修改merge_tif，使用ODM的cut，feather和merge函数
This commit is contained in:
weixin_46229132
parent
697660b5b3
commit
a25adbcc31
@ -254,7 +254,7 @@ class ImagePreprocessor:
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"""合并所有网格的影像产品"""
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self.logger.info("开始合并所有影像产品")
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merger = MergeTif(self.config.output_dir)
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merger.merge_all_tifs(grid_points, mode)
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merger.merge_orthophoto(grid_points)
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def merge_ply(self, grid_points: Dict[tuple, pd.DataFrame]):
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"""合并所有网格的PLY点云"""
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@ -1,352 +0,0 @@
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import os
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import shutil
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from datetime import timedelta
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from dataclasses import dataclass
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from typing import Dict, Tuple
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import psutil
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import pandas as pd
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from filter.cluster_filter import GPSCluster
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from filter.time_group_overlap_filter import TimeGroupOverlapFilter
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from filter.gps_filter import GPSFilter
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from utils.odm_monitor import ODMProcessMonitor
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from utils.gps_extractor import GPSExtractor
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from utils.grid_divider import GridDivider
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from utils.logger import setup_logger
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from utils.visualizer import FilterVisualizer
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from post_pro.merge_tif import MergeTif
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from post_pro.merge_obj import MergeObj
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from post_pro.merge_laz import MergePly
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from post_pro.conv_obj2 import ConvertOBJ
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@dataclass
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class PreprocessConfig:
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"""预处理配置类"""
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image_dir: str
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output_dir: str
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# 聚类过滤参数
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cluster_eps: float = 0.01
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cluster_min_samples: int = 5
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# 时间组重叠过滤参数
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time_group_overlap_threshold: float = 0.7
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time_group_interval: timedelta = timedelta(minutes=5)
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# 孤立点过滤参数
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filter_distance_threshold: float = 0.001 # 经纬度距离
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filter_min_neighbors: int = 6
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# 密集点过滤参数
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filter_grid_size: float = 0.001
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filter_dense_distance_threshold: float = 10 # 普通距离，单位：米
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filter_time_threshold: timedelta = timedelta(minutes=5)
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# 网格划分参数
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grid_overlap: float = 0.05
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grid_size: float = 500
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# 几个pipline过程是否开启
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mode: str = "快拼模式"
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accuracy: str = "medium"
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produce_dem: bool = False
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class ImagePreprocessor:
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def __init__(self, config: PreprocessConfig):
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self.config = config
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# 检查磁盘空间
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self._check_disk_space()
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# # 清理并重建输出目录
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# if os.path.exists(config.output_dir):
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# self._clean_output_dir()
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# self._setup_output_dirs()
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# 初始化其他组件
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self.logger = setup_logger(config.output_dir)
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self.gps_points = None
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self.odm_monitor = ODMProcessMonitor(
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config.output_dir, mode=config.mode)
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self.visualizer = FilterVisualizer(config.output_dir)
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def _clean_output_dir(self):
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"""清理输出目录"""
