139 lines
6.4 KiB
Python
139 lines
6.4 KiB
Python
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from tool.algorithm.image.ImageHandle import ImageHandler
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import numpy as np
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from xml.etree.ElementTree import ElementTree, Element
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class header_file_read_angle():
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"""从_incidence.xml文件中读取角度，计算雷达入射角。"""
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def __init__(self):
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pass
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def read_all_angle(self,incidence_file,hight,width):
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"""从_incidence.xml文件中读取角度，存到一个数组中"""
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tree = ElementTree()
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tree.parse(incidence_file) # 影像头文件
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root = tree.getroot()
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element_trees = list(root)
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all_angle_array=np.zeros((hight,width),dtype=float)
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a=0
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for i in range(0, len(element_trees)):
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if element_trees[i].tag=='incidenceValue':
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a=i
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break
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for j in range(0, width):
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all_angle_array[:,j]=element_trees[j+a].text
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return all_angle_array
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def get_orisim_angle(self, ori_sim_file, all_angle_array, header_height, header_width):
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"""根据ori_sim.tif文件记录的行列号去取对应的雷达入射角
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ori_sim_file:正射产品
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all_angle_array：从incidence.xml读取的所有雷达入射角数组
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header_height：轨道参数文件记录的高
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header_width：轨道参数文件记录的宽
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"""
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height_array = ImageHandler.get_band_array(ori_sim_file, 1)
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width_array = ImageHandler.get_band_array(ori_sim_file, 2)
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img_height=ImageHandler.get_img_height(ori_sim_file) # 读取影像的高度
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img_width= ImageHandler.get_img_width(ori_sim_file) # 读取影像的宽度
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angle_array=np.zeros((img_height, img_width),dtype=float) # 存放角度的空矩阵
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for i in range(0,img_height):
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for j in range(0, img_width):
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x=height_array[i,j] # 取出行数值
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y=width_array[i,j] # 取出列数值
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if x>0 and y>0:
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if x<header_height and y<header_width:
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angle_array[i, j]=all_angle_array[int(x), int(y)] # 根据行列号去取对应的雷达入射角
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else:
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angle_array[i, j] =0
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else:
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angle_array[i, j] = 0
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return angle_array
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# 数据测试模块
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# if __name__=="__main__":
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#
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# # 0、 准备数据
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# Mas_tiff_path = r"C:\Users\Administrator\Desktop\新建文件夹6\MasterSar" # 主影像头文件存放的文件夹路径 已知
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# Aux_tiff_path = r"C:\Users\Administrator\Desktop\新建文件夹6\AuxiliarySar" # 辅影像头文件存放的文件夹路径 已知
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# Mas_ori_sim = r"C:\Users\Administrator\Desktop\新建文件夹6\MasterSar\ori_sim_MasterSar_preprocessed.tif" # 主影像地面p点影像 已知
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# Aux_ori_sim = r"C:\Users\Administrator\Desktop\新建文件夹6\AuxiliarySar\ori_sim_AuxiliarySar_preprocessed.tif" # 辅影像地面p点影像 已知
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#
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# #########
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# # 1.构建主辅影像行列变换模型，生成新的影像(p点坐标):out_new_master_ori_sim
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# out_new_master_ori_sim= r"C:\Users\Administrator\Desktop\新建文件夹6\new_master_ori_sim.tif" # 写入新的地面p点路径
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# r_plsq, c_plsq2=fine_registration().least_sqrt(Mas_ori_sim, Aux_ori_sim)
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# fine_registration().get_new_master_sim(r_plsq, c_plsq2, Mas_ori_sim, out_new_master_ori_sim)
