600 lines
24 KiB
Python
600 lines
24 KiB
Python
# -*- coding: UTF-8 -*-
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"""
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@Project ：microproduct
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@File ：PreProcess.py
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@Function ：@Function: 坐标转换,坐标系转换，图像裁剪，重投影，重采样
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@Author ：LMM
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@Date ：2021/8/25 14:17
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@Version ：1.0.0
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"""
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from shapely.geometry import Polygon # 导入 gdal库要放到这一句的后面，不然会引起错误
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from osgeo import gdal
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from osgeo import gdalconst
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from osgeo import osr
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from osgeo import ogr
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import os
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import cv2
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import numpy as np
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import shutil
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import scipy.spatial.transform
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import scipy.spatial.transform._rotation_groups # 用于解决打包错误
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import scipy.special.cython_special # 用于解决打包错误
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import scipy.spatial.transform._rotation_groups # 解决打包的问题
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import shapefile
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from shapely.errors import TopologicalError
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from tool.algorithm.image.ImageHandle import ImageHandler
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import logging
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logger = logging.getLogger("mylog")
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#
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os.environ['PROJ_LIB'] = os.getcwd()
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class PreProcess:
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"""
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预处理，所有的影像配准
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"""
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def __init__(self):
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self._ImageHandler = ImageHandler()
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pass
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def cal_scopes(self, processing_paras):
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# 计算roi
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scopes = ()
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for key, value in processing_paras.items():
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if 'ori_sim' in key:
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scopes += (ImageHandler.get_scope_ori_sim(value),)
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if(processing_paras['box'] != "" or processing_paras['box'] != "empty"):
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scopes += self.box2scope(processing_paras['box'])
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return scopes
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def cal_scopes_roi(self, processing_paras):
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return self.intersect_polygon(self.cal_scopes(processing_paras))
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def cut_geoimg(self,workspace_preprocessing_path, para_names_geo, processing_paras):
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# print(os.environ['PROJ_LIB'])
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self.check_img_projection(workspace_preprocessing_path, para_names_geo, processing_paras)
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# 计算roi
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scopes = self.cal_scopes(processing_paras)
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# 计算图像的轮廓,并求相交区域
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intersect_shp_path = os.path.join(workspace_preprocessing_path, 'IntersectPolygon.shp')
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scopes_roi = self.cal_intersect_shp(intersect_shp_path, para_names_geo, processing_paras, scopes)
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# 裁剪
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# 裁剪图像：裁剪微波图像，裁剪其他图像
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cutted_img_paths = self.cut_imgs(workspace_preprocessing_path, para_names_geo, processing_paras, intersect_shp_path)
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return cutted_img_paths, scopes_roi
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def preprocessing(self, para_names, ref_img_name, processing_paras, workspace_preprocessing_path, workspace_preprocessed_path):
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# 读取每一张图像,检查图像坐标系
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self.check_img_projection(workspace_preprocessing_path, para_names, processing_paras)
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# 计算图像的轮廓,并求相交区域
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intersect_shp_path = os.path.join(workspace_preprocessing_path, 'IntersectPolygon.shp')
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self.cal_intersect_shp(intersect_shp_path, para_names, processing_paras,
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self.box2scope(processing_paras['box']))
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logger.info('create intersect shp success!')
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# 裁剪图像：裁剪微波图像，裁剪其他图像
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cutted_img_paths = self.cut_imgs(workspace_preprocessing_path, para_names, processing_paras,
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intersect_shp_path)
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logger.info('cut images success!')
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# 重采样：重采样到跟微波图像一致的分辨率，然后保存到临时目录
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preprocessed_paras = self.resampling_img(workspace_preprocessed_path, para_names, cutted_img_paths,cutted_img_paths[ref_img_name])
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# 清除预处理缓存文件
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logger.info('preprocess_handle success!')
