).predict(img),
-            Runtime::Det(_) => Err(anyhow::anyhow!("当前模型是检测模型，无法执行 OCR")),
-        }
-    }
-    pub fn detection(&self, img: &DynamicImage) -> Result<Vec<DetectionResult>> {
-        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")),
-        }
-    }
-}
-
-// struct Classification {}
-// #[derive(Debug)]
-// struct ClassificationBuilder {
-//     img: DynamicImage,
-//     png_fix: bool,
-//     color_filter_colors: Option<Vec<ColorRange>>,
-//     color_filter_custom_ranges: Option<Vec<ColorRange>>,
-// }
-// impl ClassificationBuilder {
-//     pub fn new(img: DynamicImage) -> Self {
-//         ClassificationBuilder {
-//             img,
-//             png_fix: false,
-//             color_filter_colors: None,
-//             color_filter_custom_ranges: None,
-//         }
-//     }
-//     pub fn png_fix(mut self, value: bool) -> Self {
-//         self.png_fix = value;
-//         self
-//     }
-//     pub fn color_filter_colors(mut self, value: Vec<ColorRange>) -> Self {
-//         self.color_filter_colors = Some(value);
-//         self
-//     }
-//     pub fn color_filter_custom_ranges(mut self, value: Vec<ColorRange>) -> Self {
-//         self.color_filter_custom_ranges = Some(value);
-//         self
-//     }
-//     pub fn build(self) -> Classification {
-//         Classification {}
-//     }
-// }
-
-#[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/det/builder.rs

@@ -0,0 +1,25 @@
+use crate::models::det::executor::Detector;
+use crate::models::det::session::DetSession;
+
+pub struct DetBuilder {
+    use_gpu: bool,
+    device_id: u8,
+}
+
+impl DetBuilder {
+    fn use_gpu(mut self) -> Self {
+        self.use_gpu = true;
+        self
+    }
+    fn device_id(mut self, device_id: u8) -> Self {
+        self.device_id = device_id;
+        self
+    }
+    fn build(self, session: &DetSession) -> Detector<'_> {
+        Detector {
+            session,
+            use_gpu: self.use_gpu,
+            device_id: self.device_id,
+        }
+    }
+}

src/models/det/executor.rs

@@ -1,10 +1,11 @@
-use crate::models::loader::{ModelLoader, ModelSession, ModelType};
 use anyhow::{Context, Result};
-use image::{DynamicImage, GenericImageView, imageops::FilterType};
+use image::{imageops::FilterType, DynamicImage, GenericImageView};
-use tract_onnx::prelude::tract_ndarray::{Array2, Array3, Array4, Axis, prelude::*, s};
+use std::fmt;
-use tract_onnx::prelude::{Graph, RunnableModel, Tensor, TypedFact, TypedOp, tvec};
+use tract_onnx::prelude::tract_ndarray::{prelude::*, s, Array2, Array3, Array4, Axis};
+use tract_onnx::prelude::{Tensor};
 
 
+use crate::models::det::session::DetSession;
 
 #[derive(Debug, Clone, Copy)]
 pub struct DetectionResult {
@@ -16,24 +17,35 @@ pub struct DetectionResult {
     pub class_id: u32,
 }
 
-
+impl fmt::Display for DetectionResult {
-
+    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
-pub struct Det {
+        // 结构体只管自己这一行怎么显示，不用管外部的索引 [i]
-    session: RunnableModel<TypedFact, Box<dyn TypedOp>, Graph<TypedFact, Box<dyn TypedOp>>>,
+        write!(
-}
+            f,
-impl ModelSession for Det {
+            "x1={}, y1={}, x2={}, y2={}, 分数={:.4}, 类别ID={}",
-    fn get_model_type(&self) -> ModelType {
+            self.x1, self.y1, self.x2, self.y2, self.score, self.class_id
-        todo!()
+        )
-    }
-    fn desc(&self) -> String {
-        "Detection Model 加载成功".to_string()
     }
 }
-impl Det {
+
-    pub fn new(model_path: String) -> Result<Self, anyhow::Error> {
+#[derive(Debug)]
-        let session = ModelLoader::load_model(&model_path)?.session;
+pub struct Detector<'a> {
-        Ok(Self { session })
+    pub(crate) session: &'a DetSession,
+    #[allow(dead_code)]
+    pub(crate) use_gpu: bool,
+    #[allow(dead_code)]
+    pub(crate) device_id: u8,
+}
+
+impl<'a> Detector<'a> {
+    pub fn new(session: &'a DetSession) -> Self {
+        Detector {
+            session,
+            use_gpu: false,
+            device_id: 0,
+        }
     }
+
     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)
@@ -73,11 +85,10 @@ impl Det {
                 // BGR 赋值
                 array[[0, 0, y, x]] = slice[idx + 2] as f32; // B
                 array[[0, 1, y, x]] = slice[idx + 1] as f32; // G
-                array[[0, 2, y, x]] = slice[idx] as f32;     // R
+                array[[0, 2, y, x]] = slice[idx] as f32; // R
             }
         }
 
