o :ήc@sxddlZddlmZmZedddddddZedddddd d Zed ddd d dZ      dddZdS)N)njitprangezvoid(f4[:, :, :], f4[:, :, :])T)cachenogilparallelc Cs|j\}}}|j\}}}t|D]8}t|D]1}tdt|d|||} tdt|d|||} t|D]} || | | f|||| f<q8qqdS)aB Internal method. Resize image src to dst using nearest neighbors filtering. Images must have multiple color channels, i.e. :code:`len(shape) == 3`. Parameters ---------- dst: numpy.ndarray of type np.float32 output image src: numpy.ndarray of type np.float32 input image rNshaperrangemaxmin) dstsrch_srcw_srcdepthh_dstw_dsty_dstx_dstx_srcy_srccrR/tmp/pip-target-vg8gfxp4/lib/python/pymatting/foreground/estimate_foreground_ml.py_resize_nearest_multichannels     rzvoid(f4[:, :], f4[:, :])c Cs|j\}}|j\}}t|D]/}t|D](}tdt|d|||}tdt|d|||} || |f|||f<qqdS)a2 Internal method. Resize image src to dst using nearest neighbors filtering. Images must be grayscale, i.e. :code:`len(shape) == 3`. Parameters ---------- dst: numpy.ndarray of type np.float32 output image src: numpy.ndarray of type np.float32 input image rrNr) r rrrrrrrrrrrr_resize_nearest s    rzLTuple((f4[:, :, :], f4[:, :, :]))(f4[:, :, :], f4[:, :], f4, i4, i4, i4, f4))rrc0Cs~|j\}}} tj} d} d} tj| | | f| d} tj| | | f| d}tttt||}t|dD]}t |||}t |||}tj||| f| d}tj||f| d}t ||t ||tj||| f| d}tj||| f| d}t || t ||||kr||kr|}n|}tj d| f| d}gd}gd}t|D] }t |D]}t|D]}|||f}d|}||} ||}!||}"t| D]}#|||||#f|d|#f<|||||#f|d|#f<qtdD]`}$tdt|d|||$}%tdt|d|||$}&t|||&|%f}'|||'}(| |(7} |"|(7}"t| D]%}#|d|#f|(||&|%|#f7<|d|#f|(||&|%|#f7<q)q| |"|!|!})d|)}*|*|"}+|*|! },|*| }-t| D]A}#|+|d|#f|,|d|#f}.|,|d|#f|-|d|#f}/td td|.}.td td|/}/|.||||#f<|/||||#f<qmqqq|} |}|} |} q4||fS) Nr)dtype)rrr)rrrr?rg)r npfloat32emptyintceillog2r r roundrrzerosrr abs)0 input_image input_alpharegularizationn_small_iterationsn_big_iterations small_sizegradient_weighth0w0rrw_prevh_prevF_prevB_prevn_levelsi_levelwhimagealphaFBn_iterbdxdyi_iteryxa0a1a00a01a11rdx2y2gradientda determinantinv_detb00b01b11F_cB_crrr_estimate_fb_ml=s           "&    /rXh㈵> r Fr c Cs8t|tj|tj|||||\}} |r|| fS|S)aEstimates the foreground of an image given its alpha matte. See :cite:`germer2020multilevel` for reference. Parameters ---------- image: numpy.ndarray Input image with shape :math:`h \times w \times d` alpha: numpy.ndarray Input alpha matte shape :math:`h \times w` regularization: float Regularization strength :math:`\epsilon`, defaults to :math:`10^{-5}`. Higher regularization results in smoother colors. n_small_iterations: int Number of iterations performed on small scale, defaults to :math:`10` n_big_iterations: int Number of iterations performed on large scale, defaults to :math:`2` small_size: int Threshold that determines at which size `n_small_iterations` should be used return_background: bool Whether to return the estimated background in addition to the foreground gradient_weight: float Larger values enforce smoother foregrounds, defaults to :math:`1` Returns ------- F: numpy.ndarray Extracted foreground B: numpy.ndarray Extracted background Example ------- >>> from pymatting import * >>> image = load_image("data/lemur/lemur.png", "RGB") >>> alpha = load_image("data/lemur/lemur_alpha.png", "GRAY") >>> F = estimate_foreground_ml(image, alpha, return_background=False) >>> F, B = estimate_foreground_ml(image, alpha, return_background=True) See Also ---- stack_images: This function can be used to place the foreground on a new background. )rXastyper"r#) r<r=r-r.r/r0return_backgroundr1 foreground backgroundrrrestimate_foreground_mls6   r`)rYrZrr[Fr ) numpyr"numbarrrrrXr`rrrrs    k