feat(model): 新增 ModelLoader 链式构建 API 及 ORT GPU/Tract 多线程配置

- 在 ddddocr-core 中定义 ModelBuilder Trait 及其错误类型
- ddddocr-ort 支持 use_gpu、device_id 及 num_threads 链式配置与 CUDA 硬件加速
- ddddocr-tract 基于 multithread-mm 特性支持 CPU 线程数控制
- 支持基于 tract-linalg 配置推理线程数，显式引入 tract-linalg 的 multithread-mm 特性，控制 GEMM 算子并发
- 优化线程池加载策略，适配 Tokio 异步及 CLI 等多场景

ddddocr-core/Cargo.toml

@@ -5,12 +5,12 @@ edition = { workspace = true }
 license = { workspace = true }
 
 [dependencies]
-image = "0.25.10"
-base64 = "0.22.1"
-imageproc = { version = "0.26.2", default-features = true }
-serde = { workspace = true }
-serde_json = "1.0.150"
 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}
-#serde = { workspace = true, features = ["derive"] }
+tracing={workspace = true}

ddddocr-core/src/lib.rs

@@ -1,16 +1,16 @@
-pub mod det;
+mod det;
 pub mod error;
-pub mod ocr;
+mod ocr;
 mod slide;
 pub mod utils;
 
-use crate::error::{Result, TensorError};
+use error::{Result, TensorError};
+use std::path::Path;
 
-pub use crate::slide::{SlideResult, Slider};
 pub use crate::det::{DetBuilder, DetectionResult, Detector};
-pub use crate::ocr::{Ocr, OcrBuilder, OcrResult};
-pub use crate::ocr::{ModelMetadata,Normalization};
-pub use ocr::Charset;
+pub use crate::ocr::{Charset, ModelMetadata, Normalization, Ocr, OcrBuilder, OcrResult, Resize};
+pub use crate::slide::{SlideResult, Slider};
+
 // DetSession
 
 pub enum OcrOutput {
@@ -27,7 +27,7 @@ pub enum DetOutput {
 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>) -> Result<Self::Output,TensorError>;
+    fn inference(&self, input_array: ndarray::Array4<f32>) -> Result<Self::Output, TensorError>;
 }
 
 pub trait OcrEngine: InferenceEngine<Output = OcrOutput> {
@@ -35,3 +35,11 @@ pub trait OcrEngine: InferenceEngine<Output = OcrOutput> {
 }
 
 pub trait DetEngine: InferenceEngine<Output = DetOutput> {}
+
+pub trait ModelBuilder {
+    type Session;
+    type Error;
+    fn model_for_path<P: AsRef<Path>>(&self,model_path: P) -> Result<Self::Session, Self::Error>;
+
+    fn model_from_bytes(&self,model_bytes: &[u8]) -> Result<Self::Session, Self::Error>;
+}

ddddocr-core/src/ocr/executor.rs

@@ -5,7 +5,6 @@ use crate::ocr::color_filter::{HsvRange, apply_to_image};
 use crate::utils::image_convert::png_rgba_white_preprocess;
 use crate::utils::image_processor::{convert_to_grayscale, resize_image};
 use image::DynamicImage;
-use serde::Serialize;
 use std::borrow::Cow;
 use std::fmt;
 // use tract_onnx::prelude::tract_ndarray::{ Ix2, s};
@@ -19,7 +18,7 @@ use ndarray::ArrayView2;
 use crate::error::{ImagePreprocessError, Result, TensorError};
 use crate::{OcrEngine, OcrOutput};
 use tracing::{ warn};
-#[derive(Debug, Clone, Serialize)]
+#[derive(Debug, Clone)]
 pub enum OcrResult {
     /// 纯文本分支（对应 probability = false）
     Text(String),

ddddocr-core/src/utils.rs

@@ -1,8 +1,8 @@
 pub mod image_convert;
+mod image_helper;
 pub mod image_processor;
 mod tensor_transform;
-mod image_helper;
 
 // 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;
-pub use image_convert::{ColorMode};