diff --git a/odm_preprocess.py b/odm_preprocess.py index c62d076..d4413f1 100644 --- a/odm_preprocess.py +++ b/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(overlap=self.config.grid_overlap) + grid_divider = GridDivider( + 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_points() + # self.filter_time_group_overlap() + # 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", - output_dir=r"G:\20240930091614\output", + image_dir=r"F:\error_data\20241024100834\code\images", + 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 diff --git a/utils/grid_divider.py b/utils/grid_divider.py index e51332c..f4fd251 100644 --- a/utils/grid_divider.py +++ b/utils/grid_divider.py @@ -1,63 +1,76 @@ 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}") def divide_grids(self, points_df, grid_size=500): """计算边界框并划分九宫格""" self.logger.info("开始划分九宫格") - + min_lat, max_lat = points_df['lat'].min(), points_df['lat'].max() min_lon, max_lon = points_df['lon'].min(), points_df['lon'].max() - + # 计算区域的实际距离（米） width = geodesic((min_lat, min_lon), (min_lat, max_lon)).meters height = geodesic((min_lat, min_lon), (max_lat, min_lon)).meters - + self.logger.info( f"区域宽度: {width:.2f}米, 高度: {height:.2f}米" ) # 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_height = int(height / grid_size) if int(height / grid_size) > 0 else 1 - + num_grids_width = int( + 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 - grids.append((grid_min_lat, grid_max_lat, grid_min_lon, grid_max_lon)) - + grid_max_lon = min_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): """将点分配到对应网格""" self.logger.info(f"开始将 {len(points_df)} 个点分配到网格中") - + grid_points = {i: [] for i in range(len(grids))} points_assigned = 0 multiple_grid_points = 0 - + for _, point in points_df.iterrows(): point_assigned = False for i, (min_lat, max_lat, min_lon, max_lon) in enumerate(grids): @@ -68,7 +81,7 @@ class GridDivider: else: points_assigned += 1 point_assigned = True - + self.logger.debug( f"点 {point['file']} (纬度: {point['lat']:.6f}, 经度: {point['lon']:.6f}) " f"被分配到网格" @@ -83,5 +96,41 @@ class GridDivider: f"成功分配 {points_assigned} 个点, " f"{multiple_grid_points} 个点被分配到多个网格" ) - + 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()