-
         Ok((array.into(), r))
     }
 
@@ -244,8 +255,10 @@ impl Det {
         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)?;
+        // let output_array = outputs[0]
+        let output_array = outputs
             .to_array_view::<f32>()?
             .to_owned()
             .into_dimensionality::<Ix3>()?;
@@ -273,17 +286,15 @@ impl Det {
         let detections = self.multiclass_nms(&boxes_xyxy, &scores, 0.45, 0.1);
         let final_results = detections
             .into_iter()
-            .map(|d| {
+            .map(|d| DetectionResult {
-                DetectionResult{
+                x1: (d[0] as i32).max(0).min(orig_w as i32),
-                    x1: (d[0] as i32).max(0).min(orig_w as i32),
+                y1: (d[1] as i32).max(0).min(orig_h as i32),
-                    y1: (d[1] as i32).max(0).min(orig_h as i32),
+                x2: (d[2] as i32).max(0).min(orig_w as i32),
-                    x2: (d[2] as i32).max(0).min(orig_w as i32),
+                y2: (d[3] as i32).max(0).min(orig_h as i32),
-                    y2: (d[3] as i32).max(0).min(orig_h as i32),
+                score: d[4],
-                    score: d[4],
+                class_id: d[5] as u32,
-                    class_id: d[5] as u32,
-                }
             })
             .collect();
-        Ok(final_results )
+        Ok(final_results)
     }
 }

src/models/det/mod.rs

@@ -0,0 +1,7 @@
+mod builder;
+mod executor;
+mod session;
+
+pub use builder::DetBuilder;
+pub use executor::{DetectionResult, Detector};
+pub use session::DetSession;

src/models/det/session.rs

@@ -0,0 +1,43 @@
+use crate::models::loader::{ModelLoader, ModelSession, ModelType};
+use anyhow::{Context, Result};
+use std::path::Path;
+use tract_onnx::prelude::{tvec, Graph, IntoTensor, RunnableModel, Tensor, TypedFact, TypedOp};
+
+#[derive(Debug)]
+pub struct DetSession {
+    pub(crate) session: RunnableModel<TypedFact, Box<dyn TypedOp>, Graph<TypedFact, Box<dyn TypedOp>>>,
+}
+
+impl ModelSession for DetSession {
+    fn get_model_type(&self) -> ModelType {
+        todo!()
+    }
+    fn desc(&self) -> String {
+        "Detection Model 加载成功".to_string()
+    }
+}
+
+impl DetSession {
+    pub fn new<P>(model_path: P) -> Result<Self, anyhow::Error>
+    where
+        P: AsRef<Path>,
+    {
+        let session = ModelLoader::model_for_path(&model_path)?.session;
+        Ok(Self { session })
+    }
+
+    pub fn model_from_bytes(model_bytes: &[u8]) -> Result<Self, anyhow::Error> {
+        let session = ModelLoader::model_from_bytes(model_bytes)?.session;
+        Ok(Self { session })
+    }
+    pub 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.ocr.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.swap_remove(0).into_tensor())
+    }
+}

src/models/loader.rs

@@ -1,12 +1,7 @@
 use anyhow::Context;
-use image::DynamicImage;
+use std::io::Cursor;
 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
 
@@ -26,7 +21,7 @@ pub struct ModelLoader {
 }
 
 impl ModelLoader {
-    pub fn load_model<P>(model_path: P) -> anyhow::Result<Self>
+    pub fn model_for_path<P>(model_path: P) -> anyhow::Result<Self>
     where
         P: AsRef<std::path::Path>,
     {
@@ -37,4 +32,17 @@ impl ModelLoader {
             .into_runnable()?;
         Ok(Self { session })
     }
+    /// 策略 B：从内存字节流加载模型（配合 include_bytes! 使用）
+    pub fn model_from_bytes(model_bytes: &[u8]) -> anyhow::Result<Self> {
+        // 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)
+            .with_context(|| "从内存字节流解析 ONNX 模型失败")?
+            .into_optimized()?
+            .into_runnable()?;
+
+        Ok(Self { session })
+    }
 }

src/models/mod.rs

@@ -1,5 +1,3 @@
-pub mod base;
 pub mod loader;
 pub mod ocr;
 pub mod det;
-pub mod slide;

src/models/ocr/builder.rs

@@ -0,0 +1,73 @@
+use crate::models::ocr::executor::Ocr;
+use crate::models::ocr::session::OcrSession;
+use crate::models::ocr::color_filter::ColorFilter;
+use crate::models::ocr::token_filter::TokenFilter;
+
+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 build(self, session: &OcrSession) -> 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 = &session.model_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 {
+            session,
+            png_fix: self.png_fix, // 原地解构出来
+            probability: self.probability,
+            final_color_ranges,
+            final_charset_indices,
+        }
+    }
+}

src/models/ocr/color_filter.rs

@@ -1,25 +1,24 @@
-use std::str::FromStr;
+use crate::utils::cv_ops::rgb_to_opencv_hsv;
 use anyhow::anyhow;
 use image::{DynamicImage, ImageBuffer, Rgb};
-use crate::utils::cv_ops::rgb_to_opencv_hsv;
+use std::str::FromStr;
-
-#[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)
-}
-
 
 /// 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 &&
+        h >= range.lower.0
-            s >= range.lower.1 && s <= range.upper.1 &&
+            && h <= range.upper.0
-            v >= range.lower.2 && v <= range.upper.2
+            && s >= range.lower.1
+            && s <= range.upper.1
+            && v >= range.lower.2
+            && v <= range.upper.2
     })
 }
-pub fn filter_image(image: &DynamicImage, hsv_ranges: &[HsvRange]) -> anyhow::Result<DynamicImage> {
+pub fn apply_to_image(
+    image: &DynamicImage,
+    hsv_ranges: &[HsvRange],
+) -> anyhow::Result<DynamicImage> {
     // 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();
@@ -50,6 +49,13 @@ pub fn filter_image(image: &DynamicImage, hsv_ranges: &[HsvRange]) -> anyhow::Re
 
     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 }
@@ -65,7 +71,8 @@ impl HsvRange {
         }
 
         // 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 {
+        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());
         }
 
@@ -87,25 +94,58 @@ pub enum ColorPreset {
     Custom(Vec<HsvRange>),
 }
 
-impl  ColorPreset {
+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 {
-                HsvRange { lower: (170, 50, 50), upper: (180, 255, 255) },
+                    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::Blue => &[HsvRange {
-            ColorPreset::Green  => &[HsvRange { lower: (40, 50, 50), upper: (80, 255, 255) }],
+                lower: (100, 50, 50),
-            ColorPreset::Yellow => &[HsvRange { lower: (20, 50, 50), upper: (40, 255, 255) }],
+                upper: (130, 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::Green => &[HsvRange {
-            ColorPreset::Cyan   => &[HsvRange { lower: (80, 50, 50), upper: (100, 255, 255) }],
+                lower: (40, 50, 50),
-            ColorPreset::Black  => &[HsvRange { lower: (0, 0, 0), upper: (180, 255, 50) }],
+                upper: (80, 255, 255),
-            ColorPreset::White  => &[HsvRange { lower: (0, 0, 200), upper: (180, 30, 255) }],
+            }],
-            ColorPreset::Gray   => &[HsvRange { lower: (0, 0, 50), upper: (180, 30, 200) }],
+            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,
         }
     }
@@ -204,7 +244,6 @@ impl ColorFilter for ColorPreset {
     }
 }
 
