+ Ai^RIHt^RIHt^RIHtRt!RR 4t R t R t R # ]dRt!RR]4tL(i;i) )multiscale_basic_features)NotFittedError)RandomForestClassifierTFc]tRt^ tRtR#)rN)__name__ __module__ __qualname____firstlineno____static_attributes__rc/var/www/html/photoedit/myenv/lib/python3.14/site-packages/skimage/future/trainable_segmentation.pyrr s r rc@a]tRt^toRtRRltRtRtRtRt Vt R#) TrainableSegmenteraXEstimator for classifying pixels. Parameters ---------- clf : classifier object, optional classifier object, exposing a ``fit`` and a ``predict`` method as in scikit-learn's API, for example an instance of ``RandomForestClassifier`` or ``LogisticRegression`` classifier. features_func : function, optional function computing features on all pixels of the image, to be passed to the classifier. The output should be of shape ``(m_features, *labels.shape)``. If None, :func:`skimage.feature.multiscale_basic_features` is used. Methods ------- compute_features fit predict NczVf+\'d\^dRR7VnM\R4hWnW nR#)N) n_estimatorsn_jobszOPlease install scikit-learn or pass a classifier instanceto TrainableSegmenter.) has_sklearnrclf ImportError features_func)selfrrs&&&r__init__TrainableSegmenter.__init__%s9 ;{1s2N!- H*r cdVPf \VnVPV4VnR#)N)rrfeatures)rimages&&rcompute_features#TrainableSegmenter.compute_features2s(    %!:D **51 r c hVPV4\W PVP4R#)aTrain classifier using partially labeled (annotated) image. Parameters ---------- image : ndarray Input image, which can be grayscale or multichannel, and must have a number of dimensions compatible with ``self.features_func``. labels : ndarray of ints Labeled array of shape compatible with ``image`` (same shape for a single-channel image). Labels >= 1 correspond to the training set and label 0 to unlabeled pixels to be segmented. N)r fit_segmenterrr)rrlabelss&&&rfitTrainableSegmenter.fit7s$ e$fmmTXX6r c VPf \VnVPV4p\W P4#)a@Segment new image using trained internal classifier. Parameters ---------- image : ndarray Input image, which can be grayscale or multichannel, and must have a number of dimensions compatible with ``self.features_func``. Raises ------ NotFittedError if ``self.clf`` has not been fitted yet (use ``self.fit``). )rrpredict_segmenterr)rrrs&& rpredictTrainableSegmenter.predictGs7    %!:D %%e, 8844r )rrr)NN) rr r r __doc__rrr$r(r __classdictcell__) __classdict__s@rrrs#* +2 7 55r rcnV^8pW,pW,P4pVPWE4V#)aSegmentation using labeled parts of the image and a classifier. Parameters ---------- labels : ndarray of ints Image of labels. Labels >= 1 correspond to the training set and label 0 to unlabeled pixels to be segmented. features : ndarray Array of features, with the first dimension corresponding to the number of features, and the other dimensions correspond to ``labels.shape``. clf : classifier object classifier object, exposing a ``fit`` and a ``predict`` method as in scikit-learn's API, for example an instance of ``RandomForestClassifier`` or ``LogisticRegression`` classifier. Returns ------- clf : classifier object classifier trained on ``labels`` Raises ------ NotFittedError if ``self.clf`` has not been fitted yet (use ``self.fit``). )ravelr$)r#rrmask training_datatraining_labelss&&& rr"r"Zs52 A:DNMl((*OGGM+ Jr cVPpVP^8dVPRVR,34pVPV4pTPTRR4pT# \d \ R4h\ d\pTP 'dCRTP ^,9d+\ TP ^,R,R,4hThRp?ii;i)aSegmentation of images using a pretrained classifier. Parameters ---------- features : ndarray Array of features, with the last dimension corresponding to the number of features, and the other dimensions are compatible with the shape of the image to segment, or a flattened image. clf : classifier object trained classifier object, exposing a ``predict`` method as in scikit-learn's API, for example an instance of ``RandomForestClassifier`` or ``LogisticRegression`` classifier. The classifier must be already trained, for example with :func:`skimage.future.fit_segmenter`. Returns ------- output : ndarray Labeled array, built from the prediction of the classifier. zWYou must train the classifier `clf` firstfor example with the `fit_segmenter` function.z#x must consist of vectors of length zLMaybe you did not use the same type of features for training the classifier.Nr)shapendimreshaper(r ValueErrorargs)rrshpredicted_labelserroutputs&& rr'r'zs* B}}q##RBL1;;x0 % %b"g .F M   =    888=!L `a  IsA C?CACCN) skimage.featurersklearn.exceptionsrsklearn.ensemblerrr Exceptionrr"r'rr rrAsS5  17KH5H5V@*e K   s (??