feat: 优化 slide.rs

src/models/slide.rs

@@ -1,15 +1,16 @@
-use crate::utils::cv_ops::{min_max_loc, rgb_to_gray, ndarray_to_luma8, abs_diff};
+use crate::utils::cv_ops;
+use crate::utils::cv_ops::{abs_diff, min_max_loc, ndarray_to_luma8, rgb_to_gray};
 use crate::utils::image_io::image_to_ndarray;
 use anyhow::{Context, Result, anyhow};
 use image::{DynamicImage, GenericImageView};
 use image::{ImageBuffer, Luma};
+use imageproc::contrast::{ThresholdType, threshold};
 use imageproc::distance_transform::Norm;
 use imageproc::edges::canny;
 use imageproc::morphology::{close, open};
 use imageproc::region_labelling::{Connectivity, connected_components};
 use imageproc::template_matching::{MatchTemplateMethod, match_template};
 use std::cmp::{max, min};
-use imageproc::contrast::{threshold, ThresholdType};
 use tract_onnx::prelude::tract_ndarray::{Array2, Array3, ArrayView2, ArrayView3, Axis, s};
 
 pub struct SlideResult {
@@ -78,17 +79,12 @@ impl Slide {
         // 1. 计算差异数组 (复用 cv2::absdiff)
         let diff_array = abs_diff(&target, &background);
 
-        // 2. 转换为灰度数组 (复用你的 cv2::rgb_to_gray)
+        // 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 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
@@ -98,65 +94,32 @@ impl Slide {
         let closed = close(&binary, norm, radius);
         let cleaned = open(&closed, norm, radius);
 
-        // 4. 寻找最大连通区域 (对应 findContours + max area)
         // 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）
+        // // 统计每个标签出现的频率（即面积）
-        let mut max_label = 0;
+        // 4. 寻找最大连通区域 (对应 findContours + max area)
-        let mut max_area = 0;
+        if let Some(max_label) = cv_ops::find_contours_and_max(&labelled) {
-        let mut areas = std::collections::HashMap::new();
+            // 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);
 
-        for pixel in labelled.pixels() {
+            Ok(SlideResult {
-            let label = pixel.0[0];
+                target: [center_x, center_y],
-            if label == 0 {
+                target_x: center_x,
-                continue;
+                target_y: center_y,
-            } // 跳过背景
+                confidence: 1.0, // Comparison 模式下通常认为找到即为 1.0
-            let count = areas.entry(label).or_insert(0);
+            })
-            *count += 1;
+        } else {
-            if *count > max_area {
+            Ok(SlideResult {
-                max_area = *count;
-                max_label = label;
-            }
-        }
-
-        if max_label == 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)
-        let mut min_x = w as u32;
-        let mut max_x = 0;
-        let mut min_y = h as u32;
-        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);
-            }
-        }
-
-        // 6. 计算中心点
-        let rect_w = max_x - min_x;
-        let rect_h = max_y - min_y;
-        let center_x = (min_x + rect_w / 2) as i32;
-        let center_y = (min_y + rect_h / 2) as i32;
-
-        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
-        })
     }
 
     /// 对应 Python: _perform_slide_match
@@ -210,8 +173,8 @@ impl Slide {
 
         // 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);
+        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);
@@ -256,8 +219,7 @@ impl Slide {
         // 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 = max_loc.0 as i32 + (tw as i32 / 2);
+        let (center_x, center_y) = cv_ops::calculate_center(max_loc, tw as usize, th as usize);
-        let center_y = max_loc.1 as i32 + (th as i32 / 2);
 
         // 打印调试信息，方便与 Python 对比
         // println!("Edge Match: max_val: {}, max_loc: {:?}", max_val, max_loc);
@@ -271,6 +233,4 @@ impl Slide {
             confidence: max_val as f64,
         })
     }
-
-
 }

src/utils/cv_ops.rs

@@ -1,3 +1,4 @@
+use std::cmp::{max, min};
 use image::{ImageBuffer, Luma};
 use tract_onnx::prelude::tract_ndarray::{azip, Array2, Array3, ArrayView2, ArrayView3};
 
@@ -45,6 +46,55 @@ pub fn min_max_loc(result_map: &ImageBuffer<Luma<f32>, Vec<f32>>) -> (f32, (u32,
     }
     (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(max_loc: (u32, u32), tw: usize, th: usize) -> (i32, i32) {
+    let center_x = max_loc.0 as i32 + (tw as i32 / 2);
+    let center_y = max_loc.1 as i32 + (th 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);

tests/ocr_test.rs

@@ -1,23 +1,24 @@
+use ddddocr_rs::models::slide::Slide;
+use ddddocr_rs::{DdddOcr, DdddOcrBuilder}; // 假设你的包名是这个
+use image::Rgb;
 use std::fs;
 use std::path::Path;
-use image::Rgb;
-use ddddocr_rs::{DdddOcr, DdddOcrBuilder}; // 假设你的包名是这个
-use ddddocr_rs::models::slide::Slide;
 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)
+    image::open(path_ref).map_err(|e| {
-        .map_err(|e| {
+        // 3. 此时 path_ref 依然有效，可以安全地在闭包中使用
-            // 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)
-            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( image_bytes: &[u8], bboxes: &Vec<Vec<i32>>, output_path: &str) -> anyhow::Result<()> {
+fn save_debug_image(
-
+    image_bytes: &[u8],
-
+    bboxes: &Vec<Vec<i32>>,
+    output_path: &str,
+) -> anyhow::Result<()> {
     let dynamic_img = image::load_from_memory(image_bytes)?;
     let mut img = dynamic_img.to_rgb8();
     let (width, height) = img.dimensions();
@@ -35,16 +36,24 @@ fn save_debug_image( image_bytes: &[u8], bboxes: &Vec<Vec<i32>>, output_path: &s
             img.put_pixel(x, y1, red);
             img.put_pixel(x, y2, red);
             // 如果要加粗，多画一行
-            if y1 + 1 < height { img.put_pixel(x, y1 + 1, red); }
+            if y1 + 1 < height {
-            if y2.saturating_sub(1) > 0 { img.put_pixel(x, y2 - 1, red); }
+                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);
             // 如果要加粗，多画一列
-            if x1 + 1 < width { img.put_pixel(x1 + 1, y, red); }
+            if x1 + 1 < width {
-            if x2.saturating_sub(1) > 0 { img.put_pixel(x2 - 1, y, red); }
+                img.put_pixel(x1 + 1, y, red);
+            }
+            if x2.saturating_sub(1) > 0 {
+                img.put_pixel(x2 - 1, y, red);
+            }
         }
     }
 
@@ -66,43 +75,44 @@ fn test_full_classification() {
     assert!(!result.is_empty());
 }
 #[test]
-fn test_det_load()->anyhow::Result<()>{
+fn test_det_load() -> anyhow::Result<()> {
     let det = DdddOcrBuilder::new().det().build()?;
     let image_path = "samples/det1.png";
-    let image_bytes = fs::read(image_path)
+    let image_bytes =
-        .map_err(|e| anyhow::anyhow!("无法读取图片 {}: {}", image_path, e))?;
+        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());
-    let bboxes =det.detection(&image_bytes)?;
+    let bboxes = det.detection(&image_bytes)?;
-    println!(":?{}",det);
+    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 {
         save_debug_image(&image_bytes, &bboxes, "samples/result.jpg")?;
         for (i, bbox) in bboxes.iter().enumerate() {
-            println!("目标 [{}]: x1={}, y1={}, x2={}, y2={}", i, bbox[0], bbox[1], bbox[2], bbox[3]);
+            println!(
+                "目标 [{}]: x1={}, y1={}, x2={}, y2={}",
+                i, bbox[0], bbox[1], bbox[2], bbox[3]
+            );
         }
     }
     Ok(())
 }
 
-
 #[test]
 fn test_real_slide_match() {
     let engine = Slide::new();
 
     // 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")
+    let target_img = load_image("samples/hua.png").expect("请确保 samples/hua.png 存在");
-        .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");
-    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)
+    let result = engine
+        .slide_match(&target_img, &bg_img, false)
         .expect("Slide match 执行失败");
     let duration = start.elapsed();
 
@@ -126,15 +136,14 @@ fn test_real_slide_comparison() {
 
     // 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")
+    let target_img = load_image("samples/ken.jpg").expect("请确保 samples/ken.jpg 存在");
-        .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");
-    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)
+    let result = engine
+        .slide_comparison(&target_img, &bg_img)
         .expect("Slide match 执行失败");
     let duration = start.elapsed();