+ Aib^RIHt^RIt^RIt^RIt^RIt^RIH t Rt Rt Rt Rt RRltR tRR ltRR ltR R ltR tR!RltRtR"RltRtRtRtRtR#RltR$RltRtRtR%RltRt Rt!Rt"R#)&)ImageNwrapsc0a\S4V3Rl4pV#)aDCreates a new function which operates on each channel Parameters ---------- single_channel_func: function Function that acts on a single color channel Returns ------- channel_func: function The same function that operates on all color channels Example ------- >>> from pymatting import * >>> import numpy as np >>> from scipy.signal import convolve2d >>> single_channel_fun = lambda x: convolve2d(x, np.ones((3, 3)), 'valid') >>> multi_channel_fun = apply_to_channels(single_channel_fun) >>> I = np.random.rand(480, 320, 3) >>> multi_channel_fun(I).shape (478, 318, 3) c <\VP4^8Xd S!V.VO5/VB#VPpVPV^,V^,R4p\P!\ VP^,4Uu.uF'pS!VRRV3,P 4.VO5/VBNK) up^R7pVP\VPR,4\VR,4,4#uupi)NNNaxisNrN:rNN)lenshapereshapenpstackrangecopylist)imageargskwargsrcresultsingle_channel_funcs&*, Q/var/www/html/photoedit/myenv/lib/python3.14/site-packages/pymatting/util/util.pymulti_channel_func-apply_to_channels..multi_channel_func"s u{{ q &u>t>v> >KKEMM%(E!Hb9EXX#5;;q>22(aAg(;(;(=OOO2 F>>$v||B'7"84b ?"JK Ks-C;r)rrsf rapply_to_channelsr s'2 L L" c0\P!RW4#)aComputes the dot product of two vectors. Parameters ---------- a: numpy.ndarray First vector (if np.ndim(a) > 1 the function calculates the product for the two last axes) b: numpy.ndarray Second vector (if np.ndim(b) > 1 the function calculates the product for the two last axes) Returns ------- product: scalar Dot product of `a` and `b` Example ------- >>> import numpy as np >>> from pymatting import * >>> a = np.ones(2) >>> b = np.ones(2) >>> vec_vec_dot(a,b) 2.0 z...i,...i->...reinsumabs&&r vec_vec_dotr'7s0 99%q ,,r c0\P!RW4#)aCalculates the matrix vector product for two arrays. Parameters ---------- A: numpy.ndarray Matrix (if np.ndim(A) > 2 the function calculates the product for the two last axes) b: numpy.ndarray Vector (if np.ndim(b) > 1 the function calculates the product for the two last axes) Returns ------- product: numpy.ndarray Matrix vector product of both arrays Example ------- >>> import numpy as np >>> from pymatting import * >>> A = np.eye(2) >>> b = np.ones(2) >>> mat_vec_dot(A,b) array([1., 1.]) z...ij,...j->...ir")Ar&s&&r mat_vec_dotr*Rs0 99' ..r c0\P!RW4#)aComputes the outer product of two vectors a: numpy.ndarray First vector (if np.ndim(b) > 1 the function calculates the product for the two last axes) b: numpy.ndarray Second vector (if np.ndim(b) > 1 the function calculates the product for the two last axes) Returns ------- product: numpy.ndarray Outer product of `a` and `b` as numpy.ndarray Example ------- >>> import numpy as np >>> from pymatting import * >>> a = np.arange(1,3) >>> b = np.arange(1,3) >>> vec_vec_outer(a,b) array([[1, 2], [2, 4]]) z ...i,...jr"r$s&&r vec_vec_outerr,ms. 99[! ''r cV^8gV^8d \R4hV^8gV^8d \R4hW8d \R4hW8pW8pR\P!V4,p^WT&^WS&V#)aFixes broken trimap :math:`T` by thresholding the values .. math:: T^{\text{fixed}}_{ij}= \begin{cases} 0,&\text{if } T_{ij}<\text{lower\_threshold}\\ 1,&\text{if }T_{ij}>\text{upper\_threshold}\\ 0.5, &\text{otherwise}.