492 lines
19 KiB
Python
492 lines
19 KiB
Python
# -*- coding: UTF-8 -*-
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"""
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@Project:__init__.py
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@File:AHVToPolsarpro.py
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@Function:全极化影像转成polsarpro格式T3数据
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@Contact:
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@Author:SHJ
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@Date:2021/9/18 16:44
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@Version:1.0.0
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"""
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import os
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import numpy as np
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import glob
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import struct
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from tool.algorithm.image.ImageHandle import ImageHandler
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class AHVToPolsarpro:
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"""
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全极化影像转换为bin格式T3矩阵，支持polsarpro处理
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"""
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def __init__(self, hh_hv_vh_vv_path_list=[]):
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self._hh_hv_vh_vv_path_list = hh_hv_vh_vv_path_list
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pass
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@staticmethod
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def __ahv_to_s2_veg(ahv_dir):
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"""
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全极化影像转S2矩阵
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:param ahv_dir: 全极化影像文件夹路径
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:return: 极化散射矩阵S2
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"""
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global s11
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in_tif_paths = list(glob.glob(os.path.join(ahv_dir, '*.tif')))
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in_tif_paths1 = list(glob.glob(os.path.join(ahv_dir, '*.tiff')))
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in_tif_paths += in_tif_paths1
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s11, s12, s21, s22 = None, None, None, None
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flag_list = [0, 0, 0, 0]
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for in_tif_path in in_tif_paths:
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# 读取原始SAR影像
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proj, geotrans, data = ImageHandler.read_img(in_tif_path)
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# 获取极化类型
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if '_HH' in os.path.basename(in_tif_path):
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data_real = data[0, :, :]
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data_imag = data[1, :, :]
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s11 = data_real + 1j * data_imag
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flag_list[0] = 1
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elif '_HV' in os.path.basename(in_tif_path):
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data_real = data[0, :, :]
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data_imag = data[1, :, :]
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s12 = data_real + 1j * data_imag
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flag_list[1] = 1
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elif '_VH' in os.path.basename(in_tif_path):
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data_real = data[0, :, :]
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data_imag = data[1, :, :]
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s21 = data_real + 1j * data_imag
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flag_list[2] = 1
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elif '_VV' in os.path.basename(in_tif_path):
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data_real = data[0, :, :]
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data_imag = data[1, :, :]
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s22 = data_real + 1j * data_imag
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flag_list[3] = 1
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else:
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continue
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if not flag_list == [1, 1, 1, 1]:
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raise Exception('HH or HV or VH or VV is not in path :%s', ahv_dir)
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return s11, s12, s21, s22
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@staticmethod
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def __ahv_to_s2_soil(ahv_dir):
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"""
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全极化影像转S2矩阵
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:param ahv_dir: 全极化影像文件夹路径
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:return: 极化散射矩阵S2
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"""
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global s11
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in_tif_paths = list(glob.glob(os.path.join(ahv_dir, '*.tif')))
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in_tif_paths1 = list(glob.glob(os.path.join(ahv_dir, '*.tiff')))
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in_tif_paths += in_tif_paths1
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s11, s12, s21, s22 = None, None, None, None
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flag_list = [0, 0, 0, 0]
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for in_tif_path in in_tif_paths:
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# 读取原始SAR影像
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proj, geotrans, data = ImageHandler.read_img(in_tif_path)
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# 获取极化类型
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if 'HH' in os.path.basename(in_tif_path):
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data_real = data[0, :, :]
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data_imag = data[1, :, :]
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s11 = data_real + 1j * data_imag
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flag_list[0] = 1
