0-PhddlmZ ddlmZddlmZdZn#e$rdZGddeZYnwxYwGdd Z d Z d Z d S) )multiscale_basic_features)NotFittedError)RandomForestClassifierTFceZdZdS)rN)__name__ __module__ __qualname__e/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/skimage/future/trainable_segmentation.pyrr s r rc,eZdZdZddZdZdZdZdS)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 Nc|-trtdd|_ntd||_||_dS)Nd) n_estimatorsn_jobszOPlease install scikit-learn or pass a classifier instanceto TrainableSegmenter.) has_sklearnrclf ImportError features_func)selfrrs r __init__zTrainableSegmenter.__init__%sS ; 1s2NNN!- DH*r c`|j t|_|||_dS)N)rrfeatures)rimages r compute_featuresz#TrainableSegmenter.compute_features2s,   %!:D **511 r cf||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)rrlabelss r fitzTrainableSegmenter.fit7s4 e$$$fdmTX66666r c||j t|_||}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``). )rrpredict_segmenterr)rrrs r predictzTrainableSegmenter.predictGs:   %!:D %%e,, 48444r )NN)rrr __doc__rrr!r$r r r rrs_* + + + +222 777 55555r rc|dk}||}||}||||S)a_Segmentation using labeled parts of the image and a classifier. Parameters ---------- labels : ndarray of ints Image of labels. Labels >= 1 correspond to the training set and label 0 to unlabeled pixels to be segmented. features : ndarray Array of features, with the first dimension corresponding to the number of features, and the other dimensions correspond to ``labels.shape``. clf : classifier object classifier object, exposing a ``fit`` and a ``predict`` method as in scikit-learn's API, for example an instance of ``RandomForestClassifier`` or ``LogisticRegression`` classifier. Returns ------- clf : classifier object classifier trained on ``labels`` Raises ------ NotFittedError if ``self.clf`` has not been fitted yet (use ``self.fit``). r)ravelr!)r rrmask training_datatraining_labelss r rrZsD2 A:DTNMTl((**OGGM?+++ Jr c|j}|jdkr|d|df} ||}nb#t$rt dt $r=}|jr/d|jdvr t |jddzdz|d}~wwxYw||dd}|S) aSegmentation of images using a pretrained classifier. Parameters ---------- features : ndarray Array of features, with the last dimension corresponding to the number of features, and the other dimensions are compatible with the shape of the image to segment, or a flattened image. clf : classifier object trained classifier object, exposing a ``predict`` method as in scikit-learn's API, for example an instance of ``RandomForestClassifier`` or ``LogisticRegression`` classifier. The classifier must be already trained, for example with :func:`skimage.future.fit_segmenter`. Returns ------- output : ndarray Labeled array, built from the prediction of the classifier. rzWYou must train the classifier `clf` firstfor example with the `fit_segmenter` function.z#x must consist of vectors of lengthr zLMaybe you did not use the same type of features for training the classifier.N)shapendimreshaper$r ValueErrorargs)rrshpredicted_labelserroutputs r r#r#zs* B}q##RBL11;;x00     =     8 =!LL `a  I % %b"g . .F MsA"B&)8B!!B&N) skimage.featurersklearn.exceptionsrsklearn.ensemblerrr Exceptionrrr#r r r r;s555555  111111777777KK   K           H5H5H5H5H5H5H5H5V@*****s //