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try:
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shutil.rmtree(self.config.output_dir)
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print(f"已清理输出目录: {self.config.output_dir}")
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except Exception as e:
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print(f"清理输出目录时发生错误: {str(e)}")
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raise
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def _setup_output_dirs(self):
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"""创建必要的输出目录结构"""
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try:
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# 创建主输出目录
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os.makedirs(self.config.output_dir)
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# 创建过滤图像保存目录
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os.makedirs(os.path.join(self.config.output_dir, 'filter_imgs'))
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# 创建日志目录
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os.makedirs(os.path.join(self.config.output_dir, 'logs'))
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print(f"已创建输出目录结构: {self.config.output_dir}")
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except Exception as e:
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print(f"创建输出目录时发生错误: {str(e)}")
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raise
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def _get_directory_size(self, path):
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"""获取目录的总大小（字节）"""
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total_size = 0
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for dirpath, dirnames, filenames in os.walk(path):
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for filename in filenames:
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file_path = os.path.join(dirpath, filename)
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try:
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total_size += os.path.getsize(file_path)
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except (OSError, FileNotFoundError):
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continue
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return total_size
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def _check_disk_space(self):
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"""检查磁盘空间是否足够"""
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# 获取输入目录大小
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input_size = self._get_directory_size(self.config.image_dir)
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# 获取输出目录所在磁盘的剩余空间
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output_drive = os.path.splitdrive(
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os.path.abspath(self.config.output_dir))[0]
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if not output_drive: # 处理Linux/Unix路径
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output_drive = '/home'
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disk_usage = psutil.disk_usage(output_drive)
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free_space = disk_usage.free
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# 计算所需空间（输入大小的1.5倍）
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required_space = input_size * 12
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if free_space < required_space:
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error_msg = (
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f"磁盘空间不足!\n"
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f"输入目录大小: {input_size / (1024**3):.2f} GB\n"
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f"所需空间: {required_space / (1024**3):.2f} GB\n"
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f"可用空间: {free_space / (1024**3):.2f} GB\n"
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f"在驱动器 {output_drive}"
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)
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raise RuntimeError(error_msg)
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def extract_gps(self) -> pd.DataFrame:
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"""提取GPS数据"""
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self.logger.info("开始提取GPS数据")
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extractor = GPSExtractor(self.config.image_dir)
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self.gps_points = extractor.extract_all_gps()
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self.logger.info(f"成功提取 {len(self.gps_points)} 个GPS点")
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def cluster(self):
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"""使用DBSCAN对GPS点进行聚类，只保留最大的类"""
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previous_points = self.gps_points.copy()
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clusterer = GPSCluster(
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self.gps_points,
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eps=self.config.cluster_eps,
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min_samples=self.config.cluster_min_samples
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)
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self.clustered_points = clusterer.fit()
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self.gps_points = clusterer.get_cluster_stats(self.clustered_points)
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self.visualizer.visualize_filter_step(
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self.gps_points, previous_points, "1-Clustering")
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def filter_isolated_points(self):
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"""过滤孤立点"""
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filter = GPSFilter(self.config.output_dir)
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previous_points = self.gps_points.copy()
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self.gps_points = filter.filter_isolated_points(
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self.gps_points,
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self.config.filter_distance_threshold,
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self.config.filter_min_neighbors,
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)
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self.visualizer.visualize_filter_step(
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self.gps_points, previous_points, "2-Isolated Points")
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def filter_time_group_overlap(self):
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"""过滤重叠的时间组"""
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previous_points = self.gps_points.copy()
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filter = TimeGroupOverlapFilter(
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self.config.image_dir,
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self.config.output_dir,
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overlap_threshold=self.config.time_group_overlap_threshold
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)
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self.gps_points = filter.filter_overlapping_groups(
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self.gps_points,
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time_threshold=self.config.time_group_interval
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)
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self.visualizer.visualize_filter_step(
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self.gps_points, previous_points, "3-Time Group Overlap")
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def calculate_center_coordinates(self):
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"""计算剩余点的中心经纬度坐标"""
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mean_lat = self.gps_points['lat'].mean()
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mean_lon = self.gps_points['lon'].mean()
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self.logger.info(f"区域中心坐标：纬度 {mean_lat:.6f}, 经度 {mean_lon:.6f}")
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return mean_lat, mean_lon
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def filter_alternate_images(self):
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"""按时间顺序隔一个删一个图像来降低密度"""
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previous_points = self.gps_points.copy()
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# 按时间戳排序
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self.gps_points = self.gps_points.sort_values('date')
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# 保留索引为偶数的行（即隔一个保留一个）
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self.gps_points = self.gps_points.iloc[::2].reset_index(drop=True)
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self.visualizer.visualize_filter_step(
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self.gps_points, previous_points, "4-Alternate Images")
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self.logger.info(f"交替过滤后剩余 {len(self.gps_points)} 个点")
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def divide_grids(self) -> Tuple[Dict[tuple, pd.DataFrame], Dict[tuple, tuple]]:
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"""划分网格
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Returns:
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tuple: (grid_points, translations)
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- grid_points: 网格点数据字典
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- translations: 网格平移量字典
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"""
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grid_divider = GridDivider(
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overlap=self.config.grid_overlap,
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grid_size=self.config.grid_size,
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output_dir=self.config.output_dir
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)
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grids, translations, grid_points = grid_divider.adjust_grid_size_and_overlap(
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self.gps_points
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)
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grid_divider.visualize_grids(self.gps_points, grids)
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if len(grids) >= 20:
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self.logger.warning("网格数量已超过20, 需要人工调整分区")
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return grid_points, translations
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def copy_images(self, grid_points: Dict[tuple, pd.DataFrame]):
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"""复制图像到目标文件夹"""
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self.logger.info("开始复制图像文件")
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for grid_id, points in grid_points.items():
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output_dir = os.path.join(
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self.config.output_dir,
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f"grid_{grid_id[0]}_{grid_id[1]}",
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"project",
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"images"
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)
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os.makedirs(output_dir, exist_ok=True)
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for point in points:
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src = os.path.join(self.config.image_dir, point["file"])
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dst = os.path.join(output_dir, point["file"])
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shutil.copy(src, dst)
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self.logger.info(
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f"网格 ({grid_id[0]},{grid_id[1]}) 包含 {len(points)} 张图像")
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def merge_tif(self, grid_points: Dict[tuple, pd.DataFrame], produce_dem: bool):
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"""合并所有网格的影像产品"""
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self.logger.info("开始合并所有影像产品")
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merger = MergeTif(self.config.output_dir)
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merger.merge_all_tifs(grid_points, produce_dem)
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def merge_ply(self, grid_points: Dict[tuple, pd.DataFrame]):
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"""合并所有网格的PLY点云"""
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self.logger.info("开始合并PLY点云")
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merger = MergePly(self.config.output_dir)
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merger.merge_grid_laz(grid_points)
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def merge_obj(self, grid_points: Dict[tuple, pd.DataFrame], translations: Dict[tuple, tuple]):
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"""合并所有网格的OBJ模型"""
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self.logger.info("开始合并OBJ模型")
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merger = MergeObj(self.config.output_dir)
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merger.merge_grid_obj(grid_points, translations)
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def convert_obj(self, grid_points: Dict[tuple, pd.DataFrame]):
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"""转换OBJ模型"""
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self.logger.info("开始转换OBJ模型")
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converter = ConvertOBJ(self.config.output_dir)
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converter.convert_grid_obj(grid_points)
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def post_process(self, successful_grid_points: Dict[tuple, pd.DataFrame], grid_points: Dict[tuple, pd.DataFrame], translations: Dict[tuple, tuple]):
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"""后处理：合并或复制处理结果"""
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if len(successful_grid_points) < len(grid_points):
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self.logger.warning(
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f"有 {len(grid_points) - len(successful_grid_points)} 个网格处理失败，"
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f"将只合并成功处理的 {len(successful_grid_points)} 个网格"
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)
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if self.config.mode == "快拼模式":
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self.merge_tif(successful_grid_points, self.config.produce_dem)
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elif self.config.mode == "三维模式":
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self.merge_tif(successful_grid_points, self.config.produce_dem)
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# self.merge_ply(successful_grid_points)
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# self.merge_obj(successful_grid_points, translations)
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self.convert_obj(successful_grid_points)
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else:
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self.merge_tif(successful_grid_points, self.config.produce_dem)
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# self.merge_ply(successful_grid_points)
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# self.merge_obj(successful_grid_points, translations)
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self.convert_obj(successful_grid_points)
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def process(self):
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"""执行完整的预处理流程"""
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try:
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self.extract_gps()
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self.cluster()
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# self.filter_isolated_points()
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grid_points, translations = self.divide_grids()
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# self.copy_images(grid_points)
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# self.logger.info("预处理任务完成")
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# successful_grid_points = self.odm_monitor.process_all_grids(
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# grid_points, self.config.produce_dem, self.config.accuracy)
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# successful_grid_points = self.odm_monitor.process_all_grids(
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# grid_points, self.config.produce_dem)
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successful_grid_points = grid_points
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self.post_process(successful_grid_points,
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grid_points, translations)
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except Exception as e:
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self.logger.error(f"处理过程中发生错误: {str(e)}", exc_info=True)
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raise
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if __name__ == "__main__":
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# 创建配置
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config = PreprocessConfig(
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image_dir=r"E:\datasets\UAV\134\project\images",
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output_dir=r"G:\ODM_output\134",
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cluster_eps=0.01,
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cluster_min_samples=5,
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# 添加时间组重叠过滤参数
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time_group_overlap_threshold=0.7,
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time_group_interval=timedelta(minutes=5),
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filter_distance_threshold=0.001,
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filter_min_neighbors=6,
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filter_grid_size=0.001,
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filter_dense_distance_threshold=10,
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filter_time_threshold=timedelta(minutes=5),
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grid_size=800,
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grid_overlap=0.05,
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mode="重建模式",
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produce_dem=False,
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)
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# 创建处理器并执行
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processor = ImagePreprocessor(config)
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processor.process()
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@ -5,6 +5,13 @@ from typing import Dict
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import pandas as pd
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import time
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import shutil
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import rasterio
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from rasterio.mask import mask
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from rasterio.transform import Affine, rowcol
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import fiona
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from edt import edt
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import numpy as np
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import math
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class MergeTif:
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@ -12,251 +19,271 @@ class MergeTif:
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self.output_dir = output_dir
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self.logger = logging.getLogger('UAV_Preprocess.MergeTif')
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def merge_two_tifs(self, input_tif1: str, input_tif2: str, output_tif: str):
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"""合并两张TIF影像"""
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try:
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self.logger.info("开始合并TIF影像")
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self.logger.info(f"输入影像1: {input_tif1}")
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self.logger.info(f"输入影像2: {input_tif2}")
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self.logger.info(f"输出影像: {output_tif}")
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# 检查输入文件是否存在
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if not os.path.exists(input_tif1) or not os.path.exists(input_tif2):
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error_msg = "输入影像文件不存在"
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self.logger.error(error_msg)
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raise FileNotFoundError(error_msg)
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# 打开影像，检查投影是否一致
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datasets = []
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try:
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for tif in [input_tif1, input_tif2]:
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ds = gdal.Open(tif)
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if ds is None:
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error_msg = f"无法打开影像文件: {tif}"
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self.logger.error(error_msg)
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raise ValueError(error_msg)
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datasets.append(ds)
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projections = [ds.GetProjection() for ds in datasets]
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self.logger.debug(f"影像1投影: {projections[0]}")
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self.logger.debug(f"影像2投影: {projections[1]}")
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# 检查投影是否一致
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if len(set(projections)) != 1:
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error_msg = "影像的投影不一致，请先进行重投影！"
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self.logger.error(error_msg)
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raise ValueError(error_msg)
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# 如果输出文件已存在，先删除
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if os.path.exists(output_tif):
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try:
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os.remove(output_tif)
