#命名为：geo_rpc.py """ RPC model parsers, localization, and projection """ import numpy as np from osgeo import gdal #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 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 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