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return preprocessed_paras # cutted_img_paths
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def get_ref_inf(self, ref_img_path):
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"""获取参考影像的图像信息"""
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ref_img_path = ref_img_path
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cols = ImageHandler.get_img_width(ref_img_path)
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rows = ImageHandler.get_img_height(ref_img_path)
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proj = ImageHandler.get_projection(ref_img_path)
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geo = ImageHandler.get_geotransform(ref_img_path)
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return ref_img_path, cols, rows, proj, geo
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def check_img_projection(self, out_dir, para_names, processing_paras):
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"""
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读取每一张图像,检查图像坐标系;
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将投影坐标系影像转换为地理坐标系影像(EPSG:4326)
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:param para_names:需要检查的参数名称
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"""
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if len(para_names) == 0:
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return False
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for name in para_names:
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proj = ImageHandler.get_projection(processing_paras[name])
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keyword = proj.split("[", 2)[0]
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if keyword == "PROJCS":
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# 投影坐标系 转 地理坐标系
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para_dir = os.path.split(processing_paras[name])
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out_para = os.path.join(out_dir, para_dir[1].split(".", 1)[0] + "_EPSG4326.tif")
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self.trans_epsg4326(out_para, processing_paras[name])
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processing_paras[name] = out_para
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elif len(keyword) == 0 or keyword.strip() == "" or keyword.isspace() is True:
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raise Exception('coordinate is missing!')
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def preprocessing_oh2004(self, para_names, processing_paras, workspace_preprocessing_path, workspace_preprocessed_path):
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# 读取每一张图像,检查图像坐标系
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self.check_img_projection(workspace_preprocessing_path, para_names, processing_paras)
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# 计算图像的轮廓,并求相交区域
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intersect_shp_path = os.path.join(workspace_preprocessing_path, 'IntersectPolygon.shp')
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scopes = self.cal_intersect_shp(intersect_shp_path, para_names, processing_paras,
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self.box2scope(processing_paras['box']))
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logger.info('create intersect shp success!')
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# 裁剪图像：裁剪微波图像，裁剪其他图像
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cutted_img_paths = self.cut_imgs(workspace_preprocessed_path, para_names, processing_paras,
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intersect_shp_path)
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logger.info('cut images success!')
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# 重采样：重采样到跟微波图像一致的分辨率，然后保存到临时目录
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return cutted_img_paths, scopes
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@staticmethod
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def lonlat2geo(lat, lon):
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"""
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WGS84转平面坐标
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Param: lat 为WGS_1984的纬度
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Param: lon 为WGS_1984的经度
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输出转换后的坐标x,y
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"""
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dstsrs1 = osr.SpatialReference()
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dstsrs1.ImportFromEPSG(32649)
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dstsrs2 = osr.SpatialReference()
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dstsrs2.ImportFromEPSG(4326)
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ct = osr.CoordinateTransformation(dstsrs2, dstsrs1)
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coords = ct.TransformPoint(lat, lon)
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# print("输出转换后的坐标x,y:",coords[:2])
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return coords[:2]
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@staticmethod
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def trans_geogcs2projcs(out_path, in_path):
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"""
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:param out_path:wgs84投影坐标影像保存路径
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:param in_path:地理坐标影像输入路径
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"""
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# 创建文件
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if os.path.exists(os.path.split(out_path)[0]) is False:
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os.makedirs(os.path.split(out_path)[0])
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options = gdal.WarpOptions(format='GTiff', srcSRS='EPSG:4326', dstSRS='EPSG:32649')
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gdal.Warp(out_path, in_path, options=options)
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@staticmethod
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def trans_projcs2geogcs(out_path, in_path):
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"""
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:param out_path:wgs84地理坐标影像输入路径
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:param in_path:wgs84投影坐标影像保存路径
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"""
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# 创建文件
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if os.path.exists(os.path.split(out_path)[0]) is False:
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os.makedirs(os.path.split(out_path)[0])
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options = gdal.WarpOptions(format='GTiff', srcSRS='EPSG:32649', dstSRS='EPSG:4326')
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gdal.Warp(out_path, in_path, options=options)
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@staticmethod
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def trans_projcs2geogcs(out_path, in_path ,EPSG_src=32649,EPSG_dst=4326):
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"""
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:param out_path:wgs84地理坐标影像输入路径
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:param in_path:wgs84投影坐标影像保存路径
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:param EPSG_src:原始投影系
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:param EPSG_dst:目标坐标系
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"""
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str_EPSG_src = 'EPSG:'+ str(EPSG_src)
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str_EPSG_dst = 'EPSG:'+ str(EPSG_dst)
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# 创建文件
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if os.path.exists(os.path.split(out_path)[0]) is False:
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os.makedirs(os.path.split(out_path)[0])
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options = gdal.WarpOptions(format='GTiff', srcSRS=str_EPSG_src, dstSRS=str_EPSG_dst)
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gdal.Warp(out_path, in_path, options=options)
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@staticmethod
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def trans_epsg4326(out_path, in_path):
