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

@@ -9,3 +9,12 @@ tract-onnx = { version = "0.21.10" }
 anyhow = "1.0.102"
 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"
+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

README.md

@@ -2,8 +2,43 @@
 
 带带弟弟 OCR (ddddocr) 的 Rust 移植版。高性能、低占用，支持多种验证码识别与检测。
 
+🧩 滑块识别算法核心知识点总结
+本项目实现了两种核心匹配模式，其底层逻辑与 OpenCV 的对齐情况如下：
 
+1. 匹配模式对比 (Match Modes)
+   |**模式**|**算法原理**|**适用场景**|**备注**|
+   |---|---|---|---|
+   |**边缘模式** (Edge-based)|基于 **Canny 边缘检测** 提取轮廓后再进行匹配。|**推荐方案**
+   。适用于绝大多数拼图滑块。|天然免疫拼图周边的透明/黑色留白干扰，坐标最精准。|
+   |**简单模式** (Simple/Gray)|直接基于 **灰度像素值** 进行归一化互相关计算。|适用于无明显边缘、靠颜色差异识别的场景。|对背景和透明边框敏感，可能存在重心偏移。|
 
+2. 数学公式差异 (NCC vs. CCOEFF)
+   在简单模式下，本项目采用的是 归一化互相关 (NCC)，对应 OpenCV 中的 TM_CCORR_NORMED。
+
+逻辑对齐：Rust 的 match_template 结果与 Python cv2.TM_CCORR_NORMED 完全一致。
+
+关于偏移：若拼图原始图片（Target）四周包含大量的透明留白：
+
+CCORR (本项目)：会将留白视为图像的一部分，计算出的是整张图片框的中心。
+
+CCOEFF (OpenCV 默认)：会自动进行“均值中心化”，在一定程度上能削弱留白的影响。
+
+最佳实践：若发现坐标有固定位移，建议优先切换至 边缘模式，或对滑块图进行 Bounding Box 裁剪 后再匹配。
+
+3. 图像预处理一致性
+
+   为确保识别精度，本项目在 Rust 中完美复刻了 Python OpenCV 的预处理链路：
+
+- **灰度化权重**：采用 OpenCV 标准感光公式 $0.299R + 0.587G + 0.114B$。
+
+- **Alpha 处理**：在将 PNG 转为 RGB 时，自动将透明区域填充为黑色，确保与 PIL (Python Imaging Library) 行为一致。
+
+- **坐标定义**：所有返回坐标均为匹配区域的 **几何中心点** $(x + w/2, y + h/2)$。
+
+💡 开发者建议：
+
+如果识别结果在 $X$ 轴上有大约 $10px$ 左右的固定误差，通常是因为滑块原图自带了透明边距（留白）。此时请确保
+simple_target=false。该模式会通过 Canny 边缘检测 提取轮廓特征，能自动锁定拼图实体并忽略背景留白的像素干扰。
 鸣谢 (Credits)
 
 - 本项目是 [ddddocr](https://github.com/sml2h3/ddddocr) 的 Rust 移植版本，原作者为 sml2h3。衷心感谢原作者对 OCR 社区做出的杰出贡献。

examples/simple_usage.rs

@@ -1,5 +1,5 @@
 fn main() {
-    let ocr = ddddocr_rs::DdddOcr::new("model/common.onnx").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

