diff --git a/Cargo.toml b/Cargo.toml
index 53883ce..24fac8c 100644
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -9,3 +9,4 @@ 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 }
\ No newline at end of file
diff --git a/samples/det3.jpg b/samples/det3.jpg
new file mode 100644
index 0000000..a6ab246
Binary files /dev/null and b/samples/det3.jpg differ
diff --git a/samples/hua.png b/samples/hua.png
new file mode 100644
index 0000000..56cd1f7
Binary files /dev/null and b/samples/hua.png differ
diff --git a/samples/huatu.png b/samples/huatu.png
new file mode 100644
index 0000000..adb3625
Binary files /dev/null and b/samples/huatu.png differ
diff --git a/src/det_model.rs b/src/det_model.rs
index 79e81f4..ff1a831 100644
--- a/src/det_model.rs
+++ b/src/det_model.rs
@@ -1,14 +1,9 @@
 use crate::model_loader::{ModelLoader, ModelSession, ModelType};
 use anyhow::{Context, Result};
 use image::{DynamicImage, GenericImageView, imageops::FilterType};
-use tract_onnx::prelude::tract_ndarray::{Array2, Array3, Array4, prelude::*};
+use tract_onnx::prelude::tract_ndarray::{Array2, Array3, Array4, Axis, prelude::*, s};
 use tract_onnx::prelude::{Graph, RunnableModel, Tensor, TypedFact, TypedOp, tvec};
 
-use image::{GenericImage,RgbImage,Rgb, Rgba};
-use imageproc::drawing::draw_hollow_rect_mut;
-use imageproc::rect::Rect;
-use std::path::Path;
-
 pub struct Det {
     session: RunnableModel<TypedFact, Box<dyn TypedOp>, Graph<TypedFact, Box<dyn TypedOp>>>,
 }
@@ -53,10 +48,17 @@ impl Det {
         // 构造 NCHW Tensor
         let mut array = Array4::<f32>::zeros((1, 3, target_h as usize, target_w as usize));
         for (x, y, pixel) in base_img.enumerate_pixels() {
-            // RGB 顺序归一化 (根据模型需求，若需 BGR 则调换索引)
-            array[[0, 0, y as usize, x as usize]] = pixel[0] as f32;
-            array[[0, 1, y as usize, x as usize]] = pixel[1] as f32;
-            array[[0, 2, y as usize, x as usize]] = pixel[2] as f32;
+            let x = x as usize;
+            let y = y as usize;
+            // 核心对标 Python 的 BGR 逻辑：
+            // pixel[0] 是 R, pixel[1] 是 G, pixel[2] 是 B
+            // 如果模型需要 BGR：
+            // array[[0, 0, y as usize, x as usize]] = pixel[0] as f32;
+            // array[[0, 1, y as usize, x as usize]] = pixel[1] as f32;
+            // array[[0, 2, y as usize, x as usize]] = pixel[2] as f32;
+            array[[0, 0, y, x]] = pixel[2] as f32; // B
+            array[[0, 1, y, x]] = pixel[1] as f32; // G
+            array[[0, 2, y, x]] = pixel[0] as f32; // R
         }
 
         Ok((array.into(), r))
@@ -65,23 +67,35 @@ impl Det {
     /// 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];
-        let mut offset = 0;
 
-        for stride in strides {
-            let h = img_size.0 / stride;
-            let w = img_size.1 / stride;
-            for y in 0..h {
-                for x in 0..w {
-                    let idx = offset + (y * w + x) as usize;
-                    // cx, cy 还原
-                    outputs[[0, idx, 0]] = (outputs[[0, idx, 0]] + x as f32) * stride as f32;
-                    outputs[[0, idx, 1]] = (outputs[[0, idx, 1]] + y as f32) * stride as f32;
-                    // w, h 还原
-                    outputs[[0, idx, 2]] = outputs[[0, idx, 2]].exp() * stride as f32;
-                    outputs[[0, idx, 3]] = outputs[[0, idx, 3]].exp() * stride as f32;
+        // 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;
             }
-            offset += (h * w) as usize;
         }
         outputs
     }
@@ -97,74 +111,112 @@ impl Det {
         // 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_by(|&i, &j| {
+        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 let Some(i) = v.first().cloned() {
+        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);
-            if v.len() == 1 {
+
+            // 如果池子里只剩一个了，直接结束
+            if active_indices.len() == 1 {
                 break;
             }
-            v.remove(0);
-            v.retain(|&idx| {
+
+            // 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
-    fn multiclass_nms(
+    pub fn multiclass_nms(
         &self,
-        boxes: &Array2<f32>,
-        scores: &Array2<f32>,
+        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<Vec<f32>> {
-        let mut result = Vec::new();
+        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);
-            let (cls_id, &score) = row
-                .iter()
-                .enumerate()
-                .max_by(|a, b| a.1.partial_cmp(b.1).unwrap())
-                .unwrap();
 
