def test_case_first_frame():
tracklet_file = os.path.join(cfg.RAW_DATA_SETS_DIR, '2011_09_26',
'2011_09_26_drive_0005_sync',
'tracklet_labels.xml')
num_frames = 809
objects = read_objects(tracklet_file, num_frames)
a = Tracklet_saver('./test/')
# object is counted as frame.
for i in range(num_frames):
frame_no = i
frame1 = objects[frame_no]
coordinate_3d_1, _ = obj_to_gt_boxes3d(frame1)
translation1, size1, rotation1 = boxes3d_decompose(coordinate_3d_1)
size = size1
transition = translation1
rotation = rotation1
# frame 1 has multiple cars
for i in range(len(translation1)):
a.add_tracklet(frame_no, size[i], transition[i], rotation[i])
a.write_tracklet()
def test_case_first_frame():
tracklet_file = os.path.join(cfg.RAW_DATA_SETS_DIR, '2011_09_26', '2011_09_26_drive_0005_sync',
'tracklet_labels.xml')
num_frames = 809
objects = read_objects(tracklet_file, num_frames)
a = Tracklet_saver('./test/')
# object is counted as frame.
for i in range(num_frames):
frame_no = i
frame1 = objects[frame_no]
coordinate_3d_1, _ = obj_to_gt_boxes3d(frame1)
translation1, size1, rotation1 = boxes3d_decompose(coordinate_3d_1)
size = size1
transition = translation1
rotation = rotation1
# frame 1 has multiple cars
for i in range(len(translation1)):
a.add_tracklet(frame_no, size[i], transition[i], rotation[i])
a.write_tracklet()
def run(self, iter):
# load dataset from didi
train_rgbs, train_tops, train_fronts, train_gt_labels, train_gt_boxes3d, \
frame_id = self.val_set_loader.load(1)
# '1/15/00000'.split('/')[2] == '00000':
if frame_id[0].split('/')[2] == '00000':
self.last_states_c = np.zeros((1, n_hidden))
self.last_states_h = np.zeros((1, n_hidden))
n_history_saved = 0
trans_gt, size, rotation = boxes3d.boxes3d_decompose(train_gt_boxes3d[0])
trans_history, _ = get_train_data2(n_max_objects_input, trans_gt)
feed = {
self.net_trans_history: trans_history,
self.net_trans_gt: trans_gt,
self.net_rnn_states_c: self.last_states_c,
self.net_rnn_states_h: self.last_states_h,
IS_TRAIN_PHASE: True
}
self.last_states_c, self.last_states_h = last_states.c, last_states.h
summary, total_loss, trans_reg_loss, error_reg_loss, predict_tans, error_predict, = \
sess.run([summary_op, net_loss, net_trans_reg_loss, net_score_reg_loss,
net_trans_predict, net_score_predict], feed)
self.summary_writer.add_summary(summary, iter)
def pred_and_save(tracklet_pred_dir, dataset,frame_offset=0, log_tag=None, weights_tag=None):
top_shape, front_shape, rgb_shape = dataset.get_shape()
predict = mv3d.Predictor(top_shape, front_shape, rgb_shape, log_tag=log_tag, weights_tag=weights_tag)
queue = deque(maxlen=1)
# timer
timer_step = 100
if cfg.TRACKING_TIMER:
time_it = timer()
# dataset.size - 1 for in dataset.get_shape(), a frame is used. So it'll omit first frame for prediction,
# fix this if has more time
for i in range(dataset.size-1 if fast_test == False else frame_offset + 1):
rgb, top, front, _, _, frame_id = dataset.load()
# handling multiple bags.
current_bag = frame_id.split('/')[1]
current_frame_num = int(frame_id.split('/')[2])
if i == 0:
prev_tag_bag = None
else:
prev_tag_bag = queue[0]
if current_bag != prev_tag_bag:
# print('current bag name: ', current_bag, '. previous bag name ', prev_tag_bag)
if i != 0:
tracklet.write_tracklet()
tracklet = Tracklet_saver(tracklet_pred_dir, current_bag,exist_ok=True)
# print('frame counter reset to 0. ')
queue.append(current_bag)
# frame_num = i - frame_offset
# if frame_num < 0:
# continue
# detection
boxes3d, probs = predict(top, front, rgb)
# predict.dump_log(log_subdir=os.path.join('tracking',log_tag), n_frame=i, frame_tag=frame_id)
# time timer_step iterations. Turn it on/off in config.py
if cfg.TRACKING_TIMER and i % timer_step == 0 and i != 0:
predict.track_log.write('It takes %0.2f secs for inferring %d frames. \n' % \
(time_it.time_diff_per_n_loops(), timer_step))
if len(boxes3d) != 0:
translation, size, rotation = boxes3d_decompose(boxes3d[:, :, :])
# print(translation)
# print(len(translation))
# add to tracklets
for j in range(len(translation)):
# if 0 < translation[j, 1] < 8:
# print('pose wrote. '
tracklet.add_tracklet(current_frame_num, size[j], translation[j], rotation[j], probs[j],boxes3d[j])
# print('frame_counter is here: ', current_frame_num, ' and i is here: ', i, 'frame id is here: ', frame_id)
tracklet.write_tracklet()
predict.save_weights(dir=os.path.join(log_dir, 'pretrained_model'))
if cfg.TRACKING_TIMER:
predict.log_msg.write('It takes %0.2f secs for inferring the whole test dataset. \n' % \
(time_it.total_time()))
print("tracklet file named tracklet_labels.xml is written successfully.")
