o h@sddlmZddlZddlZddlmZmZddlmZddl m Z ddZ dd d Z     dd dZ     dddddZdS))combinations_with_replacementN)filtersfeature)img_as_float32)ThreadPoolExecutorcs,fddttjdD}t|}|S)Ncs(g|]\}}tjt||dqS)axis)npgradient).0ax0ax1gaussian_filteredZ/var/www/html/scripts/venv/lib/python3.10/site-packages/skimage/feature/_basic_features.py sz#_texture_filter..)rrangendimrhessian_matrix_eigvals)rH_elemseigvalsrrr_texture_filter s  rTcCsPd}tj||dd}|r||f7}|r|t|f7}|r&|gt|R7}|S)NrF)preserve_range)rgaussiansobelr)imgsigma intensityedgestextureresultsrrrr)_singlescale_basic_features_singlechannels r#?c stt|durtt|t|d}tjt|t||ddd}t|d} t| fdd|} Wdn1sHwYt j | } | 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_workerscst|dS)N)rr r!)r#)sr rrr!rrYs z9_mutiscale_basic_features_singlechannel..) r ascontiguousarrayrintlog2logspacerlistmap itertoolschain from_iterable) rrr r! sigma_min sigma_max num_sigma num_workerssigmasex out_sigmasfeaturesrr,r'_mutiscale_basic_features_singlechannel s(,  r?) channel_axisc stgs td|durdtjfd}n |dkr$t|dfddtjdD} ttj | } tj | dd} | 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 3s0|]}td|fdVqdS).)rr r!r7r8r9r:N)r?)r dimr imagerr9r:r8r7r!rr s   z,multiscale_basic_features..r) any ValueErrorr newaxismoveaxisrshaper2r4r5r6stack) rDrr r!r7r8r9r:r@ all_resultsr>outrrCrmultiscale_basic_featurescs9  rN)TTT)TTTr$r%NN)r4rnumpyr skimagerrskimage.util.dtyperconcurrent.futuresrrr#r?rNrrrrs6     E