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OutTile = gdal.Warp(out_path, in_path,
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dstSRS='EPSG:4326',
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resampleAlg=gdalconst.GRA_NearestNeighbour
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)
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OutTile = None
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return True
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@staticmethod
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def box2scope(str_box):
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roi_box = ()
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if str_box == '' or str_box == 'empty':
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return roi_box
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box_list = [float(num) for num in list(str_box.split(';'))]
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if len(box_list) == 4:
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roi_box = ([[box_list[2], box_list[1]], [box_list[3], box_list[1]], [box_list[2], box_list[0]],
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[box_list[3], box_list[0]]],)
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return roi_box
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def cal_intersect_shp(self, shp_path, para_names,processing_paras, add_scope =()):
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"""
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:param shp_path:相交区域矢量文件保存区域
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:param para_names:判断相交影像的名称
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:return: True or False
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"""
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scopes = ()
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if len(add_scope) != 0:
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scopes += add_scope
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for name in para_names:
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scope_tuple = (self._ImageHandler.get_scope(processing_paras[name]),)
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scopes += scope_tuple
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for n, scope in zip( range(len(scopes)), scopes):
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logging.info("scope" + str(n) + ":%s", scope)
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intersect_polygon = self.intersect_polygon(scopes)
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if intersect_polygon is None:
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logger.error('image range does not overlap!')
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raise Exception('create intersect shp fail!')
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logging.info("scope roi :%s", intersect_polygon)
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if self.write_polygon_shp(shp_path, intersect_polygon, 4326) is False:
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raise Exception('create intersect shp fail!')
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return intersect_polygon
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@staticmethod
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def intersect_polygon(scopes_tuple):
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"""
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功能说明：计算多边形相交的区域坐标;注意：多边形区域会转变成凸区域再求交
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:param scopes_tuple: 输入多个区域坐标的tuple
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:return: 多边形相交的区域坐标((x0,y0),(x1,y1),..., (xn,yn))
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"""
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if len(scopes_tuple) < 2:
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logger.error('len(scopes_tuple) < 2')
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# return # todo 修改只有单景会出现无法判断相交区域问题
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try:
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# python四边形对象，会自动计算四个点，最后四个点顺序为：左上 左下 右下 右上 左上
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tmp = tuple(scopes_tuple[0])
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poly_intersect = Polygon(tmp).convex_hull
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for i in range(len(scopes_tuple)-1):
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polygon_next = Polygon(tuple(scopes_tuple[i+1])).convex_hull
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if poly_intersect.intersects(polygon_next):
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poly_intersect = poly_intersect.intersection(polygon_next)
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else:
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msg = 'Image:' + str(i) + 'range does not overlap!'
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logger.error(msg)
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return
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return list(poly_intersect.boundary.coords)[:-1]
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# except shapely.geos.TopologicalError:
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except TopologicalError:
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logger.error('shapely.geos.TopologicalError occurred!')
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return
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@staticmethod
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def write_polygon_shp(out_shp_path, point_list, EPSG =32649):
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"""
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功能说明：创建闭环的矢量文件。
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:param out_shp_path :矢量文件保存路径
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:param point_list :装有闭环点的列表[[x0,y0],[x1,y1]...[xn,yn]]
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:return: True or False
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"""
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# 为了支持中文路径，请添加下面这句代码
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gdal.SetConfigOption("GDAL_FILENAME_IS_UTF8", "NO")
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# 为了使属性表字段支持中文，请添加下面这句
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gdal.SetConfigOption("SHAPE_ENCODING", "")
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# 注册所有的驱动
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ogr.RegisterAll()
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# 创建数据，这里以创建ESRI的shp文件为例
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str_driver_name = "ESRI Shapefile"
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o_driver = ogr.GetDriverByName(str_driver_name)
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if o_driver is None:
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msg = 'driver('+str_driver_name+')is invalid value'
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logger.error(msg)
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return False
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# 创建数据源
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if os.path.exists(out_shp_path) and os.path.isfile(out_shp_path): # 如果已存在同名文件
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os.remove(out_shp_path) # 则删除之
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o_ds = o_driver.CreateDataSource(out_shp_path)
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if o_ds is None:
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msg = 'create file failed!' + out_shp_path
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logger.error(msg)
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return False
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# 创建图层，创建一个多边形图层
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srs = osr.SpatialReference()
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#srs.ImportFromEPSG(32649) # 投影坐标系，空间参考：WGS84
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srs.ImportFromEPSG(EPSG) # 地理坐标系EPSG
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o_layer = o_ds.CreateLayer("TestPolygon", srs, ogr.wkbPolygon)
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if o_layer is None:
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msg = 'create coverage failed!'
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logger.error(msg)
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return False
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# 下面创建属性表
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# 先创建一个叫FieldID的整型属性
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o_field_id = ogr.FieldDefn("FieldID", ogr.OFTInteger)
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o_layer.CreateField(o_field_id, 1)
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# 再创建一个叫FeatureName的字符型属性，字符长度为50
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o_field_name = ogr.FieldDefn("FieldName", ogr.OFTString)
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o_field_name.SetWidth(100)
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o_layer.CreateField(o_field_name, 1)
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o_defn = o_layer.GetLayerDefn()
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# 创建矩形要素
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o_feature_rectangle = ogr.Feature(o_defn)
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o_feature_rectangle.SetField(0, 1)
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o_feature_rectangle.SetField(1, "IntersectRegion")
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# 创建环对象ring
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ring = ogr.Geometry(ogr.wkbLinearRing)
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for i in range(len(point_list)):
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ring.AddPoint(point_list[i][0], point_list[i][1])
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ring.CloseRings()
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# 创建环对象polygon
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geom_rect_polygon = ogr.Geometry(ogr.wkbPolygon)
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geom_rect_polygon.AddGeometry(ring)
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o_feature_rectangle.SetGeometry(geom_rect_polygon)
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o_layer.CreateFeature(o_feature_rectangle)
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o_ds.Destroy()
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return True
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def cut_imgs(self, out_dir, para_names, processing_paras, shp_path):
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"""
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使用矢量数据裁剪影像
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:param para_names:需要检查的参数名称
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:param shp_path：裁剪的shp文件
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"""
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if len(para_names) == 0:
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return {}
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cutted_img_paths = {}
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try:
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for name in para_names:
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input_path = processing_paras[name]
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output_path = os.path.join(out_dir, name + '_cut.tif')
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self.cut_img(output_path, input_path, shp_path)
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cutted_img_paths.update({name: output_path})
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logger.info('cut %s success!', name)
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except BaseException:
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logger.error('cut_img failed!')
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return {}
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return cutted_img_paths
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def cut_imgs_VP(self, out_dir, para_names, processing_paras, shp_path, img_name):
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"""
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使用矢量数据裁剪影像
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:param para_names:需要检查的参数名称
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:param shp_path：裁剪的shp文件
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"""
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if len(para_names) == 0:
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return {}
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cutted_img_paths = {}
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try:
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for name in para_names:
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if name == 'Covering':
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img_name = img_name.split('_')[6] + '_'
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output_path = os.path.join(out_dir, img_name + name + '_cut.tif')
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else:
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output_path = os.path.join(out_dir, name + '_cut.tif')
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input_path = processing_paras[name]
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self.cut_img(output_path, input_path, shp_path)
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cutted_img_paths.update({name: output_path})
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logger.info('cut %s success!', name)
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except BaseException:
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logger.error('cut_img failed!')
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return {}
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return cutted_img_paths
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@staticmethod
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def cut_img(output_path, input_path, shp_path):
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"""
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:param output_path:剪切后的影像
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:param input_path:待剪切的影像
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:param shp_path:矢量数据
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:return: True or False
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"""
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r = shapefile.Reader(shp_path)
|
||
box = r.bbox
|
||
|
||
input_dataset = gdal.Open(input_path)
|
||
|
||
gdal.Warp(output_path, input_dataset, format='GTiff', outputBounds=box, cutlineDSName=shp_path, dstNodata=0, resampleAlg='nearest')
