精简代码
This commit is contained in:
weixin_46229132
parent
e86fe196f8
commit
5c382e1810
139
app_plugin.py
139
app_plugin.py
@ -1,12 +1,12 @@
|
||||
import os
|
||||
import shutil
|
||||
from datetime import timedelta
|
||||
from dataclasses import dataclass
|
||||
from typing import Dict, Tuple
|
||||
import psutil
|
||||
import pandas as pd
|
||||
|
||||
from filter.cluster_filter import GPSCluster
|
||||
from utils.directory_manager import DirectoryManager
|
||||
from utils.odm_monitor import ODMProcessMonitor
|
||||
from utils.gps_extractor import GPSExtractor
|
||||
from utils.grid_divider import GridDivider
|
||||
@ -19,115 +19,38 @@ from post_pro.conv_obj import ConvertOBJ
|
||||
@dataclass
|
||||
class ProcessConfig:
|
||||
"""预处理配置类"""
|
||||
|
||||
image_dir: str
|
||||
output_dir: str
|
||||
# 聚类过滤参数
|
||||
cluster_eps: float = 0.01
|
||||
cluster_min_samples: int = 5
|
||||
# 时间组重叠过滤参数
|
||||
time_group_overlap_threshold: float = 0.7
|
||||
time_group_interval: timedelta = timedelta(minutes=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
|
||||
mode: str = "快拼模式"
|
||||
accuracy: str = "medium"
|
||||
produce_dem: bool = False
|
||||
|
||||
mode: str = "三维模式"
|
||||
|
||||
|
||||
class ODM_Plugin:
|
||||
def __init__(self, config):
|
||||
self.config = config
|
||||
|
||||
# 检查磁盘空间
|
||||
self._check_disk_space()
|
||||
|
||||
# 初始化目录管理器
|
||||
self.dir_manager = DirectoryManager(config)
|
||||
# 清理并重建输出目录
|
||||
if os.path.exists(config.output_dir):
|
||||
self._clean_output_dir()
|
||||
self._setup_output_dirs()
|
||||
self.dir_manager.clean_output_dir()
|
||||
self.dir_manager.setup_output_dirs()
|
||||
# 检查磁盘空间
|
||||
self.dir_manager.check_disk_space()
|
||||
|
||||
# 初始化其他组件
|
||||
self.logger = setup_logger(config.output_dir)
|
||||
self.gps_points = None
|
||||
self.gps_points = pd.DataFrame(columns=["file", "lat", "lon"])
|
||||
self.odm_monitor = ODMProcessMonitor(
|
||||
config.output_dir, mode=config.mode)
|
||||
self.visualizer = FilterVisualizer(config.output_dir)
|
||||
|
||||
def _clean_output_dir(self):
|
||||
"""清理输出目录"""
|
||||
try:
|
||||
shutil.rmtree(self.config.output_dir)
|
||||
print(f"已清理输出目录: {self.config.output_dir}")
|
||||
except Exception as e:
|
||||
print(f"清理输出目录时发生错误: {str(e)}")
|
||||
raise
|
||||
|
||||
def _setup_output_dirs(self):
|
||||
"""创建必要的输出目录结构"""
|
||||
try:
|
||||
# 创建主输出目录
|
||||
os.makedirs(self.config.output_dir)
|
||||
|
||||
# 创建过滤图像保存目录
|
||||
os.makedirs(os.path.join(self.config.output_dir, 'filter_imgs'))
|
||||
|
||||
# 创建日志目录
|
||||
os.makedirs(os.path.join(self.config.output_dir, 'logs'))
|
||||
|
||||
print(f"已创建输出目录结构: {self.config.output_dir}")
|
||||
except Exception as e:
|
||||
print(f"创建输出目录时发生错误: {str(e)}")
|
||||
raise
|
||||
|
||||
def _get_directory_size(self, path):
|
||||
"""获取目录的总大小（字节）"""
|
||||
total_size = 0
|
||||
for dirpath, dirnames, filenames in os.walk(path):
|
||||
for filename in filenames:
|
||||
file_path = os.path.join(dirpath, filename)
|
||||
try:
|
||||
total_size += os.path.getsize(file_path)
|
||||
except (OSError, FileNotFoundError):
|
||||
continue
|
||||
return total_size
|
||||
|
||||
def _check_disk_space(self):
|
||||
"""检查磁盘空间是否足够"""
|
||||
# 获取输入目录大小
|
||||
input_size = self._get_directory_size(self.config.image_dir)
|
||||
|
||||
# 获取输出目录所在磁盘的剩余空间
|
||||
output_drive = os.path.splitdrive(
|
||||
os.path.abspath(self.config.output_dir))[0]
|
||||
if not output_drive: # 处理Linux/Unix路径
|
||||
output_drive = '/home'
|
||||
|
||||
disk_usage = psutil.disk_usage(output_drive)
|
||||
free_space = disk_usage.free
|
||||
|
||||
# 计算所需空间（输入大小的1.5倍）
|
||||
required_space = input_size * 12
|
||||
|
||||
if free_space < required_space:
|
||||
error_msg = (
|
||||
f"磁盘空间不足!\n"
|
||||
f"输入目录大小: {input_size / (1024**3):.2f} GB\n"
|
||||
f"所需空间: {required_space / (1024**3):.2f} GB\n"
|
||||
f"可用空间: {free_space / (1024**3):.2f} GB\n"
|
||||
f"在驱动器 {output_drive}"
|
||||
)
|
||||
raise RuntimeError(error_msg)
|
||||
|
||||
def extract_gps(self) -> pd.DataFrame:
|
||||
"""提取GPS数据"""
|
||||
self.logger.info("开始提取GPS数据")
|
||||
@ -150,10 +73,9 @@ class ODM_Plugin:
|
||||
self.visualizer.visualize_filter_step(
|
||||
self.gps_points, previous_points, "1-Clustering")
|
||||
|
||||
def divide_grids(self) -> Tuple[Dict[tuple, pd.DataFrame], Dict[tuple, tuple]]:
|
||||
def divide_grids(self) -> Dict[tuple, pd.DataFrame]:
|
||||
"""划分网格
|
||||
Returns:
|
