refactor: 移除 OpenCV 依赖并实现纯 Rust 图像处理流水线

- 替换 opencv 为 image 库以简化交叉编译
- 修正 nms 逻辑中的 ArrayView 借用问题
- 增加 save_debug_image 方法用于可视化检测框
- 更新 Cargo.toml 依赖项

src/lib.rs

@@ -10,84 +10,99 @@ 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 crate::det_model::Det;
 use crate::model_loader::ModelSession;
 use crate::ocr_model::Ocr;
-use crate::charset::get_default_charset;
-pub enum ModeType {
+pub enum ModelSpec {
     /// 默认 OCR (使用内置路径)
-    Ocr {
-        path: String,
-        charset: Vec<String>,
-    },
-    Det {
-        path: String,
-    },
+    OcrModel,
+    DetModel,
     /// 自定义 OCR (路径由用户提供)
-    CustomOcr {
+    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_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: ModeType,
+    mode: ModelSpec,
 }
 
 impl DdddOcrBuilder {
     pub fn new() -> Self {
         Self {
-            mode: ModeType::Ocr {
-                path: "models/common.onnx".to_string(),
-                charset: get_default_charset(),
-            },
+            mode: ModelSpec::OcrModel,
         }
     }
 
     /// 切换为检测模式
     pub fn det(mut self) -> Self {
-        self.mode = ModeType::Det {
-            path: "models/common_det.onnx".to_string(),
-        };
+        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 = ModeType::CustomOcr { path, charset };
+        self.mode = ModelSpec::CustomOcrModel { path, charset };
         self
     }
 
     /// 核心初始化逻辑
     pub fn build(self) -> Result<DdddOcr> {
-        let session: Box<dyn ModelSession> = match self.mode {
-            ModeType::Ocr { path, charset } => Box::new(Ocr::new(path, charset)?),
-            ModeType::Det { path } => Box::new(Det::new(path)?),
-            ModeType::CustomOcr { path, charset } => Box::new(Ocr::new(path, charset)?),
+        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 { session })
+        Ok(DdddOcr { runtime })
     }
 }
 
 pub struct DdddOcr {
-    session: Box<dyn ModelSession>,
+    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> {
-        self.session.predict(img, false)
-
-        // let tensor = self.preprocess_image(img, false)?;
-        //
-        // // 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))
+        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")),
+        }
     }
 }