def __init__(self, max_object_distance=50, max_node_degree=3,
max_frame_gap=3):
super(Tracker, self).__init__()
if max_frame_gap < 1 or not isinstance(max_frame_gap, int):
raise ValueError("max_frame_gap must be a positive integer")
self.graph = Graph()
self._frame_data = OrderedDict()
self.max_frame_gap = max_frame_gap
self.max_object_distance = max_object_distance
self.max_node_degree = max_node_degree
def clone_graph(self, timeholder, channel, region):
"""Clone the tracking graph with data from a different channel."""
ngraph = Graph()
# add nodes from other segmentation region
for nodeid in self.graph.node_list():
iframe, objid = Tracker.split_nodeid(nodeid)[:2]
sample = timeholder[iframe][channel].get_region(region)[objid]
ngraph.add_node(nodeid, sample)
for edge in self.graph.edges.values():
ngraph.add_edge(*edge)
return ngraph
def __init__(self, max_object_distance=50, max_node_degree=3,
max_frame_gap=3):
super(Tracker, self).__init__()
if max_frame_gap < 1 or not isinstance(max_frame_gap, int):
raise ValueError("max_frame_gap must be a positive integer")
self.graph = Graph()
self._frame_data = OrderedDict()
self.max_frame_gap = max_frame_gap
self.max_object_distance = max_object_distance
self.max_node_degree = max_node_degree
def clone_graph(self, timeholder, channel, region):
"""Clone the tracking graph with data from a different channel."""
ngraph = Graph()
# add nodes from other segmentation region
for nodeid in self.graph.node_list():
iframe, objid = Tracker.split_nodeid(nodeid)[:2]
sample = timeholder[iframe][channel].get_region(region)[objid]
ngraph.add_node(nodeid, sample)
for edge in self.graph.edges.values():
ngraph.add_edge(*edge)
return ngraph
def __init__(self):
graph = Graph()
name = SectionObjectdetection.SECTION_NAME
graph.add_node(1, (name, 'primary_image',
('raw_images_finished', [1])))
graph.add_node(2, (name, 'primary_segmentation',
('raw_images_finished', 1)))
graph.add_node(3, (name, 'secondary_image',
('raw_images_finished', [2])))
graph.add_node(4, (name, 'secondary_segmentation'))
graph.add_node(5, (name, 'secondary_registration'))
name = SectionClassification.SECTION_NAME
graph.add_node(6, (name, 'primary_features'))
graph.add_node(7, (name, 'primary_classification'))
graph.add_node(11, (name, 'secondary_features'))
graph.add_node(12, (name, 'secondary_classification'))
graph.add_edge(1, 2)
graph.add_edge(3, 4)
graph.add_edge(2, 4)
graph.add_edge(5, 1)
graph.add_edge(5, 3)
graph.add_edge(2, 6)
graph.add_edge(6, 7)
graph.add_edge(4, 11)
graph.add_edge(11, 12)
self._graph = graph
self._start = 5
class Tracker(LoggerObject):
__slots__ = [
'graph', '_frame_data', 'max_object_distance', 'max_node_degree',
'max_frame_gap'
]
def __init__(self,
max_object_distance=50,
max_node_degree=3,
max_frame_gap=3):
super(Tracker, self).__init__()
if max_frame_gap < 1 or not isinstance(max_frame_gap, int):
raise ValueError("max_frame_gap must be a positive integer")
self.graph = Graph()
self._frame_data = OrderedDict()
self.max_frame_gap = max_frame_gap
self.max_object_distance = max_object_distance
self.max_node_degree = max_node_degree
@property
def start_frame(self):
"""Return the index of the first frame."""
return min(self._frame_data.keys())
@property
def end_frame(self):
"""Returns the index of the last frame currently processed."""
return max(self._frame_data.keys())
# rename function to frames
@property
def frames(self):
"""Returns a dict with frame indices as keys and lists of
samples as values."""