||||
tuple: (grid_points, translations)
|
||||
- grid_points: 网格点数据字典
|
||||
- translations: 网格平移量字典
|
||||
"""
|
||||
@ -162,14 +84,14 @@ class ODM_Plugin:
|
||||
grid_size=self.config.grid_size,
|
||||
output_dir=self.config.output_dir
|
||||
)
|
||||
grids, translations, grid_points = grid_divider.adjust_grid_size_and_overlap(
|
||||
grids, grid_points = grid_divider.adjust_grid_size_and_overlap(
|
||||
self.gps_points
|
||||
)
|
||||
grid_divider.visualize_grids(self.gps_points, grids)
|
||||
if len(grids) >= 20:
|
||||
self.logger.warning("网格数量已超过20, 需要人工调整分区")
|
||||
|
||||
return grid_points, translations
|
||||
return grid_points
|
||||
|
||||
def copy_images(self, grid_points: Dict[tuple, pd.DataFrame]):
|
||||
"""复制图像到目标文件夹"""
|
||||
@ -192,46 +114,45 @@ class ODM_Plugin:
|
||||
self.logger.info(
|
||||
f"网格 ({grid_id[0]},{grid_id[1]}) 包含 {len(points)} 张图像")
|
||||
|
||||
def merge_tif(self, grid_points: Dict[tuple, pd.DataFrame], mode: str, produce_dem: bool):
|
||||
def merge_tif(self, grid_lt):
|
||||
"""合并所有网格的影像产品"""
|
||||
self.logger.info("开始合并所有影像产品")
|
||||
merger = MergeTif(self.config.output_dir)
|
||||
merger.merge_orthophoto(grid_points)
|
||||
merger.merge_orthophoto(grid_lt)
|
||||
|
||||
def convert_obj(self, grid_points: Dict[tuple, pd.DataFrame]):
|
||||
def convert_obj(self, grid_lt):
|
||||
"""转换OBJ模型"""
|
||||
self.logger.info("开始转换OBJ模型")
|
||||
converter = ConvertOBJ(self.config.output_dir)
|
||||
converter.convert_grid_obj(grid_points)
|
||||
converter.convert_grid_obj(grid_lt)
|
||||
|
||||
def post_process(self, successful_grid_points: Dict[tuple, pd.DataFrame], grid_points: Dict[tuple, pd.DataFrame], translations: Dict[tuple, tuple]):
|
||||
def post_process(self, successful_grid_lt: list, grid_points: Dict[tuple, pd.DataFrame]):
|
||||
"""后处理：合并或复制处理结果"""
|
||||
if len(successful_grid_points) < len(grid_points):
|
||||
if len(successful_grid_lt) < len(grid_points):
|
||||
self.logger.warning(
|
||||
f"有 {len(grid_points) - len(successful_grid_points)} 个网格处理失败，"
|
||||
f"将只合并成功处理的 {len(successful_grid_points)} 个网格"
|
||||
f"有 {len(grid_points) - len(successful_grid_lt)} 个网格处理失败，"
|
||||
f"将只合并成功处理的 {len(successful_grid_lt)} 个网格"
|
||||
)
|
||||
|
||||
self.merge_tif(successful_grid_points, self.config.mode,
|
||||
self.config.produce_dem)
|
||||
self.merge_tif(successful_grid_lt)
|
||||
if self.config.mode == "三维模式":
|
||||
|
||||
self.convert_obj(successful_grid_points)
|
||||
self.convert_obj(successful_grid_lt)
|
||||
else:
|
||||
pass
|
||||
|
||||
def process(self):
|
||||
"""执行完整的预处理流程"""
|
||||
try:
|
||||
self.extract_gps()
|
||||
self.cluster()
|
||||
grid_points, translations = self.divide_grids()
|
||||
grid_points = self.divide_grids()
|
||||
self.copy_images(grid_points)
|
||||
self.logger.info("预处理任务完成")
|
||||
|
||||
successful_grid_points = self.odm_monitor.process_all_grids(
|
||||
grid_points, self.config.produce_dem, self.config.accuracy)
|
||||
successful_grid_lt = self.odm_monitor.process_all_grids(
|
||||
grid_points)
|
||||
|
||||
self.post_process(successful_grid_points,
|
||||
grid_points, translations)
|
||||
self.post_process(successful_grid_lt, grid_points)
|
||||
self.logger.info("重建任务完成")
|
||||
|
||||
except Exception as e:
|
||||
|
||||
17
main.py
17
main.py
@ -1,5 +1,4 @@
|
||||
import argparse
|
||||
from datetime import timedelta
|
||||
from app_plugin import ProcessConfig, ODM_Plugin
|
||||
|
||||
|
||||
@ -12,13 +11,10 @@ def parse_args():
|
||||
|
||||
# 可选参数
|
||||
parser.add_argument('--mode', default='三维模式',
|
||||
choices=['快拼模式', '三维模式', '重建模式'], help='处理模式')
|
||||
parser.add_argument('--accuracy', default='medium',
|
||||
choices=['high', 'medium', 'low'], help='精度')
|
||||
choices=['快拼模式', '三维模式'], help='处理模式')
|
||||
parser.add_argument('--grid_size', type=float, default=800, help='网格大小(米)')
|
||||
parser.add_argument('--grid_overlap', type=float,
|
||||
default=0.05, help='网格重叠率')
|
||||
# parser.add_argument('--produce_dem', action='store_true', help='是否生成DEM')
|
||||
|
||||
args = parser.parse_args()
|
||||
return args
|
||||
@ -32,21 +28,12 @@ def main():
|
||||
image_dir=args.image_dir,
|
||||
output_dir=args.output_dir,
|
||||
mode=args.mode,
|
||||
accuracy=args.accuracy,
|
||||
grid_size=args.grid_size,
|
||||
grid_overlap=args.grid_overlap,
|
||||
produce_dem=True,
|
||||
|
||||
# 其他参数使用默认值
|
||||
grid_overlap=0.05,
|
||||
cluster_eps=0.01,
|
||||