|
||
# cutlineWhere="FIELD = ‘whatever’",
|
||
# optionally you can filter your cutline (shapefile) based on attribute values
|
||
# select the no data value you like
|
||
# ds = None
|
||
# do other stuff with ds object, it is your cropped dataset. in this case we only close the dataset.
|
||
del input_dataset
|
||
return True
|
||
|
||
def resampling_img(self, out_dir, para_names, img_paths, refer_img_path):
|
||
"""
|
||
以主影像为参考，对影像重采样
|
||
:param para_names:需要检查的参数名称
|
||
:param img_paths：待重采样影像路径
|
||
:param refer_img_path：参考影像路径
|
||
"""
|
||
if len(para_names) == 0 or len(img_paths) == 0:
|
||
return
|
||
prepro_imgs_path = {}
|
||
for name in para_names:
|
||
img_path = img_paths[name]
|
||
output_para = os.path.join(out_dir, name + '_ppd.tif') # + name + '_preprocessed.tif'
|
||
self.resampling_by_scale(img_path, output_para, refer_img_path)
|
||
prepro_imgs_path.update({name: output_para})
|
||
logger.info('resampling %s success!', name)
|
||
return prepro_imgs_path
|
||
|
||
@staticmethod
|
||
def resampling_by_scale(input_path, target_file, refer_img_path):
|
||
"""
|
||
按照缩放比例对影像重采样
|
||
:param input_path: GDAL地理数据路径
|
||
:param target_file: 输出影像
|
||
:param refer_img_path:参考影像
|
||
:return: True or False
|
||
"""
|
||
ref_dataset = gdal.Open(refer_img_path)
|
||
ref_cols = ref_dataset.RasterXSize # 列数
|
||
ref_rows = ref_dataset.RasterYSize # 行数
|
||
|
||
target_dataset = gdal.Open(input_path)
|
||
target_cols = target_dataset.RasterXSize # 列数
|
||
target_rows = target_dataset.RasterYSize # 行数
|
||
|
||
if(ref_cols == target_cols) and (ref_rows == target_rows):
|
||
shutil.copyfile(input_path, target_file)
|
||
return True
|
||
|
||
dataset = gdal.Open(input_path)
|
||
if dataset is None:
|
||
logger.error('resampling_by_scale:dataset is None!')
|
||
return False
|
||
|
||
band_count = dataset.RasterCount # 波段数
|
||
if (band_count == 0) or (target_file == ""):
|
||
logger.error("resampling_by_scale:Parameters of the abnormal!")
|
||
return False
|
||
|
||
cols = dataset.RasterXSize # 列数
|
||
rows = dataset.RasterYSize # 行数
|
||
scale_x = ref_cols/cols
|
||
scale_y = ref_rows/rows
|
||
|
||
cols = ref_cols
|
||
rows = ref_rows
|
||
|
||
geotrans = list(dataset.GetGeoTransform())
|
||
geotrans[1] = geotrans[1] / scale_x # 像元宽度变为原来的scale倍
|
||
geotrans[5] = geotrans[5] / scale_y # 像元高度变为原来的scale倍
|
||
|
||
if os.path.exists(target_file) and os.path.isfile(target_file): # 如果已存在同名影像
|
||
os.remove(target_file) # 则删除之
|
||
if not os.path.exists(os.path.split(target_file)[0]):
|
||
os.makedirs(os.path.split(target_file)[0])
|
||
|
||
band1 = dataset.GetRasterBand(1)
|
||
data_type = band1.DataType
|
||
target = dataset.GetDriver().Create(target_file, xsize=cols, ysize=rows, bands=band_count,
|
||
eType=data_type)
|
||
target.SetProjection(dataset.GetProjection()) # 设置投影坐标
|
||
target.SetGeoTransform(geotrans) # 设置地理变换参数
|
||
total = band_count + 1
|
||
for index in range(1, total):
|
||
# 读取波段数据
|
||
data = dataset.GetRasterBand(index).ReadAsArray(buf_xsize=cols, buf_ysize=rows)
|
||
out_band = target.GetRasterBand(index)
|
||
|
||
no_data_value = dataset.GetRasterBand(index).GetNoDataValue() # 获取没有数据的点
|
||
if not (no_data_value is None):
|
||
out_band.SetNoDataValue(no_data_value)
|
||
|
||
out_band.WriteArray(data) # 写入数据到新影像中
|
||
out_band.FlushCache()
|
||
