# -*- coding: UTF-8 -*- """ @Project ：microproduct @File ：BackscatteringAlg.py @Author ：KHZ @Date ：2021/9/2 11:14 @Version ：1.0.0 修改历史： [修改序列] [修改日期] [修改者] [修改内容] 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） """ import os import numpy as np from tool.algorithm.algtools.MetaDataHandler import MetaDataHandler from tool.algorithm.image.ImageHandle import ImageHandler from osgeo import gdal, gdalconst from concurrent.futures import ThreadPoolExecutor, as_completed # env_str = os.getcwd() # os.environ['PROJ_LIB'] = env_str class ScatteringAlg: def __init__(self): pass @staticmethod def sar_backscattering_coef(in_sar_tif, meta_file_path, out_sar_tif, replece_VV = False, is_DB = True): # 读取原始SAR影像 proj, geotrans, in_data = ImageHandler.read_img(in_sar_tif) # 计算强度信息 I = np.array(in_data[0], dtype="float32") Q = np.array(in_data[1], dtype="float32") where_9999_0 = np.where(I == -9999) where_9999_1 = np.where(Q == -9999) I[where_9999_0] = 1.0 Q[where_9999_1] = 1.0 I2 = np.square(I) Q2 = np.square(Q) intensity_arr = I2 + Q2 # 获取极化类型 if 'HH' in os.path.basename(in_sar_tif): polarization = 'HH' elif 'HV' in os.path.basename(in_sar_tif): polarization = 'HV' elif 'VH' in os.path.basename(in_sar_tif): polarization = 'VH' elif 'VV' in os.path.basename(in_sar_tif): polarization = 'VV' if replece_VV: polarization = 'HV' #土壤水分算法中可能会用HV替换VV else: raise Exception ('there are not HH、HV、VH、VV in path:',in_sar_tif) # 获取参数 QualifyValue = MetaDataHandler.get_QualifyValue(meta_file_path, polarization) Kdb = MetaDataHandler.get_Kdb(meta_file_path, polarization) # 10 * (alog10((b1 * b1 + b2 * b2) * ((3690.385986 / 32767) * (3690.385986 / 32767)))) - 32.565000 # 计算后向散射系数 #对数形式 coef_arr = 10 * (np.log10(intensity_arr * ((QualifyValue/32767)**2))) - Kdb coef_arr[np.isnan(coef_arr)] = -9999 coef_arr[np.isinf(coef_arr)] = -9999 coef_arr[where_9999_0] = -9999 coef_arr[where_9999_1] = -9999 # 输出的SAR后向散射系数产品 ImageHandler.write_img(out_sar_tif, proj, geotrans, coef_arr, -9999) return True ############### # RPC 模块 ############### def parse_rpc_file(rpc_file): # rpc_file:.rpc文件的绝对路径 # rpc_dict：符号RPC域下的16个关键字的字典 # 参考网址：http://geotiff.maptools.org/rpc_prop.html； # https://www.osgeo.cn/gdal/development/rfc/rfc22_rpc.html rpc_dict = {} with open(rpc_file) as f: text = f.read() # .rpc文件中的RPC关键词 words = ['errBias', 'errRand', 'lineOffset', 'sampOffset', 'latOffset', 'longOffset', 'heightOffset', 'lineScale', 'sampScale', 'latScale', 'longScale', 'heightScale', 'lineNumCoef', 'lineDenCoef','sampNumCoef', 'sampDenCoef',] # GDAL库对应的RPC关键词 keys = ['ERR_BIAS', 'ERR_RAND', 'LINE_OFF', 'SAMP_OFF', 'LAT_OFF', 'LONG_OFF', 'HEIGHT_OFF', 'LINE_SCALE', 'SAMP_SCALE', 'LAT_SCALE', 'LONG_SCALE', 'HEIGHT_SCALE', 'LINE_NUM_COEFF', 'LINE_DEN_COEFF', 'SAMP_NUM_COEFF', 'SAMP_DEN_COEFF'] for old, new in zip(words, keys): text = text.replace(old, new) # 以‘;\n’作为分隔符 text_list = text.split(';\n') # 删掉无用的行 text_list = text_list[3:-2] # text_list[0] = text_list[0].split('\n')[1] # 去掉制表符、换行符、空格 text_list = [item.strip('\t').replace('\n', '').replace(' ', '') for item in text_list] for item in