def extract_features(self, array, **kwargs):
"""
Extract features for the node
Parameters
----------
array : ndarray
kwargs : dict
kwargs passed to autocnet.feature_extractor.extract_features
"""
keypoint_objs, descriptors = fe.extract_features(array, **kwargs)
keypoints = np.empty((len(keypoint_objs), 7), dtype=np.float32)
for i, kpt in enumerate(keypoint_objs):
octave = kpt.octave & 8
layer = (kpt.octave >> 8) & 255
if octave < 128:
octave = octave
else:
octave = (-128 | octave)
keypoints[i] = kpt.pt[0], kpt.pt[1], kpt.response, kpt.size, kpt.angle, octave, layer # y, x
self._keypoints = pd.DataFrame(keypoints, columns=['x', 'y', 'response', 'size',
'angle', 'octave', 'layer'])
self._nkeypoints = len(self._keypoints)
self.descriptors = descriptors.astype(np.float32)
def extract_features(self, array, **kwargs):
"""
Extract features for the node
Parameters
----------
array : ndarray
kwargs : dict
KWargs passed to autocnet.feature_extractor.extract_features
"""
keypoint_objs, descriptors = fe.extract_features(array, **kwargs)
keypoints = np.empty((len(keypoint_objs), 7), dtype=np.float32)
for i, kpt in enumerate(keypoint_objs):
octave = kpt.octave & 8
layer = (kpt.octave >> 8) & 255
if octave < 128:
octave = octave
else:
octave = -128 | octave
keypoints[i] = kpt.pt[0], kpt.pt[1], kpt.response, kpt.size, kpt.angle, octave, layer # y, x
self.keypoints = pd.DataFrame(keypoints, columns=["x", "y", "response", "size", "angle", "octave", "layer"])
self._nkeypoints = len(self.keypoints)
self.descriptors = descriptors.astype(np.float32)
self.provenance[self._pid] = {"detector": "sift", "parameters": kwargs}
self._pid += 1
def extract_features(self, array, **kwargs):
"""
Extract features for the node
Parameters
----------
array : ndarray
kwargs : dict
KWargs passed to autocnet.feature_extractor.extract_features
"""
keypoint_objs, descriptors = fe.extract_features(array, **kwargs)
keypoints = np.empty((len(keypoint_objs), 7), dtype=np.float32)
for i, kpt in enumerate(keypoint_objs):
octave = kpt.octave & 8
layer = (kpt.octave >> 8) & 255
if octave < 128:
octave = octave
else:
octave = (-128 | octave)
keypoints[i] = kpt.pt[0], kpt.pt[
1], kpt.response, kpt.size, kpt.angle, octave, layer # y, x
self.keypoints = pd.DataFrame(
keypoints,
columns=['x', 'y', 'response', 'size', 'angle', 'octave', 'layer'])
self._nkeypoints = len(self.keypoints)
self.descriptors = descriptors.astype(np.float32)
def extract_features(self, array, **kwargs):
"""
Extract features for the node
Parameters
----------
array : ndarray
kwargs : dict
kwargs passed to autocnet.feature_extractor.extract_features
"""
self._keypoints, self.descriptors = fe.extract_features(array, **kwargs)
def extract_features(self, extractor_parameters={}, downsampling=1):
"""
Extracts features from each image in the graph and uses the result to assign the
node attributes for 'handle', 'image', 'keypoints', and 'descriptors'.
Parameters
----------
extractor_parameters : dict
A dictionary containing OpenCV SIFT parameters names and values.
downsampling : int
The divisor to image_size to down sample the input image.
"""
for node, attributes in self.nodes_iter(data=True):
self.get_geodataset(node)
self.get_array(node, downsampling=downsampling)
attributes['keypoints'], attributes['descriptors'] = fe.extract_features(attributes['image'],
extractor_parameters)
def extract_features(self, nfeatures) :
"""
Extracts features from each image in the graph and uses the result to assign the
node attributes for 'handle', 'image', 'keypoints', and 'descriptors'.
Parameters
----------
nfeatures : int
The number of features to be extracted.
"""
# Loop through the nodes (i.e. images) on the graph and fill in their attributes.
# These attributes are...
# geo dataset (handle and image)
# features (keypoints and descriptors)
for node, attributes in self.nodes_iter(data=True):
self.get_geodataset(node)
extraction_params = {'nfeatures' : nfeatures}
attributes['keypoints'], attributes['descriptors'] = fe.extract_features(attributes['image'],
extraction_params)
def extract_features(self, array, **kwargs):
"""
Extract features for the node
Parameters
----------
array : ndarray
kwargs : dict
kwargs passed to autocnet.feature_extractor.extract_features
"""
keypoint_objs, self.descriptors = fe.extract_features(array, **kwargs)
if self.descriptors.dtype != np.float32:
self.descriptors = self.descriptors.astype(np.float32)
# OpenCV returned keypoint objects
if isinstance(keypoint_objs, list):
keypoints = np.empty((len(keypoint_objs), 7), dtype=np.float32)
for i, kpt in enumerate(keypoint_objs):
octave = kpt.octave & 8
layer = (kpt.octave >> 8) & 255
if octave < 128:
octave = octave
else:
octave = (-128 | octave)
keypoints[i] = kpt.pt[0], kpt.pt[1], kpt.response, kpt.size, kpt.angle, octave, layer # y, x
self._keypoints = pd.DataFrame(keypoints, columns=['x', 'y', 'response', 'size',
'angle', 'octave', 'layer'])
self.nkeypoints = len(self._keypoints)
# VLFeat returned keypoint objects
elif isinstance(keypoint_objs, np.ndarray):
# Swap columns for value style access, vl_feat returns y, x
keypoint_objs[:, 0], keypoint_objs[:, 1] = keypoint_objs[:, 1], keypoint_objs[:, 0].copy()
self._keypoints = pd.DataFrame(keypoint_objs, columns=['x', 'y', 'size', 'angle'])