@@ -0,0 +1,268 @@
+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::{Result, anyhow};
+use image::DynamicImage;
+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::fmt;
+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 {
+    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 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,
+        target_image: &DynamicImage,
+        background_image: &DynamicImage,
+        simple_target: bool,
+    ) -> Result<SlideResult> {
+        let target_array = image_to_ndarray(target_image);
+        let background_array = image_to_ndarray(background_image);
+
+        self.perform_slide_match(target_array.view(), background_array.view(), simple_target)
+    }
+    /// 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,
+        target_image: &DynamicImage,
+        background_image: &DynamicImage,
+    ) -> Result<SlideResult> {
+        // 1. 转换为 ndarray (HWC RGB)
+        let target_array = image_to_ndarray(target_image);
+        let background_array = image_to_ndarray(background_image);
+
+        // 2. 执行比较逻辑 (对应 _perform_slide_comparison)
+        self.perform_slide_comparison(target_array.view(), background_array.view())
+    }
+    /// 对应 Python: _perform_slide_comparison
+    pub fn perform_slide_comparison(
+        &self,
+        target: ArrayView3<u8>,
+        background: ArrayView3<u8>,
+    ) -> Result<SlideResult> {
+
+        // 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(anyhow!(
+                "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(anyhow!("输入图像尺寸不能为0"));
+        }
+
+        let diff_array = abs_diff(&target, &background);
+
+        // 2. 转换为灰度数组 (复用你的 cv2.cvtColor)
+        let gray_array = rgb_to_gray(diff_array.view());
+        // 3. 转为 ImageBuffer 以使用 imageproc 的高级功能
+        let gray_buffer = ndarray_to_luma8(gray_array.view());
+
+        // 2. 二值化 (对应 cv2.threshold(..., 30, 255, cv2.THRESH_BINARY))
+        let binary = threshold(&gray_buffer, 30, ThresholdType::Binary);
+        // 3. 形态学操作去噪 (对应 cv2.morphologyEx)
+        // 闭运算 (Close): 先膨胀后腐蚀，用于填补缺口内的细小黑色空洞
+        // 开运算 (Open): 先腐蚀后膨胀，用于消除背景中的白色噪点点
+        let norm = Norm::LInf; // 对应 3x3 的矩形内核
+        let radius = 1u8; // 1 表示 3x3 的范围，2 表示 5x5 的范围
+        let closed = close(&binary, norm, radius);
+        let cleaned = open(&closed, norm, radius);
+
+        // connected_components 会给每个独立的白色区域打上不同的标签 (ID)
+        let background_label = Luma([0u8]);
+        let labelled = connected_components(&cleaned, Connectivity::Eight, background_label);
+
+        // // 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) {
+            // 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);
+            // 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);
+
+            Ok(SlideResult {
+                target: [center_x, center_y],
+                target_x: center_x,
+                target_y: center_y,
+                confidence: 1.0, // Comparison 模式下通常认为找到即为 1.0
+            })
+        } else {
+            Ok(SlideResult {
+                target: [0, 0],
+                target_x: 0,
+                target_y: 0,
+                confidence: 0.0,
+            })
+        }
+    }
+
+    /// 对应 Python: _perform_slide_match
+    // 在 SlideEngine 中修改此入口进行测试
+    fn perform_slide_match(
+        &self,
+        target: ArrayView3<u8>,
+        background: ArrayView3<u8>,
+        simple_target: bool, // 增加这个参数
+    ) -> Result<SlideResult> {
+        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(anyhow!("输入图像的宽度或高度不能为0"));
+        }
+        if th > bh || tw > bw {
+            return Err(anyhow!(
+                "尺寸不匹配：滑块模板(target)尺寸 [{}x{}] 不能大于背景图(background) [{}x{}]",
+                tw,
+                th,
+                bw,
+                bh
+            ));
+        }
+        if tc != bc {
+            return Err(anyhow!(
+                "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())
+        } else {
+            // 2b. 复杂模式：先提取边缘，再匹配
+
+            self.edge_based_match(target_gray.view(), background_gray.view())
+        }
+    }
+    /// 对应 Python: _simple_template_match
+    /// 使用 SAD (Sum of Absolute Differences) 算法
+    /// 核心模板匹配：SAD + 有效像素过滤
+    fn simple_template_match(
+        &self,
+        target: ArrayView2<u8>,
+        background: ArrayView2<u8>,
+    ) -> Result<SlideResult> {
+        // 1. 将 ndarray 转换为 imageproc 需要的 ImageBuffer (无拷贝或轻量转换)
+        // 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);
+        // t_buf.save("debug_rust_target.png").unwrap();
+
+        // 2. 调用 imageproc 的 NCC 算法 (等价于 cv2.TM_CCOEFF_NORMED)
+        // 模板匹配 (完全对齐 cv2.matchTemplate(..., cv2.TM_CCOEFF_NORMED))
+        let result = match_template(
+            &b_buf,
+            &t_buf,
+            MatchTemplateMethod::CrossCorrelationNormalized,
+        );
+        // save_rust_result(&result, "debug_rust_target2.png");
+        // 3. 寻找最大值 (等价于 cv2.minMaxLoc)
+        let (max_val, max_loc) = min_max_loc(&result);
+
+        // 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);
+        // println!("Rust Target Width (tw): {}", tw);
+        // println!("Rust Best Max Loc X: {}", max_loc.0);
+        // println!("Rust Final Center X: {}", center_x);
+        Ok(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
+    /// 基于边缘检测的滑块匹配 (对齐 Python _edge_based_match)
+    pub fn edge_based_match(
+        &self,
+        target: ArrayView2<u8>,
+        background: ArrayView2<u8>,
+    ) -> Result<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);
+        let b_buf = ndarray_to_luma8(background);
+
+        // 2. 边缘检测 (完全对齐 cv2.Canny(50, 150))
+        // 这步会生成黑底白线的二值化边缘图
+        let target_edges = canny(&t_buf, 50.0, 150.0);
+        let background_edges = canny(&b_buf, 50.0, 150.0);
+
+        // target_edges.save("debug_target_edges.png").ok();
+        // background_edges.save("debug_bg_edges.png").ok();
+
+        // 3. 模板匹配 (完全对齐 cv2.matchTemplate(..., cv2.TM_CCOEFF_NORMED))
+        // 在边缘图上计算归一化互相关系数
+        let result = match_template(
+            &background_edges,
+            &target_edges,
+            MatchTemplateMethod::CrossCorrelationNormalized,
+        );
+
+        // 4. 找到最佳匹配位置 (对齐 cv2.minMaxLoc)
+        let (max_val, max_loc) = min_max_loc(&result);
+        // 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);
+
+        // 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 {
+            target: [center_x, center_y],
+            target_x: center_x,
+            target_y: center_y,
+            confidence: max_val as f64,
+        })
+    }
+}