return self._frame_data
@staticmethod
def node_id(frame, object_label):
return '%d_%s' % (frame, object_label)
@staticmethod
def split_nodeid(nodeid):
return tuple([int(i) for i in nodeid.split('_')])
def track_next_frame(self, frame, samples):
self._frame_data.setdefault(frame, [])
for label, sample in samples.iteritems():
node_id = self.node_id(frame, label)
self.graph.add_node(node_id, sample)
self._frame_data[frame].append(label)
# connect time point only if any object is present
if len(self._frame_data[frame]) > 0:
self.connect_nodes(frame)
def closest_preceding_frame(self, frame):
"""Return the preceding frame or None if the gap between the to frames
is larget than max_frame_gap."""
frames = self._frame_data.keys()
icurrent = frames.index(frame)
pre_frame = None
if (0 < icurrent < len(frames)):
pre_frame = frames[icurrent - 1]
if pre_frame is not None and (frame - pre_frame) > self.max_frame_gap:
pre_frame = None
return pre_frame
def clone_graph(self, timeholder, channel, region):
"""Clone the tracking graph with data from a different channel."""
ngraph = Graph()
# add nodes from other segmentation region
for nodeid in self.graph.node_list():
iframe, objid = Tracker.split_nodeid(nodeid)[:2]
sample = timeholder[iframe][channel].get_region(region)[objid]
ngraph.add_node(nodeid, sample)
for edge in self.graph.edges.values():
ngraph.add_edge(*edge)
return ngraph
def render_tracks(self, frame, size, n=5, thick=True, radius=3):
img_conn = ccore.Image(*size)
img_split = ccore.Image(*size)
if n < 0 or frame - n + 1 < self.start_frame:
current = self.start_frame
n = frame - current + 1
else:
current = frame - n + 1
found = False
for i in range(n):
col = int(max(255. * (i + 1) / n, 255))
if current in self._frame_data:
preframe = self.closest_preceding_frame(current)
if preframe is not None:
found = True
for objIdP in self._frame_data[preframe]:
nodeIdP = self.node_id(preframe, objIdP)
objP = self.graph.node_data(nodeIdP)
if self.graph.out_degree(nodeIdP) > 1:
img = img_split
else:
img = img_conn
for edgeId in self.graph.out_arcs(nodeIdP):
nodeIdC = self.graph.tail(edgeId)
objC = self.graph.node_data(nodeIdC)
ccore.drawLine(ccore.Diff2D(*objP.oCenterAbs),
ccore.Diff2D(*objC.oCenterAbs),
img,
col,
thick=thick)
ccore.drawFilledCircle(
ccore.Diff2D(*objC.oCenterAbs), radius,
img_conn, col)
current += 1
if not found and frame in self._frame_data:
for objId in self._frame_data[frame]:
nodeId = self.node_id(frame, objId)
obj = self.graph.node_data(nodeId)
ccore.drawFilledCircle(ccore.Diff2D(*obj.oCenterAbs), radius,
img_conn, col)
return img_conn, img_split
def connect_nodes(self, iT):
max_dist2 = math.pow(self.max_object_distance, 2)
bReturnSuccess = False
# search all nodes in the previous frame
# if there is an empty frame, look for the closest frame
# that contains objects. For this go up to iMaxTrackingGap
# into the past.
iPreviousT = self.closest_preceding_frame(iT)
if iPreviousT is not None:
bReturnSuccess = True
dctMerges = {}
dctSplits = {}
# for all nodes in this layer
for iObjIdP in self._frame_data[iPreviousT]:
strNodeIdP = self.node_id(iPreviousT, iObjIdP)
oImageObjectP = self.graph.node_data(strNodeIdP)
lstNearest = []
for iObjIdC in self._frame_data[iT]:
strNodeIdC = self.node_id(iT, iObjIdC)
oImageObjectC = self.graph.node_data(strNodeIdC)
dist = oImageObjectC.squaredMagnitude(oImageObjectP)
# take all candidates within a certain distance
if dist < max_dist2:
lstNearest.append((dist, strNodeIdC))
