157 lines
5.6 KiB
Python
157 lines
5.6 KiB
Python
"""
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@Project ：microproduct
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@File ：DEMJoint
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@Function ：主函数
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@Author ：LMM
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@Date ：2021/10/19 14:39
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@Version ：1.0.0
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"""
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from osgeo import gdal, osr
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import os
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import numpy as np
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class DEMProcess:
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"""
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DEM拼接、重采样
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"""
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def __init__(self):
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pass
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@staticmethod
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def get_extent(fn):
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'''
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原文链接：https://blog.csdn.net/XBR_2014/article/details/85255412
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'''
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ds = gdal.Open(fn)
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rows = ds.RasterYSize
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cols = ds.RasterXSize
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# 获取图像角点坐标
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gt = ds.GetGeoTransform()
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minx = gt[0]
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maxy = gt[3]
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maxx = gt[0] + gt[1] * rows
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miny = gt[3] + gt[5] * cols
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return (minx, maxy, maxx, miny)
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@staticmethod
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def img_mosaic(in_files, out_dem_path):
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# 通过两两比较大小,将最终符合条件的四个角点坐标保存
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# 即为拼接图像的四个角点坐标
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minX, maxY, maxX, minY = DEMProcess.get_extent(in_files[0])
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for fn in in_files[1:]:
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minx, maxy, maxx, miny = DEMProcess.get_extent(fn)
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minX = min(minX, minx)
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maxY = max(maxY, maxy)
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maxX = max(maxX, maxx)
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minY = min(minY, miny)
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# 获取输出图像的行列数
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in_ds = gdal.Open(in_files[0])
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bands_num = in_ds.RasterCount
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gt = in_ds.GetGeoTransform()
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rows = int((maxX - minX) / abs(gt[5]))
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cols = int((maxY - minY) / gt[1])
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# 判断栅格数据的数据类型
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datatype = gdal.GDT_UInt16
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# 创建输出图像
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driver = gdal.GetDriverByName('GTiff')
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out_dem = os.path.join(out_dem_path, 'mosaic0.tif')
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out_ds = driver.Create(out_dem, cols, rows, bands_num, datatype)
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out_ds.SetProjection(in_ds.GetProjection())
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gt = list(in_ds.GetGeoTransform())
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gt[0], gt[3] = minX, maxY
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out_ds.SetGeoTransform(gt)
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for fn in in_files:
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in_ds = gdal.Open(fn)
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x_size = in_ds.RasterXSize
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y_size = in_ds.RasterYSize
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trans = gdal.Transformer(in_ds, out_ds, [])
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success, xyz = trans.TransformPoint(False, 0, 0)
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x, y, z = map(int, xyz)
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for i in range(1, bands_num + 1):
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data = in_ds.GetRasterBand(i).ReadAsArray()
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out_band = out_ds.GetRasterBand(i)
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out_data = out_band.ReadAsArray(x, y, x_size, y_size)
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data = np.maximum(data, out_data)
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out_band.WriteArray(data, x, y)
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del in_ds, out_band, out_ds
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@staticmethod
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def dem_clip(OutFilePath, DEMFilePath, SelectArea):
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'''
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根据选择范围裁剪DEM,并输出
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agrs:
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outFilePath:裁剪DEM输出地址
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DEMFilePath:被裁减DEM地址
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SelectArea:list [(xmin,ymax),(xmax,ymin)] 框选范围 左上角，右下角
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'''
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DEM_ptr = gdal.Open(DEMFilePath)
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DEM_GeoTransform = DEM_ptr.GetGeoTransform() # 读取影像的投影变换
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DEM_InvGeoTransform = gdal.InvGeoTransform(DEM_GeoTransform)
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SelectAreaArrayPoints = [gdal.ApplyGeoTransform(DEM_InvGeoTransform, p[0], p[1]) for p in SelectArea]
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SelectAreaArrayPoints = list(map(lambda p: (int(p[0]), int(p[1])), SelectAreaArrayPoints)) # 确定坐标
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[(ulx, uly), (brx, bry)] = SelectAreaArrayPoints
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rowCount, colCount = bry - uly, brx - ulx
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# 输出DEM的桌面坐标转换
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Out_Transfrom = list(DEM_GeoTransform)
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Out_Transfrom[0] = SelectArea[0][0]
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Out_Transfrom[3] = SelectArea[0][1]
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# 构建输出DEM
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Bands_num = DEM_ptr.RasterCount
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gtiff_driver = gdal.GetDriverByName('GTiff')
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datatype = gdal.GDT_UInt16
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out_dem = gtiff_driver.Create(OutFilePath, colCount, rowCount, Bands_num, datatype)
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out_dem.SetProjection(DEM_ptr.GetProjection())
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out_dem.SetGeoTransform(Out_Transfrom)
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for i in range(1, Bands_num + 1):
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data_band = DEM_ptr.GetRasterBand(i)
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out_band = out_dem.GetRasterBand(i)
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data = data_band.ReadAsArray(ulx, uly, colCount, rowCount)
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out_band.WriteArray(data)
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del out_dem
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@staticmethod
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def dem_resample(in_dem_path, out_dem_path):
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'''
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DEM重采样函数，默认坐标系为WGS84
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agrs:
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in_dem_path: 输入的DEM文件夹路径
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meta_file_path: 输入的xml元文件路径
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out_dem_path: 输出的DEM文件夹路径
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'''
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# 读取文件夹中所有的DEM
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dem_file_paths=[os.path.join(in_dem_path,dem_name) for dem_name in os.listdir(in_dem_path) if dem_name.find(".tif")>=0 and dem_name.find(".tif.")==-1]
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spatialreference=osr.SpatialReference()
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spatialreference.SetWellKnownGeogCS("WGS84") # 设置地理坐标，单位为度 degree # 设置投影坐标，单位为度 degree
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spatialproj=spatialreference.ExportToWkt() # 导出投影结果
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# 将DEM拼接成一张大图
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mergeFile =gdal.BuildVRT(os.path.join(out_dem_path,"mergeDEM.tif"), dem_file_paths)
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out_DEM=os.path.join(out_dem_path,"mosaic.tif")
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gdal.Warp(out_DEM,
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mergeFile,
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format="GTiff",
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dstSRS=spatialproj,
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dstNodata=-9999,
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outputType=gdal.GDT_Float32)
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return out_DEM
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# if __name__ == "__main__":
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# DEMProcess = DEMProcess()
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# in_dem_path = r'F:\大气延迟\out_dem'
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# out_dem_path = r'F:\大气延迟\out_dem'
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# DEMProcess.dem_resample(in_dem_path, out_dem_path)
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