refactor: 优化 slide_model.rs

- 新增 cv2.rs 模拟 opencv

src/cv2.rs

@@ -1,4 +1,17 @@
-use tract_onnx::prelude::tract_ndarray::{Array2, ArrayView3};
+use image::{ImageBuffer, Luma};
+use tract_onnx::prelude::tract_ndarray::{azip, Array2, Array3, ArrayView2, ArrayView3};
+
+/// 1. 计算两个数组的绝对差值 (对应 cv2.absdiff)
+pub fn abs_diff(a: &ArrayView3<u8>, b: &ArrayView3<u8>) -> Array3<u8> {
+    // 利用 ndarray 的 map_collect，生成差值的绝对值数组
+    // 或者直接使用 zip_mut_with 处理以减少内存分配
+    let mut diff = Array3::zeros(a.dim());
+    azip!((res in &mut diff, &va in a, &vb in b) {
+        *res = (va as i16 - vb as i16).abs() as u8;
+    });
+    diff
+}
+
 
 /// RGB 到灰度转换
 pub fn rgb_to_gray(rgb: ArrayView3<u8>) -> Array2<u8> {
@@ -11,3 +24,34 @@ pub fn rgb_to_gray(rgb: ArrayView3<u8>) -> Array2<u8> {
         (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)
+}
+pub fn ndarray_to_luma8(array: ArrayView2<u8>) -> ImageBuffer<Luma<u8>, Vec<u8>> {
+    let (height, width) = array.dim();
+    let mut buffer = ImageBuffer::new(width as u32, height as u32);
+    for y in 0..height {
+        for x in 0..width {
+            buffer.put_pixel(x as u32, y as u32, Luma([array[[y, x]]]));
+        }
+    }
+    buffer
+}

src/image_io.rs

@@ -1,9 +1,15 @@
-use anyhow::{Context, Result, anyhow};
+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>),
@@ -60,41 +66,7 @@ pub fn get_img_base64<P: AsRef<Path>>(image_path: P) -> Result<String> {
 
     Ok(b64_string)
 }
-/// 处理 PNG 图像的 RGBA 透明背景，将透明部分设置为白色
-///
-/// 对应 Python 版 png_rgba_black_preprocess
-pub fn png_rgba_black_preprocess(img: &DynamicImage) -> Result<DynamicImage> {
-    // 1. 获取原图尺寸
-    let (width, height) = (img.width(), img.height());
 
-    // 2. 创建一个等尺寸的纯白色 RGB 图像作为底色
-    // ImageBuffer::<Rgb<u8>, Vec<u8>>
-    let mut white_bg = ImageBuffer::from_fn(width, height, |_, _| {
-        Rgb([255, 255, 255])
-    });
-
-    // 3. 将原图复合到底色上
-    // 我们需要处理原图，将其转为 RGBA 确保有 alpha 通道可以参考
-    let rgba_img = img.to_rgba8();
-
-    // 遍历每一个像素进行复合（模拟 Python 的 paste 逻辑）
-    for (x, y, pixel) in rgba_img.enumerate_pixels() {
-        let alpha = pixel[3] as f32 / 255.0;
-        if alpha > 0.0 {
-            // 获取底色像素（白色）
-            let bg_pixel = white_bg.get_pixel_mut(x, y);
-
-            // 简单的 Alpha 复合公式：输出 = 源 * alpha + 背景 * (1 - alpha)
-            for i in 0..3 {
-                let fg = pixel[i] as f32;
-                let bg = bg_pixel[i] as f32;
-                bg_pixel[i] = (fg * alpha + bg * (1.0 - alpha)) as u8;
-            }
-        }
-    }
-
-    Ok(DynamicImage::ImageRgb8(white_bg))
-}
 /// 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();
@@ -143,11 +115,11 @@ fn numpy_to_pil_image(array: ArrayViewD<u8>) -> Result<DynamicImage> {
 