cluster_min_samples=5,
|
||||
time_group_overlap_threshold=0.7,
|
||||
time_group_interval=timedelta(minutes=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),
|
||||
)
|
||||
|
||||
# 创建处理器并执行
|
||||
|
||||
@ -18,13 +18,13 @@ class ConvertOBJ:
|
||||
"EPSG:32649", "EPSG:4326", always_xy=True)
|
||||
self.logger = logging.getLogger('UAV_Preprocess.ConvertOBJ')
|
||||
|
||||
def convert_grid_obj(self, grid_points):
|
||||
def convert_grid_obj(self, grid_lt):
|
||||
"""转换每个网格的OBJ文件为OSGB格式"""
|
||||
os.makedirs(os.path.join(self.output_dir,
|
||||
"osgb", "Data"), exist_ok=True)
|
||||
|
||||
# 以第一个grid的UTM坐标作为参照系
|
||||
first_grid_id = list(grid_points.keys())[0]
|
||||
first_grid_id = grid_lt[0]
|
||||
first_grid_dir = os.path.join(
|
||||
self.output_dir,
|
||||
f"grid_{first_grid_id[0]}_{first_grid_id[1]}",
|
||||
@ -34,15 +34,15 @@ class ConvertOBJ:
|
||||
first_grid_dir, "odm_orthophoto", "odm_orthophoto_log.txt")
|
||||
self.ref_east, self.ref_north = self.read_utm_offset(log_file)
|
||||
|
||||
for grid_id in grid_points.keys():
|
||||
for grid_id in grid_lt:
|
||||
try:
|
||||
self._convert_single_grid(grid_id, grid_points)
|
||||
self._convert_single_grid(grid_id)
|
||||
except Exception as e:
|
||||
self.logger.error(f"网格 {grid_id} 转换失败: {str(e)}")
|
||||
|
||||
self._create_merged_metadata()
|
||||
|
||||
def _convert_single_grid(self, grid_id, grid_points):
|
||||
def _convert_single_grid(self, grid_id):
|
||||
"""转换单个网格的OBJ文件"""
|
||||
# 构建相关路径
|
||||
grid_name = f"grid_{grid_id[0]}_{grid_id[1]}"
|
||||
@ -50,7 +50,6 @@ class ConvertOBJ:
|
||||
texturing_dir = os.path.join(project_dir, "odm_texturing")
|
||||
texturing_dst_dir = os.path.join(project_dir, "odm_texturing_dst")
|
||||
split_obj_dir = os.path.join(texturing_dst_dir, "split_obj")
|
||||
opensfm_dir = os.path.join(project_dir, "opensfm")
|
||||
log_file = os.path.join(
|
||||
project_dir, "odm_orthophoto", "odm_orthophoto_log.txt")
|
||||
os.makedirs(texturing_dst_dir, exist_ok=True)
|
||||
|
||||
@ -19,11 +19,11 @@ class MergeTif:
|
||||
self.output_dir = output_dir
|
||||
self.logger = logging.getLogger('UAV_Preprocess.MergeTif')
|
||||
|
||||
def merge_orthophoto(self, grid_points: Dict[tuple, pd.DataFrame]):
|
||||
def merge_orthophoto(self, grid_lt):
|
||||
"""合并网格的正射影像"""
|
||||
try:
|
||||
all_orthos_and_ortho_cuts = []
|
||||
for grid_id, points in grid_points.items():
|
||||
for grid_id in grid_lt:
|
||||
grid_ortho_dir = os.path.join(
|
||||
self.output_dir,
|
||||
f"grid_{grid_id[0]}_{grid_id[1]}",
|
||||
|
||||
78
utils/directory_manager.py
Normal file
78
utils/directory_manager.py
Normal file
@ -0,0 +1,78 @@
|
||||
import os
|
||||
import shutil
|
||||
import psutil
|
||||
|
||||
|
||||
class DirectoryManager:
|
||||
def __init__(self, config):
|
||||
"""
|
||||
初始化目录管理器
|
||||
Args:
|
||||
config: 配置对象，包含输入和输出目录等信息
|
||||
"""
|
||||
self.config = config
|
||||
|
||||
def clean_output_dir(self):
|
||||
"""清理输出目录"""
|
||||
try:
|
||||
shutil.rmtree(self.config.output_dir)
|
||||
print(f"已清理输出目录: {self.config.output_dir}")
|
||||
except Exception as e:
|
||||
print(f"清理输出目录时发生错误: {str(e)}")
|
||||
raise
|
||||
|
||||
def setup_output_dirs(self):
|
||||
"""创建必要的输出目录结构"""
|
||||
try:
|
||||
# 创建主输出目录
|
||||
os.makedirs(self.config.output_dir)
|
||||
|
||||
# 创建过滤图像保存目录
|
||||
os.makedirs(os.path.join(self.config.output_dir, 'filter_imgs'))
|
||||
|
||||
# 创建日志目录
|
||||
os.makedirs(os.path.join(self.config.output_dir, 'logs'))
|
||||
|
||||
print(f"已创建输出目录结构: {self.config.output_dir}")
|
||||
except Exception as e:
|
||||
print(f"创建输出目录时发生错误: {str(e)}")
|
||||
raise
|
||||
|
||||
def _get_directory_size(self, path):
|
||||
"""获取目录的总大小（字节）"""
|
||||
total_size = 0
|
||||
for dirpath, dirnames, filenames in os.walk(path):
|
||||
for filename in filenames:
|
||||
file_path = os.path.join(dirpath, filename)
|
||||
try:
|
||||
total_size += os.path.getsize(file_path)
|
||||
except (OSError, FileNotFoundError):
|
||||
continue
|
||||
return total_size
|
||||
|
||||
def check_disk_space(self):
|
||||
"""检查磁盘空间是否足够"""
|
||||
# 获取输入目录大小
|
||||
input_size = self._get_directory_size(self.config.image_dir)
|
||||
|
||||
# 获取输出目录所在磁盘的剩余空间
|
||||
output_drive = os.path.splitdrive(
|
||||
os.path.abspath(self.config.output_dir))[0]
|
||||
if not output_drive: # 处理Linux/Unix路径
|
||||
output_drive = '/home'
|
||||
|
||||
disk_usage = psutil.disk_usage(output_drive)
|
||||
