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加入快拼模式，修改参数

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This commit is contained in:
龙澳 2024-12-21 12:03:54 +08:00
parent ebe818125e
commit 0e91d125cb
5 changed files with 105 additions and 70 deletions
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50
odm_preprocess.py
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@ -1,5 +1,6 @@
import os
import shutil
from datetime import timedelta
from dataclasses import dataclass
from typing import Dict
@ -21,19 +22,25 @@ class PreprocessConfig:
image_dir: str
output_dir: str
eps: float = 0.01
min_samples: int = 5
filter_grid_size: float = 0.001
filter_dense_distance_threshold: float = 10
filter_distance_threshold: float = 0.001
# 聚类过滤参数
cluster_eps: float = 0.01
cluster_min_samples: int = 5
# 孤立点过滤参数
filter_distance_threshold: float = 0.001 # 经纬度距离
filter_min_neighbors: int = 6
# 密集点过滤参数
filter_grid_size: float = 0.001
filter_dense_distance_threshold: float = 10 # 普通距离，单位：米
filter_time_threshold: timedelta = timedelta(minutes=5)
# 网格划分参数
grid_overlap: float = 0.05
grid_size: float = 500
# 几个pipline过程是否开启
enable_filter: bool = True
enable_grid_division: bool = True
enable_visualization: bool = True
enable_copy_images: bool = True
mode: str = "快拼模式"
class ImagePreprocessor:
def __init__(self, config: PreprocessConfig):
@ -55,11 +62,13 @@ class ImagePreprocessor:
self.logger.info("开始聚类")
# 创建聚类器并执行聚类
clusterer = GPSCluster(
self.gps_points, output_dir=self.config.output_dir)
self.gps_points, output_dir=self.config.output_dir,
eps=self.config.cluster_eps, min_samples=self.config.cluster_min_samples)
# 获取主要类别的点
self.gps_points = clusterer.get_main_cluster()
self.clustered_points = clusterer.fit()
self.gps_points = clusterer.get_main_cluster(self.clustered_points)
# 获取统计信息并记录
stats = clusterer.get_cluster_stats()
stats = clusterer.get_cluster_stats(self.clustered_points)
self.logger.info(
f"聚类完成：主要类别包含 {stats['main_cluster_points']} 个点，"
f"噪声点 {stats['noise_points']} 个"
@ -90,6 +99,7 @@ class ImagePreprocessor:
self.gps_points,
grid_size=self.config.filter_grid_size,
distance_threshold=self.config.filter_dense_distance_threshold,
time_threshold=self.config.filter_time_threshold,
)
self.logger.info(f"密集点过滤后剩余 {len(self.gps_points)} 个GPS点")
return self.gps_points
@ -188,15 +198,15 @@ class ImagePreprocessor:
try:
self.extract_gps()
self.cluster()
# self.time_filter()
# self.filter_points()
self.filter_points()
grid_points = self.divide_grids()
self.copy_images(grid_points)
self.visualize_results()
# self.logger.info("预处理任务完成")
self.logger.info("预处理任务完成")
self.command_runner.run_grid_commands(
grid_points,
self.config.enable_grid_division
self.config.enable_grid_division,
self.mode
)
except Exception as e:
self.logger.error(f"处理过程中发生错误: {str(e)}", exc_info=True)
@ -206,14 +216,22 @@ class ImagePreprocessor:
if __name__ == "__main__":
# 创建配置
config = PreprocessConfig(
image_dir=r"E:\湖南省第二测绘院\11-06-项目移交文件（王辉给）\无人机二三维节点扩容生产影像\影像数据\199\code\images",
image_dir=r"E:\datasets\UAV\1815\images",
output_dir=r"test",
filter_grid_size=0.001,
filter_dense_distance_threshold=10,
cluster_eps=0.01,
cluster_min_samples=5,
filter_distance_threshold=0.001,
filter_min_neighbors=6,
filter_grid_size=0.001,
filter_dense_distance_threshold=10,
filter_time_threshold=timedelta(minutes=5),
grid_overlap=0.05,
grid_size=500,
enable_filter=True,
enable_grid_division=True,
enable_visualization=True,

19
preprocess/cluster.py
View File

@ -17,7 +17,6 @@ class GPSCluster:
self.dbscan = DBSCAN(eps=eps, min_samples=min_samples)
self.scaler = StandardScaler()
self.gps_points = gps_points
self.clustered_points = self.fit()
self.log_file = os.path.join(output_dir, 'del_imgs.txt')
def fit(self):
@ -57,7 +56,7 @@ class GPSCluster:
return result_df
def get_cluster_stats(self):
def get_cluster_stats(self, clustered_points):
"""
获取聚类统计信息
@ -67,22 +66,22 @@ class GPSCluster:
返回:
聚类统计信息的字典
"""
main_cluster_points = sum(self.clustered_points["cluster"] == 1)
main_cluster_points = sum(clustered_points["cluster"] == 1)
stats = {
"total_points": len(self.clustered_points),
"total_points": len(clustered_points),
"main_cluster_points": main_cluster_points,
"noise_points": sum(self.clustered_points["cluster"] == -1),
"noise_points": sum(clustered_points["cluster"] == -1),
}
noise_cluster = self.get_noise_cluster()
noise_cluster = self.get_noise_cluster(clustered_points)
with open(self.log_file, 'a', encoding='utf-8') as f:
for i, (_, row) in enumerate(noise_cluster.iterrows()):
f.write(row['file']+'\n')
f.write('\n')
return stats
def get_main_cluster(self):
return self.clustered_points[self.clustered_points["cluster"] == 1]
def get_main_cluster(self, clustered_points):
return clustered_points[clustered_points["cluster"] == 1]
def get_noise_cluster(self):
return self.clustered_points[self.clustered_points["cluster"] == -1]
def get_noise_cluster(self, clustered_points):
return clustered_points[clustered_points["cluster"] == -1]

8
preprocess/command_runner.py
View File

@ -35,10 +35,14 @@ i
"""
try:
grid_dir = os.path.join(self.output_dir, f'grid_{grid_idx + 1}')
command = f"docker run -ti --rm -v {grid_dir}:/datasets opendronemap/odm --project-path /datasets project --feature-quality lowest --force-gps --use-3dmesh"
if self.mode == "快拼模式":
command = f"docker run -ti --rm -v {grid_dir}:/datasets opendronemap/odm --project-path /datasets project --feature-quality lowest --force-gps --use-3dmesh --fast-orthophoto --skip-3dmodel"
else:
command = f"docker run -ti --rm -v {grid_dir}:/datasets opendronemap/odm --project-path /datasets project --feature-quality lowest --force-gps --use-3dmesh"
self.logger.info(f"开始执行命令: {command}")
success, error_msg = self.monitor.run_odm_with_monitor(command, grid_dir, grid_idx)
success, error_msg = self.monitor.run_odm_with_monitor(
command, grid_dir, grid_idx)
if not success:
raise Exception(error_msg)

