502 lines
21 KiB
Python
502 lines
21 KiB
Python
# -*- coding: UTF-8 -*-
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"""
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@Project ：microproduct
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@File ：BackscatteringAlg.py
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@Author ：KHZ
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@Date ：2021/9/2 11:14
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@Version ：1.0.0
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修改历史：
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[修改序列] [修改日期] [修改者] [修改内容]
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1 2022-6-29 石海军 1.int16矩阵转float32；2.对数形式（单位dB）转指数形式（无单位）
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"""
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import os
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import numpy as np
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from tool.algorithm.algtools.MetaDataHandler import MetaDataHandler
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from tool.algorithm.image.ImageHandle import ImageHandler
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from osgeo import gdal, gdalconst
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from concurrent.futures import ThreadPoolExecutor, as_completed
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# env_str = os.getcwd()
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# os.environ['PROJ_LIB'] = env_str
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class ScatteringAlg:
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def __init__(self):
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pass
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@staticmethod
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def sar_backscattering_coef(in_sar_tif, meta_file_path, out_sar_tif, replece_VV = False, is_DB = True):
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# 读取原始SAR影像
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proj, geotrans, in_data = ImageHandler.read_img(in_sar_tif)
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# 计算强度信息
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I = np.array(in_data[0], dtype="float32")
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Q = np.array(in_data[1], dtype="float32")
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where_9999_0 = np.where(I == -9999)
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where_9999_1 = np.where(Q == -9999)
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I[where_9999_0] = 1.0
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Q[where_9999_1] = 1.0
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I2 = np.square(I)
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Q2 = np.square(Q)
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intensity_arr = I2 + Q2
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# 获取极化类型
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if 'HH' in os.path.basename(in_sar_tif):
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polarization = 'HH'
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elif 'HV' in os.path.basename(in_sar_tif):
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polarization = 'HV'
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elif 'VH' in os.path.basename(in_sar_tif):
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polarization = 'VH'
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elif 'VV' in os.path.basename(in_sar_tif):
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polarization = 'VV'
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if replece_VV:
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polarization = 'HV' #土壤水分算法中可能会用HV替换VV
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else:
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raise Exception ('there are not HH、HV、VH、VV in path:',in_sar_tif)
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# 获取参数
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QualifyValue = MetaDataHandler.get_QualifyValue(meta_file_path, polarization)
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Kdb = MetaDataHandler.get_Kdb(meta_file_path, polarization)
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# 10 * (alog10((b1 * b1 + b2 * b2) * ((3690.385986 / 32767) * (3690.385986 / 32767)))) - 32.565000
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# 计算后向散射系数
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#对数形式
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coef_arr = 10 * (np.log10(intensity_arr * ((QualifyValue/32767)**2))) - Kdb
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coef_arr[np.isnan(coef_arr)] = -9999
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coef_arr[np.isinf(coef_arr)] = -9999
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coef_arr[where_9999_0] = -9999
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coef_arr[where_9999_1] = -9999
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# 输出的SAR后向散射系数产品
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ImageHandler.write_img(out_sar_tif, proj, geotrans, coef_arr, -9999)
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return True
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###############
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# RPC 模块
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###############
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def parse_rpc_file(rpc_file):
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# rpc_file:.rpc文件的绝对路径
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# rpc_dict：符号RPC域下的16个关键字的字典
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# 参考网址：http://geotiff.maptools.org/rpc_prop.html；
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# https://www.osgeo.cn/gdal/development/rfc/rfc22_rpc.html
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rpc_dict = {}
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with open(rpc_file) as f:
