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增加划分网格过程的可视化

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This commit is contained in:
龙澳 2024-12-23 14:21:42 +08:00
parent 17deadb25e
commit a7a08f4cc5
2 changed files with 76 additions and 24 deletions
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17
odm_preprocess.py
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@ -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

83
utils/grid_divider.py
View File

@ -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()