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elif 'HV' in os.path.basename(in_tif_path):
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data_real = data[0, :, :]
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data_imag = data[1, :, :]
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s12 = data_real + 1j * data_imag
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flag_list[1] = 1
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elif 'VH' in os.path.basename(in_tif_path):
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data_real = data[0, :, :]
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data_imag = data[1, :, :]
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s21 = data_real + 1j * data_imag
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flag_list[2] = 1
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elif 'VV' in os.path.basename(in_tif_path):
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data_real = data[0, :, :]
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data_imag = data[1, :, :]
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s22 = data_real + 1j * data_imag
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flag_list[3] = 1
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else:
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continue
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if not flag_list == [1, 1, 1, 1]:
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raise Exception('HH or HV or VH or VV is not in path :%s', ahv_dir)
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return s11, s12, s21, s22
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@staticmethod
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def __ahv_to_s2_list(ahv_path_list):
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"""
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全极化影像转S2矩阵
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:param ahv_dir: 全极化影像文件夹路径
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:return: 极化散射矩阵S2
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"""
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global s11
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in_tif_paths = ahv_path_list
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s11, s12, s21, s22 = None, None, None, None
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flag_list = [0, 0, 0, 0]
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for in_tif_path in in_tif_paths:
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# 读取原始SAR影像
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proj, geotrans, data = ImageHandler.read_img(in_tif_path)
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# 获取极化类型
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if 'HH' in os.path.basename(in_tif_path):
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data_real = data[0, :, :]
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data_imag = data[1, :, :]
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s11 = data_real + 1j * data_imag
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flag_list[0] = 1
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elif 'HV' in os.path.basename(in_tif_path):
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data_real = data[0, :, :]
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data_imag = data[1, :, :]
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s12 = data_real + 1j * data_imag
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flag_list[1] = 1
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elif 'VH' in os.path.basename(in_tif_path):
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data_real = data[0, :, :]
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data_imag = data[1, :, :]
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s21 = data_real + 1j * data_imag
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flag_list[2] = 1
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elif 'VV' in os.path.basename(in_tif_path):
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data_real = data[0, :, :]
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data_imag = data[1, :, :]
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s22 = data_real + 1j * data_imag
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flag_list[3] = 1
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else:
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continue
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if not flag_list == [1, 1, 1, 1]:
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raise Exception('HH or HV or VH or VV is not in path')
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return s11, s12, s21, s22
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@staticmethod
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def __ahv_to_s2_list_2(hh_hv_vh_vv_path_list):
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"""
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全极化影像转S2矩阵
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:param ahv_dir: 全极化影像文件夹路径
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:return: 极化散射矩阵S2
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"""
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global s11
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in_tif_paths = hh_hv_vh_vv_path_list
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s11, s12, s21, s22 = None, None, None, None
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flag_list = [0, 0, 0, 0]
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for in_tif_path, n in zip(in_tif_paths, range(len(in_tif_paths))):
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# 读取原始SAR影像
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proj, geotrans, data = ImageHandler.read_img(in_tif_path)
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# 获取极化类型
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if n == 0:
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data_real = data[0, :, :]
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data_imag = data[1, :, :]
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s11 = data_real + 1j * data_imag
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flag_list[0] = 1
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elif n == 1:
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data_real = data[0, :, :]
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data_imag = data[1, :, :]
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s12 = data_real + 1j * data_imag
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flag_list[1] = 1
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elif n == 2:
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data_real = data[0, :, :]
