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fS )a Solve linear assignment problem.
|
|
Parameters
|
----------
|
distance_metric : Callable[List[Track], List[Detection], List[int], List[int]) -> ndarray
|
The distance metric is given a list of tracks and detections as well as
|
a list of N track indices and M detection indices. The metric should
|
return the NxM dimensional cost matrix, where element (i, j) is the
|
association cost between the i-th track in the given track indices and
|
the j-th detection in the given detection_indices.
|
max_distance : float
|
Gating threshold. Associations with cost larger than this value are
|
disregarded.
|
tracks : List[track.Track]
|
A list of predicted tracks at the current time step.
|
detections : List[detection.Detection]
|
A list of detections at the current time step.
|
track_indices : List[int]
|
List of track indices that maps rows in `cost_matrix` to tracks in
|
`tracks` (see description above).
|
detection_indices : List[int]
|
List of detection indices that maps columns in `cost_matrix` to
|
detections in `detections` (see description above).
|
|
Returns
|
-------
|
(List[(int, int)], List[int], List[int])
|
Returns a tuple with the following three entries:
|
* A list of matched track and detection indices.
|
* A list of unmatched track indices.
|
* A list of unmatched detection indices.
|
|
Nr gñhãµøä>r )ÚnpÚarangeÚlenr Ú enumerateÚappend)Údistance_metricÚ max_distanceÚtracksÚ
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detectionsÚ track_indicesÚdetection_indicesÚ cost_matrixÚindicesÚmatchesÚunmatched_tracksÚunmatched_detectionsÚcolÚ detection_idxÚrowÚ track_idx© r úRD:\project\07_tracking_Re-id\04_s_yolo_tracking\lib\deep_sort\linear_assignment.pyÚmin_cost_matching s2 $
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7 }qBW t t|tdd |D } || |fS )a< Run matching cascade.
|
|
Parameters
|
----------
|
distance_metric : Callable[List[Track], List[Detection], List[int], List[int]) -> ndarray
|
The distance metric is given a list of tracks and detections as well as
|
a list of N track indices and M detection indices. The metric should
|
return the NxM dimensional cost matrix, where element (i, j) is the
|
association cost between the i-th track in the given track indices and
|
the j-th detection in the given detection indices.
|
max_distance : float
|
Gating threshold. Associations with cost larger than this value are
|
disregarded.
|
cascade_depth: int
|
The cascade depth, should be se to the maximum track age.
|
tracks : List[track.Track]
|
A list of predicted tracks at the current time step.
|
detections : List[detection.Detection]
|
A list of detections at the current time step.
|
track_indices : Optional[List[int]]
|
List of track indices that maps rows in `cost_matrix` to tracks in
|
`tracks` (see description above). Defaults to all tracks.
|
detection_indices : Optional[List[int]]
|
List of detection indices that maps columns in `cost_matrix` to
|
detections in `detections` (see description above). Defaults to all
|
detections.
|
|
Returns
|
-------
|
(List[(int, int)], List[int], List[int])
|
Returns a tuple with the following three entries:
|
* A list of matched track and detection indices.
|
* A list of unmatched track indices.
|
* A list of unmatched detection indices.
|
|
Nr c s" g | ]}| j d kr|qS )r )Útime_since_update)Ú.0Úk)Úlevelr r r ú
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<listcomp> s z$matching_cascade.<locals>.<listcomp>c s s | ]\}}|V qd S )Nr )r r Ú_r r r ú <genexpr> s z#matching_cascade.<locals>.<genexpr>)ÚlistÚranger r Úset) r r Z cascade_depthr r r r r r Ztrack_indices_lZ matches_lr" r r )r r r Úmatching_cascadeN s$ ' r' Fc st |rdnd}t j| } tj fdd|D }
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x@t|D ]4\} } || } | j| j| j|
|
|}||| || kf< q8W |S )ab Invalidate infeasible entries in cost matrix based on the state
|
distributions obtained by Kalman filtering.
|
|
Parameters
|
----------
|
kf : The Kalman filter.
|
cost_matrix : ndarray
|
The NxM dimensional cost matrix, where N is the number of track indices
|
and M is the number of detection indices, such that entry (i, j) is the
|
association cost between `tracks[track_indices[i]]` and
|
`detections[detection_indices[j]]`.
|
tracks : List[track.Track]
|
A list of predicted tracks at the current time step.
|
detections : List[detection.Detection]
|
A list of detections at the current time step.
|
track_indices : List[int]
|
List of track indices that maps rows in `cost_matrix` to tracks in
|
`tracks` (see description above).
|
detection_indices : List[int]
|
List of detection indices that maps columns in `cost_matrix` to
|
detections in `detections` (see description above).
|
gated_cost : Optional[float]
|
Entries in the cost matrix corresponding to infeasible associations are
|
set this value. Defaults to a very large value.
|
only_position : Optional[bool]
|
If True, only the x, y position of the state distribution is considered
|
during gating. Defaults to False.
|
|
Returns
|
-------
|
ndarray
|
Returns the modified cost matrix.
|
|
é é c s g | ]} | j qS r )Úto_xyah)r Úi)r r r r! ¸ s z$gate_cost_matrix.<locals>.<listcomp>)r Ú chi2inv95r Úasarrayr Úgating_distanceÚmeanÚ
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