spfunctions.py 文件源码

def feature_fourier(chBd, blk, scs, end_scale):

    rows, cols = chBd.shape
    scales_half = int(end_scale / 2)
    scales_blk = end_scale - blk
    out_len = 0
    pix_ctr = 0

    for i in range(0, rows-scales_blk, blk):
        for j in range(0, cols-scales_blk, blk):
            for k in scs:
                out_len += 2

    # set the output list
    out_list = np.zeros(out_len).astype(np.float32)

    for i in range(0, rows-scales_blk, blk):
        for j in range(0, cols-scales_blk, blk):
            for k in scs:

                ch_bd = chBd[i+scales_half-(k/2):i+scales_half-(k/2)+k, j+scales_half-(k/2):j+scales_half-(k/2)+k]

                # get the Fourier Transform
                dft = cv2.dft(np.float32(ch_bd), flags=cv2.DFT_COMPLEX_OUTPUT)
                dft_shift = np.fft.fftshift(dft)

                # get the Power Spectrum
                magnitude_spectrum = 20. * np.log(cv2.magnitude(dft_shift[:, :, 0], dft_shift[:, :, 1]))

                psd1D = azimuthal_avg(magnitude_spectrum)

                sts = list(cv2.meanStdDev(psd1D))

                # plt.subplot(121)
                # plt.imshow(ch_bd, cmap='gray')
                # plt.subplot(122)
                # plt.imshow(magnitude_spectrum, interpolation='nearest')
                # plt.show()
                # print psd1D
                # sys.exit()

                for st in sts:

                    if np.isnan(st[0][0]):
                        out_list[pix_ctr] = 0.
                    else:
                        out_list[pix_ctr] = st[0][0]

                    pix_ctr += 1

    out_list[np.isnan(out_list) | np.isinf(out_list)] = 0.

    return out_list