text_list: # 去掉‘=’ key, value = item.split('=') # 去掉多余的括号‘(’，‘)’ if '(' in value: value = value.replace('(', '').replace(')', '') rpc_dict[key] = value for key in keys[:12]: # 为正数添加符号‘+’ if not rpc_dict[key].startswith('-'): rpc_dict[key] = '+' + rpc_dict[key] # 为归一化项和误差标志添加单位 if key in ['LAT_OFF', 'LONG_OFF', 'LAT_SCALE', 'LONG_SCALE']: rpc_dict[key] = rpc_dict[key] + ' degrees' if key in ['LINE_OFF', 'SAMP_OFF', 'LINE_SCALE', 'SAMP_SCALE']: rpc_dict[key] = rpc_dict[key] + ' pixels' if key in ['ERR_BIAS', 'ERR_RAND', 'HEIGHT_OFF', 'HEIGHT_SCALE']: rpc_dict[key] = rpc_dict[key] + ' meters' # 处理有理函数项 for key in keys[-4:]: values = [] for item in rpc_dict[key].split(','): #print(item) if not item.startswith('-'): values.append('+'+item) else: values.append(item) rpc_dict[key] = ' '.join(values) return rpc_dict def write_rpc_to_tiff(inputpath,rpc_file,ap = True,outpath = None): rpc_dict = parse_rpc_file(rpc_file) if ap: # 可修改读取 dataset = gdal.Open(inputpath,gdal.GA_Update) # 向tif影像写入rpc域信息 # 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 # 尽管有些RS/GIS能加载RPC域信息，并进行动态校正 for k in rpc_dict.keys(): dataset.SetMetadataItem(k, rpc_dict[k], 'RPC') dataset.FlushCache() del dataset else: dataset = gdal.Open(inputpath,gdal.GA_Update) tiff_driver= gdal.GetDriverByName('Gtiff') out_ds = tiff_driver.CreateCopy(outpath, dataset, 1) for k in rpc_dict.keys(): out_ds.SetMetadataItem(k, rpc_dict[k], 'RPC') out_ds.FlushCache() def rpc_correction(inputpath,rpc_file,corrtiff,dem_tif_file = None): ## 设置rpc校正的参数 # 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) # 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 # 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 # 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) # RPC_DEMINTERPOLATION=bilinear 表示对DEM重采样使用双线性插值算法 # 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 # 可以在网址https://spatialreference.org/ref/epsg/32650/ 查询得到EPSG代码 write_rpc_to_tiff(inputpath,rpc_file,ap = True,outpath = None) if dem_tif_file is None: wo = gdal.WarpOptions(srcNodata=0, dstNodata=0, dstSRS='EPSG:4326', resampleAlg='bilinear', format='Gtiff',rpc=True, warpOptions=["INIT_DEST=NO_DATA"]) wr = gdal.Warp(corrtiff, inputpath, options=wo) print("RPC_GEOcorr>>>") else: wo = gdal.WarpOptions(srcNodata=0, dstNodata=0, dstSRS='EPSG:4326', resampleAlg='bilinear', format='Gtiff',rpc=True, warpOptions=["INIT_DEST=NO_DATA"], transformerOptions=["RPC_DEM=%s"%(dem_tif_file), "RPC_DEMINTERPOLATION=bilinear"]) wr = gdal.Warp(corrtiff, inputpath, options=wo) print("RPC_GEOcorr>>>") ## 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 ## 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 del wr ########################## # 输出RPC 行列号到 经纬度的变换 ########################## #最大迭代次数超过则报错 class MaxLocalizationIterationsError(Exception): """ Custom rpcm Exception. """ pass def apply_poly(poly, x, y, z): """ Evaluates a 3-variables polynom of degree 3 on a triplet of numbers. 