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text = f.read()
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# .rpc文件中的RPC关键词
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words = ['errBias', 'errRand', 'lineOffset', 'sampOffset', 'latOffset',
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'longOffset', 'heightOffset', 'lineScale', 'sampScale', 'latScale',
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'longScale', 'heightScale', 'lineNumCoef', 'lineDenCoef','sampNumCoef', 'sampDenCoef',]
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# GDAL库对应的RPC关键词
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keys = ['ERR_BIAS', 'ERR_RAND', 'LINE_OFF', 'SAMP_OFF', 'LAT_OFF', 'LONG_OFF',
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'HEIGHT_OFF', 'LINE_SCALE', 'SAMP_SCALE', 'LAT_SCALE',
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'LONG_SCALE', 'HEIGHT_SCALE', 'LINE_NUM_COEFF', 'LINE_DEN_COEFF',
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'SAMP_NUM_COEFF', 'SAMP_DEN_COEFF']
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for old, new in zip(words, keys):
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text = text.replace(old, new)
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# 以‘;\n’作为分隔符
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text_list = text.split(';\n')
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# 删掉无用的行
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text_list = text_list[3:-2]
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#
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text_list[0] = text_list[0].split('\n')[1]
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# 去掉制表符、换行符、空格
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text_list = [item.strip('\t').replace('\n', '').replace(' ', '') for item in text_list]
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for item in text_list:
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# 去掉‘=’
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key, value = item.split('=')
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# 去掉多余的括号‘(’，‘)’
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if '(' in value:
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value = value.replace('(', '').replace(')', '')
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rpc_dict[key] = value
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for key in keys[:12]:
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# 为正数添加符号‘+’
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if not rpc_dict[key].startswith('-'):
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rpc_dict[key] = '+' + rpc_dict[key]
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# 为归一化项和误差标志添加单位
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if key in ['LAT_OFF', 'LONG_OFF', 'LAT_SCALE', 'LONG_SCALE']:
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rpc_dict[key] = rpc_dict[key] + ' degrees'
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if key in ['LINE_OFF', 'SAMP_OFF', 'LINE_SCALE', 'SAMP_SCALE']:
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rpc_dict[key] = rpc_dict[key] + ' pixels'
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if key in ['ERR_BIAS', 'ERR_RAND', 'HEIGHT_OFF', 'HEIGHT_SCALE']:
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rpc_dict[key] = rpc_dict[key] + ' meters'
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# 处理有理函数项
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for key in keys[-4:]:
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values = []
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for item in rpc_dict[key].split(','):
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#print(item)
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if not item.startswith('-'):
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values.append('+'+item)
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else:
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values.append(item)
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rpc_dict[key] = ' '.join(values)
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return rpc_dict
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def write_rpc_to_tiff(inputpath,rpc_file,ap = True,outpath = None):
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rpc_dict = parse_rpc_file(rpc_file)
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if ap:
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# 可修改读取
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dataset = gdal.Open(inputpath,gdal.GA_Update)
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# 向tif影像写入rpc域信息
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# 注意，这里虽然写入了RPC域信息，但实际影像还没有进行实际的RPC校正
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# 尽管有些RS/GIS能加载RPC域信息，并进行动态校正
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for k in rpc_dict.keys():
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dataset.SetMetadataItem(k, rpc_dict[k], 'RPC')
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dataset.FlushCache()
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del dataset
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else:
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dataset = gdal.Open(inputpath,gdal.GA_Update)
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tiff_driver= gdal.GetDriverByName('Gtiff')
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out_ds = tiff_driver.CreateCopy(outpath, dataset, 1)
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for k in rpc_dict.keys():
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out_ds.SetMetadataItem(k, rpc_dict[k], 'RPC')
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out_ds.FlushCache()
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def rpc_correction(inputpath,rpc_file,corrtiff,dem_tif_file = None):
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## 设置rpc校正的参数
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# 原图像和输出影像缺失值设置为0，输出影像坐标系为WGS84(EPSG:4326), 重采样方法为双线性插值（bilinear，还有最邻近‘near’、三次卷积‘cubic’等可选)
