207 lines
8.4 KiB
Python
207 lines
8.4 KiB
Python
import logging
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from geopy.distance import geodesic
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import matplotlib.pyplot as plt
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import os
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class GridDivider:
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"""划分九宫格，并将图片分配到对应网格"""
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def __init__(self, overlap=0.1, output_dir=None):
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self.overlap = overlap
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self.output_dir = output_dir
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self.logger = logging.getLogger('UAV_Preprocess.GridDivider')
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self.logger.info(f"初始化网格划分器，重叠率: {overlap}")
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self.num_grids_width = 0 # 添加网格数量属性
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self.num_grids_height = 0
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def divide_grids(self, points_df, grid_size=500):
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"""计算边界框并划分网格
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Returns:
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tuple: (grids, translations)
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- grids: 网格边界列表
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- translations: 网格平移量字典
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"""
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self.logger.info("开始划分网格")
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min_lat, max_lat = points_df['lat'].min(), points_df['lat'].max()
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min_lon, max_lon = points_df['lon'].min(), points_df['lon'].max()
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# 计算区域的实际距离（米）
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width = geodesic((min_lat, min_lon), (min_lat, max_lon)).meters
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height = geodesic((min_lat, min_lon), (max_lat, min_lon)).meters
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self.logger.info(f"区域宽度: {width:.2f}米, 高度: {height:.2f}米")
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# 计算需要划分的网格数量
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self.num_grids_width = max(int(width / grid_size), 1)
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self.num_grids_height = max(int(height / grid_size), 1)
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# 计算每个网格对应的经纬度步长
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lat_step = (max_lat - min_lat) / self.num_grids_height
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lon_step = (max_lon - min_lon) / self.num_grids_width
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grids = []
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grid_translations = {} # 存储每个网格相对于第一个网格的平移量
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# 先创建所有网格
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for i in range(self.num_grids_height):
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for j in range(self.num_grids_width):
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grid_min_lat = min_lat + i * lat_step - self.overlap * lat_step
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grid_max_lat = min_lat + (i + 1) * lat_step + self.overlap * lat_step
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grid_min_lon = min_lon + j * lon_step - self.overlap * lon_step
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grid_max_lon = min_lon + (j + 1) * lon_step + self.overlap * lon_step
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grid_id = (i, j) # 使用(i,j)作为网格标识，i代表行，j代表列
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grid_bounds = (grid_min_lat, grid_max_lat, grid_min_lon, grid_max_lon)
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grids.append(grid_bounds)
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self.logger.debug(
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f"网格[{i},{j}]: 纬度[{grid_min_lat:.6f}, {grid_max_lat:.6f}], "
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f"经度[{grid_min_lon:.6f}, {grid_max_lon:.6f}]"
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)
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# 计算每个网格相对于第一个网格的平移量
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reference_grid = grids[0]
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for i in range(self.num_grids_height):
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for j in range(self.num_grids_width):
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grid_id = (i, j)
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grid_idx = i * self.num_grids_width + j
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if grid_idx == 0: # 参考网格
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grid_translations[grid_id] = (0, 0)
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else:
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translation = self.calculate_grid_translation(reference_grid, grids[grid_idx])
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grid_translations[grid_id] = translation
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self.logger.debug(
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f"网格[{i},{j}]相对于参考网格的平移量: x={translation[0]:.2f}m, y={translation[1]:.2f}m"
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)
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self.logger.info(
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f"成功划分为 {len(grids)} 个网格 ({self.num_grids_width}x{self.num_grids_height})")
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# 添加可视化调用
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self.visualize_grids(points_df, grids)
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return grids, grid_translations
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def assign_to_grids(self, points_df, grids):
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"""将点分配到对应网格"""
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self.logger.info(f"开始将 {len(points_df)} 个点分配到网格中")
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grid_points = {} # 使用字典存储每个网格的点
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points_assigned = 0
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multiple_grid_points = 0
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for i in range(self.num_grids_height):
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for j in range(self.num_grids_width):
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grid_points[(i, j)] = [] # 使用(i,j)元组
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for _, point in points_df.iterrows():
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point_assigned = False
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for i in range(self.num_grids_height):
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for j in range(self.num_grids_width):
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grid_idx = i * self.num_grids_width + j
