# vim: expandtab:ts=4:sw=4
from __future__ import absolute_import
import numpy as np
from . import linear_assignment
def iou(bbox, candidates):
"""Computer intersection over union.
Parameters
----------
bbox : ndarray
A bounding box in format `(top left x, top left y, width, height)`.
candidates : ndarray
A matrix of candidate bounding boxes (one per row) in the same format
as `bbox`.
Returns
-------
ndarray
The intersection over union in [0, 1] between the `bbox` and each
candidate. A higher score means a larger fraction of the `bbox` is
occluded by the candidate.
"""
bbox_tl, bbox_br = bbox[:2], bbox[:2] + bbox[2:]
candidates_tl = candidates[:, :2]
candidates_br = candidates[:, :2] + candidates[:, 2:]
tl = np.c_[np.maximum(bbox_tl[0], candidates_tl[:, 0])[:, np.newaxis],
np.maximum(bbox_tl[1], candidates_tl[:, 1])[:, np.newaxis]]
br = np.c_[np.minimum(bbox_br[0], candidates_br[:, 0])[:, np.newaxis],
np.minimum(bbox_br[1], candidates_br[:, 1])[:, np.newaxis]]
wh = np.maximum(0., br - tl)
area_intersection = wh.prod(axis=1)
area_bbox = bbox[2:].prod()
area_candidates = candidates[:, 2:].prod(axis=1)
return area_intersection / (area_bbox + area_candidates - area_intersection)
def iou_cost(tracks, detections, track_indices=None,
detection_indices=None):
"""An intersection over union distance metric.
Parameters
----------
tracks : List[deep_sort.track.Track]
A list of tracks.
detections : List[deep_sort.detection.Detection]
A list of detections.
track_indices : Optional[List[int]]
A list of indices to tracks that should be matched. Defaults to
all `tracks`.
detection_indices : Optional[List[int]]
A list of indices to detections that should be matched. Defaults
to all `detections`.
Returns
-------
ndarray
Returns a cost matrix of shape
len(track_indices), len(detection_indices) where entry (i, j) is
`1 - iou(tracks[track_indices[i]], detections[detection_indices[j]])`.
"""
if track_indices is None:
track_indices = np.arange(len(tracks))
if detection_indices is None:
detection_indices = np.arange(len(detections))
cost_matrix = np.zeros((len(track_indices), len(detection_indices)))
for row, track_idx in enumerate(track_indices):
if tracks[track_idx].time_since_update > 1:
cost_matrix[row, :] = linear_assignment.INFTY_COST
continue
bbox = tracks[track_idx].to_tlwh()
candidates = np.asarray([detections[i].tlwh for i in detection_indices])
cost_matrix[row, :] = 1. - iou(bbox, candidates)
return cost_matrix