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# 注意DEM的覆盖范围要比原影像的范围大，此外，DEM不能有缺失值，有缺失值会报错
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# 通常DEM在水域是没有值的（即缺失值的情况），因此需要将其填充设置为0，否则在RPC校正时会报错
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# 这里使用的DEM是填充0值后的SRTM V4.1 3秒弧度的DEM(分辨率为90m)
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# RPC_DEMINTERPOLATION=bilinear 表示对DEM重采样使用双线性插值算法
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# 如果要修改输出的坐标系，则要修改dstSRS参数值，使用该坐标系统的EPSG代码
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# 可以在网址https://spatialreference.org/ref/epsg/32650/ 查询得到EPSG代码
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write_rpc_to_tiff(inputpath,rpc_file,ap = True,outpath = None)
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if dem_tif_file is None:
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wo = gdal.WarpOptions(srcNodata=0, dstNodata=0, dstSRS='EPSG:4326', resampleAlg='bilinear',
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format='Gtiff',rpc=True, warpOptions=["INIT_DEST=NO_DATA"])
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wr = gdal.Warp(corrtiff, inputpath, options=wo)
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print("RPC_GEOcorr>>>")
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else:
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wo = gdal.WarpOptions(srcNodata=0, dstNodata=0, dstSRS='EPSG:4326', resampleAlg='bilinear', format='Gtiff',rpc=True, warpOptions=["INIT_DEST=NO_DATA"],
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transformerOptions=["RPC_DEM=%s"%(dem_tif_file), "RPC_DEMINTERPOLATION=bilinear"])
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wr = gdal.Warp(corrtiff, inputpath, options=wo)
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print("RPC_GEOcorr>>>")
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## 对于全海域的影像或者不使用DEM校正的话，可以将transformerOptions有关的RPC DEM关键字删掉
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## 即将上面gdal.WarpOptions注释掉，将下面的语句取消注释，无DEM时，影像范围的高程默认全为0
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del wr
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##########################
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# 输出RPC 行列号到 经纬度的变换
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##########################
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#最大迭代次数超过则报错
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class MaxLocalizationIterationsError(Exception):
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"""
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Custom rpcm Exception.
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"""
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pass
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def apply_poly(poly, x, y, z):
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"""
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Evaluates a 3-variables polynom of degree 3 on a triplet of numbers.
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将三次多项式的统一模式构建为一个单独的函数
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Args:
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poly: list of the 20 coefficients of the 3-variate degree 3 polynom,
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ordered following the RPC convention.
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x, y, z: triplet of floats. They may be numpy arrays of same length.
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Returns:
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the value(s) of the polynom on the input point(s).
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"""
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out = 0
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out += poly[0]
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out += poly[1]*y + poly[2]*x + poly[3]*z
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out += poly[4]*y*x + poly[5]*y*z +poly[6]*x*z
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out += poly[7]*y*y + poly[8]*x*x + poly[9]*z*z
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out += poly[10]*x*y*z
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out += poly[11]*y*y*y
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out += poly[12]*y*x*x + poly[13]*y*z*z + poly[14]*y*y*x
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out += poly[15]*x*x*x
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out += poly[16]*x*z*z + poly[17]*y*y*z + poly[18]*x*x*z
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out += poly[19]*z*z*z
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return out
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def apply_rfm(num, den, x, y, z):
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"""
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Evaluates a Rational Function Model (rfm), on a triplet of numbers.
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执行20个参数的分子和20个参数的除法
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Args:
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num: list of the 20 coefficients of the numerator
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den: list of the 20 coefficients of the denominator
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All these coefficients are ordered following the RPC convention.
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x, y, z: triplet of floats. They may be numpy arrays of same length.
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Returns:
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the value(s) of the rfm on the input point(s).
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"""
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return apply_poly(num, x, y, z) / apply_poly(den, x, y, z)
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def rpc_from_geotiff(geotiff_path):
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"""
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Read the RPC coefficients from a GeoTIFF file and return an RPCModel object.