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/image_io.rs

@@ -1,62 +0,0 @@
-use anyhow::{Context, Result};
-use base64::{Engine as _, engine::general_purpose};
-use image::{DynamicImage, GenericImageView, ImageBuffer, Rgb, RgbImage};
-use std::path::{Path, PathBuf};
-use tract_onnx::prelude::tract_ndarray::Array3;
-
-/// 定义支持的输入类型枚举
-pub enum ImageInput {
-    Bytes(Vec<u8>),
-    Array(Array3<u8>),
-    Path(PathBuf),
-    Base64(String),
-    DynamicImage(DynamicImage),
-}
-
-/// 模拟 Python 的 load_image_from_input
-#[allow(dead_code)]
-pub fn load_image_from_input(input: ImageInput) -> Result<DynamicImage> {
-    match input {
-        ImageInput::DynamicImage(img) => Ok(img),
-        _ => todo!("后续补充"),
-    }
-}
-
-/// 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
-#[allow(dead_code)]
-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 img.clone();
-    }
-
-    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 image.paste(img, ..., mask=img)
-    for (x, y, pixel) in rgba_img.enumerate_pixels() {
-        let alpha = pixel[3] as f32 / 255.0;
-
-        if alpha >= 1.0 {
-            // 完全不透明，直接覆盖
-            background.put_pixel(x, y, Rgb([pixel[0], pixel[1], pixel[2]]));
-        } else if alpha > 0.0 {
-            // 半透明，执行 Alpha 混合公式： (src * alpha) + (dst * (1 - alpha))
-            let bg_pixel = background.get_pixel(x, y);
-            let r = (pixel[0] as f32 * alpha + bg_pixel[0] as f32 * (1.0 - alpha)) as u8;
-            let g = (pixel[1] as f32 * alpha + bg_pixel[1] as f32 * (1.0 - alpha)) as u8;
-            let b = (pixel[2] as f32 * alpha + bg_pixel[2] as f32 * (1.0 - alpha)) as u8;
-            background.put_pixel(x, y, Rgb([r, g, b]));
-        }
-        // alpha == 0 的情况不需要处理，因为背景已经是白色了
-    }
-
-    DynamicImage::ImageRgb8(background)
-}

src/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 image 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/lib.rs

