Module: future Functionality with an experimental API. Although you can count on the functions in this package being around in the future, the API may change with any version update and will not follow the skimage two-version deprecation path. Therefore, use the functions herein with care, and do not use them in production code that will depend on updated skimage versions.  
skimage.future.fit_segmenter(labels, …) Segmentation using labeled parts of the image and a classifier.  
skimage.future.manual_lasso_segmentation(image) Return a label image based on freeform selections made with the mouse.  
skimage.future.manual_polygon_segmentation(image) Return a label image based on polygon selections made with the mouse.  
skimage.future.predict_segmenter(features, clf) Segmentation of images using a pretrained classifier.  
skimage.future.TrainableSegmenter([clf, …]) Estimator for classifying pixels.  
skimage.future.graph    fit_segmenter  
skimage.future.fit_segmenter(labels, features, clf) [source]
 
Segmentation using labeled parts of the image and a classifier.  Parameters 
 
labelsndarray of ints 

Image of labels. Labels >= 1 correspond to the training set and label 0 to unlabeled pixels to be segmented.  
featuresndarray 

Array of features, with the first dimension corresponding to the number of features, and the other dimensions correspond to labels.shape.  
clfclassifier 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 
 
clfclassifier object 

classifier trained on labels    Raises 
 
NotFittedError if self.clf has not been fitted yet (use self.fit). 
   
 Examples using skimage.future.fit_segmenter
 
  Trainable segmentation using local features and random forests   manual_lasso_segmentation  
skimage.future.manual_lasso_segmentation(image, alpha=0.4, return_all=False) [source]
 
Return a label image based on freeform selections made with the mouse.  Parameters 
 
image(M, N[, 3]) array 

Grayscale or RGB image.  
alphafloat, optional 

Transparency value for polygons drawn over the image.  
return_allbool, optional 

If True, an array containing each separate polygon drawn is returned. (The polygons may overlap.) If False (default), latter polygons “overwrite” earlier ones where they overlap.    Returns 
 
labelsarray of int, shape ([Q, ]M, N) 

The segmented regions. If mode is ‘separate’, the leading dimension of the array corresponds to the number of regions that the user drew.     Notes Press and hold the left mouse button to draw around each object. Examples >>> from skimage import data, future, io
>>> camera = data.camera()
>>> mask = future.manual_lasso_segmentation(camera)  
>>> io.imshow(mask)  
>>> io.show()  
 
 manual_polygon_segmentation  
skimage.future.manual_polygon_segmentation(image, alpha=0.4, return_all=False) [source]
 
Return a label image based on polygon selections made with the mouse.  Parameters 
 
image(M, N[, 3]) array 

Grayscale or RGB image.  
alphafloat, optional 

Transparency value for polygons drawn over the image.  
return_allbool, optional 

If True, an array containing each separate polygon drawn is returned. (The polygons may overlap.) If False (default), latter polygons “overwrite” earlier ones where they overlap.    Returns 
 
labelsarray of int, shape ([Q, ]M, N) 

The segmented regions. If mode is ‘separate’, the leading dimension of the array corresponds to the number of regions that the user drew.     Notes Use left click to select the vertices of the polygon and right click to confirm the selection once all vertices are selected. Examples >>> from skimage import data, future, io
>>> camera = data.camera()
>>> mask = future.manual_polygon_segmentation(camera)  
>>> io.imshow(mask)  
>>> io.show()  
 
 predict_segmenter  
skimage.future.predict_segmenter(features, clf) [source]
 
Segmentation of images using a pretrained classifier.  Parameters 
 
featuresndarray 

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.  
clfclassifier 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 skimage.segmentation.fit_segmenter().    Returns 
 
outputndarray 

Labeled array, built from the prediction of the classifier.     
 Examples using skimage.future.predict_segmenter
 
  Trainable segmentation using local features and random forests   TrainableSegmenter  
class skimage.future.TrainableSegmenter(clf=None, features_func=None) [source]
 
Bases: object Estimator for classifying pixels.  Parameters 
 
clfclassifier 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_funcfunction, 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, skimage.segmentation.multiscale_basic_features() is used.     Methods  
fit(image, labels) Train classifier using partially labeled (annotated) image.  
predict(image) Segment new image using trained internal classifier.    
compute_features     
__init__(clf=None, features_func=None) [source]
 
Initialize self. See help(type(self)) for accurate signature. 
  
compute_features(image) [source]

  
fit(image, labels) [source]
 
Train classifier using partially labeled (annotated) image.  Parameters 
 
imagendarray 

Input image, which can be grayscale or multichannel, and must have a number of dimensions compatible with self.features_func.  
labelsndarray 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.     
  
predict(image) [source]
 
Segment new image using trained internal classifier.  Parameters 
 
imagendarray 

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).