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该函数返回影像的Gdal格式的影像和RPCmodel
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Args:
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geotiff_path (str): path or url to a GeoTIFF file
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Returns:
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instance of the rpc_model.RPCModel class
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"""
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# with rasterio.open(geotiff_path, 'r') as src:
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#
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dataset = gdal.Open(geotiff_path, gdal.GA_ReadOnly)
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rpc_dict = dataset.GetMetadata("RPC")
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# 同时返回影像与rpc
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return dataset, RPCModel(rpc_dict,'geotiff')
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def read_rpc_file(rpc_file):
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"""
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Read RPC from a RPC_txt file and return a RPCmodel
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从TXT中直接单独读取RPC模型
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Args:
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rpc_file: RPC sidecar file path
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Returns:
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dictionary read from the RPC file, or an empty dict if fail
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"""
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rpc = parse_rpc_file(rpc_file)
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return RPCModel(rpc)
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class RPCModel:
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def __init__(self, d, dict_format="geotiff"):
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"""
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Args:
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d (dict): dictionary read from a geotiff file with
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rasterio.open('/path/to/file.tiff', 'r').tags(ns='RPC'),
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or from the .__dict__ of an RPCModel object.
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dict_format (str): format of the dictionary passed in `d`.
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Either "geotiff" if read from the tags of a geotiff file,
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or "rpcm" if read from the .__dict__ of an RPCModel object.
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"""
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if dict_format == "geotiff":
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self.row_offset = float(d['LINE_OFF'][0:d['LINE_OFF'].rfind(' ')])
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self.col_offset = float(d['SAMP_OFF'][0:d['SAMP_OFF'].rfind(' ')])
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self.lat_offset = float(d['LAT_OFF'][0:d['LAT_OFF'].rfind(' ')])
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self.lon_offset = float(d['LONG_OFF'][0:d['LONG_OFF'].rfind(' ')])
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self.alt_offset = float(d['HEIGHT_OFF'][0:d['HEIGHT_OFF'].rfind(' ')])
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self.row_scale = float(d['LINE_SCALE'][0:d['LINE_SCALE'].rfind(' ')])
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self.col_scale = float(d['SAMP_SCALE'][0:d['SAMP_SCALE'].rfind(' ')])
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self.lat_scale = float(d['LAT_SCALE'][0:d['LAT_SCALE'].rfind(' ')])
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self.lon_scale = float(d['LONG_SCALE'][0:d['LONG_SCALE'].rfind(' ')])
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self.alt_scale = float(d['HEIGHT_SCALE'][0:d['HEIGHT_SCALE'].rfind(' ')])
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self.row_num = list(map(float, d['LINE_NUM_COEFF'].split()))
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self.row_den = list(map(float, d['LINE_DEN_COEFF'].split()))
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self.col_num = list(map(float, d['SAMP_NUM_COEFF'].split()))
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self.col_den = list(map(float, d['SAMP_DEN_COEFF'].split()))
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if 'LON_NUM_COEFF' in d:
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self.lon_num = list(map(float, d['LON_NUM_COEFF'].split()))
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self.lon_den = list(map(float, d['LON_DEN_COEFF'].split()))
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self.lat_num = list(map(float, d['LAT_NUM_COEFF'].split()))
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self.lat_den = list(map(float, d['LAT_DEN_COEFF'].split()))
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elif dict_format == "rpcm":
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self.__dict__ = d
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else:
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raise ValueError(
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"dict_format '{}' not supported. "
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"Should be {{'geotiff','rpcm'}}".format(dict_format)
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)
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def projection(self, lon, lat, alt):
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"""
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Convert geographic coordinates of 3D points into image coordinates.
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正投影：从地理坐标到图像坐标
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Args:
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lon (float or list): longitude(s) of the input 3D point(s)
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lat (float or list): latitude(s) of the input 3D point(s)
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alt (float or list): altitude(s) of the input 3D point(s)
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Returns:
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float or list: horizontal image coordinate(s) (column index, ie x)
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float or list: vertical image coordinate(s) (row index, ie y)
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"""
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nlon = (np.asarray(lon) - self.lon_offset) / self.lon_scale
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nlat = (np.asarray(lat) - self.lat_offset) / self.lat_scale
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nalt = (np.asarray(alt) - self.alt_offset) / self.alt_scale
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col = apply_rfm(self.col_num, self.col_den, nlat, nlon, nalt)
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row = apply_rfm(self.row_num, self.row_den, nlat, nlon, nalt)
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col = col * self.col_scale + self.col_offset
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row = row * self.row_scale + self.row_offset
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return col, row
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def localization(self, col, row, alt, return_normalized=False):
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"""
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Convert image coordinates plus altitude into geographic coordinates.