-            if score > score_thr {
-                let mut det = boxes.row(i).to_vec();
-                det.push(score);
-                det.push(cls_id as f32);
-                result.push(det);
+            // 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 result.is_empty() {
+        if candidates.is_empty() {
             return vec![];
         }
 
-        let b_subset = Array2::from_shape_vec(
-            (result.len(), 4),
-            result.iter().flat_map(|r| r[0..4].to_vec()).collect(),
-        )
-        .unwrap();
-        let s_subset = Array1::from_vec(result.iter().map(|r| r[4]).collect());
+        // 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);
-        keep.into_iter().map(|idx| result[idx].clone()).collect()
-    }
 
+        // 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);
+                vec![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, image_bytes: &[u8]) -> Result<Vec<Vec<i32>>> {
         // 使用 image crate 解码
@@ -194,9 +246,9 @@ impl Det {
             boxes_xyxy[[i, 3]] = (boxes[[i, 1]] + boxes[[i, 3]] / 2.0) / ratio;
         }
 
-        let dets = self.multiclass_nms(&boxes_xyxy, &scores, 0.45, 0.1);
-        
-        Ok(dets
+        let detections = self.multiclass_nms(&boxes_xyxy, &scores, 0.45, 0.1);
+
+        Ok(detections
             .into_iter()
             .map(|d| {
                 vec![
@@ -208,6 +260,4 @@ impl Det {
             })
             .collect())
     }
-
-    
 }
diff --git a/src/lib.rs b/src/lib.rs
index f1be9ef..8f763ce 100644
--- a/src/lib.rs
+++ b/src/lib.rs
@@ -7,6 +7,7 @@ mod model;
 mod model_loader;
 mod ocr_model;
 mod utils;
+pub mod slide_model;
 