out_band.ComputeBandStats(False) # 计算统计信息
|
||
del dataset
|
||
del target
|
||
return True
|
||
|
||
@staticmethod
|
||
def resample_by_gdal(in_path, out_path):
|
||
src_ds = gdal.Open(in_path, gdal.GA_ReadOnly)
|
||
|
||
# 设置目标影像的投影和范围
|
||
target_projection = src_ds.GetProjection()
|
||
target_geotransform = src_ds.GetGeoTransform()
|
||
|
||
x_scale = target_geotransform[1]
|
||
y_scale = target_geotransform[5]
|
||
scale = [x_scale, np.abs(y_scale)]
|
||
new_scale = np.max(scale)
|
||
|
||
dst_geotransform = [target_geotransform[0], new_scale, target_geotransform[2], target_geotransform[3], target_geotransform[4], -new_scale]
|
||
target_x_size = int(src_ds.RasterXSize * x_scale / new_scale) # 假设我们要将影像大小缩小到原来的一半
|
||
target_y_size = int(src_ds.RasterYSize * np.abs(y_scale) / new_scale)
|
||
|
||
# 创建输出驱动
|
||
driver = gdal.GetDriverByName('GTiff')
|
||
|
||
# 创建输出文件
|
||
dst_ds = driver.Create(out_path, target_x_size, target_y_size, src_ds.RasterCount,
|
||
src_ds.GetRasterBand(1).DataType)
|
||
dst_ds.SetGeoTransform(dst_geotransform)
|
||
dst_ds.SetProjection(target_projection)
|
||
|
||
# 执行重采样
|
||
gdal.ReprojectImage(src_ds, dst_ds, None, None, gdal.GRA_Bilinear) # 使用双线性插值
|
||
|
||
# 关闭数据集
|
||
dst_ds = None
|
||
src_ds = None
|
||
|
||
@staticmethod
|
||
def cv_mean_filter(out_path, in_path, filter_size):
|
||
"""
|
||
:param out_path:滤波后的影像
|
||
:param in_path:滤波前的影像
|
||
:param filter_size:滤波尺寸
|
||
:return: True or False
|
||
"""
|
||
proj = ImageHandler.get_projection(in_path)
|
||
geotrans = ImageHandler.get_geotransform(in_path)
|
||
array = ImageHandler.get_band_array(in_path, 1)
|
||
array = cv2.blur(array, (filter_size, filter_size)) # 均值滤波
|
||
ImageHandler.write_img(out_path, proj, geotrans, array)
|
||
return True
|
||
|
||
@staticmethod
|
||
def check_LocalIncidenceAngle(out_tif_path, in_tif_path):
|
||
"""
|
||
将角度的无效值设置为nan，把角度值转为弧度值
|
||
:param out_tif_path:处理后影像路径
|
||
:param in_tif_path:处理前影像路径
|
||
"""
|
||
proj, geo, angle = ImageHandler.read_img(in_tif_path)
|
||
angle = angle.astype(np.float32, order='C')
|
||
angle[angle == -9999] = np.nan
|
||
|
||
mean = np.nanmean(angle)
|
||
if mean > np.pi:
|
||
angle = np.deg2rad(angle)# 角度转弧度
|
||
|
||
angle[np.where(angle >= 0.5 * np.pi)] = np.nan
|
||
angle[np.where(angle < 0)] = np.nan
|
||
|
||
ImageHandler.write_img(out_tif_path, proj, geo, angle)
|
||
|
||
|
||
if __name__ == '__main__':
|
||
# fn = r"F:\Tian-GF3-Wenchang\GF3_MYC_QPSI_040611_E110.6_N20.0_20240427_L1A_AHV_L10006928143-ortho\GF3_MYC_QPSI_040611_E110.6_N20.0_20240427_L1B_v_h_L10006928143-ortho.tif"
|
||
# out = r"F:\Tian-GF3-Wenchang\GF3_MYC_QPSI_040611_E110.6_N20.0_20240427_L1A_AHV_L10006928143-ortho\GF3_MYC_QPSI_040611_E110.6_N20.0_20240427_L1B_v_h_L10006928143-ortho.tif"
|
||
# PreProcess.resample_by_gdal(fn, out)
|
||
inTif = r"D:\micro\WorkSpace\VegetationPhenology\Temporary\preprocessing\20240427_Covering_cut.tif"
|
||
outTif = r"D:\micro\WorkSpace\VegetationPhenology\Temporary\preprocessing\20240427_Covering_cut_re.tif"
|
||
shpPath = r"D:\micro\test\GF3_MYC_QPSI_040611_E110.6_N20.0_20240427_L1A_AHV_L10006928143-ortho-SSAA.tif"
|
||
tarTif = r"D:\micro\VP_class\band_merged.tif"
|
||
# PreProcess.resampling_by_scale(inTif, outTif, tarTif)
|
||
|
||
print(10 * np.log10(0))
|
||
|
||
|
||
# PreProcess.cut_img(outTif, inTif, shpPath)
|
||
|
||
|