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反投影：从图像坐标到地理坐标
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Args:
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col (float or list): x image coordinate(s) of the input point(s)
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row (float or list): y image coordinate(s) of the input point(s)
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alt (float or list): altitude(s) of the input point(s)
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Returns:
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float or list: longitude(s)
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float or list: latitude(s)
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"""
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ncol = (np.asarray(col) - self.col_offset) / self.col_scale
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nrow = (np.asarray(row) - self.row_offset) / self.row_scale
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nalt = (np.asarray(alt) - self.alt_offset) / self.alt_scale
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if not hasattr(self, 'lat_num'):
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lon, lat = self.localization_iterative(ncol, nrow, nalt)
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else:
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lon = apply_rfm(self.lon_num, self.lon_den, nrow, ncol, nalt)
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lat = apply_rfm(self.lat_num, self.lat_den, nrow, ncol, nalt)
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if not return_normalized:
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lon = lon * self.lon_scale + self.lon_offset
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lat = lat * self.lat_scale + self.lat_offset
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return lon, lat
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def localization_iterative(self, col, row, alt):
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"""
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Iterative estimation of the localization function (image to ground),
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for a list of image points expressed in image coordinates.
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逆投影时的迭代函数
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Args:
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col, row: normalized image coordinates (between -1 and 1)
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alt: normalized altitude (between -1 and 1) of the corresponding 3D
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point
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Returns:
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lon, lat: normalized longitude and latitude
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Raises:
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MaxLocalizationIterationsError: if the while loop exceeds the max
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number of iterations, which is set to 100.
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"""
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# target point: Xf (f for final)
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Xf = np.vstack([col, row]).T
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# use 3 corners of the lon, lat domain and project them into the image
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# to get the first estimation of (lon, lat)
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# EPS is 2 for the first iteration, then 0.1.
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lon = -col ** 0 # vector of ones
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lat = -col ** 0
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EPS = 2
|
||
x0 = apply_rfm(self.col_num, self.col_den, lat, lon, alt)
|
||
y0 = apply_rfm(self.row_num, self.row_den, lat, lon, alt)
|
||
x1 = apply_rfm(self.col_num, self.col_den, lat, lon + EPS, alt)
|
||
y1 = apply_rfm(self.row_num, self.row_den, lat, lon + EPS, alt)
|
||
x2 = apply_rfm(self.col_num, self.col_den, lat + EPS, lon, alt)
|
||
y2 = apply_rfm(self.row_num, self.row_den, lat + EPS, lon, alt)
|
||
|
||
n = 0
|
||
while not np.all((x0 - col) ** 2 + (y0 - row) ** 2 < 1e-18):
|
||
|
||
if n > 100:
|
||
raise MaxLocalizationIterationsError("Max localization iterations (100) exceeded")