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data_imag = data[1, :, :]
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s21 = data_real + 1j * data_imag
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flag_list[2] = 1
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elif n == 3:
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data_real = data[0, :, :]
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data_imag = data[1, :, :]
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s22 = data_real + 1j * data_imag
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flag_list[3] = 1
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else:
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continue
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if not flag_list == [1, 1, 1, 1]:
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raise Exception('HH or HV or VH or VV is not in path')
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return s11, s12, s21, s22
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@staticmethod
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def __s2_to_t3(s11, s12, s21, s22):
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"""
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S2矩阵转T3矩阵
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:param s11: HH极化数据
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:param s12: HV极化数据
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:param s21: VH极化数据
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:param s22: VV极化数据
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:return: 极化相干矩阵T3
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"""
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HH = s11
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HV = s12
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VH = s21
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VV = s22
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t11 = (np.abs(HH + VV)) ** 2 / 2
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t12 = (HH + VV) * np.conj(HH - VV) / 2
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t13 = (HH + VV) * np.conj(HV + VH)
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t21 = (HH - VV) * np.conj(HH + VV) / 2
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t22 = np.abs(HH - VV) ** 2 / 2
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t23 = (HH - VV) * np.conj(HV + VH)
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t31 = (HV + VH) * np.conj(HH + VV)
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t32 = (HV + VH) * np.conj(HH - VV)
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t33 = 2 * np.abs(HV + VH) ** 2
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return t11, t12, t13, t21, t22, t23, t31, t32, t33
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def __t3_to_polsarpro_t3(self, out_dir, t11, t12, t13, t22, t23, t33):
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"""
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T3矩阵转bin格式，支持 polsarpro处理
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:param out_dir: 输出的文件夹路径
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:param t11:
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:param t12:
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:param t13:
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:param t22:
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:param t23:
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:param t33:
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:return: bin格式矩阵T3和头文件
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"""
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if not os.path.exists(out_dir):
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os.makedirs(out_dir)
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rows = t11.shape[0]
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cols = t11.shape[1]
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bins_dict = {
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'T11.bin': t11,
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'T12_real.bin': t12.real,
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'T12_imag.bin': t12.imag,
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'T13_real.bin': t13.real,
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'T13_imag.bin': t13.imag,
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'T22.bin': t22,
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'T23_real.bin': t23.real,
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'T23_imag.bin': t23.imag,
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'T33.bin': t33}
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for name, data in bins_dict.items():
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bin_path = os.path.join(out_dir, name)
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self.__write_img_bin(data, bin_path) # todo 修改T3阵保存方式
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# data.tofile(bin_path)
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out_hdr_path = bin_path + '.hdr'
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self.__write_bin_hdr(out_hdr_path, bin_path, rows, cols)
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self.__write_config_file(out_dir, rows, cols)
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def rows(self):
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"""获取影像行数"""
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return self._rows
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def cols(self):
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"""获取影像列数"""
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return self._cols
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def __write_img_bin(self, im, file_path):
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"""
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写入影像到bin文件中，保存为float32类型
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:param im : 影像矩阵数据，暂支持单通道影像数据
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:param file_path: bin文件的完整路径
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"""
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with open(file_path, 'wb') as f:
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self._rows = im.shape[0]
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self._cols = im.shape[1]
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for row in range(self._rows):
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im_bin = struct.pack("f" * self._cols, *np.reshape(im[row, :], (self._cols, 1), order='F'))
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f.write(im_bin)
