def features(self, image, shape):
r"""
Method that extracts the features for the regression, which in this
case are patch based.
Parameters
----------
image : :map:`MaskedImage`
The current image.
shape : :map:`PointCloud`
The current shape.
"""
# TODO: in the future this should be extract_local_patches_fast
patches = extract_local_patches(image, shape, self.sampling_grid)
features = [clf(np.reshape(patch, (-1, patch.shape[-1])))
for (clf, patch) in zip(self.classifiers, patches)]
return np.hstack((np.asarray(features).ravel(), 1))
def features(self, image, shape):
patches = extract_local_patches(image, shape, self.sampling_grid)
features = [clf(np.reshape(p.pixels, (-1, p.n_channels)))
for (clf, p) in zip(self.classifiers, patches)]
return np.hstack((np.asarray(features).ravel(), 1))
def features(self, image, shape):
patches = extract_local_patches(image, shape, self.sampling_grid)
features = [compute_features(p, self.regression_features).pixels.ravel()
for p in patches]
return np.hstack((np.asarray(features).ravel(), 1))
