data_association.py

"""
As implemented in https://github.com/abewley/sort but with some modifications

For each detected item, it computes the intersection over union (IOU) w.r.t. each tracked object. (IOU matrix)
Then, it applies the Hungarian algorithm (via linear_assignment) to assign each det. item to the best possible
tracked item (i.e. to the one with max. IOU).

Note: a more recent approach uses a Deep Association Metric instead.
see https://github.com/nwojke/deep_sort
"""

import numpy as np
from numba import jit
from sklearn.utils.linear_assignment_ import linear_assignment


@jit
def iou(bb_test,bb_gt):
  """
  Computes IUO between two bboxes in the form [x1,y1,x2,y2]
  """
  xx1 = np.maximum(bb_test[0], bb_gt[0])
  yy1 = np.maximum(bb_test[1], bb_gt[1])
  xx2 = np.minimum(bb_test[2], bb_gt[2])
  yy2 = np.minimum(bb_test[3], bb_gt[3])
  w = np.maximum(0., xx2 - xx1)
  h = np.maximum(0., yy2 - yy1)
  wh = w * h
  o = wh / ((bb_test[2]-bb_test[0])*(bb_test[3]-bb_test[1])
    + (bb_gt[2]-bb_gt[0])*(bb_gt[3]-bb_gt[1]) - wh)
  return(o)



def associate_detections_to_trackers(detections,trackers,iou_threshold = 0.3):
  """
  Assigns detections to tracked object (both represented as bounding boxes)

  Returns 3 lists of matches, unmatched_detections and unmatched_trackers
  """
  if(len(trackers)==0):
    return np.empty((0,2),dtype=int), np.arange(len(detections)), np.empty((0,5),dtype=int)
  iou_matrix = np.zeros((len(detections),len(trackers)),dtype=np.float32)

  for d,det in enumerate(detections):
    for t,trk in enumerate(trackers):
      iou_matrix[d,t] = iou(det,trk)
  '''The linear assignment module tries to minimise the total assignment cost.
  In our case we pass -iou_matrix as we want to maximise the total IOU between track predictions and the frame detection.'''
  matched_indices = linear_assignment(-iou_matrix)

  unmatched_detections = []
  for d,det in enumerate(detections):
    if(d not in matched_indices[:,0]):
      unmatched_detections.append(d)
  unmatched_trackers = []
  for t,trk in enumerate(trackers):
    if(t not in matched_indices[:,1]):
      unmatched_trackers.append(t)

  #filter out matched with low IOU
  matches = []
  for m in matched_indices:
    if(iou_matrix[m[0],m[1]]<iou_threshold):
      unmatched_detections.append(m[0])
      unmatched_trackers.append(m[1])
    else:
      matches.append(m.reshape(1,2))
  if(len(matches)==0):
    matches = np.empty((0,2),dtype=int)
  else:
    matches = np.concatenate(matches,axis=0)

  return matches, np.array(unmatched_detections), np.array(unmatched_trackers)