-
 /// 多路颜色“或”逻辑组合子（并集网络）
 pub struct MultiOrColorRestrict<'a> {
     pub filters: Vec<&'a dyn ColorFilter>,
@@ -246,4 +285,3 @@ macro_rules! color_any_of {
         }
     };
 }
-

src/models/ocr/executor.rs

@@ -1,25 +1,16 @@
-use crate::charset::{TokenFilter, ValidationCtx};
+use crate::models::ocr::metadata::Resize;
-use crate::model_metadata::{ModelMetadata, Resize};
+
-use crate::models::base::ModelArgs;
+use crate::models::ocr::session::OcrSession;
-use crate::models::loader::{ModelLoader, ModelSession, ModelType};
+use crate::models::ocr::color_filter::{HsvRange, apply_to_image};
-use crate::utils::color_filter::{ColorFilter, HsvRange, filter_image};
 use crate::utils::image_io::png_rgba_white_preprocess;
 use crate::utils::image_processor::{convert_to_grayscale, resize_image};
-use anyhow::Context;
+use anyhow::Result;
-use anyhow::{Result, anyhow};
+use image::DynamicImage;
-use image::{DynamicImage, ImageBuffer, Rgb};
 use serde::Serialize;
 use std::borrow::Cow;
-use std::collections::HashSet;
 use std::fmt;
 use tract_onnx::prelude::tract_ndarray::{ArrayView2, Ix2, s};
-use tract_onnx::prelude::{
+use tract_onnx::prelude::{DatumType, Tensor, tract_ndarray};
-    DatumType, Graph, IntoTensor, RunnableModel, Tensor, TypedFact, TypedOp, tract_ndarray, tvec,
-};
-// 引入 cv_ops 模块中的 OpenCV HSV 转换算子
-use crate::utils::cv_ops::rgb_to_opencv_hsv;
-
-/// 推理最终输出的强类型外壳（完全 Owned，无任何生命周期，可直接转 JSON）
 #[derive(Debug, Clone, Serialize)]
 pub enum OcrResult {
     /// 纯文本分支（对应 probability = false）
@@ -104,103 +95,40 @@ impl fmt::Display for OcrResult {
     }
 }
 
-pub struct Ocr {
+pub struct Ocr<'a> {
-    pub session: RunnableModel<TypedFact, Box<dyn TypedOp>, Graph<TypedFact, Box<dyn TypedOp>>>,
+    pub(crate) session: &'a OcrSession,
-    pub model_metadata: ModelMetadata,
+    pub(crate) png_fix: bool,
-}
+    pub(crate) probability: bool,
-impl ModelSession for Ocr {
-    fn get_model_type(&self) -> ModelType {
-        todo!("使用thiserror作为错误处理的库,thiserror 专门用于开发库（Library）");
-    }
-    fn desc(&self) -> String {
-        "Ocr Model 加载成功".to_string()
-    }
-}
-impl Ocr {
-    pub fn new(model_path: String, model_metadata: ModelMetadata) -> Result<Self, anyhow::Error> {
-        
-        let session = ModelLoader::load_model(&model_path)?.session;
-        Ok(Self {
-            session,
-            model_metadata,
-        })
-    }
-    /// 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.swap_remove(0).into_tensor())
-    }
-
-    pub fn predictor(&'_ self) -> OcrPredictor<'_> {
-        OcrPredictor::new(self)
-    }
-}
-
-pub struct OcrPredictor<'a> {
-    ocr: &'a Ocr,
-    /// 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: Result<Option<Vec<HsvRange>>, String>,
+    pub(crate) final_color_ranges: Result<Option<Vec<HsvRange>>, String>,
 
     /// 字符集范围
-    charset_restrict: Option<Vec<usize>>,
+    pub(crate) final_charset_indices: Option<Vec<usize>>,
 }
 