@@ -1,182 +1,9 @@
-mod charset;
+mod algo;
-mod image_io;
+mod error;
-mod image_processor;
+pub mod models;
-mod model;
+pub mod utils;
-mod utils;
 
-use crate::image_io::png_rgba_white_preprocess;
+pub use crate::algo::{SlideResult, Slider};
-use crate::image_processor::{convert_to_grayscale, resize_image};
+pub use crate::models::det::{DetBuilder, DetSession, DetectionResult, Detector};
-use anyhow::{Context, Result};
+pub use crate::models::ocr::{Ocr, OcrBuilder, OcrResult, OcrSession};
-use image::{DynamicImage, imageops::FilterType};
+pub use models::ocr::metadata::ModelMetadata;
-use tract_onnx::prelude::*;
-// 关键点：直接使用 tract 重导出的 ndarray
-use tract_onnx::prelude::tract_ndarray::s;
-pub struct DdddOcr {
-    session: RunnableModel<TypedFact, Box<dyn TypedOp>, Graph<TypedFact, Box<dyn TypedOp>>>,
-}
-
-impl DdddOcr {
-    pub fn new<P>(model_path: P) -> 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 })
-    }
-
-    pub fn classification(&self, img: &DynamicImage) -> Result<String> {
-        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))
-    }
-    /// 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) -> 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) -> 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) -> 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(predicted_indices: &[i64]) -> String {
-        println!("indices模型输出原始数据: {:?}", predicted_indices);
-
-        use crate::charset::CHARSET_BETA;
-        // 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) = CHARSET_BETA.get(u_idx) {
-                        res.push_str(char_str);
-                    }
-                }
-            }
-            prev_idx = idx;
-        }
-        println!("最终识别出的验证码是: {}", res);
-        res
-    }
-}
-
-
-#[cfg(test)]
-mod tests {
-    use super::*;
-    #[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/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

