330 lines
13 KiB
Python
330 lines
13 KiB
Python
#命名为：geo_rpc.py
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"""
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RPC model parsers, localization, and projection
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"""
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import numpy as np
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from osgeo import gdal
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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 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 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
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x0 = apply_rfm(self.col_num, self.col_den, lat, lon, alt)
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y0 = apply_rfm(self.row_num, self.row_den, lat, lon, alt)
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x1 = apply_rfm(self.col_num, self.col_den, lat, lon + EPS, alt)
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y1 = apply_rfm(self.row_num, self.row_den, lat, lon + EPS, alt)
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x2 = apply_rfm(self.col_num, self.col_den, lat + EPS, lon, alt)
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y2 = apply_rfm(self.row_num, self.row_den, lat + EPS, lon, alt)
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n = 0
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while not np.all((x0 - col) ** 2 + (y0 - row) ** 2 < 1e-18):
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if n > 100:
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raise MaxLocalizationIterationsError("Max localization iterations (100) exceeded")
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X0 = np.vstack([x0, y0]).T
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X1 = np.vstack([x1, y1]).T
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X2 = np.vstack([x2, y2]).T
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e1 = X1 - X0
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e2 = X2 - X0
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u = Xf - X0
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# project u on the base (e1, e2): u = a1*e1 + a2*e2
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# the exact computation is given by:
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# M = np.vstack((e1, e2)).T
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# a = np.dot(np.linalg.inv(M), u)
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# but I don't know how to vectorize this.
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# Assuming that e1 and e2 are orthogonal, a1 is given by
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# <u, e1> / <e1, e1>
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num = np.sum(np.multiply(u, e1), axis=1)
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den = np.sum(np.multiply(e1, e1), axis=1)
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a1 = np.divide(num, den).squeeze()
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num = np.sum(np.multiply(u, e2), axis=1)
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den = np.sum(np.multiply(e2, e2), axis=1)
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a2 = np.divide(num, den).squeeze()
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# use the coefficients a1, a2 to compute an approximation of the
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# point on the gound which in turn will give us the new X0
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lon += a1 * EPS
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lat += a2 * EPS
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# update X0, X1 and X2
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EPS = .1
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x0 = apply_rfm(self.col_num, self.col_den, lat, lon, alt)
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y0 = apply_rfm(self.row_num, self.row_den, lat, lon, alt)
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x1 = apply_rfm(self.col_num, self.col_den, lat, lon + EPS, alt)
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y1 = apply_rfm(self.row_num, self.row_den, lat, lon + EPS, alt)
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x2 = apply_rfm(self.col_num, self.col_den, lat + EPS, lon, alt)
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y2 = apply_rfm(self.row_num, self.row_den, lat + EPS, lon, alt)
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n += 1
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return lon, lat
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