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