U !?h@slddlZddlmZmZedddddddZedddddd d Zed ddd d dZdddZdS)N)njitprangezvoid(f4[:, :, :], f4[:, :, :])T)cachenogilparallelc Cs|j\}}}|j\}}}t|D]p}t|D]b}tdt|d|||} tdt|d|||} t|D]} || | | f|||| f<qpq,q dS)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_srccr]/var/www/html/venv/lib/python3.8/site-packages/pymatting/foreground/estimate_foreground_ml.py_resize_nearest_multichannels     rzvoid(f4[:, :], f4[:, :])c Cs|j\}}|j\}}t|D]^}t|D]P}tdt|d|||}tdt|d|||} || |f|||f<q(qdS)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))rrc4Csx|j\}}} tj} d} d} tj| | d} tj| | d}d}d}t|D]}t|D]}|||fdkrt| D]}| |||||f7<qn|d7}|||fdkrRt| D]}||||||f7<q|d7}qRqF| |d} ||d}tj| | | f| d| }tj| | | f| d|}tttt||}t|dD]$}t |||}t |||}tj ||| f| d}tj ||f| d}t ||t ||tj ||| f| d}tj ||| f| d}t ||t ||||kr||kr|}n|}tjd| f| d}ddddg} ddddg}!t|D]$}"t |D]}t|D]}|||f}#d |#}$|#|#}%|#|$}&|$|$}'t| D]:}|#||||f|d|f<|$||||f|d|f<qtd D]}(tdt|d|| |(})tdt|d||!|(}*t|#||*|)f}+|||+},|%|,7}%|'|,7}'t| D]J}|d|f|,||*|)|f7<|d|f|,||*|)|f7<qBq|%|'|&|&}-d |-}.|.|'}/|.|& }0|.|%}1t| D]}|/|d|f|0|d|f}2|0|d|f|1|d|f}3td td |2}2td td |3}3|2||||f<|3||||f<q̐qPqBq4|}|}|} |} qH||fS) Nr)dtyperg?g?h㈵>?g)r npfloat32Zzerosr intceillog2r roundemptyrrrr abs)4Z input_imageZ input_alpharegularizationn_small_iterationsn_big_iterations small_sizegradient_weightZh0Zw0rrZw_prevZh_prevZ F_mean_colorZ B_mean_colorZF_countZB_countyxrZF_prevZB_prevZn_levelsZi_levelwhimagealphaFBZn_iterbZdxZdyZi_iterZa0Za1Za00Za01Za11dZx2y2ZgradientdaZ determinantZinv_detZb00Zb01Zb11ZF_cZB_crrr_estimate_fb_ml=s                   "*    r<r r Fr!c Cs8t|tj|tj|||||\}} |r4|| 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. )r<Zastyper#r$) r4r5r+r,r-r.Zreturn_backgroundr/ foreground backgroundrrrestimate_foreground_mls6   rA)rr=rr>Fr!) numpyr#Znumbarrrrr<rArrrrs