free_space = disk_usage.free
|
||||
|
||||
# 计算所需空间（输入大小的10倍）
|
||||
required_space = input_size * 10
|
||||
|
||||
if free_space < required_space:
|
||||
error_msg = (
|
||||
f"磁盘空间不足!\n"
|
||||
f"输入目录大小: {input_size / (1024**3):.2f} GB\n"
|
||||
f"所需空间: {required_space / (1024**3):.2f} GB\n"
|
||||
f"可用空间: {free_space / (1024**3):.2f} GB\n"
|
||||
f"在驱动器 {output_drive}"
|
||||
)
|
||||
raise RuntimeError(error_msg)
|
||||
@ -3,11 +3,10 @@ from PIL import Image
|
||||
import piexif
|
||||
import logging
|
||||
import pandas as pd
|
||||
from datetime import datetime
|
||||
|
||||
|
||||
class GPSExtractor:
|
||||
"""从图像文件提取GPS坐标和拍摄日期"""
|
||||
"""从图像文件提取GPS坐标"""
|
||||
|
||||
def __init__(self, image_dir):
|
||||
self.image_dir = image_dir
|
||||
@ -18,17 +17,8 @@ class GPSExtractor:
|
||||
"""将DMS格式转换为十进制度"""
|
||||
return dms[0][0] / dms[0][1] + (dms[1][0] / dms[1][1]) / 60 + (dms[2][0] / dms[2][1]) / 3600
|
||||
|
||||
@staticmethod
|
||||
def _parse_datetime(datetime_str):
|
||||
"""解析EXIF中的日期时间字符串"""
|
||||
try:
|
||||
# EXIF日期格式通常为 'YYYY:MM:DD HH:MM:SS'
|
||||
return datetime.strptime(datetime_str.decode(), '%Y:%m:%d %H:%M:%S')
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
def get_gps_and_date(self, image_path):
|
||||
"""提取单张图片的GPS坐标和拍摄日期"""
|
||||
def get_gps(self, image_path):
|
||||
"""提取单张图片的GPS坐标"""
|
||||
try:
|
||||
image = Image.open(image_path)
|
||||
exif_data = piexif.load(image.info['exif'])
|
||||
@ -39,38 +29,21 @@ class GPSExtractor:
|
||||
if gps_info:
|
||||
lat = self._dms_to_decimal(gps_info.get(2, []))
|
||||
lon = self._dms_to_decimal(gps_info.get(4, []))
|
||||
self.logger.debug(f"成功提取图片GPS坐标: {image_path} - 纬度: {lat}, 经度: {lon}")
|
||||
|
||||
# 提取拍摄日期
|
||||
date_info = None
|
||||
if "Exif" in exif_data:
|
||||
# 优先使用DateTimeOriginal
|
||||
date_str = exif_data["Exif"].get(36867) # DateTimeOriginal
|
||||
if not date_str:
|
||||
# 备选DateTime
|
||||
date_str = exif_data["Exif"].get(36868) # DateTimeDigitized
|
||||
if not date_str:
|
||||
# 最后使用基本DateTime
|
||||
date_str = exif_data["0th"].get(306) # DateTime
|
||||
|
||||
if date_str:
|
||||
date_info = self._parse_datetime(date_str)
|
||||
self.logger.debug(f"成功提取图片拍摄日期: {image_path} - {date_info}")
|
||||
self.logger.debug(
|
||||
f"成功提取图片GPS坐标: {image_path} - 纬度: {lat}, 经度: {lon}")
|
||||
|
||||
if not gps_info:
|
||||
self.logger.warning(f"图片无GPS信息: {image_path}")
|
||||
if not date_info:
|
||||
self.logger.warning(f"图片无拍摄日期信息: {image_path}")
|
||||
|
||||
return lat, lon, date_info
|
||||
return lat, lon
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"提取图片信息时发生错误: {image_path} - {str(e)}")
|
||||
return None, None, None
|
||||
|
||||
def extract_all_gps(self):
|
||||
"""提取所有图片的GPS坐标和拍摄日期"""
|
||||
self.logger.info(f"开始从目录提取GPS坐标和拍摄日期: {self.image_dir}")
|
||||
"""提取所有图片的GPS坐标"""
|
||||
self.logger.info(f"开始从目录提取GPS坐标: {self.image_dir}")
|
||||
gps_data = []
|
||||
total_images = 0
|
||||
successful_extractions = 0
|
||||
@ -78,15 +51,15 @@ class GPSExtractor:
|
||||
for image_file in os.listdir(self.image_dir):
|
||||
total_images += 1
|
||||
image_path = os.path.join(self.image_dir, image_file)
|
||||
lat, lon, date = self.get_gps_and_date(image_path)
|
||||
lat, lon = self.get_gps(image_path)
|
||||
if lat and lon: # 仍然以GPS信息作为主要判断依据
|
||||
successful_extractions += 1
|
||||
gps_data.append({
|
||||
'file': image_file,
|
||||
'lat': lat,
|
||||
'lon': lon,
|
||||
'date': date
|
||||
})
|
||||
|
||||
self.logger.info(f"GPS坐标和拍摄日期提取完成 - 总图片数: {total_images}, 成功提取: {successful_extractions}, 失败: {total_images - successful_extractions}")
|
||||
self.logger.info(
|
||||
f"GPS坐标提取完成 - 总图片数: {total_images}, 成功提取: {successful_extractions}, 失败: {total_images - successful_extractions}")
|
||||
return pd.DataFrame(gps_data)
|
||||
|
||||
@ -16,50 +16,15 @@ class GridDivider:
|
||||
self.num_grids_width = 0 # 添加网格数量属性
|
||||
self.num_grids_height = 0
|
||||
|
||||
def adjust_grid_size(self, points_df):
|
||||
"""动态调整网格大小
|
||||
|
||||
Args:
|
||||
points_df: 包含GPS点的DataFrame
|
||||
|
||||
Returns:
|
||||
tuple: (grids, translations, grid_points, final_grid_size)
|
||||
"""
|
||||
self.logger.info(f"开始动态调整网格大小，初始大小: {self.grid_size}米")
|
||||
|
||||
while True:
|
||||
# 使用当前grid_size划分网格
|
||||
grids, translations = self.divide_grids(points_df)
|
||||
grid_points, multiple_grid_points = self.assign_to_grids(points_df, grids)
|
||||
|
||||
# 检查每个网格中的点数
|
||||
