diff --git a/odm_preprocess.py b/odm_preprocess.py
index 6dc68df..3332c91 100644
--- a/odm_preprocess.py
+++ b/odm_preprocess.py
@@ -15,6 +15,7 @@ from filter.gps_filter import GPSFilter
 from utils.grid_divider import GridDivider
 from utils.logger import setup_logger
 from filter.time_group_overlap_filter import TimeGroupOverlapFilter
+from utils.visualizer import FilterVisualizer
 
 
 @dataclass
@@ -52,9 +53,12 @@ class ImagePreprocessor:
     def __init__(self, config: PreprocessConfig):
         self.config = config
         self.logger = setup_logger(config.output_dir)
-        self.gps_points = []
-        self.command_runner = CommandRunner(
-            config.output_dir, mode=config.mode)
+        self.gps_points = None
+        self.command_runner = CommandRunner(config.output_dir, mode=config.mode)
+        self.visualizer = FilterVisualizer(config.output_dir)
+        # 用于存储每个步骤的点数据
+        self.points_history = []
+        self.step_names = []
 
     def extract_gps(self) -> pd.DataFrame:
         """提取GPS数据"""
@@ -62,24 +66,32 @@ class ImagePreprocessor:
         extractor = GPSExtractor(self.config.image_dir)
         self.gps_points = extractor.extract_all_gps()
         self.logger.info(f"成功提取 {len(self.gps_points)} 个GPS点")
+        
+        # 记录初始状态
+        self.points_history.append(self.gps_points.copy())
+        self.step_names.append("Initial")
         return self.gps_points
 
     def cluster(self) -> pd.DataFrame:
-        """使用DBSCAN对GPS点进行聚类，只保留最大的类"""
+        """使用DBSCAN对GPS点进行聚类"""
         self.logger.info("开始聚类")
-        # 创建聚类器并执行聚类
+        previous_points = self.gps_points.copy()
+        
         clusterer = GPSCluster(
             self.gps_points, output_dir=self.config.output_dir,
             eps=self.config.cluster_eps, min_samples=self.config.cluster_min_samples)
-        # 获取主要类别的点
         self.clustered_points = clusterer.fit()
         self.gps_points = clusterer.get_main_cluster(self.clustered_points)
-        # 获取统计信息并记录
-        stats = clusterer.get_cluster_stats(self.clustered_points)
-        self.logger.info(
-            f"聚类完成：主要类别包含 {stats['main_cluster_points']} 个点，"
-            f"噪声点 {stats['noise_points']} 个"
-        )
+        
+        # 可视化聚类结果
+        if self.config.enable_visualization:
+            self.visualizer.visualize_filter_step(
+                self.gps_points, previous_points, "Clustering")
+            
+        # 记录这一步的结果
+        self.points_history.append(self.gps_points.copy())
+        self.step_names.append("Clustering")
+        return self.gps_points
 
     def filter_time_group_overlap(self) -> pd.DataFrame:
         """过滤重叠的时间组"""
@@ -87,6 +99,8 @@ class ImagePreprocessor:
             return self.gps_points
 
         self.logger.info("开始过滤重叠时间组")
+        previous_points = self.gps_points.copy()
+        
         filter = TimeGroupOverlapFilter(
             self.config.image_dir,
             self.config.output_dir,
@@ -97,150 +111,31 @@ class ImagePreprocessor:
             time_threshold=self.config.time_group_interval
         )
 
-        # 更新GPS点数据，移除被删除的图像
-        self.gps_points = self.gps_points[~self.gps_points['file'].isin(
-            deleted_files)]
-        self.logger.info(f"重叠时间组过滤后剩余 {len(self.gps_points)} 个GPS点")
-
+        self.gps_points = self.gps_points[~self.gps_points['file'].isin(deleted_files)]
+        
+        # 可视化过滤结果
+        if self.config.enable_visualization:
+            self.visualizer.visualize_filter_step(
+                self.gps_points, previous_points, "Time Group Overlap")
+            
+        # 记录这一步的结果
+        self.points_history.append(self.gps_points.copy())
+        self.step_names.append("Time Group Overlap")
         return self.gps_points
 
