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.
| Segmentation using labeled parts of the image and a classifier. |
Return a label image based on freeform selections made with the mouse. | |
Return a label image based on polygon selections made with the mouse. | |
| Segmentation of images using a pretrained classifier. |
| Estimator for classifying pixels. |
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 apredict
method as in scikit-learn’s API, for example an instance ofRandomForestClassifier
orLogisticRegression
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
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 ofRandomForestClassifier
orLogisticRegression
classifier. The classifier must be already trained, for example withskimage.segmentation.fit_segmenter()
.
-
- Returns
-
-
outputndarray
-
Labeled array, built from the prediction of the classifier.
-
Examples using skimage.future.predict_segmenter
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 apredict
method as in scikit-learn’s API, for example an instance ofRandomForestClassifier
orLogisticRegression
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).
-
© 2019 the scikit-image team
Licensed under the BSD 3-clause License.
https://scikit-image.org/docs/0.18.x/api/skimage.future.html