max_points = 0
|
||||
for grid_id, points in grid_points.items():
|
||||
max_points = max(max_points, len(points))
|
||||
|
||||
self.logger.info(f"当前网格大小: {self.grid_size}米, 单个网格最大点数: {max_points}")
|
||||
|
||||
# 如果最大点数超过1600，减小网格大小
|
||||
if max_points > 1600:
|
||||
self.grid_size -= 100
|
||||
self.logger.info(f"点数超过1500，减小网格大小至: {self.grid_size}米")
|
||||
if self.grid_size < 500: # 设置一个最小网格大小限制
|
||||
self.logger.warning("网格大小已达到最小值500米，停止调整")
|
||||
break
|
||||
else:
|
||||
self.logger.info(f"找到合适的网格大小: {self.grid_size}米")
|
||||
break
|
||||
return grids
|
||||
|
||||
|
||||
def adjust_grid_size_and_overlap(self, points_df):
|
||||
"""动态调整网格重叠率"""
|
||||
grids = self.adjust_grid_size(points_df)
|
||||
self.logger.info(f"开始动态调整网格重叠率，初始重叠率: {self.overlap}")
|
||||
while True:
|
||||
# 使用调整好的网格大小划分网格
|
||||
grids, translations = self.divide_grids(points_df)
|
||||
grid_points, multiple_grid_points = self.assign_to_grids(points_df, grids)
|
||||
grids = self.divide_grids(points_df)
|
||||
grid_points, multiple_grid_points = self.assign_to_grids(
|
||||
points_df, grids)
|
||||
|
||||
if len(grids) == 1:
|
||||
self.logger.info(f"网格数量为1，跳过重叠率调整")
|
||||
@ -68,16 +33,52 @@ class GridDivider:
|
||||
self.overlap += 0.02
|
||||
self.logger.info(f"重叠率增加到: {self.overlap}")
|
||||
else:
|
||||
self.logger.info(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")
|
||||
self.logger.info(
|
||||
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")
|
||||
break
|
||||
return grids, translations, grid_points
|
||||
return grids, grid_points
|
||||
|
||||
def adjust_grid_size(self, points_df):
|
||||
"""动态调整网格大小
|
||||
|
||||
Args:
|
||||
points_df: 包含GPS点的DataFrame
|
||||
|
||||
Returns:
|
||||
tuple: grids
|
||||
"""
|
||||
self.logger.info(f"开始动态调整网格大小，初始大小: {self.grid_size}米")
|
||||
|
||||
while True:
|
||||
# 使用当前grid_size划分网格
|
||||
grids = self.divide_grids(points_df)
|
||||
grid_points, multiple_grid_points = self.assign_to_grids(
|
||||
points_df, grids)
|
||||
|
||||
# 检查每个网格中的点数
|
||||
max_points = 0
|
||||
for grid_id, points in grid_points.items():
|
||||
max_points = max(max_points, len(points))
|
||||
|
||||
self.logger.info(
|
||||
f"当前网格大小: {self.grid_size}米, 单个网格最大点数: {max_points}")
|
||||
|
||||
# 如果最大点数超过2000，减小网格大小
|
||||
if max_points > 2000:
|
||||
self.grid_size -= 100
|
||||
self.logger.info(f"点数超过2000，减小网格大小至: {self.grid_size}米")
|
||||
if self.grid_size < 500: # 设置一个最小网格大小限制
|
||||
self.logger.warning("网格大小已达到最小值500米，停止调整")
|
||||
break
|
||||
else:
|
||||
self.logger.info(f"找到合适的网格大小: {self.grid_size}米")
|
||||
break
|
||||
return grids
|
||||
|
||||
def divide_grids(self, points_df):
|
||||
"""计算边界框并划分网格
|
||||
Returns:
|
||||
tuple: (grids, translations)
|
||||
- grids: 网格边界列表
|
||||
- translations: 网格平移量字典
|
||||
tuple: grids 网格边界列表
|
||||
"""
|
||||
self.logger.info("开始划分网格")
|
||||
|
||||
@ -91,12 +92,15 @@ class GridDivider:
|
||||
self.logger.info(f"区域宽度: {width:.2f}米, 高度: {height:.2f}米")
|
||||
|
||||
# 精细调整网格的长宽，避免出现2*grid_size-1的情况的影响
|
||||
grid_size_lt = [self.grid_size -200, self.grid_size -100, self.grid_size , self.grid_size +100, self.grid_size +200]
|
||||
grid_size_lt = [self.grid_size - 200, self.grid_size - 100,
|
||||
self.grid_size, self.grid_size + 100, self.grid_size + 200]
|
||||
|
||||
width_modulus_lt = [width % grid_size for grid_size in grid_size_lt]
|
||||
grid_width = grid_size_lt[width_modulus_lt.index(min(width_modulus_lt))]
|
||||
grid_width = grid_size_lt[width_modulus_lt.index(
|
||||
min(width_modulus_lt))]
|
||||
height_modulus_lt = [height % grid_size for grid_size in grid_size_lt]
|
||||
grid_height = grid_size_lt[height_modulus_lt.index(min(height_modulus_lt))]
|
||||
grid_height = grid_size_lt[height_modulus_lt.index(
|
||||
min(height_modulus_lt))]
|
||||
self.logger.info(f"网格宽度: {grid_width:.2f}米, 网格高度: {grid_height:.2f}米")
|
||||
|
||||
# 计算需要划分的网格数量
|
||||
@ -108,18 +112,19 @@ class GridDivider:
|
||||
lon_step = (max_lon - min_lon) / self.num_grids_width
|
||||
|
||||
grids = []
|
||||
grid_translations = {} # 存储每个网格相对于第一个网格的平移量
|
||||
|
||||
# 先创建所有网格
|
||||
for i in range(self.num_grids_height):
|
||||
for j in range(self.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
|
||||
grid_max_lon = min_lon + \
|
||||
(j + 1) * lon_step + self.overlap * lon_step
|
||||
|
||||
grid_id = (i, j) # 使用(i,j)作为网格标识，i代表行，j代表列
|
||||
grid_bounds = (grid_min_lat, grid_max_lat, grid_min_lon, grid_max_lon)