41
preprocess/gps_filter.py
View File

@ -68,7 +68,7 @@ class GPSFilter:
return sorted_distances
def _group_by_time(self, points_df: pd.DataFrame, time_threshold: timedelta = timedelta(minutes=5)) -> list:
def _group_by_time(self, points_df: pd.DataFrame, time_threshold: timedelta) -> list:
"""根据拍摄时间分组图片
如果相邻两张图片的拍摄时间差超过5分钟，则进行切分
@ -97,7 +97,8 @@ class GPSFilter:
# 按时间排序
valid_date_points = valid_date_points.sort_values('date')
self.logger.info(f"有效时间范围: {valid_date_points['date'].min()} 到 {valid_date_points['date'].max()}")
self.logger.info(
f"有效时间范围: {valid_date_points['date'].min()} 到 {valid_date_points['date'].max()}")
# 计算时间差
time_diffs = valid_date_points['date'].diff()
@ -121,7 +122,8 @@ class GPSFilter:
f"时间组 {len(time_groups)}: {len(current_group)} 个点, "
f"时间范围 [{group_start_time} - {group_end_time}]"
)
self.logger.info(f"在时间 {break_time} 处发现断点，时间差为 {time_diff}")
self.logger.info(
f"在时间 {break_time} 处发现断点，时间差为 {time_diff}")
current_group_start = idx
@ -142,7 +144,7 @@ class GPSFilter:
self.logger.info(f"共分为 {len(time_groups)} 个时间组")
return time_groups
def filter_dense_points(self, points_df, grid_size=0.001, distance_threshold=13, time_interval=60):
def filter_dense_points(self, points_df, grid_size=0.001, distance_threshold=13, time_threshold=timedelta(minutes=5)):
"""
过滤密集点，先按时间分组，再在每个时间组内过滤。
空时间戳的点不进行过滤。
@ -154,10 +156,10 @@ class GPSFilter:
time_interval: 时间间隔（秒）
"""
self.logger.info(f"开始按时间分组过滤密集点 (网格大小: {grid_size}, "
f"距离阈值: {distance_threshold}米, 时间间隔: {time_interval}秒)")
f"距离阈值: {distance_threshold}米, 分组时间间隔: {time_threshold}秒)")
# 按时间分组
time_groups = self._group_by_time(points_df, time_interval)
time_groups = self._group_by_time(points_df, time_threshold)
# 存储所有要删除的图片
all_to_del_imgs = []
@ -169,7 +171,8 @@ class GPSFilter:
self.logger.info(f"跳过无时间戳组 (包含 {len(group_points)} 个点)")
continue
self.logger.info(f"处理时间组 {group_idx + 1} (包含 {len(group_points)} 个点)")
self.logger.info(
f"处理时间组 {group_idx + 1} (包含 {len(group_points)} 个点)")
# 计算该组内的点间距离
sorted_distances = self._get_distances(group_points, grid_size)
@ -200,16 +203,20 @@ class GPSFilter:
to_del_img = candidate_img1 if candidate_img1_dist < candidate_img2_dist else candidate_img2
group_to_del_imgs.append(to_del_img)
grid_del_count += 1
self.logger.debug(f"时间组 {group_idx + 1} 网格 {grid} 删除密集点: {to_del_img} (距离: {dist:.2f}米)")
distances = [d for d in distances if to_del_img not in d]
self.logger.debug(
f"时间组 {group_idx + 1} 网格 {grid} 删除密集点: {to_del_img} (距离: {dist:.2f}米)")
distances = [
d for d in distances if to_del_img not in d]
else:
break
if grid_del_count > 0:
self.logger.info(f"时间组 {group_idx + 1} 网格 {grid} 删除了 {grid_del_count} 个密集点")
self.logger.info(
f"时间组 {group_idx + 1} 网格 {grid} 删除了 {grid_del_count} 个密集点")
all_to_del_imgs.extend(group_to_del_imgs)
self.logger.info(f"时间组 {group_idx + 1} 共删除 {len(group_to_del_imgs)} 个密集点")
self.logger.info(
f"时间组 {group_idx + 1} 共删除 {len(group_to_del_imgs)} 个密集点")
# 写入删除日志
with open(self.log_file, 'a', encoding='utf-8') as f:
@ -218,13 +225,15 @@ class GPSFilter:
# 过滤数据
filtered_df = points_df[~points_df['file'].isin(all_to_del_imgs)]
self.logger.info(f"密集点过滤完成，共删除 {len(all_to_del_imgs)} 个点，剩余 {len(filtered_df)} 个点")
self.logger.info(
f"密集点过滤完成，共删除 {len(all_to_del_imgs)} 个点，剩余 {len(filtered_df)} 个点")
return filtered_df
def filter_isolated_points(self, points_df, threshold_distance=0.001, min_neighbors=6):
"""过滤孤立点"""
self.logger.info(f"开始过滤孤立点 (距离阈值: {threshold_distance}, 最小邻居数: {min_neighbors})")
self.logger.info(
f"开始过滤孤立点 (距离阈值: {threshold_distance}, 最小邻居数: {min_neighbors})")
coords = points_df[['lat', 'lon']].values
kdtree = KDTree(coords)
@ -237,9 +246,11 @@ class GPSFilter:
if neighbors_count[i] < min_neighbors:
isolated_points.append(row['file'])
f.write(row['file']+'\n')
self.logger.debug(f"删除孤立点: {row['file']} (邻居数: {neighbors_count[i]})")
self.logger.debug(
f"删除孤立点: {row['file']} (邻居数: {neighbors_count[i]})")
f.write('\n')
filtered_df = points_df[~points_df['file'].isin(isolated_points)]
self.logger.info(f"孤立点过滤完成，共删除 {len(isolated_points)} 个点，剩余 {len(filtered_df)} 个点")
self.logger.info(
f"孤立点过滤完成，共删除 {len(isolated_points)} 个点，剩余 {len(filtered_df)} 个点")
return filtered_df

7
preprocess/odm_monitor.py
View File

@ -8,7 +8,7 @@ from typing import Optional, Tuple
class ODMProcessMonitor:
"""ODM进程监控器"""
def __init__(self, max_retries: int = 3, check_interval: int = 5):
def __init__(self, max_retries: int = 3, check_interval: int = 300):
"""
初始化监控器
@ -31,7 +31,10 @@ class ODMProcessMonitor:
def _check_success(self, grid_dir: str) -> bool:
"""检查ODM是否执行成功"""
# ODM成功完成时会生成这些文件夹
success_markers = ['odm_orthophoto', 'odm_georeferencing', 'odm_texturing']
if self.mode == "快拼模式":
success_markers = ['odm_orthophoto', 'odm_georeferencing']
else:
success_markers = ['odm_orthophoto', 'odm_georeferencing', 'odm_texturing']
return all(os.path.exists(os.path.join(grid_dir, marker)) for marker in success_markers)
def run_odm_with_monitor(self, command: str, grid_dir: str, grid_idx: int) -> Tuple[bool, str]: