_, img = vc.read()
if img is None:
break
img = cv2.resize(img, (int(img.shape[1]*scale_factor), int(img.shape[0]*scale_factor)))
# detect ball in frame (just one for now)
bboxes = detector.detect(img)
bb = None
if bboxes is not None and len(bboxes)>0:
for bb in bboxes:
cv2.rectangle(img, (bb[0], bb[1]), (bb[2], bb[3]), (0, 255, 0), 2)
pass
# assume only 1 object
bb = bboxes[0]
# update kalman measurement
pred_bb = kf.get_predicted_bb()
if bb is not None:
# if we have a measurement, correct using measurement
corr_bb = kf.correct(bb)
num_missed_det = 0
else:
# if no measurement found, use prediction as measurement to correct
if num_missed_det < max_num_missed_det:
corr_bb = kf.correct(pred_bb)
num_missed_det += 1 # count as missed detection
else:
corr_bb = None
# add bbox to track
if num_missed_det < max_num_missed_det:
tracked_bbox.append(corr_bb)
else:
class Track(object):
def __init__(self):
# each history entry is numpy array [frame_id, bbox_x1, bbox_y1, bbox_x2, bbox_y2, fvec_1...128]
self.history = np.array([])
self.num_misses = 0 # num of missed assignments
self.max_misses = const.MAX_NUM_MISSES_TRACK
self.has_match = False
self.delete_me = False # set for manual deletion
self.kalman = KalmanFilter()
color = tuple(np.random.random_integers(0, 255, size=3))
self.drawing_color = color
self.predicted_next_bb = None
self.LENGTH_ESTABLISHED = 1 # number of sequential detections before we consider it a track
self.uid = uuid.uuid4()
def get_length(self):
return self.history.shape[0]
def is_singular(self):
return True if self.history.shape[
0] == self.LENGTH_ESTABLISHED else False
def is_established(self):
return True if self.history.shape[
0] > self.LENGTH_ESTABLISHED else False
def is_empty(self):
return True if self.history.shape[0] == 0 else False
def is_dead(self):
return True if (self.num_misses >= self.max_misses
or self.delete_me is True) else False
def propagate_track(self, frame_id):
# propagate track as if there was a detection, perhaps object is temporarily occluded or not detected
# use predicted bb as measurement
det = Detection.det_from_numpy_array(
self.history[self.history.shape[0] - 1])
det.bbox = self.get_predicted_next_bb()
# TODO: adjust bbox to have same width height but only propegate x,y centroid position
# pred_c_x, pred_c_y = util.centroid_from_bb(self.get_predicted_next_bb())
# wid, ht = util.wid_ht_from_bb(det.bbox)
# det.bbox = np.array([pred_c_x - wid / 2,
# pred_c_y - ht / 2,
# pred_c_x + wid / 2,
# pred_c_y + ht / 2], dtype=np.int32)
det.frame_id = frame_id
self.add_to_track(det)
def get_predicted_next_bb(self):
# # get a prediction using the latest history as a measurement
# measurement = np.array([self.get_latest_bb()], dtype=np.float32).T
return self.kalman.get_predicted_bb()
def add_to_track(self, det):
# use detection measurement to predict and correct the kalman filter
corrected_bb = self.kalman.correct(det.bbox)
# use corrected bbox
det.bbox = corrected_bb
# increment detections number of matches (could be assigned to several tracks, need to keep track of this)
det.num_matches += 1
new_history = det.as_numpy_array()
# print new_history
if self.history.size > 0:
self.history = np.vstack((self.history, new_history))
else:
self.history = new_history
def get_latest_fvec(self):
# get feature vector from the latest detection in the track (already computed during detection phase)
return self.history[self.history.shape[0] - 1][5:]
def get_latest_bb(self):
return self.history[self.history.shape[0] - 1][1:5]
def draw_history(self, img, draw_at_bottom=False):
if self.history.shape[0] > 1:
bb_latest = self.get_latest_bb()
cv2.rectangle(img, (int(bb_latest[0]), int(bb_latest[1])),
(int(bb_latest[2]), int(bb_latest[3])),
self.drawing_color, 2)
# iterate through detection history
prev_bb = self.history[0][1:5]
for det in self.history:
bb = det[1:5]
# cv2.rectangle(img, tuple(bb[:2]),tuple(bb[2:]),(255,0,0),2)
centroid = (int(bb[0] + (bb[2] - bb[0]) / 2),
int(bb[1] + (bb[3] - bb[1]) / 2))
bottom = (int(bb[0] + (bb[2] - bb[0]) / 2), int(bb[3]))
bottom_prev = (int(prev_bb[0] + (prev_bb[2] - prev_bb[0]) / 2),
int(prev_bb[3]))
# cv2.circle(img, centroid, 5, self.drawing_color, 2)
# cv2.circle(img, bottom, 3, self.drawing_color, 2)
cv2.line(img, bottom_prev, bottom, self.drawing_color, 4)
prev_bb = bb