-impl<'a> OcrPredictor<'a> {
+impl<'a> Ocr<'a> {
     // 初始化任务，设置默认参数
-    pub fn new(ocr: &'a Ocr) -> Self {
+
-        Self {
+    pub fn new(session: &'a OcrSession) -> Self {
-            ocr,
+        Ocr {
+            session,
             png_fix: false, // 默认值
             probability: false,
-            color_filter: Ok(None),
+            final_color_ranges: Ok(None),
-            charset_restrict: None,
+            final_charset_indices: 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(mut self, filter: &dyn ColorFilter) -> Self {
-        // 一句话把活全包了！错误信息无缝传递，完美熔断
-        match filter.collect_to_vec() {
-            Ok(new_ranges) => self.color_filter = Ok(new_ranges),
-            Err(err_msg) => self.color_filter = Err(err_msg), // 校验失败，Builder 正式中毒
-        }
-
-        self
-    }
-
-    pub fn charset_restrict(mut self, restrict: &dyn TokenFilter) -> Self {
-        let charset = &self.ocr.model_metadata.charset;
-        let tokens = &charset.tokens;
-        self.charset_restrict = restrict.apply_to_charset(tokens);
-        self
-    }
 }
-impl<'a> OcrPredictor<'a> {
+impl<'a> Ocr<'a> {
-    pub fn predict(self, image: &DynamicImage) -> anyhow::Result<OcrResult> {
+    pub fn predict(&self, image: &DynamicImage) -> anyhow::Result<OcrResult> {
-        println!("当前颜色过滤器状态: {:?}", self.color_filter);
+        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.color_filter {
+        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: {}",
@@ -213,13 +141,13 @@ impl<'a> OcrPredictor<'a> {
             }
             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 = filter_image(image, ranges)?;
+                let filtered_img = apply_to_image(image, ranges)?;
                 Cow::Owned(filtered_img)
             }
         };
         let tensor = self.preprocess_image(&img_cow)?;
 
-        let raw_tensor = self.ocr.inference(tensor)?;
+        let raw_tensor = self.session.inference(tensor)?;
 
         // 3. 后处理分流：直接返回 OcrResult
         let ocr_output = match raw_tensor.datum_type() {
@@ -240,7 +168,7 @@ impl<'a> OcrPredictor<'a> {
     /// 负责：透明背景修复 -> 灰度化 -> 按比例 Resize -> 归一化 -> 4维张量转换
     fn preprocess_image(&self, img: &DynamicImage) -> anyhow::Result<Tensor> {
         // 1. 获取模型元数据配置
-        let meta = &self.ocr.model_metadata;
+        let meta = &self.session.model_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)
@@ -328,13 +256,13 @@ impl<'a> OcrPredictor<'a> {
         // Ok(tensor)
     }
 }
-impl<'a> OcrPredictor<'a> {
+impl<'a> Ocr<'a> {
     fn is_valid_indices(&self, idx: usize) -> bool {
-        if idx >= self.ocr.model_metadata.charset.size() {
+        if idx >= self.session.model_metadata.charset.size() {
             return false;
         }
 
-        match &self.charset_restrict {
+        match &self.final_charset_indices {
             Some(v) => v.binary_search(&idx).is_ok(),
             None => true,
         }
@@ -342,9 +270,9 @@ impl<'a> OcrPredictor<'a> {
     /// 【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.ocr.model_metadata.charset;
+        let charset = &self.session.model_metadata.charset;
         let tokens = &charset.tokens;
-        match &self.charset_restrict {
+        match &self.final_charset_indices {
             Some(indices) => indices
                 .iter()
                 .filter_map(|&idx| tokens.get(idx).map(|cow| cow.as_ref()))
@@ -354,9 +282,9 @@ impl<'a> OcrPredictor<'a> {
         }
     }
     pub fn valid_size(&self) -> usize {
-        match &self.charset_restrict {
+        match &self.final_charset_indices {
             Some(indices) => indices.len(),
-            None => self.ocr.model_metadata.charset.tokens.len(),
+            None => self.session.model_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
@@ -504,7 +432,7 @@ impl<'a> OcrPredictor<'a> {
     /// 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.ocr.model_metadata.charset;
+        let charset = &self.session.model_metadata.charset;
         let tokens = &charset.tokens;
         // let valid_indices = &charset.valid_indices;
 
@@ -532,7 +460,7 @@ impl<'a> OcrPredictor<'a> {
 
             // 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.charset_restrict {
+            if let Some(ref indices) = self.final_charset_indices {
                 if indices.binary_search(&u_idx).is_err() {
                     continue;
                 }

src/models/ocr/metadata.rs

@@ -1,11 +1,72 @@
-use crate::charset::{CHARSET_BETA, CHARSET_OLD, Charset};
+use anyhow::{anyhow, Result};
-use anyhow::{Result, anyhow};
 use serde::Deserialize;
 use std::borrow::Cow;
-use std::collections::{HashMap, HashSet};
+use std::collections::HashMap;
-use std::fs::File;
+
-use std::io::Read;
+// ==========================================
-use std::path::Path;
+// 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(),)
+    }
+}
+
+
+
 // =====================================================================
 // 1. 辅助定义的枚举与结构体
 // =====================================================================
@@ -74,28 +135,6 @@ pub struct ModelMetadata {
 
 impl ModelMetadata {
     // --- 优雅的工厂模式构造器 ---
-    /// 从预设的旧版字符集创建
-    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,
-        )
-    }
-
     /// 通用的静态切片转换构造器
     pub fn from_static_slice(
         slice: &[&'static str],
@@ -113,19 +152,8 @@ impl ModelMetadata {
             normalization,
         }
     }
-
+    pub fn from_json_str(json_str: &str) -> Result<Self> {
-    /// 从外部外部 JSON 文件动态加载字符集
+        let dto: ModelMetadataDto = serde_json::from_str(json_str)
-    pub fn from_json_file<P: AsRef<Path>>(path: P) -> 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
@@ -163,4 +191,10 @@ impl ModelMetadata {
             normalization: dto.normalization,
         })
     }
+    /// 机制 2：从内存字节流加载（极大地方便 include_bytes! 或网络下载）
+    pub fn from_json_bytes(bytes: &[u8]) -> Result<Self> {
+        let json_str = std::str::from_utf8(bytes)
+            .map_err(|e| anyhow!("JSON 字节流不是合法的 UTF-8 编码: {}", e))?;
+        Self::from_json_str(json_str)
+    }
 }

src/models/ocr/mod.rs

@@ -0,0 +1,10 @@
+mod builder;
+mod executor;
+mod session;
+pub mod metadata;
+pub mod color_filter;
+mod token_filter;
+
+pub use builder::OcrBuilder;
+pub use executor::{Ocr, OcrResult};
+pub use session::OcrSession;

src/models/ocr/session.rs

@@ -0,0 +1,53 @@
+use crate::models::ocr::metadata::ModelMetadata;
+use crate::models::loader::{ModelLoader, ModelSession, ModelType};
+use anyhow::Context;
+use anyhow::Result;
+use std::path::Path;
+use tract_onnx::prelude::{tvec, Graph, IntoTensor, RunnableModel, Tensor, TypedFact, TypedOp};
+
+pub struct OcrSession {
+    pub session: RunnableModel<TypedFact, Box<dyn TypedOp>, Graph<TypedFact, Box<dyn TypedOp>>>,
+    pub model_metadata: ModelMetadata,
+}
+impl ModelSession for OcrSession {
+    fn get_model_type(&self) -> ModelType {
+        todo!("使用thiserror作为错误处理的库,thiserror 专门用于开发库（Library）");
+    }
+    fn desc(&self) -> String {
+        "Ocr Model 加载成功".to_string()
+    }
+}
+impl OcrSession {
+    pub fn new<P>(model_path: P, model_metadata: ModelMetadata) -> Result<Self, anyhow::Error>
+    where
+        P: AsRef<Path>,
+    {
+        let session = ModelLoader::model_for_path(model_path)?.session;
+        Ok(Self {
+            session,
+            model_metadata,
+        })
+    }
+
+    pub fn model_from_bytes(
+        model_bytes: &[u8],
+        model_metadata: ModelMetadata,
+    ) -> Result<Self, anyhow::Error> {
+        let session = ModelLoader::model_from_bytes(model_bytes)?.session;
+        Ok(Self {
+            session,
+            model_metadata,
+        })
+    }
+    /// 对应 Python 的 _inference
+    pub 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.ocr.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.swap_remove(0).into_tensor())
+    }
+}

src/models/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 ),+ ]
+        }
+    };
+}

src/utils/image_processor.rs

@@ -1,5 +1,7 @@
-use image::{DynamicImage, GrayImage, imageops::FilterType};
+use image::{DynamicImage, GrayImage, imageops::FilterType, Rgb, ImageBuffer};
-use anyhow::Result;
+use anyhow::{anyhow, Result};
+use crate::models::ocr::color_filter::HsvRange;
+use crate::utils::cv_ops::rgb_to_opencv_hsv;
 