@@ -0,0 +1,300 @@
+use anyhow::{Context, Result};
+use image::{imageops::FilterType, DynamicImage, GenericImageView};
+use std::fmt;
+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 {
+    pub x1: i32,
+    pub y1: i32,
+    pub x2: i32,
+    pub y2: i32,
+    pub score: f32,
+    pub class_id: u32,
+}
+
+impl fmt::Display for DetectionResult {
+    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]
+        write!(
+            f,
+            "x1={}, y1={}, x2={}, y2={}, 分数={:.4}, 类别ID={}",
+            self.x1, self.y1, self.x2, self.y2, self.score, self.class_id
+        )
+    }
+}
+
+#[derive(Debug)]
+pub struct Detector<'a> {
+    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)
+    }
+    /// 2. preproc: 纯 Rust 实现 (替代 OpenCV)
+    fn preproc(&self, image: &DynamicImage, input_size: (u32, u32)) -> Result<(Tensor, f32)> {
+        let (target_h, target_w) = input_size;
+        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);
+        let new_h = (img_h as f32 * r) as u32;
+        let new_w = (img_w as f32 * r) as u32;
+
+        // Resize 图像
+        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
+        let mut base_img =
+            image::ImageBuffer::from_pixel(target_w, target_h, image::Rgb([114u8, 114, 114]));
+
+        // 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));
+
+        // 用连续的 stride 步长进行写入，提高 CPU 缓存利用率
+        for y in 0..target_h as usize {
+            for x in 0..target_w as usize {
+                let idx = (y * target_w as usize + x) * 3;
+                // 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
+            }
+        }
+
+        Ok((array.into(), r))
+    }
+
+    /// 3. demo_postprocess (逻辑与 Python 一致)
+    fn demo_postprocess(&self, mut outputs: Array3<f32>, img_size: (i32, i32)) -> Array3<f32> {
+        let strides = [8, 16, 32];
+
+        // 遍历每一个 Batch（支持动态 Batch 推理）
+        for mut batch in outputs.axis_iter_mut(Axis(0)) {
+            let mut offset = 0;
+
+            for &stride in &strides {
+                // 计算当前特征图的尺寸
+                let h = img_size.0 / stride;
+                let w = img_size.1 / stride;
+                let f_stride = stride as f32;
+
+                for y in 0..h {
+                    for x in 0..w {
+                        // 计算当前格子在 25200 个锚点中的线性索引
+                        let idx = offset + (y * w + x) as usize;
+                        // 1. 还原中心点坐标 (cx, cy)
+                        // 公式: (output + grid_offset) * stride
+                        batch[[idx, 0]] = (batch[[idx, 0]] + x as f32) * f_stride;
+                        batch[[idx, 1]] = (batch[[idx, 1]] + y as f32) * f_stride;
+
+                        // 2. 还原宽高 (w, h)
+                        // 公式: exp(output) * stride
+                        batch[[idx, 2]] = batch[[idx, 2]].exp() * f_stride;
+                        batch[[idx, 3]] = batch[[idx, 3]].exp() * f_stride;
+                    }
+                }
+                // 移动到下一个步长的起始位置
+                offset += (h * w) as usize;
+            }
+        }
+        outputs
+    }
+
+    /// 4. nms
+    fn nms(&self, boxes: &Array2<f32>, scores: &Array1<f32>, nms_thr: f32) -> Vec<usize> {
+        let mut keep = Vec::new();
+        let x1 = boxes.column(0);
+        let y1 = boxes.column(1);
+        let x2 = boxes.column(2);
+        let y2 = boxes.column(3);
+        // 在每一项前加上 &，并确保括号内的计算顺序
+        // 注意：ndarray 的 View 运算需要 &view1 - &view2
+        let areas = (&x2 - &x1 + 1.0) * (&y2 - &y1 + 1.0);
+
+        // 初始排序索引
+        let mut v: Vec<usize> = (0..scores.len()).collect();
+        v.sort_unstable_by(|&i, &j| {
+            scores[j]
+                .partial_cmp(&scores[i])
+                .unwrap_or(std::cmp::Ordering::Equal)
+        });
+        // 我们不使用 v.remove(0)，而是直接通过索引池操作
+        let mut active_indices = v;
+
+        while !active_indices.is_empty() {
+            // 取出当前池子中得分最高的框（即第一个元素）
+            let i = active_indices[0];
+            keep.push(i);
+
+            // 如果池子里只剩一个了，直接结束
+            if active_indices.len() == 1 {
+                break;
