233 lines
9.3 KiB
Python
233 lines
9.3 KiB
Python
##########################
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# 地理编码
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##########################
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from osgeo import osr
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from osgeo import gdal
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import numpy as np
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import os
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import math
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from sklearn.neighbors import KDTree
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from xml.etree import ElementTree as ET
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from tool.algorithm.image.ImageHandle import ImageHandler
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# os.environ['PROJ_LIB'] = os.getcwd()
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def get_lambel_sentinel(isce_work_path):
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refrence_path=os.path.join(isce_work_path,"reference")
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IW_path=None
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for temp_file in os.listdir(refrence_path):
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if temp_file.find("IW")==0 and temp_file.find(".xml")>0:
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IW_path=os.path.join(refrence_path,temp_file)
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# 解析波长
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root=ET.parse(IW_path).getroot()
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for node in root.iter():
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if 'name' in node.attrib and node.attrib['name']=="radarwavelength":
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for n in node.iter():
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if n.tag=="value":
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return float(n.text.replace("\n",""))
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pass
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pass
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def get_long_lat_path(isce_work_path):
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long_path=os.path.join(isce_work_path,"merged","geom_reference","lon.rdr.vrt")
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lat_path=os.path.join(isce_work_path,"merged","geom_reference","lat.rdr.vrt")
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# long_path = os.path.join(isce_work_path, "geom_reference", "lon.rdr.vrt")
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# lat_path = os.path.join(isce_work_path, "geom_reference", "lat.rdr.vrt")
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return [long_path,lat_path]
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def get_hgt_path(isce_work_path):
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# return os.path.join(isce_work_path, "geom_reference","hgt.rdr.vrt")
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return os.path.join(isce_work_path, "merged", "geom_reference","hgt.rdr.vrt")
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def get_Los_path(isce_work_path):
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# return os.path.join(isce_work_path, "geom_reference","los.rdr.vrt")
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return os.path.join(isce_work_path, "merged", "geom_reference","los.rdr.vrt")
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def get_filt_fine_unw_path(isce_work_path):
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flit_fine_unw_path_ls=[]
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interferograms_path=os.path.join(isce_work_path,"Igrams")
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for rootdir, dirs, files in os.walk(interferograms_path):
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for filename in files:
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# if filename=="filt_fine.unw.vrt":
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if filename.endswith(".unw.vrt"):
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flit_fine_unw_path_ls.append(os.path.join(rootdir, filename))
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return flit_fine_unw_path_ls
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def info_VRT(vrt_path):
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vrt_data=gdal.Open(vrt_path,gdal.GA_ReadOnly)
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print("width:\t",vrt_data.RasterXSize)
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print("height:\t",vrt_data.RasterYSize)
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print("bands numbers:\t",vrt_data.RasterCount)
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for i in range(vrt_data.RasterCount):
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data=vrt_data.ReadAsArray(0,0,vrt_data.RasterXSize,vrt_data.RasterYSize)
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print("band {} ,dtype:{},\tmin:{}\tmax:\t{}".format(i+1,data.dtype,np.min(data),np.max(data)))
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return vrt_data
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def vrt2tiff(vrt_path,tiff_path,band_idx=0):
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driver = gdal.GetDriverByName('GTiff') # 数据类型必须有，因为要计算需要多大内存空间
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vrt_data=info_VRT(vrt_path)
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tiff_dataset=driver.Create(tiff_path,vrt_data.RasterXSize, vrt_data.RasterYSize, 1, gdal.GDT_Float32)
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if vrt_data.RasterCount>1:
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data=vrt_data.ReadAsArray(0,0,vrt_data.RasterXSize,vrt_data.RasterYSize)[band_idx,:,:]
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tiff_dataset.GetRasterBand(1).WriteArray(data)
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else:
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data=vrt_data.ReadAsArray(0,0,vrt_data.RasterXSize,vrt_data.RasterYSize)
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tiff_dataset.GetRasterBand(1).WriteArray(data)
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del tiff_dataset
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def read_tiff_dataset(tiff_data,band_idx=0):
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dataset=gdal.Open(tiff_data,gdal.GA_ReadOnly)
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if dataset.RasterCount>1:
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data=dataset.ReadAsArray(0,0,dataset.RasterXSize,dataset.RasterYSize)[band_idx,:,:]
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else:
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data=dataset.ReadAsArray(0,0,dataset.RasterXSize,dataset.RasterYSize)
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del dataset
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return data
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def saveTiff(target_data_path,xsize,ysize,gt,srs,target_arr):
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driver = gdal.GetDriverByName('GTiff') # 数据类型必须有，因为要计算需要多大内存空间
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tiff_geo=driver.Create(target_data_path, int(xsize)+1, int(ysize)+1, 1, gdal.GDT_Float32)
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tiff_geo.GetRasterBand(1).WriteArray(target_arr)
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tiff_geo.SetGeoTransform(gt)
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tiff_geo.SetProjection(srs.ExportToWkt())
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del tiff_geo
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# 插值地理编码方法
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def geoCoding(tree,X_min,X_max,Y_min,Y_max,block_size,value_data,target_arr):
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for i in range(X_min,X_max+block_size,block_size):
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for j in range(Y_min,Y_max+block_size,block_size):
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end_i=i+block_size if i+block_size<X_max else X_max
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end_j=j+block_size if j+block_size<Y_max else Y_max
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X_ids=list(range(i,end_i+1))
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Y_ids=list(range(j,end_j+1))
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[X_ids,Y_ids]=np.meshgrid(X_ids,Y_ids)
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XY_query=np.concatenate([X_ids.reshape(-1,1),Y_ids.reshape(-1,1)],axis=1)
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if XY_query.shape[0]==0:
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continue
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dist, ind = tree.query(XY_query, k=4)
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# 处理距离为0
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dist_mask=dist==0
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dist_mask=np.sum(dist_mask,1)
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idx=np.where(dist_mask>=1)
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ind0=ind[idx,0]
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X_ids=XY_query[idx,0] # x
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Y_ids=XY_query[idx,1] # y
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target_arr[Y_ids,X_ids]=value_data[ind0]
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# 处理距离非0 这部分插值重写
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idx=np.where(dist_mask==0)
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ind=ind[idx,:][0,:,:]
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dist=dist[idx,:][0,:,:]
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X_ids=XY_query[idx,0] # x
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Y_ids=XY_query[idx,1] # y
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XY_query=np.concatenate([X_ids.reshape(-1,1),Y_ids.reshape(-1,1)],axis=1)
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dist_mask=dist[:,3]<=50 ####################################################################################
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idx=np.where(dist_mask==1)
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ind=ind[idx,:][0,:,:]
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dist=dist[idx,:][0,:,:]
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X_ids=XY_query[idx,0] # x
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Y_ids=XY_query[idx,1] # y
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XY_query=np.concatenate([X_ids.reshape(-1,1),Y_ids.reshape(-1,1)],axis=1)
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w=1/dist
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w=w/np.sum(w,1).reshape(-1,1) # 权重
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target_arr[XY_query[:,1],XY_query[:,0]]=np.sum(w*value_data[ind],1)
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print(end_i/X_max,"...",end="")
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return target_arr
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def get_Dem(isce_work_path,temp_work_path,pack_path,product_name, array):
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# lamda=get_lambel_sentinel(isce_work_path)
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# filt_topophase_unw_path=get_filt_fine_unw_path(isce_work_path)[0]
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# unw_tiff_path=os.path.join(temp_work_path,"unw.tiff")
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# vrt2tiff(filt_topophase_unw_path,unw_tiff_path,1)
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[lon_path,lat_path]=get_long_lat_path(isce_work_path)
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lon_tiff_path=os.path.join(temp_work_path,"lon.tiff")
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vrt2tiff(lon_path,lon_tiff_path)
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lat_tiff_path=os.path.join(temp_work_path,"lat.tiff")
