def assign_detections_to_trackers(trackers,
detections,
label_indices,
iou_thrd=0.3):
'''
From current list of trackers and new detections, output matched detections,
unmatchted trackers, unmatched detections.
'''
IOU_mat = np.zeros((len(trackers), len(detections)), dtype=np.float32)
for t, trk in enumerate(trackers):
#trk = convert_to_cv2bbox(trk)
for d, det in enumerate(detections):
# det = convert_to_cv2bbox(det)
IOU_mat[t, d] = helpers.box_iou2(trk, det)
# Produces matches
# Solve the maximizing the sum of IOU assignment problem using the
# Hungarian algorithm (also known as Munkres algorithm)
indices = linear_sum_assignment(-IOU_mat)
indices = np.asarray(indices)
matched_idx = np.transpose(indices)
unmatched_trackers, unmatched_detections, unmatched_indices = [], [], []
for t, trk in enumerate(trackers):
if (t not in matched_idx[:, 0]):
unmatched_trackers.append(t)
for d, det in enumerate(detections):
if (d not in matched_idx[:, 1]):
unmatched_detections.append(d)
unmatched_indices.append(label_indices[d])
matches = []
# For creating trackers we consider any detection with an
# overlap less than iou_thrd to signifiy the existence of
# an untracked object
for m in matched_idx:
if (IOU_mat[m[0], m[1]] < iou_thrd):
unmatched_trackers.append(m[0])
unmatched_detections.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), unmatched_indices
def assign_detections_to_trackers(trackers, detections, iou_thrd=0.3):
'''
From current list of trackers and new detections, output matched detections,
unmatchted trackers, unmatched detections.
'''
IOU_mat = np.zeros((len(trackers), len(detections)), dtype=np.float32)
# at the end of this loop, each tracker box should be paired with all detection boxes, with the corresponding IOU score stored at that position.
for t, trk in enumerate(trackers):
#trk = convert_to_cv2bbox(trk)
for d, det in enumerate(detections):
# det = convert_to_cv2bbox(det)
IOU_mat[t, d] = helpers.box_iou2(
trk, det) # get IOU score of tracker box and detection box.
# Matching the tracker boxes to each detection now becomes an Assignment Problem. To solve this,
# use the Hungarian algorithm (also known as Munkres algorithm)
# The IOU_mat creates a very useful real-valued matrix C. The cost function used was the IOU.
# return matches as a pair. Tracker on left side, Detection on right side.
IOU_mat = 1 / (IOU_mat + 1) # offset by 1 to prevent division by zero
matched_idx = linear_assignment(IOU_mat)
unmatched_trackers, unmatched_detections = [], []
for t, trk in enumerate(trackers):
if (t not in matched_idx[:, 0]):
unmatched_trackers.append(t)
for d, det in enumerate(detections):
if (d not in matched_idx[:, 1]):
unmatched_detections.append(d)
matches = []
# For creating trackers we consider any detection with an
# overlap less than iou_thrd to signifiy the existence of
# an untracked object
for m in matched_idx:
if (IOU_mat[m[0], m[1]] < iou_thrd):
unmatched_trackers.append(m[0])
unmatched_detections.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) # need make sure it is a numpy array
return matches, np.array(unmatched_detections), np.array(
unmatched_trackers)
def assign_detections_to_trackers(trackers, detections, iou_thrd=0.3):
'''
从当前的跟踪器和新检测列表中 未匹配的追踪器，不匹配的检测。
'''
IOU_mat = np.zeros((len(trackers), len(detections)), dtype=np.float32)
for t, trk in enumerate(trackers):
# trk = convert_to_cv2bbox(trk)
for d, det in enumerate(detections):
# det = convert_to_cv2bbox(det)
IOU_mat[t, d] = helpers.box_iou2(trk, det)
matched_idx = linear_assignment(-IOU_mat)
unmatched_trackers, unmatched_detections = [], []
for t, trk in enumerate(trackers):
if (t not in matched_idx[:, 0]):
unmatched_trackers.append(t)
for d, det in enumerate(detections):
if (d not in matched_idx[:, 1]):
unmatched_detections.append(d)
matches = []
# 对于创建追踪器，我们考虑重叠小于iou thrd的任何检测，以表示存在未追踪的对象
for m in matched_idx:
if (IOU_mat[m[0], m[1]] < iou_thrd):
unmatched_trackers.append(m[0])
unmatched_detections.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)
def assign_detections_to_trackers(trackers, detections, iou_thresh=0.3):
IOU_mat = np.zeros((len(trackers), len(detections)), dtype=np.float32)
for t, trk in enumerate(trackers):
# trk = convert_to_cv2bbox(trk)
for d, detection in enumerate(detections):
# det = convert_to_cv2bbox(det)
IOU_mat[t, d] = helpers.box_iou2(trk, detection)
matched_idx = linear_assignment(-IOU_mat)
unmatched_trackers, unmatched_detections = [], []
for t, trk in enumerate(trackers):
if t not in matched_idx[:, 0]:
unmatched_trackers.append(t)
for d, detection in enumerate(detections):
if d not in matched_idx[:, 1]:
unmatched_detections.append(d)
matches = []
# For creating trackers we consider any detection with an
# overlap less than iou_thrd to signifiy the existence of
# an untracked object
for m in matched_idx:
if IOU_mat[m[0], m[1]] < iou_thresh:
unmatched_trackers.append(m[0])
unmatched_detections.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)
def assign_detections_to_trackers(trackers, detections, iou_thrd=.3):
"""
From current list of trackers and new detections, output matched detections,
unmatchted trackers, unmatched detections.