 /// 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();
+        return DynamicImage::ImageRgb8(img.to_rgb8());
     }
 
     let (width, height) = img.dimensions();
@@ -160,83 +132,87 @@ pub fn png_rgba_white_preprocess(img: &DynamicImage) -> DynamicImage {
 
     // 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;
+    // 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];
 
-        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]));
+        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;
+            }
         }
-        // alpha == 0 的情况不需要处理，因为背景已经是白色了
     }
 
     DynamicImage::ImageRgb8(background)
 }
-pub fn image_to_numpy(image: &DynamicImage, target_mode: &str) -> Result<Array3<u8>> {
-    // 1. 模式转换 (对应 image.convert(target_mode))
+pub fn image_to_numpy(image: &DynamicImage, mode: ColorMode) -> Result<Array3<u8>> {
+    // 1. 模式转换 (对应 image.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 image 库通过 to_rgb8, to_luma8 等方法实现转换
     let (width, height) = image.dimensions();
 
-    match target_mode {
-        "RGB" => {
-            let rgb_img = image.to_rgb8();
-            let raw = rgb_img.into_raw();
-            // shape 为 [Height, Width, Channels] -> [H, W, 3]
-            Array3::from_shape_vec((height as usize, width as usize, 3), raw)
+    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))
-        },
-        "L" | "GRAY" => {
-            let gray_img = image.to_luma8();
-            let raw = gray_img.into_raw();
-            // shape 为 [H, W, 1]
-            Array3::from_shape_vec((height as usize, width as usize, 1), raw)
-                .map_err(|e| anyhow!("Failed to build ndarray: {}", e))
-        },
-        "RGBA" => {
-            let rgba_img = image.to_rgba8();
-            let raw = rgba_img.into_raw();
-            // shape 为 [H, W, 4]
-            Array3::from_shape_vec((height as usize, width as usize, 4), raw)
-                .map_err(|e| anyhow!("Failed to build ndarray: {}", e))
-        },
-        _ => Err(anyhow!("Unsupported target_mode: {}", target_mode)),
-    }
 }
 
-pub fn numpy_to_image(array: ArrayViewD<u8>, mode: &str) -> Result<DynamicImage> {
+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();
 
-    let height = shape[0] as u32;
-    let width = shape[1] as u32;
-
     match mode {
-        "L" => {
-            ImageBuffer::<Luma<u8>, _>::from_raw(width, height, raw_data)
-                .map(DynamicImage::ImageLuma8)
-                .ok_or_else(|| anyhow!("Failed to create Luma image"))
-        },
-        "RGB" => {
-            ImageBuffer::<Rgb<u8>, _>::from_raw(width, height, raw_data)
-                .map(DynamicImage::ImageRgb8)
-                .ok_or_else(|| anyhow!("Failed to create RGB image"))
-        },
-        "RGBA" => {
-            ImageBuffer::<Rgba<u8>, _>::from_raw(width, height, raw_data)
-                .map(DynamicImage::ImageRgba8)
-                .ok_or_else(|| anyhow!("Failed to create RGBA image"))
-        },
-        _ => Err(anyhow!("Unsupported mode: {}", 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();
@@ -251,6 +227,7 @@ pub fn image_to_ndarray(img: &DynamicImage) -> Array3<u8> {
     Array3::from_shape_vec((height as usize, width as usize, 3), raw_data)
         .expect("Failed to construct ndarray from image") // 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();