\\ \end{cases} Parameters ---------- trimap: numpy.ndarray Possibly broken trimap lower_threshold: float Threshold used to determine background pixels, defaults to 0.1 upper_threshold: float Threshold used to determine foreground pixels, defaults to 0.9 Returns ------- fixed_trimap: numpy.ndarray Trimap having values in :math:`\{0, 0.5, 1\}` Example ------- >>> from pymatting import * >>> import numpy as np >>> trimap = np.array([0,0.1, 0.4, 0.9, 1]) >>> fix_trimap(trimap, 0.2, 0.8) array([0. , 0. , 0.5, 1. , 1. ]) zInvalid lower thresholdzInvalid upper thresholdz4Lower threshold must be smaller than upper thresholdg?) ValueErrorr ones_like)trimaplower_thresholdupper_thresholdis_bgis_fgfixeds&&& r fix_trimapr6sDo1233o1233(OPP  $E  $E ",,v& &EELEL Lr cB\V4R# \dR#i;i)aChecks if an object is iterable Parameters ---------- obj: object Object to check Returns ------- is_iterable: bool Boolean variable indicating whether the object is iterable Example ------- >>> from pymatting import * >>> l = [] >>> isiterable(l) True TF)iter TypeError)objs&r isiterabler;s#( S  s  cR\PR\PR\PR\PR\P R\P R\P /p\V4'g9\VPV,4\VPV,43pVPWVP4,4pV#)bicubicbilinearboxhamminglanczosnearestnone) rBICUBICBILINEARBOXHAMMINGLANCZOSNEARESTr;intwidthheightresizelower)rsizeresamplefilterss&&& r_resize_pil_imagerRs5==ENN uyy5==5==5== G d  EKK$&'U\\D-@)AB LLx~~'78 9E Lr c\P!V4pVe-VP4pVR8XdRMTpVPV4pVe \ WBV4p\ P !V4R, pV#)zThis function can be used to load an image from a file. Parameters ---------- path: str Path of image to load. mode: str Can be "GRAY", "RGB" or something else (see PIL.convert()) Returns ------- image: numpy.ndarray Loaded image GRAYLgo@)ropenupperconvertrRrarray)pathmoderOrPrs&&&& r load_imager\sg JJt E zz|fns$ d# !%x8 HHUOe #E Lr cHVP\P\P\P39gQhVP\P\P39d=\P !V^,^^4P \P4pV'dJ\PPV4wr4\V4^8d\P!VRR7\P!V4PV4R#)axGiven a path, save an image there. Parameters ---------- path: str Where to save the image. image: numpy.ndarray, dtype in [np.uint8, np.float32, np.float64] Image to save. Images of float dtypes should be in range [0, 1]. Images of uint8 dtype should be in range [0, 255] make_directory: bool Whether to create the directories needed for the image path. T)exist_okN)dtyperuint8float32float64clipastypeosrZsplitrmakedirsr fromarraysave)rZrmake_directory directory_s&&& r save_imagerms ;;288RZZ< << < {{rzz2::.. Q,33BHH=ww}}T* y>A  KK D 1 OOE%r c\VP4R9gQhVP\P\P \P 39gQhVP\P \P 39d=\P!V^,^^4P\P4p\VP4^8Xd!\P!V.^,^R7#VP^,^8Xd!\P!V.^,^R7#VP^,^8XdV#VP^,^8Xd VR,#\RVP4h)aConvertes an image to rgb8 color space Parameters ---------- image: numpy.ndarray Image to convert Returns ------- image: numpy.ndarray Converted image with same height and width as input image but with three color channels Example ------- >>> from pymatting import * >>> import numpy as np >>> I = np.eye(2) >>> to_rgb8(I) array([[[255, 255, 255], [ 0, 0, 0]], [[ 0, 0, 0], [255, 255, 255]]], dtype=uint8) r zInvalid image shape:)r)r r :NroN) rrr_rr`rarbrcrdr concatenater.)rs&rto_rgb8rq"s. u{{ v %% % ;;288RZZ< << < {{rzz2::.. Q,33BHH= 5;;1xx! !,, {{1~~~ugk22 Q1  Q1 X +U[[ 99r c VF:pVfK VP\P\P39dK:Qh \ V4pV^8XdR#VfOVfK\ \P !\P!V444pWQ,^, V,pM5VfWQ,^, V,pMVfWR,^, V,pVUu.uFqDfKVPNK pp\RV44p\RV44p\VU u.uFp \ V 4^8gKV ^,NK up ^R7p V ^8d\V4FwrVfK \ VP4^8XdVRR\P3,pVP^,^8Xd!