def getInitialGuesses(self, cameras):
if not self.G:
raise RuntimeError("Graph is uninitialized!")
#################################################################
## STEP 0: check if all cameras in the chain are connected
## through common target point observations
## (=all vertices connected?)
#################################################################
if not self.isGraphConnected():
sm.logError(
"The cameras are not connected through mutual target observations! "
"Please provide another dataset...")
self.plotGraph()
sys.exit(0)
#################################################################
## STEP 1: get baseline initial guesses by calibrating good
## camera pairs using a stereo calibration
##
#################################################################
#first we need to find the best camera pairs to obtain the initial guesses
#--> use the pairs that share the most common observed target corners
#The graph is built with weighted edges that represent the number of common
#target corners, so we can use dijkstras algorithm to get the best pair
#configuration for the initial pair calibrations
weights = [1.0 / commonPoints for commonPoints in self.G.es["weight"]]
#choose the cam with the least edges as base_cam
outdegrees = self.G.vs.outdegree()
base_cam_id = outdegrees.index(min(outdegrees))
#solve for shortest path (=optimal transformation chaining)
edges_on_path = self.G.get_shortest_paths(0,
weights=weights,
output="epath")
self.optimal_baseline_edges = set(
[item for sublist in edges_on_path for item in sublist])
#################################################################
## STEP 2: solve stereo calibration problem for the baselines
## (baselines are always from lower_id to higher_id cams!)
#################################################################
#calibrate all cameras in pairs
for baseline_edge_id in self.optimal_baseline_edges:
#get the cam_nrs from the graph edge (calibrate from low to high id)
vertices = self.G.es[baseline_edge_id].tuple
if vertices[0] < vertices[1]:
camL_nr = vertices[0]
camH_nr = vertices[1]
else:
camL_nr = vertices[1]
camH_nr = vertices[0]
print "\t initializing camera pair ({0},{1})... ".format(
camL_nr, camH_nr)
#run the pair extrinsic calibration
obs_list = self.obs_db.getAllObsTwoCams(camL_nr, camH_nr)
success, baseline_HL = kcc.stereoCalibrate(cameras[camL_nr],
cameras[camH_nr],
obs_list,
distortionActive=False)
if success:
sm.logDebug("baseline_{0}_{1}={2}".format(
camL_nr, camH_nr, baseline_HL.T()))
else:
sm.logError(
"initialization of camera pair ({0},{1}) failed ".format(
camL_nr, camH_nr))
sm.logError("estimated baseline_{0}_{1}={2}".format(
camL_nr, camH_nr, baseline_HL.T()))
#store the baseline in the graph
self.G.es[self.G.get_eid(camL_nr,
camH_nr)]["baseline_HL"] = baseline_HL
#################################################################
## STEP 3: transform from the "optimal" baseline chain to camera chain ordering
## (=> baseline_0 = T_c1_c0 |
#################################################################
#construct the optimal path graph
G_optimal_baselines = self.G.copy()
eid_not_optimal_path = set(range(0, len(G_optimal_baselines.es)))
for eid in self.optimal_baseline_edges:
eid_not_optimal_path.remove(eid)
G_optimal_baselines.delete_edges(eid_not_optimal_path)
#now we convert the arbitary baseline graph to baselines starting from
# cam0 and traverse the chain (cam0->cam1->cam2->camN)
baselines = []
for baseline_id in range(0, self.numCams - 1):
#find the shortest path on the graph
path = G_optimal_baselines.get_shortest_paths(
baseline_id, baseline_id + 1)[0]
#get the baseline from cam with id baseline_id to baseline_id+1
baseline_HL = sm.Transformation()
for path_idx in range(0, len(path) - 1):
source_vert = path[path_idx]
target_vert = path[path_idx + 1]
T_edge = self.G.es[self.G.get_eid(source_vert,
target_vert)]["baseline_HL"]
#correct the direction (baselines always from low to high cam id!)
T_edge = T_edge if source_vert < target_vert else T_edge.inverse(
)
#chain up
baseline_HL = T_edge * baseline_HL
#store in graph
baselines.append(baseline_HL)
#################################################################
## STEP 4: refine guess in full batch
#################################################################
success, baselines = kcc.solveFullBatch(cameras, baselines, self)
if not success:
sm.logWarn("Full batch refinement failed!")
return baselines
def getInitialGuesses(self, cameras):
if not self.G:
raise RuntimeError("Graph is uninitialized!")
#################################################################
## STEP 0: check if all cameras in the chain are connected
## through common target point observations
## (=all vertices connected?)
#################################################################
if not self.isGraphConnected():
sm.logError("The cameras are not connected through mutual target observations! "
"Please provide another dataset...")
self.plotGraph()
sys.exit(0)
#################################################################
## STEP 1: get baseline initial guesses by calibrating good
## camera pairs using a stereo calibration
##
#################################################################
#first we need to find the best camera pairs to obtain the initial guesses
#--> use the pairs that share the most common observed target corners
#The graph is built with weighted edges that represent the number of common
#target corners, so we can use dijkstras algorithm to get the best pair
#configuration for the initial pair calibrations
weights = [1.0/commonPoints for commonPoints in self.G.es["weight"]]
#choose the cam with the least edges as base_cam
outdegrees = self.G.vs.outdegree()
base_cam_id = outdegrees.index(min(outdegrees))
#solve for shortest path (=optimal transformation chaining)
edges_on_path = self.G.get_shortest_paths(0, weights=weights, output="epath")
self.optimal_baseline_edges = set([item for sublist in edges_on_path for item in sublist])
#################################################################
## STEP 2: solve stereo calibration problem for the baselines
## (baselines are always from lower_id to higher_id cams!)
#################################################################
#calibrate all cameras in pairs
for baseline_edge_id in self.optimal_baseline_edges:
#get the cam_nrs from the graph edge (calibrate from low to high id)
vertices = self.G.es[baseline_edge_id].tuple
if vertices[0]<vertices[1]:
camL_nr = vertices[0]
camH_nr = vertices[1]
else:
camL_nr = vertices[1]
camH_nr = vertices[0]
print "\t initializing camera pair ({0},{1})... ".format(camL_nr, camH_nr)
#run the pair extrinsic calibration
obs_list = self.obs_db.getAllObsTwoCams(camL_nr, camH_nr)
success, baseline_HL = kcc.stereoCalibrate(cameras[camL_nr],
cameras[camH_nr],
obs_list,
distortionActive=False)
if success:
sm.logDebug("baseline_{0}_{1}={2}".format(camL_nr, camH_nr, baseline_HL.T()))
else:
sm.logError("initialization of camera pair ({0},{1}) failed ".format(camL_nr, camH_nr))
sm.logError("estimated baseline_{0}_{1}={2}".format(camL_nr, camH_nr, baseline_HL.T()))
#store the baseline in the graph
self.G.es[ self.G.get_eid(camL_nr, camH_nr) ]["baseline_HL"] = baseline_HL
#################################################################
## STEP 3: transform from the "optimal" baseline chain to camera chain ordering
## (=> baseline_0 = T_c1_c0 |
#################################################################
#construct the optimal path graph
G_optimal_baselines = self.G.copy()
eid_not_optimal_path = set(range(0,len(G_optimal_baselines.es)))
for eid in self.optimal_baseline_edges:
eid_not_optimal_path.remove(eid)
G_optimal_baselines.delete_edges( eid_not_optimal_path )
#now we convert the arbitary baseline graph to baselines starting from
# cam0 and traverse the chain (cam0->cam1->cam2->camN)
baselines = []
for baseline_id in range(0, self.numCams-1):
#find the shortest path on the graph
path = G_optimal_baselines.get_shortest_paths(baseline_id, baseline_id+1)[0]
#get the baseline from cam with id baseline_id to baseline_id+1
baseline_HL = sm.Transformation()
for path_idx in range(0, len(path)-1):
source_vert = path[path_idx]
target_vert = path[path_idx+1]
T_edge = self.G.es[ self.G.get_eid(source_vert, target_vert) ]["baseline_HL"]
#correct the direction (baselines always from low to high cam id!)
T_edge = T_edge if source_vert<target_vert else T_edge.inverse()
#chain up
baseline_HL = T_edge * baseline_HL
#store in graph
baselines.append(baseline_HL)
#################################################################
## STEP 4: refine guess in full batch
#################################################################
success, baselines = kcc.solveFullBatch(cameras, baselines, self)
if not success:
sm.logWarn("Full batch refinement failed!")
return baselines