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min_lat, max_lat, min_lon, max_lon = grids[grid_idx]
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if min_lat <= point['lat'] <= max_lat and min_lon <= point['lon'] <= max_lon:
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grid_points[(i, j)].append(point.to_dict())
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if point_assigned:
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multiple_grid_points += 1
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else:
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points_assigned += 1
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point_assigned = True
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# 记录每个网格的点数
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for grid_id, points in grid_points.items():
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self.logger.info(f"网格 {grid_id} 包含 {len(points)} 个点")
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self.logger.info(
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f"点分配完成: 总点数 {len(points_df)}, "
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f"成功分配 {points_assigned} 个点, "
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f"{multiple_grid_points} 个点被分配到多个网格"
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)
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return grid_points
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def visualize_grids(self, points_df, grids):
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"""可视化网格划分和GPS点的分布"""
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self.logger.info("开始可视化网格划分")
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plt.figure(figsize=(12, 8))
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# 绘制GPS点
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plt.scatter(points_df['lon'], points_df['lat'],
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c='blue', s=10, alpha=0.6, label='GPS点')
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# 绘制网格
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for i in range(self.num_grids_height):
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for j in range(self.num_grids_width):
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grid_idx = i * self.num_grids_width + j
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min_lat, max_lat, min_lon, max_lon = grids[grid_idx]
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plt.plot([min_lon, max_lon, max_lon, min_lon, min_lon],
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[min_lat, min_lat, max_lat, max_lat, min_lat],
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'r-', alpha=0.5)
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# 在网格中心添加网格编号
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center_lon = (min_lon + max_lon) / 2
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center_lat = (min_lat + max_lat) / 2
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plt.text(center_lon, center_lat, f"({i},{j})", # 显示(i,j)
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horizontalalignment='center', verticalalignment='center')
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plt.title('Grid Division and GPS Point Distribution')
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plt.xlabel('Longitude')
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plt.ylabel('Latitude')
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plt.legend()
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plt.grid(True)
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# 如果提供了输出目录，保存图像
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if self.output_dir:
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save_path = os.path.join(
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self.output_dir, 'filter_imgs', 'grid_division.png')
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plt.savefig(save_path, dpi=300, bbox_inches='tight')
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self.logger.info(f"网格划分可视化图已保存至: {save_path}")
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plt.close()
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def get_grid_center(self, grid_bounds) -> tuple:
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"""计算网格中心点的经纬度
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Args:
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grid_bounds: (min_lat, max_lat, min_lon, max_lon)
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Returns:
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(center_lat, center_lon)
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"""
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min_lat, max_lat, min_lon, max_lon = grid_bounds
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return ((min_lat + max_lat) / 2, (min_lon + max_lon) / 2)
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def calculate_grid_translation(self, reference_grid: tuple, target_grid: tuple) -> tuple:
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"""计算目标网格相对于参考网格的平移距离（米）
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Args:
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reference_grid: 参考网格的边界 (min_lat, max_lat, min_lon, max_lon)
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target_grid: 目标网格的边界 (min_lat, max_lat, min_lon, max_lon)
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Returns:
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(x_translation, y_translation): 在米制单位下的平移量
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"""
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ref_center = self.get_grid_center(reference_grid)
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target_center = self.get_grid_center(target_grid)
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# 计算经度方向的距离（x轴）
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x_distance = geodesic(
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(ref_center[0], ref_center[1]),
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(ref_center[0], target_center[1])
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).meters
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# 如果目标在参考点西边，距离为负
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if target_center[1] < ref_center[1]:
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x_distance = -x_distance
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# 计算纬度方向的距离（y轴）
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y_distance = geodesic(
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(ref_center[0], ref_center[1]),
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(target_center[0], ref_center[1])
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).meters
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# 如果目标在参考点南边，距离为负
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if target_center[0] < ref_center[0]:
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y_distance = -y_distance
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return (x_distance, y_distance)
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