 /// 对应 Python 的 convert_to_grayscale
 /// 将图像转换为灰度图 (L模式)
@@ -35,3 +37,4 @@ pub fn resize_image(
 //         FilterType::Lanczos3
 //     )
 // }
+

src/utils/mod.rs

@@ -1,4 +1,3 @@
 pub mod image_io;
 pub mod image_processor;
 pub mod cv_ops;
-pub mod color_filter;

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_rs::models::ocr::metadata::Charset;
+use ddddocr_rs::models::ocr::metadata::{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",
@@ -517,212 +524,77 @@ pub const CHARSET_BETA: &[&str] = &[
 
 pub const CHARSET_OLD: &[&str] = &["", "笤", "谴", "膀", "荔"];
 
-use std::borrow::Cow;
-use std::collections::{HashMap, HashSet};
 
-/// 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
+// pub fn from_builtin_old() -> Self {
-        let mut temp_indices = Vec::with_capacity(estimated_capacity.max(16));
+//     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,
+//     )
+// }
 
-        // 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
+// /// 从外部外部 JSON 文件动态加载字符集(在后续优化中移除)
-            if token_str.is_empty() || idx == 0 {
+// pub fn from_json_file<P: AsRef<Path>>(path: P) -> anyhow::Result<Self> {
-                temp_indices.push(idx);
+//     let path = path.as_ref();
-                continue; // 关键：直接跳过，防止后续 matches 匹配成功导致重复 push 产生 Bug
+//     if !path.exists() {
-            }
+//         return Err(anyhow!("模型元数据配置文件不存在: {:?}", path));
-
+//     }
-            // 规则 B: 组装无拷贝上下文
+// 
-            let ctx = ValidationCtx {
+//     let mut file = File::open(path)?;
-                text: token_str,
+//     let mut content = String::new();
-                token_id: idx,
+//     file.read_to_string(&mut content)?;
-            };
+// 
-
+//     let dto: ModelMetadataDto = serde_json::from_str(&content)
-            // 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）
+//         .map_err(|e| anyhow!("JSON 反序列化失败，请检查字段是否完整: {}", e))?;
-            if self.matches(&ctx) {
+// 
-                temp_indices.push(idx);
+//     // 1. 将 DTO 的字符串数组转化为强类型的 Charset
-                has_any_match = true;
+//     let tokens: Vec<Cow<'static, str>> =
-            }
+//         dto.charset.into_iter().map(|s| Cow::Owned(s)).collect();
-        }
+//     let charset = Charset::new(tokens);
-
+// 
-        // 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）
+//     // 2. 解析 resize 策略（重现 Python 的复杂条件判断）
-        if !has_any_match {
+//     if dto.resize.len() != 2 {
-            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。");
+//         return Err(anyhow!(
-            None
+//                 "'resize (or image)' 字段必须是包含两个元素的数组，例如 [-1, 64]"
-        } 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();
+//     let r0 = dto.resize[0];
-            temp_indices.dedup();
+//     let r1 = dto.resize[1];
-            Some(temp_indices)
+// 
-        }
+//     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)
-#[derive(Debug, Clone, PartialEq, Eq)]
+//         } else {
-pub enum CharRestrict {
+//             // 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
-    Digit,
+//             Resize::DynamicWidth(r1 as u32)
-    Lowercase,
+//         }
-    Uppercase,
+//     } else {
-    CustomList(Vec<String>),
+//         // 正常的固定宽高
-}
+//         Resize::Fixed(r0 as u32, r1 as u32)
-
+//     };
-impl TokenFilter for CharRestrict {
+// 
-    fn matches(&self, ctx: &ValidationCtx) -> bool {
+//     Ok(Self {
-        match self {
+//         charset,
-            Self::Digit => ctx.text.len() == 1 && ctx.text.as_bytes()[0].is_ascii_digit(),
+//         word: dto.word,
-            Self::Lowercase => ctx.text.len() == 1 && ctx.text.as_bytes()[0].is_ascii_lowercase(),
+//         resize,
-            Self::Uppercase => ctx.text.len() == 1 && ctx.text.as_bytes()[0].is_ascii_uppercase(),
+//         channel: dto.channel,
-            Self::CustomList(vec) => vec.iter().any(|t| t == ctx.text),
+//         normalization: dto.normalization,
-        }
+//     })
-    }
+// }
-    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 ),+ ]
-        }
-    };
-}
-
-// ==========================================
-// 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(),)
-    }
-}

