1-PhzddlmZddlZddlZddlmZmZddlmZddl m Z e sddl m Z nddlmZGd d eZ d Z dd Z ddZ ddddZdS))combinations_with_replacementN)filtersfeature)img_as_float32)is_wasm)ThreadPoolExecutor)AbstractContextManagerc eZdZdZdZdZdS) PoolExecutorcdSN)self___s _/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/skimage/feature/_basic_features.py__init__zPoolExecutor.__init__ DcdSrr)rexc_typeexc_valexc_tbs r__exit__zPoolExecutor.__exit__rrc"t||Sr)map)rfn iterabless rrzPoolExecutor.mapsr9%% %rN)__name__ __module__ __qualname__rrrrrrr r sA       & & & & &rr cfdttjdD}tj|}|S)Ncrg|]3\}}tjtj||4S)axis)npgradient).0ax0ax1gaussian_filtereds r z#_texture_filter..sL C BK 12237cBBBrr)rrangendimrhessian_matrix_eigvals)r,H_elemseigvalss` r_texture_filterr3sY5e"sG(E%PPP(%'' 7GM"34466:9_%677999 Nr?c  tjt|9ttj|tj|z dz}tjtj|tj||dd}t |5} t| fd|} dddn #1swxYwYtj | } | S)aZFeatures for a single channel nd image. Parameters ---------- img : 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. Returns ------- features : list List of features, each element of the list is an array of shape as img. NrT)numbaseendpoint) max_workersc,t|S)N)r:r;r<)r>)sr;r9r:r<s rz9_mutiscale_basic_features_singlechannel..is"Ciugr) r'ascontiguousarrayrintlog2logspacer listr itertoolschain from_iterable) r9r:r;r< sigma_min sigma_max num_sigma num_workerssigmasex out_sigmasfeaturess ```` r'_mutiscale_basic_features_singlechannelrZ0sTX ~c22 3 3C **RWY-?-??!CDD [       F + . . . " FF                    ,,Z88H Os.*C$$C(+C() channel_axisctgstd|dtjfd}n|dkrtj|dfdt jdD} ttj | } tj | d} | S)aLocal 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.N.c 3VK|]#}td|f V$dS).)r:r;r<rRrSrTrUN)rZ) r)dimr;imager:rTrUrSrRr<s r z,multiscale_basic_features..sh    0 #s(O#       rr%) any ValueErrorr'newaxismoveaxisr.shaperNrOrPrQstack) r`r:r;r<rRrSrTrUr[ all_resultsrYouts ```````` rmultiscale_basic_featuresrjss r  5'* + +  8   c2:o&    E<44           R))   KIO11+>>??H (8" % % %C Jr)TTT)TTTr?r@NN)rOrnumpyr'skimagerrskimage.util.dtyper_shared._dependency_checksrconcurrent.futuresr r contextlibr r3r>rZrjrrrrqsi333333$$$$$$$$------000000&EEEEEEE111111&&&&&-&&&59       @@@@J  SSSSSSSSr