How to use the histomicstk.preprocessing.color_deconvolution.color_deconvolution function in histomicstk

**it_kwargs)

    # get tile image
    im_tile = tile_info['tile'][:, :, :3]

    # perform color normalization
    im_nmzd = htk_cnorm.reinhard(im_tile,
                                 args.reference_mu_lab,
                                 args.reference_std_lab,
                                 src_mu=src_mu_lab,
                                 src_sigma=src_sigma_lab)

    # perform color decovolution
    w = cli_utils.get_stain_matrix(args)

    im_stains = htk_cdeconv.color_deconvolution(im_nmzd, w).Stains

    im_nuclei_stain = im_stains[:, :, 0].astype(np.float)

    # segment nuclear foreground
    im_nuclei_fgnd_mask = im_nuclei_stain < args.foreground_threshold

    # segment nuclei
    im_nuclei_seg_mask = htk_nuclear.detect_nuclei_kofahi(
        im_nuclei_stain,
        im_nuclei_fgnd_mask,
        args.min_radius,
        args.max_radius,
        args.min_nucleus_area,
        args.local_max_search_radius
    )

ts = large_image.getTileSource(args.inputImageFile)

    im_input = ts.getRegion(
        format=large_image.tilesource.TILE_FORMAT_NUMPY,
        **utils.get_region_dict(args.region, args.maxRegionSize, ts)
    )[0]

    # Create stain matrix
    print('>> Creating stain matrix')

    w = utils.get_stain_matrix(args)
    print(w)

    # Perform color deconvolution
    print('>> Performing color deconvolution')
    im_stains = htk_cd.color_deconvolution(im_input, w).Stains

    # write stain images to output
    print('>> Outputting individual stain images')

    print(args.outputStainImageFile_1)
    skimage.io.imsave(args.outputStainImageFile_1, im_stains[:, :, 0])

    print(args.outputStainImageFile_2)
    skimage.io.imsave(args.outputStainImageFile_2, im_stains[:, :, 1])

    print(args.outputStainImageFile_3)
    skimage.io.imsave(args.outputStainImageFile_3, im_stains[:, :, 2])

im_input = skimage.io.imread(args.inputImageFile)[:, :, :3]

    # Create stain matrix
    print('>> Creating stain matrix')

    w_init = np.array([args.stainColor_1, args.stainColor_2]).T

    print w_init

    # Perform color deconvolution
    print('>> Performing color deconvolution')

    res = htk_cdeconv.sparse_color_deconvolution(
        im_input, w_init, args.beta)
    w_est = np.concatenate((res.Wc, np.zeros((3, 1))), 1)
    res = htk_cdeconv.color_deconvolution(im_input, w_est)

    # write stain images to output
    print('>> Outputting individual stain images')

    print args.outputStainImageFile_1
    skimage.io.imsave(args.outputStainImageFile_1, res.Stains[:, :, 0])

    print args.outputStainImageFile_2
    skimage.io.imsave(args.outputStainImageFile_2, res.Stains[:, :, 1])

    print args.outputStainImageFile_3
    skimage.io.imsave(args.outputStainImageFile_3, res.Stains[:, :, 2])

REFERENCE_STD_LAB,
        src_mu=src_mu_lab,
        src_sigma=src_sigma_lab
    )

    # perform color decovolution
    if args.deconv_method == 'ruifrok':

        w = cli_utils.get_stain_matrix(args)
        im_stains = htk_cdeconv.color_deconvolution(
            im_nmzd, w).Stains.astype(np.float)[:, :, :2]

    elif args.deconv_method == 'macenko':

        w_est = htk_cdeconv.rgb_separate_stains_macenko_pca(im_tile, 255)
        im_stains = htk_cdeconv.color_deconvolution(
            im_tile, w_est, 255).Stains.astype(np.float)
        ch1 = htk_cdeconv.find_stain_index(
            htk_cdeconv.stain_color_map[args.stain_1], w_est)
        ch2 = htk_cdeconv.find_stain_index(
            htk_cdeconv.stain_color_map[args.stain_2], w_est)
        im_stains = im_stains[:, :, [ch1, ch2]]

    else:

        raise ValueError('Invalid deconvolution method parameter.')

    # =========================================================================
    # ====================== Fuse the stain1 & stain2 pix======================
    # =========================================================================

    # compute nuclear foreground mask