def source_to_target_distances(sourceIDs,
targetIDs,
points,
segmentIDs=[],
excludeIDs=[-1]):
"""
Create a Euclidean distance matrix between source and target points.
Compute the Euclidean distance from each source point to
its nearest target point, optionally within each segment.
Example::
Compute fundus-to-feature distances, the minimum distance
from each label boundary vertex (corresponding to a fundus
in the DKT cortical labeling protocol) to all of the
feature vertices in the same fold.
Parameters
----------
sourceIDs : list of N integers (N is the number of vertices)
source IDs, where any ID not in excludeIDs is a source point
targetIDs : list of N integers (N is the number of vertices)
target IDs, where any ID not in excludeIDs is a target point
points : list of N lists of three floats (N is the number of vertices)
coordinates of all vertices
segmentIDs : list of N integers (N is the number of vertices)
segment IDs, where each ID not in excludeIDs is considered a
different segment (unlike above, where value in sourceIDs or
targetIDs doesn't matter, so long as its not in excludeIDs);
source/target distances are computed within each segment
excludeIDs : list of integers
IDs to exclude
Returns
-------
distances : numpy array
distance value for each vertex (default -1)
distance_matrix : numpy array [#points by maximum segment ID + 1]
distances organized by segments (columns)
"""
import numpy as np
from mindboggle.guts.compute import point_distance
if isinstance(points, list):
points = np.asarray(points)
npoints = len(points)
# Extract unique segment IDs (or use all points as a single segment):
if np.size(segmentIDs):
segments = [x for x in np.unique(segmentIDs) if x not in excludeIDs]
else:
segmentIDs = np.zeros(npoints)
segments = [0]
nsegments = max(segments) + 1
# Initialize outputs:
distances = -1 * np.ones(npoints)
distance_matrix = -1 * np.ones((npoints, nsegments))
# For each segment:
for segment in segments:
segment_indices = [i for i, x in enumerate(segmentIDs) if x == segment]
# Find all source points in the segment:
source_indices = [
i for i, x in enumerate(sourceIDs) if x not in excludeIDs
if i in segment_indices
]
# Find all target points in the segment:
target_indices = [
i for i, x in enumerate(targetIDs) if x not in excludeIDs
if i in segment_indices
]
if source_indices and target_indices:
# For each source point in the segment:
for isource in source_indices:
# Find the closest target point:
d, i = point_distance(points[isource], points[target_indices])
distances[isource] = d
distance_matrix[isource, segment] = d
return distances, distance_matrix
def source_to_target_distances(sourceIDs, targetIDs, points,
segmentIDs=[], excludeIDs=[-1]):
"""
Create a Euclidean distance matrix between source and target points.
Compute the Euclidean distance from each source point to
its nearest target point, optionally within each segment.
Example::
Compute fundus-to-feature distances, the minimum distance
from each label boundary vertex (corresponding to a fundus
in the DKT cortical labeling protocol) to all of the
feature vertices in the same fold.
Parameters
----------
sourceIDs : list of N integers (N is the number of vertices)
source IDs, where any ID not in excludeIDs is a source point
targetIDs : list of N integers (N is the number of vertices)
target IDs, where any ID not in excludeIDs is a target point
points : list of N lists of three floats (N is the number of vertices)
coordinates of all vertices
segmentIDs : list of N integers (N is the number of vertices)
segment IDs, where each ID not in excludeIDs is considered a
different segment (unlike above, where value in sourceIDs or
targetIDs doesn't matter, so long as its not in excludeIDs);
source/target distances are computed within each segment
excludeIDs : list of integers
IDs to exclude
Returns
-------
distances : numpy array
distance value for each vertex (default -1)
distance_matrix : numpy array [#points by maximum segment ID + 1]
distances organized by segments (columns)
"""
import numpy as np
from mindboggle.guts.compute import point_distance
if isinstance(points, list):
points = np.asarray(points)
npoints = len(points)
# Extract unique segment IDs (or use all points as a single segment):
if np.size(segmentIDs):
segments = [x for x in np.unique(segmentIDs) if x not in excludeIDs]
else:
segmentIDs = np.zeros(npoints)
segments = [0]
nsegments = max(segments) + 1
# Initialize outputs:
distances = -1 * np.ones(npoints)
distance_matrix = -1 * np.ones((npoints, nsegments))
# For each segment:
for segment in segments:
segment_indices = [i for i,x in enumerate(segmentIDs)
if x == segment]
# Find all source points in the segment:
source_indices = [i for i,x in enumerate(sourceIDs)
if x not in excludeIDs
if i in segment_indices]
# Find all target points in the segment:
target_indices = [i for i,x in enumerate(targetIDs)
if x not in excludeIDs
if i in segment_indices]
if source_indices and target_indices:
# For each source point in the segment:
for isource in source_indices:
# Find the closest target point:
d, i = point_distance(points[isource],
points[target_indices])
distances[isource] = d
distance_matrix[isource, segment] = d
return distances, distance_matrix