-    # TODO 过滤算法还需要更新
-    def filter_points(self) -> pd.DataFrame:
-        """过滤GPS点"""
-        if not self.config.enable_filter:
-            return self.gps_points
-
-        self.logger.info("开始过滤GPS点")
-        filter = GPSFilter(self.config.output_dir)
-
-        self.logger.info(
-            f"开始过滤孤立点(距离阈值: {self.config.filter_distance_threshold}, 最小邻居数: {self.config.filter_min_neighbors})"
-        )
-        self.gps_points = filter.filter_isolated_points(
-            self.gps_points,
-            self.config.filter_distance_threshold,
-            self.config.filter_min_neighbors,
-        )
-        self.logger.info(f"孤立点过滤后剩余 {len(self.gps_points)} 个GPS点")
-
-        self.logger.info(
-            f"开始过滤密集点(网格大小: {self.config.filter_grid_size}, 距离阈值: {self.config.filter_dense_distance_threshold})"
-        )
-        self.gps_points = filter.filter_dense_points(
-            self.gps_points,
-            grid_size=self.config.filter_grid_size,
-            distance_threshold=self.config.filter_dense_distance_threshold,
-            time_threshold=self.config.filter_time_threshold,
-        )
-        self.logger.info(f"密集点过滤后剩余 {len(self.gps_points)} 个GPS点")
-        return self.gps_points
-
-    def divide_grids(self) -> Dict[int, pd.DataFrame]:
-        """划分网格"""
-        if not self.config.enable_grid_division:
-            return {0: self.gps_points}  # 不划分网格时，所有点放在一个网格中
-
-        self.logger.info(f"开始划分网格 (重叠率: {self.config.grid_overlap})")
-        grid_divider = GridDivider(overlap=self.config.grid_overlap)
-        grids = grid_divider.divide_grids(
-            self.gps_points, grid_size=self.config.grid_size
-        )
-        grid_points = grid_divider.assign_to_grids(self.gps_points, grids)
-        self.logger.info(f"成功划分为 {len(grid_points)} 个网格")
-        return grid_points
-
-    def copy_images(self, grid_points: Dict[int, pd.DataFrame]):
-        """复制图像到目标文件夹"""
-        if not self.config.enable_copy_images:
-            return
-
-        self.logger.info("开始复制图像文件")
-
-        for grid_idx, points in grid_points.items():
-            if self.config.enable_grid_division:
-                output_dir = os.path.join(
-                    self.config.output_dir, f"grid_{grid_idx + 1}", "project", "images"
-                )
-            else:
-                output_dir = os.path.join(
-                    self.config.output_dir, "project", "images")
-
-            os.makedirs(output_dir, exist_ok=True)
-
-            for point in tqdm(points, desc=f"复制网格 {grid_idx + 1} 的图像"):
-                src = os.path.join(self.config.image_dir, point["file"])
-                dst = os.path.join(output_dir, point["file"])
-                shutil.copy(src, dst)
-            self.logger.info(f"网格 {grid_idx + 1} 包含 {len(points)} 张图像")
-
-    def visualize_results(self):
-        """可视化处理结果"""
-        if not self.config.enable_visualization:
-            return
-
-        self.logger.info("开始生成可视化结果")
-        extractor = GPSExtractor(self.config.image_dir)
-        original_points_df = extractor.extract_all_gps()
-
-        # 读取被过滤的图片列表
-        with open(
-            os.path.join(self.config.output_dir, "del_imgs.txt"), "r", encoding="utf-8"
-        ) as file:
-            filtered_files = [line.strip() for line in file if line.strip()]
-
-        # 创建一个新的图形
-        plt.figure(figsize=(20, 16))
-
-        # 绘制所有原始点
-        plt.scatter(
-            original_points_df["lon"],
-            original_points_df["lat"],
-            color="blue",
-            label="Original Points",
-            alpha=0.6,
-        )
-
-        # 绘制被过滤的点
-        filtered_points_df = original_points_df[
-            original_points_df["file"].isin(filtered_files)
-        ]
-        plt.scatter(
-            filtered_points_df["lon"],
-            filtered_points_df["lat"],
-            color="red",
-            marker="x",
-            label="Filtered Points",
-            alpha=0.6,
-        )
-
-        # 设置图形属性
-        plt.title("GPS Coordinates of Images", fontsize=14)
-        plt.xlabel("Longitude", fontsize=12)
-        plt.ylabel("Latitude", fontsize=12)
-        plt.grid(True)
-        plt.legend()
-
-        # 保存图形
-        plt.savefig(os.path.join(self.config.output_dir, "filter_GPS.png"))
-        plt.close()
-        self.logger.info("预处理结果图已保存")
-
     def process(self):
         """执行完整的预处理流程"""
         try:
             self.extract_gps()
             self.cluster()
             self.filter_time_group_overlap()
-            # self.filter_points()
-            # grid_points = self.divide_grids()
-            # self.copy_images(grid_points)
-            # self.visualize_results()
-            # self.logger.info("预处理任务完成")
-            # self.command_runner.run_grid_commands(