+            }
+
+            // 5. 核心逻辑：使用 retain 一次性过滤掉：
+            // (a) 当前框自己 (idx == i)
+            // (b) 与当前框重叠度过高的框 (iou > nms_thr)
+            active_indices.retain(|&idx| {
+                // 如果是当前正在处理的框，不保留（因为它已经进入 keep 了）
+                if idx == i {
+                    return false;
+                }
+
+                // 计算 IoU
+                let xx1 = x1[i].max(x1[idx]);
+                let yy1 = y1[i].max(y1[idx]);
+                let xx2 = x2[i].min(x2[idx]);
+                let yy2 = y2[i].min(y2[idx]);
+
+                let w = (xx2 - xx1 + 1.0).max(0.0);
+                let h = (yy2 - yy1 + 1.0).max(0.0);
+                let inter = w * h;
+
+                let iou = inter / (areas[i] + areas[idx] - inter);
+
+                // 只保留 IoU 小于阈值的框
+                iou <= nms_thr
+            });
+        }
+
+        keep
+    }
+
+    /// 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<[f32; 6]> {
+        let mut candidates = Vec::new();
+
+        // 1. 筛选高分框 (单次遍历完成 Argmax 和 Threshold 过滤)
+        for i in 0..scores.nrows() {
+            let row = scores.row(i);
+
+            // 找到当前行（即当前锚点）得分最高的类别
+            let mut max_score = 0.0;
+            let mut cls_id = 0;
+            for (j, &s) in row.iter().enumerate() {
+                if s > max_score {
+                    max_score = s;
+                    cls_id = j;
+                }
+            }
+
+            // 仅保留超过阈值的候选框
+            if max_score > score_thr {
+                // 暂时存储索引和元数据，避免频繁创建大数组
+                candidates.push((i, max_score, cls_id));
+            }
+        }
+
+        if candidates.is_empty() {
+            return vec![];
+        }
+
+        // 2. 准备 NMS 输入
+        // 构造 NMS 需要的子集数组
+        let mut b_subset = Array2::<f32>::zeros((candidates.len(), 4));
+        let mut s_subset = Array1::<f32>::zeros(candidates.len());
+
+        for (new_idx, &(orig_idx, score, _)) in candidates.iter().enumerate() {
+            b_subset.row_mut(new_idx).assign(&boxes.row(orig_idx));
+            s_subset[new_idx] = score;
+        }
+
+        // 3. 执行 NMS (返回保留下来的子集索引)
+        let keep = self.nms(&b_subset, &s_subset, nms_thr);
+
+        // 4. 组装最终结果 [x1, y1, x2, y2, score, class_id]
+        keep.into_iter()
+            .map(|k_idx| {
+                let (orig_idx, score, cls_id) = candidates[k_idx];
+                let b = boxes.row(orig_idx);
+                [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, dynamic_img: &DynamicImage) -> Result<Vec<DetectionResult>> {
+        // 使用 utils crate 解码
+        // 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))?;
+
+        // tract 推理
+        // let outputs = self.session.session.run(tvec!(input_tensor.into()))?;
+        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>()?;
+
+        let predictions = self.demo_postprocess(output_array, (416, 416));
+        let pred = predictions.slice(s![0, .., ..]);
+
+        let boxes = pred.slice(s![.., 0..4]);
+        let obj_conf = pred.slice(s![.., 4..5]);
+        let cls_conf = pred.slice(s![.., 5..]);
+        let obj_broadcast = obj_conf
+            .broadcast(cls_conf.dim())
+            .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() {
+            boxes_xyxy[[i, 0]] = (boxes[[i, 0]] - boxes[[i, 2]] / 2.0) / ratio;
+            boxes_xyxy[[i, 1]] = (boxes[[i, 1]] - boxes[[i, 3]] / 2.0) / ratio;
+            boxes_xyxy[[i, 2]] = (boxes[[i, 0]] + boxes[[i, 2]] / 2.0) / ratio;
+            boxes_xyxy[[i, 3]] = (boxes[[i, 1]] + boxes[[i, 3]] / 2.0) / ratio;
+        }
+
+        let detections = self.multiclass_nms(&boxes_xyxy, &scores, 0.45, 0.1);
+        let final_results = detections
+            .into_iter()
+            .map(|d| DetectionResult {
+                x1: (d[0] as i32).max(0).min(orig_w 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),
+                y2: (d[3] as i32).max(0).min(orig_h as i32),
+                score: d[4],
+                class_id: d[5] as u32,
+            })
+            .collect();
+        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

