mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-11-21 19:07:46 +01:00
group by label before tracking objects
This commit is contained in:
Blake Blackshear
parent
32b212c7b6
commit
be5a114f6a
@ -251,6 +251,7 @@ class ObjectTracker(threading.Thread):
|
||||
|
||||
def register(self, index, obj):
|
||||
id = f"{str(obj['frame_time'])}-{index}"
|
||||
obj['id'] = id
|
||||
self.tracked_objects[id] = obj
|
||||
self.disappeared[id] = 0
|
||||
|
||||
@ -280,96 +281,106 @@ class ObjectTracker(threading.Thread):
|
||||
# return early as there are no centroids or tracking info
|
||||
# to update
|
||||
return
|
||||
|
||||
# compute centroids
|
||||
|
||||
# group by name
|
||||
new_object_groups = defaultdict(lambda: [])
|
||||
for obj in new_objects:
|
||||
centroid_x = int((obj['box']['xmin']+obj['box']['xmax']) / 2.0)
|
||||
centroid_y = int((obj['box']['ymin']+obj['box']['ymax']) / 2.0)
|
||||
obj['centroid'] = (centroid_x, centroid_y)
|
||||
|
||||
if len(self.tracked_objects) == 0:
|
||||
for index, obj in enumerate(new_objects):
|
||||
self.register(index, obj)
|
||||
return
|
||||
new_object_groups[obj['name']].append(obj)
|
||||
|
||||
new_centroids = np.array([o['centroid'] for o in new_objects])
|
||||
current_ids = list(self.tracked_objects.keys())
|
||||
current_centroids = np.array([o['centroid'] for o in self.tracked_objects.values()])
|
||||
# track objects for each label type
|
||||
# TODO: this is going to miss deregistering objects that are not in the new groups
|
||||
for label, group in new_object_groups.items():
|
||||
current_objects = [o for o in self.tracked_objects.values() if o['name'] == label]
|
||||
current_ids = [o['id'] for o in current_objects]
|
||||
current_centroids = np.array([o['centroid'] for o in current_objects])
|
||||
|
||||
# compute the distance between each pair of tracked
|
||||
# centroids and new centroids, respectively -- our
|
||||
# goal will be to match each new centroid to an existing
|
||||
# object centroid
|
||||
D = dist.cdist(current_centroids, new_centroids)
|
||||
# compute centroids
|
||||
for obj in group:
|
||||
centroid_x = int((obj['box']['xmin']+obj['box']['xmax']) / 2.0)
|
||||
centroid_y = int((obj['box']['ymin']+obj['box']['ymax']) / 2.0)
|
||||
obj['centroid'] = (centroid_x, centroid_y)
|
||||
|
||||
# in order to perform this matching we must (1) find the
|
||||
# smallest value in each row and then (2) sort the row
|
||||
# indexes based on their minimum values so that the row
|
||||
# with the smallest value is at the *front* of the index
|
||||
# list
|
||||
rows = D.min(axis=1).argsort()
|
||||
if len(current_objects) == 0:
|
||||
for index, obj in enumerate(group):
|
||||
self.register(index, obj)
|
||||
return
|
||||
|
||||
new_centroids = np.array([o['centroid'] for o in group])
|
||||
|
||||
# next, we perform a similar process on the columns by
|
||||
# finding the smallest value in each column and then
|
||||
# sorting using the previously computed row index list
|
||||
cols = D.argmin(axis=1)[rows]
|
||||
# compute the distance between each pair of tracked
|
||||
# centroids and new centroids, respectively -- our
|
||||
# goal will be to match each new centroid to an existing
|
||||
# object centroid
|
||||
D = dist.cdist(current_centroids, new_centroids)
|
||||
|
||||
# in order to determine if we need to update, register,
|
||||
# or deregister an object we need to keep track of which
|
||||
# of the rows and column indexes we have already examined
|
||||
usedRows = set()
|
||||
usedCols = set()
|
||||
# in order to perform this matching we must (1) find the
|
||||
# smallest value in each row and then (2) sort the row
|
||||
# indexes based on their minimum values so that the row
|
||||
# with the smallest value is at the *front* of the index
|
||||
# list
|
||||
rows = D.min(axis=1).argsort()
|
||||
|
||||
# loop over the combination of the (row, column) index
|
||||
# tuples
|
||||
for (row, col) in zip(rows, cols):
|
||||
# if we have already examined either the row or
|
||||
# column value before, ignore it
|
||||
# val
|
||||
if row in usedRows or col in usedCols:
|
||||
continue
|
||||
# next, we perform a similar process on the columns by
|