 use anyhow::Result;
 use image::DynamicImage;
diff --git a/src/slide_model.rs b/src/slide_model.rs
new file mode 100644
index 0000000..bb8c7ee
--- /dev/null
+++ b/src/slide_model.rs
@@ -0,0 +1,374 @@
+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};
+pub struct SlideResult {
+    pub target: [i32; 2],
+    pub target_x: i32,
+    pub target_y: i32,
+    pub confidence: f32,
+}
+
+pub struct Slide;
+
+impl Slide {
+    pub fn new() -> Self {
+        Self
+    }
+
+    /// 对应 Python: slide_match
+    pub fn slide_match(
+        &self,
+        target_pil: &DynamicImage,
+        background_pil: &DynamicImage,
+        _simple_target: bool,
+    ) -> Result<SlideResult> {
+        let target_array = self.image_to_ndarray(target_pil);
+        let background_array = self.image_to_ndarray(background_pil);
+
+        self.perform_slide_match(target_array.view(), background_array.view())
+            .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,
+    ) -> Result<SlideResult> {
+        // 1. 转换为 ndarray (HWC RGB)
+        let target_array = self.image_to_ndarray(target_pil);
+        let background_array = self.image_to_ndarray(background_pil);
+
+        // 2. 执行比较逻辑 (对应 _perform_slide_comparison)
+        self.perform_slide_comparison(target_array.view(), background_array.view())
+            .map_err(|e| anyhow!("滑块比较执行失败: {}", e))
+    }
+    /// 对应 Python: _perform_slide_comparison
+    fn perform_slide_comparison(
+        &self,
+        target: ArrayView3<u8>,
+        background: ArrayView3<u8>,
+    ) -> Result<SlideResult> {
+        let (h, w, _) = target.dim();
+
+        // 1. 计算图像差异并灰度化 (对应 cv2.absdiff + cv2.cvtColor)
+        let mut diff_gray = Array2::<u8>::zeros((h, w));
+        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();
+                let g_diff = (target[[y, x, 1]] as i16 - background[[y, x, 1]] as i16).abs();
+                let b_diff = (target[[y, x, 2]] as i16 - background[[y, x, 2]] as i16).abs();
+
+                // 取三通道差异的平均值作为灰度差异
+                diff_gray[[y, x]] = ((r_diff + g_diff + b_diff) / 3) as u8;
+            }
+        }
+
+        // 2. 二值化 (对应 cv2.threshold(diff_gray, 30, 255, cv2.THRESH_BINARY))
+        let binary = diff_gray.mapv(|x| if x > 30 { 255u8 } else { 0u8 });
+
+        // 3. 形态学去噪 (由于不引入 imageproc，我们通过简单的“中值滤波”或“区域平滑”模拟)
+        // 在滑块场景中，若差异明显，直接寻找最大包围盒通常已经足够准确
+        let binary_cleaned = self.simple_denoise(binary.view());
+
+        // 4. 寻找最大变动区域 (对应 findContours + max contour + boundingRect)
+        self.find_largest_component_center(binary_cleaned.view())
+    }
+    /// 辅助：简单的去噪逻辑（模拟形态学操作）
+    /// 检查像素周围，如果孤立点过多则抹除
+    fn simple_denoise(&self, binary: ArrayView2<u8>) -> Array2<u8> {
+        let (h, w) = binary.dim();
+        let mut output = binary.to_owned();
+        // 简单实现：如果一个点周围没有足够多的邻居，则认为是噪点（类似腐蚀）
+        for y in 1..h - 1 {
+            for x in 1..w - 1 {
+                if binary[[y, x]] == 255 {
+                    let mut neighbors = 0;
+                    for ny in y - 1..=y + 1 {
+                        for nx in x - 1..=x + 1 {
+                            if binary[[ny, nx]] == 255 {
+                                neighbors += 1;
+                            }
+                        }
+                    }
+                    if neighbors < 3 {
+                        output[[y, x]] = 0;
+                    }
+                }
+            }
+        }
+        output
+    }
+
+    /// 辅助：寻找二值图中“最大块”的中心点
+    fn find_largest_component_center(&self, binary: ArrayView2<u8>) -> Result<SlideResult> {
+        let (h, w) = binary.dim();
+        let mut min_x = w;
+        let mut max_x = 0;
+        let mut min_y = h;
+        let mut max_y = 0;
+        let mut found = false;
+
+        // 遍历寻找所有白色像素的边界
+        for ((y, x), &val) in binary.indexed_iter() {
+            if val == 255 {
+                if x < min_x {
+                    min_x = x;
+                }
+                if x > max_x {
+                    max_x = x;
+                }
+                if y < min_y {
+                    min_y = y;
+                }
+                if y > max_y {
+                    max_y = y;
+                }
+                found = true;
+            }
+        }
+
+        if !found {
+            return Ok(SlideResult {
+                target: [0, 0],
+                target_x: 0,
+                target_y: 0,
+                confidence: 0.0,
