def pipeline(img):
'''
Pipeline function for detection and tracking
'''
global frame_count
global tracked_count
global tracker_list
global max_age
global min_hits
global track_id_list
global debug
global z_box
img_dim = (img.shape[1], img.shape[0])
if frame_count % 10 < 2:
z_box = det.get_localization(img) # measurement
if len(z_box) > 0:
for i in range(len(z_box)):
box = z_box[i]
tmp_trk = Tracker() # Create a new tracker
x = np.array([[box[0], 0, box[1], 0, box[2], 0, box[3], 0]]).T
tmp_trk.x_state = x
tmp_trk.predict_only()
if len(tracker_list) != 0 and i < len(tracker_list):
tracker_list[i] = tmp_trk
else:
tracker_list.append(tmp_trk)
print(tracker_list)
img = helpers.draw_box_label(img, box)
else:
if len(z_box) > 0:
print(tracker_list)
nb = []
for i in range(len(z_box)):
z = z_box[i]
z = np.expand_dims(z, axis=0).T
tmp_trk = tracker_list[i]
tmp_trk.kalman_filter(z)
xx = tmp_trk.x_state.T[0].tolist()
xx = [xx[0], xx[2], xx[4], xx[6]]
nb.append(xx)
img = helpers.draw_box_label(img, xx)
for i in range(len(nb)):
print(nb[i])
print(z_box[i])
z_box = nb
frame_count += 1
if debug:
print('Frame:', frame_count)
return img
def pipeline(img):
'''
Pipeline function for detection and tracking
'''
global frame_count
global tracker_list
global max_age
global min_hits
global track_id_list
global debug
frame_count += 1
start = time.time()
img_dim = (img.shape[1], img.shape[0])
z_box = det.get_localization(img) # measurement
if debug:
print('Frame:', frame_count)
x_box = []
if debug:
for i in range(len(z_box)):
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:
x_box.append(trk.box)
matched, unmatched_dets, unmatched_trks \
= assign_detections_to_trackers(x_box, z_box, iou_thrd = 0.3)
if debug:
print('Detection: ', z_box)
print('x_box: ', x_box)
print('matched:', matched)
print('unmatched_det:', unmatched_dets)
print('unmatched_trks:', unmatched_trks)
# Deal with matched detections
if matched.size > 0:
for trk_idx, det_idx in matched:
z = z_box[det_idx]
z = np.expand_dims(z, axis=0).T
tmp_trk = tracker_list[trk_idx]
tmp_trk.kalman_filter(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.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]
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
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.id = track_id_list.popleft(
) # assign an ID for the tracker
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]
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:
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()
img = helpers.draw_box_label(
img, x_cv2, helpers.trk_id_to_color(
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:
track_id_list.append(trk.id)
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 pipeline(img):
"""
Pipeline function for detection and tracking
"""
global frame_count
global tracker_list
global max_age
global min_hits
global track_id_list
global debug
global points
frame_count += 1
img_dim = (img.shape[1], img.shape[0])
z_box = det.get_localization(img) # measurement
if debug:
print('Frame:', frame_count)
x_box =[]
if debug:
img1 = img.copy()
for i in range(len(z_box)):
img1 = helpers.draw_box_label(i, img1, z_box[i], box_color=(255, 0, 0))
plt.imshow(img1)
plt.show()
if len(tracker_list) > 0:
for trk in tracker_list:
x_box.append(trk.box)
matched, unmatched_dets, unmatched_trks \
= assign_detections_to_trackers(x_box, z_box, iou_thrd = 0.3)
if debug:
print('Detection: ', z_box)
print('x_box: ', x_box)
print('matched:', matched)
print('unmatched_det:', unmatched_dets)
print('unmatched_trks:', unmatched_trks)
# Deal with matched detections
if matched.size > 0:
for trk_idx, det_idx in matched:
z = z_box[det_idx]
z = np.expand_dims(z, axis=0).T
tmp_trk = tracker_list[trk_idx]
tmp_trk.kalman_filter(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.hits += 1
# Deal with unmatched detections
if len(unmatched_dets) > 0:
for idx in unmatched_dets:
z = 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
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.id = track_id_list.popleft() # assign an ID for the tracker
print(tmp_trk.id)
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]
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 = []
if (len(tracker_list) == 0):
print('list should be cleared now')
points = []
for trk in tracker_list:
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()
for good_track in good_tracker_list:
box = good_track.box
if (len(z_box) != 0):
y_up, x_left, y_down, x_right = box
#center = (int((x_left + x_right) / 2), int((y_up + y_down) / 2))
legs = (int((x_left + x_right) / 2), y_down)
points.append(legs)
img = cv2.circle(img, legs, 20, (255, 0, 0), thickness=-1)
img = helpers.draw_box_label(good_track.id, img, box) # Draw the bounding boxes on the images
if (len(points) > 1):
for i in range(len(points) - 1):
cv2.line(img, points[i], points[i + 1], (255, 0, 0), 2)
# Book keeping ???
deleted_tracks = filter(lambda x: x.no_losses > max_age, tracker_list)
for trk in deleted_tracks:
track_id_list.append(trk.id)
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))
cv2.imshow("frame", img)
return img
# Updated state
x_update =trk.x_state
x_updated_box = [x_update[0], x_update[2], x_update[4], x_update[6]]
print('The initial state is: ', x_init)
print('The measurement is: ', z)
print('The update state is: ', x_update)
# Visualize the Kalman filter process and the
# impact of measurement nosie convariance matrix
images = [plt.imread(file) for file in glob.glob('./test_images/*.jpg')]
img=images[3]
plt.figure(figsize=(10, 14))
helpers.draw_box_label(img, x_init_box, box_color=(0, 255, 0))
ax = plt.subplot(3, 1, 1)
plt.imshow(img)
plt.title('Initial: '+str(x_init_box))
helpers.draw_box_label(img, z, box_color=(255, 0, 0))
ax = plt.subplot(3, 1, 2)
plt.imshow(img)
plt.title('Measurement: '+str(z))
helpers.draw_box_label(img, x_updated_box)
ax = plt.subplot(3, 1, 3)
plt.imshow(img)
plt.title('Updated: '+str(x_updated_box))
plt.show()
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 pipeline(img_in):
global frame_count
global tracker_list
global max_age
global min_hits
global track_id_list
global debug
global track_id
frame_count += 1
z_box = det.get_localization(img_in) # measurement
if debug:
print('Frame:', frame_count)
x_box = []
if len(tracker_list) > 0:
for trk in tracker_list:
x_box.append(trk.box)
matched, unmatched_dets, unmatched_trks = assign_detections_to_trackers(
x_box, z_box, iou_thresh=0.3)
if debug:
print('Detection: ', z_box)
print('x_box: ', x_box)
print('matched:', matched)
print('unmatched_det:', unmatched_dets)
print('unmatched_trks:', unmatched_trks)
# Deal with matched detections
if matched.size > 0:
for trk_idx, det_idx in matched:
z = z_box[det_idx]
z = np.expand_dims(z, axis=0).T
tmp_trk = tracker_list[trk_idx]
tmp_trk.kalman_filter(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.hits += 1
# Deal with unmatched detections
if len(unmatched_dets) > 0:
for idx in unmatched_dets:
z = 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
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
track_id += 1
tmp_trk.id = track_id # assign an ID for the tracker
print(tmp_trk.id)
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]
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:
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)
img_in, img_crop = helpers.draw_box_label(trk.id, img_in, x_cv2,
frame_count)
if img_crop is not None:
img_trk = [img_in, img_crop, x_cv2]
if trk.id in final_dict.keys():
l = final_dict[trk.id]
l.append(img_trk)
final_dict.update({trk.id: l})
else:
l = []
l.append(img_trk)
final_dict.update({trk.id: l})
# Book keeping
deleted_tracks = filter(lambda x: x.no_losses > max_age, tracker_list)
for trk in deleted_tracks:
track_id_list.append(trk.id)
tracker_list = [x for x in tracker_list if x.no_losses <= max_age]
fileObject = open("final_dict_pickle", 'wb')
pickle.dump(final_dict, fileObject)
fileObject.close()
if debug:
print('Ending tracker_list: ', len(tracker_list))
print('Ending good tracker_list: ', len(good_tracker_list))
cv2.imshow("frame", img_in)
return img_in
def face_pipeline(img, det):
global frame_count
global tracker_list
global min_hits
global track_id_list
global max_age
frame_count += 1
boxs = detect_faces(img, det)
x_box = []
if len(tracker_list) > 0:
for trk in tracker_list:
x_box.append(trk.box)
matched, unmatched_dets, unmatched_trks = assign_detections_to_trackers(x_box, boxs, iou_thrd=.3)
if matched.size > 0:
for trk_idx, det_idx in matched:
z = boxs[det_idx]
z = np.expand_dims(z, axis=0).T
tmp_trk = tracker_list[trk_idx]
tmp_trk.kalman_filter(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.hits += 1
if len(unmatched_dets) > 0:
for idx in unmatched_dets:
z = boxs[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
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.id = track_id_list.popleft() # assign an ID for the tracker
print(tmp_trk.id)
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]
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:
if ((trk.hits >= min_hits) and (trk.no_losses <= max_age)):
good_tracker_list.append(trk)
x_cv2 = trk.box
img = helpers.draw_box_label(trk.id, img, x_cv2) # 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:
track_id_list.append(trk.id)
tracker_list = [x for x in tracker_list if x.no_losses <= max_age]
# cv2.imshow("frame",img)
return img
# cv2.FONT_HERSHEY_PLAIN, 1, (0, 255, 0), 2)
# cv2.imwrite('r1.jpg', cv2.cvtColor(image, cv2.COLOR_RGB2BGR))
# detector.close()
matplotlib.use("TkAgg")
det = PersonDetector()
os.chdir(cwd)
TEST_IMAGE_PATHS = glob(os.path.join('test_images/', '*.jpg'))
# print(len(TEST_IMAGE_PATHS))
for i, image_path in enumerate(TEST_IMAGE_PATHS):
print('')
print('*************************************************')
img_full = Image.open(image_path)
img_full_np = det.load_image_into_numpy_array(img_full)
start = time.time()
b = det.get_localization(img_full_np, visual=False)
end = time.time()
print('Localization time: ', end - start)
for i in range(len(b)):
img1 = helpers.draw_box_label(i,
img_full_np,
b[i],
box_color=(255, 0, 0))
plt.imshow(img1)
plt.show()
#
def pipeline(img):
'''
用于检测和跟踪的功能
'''
global frame_count
global tracker_list
global max_age
global min_hits
global track_id_list
global debug
global str1
global str555
frame_count += 1
img_dim = (img.shape[1], img.shape[0])
z_box = det.get_localization(img) # measurement
if debug:
print('Frame:', frame_count)
x_box = []
if debug:
str4 = ""
str5552 = ""
stryao = ""
for i in range(len(z_box)):
img1, str3, str5551 = helpers.draw_box_label(img,
z_box[i],
box_color=(255, 0, 0))
str4 = str4 + "\n" + str3
if (str5551 == "注意前方车辆！"):
stryao = "注意前方车辆！"
plt.imshow(img1)
print("AAAAaaaaa" + str4)
str1 = str4
str555 = str5552
print("WWWWWaaaaa" + str555)
plt.show()
if (z_box == []):
str1 = " "
str555 = " "
if len(tracker_list) > 0:
for trk in tracker_list:
x_box.append(trk.box)
#当在第一个视频帧中首次检测到车辆时，运行以下代码行分别返回匹配、不匹配和不匹配的空列表、一个元素列表和空列表。
matched, unmatched_dets, unmatched_trks \
= assign_detections_to_trackers(x_box, z_box, iou_thrd=0.3)
if debug:
print('Detection: ', z_box)
print('x_box: ', x_box)
print('matched:', matched)
print('unmatched_det:', unmatched_dets)
print('unmatched_trks:', unmatched_trks)
# 处理匹配的检测
#当在第二个视频帧中再次检测到车辆时，运行以下assign_detections_to_tracker将分别返回一个元素列表、一个空列表和一个匹配、不匹配和不匹配的_dets的空列表。如下图所示，我们有一个匹配的检测，将由以下代码块处理：
if matched.size > 0:
for trk_idx, det_idx in matched:
z = z_box[det_idx]
z = np.expand_dims(z, axis=0).T
tmp_trk = tracker_list[trk_idx]
tmp_trk.kalman_filter(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.hits += 1
tmp_trk.no_losses = 0
# 处理不匹配的检测
#不匹配的检测由以下代码块处理：
if len(unmatched_dets) > 0:
for idx in unmatched_dets:
z = z_box[idx]
z = np.expand_dims(z, axis=0).T
tmp_trk = tracker.Tracker() # 创建新的跟踪器
x = np.array([[z[0], 0, z[1], 0, z[2], 0, z[3], 0]]).T
tmp_trk.x_state = x
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
try:
tmp_trk.id = track_id_list.popleft() # 为跟踪器分配一个id
tracker_list.append(tmp_trk)
x_box.append(xx)
except IndexError:
tracker_list.append(tmp_trk)
x_box.append(xx)
# 处理不匹配的曲目
if len(unmatched_trks) > 0:
for trk_idx in unmatched_trks:
tmp_trk = tracker_list[trk_idx]
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
# 要注释的曲目列表
good_tracker_list = []
for trk in tracker_list:
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()
img, str2, str6 = helpers.draw_box_label(img, x_cv2) # 在图像上绘制边界框
deleted_tracks = filter(lambda x: x.no_losses > max_age, tracker_list)
for trk in deleted_tracks:
track_id_list.append(trk.id)
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, str1, stryao
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:
if ((trk.hits >= min_hits) and (trk.no_losses <= max_age)):
good_tracker_list.append(trk)
x_cv2 = trk.box
<<<<<<< HEAD
img = helpers.draw_box_label(img, x_cv2) # Draw the bounding boxes on the
=======
left, top, right, bottom = x_cv2[1], x_cv2[0], x_cv2[3], x_cv2[2]
car = img[top:bottom, left:right]
img = helpers.draw_box_label(img, trk.id, x_cv2) # Draw the bounding boxes on the
>>>>>>> tanyinghui/master
# images
# Book keeping
deleted_tracks = filter(lambda x: x.no_losses > max_age, tracker_list)
<<<<<<< HEAD
for trk in deleted_tracks:
track_id_list.append(trk.id)
=======
>>>>>>> tanyinghui/master
def pipeline(boxes, dims):
'''
Pipeline function for detection and tracking
'''
global frame_count
global tracker_list
global max_age
global min_hits
global track_id_list
global debug
global next_image
frame_count += 1
#img_dim = (img.shape[1], img.shape[0])
pixel_boxes = helpers.box_array_to_pixels(boxes, dims)
z_box = pixel_boxes #det.get_localization(img) # measurement
if debug:
print('Frame:', frame_count)
x_box = []
if debug:
for i in range(len(z_box)):
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:
x_box.append(trk.box)
matched, unmatched_dets, unmatched_trks \
= assign_detections_to_trackers(x_box, z_box, iou_thrd = 0.3)
if debug:
print('Detection: ', z_box)
print('x_box: ', x_box)
print('matched:', matched)
print('unmatched_det:', unmatched_dets)
print('unmatched_trks:', unmatched_trks)
# Deal with matched detections
if matched.size > 0:
for trk_idx, det_idx in matched:
z = z_box[det_idx]
z = np.expand_dims(z, axis=0).T
tmp_trk = tracker_list[trk_idx]
tmp_trk.kalman_filter(z)
xx = tmp_trk.x_state.T[0].tolist()
xx = [xx[0], xx[2], xx[4], xx[6]]
x_box[trk_idx] = xx
print(xx)
tmp_trk.box = xx
tmp_trk.hits += 1
print("NUM HITS:")
print(tmp_trk.hits)
#print(tmp_trk.location_history)
#print(np.asarray([np.array(xx)]))
#print("Now to concatenate")
tmp_trk.location_history = np.concatenate(
(tmp_trk.location_history, np.asarray([np.array(xx)])))
#print(np.array(xx))
print(tmp_trk.location_history)
tmp_trk.no_losses = 0
# Deal with unmatched detections
if len(unmatched_dets) > 0:
for idx in unmatched_dets:
z = z_box[idx]
print("UNMATCHED DETECTINOS")
#print(z)
z = np.expand_dims(z, axis=0).T
#print(z)
tmp_trk = tracker.Tracker() # Create a new tracker
x = np.array([[z[0], 0, z[1], 0, z[2], 0, z[3], 0]]).T
#print("trouble x")
#print(x)
#print("end trouble x")
tmp_trk.x_state = x
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
print(track_id_list)
tmp_trk.id = track_id_list.popleft(
) # assign an ID for the tracker
tracker_list.append(tmp_trk)
x_box.append(xx)
print("END UNMATCHED DETECTIONS")
# Deal with unmatched tracks
if len(unmatched_trks) > 0:
for trk_idx in unmatched_trks:
tmp_trk = tracker_list[trk_idx]
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:
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()
#img = helpers.draw_box_label(img, x_cv2) # Draw the bounding boxes on the
next_image = helpers.draw_box_label(next_image, x_cv2)
# images
# Book keeping
deleted_tracks = filter(lambda x: x.no_losses > max_age, tracker_list)
