import numpy as np

from tool.algorithm.image.ImageHandle import ImageHandler
from matplotlib import pyplot as plt
import matplotlib as mpl
from scipy import signal
import scipy
cmap = mpl.cm.jet_r

def show_filt():
    mas = r"D:\micro\LWork\VegetationHeight\Temporary\preprocessed\master_s2\s11.bin"
    aux = r"D:\micro\LWork\VegetationHeight\Temporary\preprocessed\slave_s2\s11.bin"
    out_file = r'D:\micro\LWork\VegetationHeight\Temporary\preprocessed\test_def.tif'

    mas_arr = np.fromfile(mas, np.complex64)
    aux_arr = np.fromfile(aux, np.complex64)
    # mas_new = mas_arr.reshape(1307, 1900)
    # aux_new = aux_arr.reshape(1307, 1900)
    mas_new = mas_arr.reshape(6419, 5117)
    aux_new = aux_arr.reshape(6419, 5117)
    # mas_arr = ImageHandler.get_all_band_array(mas)
    # aux_arr = ImageHandler.get_all_band_array(aux)
    # mas_new = mas_arr[:,:,0] + mas_arr[:,:,1] * 1j
    # aux_new = aux_arr[:,:,0] + aux_arr[:,:,1] * 1j

    res = mas_new * np.conj(aux_new)

    ang1 = np.angle(res, deg=False)
    ImageHandler.write_img(out_file, '', [0, 0, 0, 0, 0, 0], ang1)
    plt.imshow(ang1, cmap="hsv")
    plt.colorbar()
    plt.set_cmap(cmap)
    plt.show()


def compute_flat_phase_estimate(sar_image):
    # 假设sar_image是输入的SAR图像数据，是一个二维的复数数组，其中每个元素包含振幅和相位信息
    # 首先，计算SAR图像的幅度
    sar_amplitude = np.abs(sar_image)

    # 对幅度进行多普勒校正，这个步骤通常包括对图像进行配准和校正
    # 这里仅仅做示例，实际情况下你可能需要更复杂的配准和校正步骤
    sar_amplitude_corrected = sar_amplitude

    # 计算多普勒校正后的SAR图像的相位
    sar_phase = np.angle(sar_image)

    # 使用信号处理中的方法（例如2D FFT）估计平地相位
    # 在这个示例中，我们使用3x3的中值滤波器来平滑相位图像
    flat_phase_estimate = signal.medfilt(sar_phase, kernel_size=3)
    plt.imshow(flat_phase_estimate[:,:,1], cmap="hsv")
    plt.colorbar()
    plt.set_cmap(cmap)
    plt.show()

    return flat_phase_estimate



if __name__ == '__main__':

    show_filt()