\P!V.V ,^R7pVP^,^8XdEV ^8Xd>\V\P!VPR,VPR74pW@V &K Vf\R V44p\P !Wr,W,V 3VR7p \#V4Fp \#V4FpWV,,p V \ V48dK3W ,pVfK3VP%VP^,VP^,R 4pVV W,W,VP^,,1W,W,VP^,,13&K K V P^,^8Xd V R ,p V #uupiuup i) aPlots a grid of images. Parameters ---------- images : list of numpy.ndarray List of images to plot nx: int Number of rows ny: int Number of columns dtype: type Data type of output array Returns ------- grid: numpy.ndarray Grid of images with datatype `dtype` Nc32"TF q^,xK R#5i)rN.0rs& r make_grid..v )&!HH&c32"TF q^,xK R#5i)Nrtrus& rrwrxwryrz)defaultr r r r_c3D"TFqfKVPxK R#5i)Nr~)rvrs& rrwrxsJfU[U[[fs  r )r r r)r_rrarbrrJceilsqrtrmax enumeratenewaxisrp stack_imagesonesnextzerosrr)imagesnxnyr_rnshapeshwrdiryxs&&&& r make_gridrNs&  ;;2::rzz":: :: F AAv zbj $ %fqjR  fqjR  fqjR '- CvekekkvF C )& ))A )& ))A 6 <6%SZ!^XU1XX6 Q&NNE7Q;Q?E;;q>Q&16(rwwu{{2ekkJE"q * }JfJJ XXqvqvq) 7F 2YrAF ACKIE  ekk!nekk!nbIEAEEKKN22AEAEEKKPQN>> import numpy as np >>> from pymatting import * >>> trimap = np.array([[1,0],[0.5,0.2]]) >>> is_fg, is_bg, is_known, is_unknown = trimap_split(trimap) >>> is_fg array([ True, False, False, False]) >>> is_bg array([False, True, False, False]) >>> is_known array([ True, True, False, False]) >>> is_unknown array([False, False, True, True]) z:Trimap values should be in [0, 1], but trimap.min() is %s. stacklevel?z:Trimap values should be in [0, 1], but trimap.max() is %s.zfUnexpected trimap.dtype %s. Are you sure that you do not want to use np.float32 or np.float64 instead?z7Trimap did not contain background values (values <= %f)z7Trimap did not contain foreground values (values >= %f)) flattenminrwarningswarnr_rrarbsumr.) r0r bg_threshold fg_threshold min_value max_valuer4r3is_known is_unknowns &&&& r trimap_splitrsZ! I I3 H9 T 3 H9 T ||BJJ 33 tll   "E  "E yy{a E T   yy{a E T  }HJ  --r cZ\VP4^8wgVP^,^8wd3\P!R\ VP4,^R7VP 4pVP 4pVR8d \P!RV,^R7VR8d \P!RV,^R7VP\P\P39d,\P!RVP,^R7R#R#) aGPerforms a sanity check for input images. Image values should be in the range [0, 1], the `dtype` should be `np.float32` or `np.float64` and the image shape should be `(?, ?, 3)`. Parameters ---------- image: numpy.ndarray Image with shape :math:`h \times w \times 3` Example ------- >>> import numpy as np >>> from pymatting import check_image >>> image = (np.random.randn(64, 64, 2) * 255).astype(np.int32) >>> sanity_check_image(image) __main__:1: UserWarning: Expected RGB image of shape (?, ?, 3), but image.shape is (64, 64, 2). __main__:1: UserWarning: Image values should be in [0, 1], but image.min() is -933. __main__:1: UserWarning: Image values should be in [0, 1], but image.max() is 999. __main__:1: UserWarning: Unexpected image.dtype int32. Are you sure that you do not want to use np.float32 or np.float64 instead? z=Expected RGB image of shape (?, ?, 3), but image.shape is %s.rrz8Image values should be in [0, 1], but image.min() is %s.rz8Image values should be in [0, 1], but image.max() is %s.zeUnexpected image.dtype %s. Are you sure that you do not want to use np.float32 or np.float64 instead?N) rrrrstrrrr_rrarb)rrrs& rsanity_check_imagers. 