U ?h@sxddlmZz ddlmZddlmZdZWn(ek rTdZGdddeZYnXGdd d Z d d Z d d Z dS))multiscale_basic_features)NotFittedError)RandomForestClassifierTFc@s eZdZdS)rN)__name__ __module__ __qualname__rrW/var/www/html/venv/lib/python3.8/site-packages/skimage/future/trainable_segmentation.pyr src@s2eZdZdZd ddZddZddZd d ZdS) TrainableSegmenteraEstimator 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 NcCs6|dkr&trtddd|_q,tdn||_||_dS)Nd)Z n_estimatorsZn_jobszOPlease install scikit-learn or pass a classifier instanceto TrainableSegmenter.) has_sklearnrclf ImportError features_func)selfrrrrr __init__$szTrainableSegmenter.__init__cCs |jdkrt|_|||_dS)N)rrfeatures)rimagerrr compute_features1s z#TrainableSegmenter.compute_featurescCs||t||j|jdS)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)rrlabelsrrr fit6s zTrainableSegmenter.fitcCs&|jdkrt|_||}t||jS)aSegment 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``). N)rrpredict_segmenterr)rrrrrr predictFs  zTrainableSegmenter.predict)NN)rrr__doc__rrrrrrrr r s  r cCs,|dk}||}||}||||S)a_Segmentation 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)Zravelr)rrrmaskZ training_dataZtraining_labelsrrr rYs   rc Cs|j}|jdkr"|d|df}z||}Wnltk rLtdYnRtk r}z4|jrd|jdkrt|jdddn|W5d}~XYnX||dd}|S) a Segmentation 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.segmentation.fit_segmenter`. Returns ------- output : ndarray Labeled array, built from the prediction of the classifier. r zWYou 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)shapendimZreshaperr ValueErrorargs)rrshZpredicted_labelserroutputrrr rys&  rN) Zskimage.featurerZsklearn.exceptionsrZsklearn.ensemblerr r Exceptionr rrrrrr s   K