将三次多项式的统一模式构建为一个单独的函数 Args: poly: list of the 20 coefficients of the 3-variate degree 3 polynom, ordered following the RPC convention. x, y, z: triplet of floats. They may be numpy arrays of same length. Returns: the value(s) of the polynom on the input point(s). """ out = 0 out += poly[0] out += poly[1]*y + poly[2]*x + poly[3]*z out += poly[4]*y*x + poly[5]*y*z +poly[6]*x*z out += poly[7]*y*y + poly[8]*x*x + poly[9]*z*z out += poly[10]*x*y*z out += poly[11]*y*y*y out += poly[12]*y*x*x + poly[13]*y*z*z + poly[14]*y*y*x out += poly[15]*x*x*x out += poly[16]*x*z*z + poly[17]*y*y*z + poly[18]*x*x*z out += poly[19]*z*z*z return out def apply_rfm(num, den, x, y, z): """ Evaluates a Rational Function Model (rfm), on a triplet of numbers. 执行20个参数的分子和20个参数的除法 Args: num: list of the 20 coefficients of the numerator den: list of the 20 coefficients of the denominator All these coefficients are ordered following the RPC convention. x, y, z: triplet of floats. They may be numpy arrays of same length. Returns: the value(s) of the rfm on the input point(s). """ return apply_poly(num, x, y, z) / apply_poly(den, x, y, z) def rpc_from_geotiff(geotiff_path): """ Read the RPC coefficients from a GeoTIFF file and return an RPCModel object. 该函数返回影像的Gdal格式的影像和RPCmodel Args: geotiff_path (str): path or url to a GeoTIFF file Returns: instance of the rpc_model.RPCModel class """ # with rasterio.open(geotiff_path, 'r') as src: # dataset = gdal.Open(geotiff_path, gdal.GA_ReadOnly) rpc_dict = dataset.GetMetadata("RPC") # 同时返回影像与rpc return dataset, RPCModel(rpc_dict,'geotiff') def read_rpc_file(rpc_file): """ Read RPC from a RPC_txt file and return a RPCmodel 从TXT中直接单独读取RPC模型 Args: rpc_file: RPC sidecar file path Returns: dictionary read from the RPC file, or an empty dict if fail """ rpc = parse_rpc_file(rpc_file) return RPCModel(rpc) class RPCModel: def __init__(self, d, dict_format="geotiff"): """ Args: d (dict): dictionary read from a geotiff file with rasterio.open('/path/to/file.tiff', 'r').tags(ns='RPC'), or from the .__dict__ of an RPCModel object. dict_format (str): format of the dictionary passed in `d`. Either "geotiff" if read from the tags of a geotiff file, or "rpcm" if read from the .__dict__ of an RPCModel object. """ if dict_format == "geotiff": self.row_offset = float(d['LINE_OFF'][0:d['LINE_OFF'].rfind(' ')]) self.col_offset = float(d['SAMP_OFF'][0:d['SAMP_OFF'].rfind(' ')]) self.lat_offset = float(d['LAT_OFF'][0:d['LAT_OFF'].rfind(' ')]) self.lon_offset = float(d['LONG_OFF'][0:d['LONG_OFF'].rfind(' ')]) self.alt_offset = float(d['HEIGHT_OFF'][0:d['HEIGHT_OFF'].rfind(' ')]) self.row_scale = float(d['LINE_SCALE'][0:d['LINE_SCALE'].rfind(' ')]) self.col_scale = float(d['SAMP_SCALE'][0:d['SAMP_SCALE'].rfind(' ')]) self.lat_scale = float(d['LAT_SCALE'][0:d['LAT_SCALE'].rfind(' ')]) self.lon_scale = float(d['LONG_SCALE'][0:d['LONG_SCALE'].rfind(' ')]) self.alt_scale = float(d['HEIGHT_SCALE'][0:d['HEIGHT_SCALE'].rfind(' ')]) self.row_num = list(map(float, d['LINE_NUM_COEFF'].split())) self.row_den = list(map(float, d['LINE_DEN_COEFF'].split())) self.col_num = list(map(float, d['SAMP_NUM_COEFF'].split())) self.col_den = list(map(float, d['SAMP_DEN_COEFF'].split())) if 'LON_NUM_COEFF' in d: self.lon_num = list(map(float, d['LON_NUM_COEFF'].split())) self.lon_den = list(map(float, d['LON_DEN_COEFF'].split())) self.lat_num = list(map(float, d['LAT_NUM_COEFF'].split())) self.lat_den = list(map(float, d['LAT_DEN_COEFF'].split())) elif dict_format == "rpcm": self.