# lstNearest is the list of nodes in the current frame
# whose distance to the previous node is smaller than the
# fixed threshold.
if len(lstNearest) > 0:
# sort ascending by distance (first tuple element)
lstNearest.sort(key=lambda x: x[0])
# take only a certain number as merge candidates (the N closest)
# and split candidates (this number is identical).
for dist, strNodeIdC in lstNearest[:self.max_node_degree]:
try:
dctMerges[strNodeIdC].append((dist, strNodeIdP))
except KeyError:
dctMerges[strNodeIdC] = [(dist, strNodeIdP)]
try:
dctSplits[strNodeIdP].append((dist, strNodeIdC))
except KeyError:
dctSplits[strNodeIdP] = [(dist, strNodeIdC)]
# dctSplits contains for each node the list of potential
# successors with distance smaller than threshold.
# dctMerges contains for each node the list of potential
# predecessors with distance smaller than threshold.
# prevent split and merge for one node at the same time
for id_c in dctMerges:
found_connection = False
nodes = dctMerges[id_c]
# for all objects that have only one predecessor within the defined radius,
# take this predecessor.
if len(nodes) == 1:
self.graph.add_edge(nodes[0][1], id_c)
found_connection = True
else:
# If there are several candidates (previous objects fulfilling the condition)
# take those candidates in the previous frame that have only one possible
# predecessor.
for dist, id_p in nodes:
if len(dctSplits[id_p]) == 1:
self.graph.add_edge(id_p, id_c)
found_connection = True
# if there was no connection found, take the closest predecessor,
# unless there is none.
if not found_connection:
if len(nodes) > 0:
self.graph.add_edge(nodes[0][1], id_c)
return iPreviousT, bReturnSuccess
class Tracker(LoggerObject):
__slots__ = ['graph', '_frame_data','max_object_distance',
'max_node_degree', 'max_frame_gap']
def __init__(self, max_object_distance=50, max_node_degree=3,
max_frame_gap=3):
super(Tracker, self).__init__()
if max_frame_gap < 1 or not isinstance(max_frame_gap, int):
raise ValueError("max_frame_gap must be a positive integer")
self.graph = Graph()
self._frame_data = OrderedDict()
self.max_frame_gap = max_frame_gap
self.max_object_distance = max_object_distance
self.max_node_degree = max_node_degree
@property
def start_frame(self):
"""Return the index of the first frame."""
return min(self._frame_data.keys())
@property
def end_frame(self):
"""Returns the index of the last frame currently processed."""
return max(self._frame_data.keys())
# rename function to frames
@property
def frames(self):
"""Returns a dict with frame indices as keys and lists of
samples as values."""
return self._frame_data
@staticmethod
def node_id(frame, object_label):
return '%d_%s' %(frame, object_label)
@staticmethod
def split_nodeid(nodeid):
return tuple([int(i) for i in nodeid.split('_')])
def track_next_frame(self, frame, samples):
self._frame_data.setdefault(frame, [])
for label, sample in samples.iteritems():
node_id = self.node_id(frame, label)
self.graph.add_node(node_id, sample)
self._frame_data[frame].append(label)
# connect time point only if any object is present
if len(self._frame_data[frame]) > 0:
self.connect_nodes(frame)
def closest_preceding_frame(self, frame):
"""Return the preceding frame or None if the gap between the to frames
is larget than max_frame_gap."""