|
||||
grid_bounds = (grid_min_lat, grid_max_lat,
|
||||
grid_min_lon, grid_max_lon)
|
||||
grids.append(grid_bounds)
|
||||
|
||||
self.logger.debug(
|
||||
@ -127,26 +132,10 @@ class GridDivider:
|
||||
f"经度[{grid_min_lon:.6f}, {grid_max_lon:.6f}]"
|
||||
)
|
||||
|
||||
# 计算每个网格相对于第一个网格的平移量
|
||||
reference_grid = grids[0]
|
||||
for i in range(self.num_grids_height):
|
||||
for j in range(self.num_grids_width):
|
||||
grid_id = (i, j)
|
||||
grid_idx = i * self.num_grids_width + j
|
||||
if grid_idx == 0: # 参考网格
|
||||
grid_translations[grid_id] = (0, 0)
|
||||
else:
|
||||
translation = self.calculate_grid_translation(reference_grid, grids[grid_idx])
|
||||
grid_translations[grid_id] = translation
|
||||
self.logger.debug(
|
||||
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"
|
||||
)
|
||||
|
||||
self.logger.info(
|
||||
f"成功划分为 {len(grids)} 个网格 ({self.num_grids_width}x{self.num_grids_height})")
|
||||
|
||||
return grids, grid_translations
|
||||
|
||||
return grids
|
||||
|
||||
def assign_to_grids(self, points_df, grids):
|
||||
"""将点分配到对应网格"""
|
||||
@ -205,7 +194,8 @@ class GridDivider:
|
||||
|
||||
# 计算网格的实际长度和宽度（米）
|
||||
width = geodesic((min_lat, min_lon), (min_lat, max_lon)).meters
|
||||
height = geodesic((min_lat, min_lon), (max_lat, min_lon)).meters
|
||||
height = geodesic((min_lat, min_lon),
|
||||
(max_lat, min_lon)).meters
|
||||
|
||||
plt.plot([min_lon, max_lon, max_lon, min_lon, min_lon],
|
||||
[min_lat, min_lat, max_lat, max_lat, min_lat],
|
||||
|
||||
@ -1,134 +1,22 @@
|
||||
import os
|
||||
import time
|
||||
import logging
|
||||
import subprocess
|
||||
from typing import Dict, Tuple
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from osgeo import gdal
|
||||
|
||||
|
||||
class NotOverlapError(Exception):
|
||||
"""图像重叠度不足异常"""
|
||||
pass
|
||||
import docker
|
||||
|
||||
|
||||
class ODMProcessMonitor:
|
||||
"""ODM处理监控器"""
|
||||
|
||||
def __init__(self, output_dir: str, mode: str = "快拼模式"):
|
||||
def __init__(self, output_dir: str, mode: str = "三维模式"):
|
||||
self.output_dir = output_dir
|
||||
self.logger = logging.getLogger('UAV_Preprocess.ODMMonitor')
|
||||
self.mode = mode
|
||||
|
||||
def _check_success(self, grid_dir: str) -> bool:
|
||||
"""检查ODM是否执行成功
|
||||
|
||||
检查项目:
|
||||
1. 必要的文件夹和文件是否存在
|
||||
2. 产品文件是否有效
|
||||
"""
|
||||
project_dir = os.path.join(grid_dir, 'project')
|
||||
|
||||
# 根据不同模式检查不同的产品
|
||||
if self.mode == "快拼模式":
|
||||
# 只检查正射影像
|
||||
# if not self._check_orthophoto(project_dir):
|
||||
# return False
|
||||
pass
|
||||
|
||||
elif self.mode == "三维模式":
|
||||
# 检查点云和实景三维
|
||||
if not all([
|
||||
os.path.exists(os.path.join(
|
||||
project_dir, 'odm_georeferencing', 'odm_georeferenced_model.laz')),
|
||||
os.path.exists(os.path.join(
|
||||
project_dir, 'odm_texturing', 'odm_textured_model_geo.obj'))
|
||||
]):
|
||||
self.logger.error("点云或实景三维文件夹未生成")
|
||||
return False
|
||||
|
||||
# TODO: 添加点云和实景三维的质量检查
|
||||
|
||||
elif self.mode == "重建模式":
|
||||
# 检查所有产品
|
||||
if not all([
|
||||
os.path.exists(os.path.join(
|
||||
project_dir, 'odm_georeferencing', 'odm_georeferenced_model.laz')),
|
||||
os.path.exists(os.path.join(
|
||||
project_dir, 'odm_texturing', 'odm_textured_model_geo.obj'))
|
||||
]):
|
||||
self.logger.error("部分必要的文件夹未生成")
|
||||
return False
|
||||
|
||||
# 检查正射影像
|
||||
# if not self._check_orthophoto(project_dir):
|
||||
# return False
|
||||
|
||||
# TODO: 添加点云和实景三维的质量检查
|
||||
|
||||
return True
|
||||
|
||||
# TODO 正射影像怎么检查最好
|
||||
def _check_orthophoto(self, project_dir: str) -> bool:
|
||||
"""检查正射影像的质量"""
|
||||
ortho_path = os.path.join(
|
||||
project_dir, 'odm_orthophoto', 'odm_orthophoto.original.tif')
|
||||
|
||||
if not os.path.exists(ortho_path):
|
||||
self.logger.error("正射影像文件未生成")
|
||||
return False
|
||||
|
||||
# 检查文件大小
|
||||
file_size_mb = os.path.getsize(ortho_path) / (1024 * 1024) # 转换为MB
|
||||
if file_size_mb < 1:
|
||||
self.logger.error(f"正射影像文件过小: {file_size_mb:.2f}MB")
|
||||
return False
|
||||
|
||||
try:
|
||||
# 打开影像文件
|
||||
ds = gdal.Open(ortho_path)
|
||||
if ds is None:
|
||||
self.logger.error("无法打开正射影像文件")
|
||||
return False
|
||||
|
||||
# 读取第一个波段
|
||||
band = ds.GetRasterBand(1)
|
||||
|
||||
# 获取统计信息
|
||||
stats = band.GetStatistics(False, True)
|
||||
if stats is None:
|
||||
self.logger.error("无法获取影像统计信息")
|
||||
return False
|
||||
|
||||