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# # 2.计算卫星的空间位置和p0点的位置
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#
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# Orthorectification_RD_object=Orthorectification_RD() # 使用生成的out_new_master_ori_sim来计算空间位置
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# Mas_p0_array, Mas_sat_array, Mas_lamda= Orthorectification_RD_object.Orthorectification(Mas_tiff_path, Mas_ori_sim)
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# Aux_p0_array, Aux_sat_array, Aux_lamda= Orthorectification_RD_object.Orthorectification(Aux_tiff_path, out_new_master_ori_sim)
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#
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# S1=Mas_sat_array # 主影像卫星位置
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# S2=Aux_sat_array # 辅影像卫星位置
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# P=Mas_p0_array # 主影像计算出的p0
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# # 3.计算垂直基线数组B_vertical、斜距len_S1_P、斜距len_S2_P
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# B_vertical, len_S1_P, len_S2_P=fine_registration().calu_vector_angle(S1, S2, P)
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# print("垂直基线计算完成")
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# ########
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#
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# # 开始计算kz
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# geo = ImageHandler.get_geotransform(Mas_ori_sim)
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# pro = ImageHandler.get_projection(Mas_ori_sim)
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# B_vertical_path=r"C:\Users\Administrator\Desktop\新建文件夹6\B_vertical_path.tif"
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# ImageHandler.write_img(B_vertical_path, pro, geo, B_vertical, no_data='null')
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#
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# len_S1_P_path=r"C:\Users\Administrator\Desktop\新建文件夹6\len_S1_P_path.tif"
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# ImageHandler.write_img(len_S1_P_path, pro, geo, len_S1_P, no_data='null')
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# len_S2_P_path=r"C:\Users\Administrator\Desktop\新建文件夹6\len_S2_P_path.tif"
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# ImageHandler.write_img(len_S2_P_path, pro, geo, len_S2_P, no_data='null')
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#
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#
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# master_incident_file=r"C:\Users\Administrator\Desktop\新建文件夹6\MasterSar\IncidenceAngle.tif"
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# Mas_lamda=0.055517
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# kz_array=fine_registration().calu_kz(master_incident_file, B_vertical_path,len_S1_P_path, Mas_lamda)
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# kz_array_path=r"C:\Users\Administrator\Desktop\新建文件夹6\kz_array_path.tif"
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# ImageHandler.write_img(len_S2_P_path, pro, geo, kz_array, no_data='null')
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# pass
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# m_incidence_file=r"I:\西藏未正射\西藏\GF3_MYN_QPSI_011437_E98.7_N31.1_20181012_L1A_AHV_L10003514923\GF3_MYN_QPSI_011437_E98.7_N31.1_20181012_L1A_AHV_L10003514923.incidence.xml"
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# m_ori_sim_file =r"D:\MicroWorkspace\L-SAR\VegetationHeight\Input\MasterSar\ori_sim.tif"
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# m_all_angle_array=header_file_read_angle().read_all_angle(m_incidence_file,6000,8062)
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# m_angle_array=header_file_read_angle().get_orisim_angle(m_ori_sim_file, m_all_angle_array)
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#
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#
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# a_incidence_file = r"I:\西藏未正射\西藏\GF3_MYN_QPSI_011437_E98.7_N31.3_20181012_L1A_AHV_L10003514924\GF3_MYN_QPSI_011437_E98.7_N31.3_20181012_L1A_AHV_L10003514924.incidence.xml"
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# a_ori_sim_file =r"D:\MicroWorkspace\L-SAR\VegetationHeight\Input\AuxiliarySar\ori_sim2.tif"
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# a_all_angle_array = header_file_read_angle().read_all_angle(a_incidence_file, 6000, 8062)
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# a_angle_array = header_file_read_angle().get_orisim_angle(a_ori_sim_file, a_all_angle_array)
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#
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# v1=m_angle_array[0:561,0:1262]
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# v2=a_angle_array[0:561,0:1262]
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# v1=v1/180*math.pi
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# v2=v2/180 * math.pi
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#
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# mean = (v1 + v2) / 2
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# chazhi = v1 - v2
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# kz_array = 4 * math.pi * (abs(v2 - v1)) / (np.sin(mean) * 0.055517)
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# pass
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