250 lines
10 KiB
Python
250 lines
10 KiB
Python
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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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"""划分网格，并将图片分配到对应网格"""
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def __init__(self, overlap, grid_size, project_path, output_dir):
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self.overlap = overlap
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self.grid_size = grid_size
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self.project_path = project_path
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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 adjust_grid_size_and_overlap(self, points_df):
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"""动态调整网格重叠率"""
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grids = self.adjust_grid_size(points_df)
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self.logger.info(f"开始动态调整网格重叠率，初始重叠率: {self.overlap}")
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while True:
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# 使用调整好的网格大小划分网格
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grids = self.divide_grids(points_df)
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grid_points, multiple_grid_points = self.assign_to_grids(
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points_df, grids)
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if len(grids) == 1:
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self.logger.info(f"网格数量为1，跳过重叠率调整")
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break
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elif multiple_grid_points < 0.1*len(points_df):
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self.overlap += 0.02
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self.logger.info(f"重叠率增加到: {self.overlap}")
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else:
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self.logger.info(
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f"找到合适的重叠率: {self.overlap}, 有{multiple_grid_points}个点被分配到多个网格")
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break
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return grids, grid_points
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def adjust_grid_size(self, points_df):
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"""动态调整网格大小
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Args:
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points_df: 包含GPS点的DataFrame
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Returns:
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tuple: (grids, translations, grid_points, final_grid_size)
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"""
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self.logger.info(f"开始动态调整网格大小，初始大小: {self.grid_size}米")
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while True:
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# 使用当前grid_size划分网格
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grids = self.divide_grids(points_df)
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grid_points, multiple_grid_points = self.assign_to_grids(
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points_df, grids)
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# 检查每个网格中的点数
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max_points = 0
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for grid_id, points in grid_points.items():
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max_points = max(max_points, len(points))
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self.logger.info(
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f"当前网格大小: {self.grid_size}米, 单个网格最大点数: {max_points}")
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# 如果最大点数超过1600，减小网格大小
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if max_points > 1600:
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self.grid_size -= 100
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self.logger.info(f"点数超过1500，减小网格大小至: {self.grid_size}米")
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if self.grid_size < 500: # 设置一个最小网格大小限制
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self.logger.warning("网格大小已达到最小值500米，停止调整")
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break
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else:
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self.logger.info(f"找到合适的网格大小: {self.grid_size}米")
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break
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return grids
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def divide_grids(self, points_df):
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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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# 精细调整网格的长宽，避免出现2*grid_size-1的情况的影响
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grid_size_lt = [self.grid_size - 200, self.grid_size - 100,
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self.grid_size, self.grid_size + 100, self.grid_size + 200]
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width_modulus_lt = [width % grid_size for grid_size in grid_size_lt]
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grid_width = grid_size_lt[width_modulus_lt.index(
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min(width_modulus_lt))]
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height_modulus_lt = [height % grid_size for grid_size in grid_size_lt]
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grid_height = grid_size_lt[height_modulus_lt.index(
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min(height_modulus_lt))]
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self.logger.info(f"网格宽度: {grid_width:.2f}米, 网格高度: {grid_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_width), 1)
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self.num_grids_height = max(int(height / grid_height), 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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# 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 + \
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(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 + \
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(j + 1) * lon_step + self.overlap * lon_step
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grid_bounds = (grid_min_lat, grid_max_lat,
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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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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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return grids
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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, multiple_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 points')
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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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# 计算网格的实际长度和宽度（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),
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(max_lat, min_lon)).meters
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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和尺寸信息
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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,
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f"({i},{j})\n{width:.0f}m×{height:.0f}m", # 显示(i,j)和尺寸
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horizontalalignment='center',
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verticalalignment='center',
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fontsize=8)
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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 save_image_groups(self, grid_points, output_file_name="image_groups.txt"):
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"""保存图像分组信息到文件
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Args:
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grid_points (dict): 每个网格的点信息，键为(i, j)，值为点的列表
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output_file (str): 输出文件路径
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"""
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self.logger.info(f"开始保存图像分组信息到 {output_file_name}")
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output_file = os.path.join(
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self.project_path, 'project', output_file_name)
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with open(output_file, 'w') as f:
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for (i, j), points in grid_points.items():
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# 计算组编号（按行展开的顺序）
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group_id = i * self.num_grids_width + j + 1
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for point in points:
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image_name = point.get('file', 'unknown')
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f.write(f"{image_name} {group_id}\n")
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self.logger.info(f"图像分组信息已保存到 {output_file}")
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