min_val, max_val, mean, std = stats
|
||||
|
||||
# 计算空值比例
|
||||
no_data_value = band.GetNoDataValue()
|
||||
array = band.ReadAsArray()
|
||||
if no_data_value is not None:
|
||||
no_data_ratio = np.sum(array == no_data_value) / array.size
|
||||
else:
|
||||
no_data_ratio = 0
|
||||
|
||||
# 检查空值比例是否过高（超过50%）
|
||||
if no_data_ratio > 0.5:
|
||||
self.logger.error(f"正射影像空值比例过高: {no_data_ratio:.2%}")
|
||||
return False
|
||||
|
||||
# 检查影像是否全黑或全白
|
||||
if max_val - min_val < 1:
|
||||
self.logger.error("正射影像可能无效：像素值范围过小")
|
||||
return False
|
||||
|
||||
ds = None # 关闭数据集
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"检查正射影像时发生错误: {str(e)}")
|
||||
return False
|
||||
|
||||
def run_odm_with_monitor(self, grid_dir: str, grid_id: tuple, produce_dem: bool = False, accuracy='medium') -> Tuple[bool, str]:
|
||||
def run_odm_with_monitor(self, grid_dir: str, grid_id: tuple) -> Tuple[bool, str]:
|
||||
"""运行ODM命令"""
|
||||
self.logger.info(f"开始处理网格 ({grid_id[0]},{grid_id[1]})")
|
||||
success = False
|
||||
@ -136,21 +24,21 @@ class ODMProcessMonitor:
|
||||
max_retries = 3
|
||||
current_try = 0
|
||||
|
||||
# 根据模式设置是否使用lowest quality
|
||||
use_lowest_quality = self.mode == "快拼模式"
|
||||
# 初始化 Docker 客户端
|
||||
client = docker.from_env()
|
||||
|
||||
while current_try < max_retries:
|
||||
current_try += 1
|
||||
self.logger.info(
|
||||
f"第 {current_try} 次尝试处理网格 ({grid_id[0]},{grid_id[1]})")
|
||||
|
||||
try:
|
||||
# 构建Docker命令
|
||||
grid_dir = grid_dir[0].lower()+grid_dir[1:].replace('\\', '/')
|
||||
docker_command = (
|
||||
f"docker run --gpus all -ti --rm "
|
||||
f"-v {grid_dir}:/datasets "
|
||||
f"opendronemap/odm:gpu "
|
||||
# 构建 Docker 容器运行参数
|
||||
grid_dir = grid_dir[0].lower(
|
||||
) + grid_dir[1:].replace('\\', '/')
|
||||
volumes = {
|
||||
grid_dir: {'bind': '/datasets', 'mode': 'rw'}
|
||||
}
|
||||
command = (
|
||||
f"--project-path /datasets project "
|
||||
f"--max-concurrency 15 "
|
||||
f"--force-gps "
|
||||
@ -159,117 +47,70 @@ class ODMProcessMonitor:
|
||||
f"--optimize-disk-space "
|
||||
f"--orthophoto-cutline "
|
||||
f"--feature-type sift "
|
||||
# f"--orthophoto-resolution 8 "
|
||||
)
|
||||
if accuracy == "high":
|
||||
docker_command += (
|
||||
f"--feature-quality ultra "
|
||||
f"--pc-quality ultra "
|
||||
f"--mesh-size 3000000 "
|
||||
f"--mesh-octree-depth 12 "
|
||||
f"--orthophoto-resolution 2 "
|
||||
f"--orthophoto-resolution 8 "
|
||||
)
|
||||
|
||||
if produce_dem and self.mode != "快拼模式":
|
||||
docker_command += (
|
||||
if self.mode == "快拼模式":
|
||||
command += (
|
||||
f"--fast-orthophoto "
|
||||
f"--skip-3dmodel "
|
||||
)
|
||||
else: # 三维模式参数
|
||||
command += (
|
||||
f"--dsm "
|
||||
f"--dtm "
|
||||
)
|
||||
|
||||
# 根据是否使用lowest quality添加参数
|
||||
if use_lowest_quality:
|
||||
# docker_command += f"--feature-quality lowest "
|
||||
pass
|
||||
command += "--rerun-all"
|
||||
|
||||
if self.mode == "快拼模式":
|
||||
docker_command += (
|
||||
f"--fast-orthophoto "
|
||||
f"--skip-3dmodel "
|
||||
self.logger.info(f"Docker 命令: {command}")
|
||||
|
||||
# 运行 Docker 容器
|
||||
container = client.containers.run(
|
||||
image="opendronemap/odm:gpu",
|
||||
command=command,
|
||||
volumes=volumes,
|
||||
detach=True,
|
||||
remove=False,
|
||||
runtime="nvidia", # 使用 GPU
|
||||
)
|
||||
# elif self.mode == "三维模式":
|
||||
# docker_command += (
|
||||
# f"--skip-orthophoto "
|
||||
# )
|
||||
|
||||
docker_command += "--rerun-all"
|
||||
self.logger.info(docker_command)
|
||||
|
||||
process = subprocess.Popen(
|
||||
docker_command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
|
||||
logging.info(f"进程{process.pid}开始执行")
|
||||
stdout, stderr = process.communicate()
|
||||
stdout = stdout.decode('utf-8')
|
||||
stderr = stderr.decode('utf-8')
|
||||
# 关闭process，防止内存堆积
|
||||
process.terminate()
|
||||
return_code = process.poll() # 获取进程返回码，如果返回 None 说明进程仍在运行
|
||||
if return_code is None:
|
||||
logging.info(f"进程{process.pid}仍在运行")
|
||||
# 等待容器运行完成
|
||||
exit_status = container.wait()
|
||||
if exit_status["StatusCode"] != 0:
|
||||
self.logger.error(
|
||||
f"容器运行失败，退出状态码: {exit_status['StatusCode']}")
|
||||
# 获取容器的错误日志
|
||||
error_msg = container.logs(
|
||||
stderr=True).decode("utf-8").splitlines()
|
||||
self.logger.error("容器运行失败的详细错误日志：")
|
||||
for line in error_msg:
|
||||
self.logger.error(line)
|
||||
else:
|
||||
logging.info(f"进程{process.pid}已被关闭，返回码：{return_code}.")
