from tool.algorithm.image.ImageHandle import ImageHandler
import numpy as np
from xml.etree.ElementTree import ElementTree, Element


class header_file_read_angle():
    """从_incidence.xml文件中读取角度，计算雷达入射角。"""
    def __init__(self):
        pass
    def read_all_angle(self,incidence_file,hight,width):
        """从_incidence.xml文件中读取角度，存到一个数组中"""
        tree = ElementTree()
        tree.parse(incidence_file)    # 影像头文件
        root = tree.getroot()
        element_trees = list(root)

        all_angle_array=np.zeros((hight,width),dtype=float)
        a=0
        for i in range(0, len(element_trees)):
            if element_trees[i].tag=='incidenceValue':
                a=i
                break
        for j in range(0, width):

            all_angle_array[:,j]=element_trees[j+a].text
        return all_angle_array

    def get_orisim_angle(self, ori_sim_file, all_angle_array, header_height, header_width):
        """根据ori_sim.tif文件记录的行列号去取对应的雷达入射角
        ori_sim_file:正射产品
        all_angle_array：从incidence.xml读取的所有雷达入射角数组
        header_height：轨道参数文件记录的高
        header_width：轨道参数文件记录的宽
        """

        height_array = ImageHandler.get_band_array(ori_sim_file, 1)
        width_array = ImageHandler.get_band_array(ori_sim_file, 2)

        img_height=ImageHandler.get_img_height(ori_sim_file)   # 读取影像的高度
        img_width= ImageHandler.get_img_width(ori_sim_file)    # 读取影像的宽度

        angle_array=np.zeros((img_height, img_width),dtype=float)  # 存放角度的空矩阵
        for i in range(0,img_height):
            for j in range(0, img_width):
                x=height_array[i,j]      # 取出行数值
                y=width_array[i,j]       # 取出列数值
                if x>0 and y>0:
                    if x<header_height and y<header_width:
                        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
                    else:
                        angle_array[i, j] =0
                else:
                    angle_array[i, j] = 0
        return angle_array

    # 数据测试模块
# if __name__=="__main__":
#
#     # 0、 准备数据
#     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
#     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
#     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
#     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
#
#     #########
#     # 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
#     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
#     r_plsq, c_plsq2=fine_registration().least_sqrt(Mas_ori_sim, Aux_ori_sim)
#     fine_registration().get_new_master_sim(r_plsq, c_plsq2, Mas_ori_sim, out_new_master_ori_sim)
#     # 2.计算卫星的空间位置和p0点的位置
#
#     Orthorectification_RD_object=Orthorectification_RD()    # 使用生成的out_new_master_ori_sim来计算空间位置
#     Mas_p0_array, Mas_sat_array, Mas_lamda= Orthorectification_RD_object.Orthorectification(Mas_tiff_path, Mas_ori_sim)
#     Aux_p0_array, Aux_sat_array, Aux_lamda= Orthorectification_RD_object.Orthorectification(Aux_tiff_path, out_new_master_ori_sim)
#
#     S1=Mas_sat_array   # 主影像卫星位置
#     S2=Aux_sat_array   # 辅影像卫星位置
#     P=Mas_p0_array     # 主影像计算出的p0
#     # 3.计算垂直基线数组B_vertical、斜距len_S1_P、斜距len_S2_P
#     B_vertical, len_S1_P, len_S2_P=fine_registration().calu_vector_angle(S1, S2, P)
#     print("垂直基线计算完成")
#     ########
#
#     # 开始计算kz
#     geo = ImageHandler.get_geotransform(Mas_ori_sim)
#     pro = ImageHandler.get_projection(Mas_ori_sim)
#     B_vertical_path=r"C:\Users\Administrator\Desktop\新建文件夹6\B_vertical_path.tif"
#     ImageHandler.write_img(B_vertical_path, pro, geo, B_vertical, no_data='null')
#

#     len_S1_P_path=r"C:\Users\Administrator\Desktop\新建文件夹6\len_S1_P_path.tif"
#     ImageHandler.write_img(len_S1_P_path, pro, geo, len_S1_P, no_data='null')
#     len_S2_P_path=r"C:\Users\Administrator\Desktop\新建文件夹6\len_S2_P_path.tif"
#     ImageHandler.write_img(len_S2_P_path, pro, geo, len_S2_P, no_data='null')
#
#
#     master_incident_file=r"C:\Users\Administrator\Desktop\新建文件夹6\MasterSar\IncidenceAngle.tif"
#     Mas_lamda=0.055517
#     kz_array=fine_registration().calu_kz(master_incident_file, B_vertical_path,len_S1_P_path, Mas_lamda)
#     kz_array_path=r"C:\Users\Administrator\Desktop\新建文件夹6\kz_array_path.tif"
#     ImageHandler.write_img(len_S2_P_path, pro, geo, kz_array, no_data='null')
#     pass



    # 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"
    # m_ori_sim_file =r"D:\MicroWorkspace\L-SAR\VegetationHeight\Input\MasterSar\ori_sim.tif"
    # m_all_angle_array=header_file_read_angle().read_all_angle(m_incidence_file,6000,8062)
    # m_angle_array=header_file_read_angle().get_orisim_angle(m_ori_sim_file, m_all_angle_array)
    #
    #
    # 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"
    # a_ori_sim_file =r"D:\MicroWorkspace\L-SAR\VegetationHeight\Input\AuxiliarySar\ori_sim2.tif"
    # a_all_angle_array = header_file_read_angle().read_all_angle(a_incidence_file, 6000, 8062)
    # a_angle_array = header_file_read_angle().get_orisim_angle(a_ori_sim_file, a_all_angle_array)
    #
    # v1=m_angle_array[0:561,0:1262]
    # v2=a_angle_array[0:561,0:1262]
    # v1=v1/180*math.pi
    # v2=v2/180 * math.pi
    #
    # mean = (v1 + v2) / 2
    # chazhi = v1 - v2
    # kz_array = 4 * math.pi * (abs(v2 - v1)) / (np.sin(mean) * 0.055517)
    # pass