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f.close()
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@staticmethod
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def __write_bin_hdr(out_hdr_path, bin_path, rows, cols):
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"""
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写入影像的头文件
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:param out_hdr_path : 头文件的路径
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:param bin_path: bin文件的路径
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:param rows: 影像的行数
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:param cols: 影像的列数
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"""
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h1 = 'ENVI'
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h2 = 'description = {'
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h3 = 'File Imported into ENVI. }'
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h4 = 'samples = ' + str(cols) # 列
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h5 = 'lines = ' + str(rows) # 行
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h6 = 'bands = 1 ' # 波段数
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h7 = 'header offset = 0'
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h8 = 'file type = ENVI Standard'
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h9 = 'data type = 4' # 数据格式
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h10 = 'interleave = bsq' # 存储格式
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h11 = 'sensor type = Unknown'
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h12 = 'byte order = 0'
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h13 = 'band names = {'
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h14 = bin_path + '}'
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# h = [h1, h2, h3, h4, h5, h6, h7, h8, h9, h10, h11, h12, h13, h14]
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# doc = open(out_hdr_path, 'w')
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# for i in range(0, 14):
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# print(h[i], end='', file=doc)
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# print('\n', end='', file=doc)
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h = [h1, h4, h5, h6, h7, h8, h9, h10, h12]
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doc = open(out_hdr_path, 'w')
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for i in range(0, 9):
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print(h[i], end='', file=doc)
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print('\n', end='', file=doc)
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doc.close()
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@staticmethod
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def __write_config_file(out_config_dir, rows, cols):
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"""
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写入polsarpro配置文件
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:param out_config_dir : 配置文件路径
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:param rows: 影像的行数
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:param cols: 影像的列数
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"""
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h1 = 'Nrow'
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h2 = str(rows)
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h3 = '---------'
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h4 = 'Ncol'
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h5 = str(cols)
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h6 = '---------'
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h7 = 'PolarCase'
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h8 = 'monostatic'
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h9 = '---------'
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h10 = 'PolarType'
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h11 = 'full'
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h = [h1, h2, h3, h4, h5, h6, h7, h8, h9, h10, h11]
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out_config_path = os.path.join(out_config_dir, 'config.txt')
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doc = open(out_config_path, 'w')
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for i in range(0, 11):
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print(h[i], end='', file=doc)
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print('\n', end='', file=doc)
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doc.close()
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def incidence_tif2bin(self, incidence_file, out_path):
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if not os.path.exists(out_path):
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os.mkdir(out_path)
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incidence_bin = os.path.join(out_path, 'incidence.bin')
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data = ImageHandler().get_data(incidence_file)
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rows = data.shape[0]
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cols = data.shape[1]
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self.__write_img_bin(data, incidence_bin)
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if not os.path.exists(incidence_bin):
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raise Exception('incidence to bin failed')
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out_hdr_path = incidence_bin + '.hdr'
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self.__write_bin_hdr(out_hdr_path, incidence_bin, rows, cols)
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return incidence_bin
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def ahv_to_polsarpro_t3_veg(self, out_file_dir, in_ahv_dir=''):
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if self._hh_hv_vh_vv_path_list == [] :
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s11, s12, s21, s22 = self.__ahv_to_s2_veg(in_ahv_dir)
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else:
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s11, s12, s21, s22 = self.__ahv_to_s2_list_2(self._hh_hv_vh_vv_path_list)
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t11, t12, t13, t21, t22, t23, t31, t32, t33 = self.__s2_to_t3(
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s11, s12, s21, s22)
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self.__t3_to_polsarpro_t3(out_file_dir, t11, t12, t13, t22, t23, t33)
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def ahv_to_polsarpro_t3_soil(self, out_file_dir, in_ahv_dir=''):