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except Exception as e:
|
||||
self.logger.warning(f"删除已存在的输出文件失败: {str(e)}")
|
||||
# 生成一个新的输出文件名
|
||||
base, ext = os.path.splitext(output_tif)
|
||||
output_tif = f"{base}_{int(time.time())}{ext}"
|
||||
self.logger.info(f"使用新的输出文件名: {output_tif}")
|
||||
|
||||
# 创建 GDAL Warp 选项
|
||||
warp_options = gdal.WarpOptions(
|
||||
format="GTiff",
|
||||
resampleAlg="average",
|
||||
srcNodata=0,
|
||||
dstNodata=0,
|
||||
multithread=True
|
||||
)
|
||||
|
||||
self.logger.info("开始执行影像拼接...")
|
||||
result = gdal.Warp(output_tif, datasets, options=warp_options)
|
||||
|
||||
if result is None:
|
||||
error_msg = "影像拼接失败"
|
||||
self.logger.error(error_msg)
|
||||
raise RuntimeError(error_msg)
|
||||
|
||||
# 获取输出影像的基本信息
|
||||
output_dataset = gdal.Open(output_tif)
|
||||
if output_dataset:
|
||||
width = output_dataset.RasterXSize
|
||||
height = output_dataset.RasterYSize
|
||||
bands = output_dataset.RasterCount
|
||||
self.logger.info(
|
||||
f"拼接完成，输出影像大小: {width}x{height}，波段数: {bands}")
|
||||
output_dataset = None # 显式关闭数据集
|
||||
|
||||
self.logger.info(f"影像拼接成功，输出文件保存至: {output_tif}")
|
||||
|
||||
finally:
|
||||
# 确保所有数据集都被正确关闭
|
||||
for ds in datasets:
|
||||
if ds:
|
||||
ds = None
|
||||
result = None
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"影像拼接过程中发生错误: {str(e)}", exc_info=True)
|
||||
raise
|
||||
|
||||
def merge_grid_tif(self, grid_points: Dict[tuple, pd.DataFrame], product_info: dict):
|
||||
"""合并指定产品的所有网格"""
|
||||
product_name = product_info['name']
|
||||
product_path = product_info['path']
|
||||
filename_original = product_info['filename']
|
||||
filename = filename_original.replace(".original", "")
|
||||
|
||||
self.logger.info(f"开始合并{product_name}")
|
||||
|
||||
input_tif1, input_tif2 = None, None
|
||||
merge_count = 0
|
||||
temp_files = []
|
||||
|
||||
def merge_orthophoto(self, grid_points: Dict[tuple, pd.DataFrame]):
|
||||
"""合并网格的正射影像"""
|
||||
try:
|
||||
all_orthos_and_ortho_cuts = []
|
||||
for grid_id, points in grid_points.items():
|
||||
grid_tif_original = os.path.join(
|
||||
grid_ortho_dir = os.path.join(
|
||||
self.output_dir,
|
||||
f"grid_{grid_id[0]}_{grid_id[1]}",
|
||||
"project",
|
||||
product_path,
|
||||
filename_original
|
||||
"odm_orthophoto",
|
||||
)
|
||||
grid_tif = os.path.join(
|
||||
self.output_dir,
|
||||
f"grid_{grid_id[0]}_{grid_id[1]}",
|
||||
"project",
|
||||
product_path,
|
||||
filename
|
||||
)
|
||||
if os.path.exists(grid_tif_original) and os.path.exists(grid_tif):
|
||||
self.logger.info(
|
||||
f"网格 ({grid_id[0]},{grid_id[1]}) 的{product_name}存在: {grid_tif_original, grid_tif}")
|
||||
# 如果文件大于600MB，则不使用original文件
|
||||
file_size_mb_original = os.path.getsize(
|
||||
grid_tif_original) / (1024 * 1024) # 转换为MB
|
||||
if file_size_mb_original > 600:
|
||||
to_merge_tif = grid_tif
|
||||
else:
|
||||
to_merge_tif = grid_tif_original
|
||||
elif os.path.exists(grid_tif_original) and not os.path.exists(grid_tif):
|
||||
to_merge_tif = grid_tif_original
|
||||
elif not os.path.exists(grid_tif_original) and os.path.exists(grid_tif):
|
||||
to_merge_tif = grid_tif
|
||||
else:
|
||||
self.logger.warning(
|
||||
f"网格 ({grid_id[0]},{grid_id[1]}) 的{product_name}不存在: {grid_tif_original, grid_tif}")
|
||||
continue
|
||||
tif_path = os.path.join(grid_ortho_dir, "odm_orthophoto.tif")
|
||||
tif_mask = os.path.join(grid_ortho_dir, "cutline.gpkg")
|
||||
output_cut_tif = os.path.join(
|
||||
grid_ortho_dir, "odm_orthophoto_cut.tif")
|
||||
output_feathered_tif = os.path.join(
|
||||
grid_ortho_dir, "odm_orthophoto_feathered.tif")
|
||||
|
||||
if input_tif1 is None:
|
||||
input_tif1 = to_merge_tif
|
||||
self.logger.info(f"设置第一个输入{product_name}: {input_tif1}")
|
||||
else:
|
||||
input_tif2 = to_merge_tif
|
||||
# 生成带时间戳的临时输出文件名
|
||||
temp_output = os.path.join(
|
||||
self.output_dir,
|
||||
f"temp_merged_{int(time.time())}_{product_info['output']}"
|
||||
)
|
||||
self.compute_mask_raster(
|
||||
tif_path, tif_mask, output_cut_tif, blend_distance=20)
|
||||
self.feather_raster(
|
||||
tif_path, output_feathered_tif, blend_distance=20)
|
||||
all_orthos_and_ortho_cuts.append(
|
||||
[output_feathered_tif, output_cut_tif])
|
||||
|
||||
self.logger.info(
|
||||
f"开始合并{product_name}第 {merge_count + 1} 次:\n"
|
||||
f"输入1: {input_tif1}\n"
|
||||
f"输入2: {input_tif2}\n"
|
||||
f"输出: {temp_output}"
|
||||
)
|
||||
|
||||
self.merge_two_tifs(input_tif1, input_tif2, temp_output)
|
||||
merge_count += 1
|
||||
|
||||
input_tif1 = temp_output
|
||||
input_tif2 = None
|
||||
temp_files.append(temp_output)
|
||||
|
||||
final_output = os.path.join(
|
||||
self.output_dir, product_info['output'])
|
||||
shutil.copy2(input_tif1, final_output)
|
||||
|
||||
# 清理所有临时文件
|
||||
for temp_file in temp_files:
|
||||
try:
|
||||
os.remove(temp_file)
|
||||
except Exception as e:
|
||||
self.logger.warning(f"删除临时文件失败: {str(e)}")
|
||||
|
||||
self.logger.info(
|
||||
f"{product_name}合并完成，共执行 {merge_count} 次合并，"
|
||||
f"最终输出文件: {final_output}"
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(
|
||||
f"{product_name}合并过程中发生错误: {str(e)}", exc_info=True)
|
||||
raise
|
||||
|
||||
def merge_all_tifs(self, grid_points: Dict[tuple, pd.DataFrame], mode: str):
|
||||
"""合并所有产品（正射影像、DSM和DTM）"""
|
||||
try:
|
||||
products = [
|
||||
{
|
||||
'name': '正射影像',
|
||||
'path': 'odm_orthophoto',
|
||||
'filename': 'odm_orthophoto.original.tif',
|
||||
'output': 'orthophoto.tif'
|
||||
},
|
||||
|
||||
]
|
||||
if mode == '三维模式':
|
||||
products.append(
|
||||
{
|
||||
'name': 'DSM',
|
||||
'path': 'odm_dem',
|
||||
'filename': 'dsm.original.tif',
|
||||
'output': 'dsm.tif'
|
||||
orthophoto_vars = {
|
||||
'TILED': 'NO',
|
||||
'COMPRESS': False,
|
||||
'PREDICTOR': '1',
|
||||
'BIGTIFF': 'IF_SAFER',
|
||||
'BLOCKXSIZE': 512,
|
||||
'BLOCKYSIZE': 512,
|
||||
'NUM_THREADS': 15
|
||||
}
|
||||
