microproduct/atmosphericDelay-s1a/Delay_test.py

from AtmosphericDelayAuxData import NcHandle,ReadImage
from AtmosphericDelayAlg import AtmosphericDelay as Ady
from tool.algorithm.image import ImageHandle
from tool.algorithm.algtools.PreProcess import PreProcess as pp
import gc
import os
from pathlib import Path

env_str = os.path.split(os.path.realpath(__file__))[0]
os.environ['PROJ_LIB'] = env_str


def searchfile(source, pattern='*', recursive=False):

    if not isinstance(source, Path):
        source = Path(source)
    if source.is_dir() and source.exists():
        if recursive:
            res = list(source.rglob(pattern))
        else:
            res = list(source.glob(pattern))
    else:
        print(f"{source} is a file path or folder path is not exists!")
        return []   # False
    file_list = [i for i in res if i.is_file()]
    return file_list


def get_nc_dic(nc_dir):
    imageHandler = ImageHandle.ImageHandler()
    arr_dict = {}
    nc_list = ["AuxiliaryNC_geo_h", "AuxiliaryNC_re_hum", "AuxiliaryNC_temp",
               "MasterNC_geo_h", "MasterNC_re_hum", "MasterNC_temp"]
    arr_name = ["a_geo", "a_re_hum", "a_temp", "m_geo", "m_re_hum", "m_temp"]
    path = nc_dir
    for i in range(0, len(nc_list)):
        in_path = path + nc_list[i] + ".tif"
        array = imageHandler.get_all_band_array(in_path)
        arr_dict.update({arr_name[i]: array})
    return arr_dict["a_geo"], arr_dict["a_re_hum"], arr_dict["a_temp"], arr_dict["m_geo"], arr_dict["m_re_hum"], arr_dict["m_temp"]


def resampe_image(aux_dry_wet_unresame_path, mas_dry_wet_unresame_path, temp_dir):
    """
    将延迟图（ztd）恢复为非正方形的样子
    para: aux_dry_wet_unresame_path：待裁剪的辅影像延迟图路径
    para: mas_dry_wet_unresame_path：待裁剪的主影像延迟图路径
    return :裁剪后的辅影像延迟图路径、主影像延迟图路径
    """
    # 1.3 裁剪主辅影像延迟值
    out_aux_dry_wet_resamed_path = temp_dir + "resamed_aux_dry_wet.tif"
    out_mas_dry_wet_resamed_path = temp_dir + "resamed_mas_dry_wet.tif"

    pp.resampling_by_scale(aux_dry_wet_unresame_path, out_aux_dry_wet_resamed_path,
                           temp_dir + "MasterNC_geo_h.tif")
    pp.resampling_by_scale(mas_dry_wet_unresame_path, out_mas_dry_wet_resamed_path,
                           temp_dir + "MasterNC_geo_h.tif")

    return out_aux_dry_wet_resamed_path, out_mas_dry_wet_resamed_path


if __name__ == '__main__':
    import cv2
    import numpy as np
    import matplotlib.pyplot as plt

    # 读取图像
    fn = r'C:\Users\sxwcc\Documents\WeChat Files\wxid_faon3oi4svdh22\FileStorage\File\2023-12\Byte.tif'
    out = r'D:\micro\LWork\VegetationHeight\Temporary\test.tif'
    image = cv2.imread(fn, 1)  # 以灰度模式读取图像
    # image[np.where(image == 150)] = 0

    # 定义膨胀核
    kernel = np.ones((50, 50), np.uint8)  # 5x5 的全 1 矩阵

    # 进行膨胀操作
    dilated_image = cv2.dilate(image, kernel, iterations=1)
    cv2.imwrite(out, dilated_image)
    plt.figure()
    plt.matshow(dilated_image)
    plt.title("ref flat unwrap angle")  # 绝对相位
    plt.show()
    # mas_nc = r'F:\干涉大气延迟校正\大气延迟检验\大气延迟检验\ERA5_N25_N35_E115_E125_20220910_10.nc'
    # aux_nc = r'F:\干涉大气延迟校正\大气延迟检验\大气延迟检验\ERA5_N25_N35_E115_E125_20220922_10.nc'
    # dem_file = r''
    # temp_dir = 'F://干涉大气延迟校正//大气延迟检验//大气延迟检验//nc_file//test//'
    # out_path = 'F://干涉大气延迟校正//大气延迟检验//大气延迟检验//nc_file//'
    #
    # # 读取气象数据
    # ncHandle = NcHandle()
    # imageHandle = ImageHandle.ImageHandler()
    # ncHandle.tran_tif(mas_nc, out_path, "MasterNC")
    # ncHandle.tran_tif(aux_nc, out_path, "AuxiliaryNC")
    #
    # a_geo, a_re_hum, a_temp, m_geo, m_re_hum, m_temp = get_nc_dic(out_path)
    #
    # base_file = out_path + "MasterNC_geo_h.tif"  # 用来获取裁剪后气象数据的经纬度、分辨率等信息
    #
    # # 1.2 计算主、辅影像垂直分层湿干延迟值，并保存干湿延迟图
    # m_cdstack_dry_wet = Ady().aps_wrf_sar(base_file, m_temp, m_re_hum, m_geo, dem_file)  # 主影像垂直分层干湿延迟值数组
    # m_dry_wet_path = out_path + "m_dry_wet.tif"  # 主影像干湿延迟图保存路径
    # Ady().write_dry_wet_tif(dem_file, base_file, m_cdstack_dry_wet, m_dry_wet_path)
    #
    # a_cdstack_dry_wet = Ady().aps_wrf_sar(base_file, a_temp, a_re_hum, a_geo, dem_file)  # 辅影像垂直分层干湿延迟值数组
    # a_dry_wet_path = out_path + "a_dry_wet.tif"  # 辅影像干湿延迟图保存路径
    # Ady().write_dry_wet_tif(dem_file, base_file, a_cdstack_dry_wet, a_dry_wet_path)
    # #
    #
    # aux_dry_wet_resamed_path, mas_dry_wet_resamed_path = resampe_image(a_dry_wet_path, m_dry_wet_path, temp_dir)  # 参照气象数据分辨率进行重采样
    #
    # gc.collect()  # 回收内存
    # # 1.4 输出主辅影像ztd数组
    # a_ztd = Ady().calc_ztd(a_dry_wet_path, dem_file)  # 辅影像的ztd数组
    # m_ztd = Ady().calc_ztd(m_dry_wet_path, dem_file)  # 主影像的ztd数组
    # out_aux_ztd_path = out_path + "aux_ztd.tif"
    # out_mas_ztd_path = out_path + "mas_ztd.tif"
    # Ady().write_ztd_tif(dem_file, base_file, a_ztd, out_aux_ztd_path)  # ztd数组->ztd影像
    # Ady().write_ztd_tif(dem_file, base_file, m_ztd, out_mas_ztd_path)  # ztd数组->ztd影像