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Improve handling of object matching

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Use `np.unique` to determine the correct set of row/col pairs to iterate
over when doing the object matching without needing to track which rows
or columns have already been seen.  Add to some of the accompanying
documentation to clarify this algorithm.

Also fix what looks to be an erroneous early return, and change this to
a continue.
This commit is contained in:
Sean Vig 2021-05-21 22:11:36 -04:00 committed by Blake Blackshear
parent 57864f2be6
commit 80f8256422
1 changed files with 23 additions and 34 deletions
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57
frigate/objects.py
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@ -82,56 +82,45 @@ class ObjectTracker:
if len(current_objects) == 0:
for index, obj in enumerate(group):
self.register(index, obj)
return
continue
new_centroids = np.array([o["centroid"] for o in group])
# 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
# goal will be to match each current centroid to a new
# object centroid
D = dist.cdist(current_centroids, new_centroids)
# 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
# in order to perform this matching we must (1) find the smallest
# value in each row (i.e. the distance from each current object to
# the closest new object) and then (2) sort the row indexes based
# on their minimum values so that the row with the smallest
# distance (the best match) is at the *front* of the index list
rows = D.min(axis=1).argsort()
# 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
# next, we determine which new object each existing object matched
# against, and apply the same sorting as was applied previously
cols = D.argmin(axis=1)[rows]
# 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()
# many current objects may register with each new object, so only
# match the closest ones. unique returns the indices of the first
# occurrences of each value, and because the rows are sorted by
# distance, this will be index of the closest match
_, index = np.unique(cols, return_index=True)
rows = rows[index]
cols = cols[index]
# 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
if row in usedRows or col in usedCols:
continue
# otherwise, grab the object ID for the current row,
# set its new centroid, and reset the disappeared
# counter
# loop over the combination of the (row, column) index tuples
for row, col in zip(rows, cols):
# grab the object ID for the current row, set its new centroid,
# and reset the disappeared counter
objectID = current_ids[row]
self.update(objectID, group[col])
# indicate that we have examined each of the row and
# column indexes, respectively
usedRows.add(row)
usedCols.add(col)
# compute the 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)
# compute the row and column indices we have NOT yet examined
unusedRows = set(range(D.shape[0])).difference(rows)
unusedCols = set(range(D.shape[1])).difference(cols)
# in the event that the number of object centroids is
# equal or greater than the number of input centroids