)
|
||||
products.append(
|
||||
{
|
||||
'name': 'DTM',
|
||||
'path': 'odm_dem',
|
||||
'filename': 'dtm.original.tif',
|
||||
'output': 'dtm.tif'
|
||||
}
|
||||
)
|
||||
|
||||
for product in products:
|
||||
self.merge_grid_tif(grid_points, product)
|
||||
self.merge(all_orthos_and_ortho_cuts, os.path.join(
|
||||
self.output_dir, "orthophoto.tif"), orthophoto_vars)
|
||||
|
||||
self.logger.info("所有产品合并完成")
|
||||
except Exception as e:
|
||||
self.logger.error(f"产品合并过程中发生错误: {str(e)}", exc_info=True)
|
||||
raise
|
||||
|
||||
def compute_mask_raster(self, input_raster, vector_mask, output_raster, blend_distance=20, only_max_coords_feature=False):
|
||||
if not os.path.exists(input_raster):
|
||||
print("Cannot mask raster, %s does not exist" % input_raster)
|
||||
return
|
||||
|
||||
if __name__ == "__main__":
|
||||
import sys
|
||||
sys.path.append(os.path.dirname(
|
||||
os.path.dirname(os.path.abspath(__file__))))
|
||||
from utils.logger import setup_logger
|
||||
import pandas as pd
|
||||
if not os.path.exists(vector_mask):
|
||||
print("Cannot mask raster, %s does not exist" % vector_mask)
|
||||
return
|
||||
|
||||
# 设置输出目录和日志
|
||||
output_dir = r"G:\ODM_output\1009"
|
||||
setup_logger(output_dir)
|
||||
print("Computing mask raster: %s" % output_raster)
|
||||
|
||||
# 构造测试用的grid_points字典
|
||||
# 假设我们有两个网格，每个网格包含一些GPS点的DataFrame
|
||||
grid_points = {
|
||||
(0, 0): pd.DataFrame({
|
||||
'latitude': [39.9, 39.91],
|
||||
'longitude': [116.3, 116.31],
|
||||
'altitude': [100, 101]
|
||||
}),
|
||||
(0, 1): pd.DataFrame({
|
||||
'latitude': [39.92, 39.93],
|
||||
'longitude': [116.32, 116.33],
|
||||
'altitude': [102, 103]
|
||||
})
|
||||
}
|
||||
with rasterio.open(input_raster, 'r') as rast:
|
||||
with fiona.open(vector_mask) as src:
|
||||
burn_features = src
|
||||
|
||||
# 创建MergeTif实例并执行合并
|
||||
merge_tif = MergeTif(output_dir)
|
||||
merge_tif.merge_all_tifs(grid_points)
|
||||
if only_max_coords_feature:
|
||||
max_coords_count = 0
|
||||
max_coords_feature = None
|
||||
for feature in src:
|
||||
if feature is not None:
|
||||
# No complex shapes
|
||||
if len(feature['geometry']['coordinates'][0]) > max_coords_count:
|
||||
max_coords_count = len(
|
||||
feature['geometry']['coordinates'][0])
|
||||
max_coords_feature = feature
|
||||
if max_coords_feature is not None:
|
||||
burn_features = [max_coords_feature]
|
||||
|
||||
shapes = [feature["geometry"] for feature in burn_features]
|
||||
out_image, out_transform = mask(rast, shapes, nodata=0)
|
||||
|
||||
if blend_distance > 0:
|
||||
if out_image.shape[0] >= 4:
|
||||
# alpha_band = rast.dataset_mask()
|
||||
alpha_band = out_image[-1]
|
||||
dist_t = edt(alpha_band, black_border=True, parallel=0)
|
||||
dist_t[dist_t <= blend_distance] /= blend_distance
|
||||
dist_t[dist_t > blend_distance] = 1
|
||||
np.multiply(alpha_band, dist_t,
|
||||
out=alpha_band, casting="unsafe")
|
||||
else:
|
||||
print(
|
||||
"%s does not have an alpha band, cannot blend cutline!" % input_raster)
|
||||
|
||||
with rasterio.open(output_raster, 'w', BIGTIFF="IF_SAFER", **rast.profile) as dst:
|
||||
dst.colorinterp = rast.colorinterp
|
||||
dst.write(out_image)
|
||||
|
||||
return output_raster
|
||||
|
||||
def feather_raster(self, input_raster, output_raster, blend_distance=20):
|
||||
if not os.path.exists(input_raster):
|
||||
print("Cannot feather raster, %s does not exist" % input_raster)
|
||||
return
|
||||
|
||||
print("Computing feather raster: %s" % output_raster)
|
||||
|
||||
with rasterio.open(input_raster, 'r') as rast:
|
||||
out_image = rast.read()
|
||||
if blend_distance > 0:
|
||||
if out_image.shape[0] >= 4:
|
||||
alpha_band = out_image[-1]
|
||||
dist_t = edt(alpha_band, black_border=True, parallel=0)
|
||||
dist_t[dist_t <= blend_distance] /= blend_distance
|
||||
dist_t[dist_t > blend_distance] = 1
|
||||
np.multiply(alpha_band, dist_t,
|
||||
out=alpha_band, casting="unsafe")
|
||||
else:
|
||||
print(
|
||||
"%s does not have an alpha band, cannot feather raster!" % input_raster)
|
||||
|
||||
with rasterio.open(output_raster, 'w', BIGTIFF="IF_SAFER", **rast.profile) as dst:
|
||||
dst.colorinterp = rast.colorinterp
|
||||
dst.write(out_image)
|
||||
|
||||
return output_raster
|
||||
|
||||
def merge(self, input_ortho_and_ortho_cuts, output_orthophoto, orthophoto_vars={}):
|
||||
"""
|
||||
Based on https://github.com/mapbox/rio-merge-rgba/
|
||||
Merge orthophotos around cutlines using a blend buffer.
|
||||
"""
|
||||
inputs = []
|
||||
bounds = None
|
||||
precision = 7
|
||||
|
||||
for o, c in input_ortho_and_ortho_cuts:
|
||||
inputs.append((o, c))
|
||||
|
||||
with rasterio.open(inputs[0][0]) as first:
|
||||
res = first.res
|
||||
dtype = first.dtypes[0]
|
||||
profile = first.profile
|
||||
num_bands = first.meta['count'] - 1 # minus alpha
|
||||
colorinterp = first.colorinterp
|
||||
|
||||
print("%s valid orthophoto rasters to merge" % len(inputs))
|
||||
sources = [(rasterio.open(o), rasterio.open(c)) for o, c in inputs]
|
||||
|
||||
# scan input files.
|
||||
# while we're at it, validate assumptions about inputs
|
||||
xs = []
|
||||
ys = []
|
||||
for src, _ in sources:
|
||||
left, bottom, right, top = src.bounds
|
||||
xs.extend([left, right])
|
||||
ys.extend([bottom, top])
|
||||
if src.profile["count"] < 2:
|
||||
raise ValueError("Inputs must be at least 2-band rasters")
|
||||
dst_w, dst_s, dst_e, dst_n = min(xs), min(ys), max(xs), max(ys)
|
||||
print("Output bounds: %r %r %r %r" % (dst_w, dst_s, dst_e, dst_n))
|
||||
|
||||
output_transform = Affine.translation(dst_w, dst_n)
|
||||
output_transform *= Affine.scale(res[0], -res[1])
|
||||
|
||||
# Compute output array shape. We guarantee it will cover the output
|
||||
# bounds completely.
|
||||
output_width = int(math.ceil((dst_e - dst_w) / res[0]))
|
||||
output_height = int(math.ceil((dst_n - dst_s) / res[1]))