:param trackers:
:param detections:
:param iou_thrd:
:return:
"""
IOU_mat = np.zeros((len(trackers), len(detections)), dtype=np.float32)
for t, trk in enumerate(trackers):
for d, det in enumerate(detections):
IOU_mat[t, d] = helpers.box_iou2(trk, det)
matched_idx = linear_assignment(IOU_mat)
unmatched_trackers, unmatched_detections = [], []
for t, trk in enumerate(trackers):
if (t not in matched_idx[:, 0]):
unmatched_trackers.append(t)
for d, det in enumerate(detections):
if (d not in matched_idx[:, 1]):
unmatched_detections.append(d)
matches = []
for m in matched_idx:
if (IOU_mat[m[0], m[1]] < iou_thrd):
unmatched_trackers.append(m[0])
unmatched_detections.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)
def pipeline(img):
'''
Pipeline function for detection and tracking
'''
global frame_count
global tracker_list
global dead_tracker_list
global track_id_start_value
global max_age
global min_hits
global track_id_list
global debug
global args
frame_count += 1
img_dim = (img.shape[1], img.shape[0])
z_box = det.get_localization(img) # measurement
img_raw = np.copy(img)
if debug:
print('Frame:', frame_count)
x_box = []
if debug:
for i in range(len(z_box)):
if not args['dots']:
img1 = helpers.draw_box_label(img,
z_box[i],
box_color=(255, 0, 0))
#plt.imshow(img1)
plt.show()
if len(tracker_list) > 0:
for trk in tracker_list:
trk.predict_only()
xx = trk.x_state
xx = xx.T[0].tolist()
xx = [xx[0], xx[2], xx[4], xx[6]]
trk.box = xx
x_box.append(trk.box)
matched, unmatched_dets, unmatched_trks \
= assign_detections_to_trackers(x_box, z_box, iou_thrd = 0.1)
if debug:
print('Detection: ', z_box)
print('x_box: ', x_box)
print('matched:', matched)
print('unmatched_det:', unmatched_dets)
print('unmatched_trks:', unmatched_trks)
font = cv2.FONT_HERSHEY_SIMPLEX
font_size = 1
font_color = (255, 255, 255)
cv2.putText(img, str(len(z_box)), (100, 100), font, font_size, font_color,
1, cv2.LINE_AA)
pos = 30
for trk_idx, det_idx in matched:
iou = helpers.box_iou2(tracker_list[trk_idx].box, z_box[det_idx])
cv2.putText(img, tracker_list[trk_idx].id + " " + str(iou),
(100, 100 + pos), font, font_size, font_color, 1,
cv2.LINE_AA)
pos += 30
cv2.putText(img, str(frame_count), (100, 100 + pos), font, font_size,
font_color, 1, cv2.LINE_AA)
# Deal with matched detections
if matched.size > 0:
for trk_idx, det_idx in matched:
z = z_box[det_idx]
ymin, xmin, ymax, xmax = z_box[det_idx]
z = np.expand_dims(z, axis=0).T
tmp_trk = tracker_list[trk_idx]
person_im = img_raw[ymin:ymax, xmin:xmax]
logo_boxes = logo_det.get_localization(person_im)
if (len(logo_boxes) > 0):
l_ymin, l_xmin, l_ymax, l_xmax = logo_boxes[0]
img = helpers.draw_box_label(img, [
l_ymin + z[0], l_xmin + z[1], l_ymax + z[0], l_xmax + z[1]
],
id="logo",
box_color=(255, 0, 0))
logo_x = (l_xmax - l_xmin) / 2 + (l_xmin + z[1])
logo_y = (l_ymax - l_ymin) / 2 + (l_ymin + z[0])
img = cv2.circle(img, (logo_x, logo_y), 5, trk.color, 2)
tmp_trk.logo_x_coords.append(logo_x)
tmp_trk.logo_y_coords.append(logo_y)
if not args['dots']:
img = helpers.draw_box_label(img,
tmp_trk.box,
id=tmp_trk.id,
box_color=(0, 255, 0))
#tmp_trk.kalman_filter(z)
tmp_trk.update_only(z)
xx = tmp_trk.x_state.T[0].tolist()
xx = [xx[0], xx[2], xx[4], xx[6]]
x_box[trk_idx] = xx
tmp_trk.box = xx
tmp_trk.y_coords.append(int((xx[2] - xx[0]) / 2 + xx[0]))
tmp_trk.x_coords.append(int((xx[3] - xx[1]) / 2 + xx[1]))
tmp_trk.hits += 1
tmp_trk.no_losses = 0
# Deal with unmatched detections
if len(unmatched_dets) > 0:
for idx in unmatched_dets:
z = z_box[idx]
ymin, xmin, ymax, xmax = z_box[idx]
z = np.expand_dims(z, axis=0).T
tmp_trk = tracker.Tracker() # Create a new tracker
x = np.array([[z[0], 0, z[1], 0, z[2], 0, z[3], 0]]).T
tmp_trk.x_state = x
person_im = img_raw[ymin:ymax, xmin:xmax]
logo_boxes = logo_det.get_localization(person_im)
if (len(logo_boxes) > 0):
l_ymin, l_xmin, l_ymax, l_xmax = logo_boxes[0]
img = helpers.draw_box_label(img, [
l_ymin + z[0], l_xmin + z[1], l_ymax + z[0], l_xmax + z[1]
],
id="logo",
box_color=(255, 0, 0))
logo_x = (l_xmax - l_xmin) / 2 + (l_xmin + z[1])
logo_y = (l_ymax - l_ymin) / 2 + (l_ymin + z[0])
img = cv2.circle(img, (logo_x, logo_y), 5, trk.color, 2)
tmp_trk.logo_x_coords.append(logo_x)
tmp_trk.logo_y_coords.append(logo_y)
tmp_trk.predict_only()
xx = tmp_trk.x_state
xx = xx.T[0].tolist()
xx = [xx[0], xx[2], xx[4], xx[6]]
tmp_trk.box = xx
tmp_trk.y_coords.append(int((xx[2] - xx[0]) / 2 + xx[0]))
tmp_trk.x_coords.append(int((xx[3] - xx[1]) / 2 + xx[1]))
tmp_trk.id = str(
track_id_start_value) # assign an ID for the tracker
track_id_start_value += 1
tracker_list.append(tmp_trk)
x_box.append(xx)
# Deal with unmatched tracks
if len(unmatched_trks) > 0:
for trk_idx in unmatched_trks:
tmp_trk = tracker_list[trk_idx]
if not args['dots']:
img = helpers.draw_box_label(img,
tmp_trk.box,
id=tmp_trk.id,
box_color=(255, 255, 0))
tmp_trk.no_losses += 1
#tmp_trk.predict_only()
#xx = tmp_trk.x_state
#xx = xx.T[0].tolist()
#xx =[xx[0], xx[2], xx[4], xx[6]]
#tmp_trk.box =xx
#x_box[trk_idx] = xx
# The list of tracks to be annotated
good_tracker_list = []
for trk in tracker_list:
print(trk.id)
if ((trk.hits >= min_hits) and (trk.no_losses <= max_age)):
good_tracker_list.append(trk)
x_cv2 = trk.box
if debug:
print('updated box: ', x_cv2)
print()
if args['dots']:
print("COLOR IS: {}".format(trk.color))
for i in zip(trk.x_coords, trk.y_coords):
img = cv2.circle(img, i, 10, trk.color, 5)
if not args['dots']:
img = helpers.draw_box_label(
img, x_cv2, id=trk.id) # Draw the bounding boxes on the
# images
# Book keeping
deleted_tracks = filter(lambda x: x.no_losses > max_age, tracker_list)
for trk in deleted_tracks:
dead_tracker_list.append(trk)
tracker_list = [x for x in tracker_list if x.no_losses <= max_age]
if debug:
print('Ending tracker_list: ', len(tracker_list))
print('Ending good tracker_list: ', len(good_tracker_list))
return img
def assign_detections_to_trackers(trackers, detections, iou_thrd=0.3, current_frame=0):
'''
From current list of trackers and new detections, output matched detections,
unmatchted trackers, unmatched detections.
'''
# print("ASSIGN_DETECTIONS_TO_TRACKERS")
# print(trackers)
# print(detections)
# print("END ASSIGN_DETECTIONS_TO_TRACKERS")
unmatched_trackers, unmatched_detections = [], []
matches = []
if current_frame == 1:
print("First Frame")
matches = np.empty((0, 2), dtype=int)
for d, det in enumerate(detections):
unmatched_detections.append(d)
return matches, np.array(unmatched_detections), np.array(unmatched_trackers)
IOU_mat = np.zeros((len(trackers), len(detections)), dtype=np.float32)
for t, trk in enumerate(trackers):
# trk = convert_to_cv2bbox(trk)
for d, det in enumerate(detections):
# det = convert_to_cv2bbox(det)
IOU_mat[t, d] = helpers.box_iou2(trk, det)
# Produces matches
# Solve the maximizing the sum of IOU assignment problem using the
# Hungarian algorithm (also known as Munkres algorithm)
matched_idx = linear_assignment.linear_assignment(-IOU_mat)
for t, trk in enumerate(trackers):
if (t not in matched_idx[:, 0]):
unmatched_trackers.append(t)
for d, det in enumerate(detections):
if (d not in matched_idx[:, 1]):
unmatched_detections.append(d)
# For creating trackers we consider any detection with an
# overlap less than iou_thrd to signifiy the existence of
# an untracked object
for m in matched_idx:
if (IOU_mat[m[0], m[1]] < iou_thrd):
unmatched_trackers.append(m[0])
unmatched_detections.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)
