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trainable_segmentation.py
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trainable_segmentation.py
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from skimage.feature import multiscale_basic_features
try:
from sklearn.exceptions import NotFittedError
from sklearn.ensemble import RandomForestClassifier
has_sklearn = True
except ImportError:
has_sklearn = False
class NotFittedError(Exception):
pass
class TrainableSegmenter:
"""Estimator 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
"""
def __init__(self, clf=None, features_func=None):
if clf is None:
if has_sklearn:
self.clf = RandomForestClassifier(n_estimators=100, n_jobs=-1)
else:
raise ImportError(
"Please install scikit-learn or pass a classifier instance"
"to TrainableSegmenter."
)
else:
self.clf = clf
self.features_func = features_func
def compute_features(self, image):
if self.features_func is None:
self.features_func = multiscale_basic_features
self.features = self.features_func(image)
def fit(self, image, labels):
"""Train 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.
"""
self.compute_features(image)
fit_segmenter(labels, self.features, self.clf)
def predict(self, image):
"""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``).
"""
if self.features_func is None:
self.features_func = multiscale_basic_features
features = self.features_func(image)
return predict_segmenter(features, self.clf)
def fit_segmenter(labels, features, clf):
"""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``).
"""
mask = labels > 0
training_data = features[mask]
training_labels = labels[mask].ravel()
clf.fit(training_data, training_labels)
return clf
def predict_segmenter(features, clf):
"""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.
"""
sh = features.shape
if features.ndim > 2:
features = features.reshape((-1, sh[-1]))
try:
predicted_labels = clf.predict(features)
except NotFittedError:
raise NotFittedError(
"You must train the classifier `clf` first"
"for example with the `fit_segmenter` function."
)
except ValueError as err:
if err.args and 'x must consist of vectors of length' in err.args[0]:
raise ValueError(
err.args[0] + '\n' +
"Maybe you did not use the same type of features for training the classifier."
)
else:
raise err
output = predicted_labels.reshape(sh[:-1])
return output