|
||||
|
||||
# Adjust bounds to fit.
|
||||
dst_e, dst_s = output_transform * (output_width, output_height)
|
||||
print("Output width: %d, height: %d" %
|
||||
(output_width, output_height))
|
||||
print("Adjusted bounds: %r %r %r %r" % (dst_w, dst_s, dst_e, dst_n))
|
||||
|
||||
profile["transform"] = output_transform
|
||||
profile["height"] = output_height
|
||||
profile["width"] = output_width
|
||||
profile["tiled"] = orthophoto_vars.get('TILED', 'YES') == 'YES'
|
||||
profile["blockxsize"] = orthophoto_vars.get('BLOCKXSIZE', 512)
|
||||
profile["blockysize"] = orthophoto_vars.get('BLOCKYSIZE', 512)
|
||||
profile["compress"] = orthophoto_vars.get('COMPRESS', 'LZW')
|
||||
profile["predictor"] = orthophoto_vars.get('PREDICTOR', '2')
|
||||
profile["bigtiff"] = orthophoto_vars.get('BIGTIFF', 'IF_SAFER')
|
||||
profile.update()
|
||||
|
||||
# create destination file
|
||||
with rasterio.open(output_orthophoto, "w", **profile) as dstrast:
|
||||
dstrast.colorinterp = colorinterp
|
||||
for idx, dst_window in dstrast.block_windows():
|
||||
left, bottom, right, top = dstrast.window_bounds(dst_window)
|
||||
|
||||
blocksize = dst_window.width
|
||||
dst_rows, dst_cols = (dst_window.height, dst_window.width)
|
||||
|
||||
# initialize array destined for the block
|
||||
dst_count = first.count
|
||||
dst_shape = (dst_count, dst_rows, dst_cols)
|
||||
|
||||
dstarr = np.zeros(dst_shape, dtype=dtype)
|
||||
|
||||
# First pass, write all rasters naively without blending
|
||||
for src, _ in sources:
|
||||
src_window = tuple(zip(rowcol(
|
||||
src.transform, left, top, op=round, precision=precision
|
||||
), rowcol(
|
||||
src.transform, right, bottom, op=round, precision=precision
|
||||
)))
|
||||
|
||||
temp = np.zeros(dst_shape, dtype=dtype)
|
||||
temp = src.read(
|
||||
out=temp, window=src_window, boundless=True, masked=False
|
||||
)
|
||||
|
||||
# pixels without data yet are available to write
|
||||
write_region = np.logical_and(
|
||||
(dstarr[-1] == 0), (temp[-1] != 0) # 0 is nodata
|
||||
)
|
||||
np.copyto(dstarr, temp, where=write_region)
|
||||
|
||||
# check if dest has any nodata pixels available
|
||||
if np.count_nonzero(dstarr[-1]) == blocksize:
|
||||
break
|
||||
|
||||
# Second pass, write all feathered rasters
|
||||
# blending the edges
|
||||
for src, _ in sources:
|
||||
src_window = tuple(zip(rowcol(
|
||||
src.transform, left, top, op=round, precision=precision
|
||||
), rowcol(
|
||||
src.transform, right, bottom, op=round, precision=precision
|
||||
)))
|
||||
|
||||
temp = np.zeros(dst_shape, dtype=dtype)
|
||||
temp = src.read(
|
||||
out=temp, window=src_window, boundless=True, masked=False
|
||||
)
|
||||
|
||||
where = temp[-1] != 0
|
||||
for b in range(0, num_bands):
|
||||
blended = temp[-1] / 255.0 * temp[b] + \
|
||||
(1 - temp[-1] / 255.0) * dstarr[b]
|
||||
np.copyto(dstarr[b], blended,
|
||||
casting='unsafe', where=where)
|
||||
dstarr[-1][where] = 255.0
|
||||
|
||||
# check if dest has any nodata pixels available
|
||||
if np.count_nonzero(dstarr[-1]) == blocksize:
|
||||
break
|
||||
|
||||
# Third pass, write cut rasters
|
||||
# blending the cutlines
|
||||
for _, cut in sources:
|
||||
src_window = tuple(zip(rowcol(
|
||||
cut.transform, left, top, op=round, precision=precision
|
||||
), rowcol(
|
||||
cut.transform, right, bottom, op=round, precision=precision
|
||||
)))
|
||||
|
||||
temp = np.zeros(dst_shape, dtype=dtype)
|
||||
temp = cut.read(
|
||||
out=temp, window=src_window, boundless=True, masked=False
|
||||
)
|
||||
|
||||
# For each band, average alpha values between
|
||||
# destination raster and cut raster
|
||||
for b in range(0, num_bands):
|
||||
blended = temp[-1] / 255.0 * temp[b] + \
|
||||
(1 - temp[-1] / 255.0) * dstarr[b]
|
||||
np.copyto(dstarr[b], blended,
|
||||
casting='unsafe', where=temp[-1] != 0)
|
||||
|
||||
dstrast.write(dstarr, window=dst_window)
|
||||
|
||||
return output_orthophoto
|
||||
|
||||
@ -157,8 +157,8 @@ class ODMProcessMonitor:
|
||||
f"--use-exif "
|
||||
f"--use-hybrid-bundle-adjustment "
|
||||
f"--optimize-disk-space "
|
||||
# f"--3d-tiles "
|
||||
# f"--feature-type sift "
|
||||
f"--orthophoto-cutline "
|
||||
f"--feature-type sift "
|
||||
# f"--orthophoto-resolution 8 "
|
||||
)
|
||||
if accuracy == "high":
|
||||
@ -178,7 +178,8 @@ class ODMProcessMonitor:
|
||||
|
||||
# 根据是否使用lowest quality添加参数
|
||||
if use_lowest_quality:
|
||||
docker_command += f"--feature-quality lowest "
|
||||
# docker_command += f"--feature-quality lowest "
|
||||
pass
|
||||
|
||||
if self.mode == "快拼模式":
|
||||
docker_command += (
|
||||
|
||||
Loading…
Reference in New Issue
Block a user