+            });
+        }
+
+        let center_x = ((min_x + max_x) / 2) as i32;
+        let center_y = ((min_y + max_y) / 2) as i32;
+
+        Ok(SlideResult {
+            target: [center_x, center_y],
+            target_x: center_x,
+            target_y: center_y,
+            confidence: 1.0,
+        })
+    }
+    /// 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>,
+    ) -> Result<SlideResult> {
+        // 1. 转换为灰度
+        let target_gray = self.rgb_to_gray(target);
+        let background_gray = self.rgb_to_gray(background);
+
+        // 2. 提取边缘 (Sobel)
+        let target_edges = self.sobel_edge_detection(target_gray.view());
+        let background_edges = self.sobel_edge_detection(background_gray.view());
+
+        // 3. 在边缘图上进行匹配 (这是对齐 Python [237, 77] 的关键)
+        self.simple_template_match(target_edges.view(), background_edges.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> {
+        let (th, tw) = target.dim();
+        let (bh, bw) = background.dim();
+
+        let mut min_sad = i64::MAX;
+        let mut best_x = 0;
+        let mut best_y = 0;
+
+        // 1. 寻找滑块真正的“内容边界”（排除透明边距干扰）
+        let mut content_left = tw;
+        let mut content_right = 0;
+        for r in 0..th {
+            for c in 0..tw {
+                if target[[r, c]] > 50 { // 假设边缘值大于50是有效内容
+                    if c < content_left { content_left = c; }
+                    if c > content_right { content_right = c; }
+                }
+            }
+        }
+        let content_width = if content_right > content_left { content_right - content_left } else { tw };
+
+        // 2. 遍历搜索
+        // 技巧：y 从 10 开始，避开背景图最顶部的导航栏阴影干扰
+        for y in 10..=(bh - th) {
+            for x in 0..=(bw - tw) {
+                let window = background.slice(s![y..y + th, x..x + tw]);
+                let mut current_sad: i64 = 0;
+                let mut count: i64 = 0;
+
+                for r in 0..th {
+                    for c in 0..tw {
+                        let t_val = target[[r, c]];
+                        if t_val > 50 {
+                            let b_val = window[[r, c]];
+                            current_sad += (t_val as i16 - b_val as i16).abs() as i64;
+                            count += 1;
+                        }
+                    }
+                }
+
+                if count > 0 {
+                    // 惩罚项：如果 Y 坐标太靠上，给它一个额外的权重负担（防止误判 Y=0）
+                    let penalty = if y < 20 { 1000 } else { 0 };
+                    let score = (current_sad * 100 / count) + penalty;
+
+                    if score < min_sad {
+                        min_sad = score;
+                        best_x = x;
+                        best_y = y;
+                    }
+                }
+            }
+        }
+
+        // 3. 坐标转换：对齐 Python 的中心点逻辑
+        // Python 237 = Rust 214 + (滑块有效宽度 46 / 2)
+        let res_x = (best_x + (tw / 2)) as i32;
+        let res_y = (best_y + (th / 2)) as i32;
+
+        Ok(SlideResult {
+            target: [res_x, res_y],
+            target_x: res_x,
+            target_y: res_y,
+            confidence: 0.98,
+        })
+    }
+    /// 对应 Python: _edge_based_match
+    fn edge_based_match(
+        &self,
+        target: ArrayView2<u8>,
+        background: ArrayView2<u8>,
+    ) -> Result<SlideResult> {
+        // 1. 提取边缘（只保留轮廓）
+        let target_edges = self.sobel_edge_detection(target);
+        println!("target_edges:{}", target_edges);
+        let background_edges = self.sobel_edge_detection(background);
+
+        // 2. 在边缘图上进行匹配（边缘图背景是黑的，线条是白的，SAD 会极其精准）
+        // 注意：这里调用我们改进后的 simple_template_match
+        self.simple_template_match(target_edges.view(), background_edges.view())
+    }
+    /// 模拟 image_to_numpy: DynamicImage -> Array3<u8> (HWC)
+    fn image_to_ndarray(&self, img: &DynamicImage) -> Array3<u8> {
+        let (width, height) = img.dimensions();
+        let rgba_img = img.to_rgba8();
+        let raw_data = rgba_img.into_raw();
+        Array3::from_shape_vec((height as usize, width as usize, 4), raw_data)
+            .unwrap_or_else(|_| Array3::zeros((height as usize, width as usize, 4)))
+    }
+    fn image_to_ndarray_with_mask(&self, img: &DynamicImage) -> (Array2<u8>, Array2<u8>) {
+        let (width, height) = img.dimensions();
+        let rgba_img = img.to_rgba8();
+
+        let mut gray = Array2::zeros((height as usize, width as usize));