for trk in deleted_tracks:
track_id_list.append(trk.id)
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))
def pipeline(img):
'''
Pipeline function for detection and tracking
'''
global frame_count
global tracker_list
global max_age
global min_hits
global track_id_list
global debug
global avg_fps
frame_count += 1
#print("")
#print(frame_count)
#print("")
start = time.time()
img_dim = (img.shape[1], img.shape[0])
# YOLO detection for vehicle
yolo_start = time.time()
z_box = yolo_det.get_detected_boxes(img)
#z_box_cpy= z_box
yolo_end = time.time()
# Lpd
#print("Time taken for yolo detection is", yolo_end-yolo_start)
track_start = time.time()
if debug:
print('Frame:', frame_count)
x_box = []
if debug:
for i in range(len(z_box)):
img1 = helpers.draw_box_label(img, z_box[i], box_color=(255, 0, 0))
cv2.imshow("frame", img1)
k = cv2.waitKey(10)
if k == ord('e'):
cv2.destroyAllWindows()
sys.exit(-1)
# plt.show()
if len(tracker_list) > 0:
for trk in tracker_list:
x_box.append(trk.box)
matched, unmatched_dets, unmatched_trks \
= assign_detections_to_trackers(x_box, z_box, iou_thrd=0.3)
if debug:
print('Detection: ', z_box)
print('x_box: ', x_box)
print('matched:', matched)
print('unmatched_det:', unmatched_dets)
print('unmatched_trks:', unmatched_trks)
# Deal with matched detections
if matched.size > 0:
for trk_idx, det_idx in matched:
z = z_box[det_idx]
tmp_trk = tracker_list[trk_idx]
tmp_trk.features.append(extract_feature(img, z))
z = np.expand_dims(z, axis=0).T
tmp_trk.kalman_filter(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.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]
if len(unmatched_trks) > 0:
min_score = 10000000
tmp_idx = -1
for trk_idx in unmatched_trks:
trk = tracker_list[trk_idx]
#print(len(trk.features))
if len(trk.features) == 0:
continue
score = trk.feature_match(extract_feature(
img, z)) ## find closest feature match
if score < min_score:
min_score = score
tmp_idx = trk_idx
if min_score < feature_thresh and tmp_idx != -1:
z = np.expand_dims(z, axis=0).T
tmp_trk = tracker_list[tmp_idx]
tmp_trk.kalman_filter(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.hits += 1
tmp_trk.no_losses = 0
continue
#new_boxes.append(z)
z = np.expand_dims(z, axis=0).T
tmp_trk = tr.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
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.id = track_id_list.popleft(
) # assign an ID for the tracker
tracker_list.append(tmp_trk)
x_box.append(xx)
# Deal with unmatched tracks*100
if len(unmatched_trks) > 0:
for trk_idx in unmatched_trks:
tmp_trk = tracker_list[trk_idx]
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
img_vis = img.copy()
good_tracker_list = []
#print(img_dim)
good_boxes = []
for trk in tracker_list:
if ((trk.hits >= min_hits) and (trk.no_losses <= max_age)):
good_tracker_list.append(trk)
good_boxes.append(trk.box)
#for trk in good_tracker_list:
selected_ids = nms(torch.FloatTensor(np.array(good_boxes)),
torch.FloatTensor([1.0] * len(good_boxes)), 0.45)
for idx in selected_ids:
trk = good_tracker_list[idx]
x_cv2 = trk.box
idx = trk.id
if debug:
print('updated box: ', x_cv2)
print()
# Draw the bounding boxes on the
img_vis = helpers.draw_box_label(img_vis, x_cv2, idx)
if frame_count % 5 == 0:
y1_temp, x1_temp, y2_temp, x2_temp = x_cv2
w_temp = x2_temp - x1_temp
h_temp = y2_temp - y1_temp
if w_temp * h_temp < 400 or w_temp <= 0 or h_temp <= 0 or min(
x_cv2) < 0:
continue
plates = []
#print(x_cv2)
dt_start = time.time()
Ivehicle = img[y1_temp:y2_temp, x1_temp:x2_temp]
ratio = float(max(Ivehicle.shape[:2])) / min(Ivehicle.shape[:2])
side = int(ratio * 288.)