__dict__ = d else: raise ValueError( "dict_format '{}' not supported. " "Should be {{'geotiff','rpcm'}}".format(dict_format) ) def projection(self, lon, lat, alt): """ Convert geographic coordinates of 3D points into image coordinates. 正投影：从地理坐标到图像坐标 Args: lon (float or list): longitude(s) of the input 3D point(s) lat (float or list): latitude(s) of the input 3D point(s) alt (float or list): altitude(s) of the input 3D point(s) Returns: float or list: horizontal image coordinate(s) (column index, ie x) float or list: vertical image coordinate(s) (row index, ie y) """ nlon = (np.asarray(lon) - self.lon_offset) / self.lon_scale nlat = (np.asarray(lat) - self.lat_offset) / self.lat_scale nalt = (np.asarray(alt) - self.alt_offset) / self.alt_scale col = apply_rfm(self.col_num, self.col_den, nlat, nlon, nalt) row = apply_rfm(self.row_num, self.row_den, nlat, nlon, nalt) col = col * self.col_scale + self.col_offset row = row * self.row_scale + self.row_offset return col, row def localization(self, col, row, alt, return_normalized=False): """ Convert image coordinates plus altitude into geographic coordinates. 反投影：从图像坐标到地理坐标 Args: col (float or list): x image coordinate(s) of the input point(s) row (float or list): y image coordinate(s) of the input point(s) alt (float or list): altitude(s) of the input point(s) Returns: float or list: longitude(s) float or list: latitude(s) """ ncol = (np.asarray(col) - self.col_offset) / self.col_scale nrow = (np.asarray(row) - self.row_offset) / self.row_scale nalt = (np.asarray(alt) - self.alt_offset) / self.alt_scale if not hasattr(self, 'lat_num'): lon, lat = self.localization_iterative(ncol, nrow, nalt) else: lon = apply_rfm(self.lon_num, self.lon_den, nrow, ncol, nalt) lat = apply_rfm(self.lat_num, self.lat_den, nrow, ncol, nalt) if not return_normalized: lon = lon * self.lon_scale + self.lon_offset lat = lat * self.lat_scale + self.lat_offset return lon, lat def localization_iterative(self, col, row, alt): """ Iterative estimation of the localization function (image to ground), for a list of image points expressed in image coordinates. 逆投影时的迭代函数 Args: col, row: normalized image coordinates (between -1 and 1) alt: normalized altitude (between -1 and 1) of the corresponding 3D point Returns: lon, lat: normalized longitude and latitude Raises: MaxLocalizationIterationsError: if the while loop exceeds the max number of iterations, which is set to 100. """ # target point: Xf (f for final) Xf = np.vstack([col, row]).T # use 3 corners of the lon, lat domain and project them into the image # to get the first estimation of (lon, lat) # EPS is 2 for the first iteration, then 0.1. lon = -col ** 0 # vector of ones lat = -col ** 0 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 # / 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