def match_objects(self, set_a, set_b, time_a, time_b):
"""
Match two sets of objects at particular times.
Args:
set_a: list of STObjects
set_b: list of STObjects
time_a: time at which set_a is being evaluated for matching
time_b: time at which set_b is being evaluated for matching
Returns:
List of tuples containing (set_a index, set_b index) for each match
"""
costs = self.cost_matrix(set_a, set_b, time_a, time_b) * 100
min_row_costs = costs.min(axis=1)
min_col_costs = costs.min(axis=0)
good_rows = np.where(min_row_costs < 100)[0]
good_cols = np.where(min_col_costs < 100)[0]
assignments = []
if len(good_rows) > 0 and len(good_cols) > 0:
munk = Munkres()
initial_assignments = munk.compute(costs[np.meshgrid(
good_rows, good_cols, indexing='ij')].tolist())
initial_assignments = [(good_rows[x[0]], good_cols[x[1]])
for x in initial_assignments]
for a in initial_assignments:
if costs[a[0], a[1]] < 100:
assignments.append(a)
return assignments
def match_objects(self, set_a, set_b, time_a, time_b):
"""
Match two sets of objects at particular times.
Args:
set_a: list of STObjects
set_b: list of STObjects
time_a: time at which set_a is being evaluated for matching
time_b: time at which set_b is being evaluated for matching
Returns:
List of tuples containing (set_a index, set_b index) for each match
"""
costs = self.cost_matrix(set_a, set_b, time_a, time_b) * 100
min_row_costs = costs.min(axis=1)
min_col_costs = costs.min(axis=0)
good_rows = np.where(min_row_costs < 100)[0]
good_cols = np.where(min_col_costs < 100)[0]
assignments = []
if len(good_rows) > 0 and len(good_cols) > 0:
munk = Munkres()
initial_assignments = munk.compute(costs[tuple(np.meshgrid(good_rows, good_cols, indexing='ij'))].tolist())
initial_assignments = [(good_rows[x[0]], good_cols[x[1]]) for x in initial_assignments]
for a in initial_assignments:
if costs[a[0], a[1]] < 100:
assignments.append(a)
return assignments
def match_tracks(self, set_a, set_b, closest_matches=False):
"""
Find the optimal set of matching assignments between set a and set b. This function supports optimal 1:1
matching using the Munkres method and matching from every object in set a to the closest object in set b.
In this situation set b accepts multiple matches from set a.
Args:
set_a:
set_b:
closest_matches:
Returns:
"""
costs = self.track_cost_matrix(set_a, set_b) * 100
min_row_costs = costs.min(axis=1)
min_col_costs = costs.min(axis=0)
good_rows = np.where(min_row_costs < 100)[0]
good_cols = np.where(min_col_costs < 100)[0]
assignments = []
if len(good_rows) > 0 and len(good_cols) > 0:
if closest_matches:
b_matches = costs[np.meshgrid(good_rows, good_cols, indexing='ij')].argmin(axis=1)
a_matches = np.arange(b_matches.size)
initial_assignments = [(good_rows[a_matches[x]], good_cols[b_matches[x]])
for x in range(b_matches.size)]
else:
munk = Munkres()
initial_assignments = munk.compute(costs[np.meshgrid(good_rows, good_cols, indexing='ij')].tolist())
initial_assignments = [(good_rows[x[0]], good_cols[x[1]]) for x in initial_assignments]
for a in initial_assignments:
if costs[a[0], a[1]] < 100:
assignments.append(a)
return assignments
def match_tracks(self, set_a, set_b, closest_matches=False):
"""
Find the optimal set of matching assignments between set a and set b. This function supports optimal 1:1
matching using the Munkres method and matching from every object in set a to the closest object in set b.
In this situation set b accepts multiple matches from set a.
Args:
set_a:
set_b:
closest_matches:
Returns:
"""
costs = self.track_cost_matrix(set_a, set_b) * 100
min_row_costs = costs.min(axis=1)
min_col_costs = costs.min(axis=0)
good_rows = np.where(min_row_costs < 100)[0]
good_cols = np.where(min_col_costs < 100)[0]
assignments = []
if len(good_rows) > 0 and len(good_cols) > 0:
if closest_matches:
b_matches = costs[np.meshgrid(good_rows, good_cols, indexing='ij')].argmin(axis=1)
a_matches = np.arange(b_matches.size)
initial_assignments = [(good_rows[a_matches[x]], good_cols[b_matches[x]])
for x in range(b_matches.size)]
else:
munk = Munkres()
initial_assignments = munk.compute(costs[np.meshgrid(good_rows, good_cols, indexing='ij')].tolist())
initial_assignments = [(good_rows[x[0]], good_cols[x[1]]) for x in initial_assignments]
for a in initial_assignments:
if costs[a[0], a[1]] < 100:
assignments.append(a)
return assignments
def match_tracks(self, set_a, set_b):
costs = self.track_cost_matrix(set_a, set_b) * 100
min_row_costs = costs.min(axis=1)
min_col_costs = costs.min(axis=0)
good_rows = np.where(min_row_costs < 100)[0]
good_cols = np.where(min_col_costs < 100)[0]
assignments = []
if len(good_rows) > 0 and len(good_cols) > 0:
munk = Munkres()
initial_assignments = munk.compute(costs[np.meshgrid(
good_rows, good_cols, indexing='ij')].tolist())
initial_assignments = [(good_rows[x[0]], good_cols[x[1]])
for x in initial_assignments]
for a in initial_assignments:
if costs[a[0], a[1]] < 100:
assignments.append(a)
return assignments