5;;1 A! 3 K%++   I I3 F R 3 F R  {{2::rzz22 skk  3r c\VP4^8XdVRR\P3,pW ,^V, V,,#)aThis function composes a new image for given foreground image, background image and alpha matte. This is done by applying the composition equation .. math:: I = \alpha F + (1-\alpha)B. Parameters ---------- foreground: numpy.ndarray Foreground image background: numpy.ndarray Background image alpha: numpy.ndarray Alpha matte Returns ------- image: numpy.ndarray Composed image as numpy.ndarray Example ------- >>> from pymatting import * >>> foreground = load_image("data/lemur/lemur_foreground.png", "RGB") >>> background = load_image("data/lemur/beach.png", "RGB") >>> alpha = load_image("data/lemur/lemur_alpha.png", "GRAY") >>> I = blend(foreground, background, alpha) r )rrrr) foreground backgroundalphas&&&rblendrEsA< 5;;1aBJJ&'  Uj 8 88r cVUu.uF9p\VP4^8XdTMVRR\P3,NK; pp\P!V^R7#uupi)aThis function stacks images along the third axis. This is useful for combining e.g. rgb color channels or color and alpha channels. Parameters ---------- *images: numpy.ndarray Images to be stacked. Returns ------- image: numpy.ndarray Stacked images as numpy.ndarray Example ------- >>> from pymatting.util.util import stack_images >>> import numpy as np >>> I = stack_images(np.random.rand(4,5,3), np.random.rand(4,5,3)) >>> I.shape (4, 5, 6) r r )rrrrrp)rrs* rrrisa0Eekk"a'U1a3C-D D  >>&q )) s?AcVP\P!VP^,VP44pV#)aCalculate the sum of each row of a matrix Parameters ---------- A: np.ndarray or scipy.sparse.spmatrix Matrix to sum rows of Returns ------- row_sums: np.ndarray Vector of summed rows Example ------- >>> from pymatting import * >>> import numpy as np >>> A = np.random.rand(2,2) >>> A array([[0.62750946, 0.12917617], [0.8599449 , 0.5777254 ]]) >>> row_sum(A) array([0.75668563, 1.4376703 ]) )dotrrrr_)r)row_sumss& rrow_sumrs.0uuRWWQWWQZ12H Or c\V4pRW"V8&RV, p\PPV4pVP V4pV#)aNormalize the rows of a matrix Rows with sum below threshold are left as-is. Parameters ---------- A: scipy.sparse.spmatrix Matrix to normalize threshold: float Threshold to avoid division by zero Returns ------- A: scipy.sparse.spmatrix Matrix with normalized rows Example ------- >>> from pymatting import * >>> import numpy as np >>> A = np.arange(4).reshape(2,2) >>> normalize_rows(A) array([[0. , 1. ], [0.4, 0.6]]) r)rscipysparsediagsr)r) thresholdrrow_normalization_factorsDs&& rnormalize_rowsrsM4qzH&)H !" #h 45A aA Hr cRV'dZ\P!\P!V4V4p\P!\P!V4V4pW43#\P!V4p\P!V4p\P!W44wr4W43#)a Calculates image pixel coordinates for an image with a specified shape Parameters ---------- width: int Width of the input image height: int Height of the input image flatten: bool Whether the array containing the coordinates should be flattened or not, defaults to False Returns ------- x: numpy.ndarray x coordinates y: numpy.ndarray y coordinates Example ------- >>> from pymatting import * >>> x, y = grid_coordinates(2,2) >>> x array([[0, 1], [0, 1]]) >>> y array([[0, 0], [1, 1]]) )rtilearangerepeatmeshgrid)rKrLrrrs&&& rgrid_coordinatesrsw< GGBIIe$f - IIbii' / 4K IIe  IIf {{1  4Kr c*\P!V4P4p\V4pW,p\P!Wv,\P R7p\P!Wv,\P R7p \P!Wv,\P R7p ^p \WRR7wr\W4V4Fwrp\P!W,^V^, 4p\P!W,^V^, 4pWV,,WW,%VVV,,WW,%VWW,%W, p K \PPWV 33Ww3R7pV#)ajCalculates a convolution matrix that can be applied to a vectorized image Additionally, this function allows to specify which pixels should be used for the convoltion, i.e. .. math:: \left(I * K\right)_{ij} = \sum_k K_k I_{i+{\Delta_y}_k,j+{\Delta_y}_k}, where :math:`K` is the flattened convolution kernel. Parameters ---------- width: int Width of the input image height: int Height of the input image kernel: numpy.ndarray Convolutional kernel dx: numpy.ndarray Offset in x direction dy: nunpy.ndarray Offset in y direction Returns ------- M: scipy.sparse.csr_matrix Convolution matrix r~Tr)r) rasarrayrrrint32rbrziprcrr csr_matrix)rKrLkerneldxdyweightscountri_indsj_indsvalueskrrdx2dy2weightx2y2r)s&&&&& rsparse_conv_matrix_with_offsetsrs)6jj ((*G LE A XXairxx 0F XXairxx 0F XXairzz 2F A E4 8DA0& WWQWa + WWQWa! ,E M15eO15"15  1  &)9 :1&IA Hr cVPwr4\WCRR7wrVWT^,,pWc^,,p\WW%V4#)aCalculates a convolution matrix that can be applied to a vectorized image Parameters ---------- width: int Width of the input image height: int Height of the input image kernel: numpy.ndarray Convolutional kernel Returns ------- M: scipy.sparse.csr_matrix Convolution matrix Example ------- >>> from pymatting import * >>> import numpy as np >>> sparse_conv_matrix(3,3,np.ones((3,3))) <9x9 sparse matrix of type '' with 49 stored elements in Compressed Sparse Row format> Tr)rrr)rKrLrkhkwrrs&&& rsparse_conv_matrixr'sB2\\FB BD 1DAqLAqLA *5&Q GGr cV'd \V4pV\V4,p\PP V4pW@, pV#)a~Calculates the random walk normalized Laplacian matrix from the weight matrix Parameters ---------- W: numpy.ndarray Array of weights normalize: bool Whether the rows of W should be normalized to 1, defaults to True regularization: float Regularization strength, defaults to 0, i.e. no regularizaion Returns ------- L: scipy.sparse.spmatrix Laplacian matrix Example ------- >>> from pymatting import * >>> import numpy as np >>> weights_to_laplacian(np.ones((4,4))) matrix([[ 0.75, -0.25, -0.25, -0.25], [-0.25, 0.75, -0.25, -0.25], [-0.25, -0.25, 0.75, -0.25], [-0.25, -0.25, -0.25, 0.75]]) )rrrrr)W normalizeregularizationrrrUs&&& rweights_to_laplacianrHs?6 1 #A 1A A Hr c\P!V4pVP4pVP4pW, W!, , #)aDNormalizes an array such that all values are between 0 and 1 Parameters ---------- values: numpy.ndarray Array to normalize Returns ------- result: numpy.ndarray Normalized array Example ------- >>> from pymatting import * >>> import numpy as np >>> normalize(np.array([0, 1, 3, 10])) array([0. , 0.1, 0.3, 1. ]) )rrrr)rr%r&s& rrrns7(ZZ F A A J15 !!r c.W,^, V,#)zDivides a number x by another integer n and rounds up the result Parameters ---------- x: int Numerator n: int Denominator Returns ------- result: int Result Example ------- >>> from pymatting import * >>> div_round_up(3,2) 2 rt)rrs&&r div_round_uprs* EAI! r cW, p\P!W, V,RR7\P!\P!V44, p\P!VRR4pVR,p\P!V^V, V,, V\P !V4VR8gR7p\P!VRR4p\P !Wu.4pV#)a]Remove background from image with at most two colors. Might not work if image has more than two colors. Parameters ---------- image: numpy.ndarray RGB input image fg_color: numpy.ndarray RGB Foreground color bg_color: numpy.ndarray RGB Background color Returns ------- output: numpy.ndarray RGBA output image Example ------- >>> from pymatting import * >>> import numpy as np >>> image = np.random.rand(480, 320, 3) >>> fg_color = np.random.rand(3) >>> bg_color = np.random.rand(3) >>> output = remove_background_bicolor(image, fg_color, bg_color) >>> print(output.shape) (480, 320, 4) r rr)outwherer )r r N)rrsquarercdivide zeros_likedstack) rfg_colorbg_colorfg_bgurr% actual_coloroutputs &&& rremove_background_bicolorrs<  E  E)3bffRYYu=M6NNA GGAsC E jA99 !a%8# #aR]]5-AcL77<c2L YY , -F Mr )皙??)r=)NNr?)T)NNN)Trr)r)F)Tr)#PILrnumpyr scipy.sparserrer functoolsrrr'r*r,r6r;rRr\rmrqrrrrrrrrrrrrrrrrtr rrs +\-6/6(40f6&>&6):XTn$V.r2 j!9H*::% P'T/ dHB# L"401r