utils.py 文件源码

def decompose_vol2cube(vol_data, batch_size, cube_size, n_chn, ita):
    cube_list = []
    # get parameters for decompose
    fold, ovlap = fit_cube_param(vol_data.shape, cube_size, ita)
    dim = np.asarray(vol_data.shape)
    # decompose
    for R in range(0, fold[0]):
        r_s = R*cube_size - R*ovlap[0]
        r_e = r_s + cube_size
        if r_e >= dim[0]:
            r_s = dim[0] - cube_size
            r_e = r_s + cube_size
        for C in range(0, fold[1]):
            c_s = C*cube_size - C*ovlap[1]
            c_e = c_s + cube_size
            if c_e >= dim[1]:
                c_s = dim[1] - cube_size
                c_e = c_s + cube_size
            for H in range(0, fold[2]):
                h_s = H*cube_size - H*ovlap[2]
                h_e = h_s + cube_size
                if h_e >= dim[2]:
                    h_s = dim[2] - cube_size
                    h_e = h_s + cube_size
                # partition multiple channels
                cube_temp = vol_data[r_s:r_e, c_s:c_e, h_s:h_e]
                cube_batch = np.zeros([batch_size, cube_size, cube_size, cube_size, n_chn]).astype('float32')
                cube_batch[0, :, :, :, 0] = copy.deepcopy(cube_temp)
                # save
                cube_list.append(cube_batch)

    return cube_list


# compose list of label cubes into a label volume