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vrt2tiff(lat_path,lat_tiff_path)
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hgt_path=get_hgt_path(isce_work_path)
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hgt_tiff_path=os.path.join(temp_work_path,"hgt.tiff")
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vrt2tiff(hgt_path,hgt_tiff_path)
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los_path=get_Los_path(isce_work_path)
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los_tiff_path=os.path.join(temp_work_path,"los_uwm.tiff")
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vrt2tiff(los_path,los_tiff_path,0)
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los_data=read_tiff_dataset(los_tiff_path,band_idx=0)
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lon_data=read_tiff_dataset(lon_tiff_path,band_idx=0)
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lon = lon_data[0, :]
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lon_min = np.min(lon)
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lon_max = np.max(lon)
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lat_data=read_tiff_dataset(lat_tiff_path,band_idx=0)
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lat = lat_data[:, lat_data.shape[1]-1]
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lat_min = np.min(lat)
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lat_max = np.max(lat)
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los_data=los_data.reshape(-1)
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lon_data=lon_data.reshape(-1)
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lat_data=lat_data.reshape(-1)
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array = array.reshape(-1)
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mask_idx=np.where(los_data>0)
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lon_data=lon_data[mask_idx]
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lat_data=lat_data[mask_idx]
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array = array[mask_idx]
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# print(unw_data.shape,np.min(unw_data),np.max(unw_data))
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del mask_idx,los_data
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box = str(lat_min) + ';' + str(lat_max) + ';' + str(lon_min) + ';' + str(lon_max)
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# 形变值计算
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hgt_data=array # 叠加isce高程
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# hgt_data=def_data # 叠加isce高程
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hgt_tiff_path = os.path.join(temp_work_path, "hgt_data.tiff")
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# ImageHandler.write_img(hgt_tiff_path, '', [0.0, 1.0, 0.0, 0.0, 0.0, 1.0], hgt_data)
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#
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gt=[np.min(lon_data), 0.0002777777777777778, 0.0, np.max(lat_data), 0.0, -0.0002777777777777778]
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xsize=((np.max(lon_data)-np.min(lon_data))/0.0002777777777777778)+1
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ysize=((np.min(lat_data)-np.max(lat_data))/-0.0002777777777777778)+1
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# 获取地理坐标系统信息，用于选取需要的地理坐标系统
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srs = osr.SpatialReference()
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srs.ImportFromEPSG(4326) # 定义输出的坐标系为"WGS 84"
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inv_gt=gdal.InvGeoTransform(gt)
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print(xsize,ysize)
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def_arr=np.zeros((int(ysize)+1,int(xsize)+1))-9999
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dem_arr=np.zeros((int(ysize)+1,int(xsize)+1))-9999
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X=inv_gt[0]+lon_data*inv_gt[1]+inv_gt[2]*lat_data
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Y=inv_gt[3]+lon_data*inv_gt[4]+inv_gt[5]*lat_data
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XY=np.zeros((X.shape[0],2))
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XY[:,0]=X
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XY[:,1]=Y
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tree = KDTree(XY, leaf_size=2)
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X_min=math.ceil(np.min(X))
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X_max=math.floor(np.max(X))
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Y_min=math.ceil(np.min(Y))
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Y_max=math.floor(np.max(Y))
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block_size=1000
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dem_arr=geoCoding(tree,X_min,X_max,Y_min,Y_max,block_size,hgt_data,dem_arr)
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if not os.path.exists(pack_path):
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os.mkdir(pack_path)
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dem_target_data_path=product_name
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saveTiff(dem_target_data_path,xsize,ysize,gt,srs,dem_arr)
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# return hgt_tiff_path, box
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return dem_target_data_path
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if __name__ == '__main__':
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isce_work_path = r"D:\micro\microproduct_depdence\GF3-Deformation\isce_work"
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temp_work_path = r"D:\micro\microproduct_depdence\GF3-Deformation\test"
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out_work_path = r"D:\micro\microproduct_depdence\GF3-Deformation\test"
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product = r'D:\micro\microproduct_depdence\GF3-Deformation\test\dem.tiff'
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get_Dem(isce_work_path, temp_work_path, out_work_path, product)
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pass |