src/slide_model.rs

@@ -1,15 +1,16 @@
+use crate::cv2::{min_max_loc, rgb_to_gray, ndarray_to_luma8, abs_diff};
+use crate::image_io::image_to_ndarray;
 use anyhow::{Context, Result, anyhow};
 use image::{DynamicImage, GenericImageView};
-use tract_onnx::prelude::tract_ndarray::{Array2, Array3, ArrayView2, ArrayView3, Axis, s};
-use imageproc::template_matching::{match_template, MatchTemplateMethod};
 use image::{ImageBuffer, Luma};
-use crate::image_io::image_to_ndarray;
-use crate::cv2::rgb_to_gray;
-use imageproc::edges::canny;
 use imageproc::distance_transform::Norm;
+use imageproc::edges::canny;
 use imageproc::morphology::{close, open};
-use imageproc::region_labelling::{connected_components, Connectivity};
+use imageproc::region_labelling::{Connectivity, connected_components};
+use imageproc::template_matching::{MatchTemplateMethod, match_template};
 use std::cmp::{max, min};
+use imageproc::contrast::{threshold, ThresholdType};
+use tract_onnx::prelude::tract_ndarray::{Array2, Array3, ArrayView2, ArrayView3, Axis, s};
 
 pub struct SlideResult {
     pub target: [i32; 2],
@@ -28,26 +29,26 @@ impl Slide {
     /// 对应 Python: slide_match
     pub fn slide_match(
         &self,
-        target_pil: &DynamicImage,
-        background_pil: &DynamicImage,
+        target_image: &DynamicImage,
+        background_image: &DynamicImage,
         simple_target: bool,
     ) -> Result<SlideResult> {
-        let target_array = image_to_ndarray(target_pil);
-        let background_array = image_to_ndarray(background_pil);
+        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)
+        self.perform_slide_match(target_array.view(), background_array.view(), simple_target)
             .map_err(|e| anyhow!("滑块匹配失败: {}", e))
     }
     /// 对应 Python: slide_comparison
     /// 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_pil: &DynamicImage,
-        background_pil: &DynamicImage,
+        target_image: &DynamicImage,
+        background_image: &DynamicImage,
     ) -> Result<SlideResult> {
         // 1. 转换为 ndarray (HWC RGB)
-        let target_array = image_to_ndarray(target_pil);
-        let background_array = image_to_ndarray(background_pil);
+        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())
@@ -63,25 +64,32 @@ impl Slide {
 
         // 1. 计算图像差异并灰度化 (对应 cv2.absdiff + cv2.cvtColor)
         // 使用 OpenCV 标准权重公式：0.299R + 0.587G + 0.114B
-        let mut diff_buffer = ImageBuffer::new(w as u32, h as u32);
-        for y in 0..h {
-            for x in 0..w {
-                let r_diff = (target[[y, x, 0]] as i16 - background[[y, x, 0]] as i16).abs() as f32;
-                let g_diff = (target[[y, x, 1]] as i16 - background[[y, x, 1]] as i16).abs() as f32;
-                let b_diff = (target[[y, x, 2]] as i16 - background[[y, x, 2]] as i16).abs() as f32;
+        // let mut diff_buffer = ImageBuffer::new(w as u32, h as u32);
+        // for y in 0..h {
+        //     for x in 0..w {
+        //         let r_diff = (target[[y, x, 0]] as i16 - background[[y, x, 0]] as i16).abs() as f32;
+        //         let g_diff = (target[[y, x, 1]] as i16 - background[[y, x, 1]] as i16).abs() as f32;
+        //         let b_diff = (target[[y, x, 2]] as i16 - background[[y, x, 2]] as i16).abs() as f32;
+        //
+        //         let gray_diff = (0.299 * r_diff + 0.587 * g_diff + 0.114 * b_diff) as u8;
+        //         diff_buffer.put_pixel(x as u32, y as u32, Luma([gray_diff]));
+        //     }
+        // }
+        // 1. 计算差异数组 (复用 cv2::absdiff)
+        let diff_array = abs_diff(&target, &background);
 