tests/ocr_test.rs

@@ -1,9 +1,11 @@
-use ddddocr_rs::models::slide::Slide;
+use ddddocr_rs::models::det::DetectionResult;
-use ddddocr_rs::{DdddOcr, DdddOcrBuilder}; // 假设你的包名是这个
+use ddddocr_rs::{DetBuilder, DetSession, Detector, ModelMetadata, Ocr, OcrSession, Slider}; // 假设你的包名是这个
 use image::{DynamicImage, Rgb};
 use std::fs;
 use std::path::Path;
-use ddddocr_rs::models::det::DetectionResult;
+mod char_slice;
+use char_slice::CHARSET_BETA;
+use ddddocr_rs::models::ocr::metadata::{Normalization, Resize};
 
 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
@@ -17,8 +19,8 @@ fn load_image<P: AsRef<Path>>(path: P) -> anyhow::Result<image::DynamicImage> {
 }
 /// 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
+    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
+    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)?;
@@ -60,23 +62,37 @@ fn save_debug_image(
     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");
+    let ocr = OcrSession::new(
+        "D:\\CNWei\\CNW\\Rust\\ddddocr-rs\\models\\common_sml2h3_f32.onnx",
+        ModelMetadata::from_static_slice(
+            CHARSET_BETA,
+            false,
+            Resize::DynamicWidth(64),
+            1,
+            Normalization::MinusOneToOne,
+        ),
+    )
+    .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");
+    let result = Ocr::new(&ocr)
+        .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 = DdddOcrBuilder::new().det().build()?;
+    let det = DetSession::new("D:\\CNWei\\CNW\\Rust\\ddddocr-rs\\models\\common_det.onnx")?;
     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))?;
@@ -88,8 +104,8 @@ fn test_det_load() -> anyhow::Result<()> {
         .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 = det.detection(&img)?;
+    let bboxes = Detector::new(&det).predict(&img)?;
-    println!(":?{}", det);
+    // println!("{:?}", det);
     println!("检测到的目标数量: {}", bboxes.len());
 
     if bboxes.is_empty() {
@@ -100,10 +116,7 @@ fn test_det_load() -> anyhow::Result<()> {
 
         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!(
+            println!("目标 [{}]: {}", i, bbox);
-                "目标 [{}]: x1={}, y1={}, x2={}, y2={}, 分数={:.4}, 类别ID={}",
-                i, bbox.x1, bbox.y1, bbox.x2, bbox.y2, bbox.score, bbox.class_id
-            );
         }
     }
     Ok(())
@@ -111,7 +124,7 @@ fn test_det_load() -> anyhow::Result<()> {
 
 #[test]
 fn test_real_slide_match() {
-    let engine = Slide::new();
+    let engine = Slider::new().unwrap();
 
     // 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
@@ -128,9 +141,7 @@ fn test_real_slide_match() {
 
     // 3. 打印结果
     println!("-------------------------------------------");
-    println!("滑块匹配测试结果:");
+    println!("{}", result);
-    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!("-------------------------------------------");
 
@@ -142,7 +153,7 @@ fn test_real_slide_match() {
 
 #[test]
 fn test_real_slide_comparison() {
-    let engine = Slide::new();
+    let engine = Slider::new().unwrap();
 
     // 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