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if self._hh_hv_vh_vv_path_list == [] :
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s11, s12, s21, s22 = self.__ahv_to_s2_soil(in_ahv_dir)
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else:
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s11, s12, s21, s22 = self.__ahv_to_s2_list_2(self._hh_hv_vh_vv_path_list)
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t11, t12, t13, t21, t22, t23, t31, t32, t33 = self.__s2_to_t3(
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s11, s12, s21, s22)
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self.__t3_to_polsarpro_t3(out_file_dir, t11, t12, t13, t22, t23, t33)
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def calibration(self, calibration_value, in_ahv_dir='', name=''):
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if name == '':
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out_dir = os.path.join(in_ahv_dir, 'calibration')
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else:
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out_dir = os.path.join(in_ahv_dir, name, 'calibration')
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flag_list = [0, 0, 0, 0]
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if self._hh_hv_vh_vv_path_list == []: # 地表覆盖、土壤盐碱度
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in_tif_paths = list(glob.glob(os.path.join(in_ahv_dir, '*.tif')))
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in_tif_paths1 = list(glob.glob(os.path.join(in_ahv_dir, '*.tiff')))
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in_tif_paths += in_tif_paths1
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for in_tif_path in in_tif_paths:
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# 读取原始SAR影像
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proj, geotrans, data = ImageHandler.read_img(in_tif_path)
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name = os.path.basename(in_tif_path)
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data_new = np.zeros(data.shape)
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# 获取极化类型
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if 'HH' in os.path.basename(in_tif_path):
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data_new[0, :, :] = data[0, :, :] * calibration_value[0]
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data_new[1, :, :] = data[1, :, :] * calibration_value[0]
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ImageHandler.write_img(os.path.join(out_dir, name), proj, geotrans, data_new)
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flag_list[0] = 1
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elif 'HV' in os.path.basename(in_tif_path):
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data_new[0, :, :] = data[0, :, :] * calibration_value[1]
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data_new[1, :, :] = data[1, :, :] * calibration_value[1]
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ImageHandler.write_img(os.path.join(out_dir, name), proj, geotrans, data_new)
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flag_list[1] = 1
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elif 'VH' in os.path.basename(in_tif_path):
|
||
data_new[0, :, :] = data[0, :, :] * calibration_value[2]
|
||
data_new[1, :, :] = data[1, :, :] * calibration_value[2]
|
||
ImageHandler.write_img(os.path.join(out_dir, name), proj, geotrans, data_new)
|
||
flag_list[2] = 1
|
||
elif 'VV' in os.path.basename(in_tif_path):
|
||
data_new[0, :, :] = data[0, :, :] * calibration_value[3]
|
||
data_new[1, :, :] = data[1, :, :] * calibration_value[3]
|
||
ImageHandler.write_img(os.path.join(out_dir, name), proj, geotrans, data_new)
|
||
flag_list[3] = 1
|
||
if not flag_list == [1, 1, 1, 1]:
|
||
raise Exception('calibration error! ')
|
||
else:
|
||
for in_tif_path in self._hh_hv_vh_vv_path_list: # 植被物候
|
||
# 读取原始SAR影像
|
||
proj, geotrans, data = ImageHandler.read_img(in_tif_path)
|
||
name = os.path.basename(in_tif_path)
|
||
data_new = np.zeros(data.shape)
|
||
|
||
# 获取极化类型
|
||
if '_HH' in os.path.basename(in_tif_path):
|
||
data_new[0, :, :] = data[0, :, :] * calibration_value[0]
|
||
data_new[1, :, :] = data[1, :, :] * calibration_value[0]
|
||
ImageHandler.write_img(os.path.join(out_dir, name), proj, geotrans, data_new)
|
||
flag_list[0] = 1
|
||
elif '_HV' in os.path.basename(in_tif_path):
|
||
data_new[0, :, :] = data[0, :, :] * calibration_value[1]
|
||
data_new[1, :, :] = data[1, :, :] * calibration_value[1]
|
||
ImageHandler.write_img(os.path.join(out_dir, name), proj, geotrans, data_new)
|
||
flag_list[1] = 1
|
||
elif '_VH' in os.path.basename(in_tif_path):
|
||
data_new[0, :, :] = data[0, :, :] * calibration_value[2]
|
||
data_new[1, :, :] = data[1, :, :] * calibration_value[2]
|
||
ImageHandler.write_img(os.path.join(out_dir, name), proj, geotrans, data_new)
|
||
flag_list[2] = 1
|
||
elif '_VV' in os.path.basename(in_tif_path):
|
||
data_new[0, :, :] = data[0, :, :] * calibration_value[3]
|
||
data_new[1, :, :] = data[1, :, :] * calibration_value[3]
|
||
ImageHandler.write_img(os.path.join(out_dir, name), proj, geotrans, data_new)
|
||
flag_list[3] = 1
|
||
if not flag_list == [1, 1, 1, 1]:
|
||
raise Exception('calibration error! ')
|
||
self._hh_hv_vh_vv_path_list = []
|
||
return out_dir
|
||
|
||
|
||
|
||
if __name__ == '__main__':
|
||
#实例1：
|
||
# atp = AHVToPolsarpro()
|
||
# ahv_path = 'D:\\DATA\\GAOFEN3\\2-GF3_MYN_WAV_020086_E107.2_N27.6_20200603_L1A_AHV_L10004843087\\'
|
||
# # ahv_path = 'D:\\DATA\\GAOFEN3\\2598957_Paris\\'
|
||
# out_file_path = 'D:\\bintest0923\\'
|
||
# atp.ahv_to_polsarpro_t3(out_file_path, ahv_path)
|
||
|
||
# # 极化分解得到T3矩阵
|
||
# atp = AHVToPolsarpro()
|
||
# ahv_path = r"I:\MicroWorkspace\product\C-SAR\SoilSalinity\GF3B_MYC_QPSI_003581_E120.6_N31.3_20220729_L1A_AHV_L10000073024_RPC"
|
||
# t3_path = ahv_path + 'psp_t3\\'
|
||
# atp.ahv_to_polsarpro_t3(t3_path, ahv_path)
|
||
|
||
#实例2：
|
||
# dir = r'D:\MicroWorkspace\product\C-SAR\VegetationPhenology\Temporary\preprocessed/'
|
||
# path_list = [dir +'GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422_RPC_HH_preprocessed.tif',
|
||
# dir +'GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422_RPC_HV_preprocessed.tif',
|
||
# dir +'GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422_RPC_VH_preprocessed.tif',
|
||
# dir +'GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422_RPC_VV_preprocessed.tif']
|
||
#
|
||
#
|
||
# atp = AHVToPolsarpro(path_list)
|
||
# atp.ahv_to_polsarpro_t3(r'D:\MicroWorkspace\product\C-SAR\VegetationPhenology\Temporary\processing\GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422_RPC/t3')
|
||
|
||
print("done")
|