-            #     grid_points,
-            #     self.config.enable_grid_division,
-            # )
-            # TODO 拼图
+            
+            # 在处理结束时生成所有步骤的可视化
+            if self.config.enable_visualization:
+                self.visualizer.visualize_all_steps(
+                    self.points_history, self.step_names)
+                
+            self.logger.info("预处理任务完成")
         except Exception as e:
             self.logger.error(f"处理过程中发生错误: {str(e)}", exc_info=True)
             raise
diff --git a/utils/visualizer.py b/utils/visualizer.py
new file mode 100644
index 0000000..015a1a5
--- /dev/null
+++ b/utils/visualizer.py
@@ -0,0 +1,115 @@
+import os
+import matplotlib.pyplot as plt
+import pandas as pd
+import logging
+from typing import List, Optional
+
+class FilterVisualizer:
+    """过滤结果可视化器"""
+    
+    def __init__(self, output_dir: str):
+        self.output_dir = output_dir
+        self.logger = logging.getLogger('UAV_Preprocess.Visualizer')
+        
+    def visualize_filter_step(self, 
+                            current_points: pd.DataFrame,
+                            previous_points: pd.DataFrame,
+                            step_name: str,
+                            save_name: Optional[str] = None):
+        """
+        可视化单个过滤步骤的结果
+        
+        Args:
+            current_points: 当前步骤后的点
+            previous_points: 上一步骤的点
+            step_name: 步骤名称
+            save_name: 保存文件名，默认为step_name
+        """
+        self.logger.info(f"开始生成{step_name}的可视化结果")
+        
+        # 找出被过滤掉的点
+        filtered_files = set(previous_points['file']) - set(current_points['file'])
+        filtered_points = previous_points[previous_points['file'].isin(filtered_files)]
+        
+        # 创建图形
+        plt.figure(figsize=(20, 16))
+        
+        # 绘制保留的点
+        plt.scatter(current_points['lon'], current_points['lat'],
+                   color='blue', label='Retained Points',
+                   alpha=0.6, s=50)
+        
+        # 绘制被过滤的点
+        plt.scatter(filtered_points['lon'], filtered_points['lat'],
+                   color='red', marker='x', label='Filtered Points',
+                   alpha=0.6, s=100)
+        
+        # 设置图形属性
+        plt.title(f"GPS Points After {step_name}", fontsize=14)
+        plt.xlabel("Longitude", fontsize=12)
+        plt.ylabel("Latitude", fontsize=12)
+        plt.grid(True)
+        
+        # 添加统计信息
+        stats_text = (f"Total Points: {len(previous_points)}\n"
+                     f"Filtered Points: {len(filtered_points)}\n"
+                     f"Remaining Points: {len(current_points)}")
+        plt.figtext(0.02, 0.02, stats_text, fontsize=10,
+                   bbox=dict(facecolor='white', alpha=0.8))
+        
+        plt.legend()
+        
+        # 保存图形
+        save_name = save_name or step_name.lower().replace(' ', '_')
+        save_path = os.path.join(self.output_dir, f'filter_{save_name}.png')
+        plt.savefig(save_path, dpi=300, bbox_inches='tight')
+        plt.close()
+        
+        self.logger.info(f"{step_name}过滤可视化结果已保存至 {save_path}")
+        
+    def visualize_all_steps(self, 
+                           points_history: List[pd.DataFrame],
+                           step_names: List[str]):
+        """
+        可视化所有过滤步骤的结果
+        
+        Args:
+            points_history: 每个步骤的点数据列表
+            step_names: 步骤名称列表
+        """
+        if len(points_history) != len(step_names):
+            raise ValueError("点数据列表和步骤名称列表长度不匹配")
+            
+        plt.figure(figsize=(20, 16))
+        
+        # 使用不同的颜色表示不同步骤
+        colors = plt.cm.rainbow(np.linspace(0, 1, len(points_history)))
+        
+        # 绘制每个步骤的点
+        for i, (points, color) in enumerate(zip(points_history, colors)):
+            plt.scatter(points['lon'], points['lat'],
+                       color=color, label=f'After {step_names[i]}',
+                       alpha=0.6, s=50)
+        
+        # 设置图形属性
+        plt.title("GPS Points Filtering Process", fontsize=14)
+        plt.xlabel("Longitude", fontsize=12)
+        plt.ylabel("Latitude", fontsize=12)
+        plt.grid(True)
+        
+        # 添加统计��息
+        stats_text = "Points Count:\n" + "\n".join(
+            f"{step_names[i]}: {len(points)}"
+            for i, points in enumerate(points_history)
+        )
+        plt.figtext(0.02, 0.02, stats_text, fontsize=10,
+                   bbox=dict(facecolor='white', alpha=0.8))
+        
+        plt.legend()
+        
+        # 保存图形
+        save_path = os.path.join(self.output_dir, 'filter_all_steps.png')
+        plt.savefig(save_path, dpi=300, bbox_inches='tight')
+        plt.close()
+        
+        self.logger.info(f"所有过滤步骤的可视化结果已保存至 {save_path}") 
\ No newline at end of file