|
||||
# 获取所有日志
|
||||
logs = container.logs().decode("utf-8").splitlines()
|
||||
|
||||
stdout_lines = stdout.strip().split('\n')
|
||||
last_lines = '\n'.join(
|
||||
stdout_lines[-50:] if len(stdout_lines) > 10 else stdout_lines)
|
||||
self.logger.info(f"==========stdout==========: {last_lines}")
|
||||
|
||||
if stderr:
|
||||
self.logger.error(f"docker run指令执行失败")
|
||||
self.logger.error(f"==========stderr==========: {stderr}")
|
||||
if "error during connect" in stderr or "The system cannot find the file specified" in stderr:
|
||||
error_msg = "Docker没有启动，请启动Docker"
|
||||
elif "user declined directory sharing" in stderr:
|
||||
error_msg = "Docker无法访问目录，请检查目录权限和共享设置"
|
||||
else:
|
||||
error_msg = "Docker运行失败，需要人工排查错误"
|
||||
break
|
||||
else:
|
||||
self.logger.info("docker run指令执行成功")
|
||||
if "ODM app finished" in last_lines:
|
||||
self.logger.info("ODM处理完成")
|
||||
if self._check_success(grid_dir):
|
||||
self.logger.info(
|
||||
f"网格 ({grid_id[0]},{grid_id[1]}) 处理成功")
|
||||
# 输出最后 50 行日志
|
||||
self.logger.info("容器运行完成，以下是最后 50 行日志：")
|
||||
for line in logs[-50:]:
|
||||
self.logger.info(line)
|
||||
success = True
|
||||
error_msg = ""
|
||||
break
|
||||
else:
|
||||
self.logger.error(
|
||||
f"虽然ODM处理完成，但是生产产品质量可能不合格，需要人工检查")
|
||||
raise NotOverlapError
|
||||
# TODO 先写成这样，后面这三种情况可能处理不一样
|
||||
elif "enough overlap" in last_lines:
|
||||
raise NotOverlapError
|
||||
elif "out of memory" in last_lines:
|
||||
raise NotOverlapError
|
||||
elif "strange values" in last_lines:
|
||||
raise NotOverlapError
|
||||
else:
|
||||
raise NotOverlapError
|
||||
|
||||
except NotOverlapError:
|
||||
if use_lowest_quality and self.mode == "快拼模式":
|
||||
self.logger.warning(
|
||||
"检测到not overlap错误，移除lowest quality参数后重试")
|
||||
use_lowest_quality = False
|
||||
time.sleep(10)
|
||||
continue
|
||||
else:
|
||||
self.logger.error(
|
||||
"即使移除lowest quality参数后仍然出现错误")
|
||||
error_msg = "图像重叠度不足，需要人工检查数据集的采样间隔情况"
|
||||
break
|
||||
# 删除容器
|
||||
container.remove()
|
||||
time.sleep(5)
|
||||
|
||||
return success, error_msg
|
||||
|
||||
def process_all_grids(self, grid_points: Dict[tuple, pd.DataFrame], produce_dem: bool, accuracy: str) -> Dict[tuple, pd.DataFrame]:
|
||||
def process_all_grids(self, grid_points: Dict[tuple, pd.DataFrame]) -> list:
|
||||
"""处理所有网格
|
||||
|
||||
Returns:
|
||||
Dict[tuple, pd.DataFrame]: 成功处理的网格点数据字典
|
||||
"""
|
||||
self.logger.info("开始执行网格处理")
|
||||
successful_grid_points = {}
|
||||
successful_grid_lt = []
|
||||
failed_grids = []
|
||||
|
||||
for grid_id, points in grid_points.items():
|
||||
@ -281,12 +122,10 @@ class ODMProcessMonitor:
|
||||
success, error_msg = self.run_odm_with_monitor(
|
||||
grid_dir=grid_dir,
|
||||
grid_id=grid_id,
|
||||
produce_dem=produce_dem,
|
||||
accuracy=accuracy
|
||||
)
|
||||
|
||||
if success:
|
||||
successful_grid_points[grid_id] = points
|
||||
successful_grid_lt.append(grid_id)
|
||||
else:
|
||||
self.logger.error(
|
||||
f"网格 ({grid_id[0]},{grid_id[1]}) 处理失败: {error_msg}")
|
||||
@ -301,7 +140,7 @@ class ODMProcessMonitor:
|
||||
# 汇总处理结果
|
||||
total_grids = len(grid_points)
|
||||
failed_count = len(failed_grids)
|
||||
success_count = len(successful_grid_points)
|
||||
success_count = len(successful_grid_lt)
|
||||
|
||||
self.logger.info(
|
||||
f"网格处理完成。总计: {total_grids}, 成功: {success_count}, 失败: {failed_count}")
|
||||
@ -311,7 +150,7 @@ class ODMProcessMonitor:
|
||||
self.logger.error(
|
||||
f"网格 ({grid_id[0]},{grid_id[1]}): {error_msg}")
|
||||
|
||||
if len(successful_grid_points) == 0:
|
||||
if len(successful_grid_lt) == 0:
|
||||
raise Exception("所有网格处理都失败，无法继续处理")
|
||||
|
||||
return successful_grid_points
|
||||
return successful_grid_lt
|
||||
|
||||
@ -65,7 +65,7 @@ class FilterVisualizer:
|
||||
# 创建图形
|
||||
plt.rcParams['font.sans-serif']=['SimHei']#黑体
|
||||
plt.rcParams['axes.unicode_minus'] = False
|
||||
plt.figure(figsize=(20, 16))
|
||||
plt.figure()
|
||||
|
||||
# 绘制保留的点
|
||||
plt.scatter(current_x, current_y,
|
||||
|
||||
Loading…
Reference in New Issue
Block a user