增加划分网格过程的可视化

odm_preprocess.py

@@ -197,8 +197,10 @@ class ImagePreprocessor:
     def divide_grids(self) -> Dict[int, pd.DataFrame]:
         """划分网格"""
         self.logger.info(f"开始划分网格 (重叠率: {self.config.grid_overlap})")
-        self.logger.info(f"开始划分网格 (重叠率: {self.config.grid_overlap})")
+        grid_divider = GridDivider(
-        grid_divider = GridDivider(overlap=self.config.grid_overlap)
+            overlap=self.config.grid_overlap,
+            output_dir=self.config.output_dir
+        )
         grids = grid_divider.divide_grids(
             self.gps_points, grid_size=self.config.grid_size
         )
@@ -288,8 +290,8 @@ class ImagePreprocessor:
         try:
             self.extract_gps()
             self.cluster()
-            self.filter_time_group_overlap()
+            # self.filter_time_group_overlap()
-            self.filter_points()
+            # self.filter_points()
             grid_points = self.divide_grids()
             self.copy_images(grid_points)
             self.logger.info("预处理任务完成")
@@ -306,8 +308,8 @@ class ImagePreprocessor:
 if __name__ == "__main__":
     # 创建配置
     config = PreprocessConfig(
-        image_dir=r"F:\error_data\20240930091614\project\images",
+        image_dir=r"F:\error_data\20241024100834\code\images",
-        output_dir=r"G:\20240930091614\output",
+        output_dir=r"G:\20241024100834\output",
 
         cluster_eps=0.01,
         cluster_min_samples=5,
@@ -324,9 +326,10 @@ if __name__ == "__main__":
         filter_time_threshold=timedelta(minutes=5),
 
         grid_size=1000,
+        grid_overlap=0.03,
 
 
-        mode="快拼模式",
+        mode="重建模式",
     )
 
     # 创建处理器并执行

utils/grid_divider.py

@@ -1,11 +1,15 @@
 import logging
 from geopy.distance import geodesic
+import matplotlib.pyplot as plt
+import os
+
 
 class GridDivider:
     """划分九宫格，并将图片分配到对应网格"""
 
-    def __init__(self, overlap=0.1):
+    def __init__(self, overlap=0.1, output_dir=None):
         self.overlap = overlap
+        self.output_dir = output_dir
         self.logger = logging.getLogger('UAV_Preprocess.GridDivider')
         self.logger.info(f"初始化网格划分器，重叠率: {overlap}")
 
@@ -25,29 +29,38 @@ class GridDivider:
         )
 
         # 计算需要划分的网格数量
-        num_grids_width = int(width / grid_size) if int(width / grid_size) > 0 else 1
+        num_grids_width = int(
-        num_grids_height = int(height / grid_size) if int(height / grid_size) > 0 else 1
+            width / grid_size) if int(width / grid_size) > 0 else 1
+        num_grids_height = int(
+            height / grid_size) if int(height / grid_size) > 0 else 1
 
         # 计算每个网格对应的经纬度步长
         lat_step = (max_lat - min_lat) / num_grids_height
         lon_step = (max_lon - min_lon) / num_grids_width
 
-
         grids = []
         for i in range(num_grids_height):
             for j in range(num_grids_width):
                 grid_min_lat = min_lat + i * lat_step - self.overlap * lat_step
-                grid_max_lat = min_lat + (i + 1) * lat_step + self.overlap * lat_step
+                grid_max_lat = min_lat + \
+                    (i + 1) * lat_step + self.overlap * lat_step
                 grid_min_lon = min_lon + j * lon_step - self.overlap * lon_step
-                grid_max_lon = min_lon + (j + 1) * lon_step + self.overlap * lon_step
+                grid_max_lon = min_lon + \
-                grids.append((grid_min_lat, grid_max_lat, grid_min_lon, grid_max_lon))
+                    (j + 1) * lon_step + self.overlap * lon_step
+                grids.append((grid_min_lat, grid_max_lat,
+                             grid_min_lon, grid_max_lon))
 
                 self.logger.debug(
                     f"网格[{i},{j}]: 纬度[{grid_min_lat:.6f}, {grid_max_lat:.6f}], "
                     f"经度[{grid_min_lon:.6f}, {grid_max_lon:.6f}]"
                 )
 
-        self.logger.info(f"成功划分为 {len(grids)} 个网格 ({num_grids_width}x{num_grids_height})")
+        self.logger.info(
+            f"成功划分为 {len(grids)} 个网格 ({num_grids_width}x{num_grids_height})")
+
+        # 添加可视化调用
+        self.visualize_grids(points_df, grids)
+
         return grids
 
     def assign_to_grids(self, points_df, grids):
@@ -85,3 +98,39 @@ class GridDivider:
         )
 
         return grid_points
+
+    def visualize_grids(self, points_df, grids):
+        """可视化网格划分和GPS点的分布"""
+        self.logger.info("开始可视化网格划分")
+
+        plt.figure(figsize=(12, 8))
+
+        # 绘制GPS点
+        plt.scatter(points_df['lon'], points_df['lat'],
+                    c='blue', s=10, alpha=0.6, label='GPS点')
+
+        # 绘制网格
+        for i, (min_lat, max_lat, min_lon, max_lon) in enumerate(grids):
+            plt.plot([min_lon, max_lon, max_lon, min_lon, min_lon],
+                     [min_lat, min_lat, max_lat, max_lat, min_lat],
+                     'r-', alpha=0.5)
+            # 在网格中心添加网格编号
+            center_lon = (min_lon + max_lon) / 2
+            center_lat = (min_lat + max_lat) / 2
+            plt.text(center_lon, center_lat, str(i),
+                     horizontalalignment='center', verticalalignment='center')
+
+        plt.title('网格划分与GPS点分布图')
+        plt.xlabel('经度')
+        plt.ylabel('纬度')
+        plt.legend()
+        plt.grid(True)
+
+        # 如果提供了输出目录，保存图像
+        if self.output_dir:
+            save_path = os.path.join(
+                self.output_dir, 'filter_imgs', 'grid_division.png')
+            plt.savefig(save_path, dpi=300, bbox_inches='tight')
+            self.logger.info(f"网格划分可视化图已保存至: {save_path}")
+
+        plt.close()