9iSSKJr SSKJr SSKJr Sr"SS 5r S r S r g !\a Sr"SS\5rN&f=f) )multiscale_basic_features)NotFittedError)RandomForestClassifierTFc\rSrSrSrg)r N)__name__ __module__ __qualname____firstlineno____static_attributes__re/var/www/html/land-doc-ocr/venv/lib/python3.13/site-packages/skimage/future/trainable_segmentation.pyrr s rrc4\rSrSrSrS SjrSrSrSrSr g) 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 NcnUc&[(a[SSS9UlO[S5eXlX lg)Nd) 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*rc`URc [UlURU5Ulg)N)rrfeatures)rimages rcompute_features#TrainableSegmenter.compute_features2s(    %!:D **51 rcfURU5 [X RUR5 g)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_segmenterr r)rr!labelss rfitTrainableSegmenter.fit7s$ e$fmmTXX6rc~URc [UlURU5n[X R5$)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)rr!r s rpredictTrainableSegmenter.predictGs7    %!:D %%e, 8844r)rr r)NN) r r r r __doc__rr"r'r+r rrrrrs* +2 7 5rrc^US:nXnXR5nURXE5 U$)aSegmentation using labeled parts of the image and a classifier. Parameters ---------- labels : ndarray of ints Image of labels. Labels >= 1 correspond to the training set and label 0 to unlabeled pixels to be segmented. features : ndarray Array of features, with the first dimension corresponding to the number of features, and the other dimensions correspond to ``labels.shape``. clf : classifier object classifier object, exposing a ``fit`` and a ``predict`` method as in scikit-learn's API, for example an instance of ``RandomForestClassifier`` or ``LogisticRegression`` classifier. Returns ------- clf : classifier object classifier trained on ``labels`` Raises ------ NotFittedError if ``self.clf`` has not been fitted yet (use ``self.fit``). r)ravelr')r&r rmask training_datatraining_labelss rr%r%Zs52 A:DNMl((*OGGM+ JrcURnURS:aURSUS45nURU5nURUSS5nU$![a [ S5e[ aInUR (a1SUR S;a[ UR SS-S-5eUeSnAff=f) 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. rzWYou must train the classifier `clf` firstfor example with the `fit_segmenter` function.z#x must consist of vectors of lengthr zLMaybe you did not use the same type of features for training the classifier.N)shapendimreshaper+r ValueErrorargs)r rshpredicted_labelserroutputs rr*r*zs* B}}q##RBL1;;x0 % %b"g .F M   =    88=!L `a  IsAC9AB==CN) skimage.featurersklearn.exceptionsrsklearn.ensemblerrr Exceptionrr%r*rrrrCsS5  17KH5H5V@*e K   s '==