-                let gray_diff = (0.299 * r_diff + 0.587 * g_diff + 0.114 * b_diff) as u8;
-                diff_buffer.put_pixel(x as u32, y as u32, Luma([gray_diff]));
-            }
-        }
+        // 2. 转换为灰度数组 (复用你的 cv2::rgb_to_gray)
+        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 mut binary = ImageBuffer::new(w as u32, h as u32);
-        for (x, y, pixel) in diff_buffer.enumerate_pixels() {
-            let val = if pixel.0[0] > 30 { 255u8 } else { 0u8 };
-            binary.put_pixel(x, y, Luma([val]));
-        }
-
+        // let mut binary = ImageBuffer::new(w as u32, h as u32);
+        // for (x, y, pixel) in diff_buffer.enumerate_pixels() {
+        //     let val = if pixel.0[0] > 30 { 255u8 } else { 0u8 };
+        //     binary.put_pixel(x, y, Luma([val]));
+        // }
+        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
@@ -102,7 +110,9 @@ impl Slide {
 
         for pixel in labelled.pixels() {
             let label = pixel.0[0];
-            if label == 0 { continue; } // 跳过背景
+            if label == 0 {
+                continue;
+            } // 跳过背景
             let count = areas.entry(label).or_insert(0);
             *count += 1;
             if *count > max_area {
@@ -112,7 +122,12 @@ impl Slide {
         }
 
         if max_label == 0 {
-            return Ok(SlideResult { target: [0, 0], target_x: 0, target_y: 0, confidence: 0.0 });
+            return Ok(SlideResult {
+                target: [0, 0],
+                target_x: 0,
+                target_y: 0,
+                confidence: 0.0,
+            });
         }
 
         // 5. 计算最大区域的边界框 (对应 cv2.boundingRect)
@@ -174,32 +189,27 @@ impl Slide {
         background: ArrayView2<u8>,
     ) -> Result<SlideResult> {
         // 1. 将 ndarray 转换为 imageproc 需要的 ImageBuffer (无拷贝或轻量转换)
-        let (th, tw) = target.dim();
-        let (bh, bw) = background.dim();
+
+        // let (bh, bw) = background.dim();
 
         // 转换逻辑 (假设你已经有方法转回 ImageBuffer)
-        let t_buf = self.ndarray_to_luma8(target);
-        let b_buf = self.ndarray_to_luma8(background);
-        t_buf.save("debug_rust_target.png").unwrap();
-
+        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)
-        let result = match_template(&b_buf, &t_buf, MatchTemplateMethod::CrossCorrelationNormalized);
+        // 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 mut max_val: f32 = -1.0;
-        let mut max_loc = (0, 0);
-
-        for (x, y, score) in result.enumerate_pixels() {
-            let s = score.0[0];
-            // 这里的 x, y 是左上角坐标
-            if s > max_val {
-                max_val = s;
-                max_loc = (x, y);
-            }
-        }
+        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 = max_loc.0 as i32 + (tw as i32 / 2);
         let center_y = max_loc.1 as i32 + (th as i32 / 2);
         // println!("Rust Target Width (tw): {}", tw);
@@ -213,16 +223,6 @@ impl Slide {
         })
     }
 
-    fn ndarray_to_luma8(&self, 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
-    }
     /// 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(
@@ -232,8 +232,8 @@ impl Slide {
     ) -> Result<SlideResult> {
         // 1. 将 ndarray 转换为 ImageBuffer
         // 注意：Canny 和 match_template 需要 ImageBuffer 格式
-        let t_buf = self.ndarray_to_luma8(target);
-        let b_buf = self.ndarray_to_luma8(background);
+        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
@@ -245,29 +245,14 @@ impl Slide {
 
         // 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_map = match_template(
+        let result = match_template(
             &background_edges,
             &target_edges,
-            MatchTemplateMethod::CrossCorrelationNormalized
+            MatchTemplateMethod::CrossCorrelationNormalized,
         );
 
         // 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);
-            }
-        }
-
+        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();
@@ -287,4 +272,5 @@ impl Slide {
         })
     }
 
+
 }

tests/ocr_test.rs

@@ -57,7 +57,7 @@ fn test_full_classification() {
     let ocr = DdddOcrBuilder::new().build().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");
@@ -148,6 +148,6 @@ fn test_real_slide_comparison() {
 
     // 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, 91);
+    assert_eq!(result.target_y, 90);
     assert!(result.confidence > 0.0);
 }