bname = 'frame{}_{}.png'.format(frame_count, idx)
bound_dim = min(side + (side % (2**4)), size)
# print "\t\tBound dim: %d, ratio: %f" % (bound_dim,ratio)
#dt_plates_start = time.time()
Llp, LlpImgs, _ = detect_lp(wpod_net, im2single(Ivehicle),
bound_dim, 2**4, (240, 80),
lp_threshold)
if len(LlpImgs):
plates = [Llp[0].pts]
cv2.imwrite("%s/%s" % (detected_plates_dir, bname),
LlpImgs[0] * 255.)
plate_string = _lpr.plates_ocr(LlpImgs[0] * 255.)
for plate in plates:
x1 = (plate[0][0] * w_temp + x1_temp).astype('int')
y1 = (plate[1][0] * h_temp + y1_temp).astype('int')
x2 = (plate[0][1] * w_temp + x1_temp).astype('int')
y2 = (plate[1][1] * h_temp + y1_temp).astype('int')
x3 = (plate[0][2] * w_temp + x1_temp).astype('int')
y3 = (plate[1][2] * h_temp + y1_temp).astype('int')
x4 = (plate[0][3] * w_temp + x1_temp).astype('int')
y4 = (plate[1][3] * h_temp + y1_temp).astype('int')
plate = np.array([[x1, y1], [x2, y2], [x3, y3], [x4, y4]],
np.int32)
plate = plate.reshape((-1, 1, 2))
cv2.polylines(img_vis, [plate], True, (255, 0, 0), 4)
cv2.putText(img_vis, plate_string, (x1, y1),
cv2.FONT_HERSHEY_SIMPLEX, 1.1, (0, 0, 255), 2)
cv2.imwrite("%s/%s" % (detected_cars_dir, bname),
img_vis[y1_temp:y2_temp, x1_temp:x2_temp])
track_end = time.time()
# images
# dt_start = time.time()
print("Time taken to track the boxes is", track_end - track_start)
end = time.time()
fps = 1.0 / (end - start)
#dt_fps = 1.0/(dt_dr+yolo_end-yolo_start)
avg_fps += fps
cv2.putText(img_vis, "FPS: {:.4f}".format(fps),
(int(0.8 * img_dim[0]), 100), cv2.FONT_HERSHEY_SIMPLEX, 1.1,
(255, 255, 0), 4)
#cv2.putText(img_vis, "Detect FPS: {:.4f}".format(
# dt_fps), (10, 100), cv2.FONT_HERSHEY_SIMPLEX, 1.1, (255,255, 0), 4)
# Book keeping
deleted_tracks = filter(lambda x: x.no_losses > feature_tp, tracker_list)
for trk in deleted_tracks:
track_id_list.append(trk.id)
tracker_list = [x for x in tracker_list if x.no_losses <= feature_tp]
if debug:
print('Ending tracker_list: ', len(tracker_list))
print('Ending good tracker_list: ', len(good_tracker_list))
return img_vis