|
||
|
||
X0 = np.vstack([x0, y0]).T
|
||
X1 = np.vstack([x1, y1]).T
|
||
X2 = np.vstack([x2, y2]).T
|
||
e1 = X1 - X0
|
||
e2 = X2 - X0
|
||
u = Xf - X0
|
||
|
||
# project u on the base (e1, e2): u = a1*e1 + a2*e2
|
||
# the exact computation is given by:
|
||
# M = np.vstack((e1, e2)).T
|
||
# a = np.dot(np.linalg.inv(M), u)
|
||
# but I don't know how to vectorize this.
|
||
# Assuming that e1 and e2 are orthogonal, a1 is given by
|
||
# <u, e1> / <e1, e1>
|
||
num = np.sum(np.multiply(u, e1), axis=1)
|
||
den = np.sum(np.multiply(e1, e1), axis=1)
|
||
a1 = np.divide(num, den).squeeze()
|
||
|
||
num = np.sum(np.multiply(u, e2), axis=1)
|
||
den = np.sum(np.multiply(e2, e2), axis=1)
|
||
a2 = np.divide(num, den).squeeze()
|
||
|
||
# use the coefficients a1, a2 to compute an approximation of the
|
||
# point on the gound which in turn will give us the new X0
|
||
lon += a1 * EPS
|
||
lat += a2 * EPS
|
||
|
||
# update X0, X1 and X2
|
||
EPS = .1
|
||
x0 = apply_rfm(self.col_num, self.col_den, lat, lon, alt)
|
||
y0 = apply_rfm(self.row_num, self.row_den, lat, lon, alt)
|
||
x1 = apply_rfm(self.col_num, self.col_den, lat, lon + EPS, alt)
|
||
y1 = apply_rfm(self.row_num, self.row_den, lat, lon + EPS, alt)
|
||
x2 = apply_rfm(self.col_num, self.col_den, lat + EPS, lon, alt)
|
||
y2 = apply_rfm(self.row_num, self.row_den, lat + EPS, lon, alt)
|
||
n += 1
|
||
|
||
return lon, lat
|
||
#############################################
|
||
# 校正影像 输出影像转换表
|
||
#############################################
|
||
|
||
"""
|
||
[pool.putRequest(req) for req in requests]等同于
|
||
for req in requests:
|
||
pool.putRequest(req)
|
||
"""
|
||
|
||
def rpc_row_col(params):
|
||
rpc_mdl,r_block,c_block,ati_block,i,block_shape=params
|
||
r_block ,c_block = rpc_mdl.localization(r_block.reshape(-1).astype(np.int32),c_block.reshape(-1).astype(np.int32) ,ati_block )
|
||
return [i,r_block ,c_block,block_shape]
|
||
|
||
def getRCImageRC(inputPath,out_rpc_rc_path,rpc_file_path):
|
||
rpc_tool = read_rpc_file(rpc_file_path)
|
||
#shutil.copy2(inputPath, out_rpc_rc_path)
|
||
# 创建重采样输出文件（设置投影及六参数）
|
||
input_rpc_sar=gdal.Open(inputPath)
|
||
|
||
driver = gdal.GetDriverByName('GTiff')
|
||
output = driver.Create(out_rpc_rc_path, input_rpc_sar.RasterXSize,input_rpc_sar.RasterYSize, 2,gdal.GDT_Float32)
|
||
output.SetGeoTransform(list(input_rpc_sar.GetGeoTransform()))
|
||
output.SetProjection(input_rpc_sar.GetProjection())
|
||
# 参数说明 输入数据集、输出文件、输入投影、参考投影、重采样方法(最邻近内插\双线性内插\三次卷积等)、回调函数
|
||
|
||
rpc_rc_img=output
|
||
# 计算行列号
|
||
rc_height=rpc_rc_img.RasterYSize
|
||
rc_width=rpc_rc_img.RasterXSize
|
||
|
||
with ThreadPoolExecutor(max_workers=8) as t:
|
||
plist=[]
|
||
for i in range(0,rc_height,100):
|
||
c_block=np.ones((100,rc_width)).astype(np.float32)*(np.array(list(range(rc_width))).reshape(1,rc_width))
|
||
r_block=np.ones((100,rc_width)).astype(np.float32)*(np.array(list(range(100))).reshape(100,1))
|
||
r_block=r_block+i
|
||
if not rc_height-i>100:
|
||
num=rc_height-i
|
||
r_block=r_block[:num,:].astype(np.float32)
|
||
c_block=c_block[:num,:].astype(np.float32)
|
||
block_shape=r_block.shape
|
||
|
||
plist.append(t.submit(rpc_row_col,[rpc_tool,r_block.reshape(-1).astype(np.int32),c_block.reshape(-1).astype(np.int32) ,c_block.reshape(-1)*0+0,i,block_shape]))
|
||
for future in as_completed(plist):
|
||
data = future.result()
|
||
i,r_block ,c_block,block_shape=data
|
||
rpc_rc_img.GetRasterBand(1).WriteArray(r_block.reshape(block_shape).astype(np.float32),0,i)
|
||
rpc_rc_img.GetRasterBand(2).WriteArray(c_block.reshape(block_shape).astype(np.float32),0,i)
|
||
del rpc_rc_img,output,input_rpc_sar |