@@ -0,0 +1,48 @@
+use anyhow::Context;
+use std::io::Cursor;
+use tract_onnx::onnx;
+use tract_onnx::prelude::*;
+
+/// 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 model_for_path<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 })
+    }
+    /// 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

@@ -0,0 +1,3 @@
+pub mod loader;
+pub mod ocr;
+pub mod det;

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

@@ -0,0 +1,287 @@
+use crate::utils::cv_ops::rgb_to_opencv_hsv;
+use anyhow::anyhow;
+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],
+) -> 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();
+    let mut raw_pixels = rgb_img.into_raw();
+
+    // 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(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<(), String> {
+        // 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());
+        }
+
+        // 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());
+        }
+
+        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<(), String> {
+        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 = String;
+    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)),
+        }
+    }
+}
+
+// =====================================================================
+// 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<(), String> {
+        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>>, String> {
+        // 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<(), String> {
+        // 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<(), String> {
+        // 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 ),+ ]
+        }
+    };
+}

src/models/ocr/executor.rs

@@ -0,0 +1,478 @@
+use crate::models::ocr::metadata::Resize;
+
+use crate::models::ocr::session::OcrSession;
+use crate::models::ocr::color_filter::{HsvRange, apply_to_image};
+use crate::utils::image_io::png_rgba_white_preprocess;
+use crate::utils::image_processor::{convert_to_grayscale, resize_image};
+use anyhow::Result;
+use image::DynamicImage;
+use serde::Serialize;
+use std::borrow::Cow;
+use std::fmt;
+use tract_onnx::prelude::tract_ndarray::{ArrayView2, Ix2, s};
+use tract_onnx::prelude::{DatumType, Tensor, tract_ndarray};
+#[derive(Debug, Clone, Serialize)]
+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) session: &'a OcrSession,
+    pub(crate) png_fix: bool,
+    pub(crate) probability: bool,
+    /// 颜色过滤：保留的颜色列表
+    pub(crate) final_color_ranges: Result<Option<Vec<HsvRange>>, String>,
+
+    /// 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(session: &'a OcrSession) -> Self {
+        Ocr {
+            session,
+            png_fix: false, // 默认值
+            probability: false,
+            final_color_ranges: Ok(None),
+            final_charset_indices: None,
+        }
+    }
+}
+impl<'a> Ocr<'a> {
+    pub fn predict(&self, image: &DynamicImage) -> anyhow::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
+                ));
+            }
+            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.session.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);
+
+        Ok(ocr_output)
+    }
+    /// 对应 Python 的 _preprocess_image
+    /// 负责：透明背景修复 -> 灰度化 -> 按比例 Resize -> 归一化 -> 4维张量转换
+    fn preprocess_image(&self, img: &DynamicImage) -> anyhow::Result<Tensor> {
+        // 1. 获取模型元数据配置
+        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)
+        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 tensor = 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 = tract_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)
+                    },
+                );
+                Tensor::from(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 = tract_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)
+            }
+
+            _ => return Err(anyhow::anyhow!("不支持的通道数配置: {}", meta.channel)),
+        };
+
+        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)
+    }
+}
+impl<'a> Ocr<'a> {
+    fn is_valid_indices(&self, idx: usize) -> bool {
+        if idx >= self.session.model_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.session.model_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.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
+    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 = tract_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.session.model_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 {
+                eprintln!("警告: 预测索引 {} 超出字符集范围", u_idx);
+            }
+        }
+        res
+    }
+}

src/models/ocr/metadata.rs

@@ -0,0 +1,200 @@
+use anyhow::{anyhow, Result};
+use serde::Deserialize;
+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(),)
+    }
+}
+
+
+
+// =====================================================================
+// 1. 辅助定义的枚举与结构体
+// =====================================================================
+
+#[derive(Debug, Clone, Copy, Deserialize)]
+#[serde(rename_all = "snake_case")] // 支持 json 中写 "zero_to_one" 或 "minus_one_to_one"
+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),
+}
+
+/// 仅用于反序列化 JSON 的中间临时结构体（DTO）
+#[derive(Deserialize)]
+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: Normalization,
+}
+fn default_normalization() -> Normalization {
+    Normalization::ZeroToOne
+}
+
+#[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 {
+    // --- 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,
+        }
+    }
+    pub fn from_json_str(json_str: &str) -> Result<Self> {
+        let dto: ModelMetadataDto = serde_json::from_str(json_str)
+            .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,
+        })
+    }
+    /// 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/cv_ops.rs

@@ -0,0 +1,161 @@
+use image::{ImageBuffer, Luma};
+use std::cmp::{max, min};
+use tract_onnx::prelude::tract_ndarray::{Array2, Array3, ArrayView2, ArrayView3, azip};
+
+/// 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(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)
+}
+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
+}
+// =====================================================================
+// 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 mut 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)
+}

src/utils/image_io.rs

@@ -0,0 +1,264 @@
+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

@@ -0,0 +1,40 @@
+use image::{DynamicImage, GrayImage, imageops::FilterType, Rgb, ImageBuffer};
+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)
+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)
+}
+// 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

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

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",
@@ -514,3 +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 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,
+//     })
+// }

tests/ocr_test.rs

@@ -1,16 +1,183 @@
-use ddddocr_rs::DdddOcr; // 假设你的包名是这个
+use ddddocr_rs::models::det::DetectionResult;
+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;
+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
+    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(())
+}
 
 #[test]
 fn test_full_classification() {
     // 1. 初始化模型
-    let ocr = DdddOcr::new("model/common.onnx").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/code3.png").expect("测试图片不存在");
+    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 = 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))?;
+
+    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, "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().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
+    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!("{}", 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().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
+    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);
+}