Inheritance diagram for deep_sort.nn_matching.NearestNeighborDistanceMetric:
Public Member Functions | |
| def | __init__ (self, metric, matching_threshold, budget=None) |
| def | distance (self, features, targets) |
| def | partial_fit (self, features, targets, active_targets) |
Public Attributes | |
| budget | |
| matching_threshold | |
| samples | |
 Public Attributes inherited from object | |
| pointer | iv [2] |
Private Attributes | |
| _metric | |
Detailed Description
A nearest neighbor distance metric that, for each target, returns
the closest distance to any sample that has been observed so far.
Parameters
----------
metric : str
Either "euclidean" or "cosine".
matching_threshold: float
The matching threshold. Samples with larger distance are considered an
invalid match.
budget : Optional[int]
If not None, fix samples per class to at most this number. Removes
the oldest samples when the budget is reached.
Attributes
----------
samples : Dict[int -> List[ndarray]]
A dictionary that maps from target identities to the list of samples
that have been observed so far.
Definition at line 99 of file nn_matching.py.
Constructor & Destructor Documentation
| def deep_sort.nn_matching.NearestNeighborDistanceMetric.__init__ | ( | self, | |
| metric, | |||
| matching_threshold, | |||
budget = None |
|||
| ) |
Definition at line 123 of file nn_matching.py.
Member Function Documentation
| def deep_sort.nn_matching.NearestNeighborDistanceMetric.distance | ( | self, | |
| features, | |||
| targets | |||
| ) |
Compute distance between features and targets.
Parameters
----------
features : ndarray
An NxM matrix of N features of dimensionality M.
targets : List[int]
A list of targets to match the given `features` against.
Returns
-------
ndarray
Returns a cost matrix of shape len(targets), len(features), where
element (i, j) contains the closest squared distance between
`targets[i]` and `features[j]`.
Definition at line 156 of file nn_matching.py.
| def deep_sort.nn_matching.NearestNeighborDistanceMetric.partial_fit | ( | self, | |
| features, | |||
| targets, | |||
| active_targets | |||
| ) |
Update the distance metric with new data.
Parameters
----------
features : ndarray
An NxM matrix of N features of dimensionality M.
targets : ndarray
An integer array of associated target identities.
active_targets : List[int]
A list of targets that are currently present in the scene.
Definition at line 137 of file nn_matching.py.
Member Data Documentation
|
private |
Definition at line 127 of file nn_matching.py.
| deep_sort.nn_matching.NearestNeighborDistanceMetric.budget |
Definition at line 134 of file nn_matching.py.
| deep_sort.nn_matching.NearestNeighborDistanceMetric.matching_threshold |
Definition at line 133 of file nn_matching.py.
| deep_sort.nn_matching.NearestNeighborDistanceMetric.samples |
Definition at line 135 of file nn_matching.py.
The documentation for this class was generated from the following file: