9izSSKJr SSKrSSKrSSKJrJr SSKJr SSK J r \ (dSSK J r OSSKJr "S S \5r S rSS jrSS jrSSS.Sjjrg))combinations_with_replacementN)filtersfeature)img_as_float32)is_wasm)ThreadPoolExecutor)AbstractContextManagerc&\rSrSrSrSrSrSrg) PoolExecutorcgN)self___s _/var/www/html/land-doc-ocr/venv/lib/python3.13/site-packages/skimage/feature/_basic_features.py__init__PoolExecutor.__init__ cgrr)rexc_typeexc_valexc_tbs r__exit__PoolExecutor.__exit__rrc[X5$r)map)rfn iterabless rr PoolExecutor.maps r% %rrN)__name__ __module__ __qualname____firstlineno__rrr __static_attributes__rrrr r s   &rr c [[UR5S5VVs/sH1up[R"[R"U5UUS9PM3 nnn[ R "U5nU$s snnf)Nraxis)rrangendimnpgradientrhessian_matrix_eigvals)gaussian_filteredax0ax1H_elemseigvalss r_texture_filterr6sp6e[TUTTTS9$)N)r=r>r?)rA)sr>r<r=r?s r9_mutiscale_basic_features_singlechannel..isCiugr) r.ascontiguousarrayrintlog2logspacer listr itertoolschain from_iterable) r<r=r>r? sigma_min sigma_max num_sigma num_workerssigmasex out_sigmasfeaturess ```` r'_mutiscale_basic_features_singlechannelr\0sX  ~c2 3C *RWWY-??!CD [[       F + ." FF    /,,Z8H O / .s )#C55 D) channel_axisc^^^^^^^^[TTT/5(d [S5eUcTS[R4mSnOUS:wa[R"TUS5mUUUUUUUU4Sj[ TR S55n [[RRU 55n [R"U SS9n U $)a Local features for a single- or multi-channel nd image. Intensity, gradient intensity and local structure are computed at different scales thanks to Gaussian blurring. Parameters ---------- image : ndarray Input image, which can be grayscale or multichannel. intensity : bool, default True If True, pixel intensities averaged over the different scales are added to the feature set. edges : bool, default True If True, intensities of local gradients averaged over the different scales are added to the feature set. texture : bool, default True If True, eigenvalues of the Hessian matrix after Gaussian blurring at different scales are added to the feature set. sigma_min : float, optional Smallest value of the Gaussian kernel used to average local neighborhoods before extracting features. sigma_max : float, optional Largest value of the Gaussian kernel used to average local neighborhoods before extracting features. num_sigma : int, optional Number of values of the Gaussian kernel between sigma_min and sigma_max. If None, sigma_min multiplied by powers of 2 are used. num_workers : int or None, optional The number of parallel threads to use. If set to ``None``, the full set of available cores are used. channel_axis : int or None, optional If None, the image is assumed to be a grayscale (single channel) image. Otherwise, this parameter indicates which axis of the array corresponds to channels. .. versionadded:: 0.19 ``channel_axis`` was added in 0.19. Returns ------- features : np.ndarray Array of shape ``image.shape + (n_features,)``. When `channel_axis` is not None, all channels are concatenated along the features dimension. (i.e. ``n_features == n_features_singlechannel * n_channels``) z[At least one of `intensity`, `edges` or `textures`must be True for features to be computed..c 3N># UHn[TSU4TTT TTTTS9v M g7f).)r=r>r?rTrUrVrWN)r\) .0dimr>imager=rVrWrUrTr?s r ,multiscale_basic_features..sA *C 0 #s(O# *s"%r*) any ValueErrorr.newaxismoveaxisr,shaperPrQrRrSstack) rcr=r>r?rTrUrVrWr] all_resultsr[outs ```````` rmultiscale_basic_featuresrnssr  5'* + + 8  c2::o&   E<4  R) KIOO11+>?H ((8" %C Jr)TTT)TTTg?NN)rQrnumpyr.skimagerrskimage.util.dtyper_shared._dependency_checksrconcurrent.futuresr r contextlibr r6rAr\rnrrrrvs3$-0E1&-&59    @J  SSr