||||
# finding the smallest value in each column and then
|
||||
# sorting using the previously computed row index list
|
||||
cols = D.argmin(axis=1)[rows]
|
||||
|
||||
# otherwise, grab the object ID for the current row,
|
||||
# set its new centroid, and reset the disappeared
|
||||
# counter
|
||||
objectID = current_ids[row]
|
||||
self.update(objectID, new_objects[col])
|
||||
self.disappeared[objectID] = 0
|
||||
# in order to determine if we need to update, register,
|
||||
# or deregister an object we need to keep track of which
|
||||
# of the rows and column indexes we have already examined
|
||||
usedRows = set()
|
||||
usedCols = set()
|
||||
|
||||
# indicate that we have examined each of the row and
|
||||
# column indexes, respectively
|
||||
usedRows.add(row)
|
||||
usedCols.add(col)
|
||||
# loop over the combination of the (row, column) index
|
||||
# tuples
|
||||
for (row, col) in zip(rows, cols):
|
||||
# if we have already examined either the row or
|
||||
# column value before, ignore it
|
||||
# val
|
||||
if row in usedRows or col in usedCols:
|
||||
continue
|
||||
|
||||
# compute both the row and column index we have NOT yet
|
||||
# examined
|
||||
unusedRows = set(range(0, D.shape[0])).difference(usedRows)
|
||||
unusedCols = set(range(0, D.shape[1])).difference(usedCols)
|
||||
|
||||
# in the event that the number of object centroids is
|
||||
# equal or greater than the number of input centroids
|
||||
# we need to check and see if some of these objects have
|
||||
# potentially disappeared
|
||||
if D.shape[0] >= D.shape[1]:
|
||||
# loop over the unused row indexes
|
||||
for row in unusedRows:
|
||||
# grab the object ID for the corresponding row
|
||||
# index and increment the disappeared counter
|
||||
# otherwise, grab the object ID for the current row,
|
||||
# set its new centroid, and reset the disappeared
|
||||
# counter
|
||||
objectID = current_ids[row]
|
||||
self.disappeared[objectID] += 1
|
||||
self.update(objectID, new_objects[col])
|
||||
self.disappeared[objectID] = 0
|
||||
|
||||
# check to see if the number of consecutive
|
||||
# frames the object has been marked "disappeared"
|
||||
# for warrants deregistering the object
|
||||
if self.disappeared[objectID] > self.max_disappeared:
|
||||
self.deregister(objectID)
|
||||
# indicate that we have examined each of the row and
|
||||
# column indexes, respectively
|
||||
usedRows.add(row)
|
||||
usedCols.add(col)
|
||||
|
||||
# otherwise, if the number of input centroids is greater
|
||||
# than the number of existing object centroids we need to
|
||||
# register each new input centroid as a trackable object
|
||||
else:
|
||||
for col in unusedCols:
|
||||
self.register(col, new_objects[col])
|
||||
# compute both the row and column index we have NOT yet
|
||||
# examined
|
||||
unusedRows = set(range(0, D.shape[0])).difference(usedRows)
|
||||
unusedCols = set(range(0, D.shape[1])).difference(usedCols)
|
||||
|
||||
# in the event that the number of object centroids is
|
||||
# equal or greater than the number of input centroids
|
||||
# we need to check and see if some of these objects have
|
||||
# potentially disappeared
|
||||
if D.shape[0] >= D.shape[1]:
|
||||
# loop over the unused row indexes
|
||||
for row in unusedRows:
|
||||
# grab the object ID for the corresponding row
|
||||
# index and increment the disappeared counter
|
||||
objectID = current_ids[row]
|
||||
self.disappeared[objectID] += 1
|
||||
|
||||
# check to see if the number of consecutive
|
||||
# frames the object has been marked "disappeared"
|
||||
# for warrants deregistering the object
|
||||
if self.disappeared[objectID] > self.max_disappeared:
|
||||
self.deregister(objectID)
|
||||
|
||||
# otherwise, if the number of input centroids is greater
|
||||
# than the number of existing object centroids we need to
|
||||
# register each new input centroid as a trackable object
|
||||
else:
|
||||
for col in unusedCols:
|
||||
self.register(col, new_objects[col])
|
||||
|
||||
# Maintains the frame and object with the highest score
|
||||
class BestFrames(threading.Thread):
|
||||
|
||||
Loading…
Reference in New Issue
Block a user