frames = self._frame_data.keys()
icurrent = frames.index(frame)
pre_frame = None
if (0 < icurrent < len(frames)):
pre_frame = frames[icurrent-1]
if pre_frame is not None and (frame - pre_frame) > self.max_frame_gap:
pre_frame = None
return pre_frame
def render_tracks(self, frame, size, n=5, thick=True, radius=3):
img_conn = ccore.Image(*size)
img_split = ccore.Image(*size)
if n < 0 or frame-n+1 < self.start_frame:
current = self.start_frame
n = frame-current+1
else:
current = frame-n+1
found = False
for i in range(n):
col = int(max(255.*(i+1)/n, 255))
if current in self._frame_data:
preframe = self.closest_preceding_frame(current)
if preframe is not None:
found = True
for objIdP in self._frame_data[preframe]:
nodeIdP = self.node_id(preframe, objIdP)
objP = self.graph.node_data(nodeIdP)
if self.graph.out_degree(nodeIdP) > 1:
img = img_split
else:
img = img_conn
for edgeId in self.graph.out_arcs(nodeIdP):
nodeIdC = self.graph.tail(edgeId)
objC = self.graph.node_data(nodeIdC)
ccore.drawLine(ccore.Diff2D(*objP.oCenterAbs),
ccore.Diff2D(*objC.oCenterAbs),
img, col,
thick=thick)
ccore.drawFilledCircle(ccore.Diff2D(*objC.oCenterAbs),
radius, img_conn, col)
current += 1
if not found and frame in self._frame_data:
for objId in self._frame_data[frame]:
nodeId = self.node_id(frame, objId)
obj = self.graph.node_data(nodeId)
ccore.drawFilledCircle(ccore.Diff2D(*obj.oCenterAbs),
radius, img_conn, col)
return img_conn, img_split
def connect_nodes(self, iT):
max_dist2 = math.pow(self.max_object_distance, 2)
bReturnSuccess = False
oGraph = self.graph
# search all nodes in the previous frame
# if there is an empty frame, look for the closest frame
# that contains objects. For this go up to iMaxTrackingGap
# into the past.
iPreviousT = self.closest_preceding_frame(iT)
if not iPreviousT is None:
bReturnSuccess = True
dctMerges = {}
dctSplits = {}
# for all nodes in this layer
for iObjIdP in self._frame_data[iPreviousT]:
strNodeIdP = self.node_id(iPreviousT, iObjIdP)
oImageObjectP = oGraph.node_data(strNodeIdP)
lstNearest = []
for iObjIdC in self._frame_data[iT]:
strNodeIdC = self.node_id(iT, iObjIdC)
oImageObjectC = oGraph.node_data(strNodeIdC)
dist = oImageObjectC.squaredMagnitude(oImageObjectP)
# take all candidates within a certain distance
if dist < max_dist2:
lstNearest.append((dist, strNodeIdC))
# lstNearest is the list of nodes in the current frame
# whose distance to the previous node is smaller than the
# fixed threshold.
if len(lstNearest) > 0:
# sort ascending by distance (first tuple element)
lstNearest.sort(key=lambda x: x[0])
# take only a certain number as merge candidates (the N closest)
# and split candidates (this number is identical).
for dist, strNodeIdC in lstNearest[:self.max_node_degree]:
try:
dctMerges[strNodeIdC].append((dist, strNodeIdP))
except KeyError:
dctMerges[strNodeIdC] = [(dist, strNodeIdP)]
try:
dctSplits[strNodeIdP].append((dist, strNodeIdC))
except KeyError:
dctSplits[strNodeIdP] = [(dist, strNodeIdC)]
# dctSplits contains for each node the list of potential
# successors with distance smaller than threshold.
# dctMerges contains for each node the list of potential
# predecessors with distance smaller than threshold.
# prevent split and merge for one node at the same time
for id_c in dctMerges:
found_connection = False
nodes = dctMerges[id_c]
# for all objects that have only one predecessor within the defined radius,
# take this predecessor.
if len(nodes) == 1:
oGraph.add_edge(nodes[0][1], id_c)
found_connection = True
else:
# If there are several candidates (previous objects fulfilling the condition)
# take those candidates in the previous frame that have only one possible
# predecessor.
for dist, id_p in nodes:
if len(dctSplits[id_p]) == 1:
oGraph.add_edge(id_p, id_c)
found_connection = True
# if there was no connection found, take the closest predecessor,
# unless there is none.
if not found_connection:
if len(nodes) > 0:
oGraph.add_edge(nodes[0][1], id_c)
return iPreviousT, bReturnSuccess