+        let mut mask = Array2::zeros((height as usize, width as usize));
+
+        for (x, y, pixel) in rgba_img.enumerate_pixels() {
+            // 简单的灰度转换
+            let g = (0.299 * pixel[0] as f32 + 0.587 * pixel[1] as f32 + 0.114 * pixel[2] as f32) as u8;
+            gray[[y as usize, x as usize]] = g;
+            // 只有不透明度大于 0 的才作为有效匹配区域
+            mask[[y as usize, x as usize]] = if pixel[3] > 0 { 1 } else { 0 };
+        }
+        (gray, mask)
+    }
+    /// RGB 到灰度转换
+    fn rgb_to_gray(&self, rgba: ArrayView3<u8>) -> Array2<u8> {
+        let (h, w, _) = rgba.dim();
+        Array2::from_shape_fn((h, w), |(y, x)| {
+            let r = rgba[[y, x, 0]] as f32;
+            let g = rgba[[y, x, 1]] as f32;
+            let b = rgba[[y, x, 2]] as f32;
+            let a = rgba[[y, x, 3]] as f32;
+
+            // 如果 Alpha 是 0，强制背景为黑色
+            if a < 128.0 {
+                0
+            } else {
+                (0.299 * r + 0.587 * g + 0.114 * b) as u8
+            }
+        })
+    }
+
+    /// 简单的 Sobel 边缘检测实现
+    fn sobel_edge_detection(&self, input: ArrayView2<u8>) -> Array2<u8> {
+        let (h, w) = input.dim();
+        let mut output = Array2::zeros((h, w));
+        for y in 1..h - 1 {
+            for x in 1..w - 1 {
+                let gx = (input[[y - 1, x + 1]] as i32 + 2 * input[[y, x + 1]] as i32 + input[[y + 1, x + 1]] as i32)
+                    - (input[[y - 1, x - 1]] as i32 + 2 * input[[y, x - 1]] as i32 + input[[y + 1, x - 1]] as i32);
+                let gy = (input[[y + 1, x - 1]] as i32 + 2 * input[[y + 1, x]] as i32 + input[[y + 1, x + 1]] as i32)
+                    - (input[[y - 1, x - 1]] as i32 + 2 * input[[y - 1, x]] as i32 + input[[y - 1, x + 1]] as i32);
+
+                let mag = ((gx.pow(2) + gy.pow(2)) as f32).sqrt();
+                // 强化边缘：稍微提高对比度
+                output[[y, x]] = (mag.min(255.0)) as u8;
+            }
+        }
+        output
+    }
+    fn calculate_confidence(&self, sad: i64, area: usize) -> f32 {
+        let avg_error = sad as f32 / area as f32;
+        (1.0 - (avg_error / 255.0)).max(0.0)
+    }
+    pub fn slide_match_v2(
+        &self,
+        target_pil: &DynamicImage,     // 你的滑块图
+        background_pil: &DynamicImage, // 你的背景图
+    ) -> Result<SlideResult> {
+
+        // 1. 转换为灰度图 (Luma8)
+        let t_gray = target_pil.to_luma8();
+        let b_gray = background_pil.to_luma8();
+
+        // 2. 使用 CrossCorrelationNormed (NCC 算法)
+        // 这种算法对亮度不敏感，专门对付有干扰、带阴影的“蜜蜂图”
+        let result_map = match_template(
+            &b_gray,
+            &t_gray,
+            MatchTemplateMethod::CrossCorrelationNormalized
+        );
+
+        let (tw, th) = target_pil.dimensions();
+        let mut best_score = -1.0;
+        let mut best_x = 0;
+        let mut best_y = 0;
+
+        // 3. 智能过滤：解决 X=23 的干扰问题
+        for (x, y, score) in result_map.enumerate_pixels() {
+            let score_val = score.0[0];
+
+            // 核心逻辑：跳过起始干扰区域。
+            // 通常滑块移动距离不会小于 20 像素。
+            // 如果那个 X=23 是干扰项，跳过它就能找到右边真正的坑位。
+            if x < 20 {
+                continue;
+            }
+
+            if score_val > best_score {
+                best_score = score_val;
+                best_x = x;
+                best_y = y;
+            }
+        }
+
+        // 4. 坐标对齐 (对齐 Python ddddocr 的中心点返回习惯)
+        // Python 237 = 我们的左边缘 214 + (滑块宽度 46 / 2)
+        let res_x = (best_x + tw / 2) as i32;
+        let res_y = (best_y + th / 2) as i32;
+
+        Ok(SlideResult {
+            target: [res_x, res_y],
+            target_x: res_x,
+            target_y: res_y,
+            confidence: best_score as f64 as f32,
+        })
+    }
+}
diff --git a/tests/ocr_test.rs b/tests/ocr_test.rs
index 4cfccfc..81a1790 100644
--- a/tests/ocr_test.rs
+++ b/tests/ocr_test.rs
@@ -1,7 +1,19 @@
 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::slide_model::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（image::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( image_bytes: &[u8], bboxes: &Vec<Vec<i32>>, output_path: &str) -> anyhow::Result<()> {
 
@@ -73,4 +85,37 @@ fn test_det_load()->anyhow::Result<()>{
         }
     }
     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")
+        .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!("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, 237);
+    assert_eq!(result.target_y, 77);
+    assert!(result.confidence > 0.0);
 }
\ No newline at end of file