return tracklet.path
def pred_and_save(tracklet_pred_dir,
dataset,
generate_video=False,
frame_offset=16,
log_tag=None,
weights_tag=None):
# Tracklet_saver will check whether the file already exists.
tracklet = Tracklet_saver(tracklet_pred_dir)
os.makedirs(os.path.join(log_dir, 'image'), exist_ok=True)
top_shape, front_shape, rgb_shape = dataset.get_shape()
predict = mv3d.Predictor(top_shape,
front_shape,
rgb_shape,
log_tag=log_tag,
weights_tag=weights_tag)
if generate_video:
vid_in = skvideo.io.FFmpegWriter(os.path.join(log_dir, 'output.mp4'))
# timer
timer_step = 100
if cfg.TRACKING_TIMER:
time_it = timer()
frame_num = 0
for i in range(dataset.size if fast_test == False else frame_offset + 1):
rgb, top, front, _, _, _ = dataset.load()
frame_num = i - frame_offset
if frame_num < 0:
continue
boxes3d, probs = predict(top, front, rgb)
predict.dump_log(log_subdir=log_subdir, n_frame=i)
# time timer_step iterations. Turn it on/off in config.py
if cfg.TRACKING_TIMER and i % timer_step == 0 and i != 0:
predict.track_log.write('It takes %0.2f secs for inferring %d frames. \n' % \
(time_it.time_diff_per_n_loops(), timer_step))
# for debugging: save image and show image.
top_image = draw_top_image(top[0])
rgb_image = rgb[0]
if len(boxes3d) != 0:
top_image = draw_box3d_on_top(top_image,
boxes3d[:, :, :],
color=(80, 80, 0),
thickness=3)
rgb_image = draw_box3d_on_camera(rgb_image,
boxes3d[:, :, :],
color=(0, 0, 80),
thickness=3)
translation, size, rotation = boxes3d_decompose(boxes3d[:, :, :])
# todo: remove it after gtbox is ok
size[:, 1:3] = size[:, 1:3] / cfg.TRACKLET_GTBOX_LENGTH_SCALE
for j in range(len(translation)):
if 0 < translation[j, 1] < 8:
tracklet.add_tracklet(frame_num, size[j], translation[j],
rotation[j])
resize_scale = top_image.shape[0] / rgb_image.shape[0]
rgb_image = cv2.resize(
rgb_image,
(int(rgb_image.shape[1] * resize_scale), top_image.shape[0]))
rgb_image = cv2.cvtColor(rgb_image, cv2.COLOR_BGR2RGB)
new_image = np.concatenate((top_image, rgb_image), axis=1)
cv2.imwrite(os.path.join(log_dir, 'image', '%5d_image.jpg' % i),
new_image)
if generate_video:
vid_in.writeFrame(new_image)
vid_in.close()
tracklet.write_tracklet()
predict.dump_weigths(os.path.join(log_dir, 'pretrained_model'))
if cfg.TRACKING_TIMER:
predict.log_msg.write('It takes %0.2f secs for inferring the whole test dataset. \n' % \
(time_it.total_time()))
print("tracklet file named tracklet_labels.xml is written successfully.")
return tracklet.path
def pred_and_save(tracklet_pred_dir, dataset, generate_video=False,
frame_offset=16, log_tag=None, weights_tag=None):
# Tracklet_saver will check whether the file already exists.
tracklet = Tracklet_saver(tracklet_pred_dir)
os.makedirs (os.path.join(log_dir,'image'),exist_ok=True)
top_shape, front_shape, rgb_shape=dataset.get_shape()
predict=mv3d.Predictor(top_shape, front_shape, rgb_shape, log_tag=log_tag, weights_tag=weights_tag)
if generate_video:
vid_in = skvideo.io.FFmpegWriter(os.path.join(log_dir,'output.mp4'))
# timer
timer_step = 100
if cfg.TRACKING_TIMER:
time_it = timer()
frame_num = 0
for i in range(dataset.size):
rgb, top, front, _, _,_= dataset.load(1)
frame_num = i - frame_offset
if frame_num < 0:
continue
boxes3d,probs=predict(top, front, rgb)
predict.dump_log(log_subdir=log_subdir,n_frame=i)
# time timer_step iterations. Turn it on/off in config.py
if cfg.TRACKING_TIMER and i%timer_step ==0 and i!=0:
predict.track_log.write('It takes %0.2f secs for inferring %d frames. \n' % \
(time_it.time_diff_per_n_loops(), timer_step))
# for debugging: save image and show image.
top_image = draw_top_image(top[0])
rgb_image = rgb[0]
if len(boxes3d)!=0:
top_image = draw_box3d_on_top(top_image, boxes3d[:,:,:], color=(80, 80, 0), thickness=3)
rgb_image = draw_box3d_on_camera(rgb_image, boxes3d[:, :, :], color=(0, 0, 80), thickness=3)
translation, size, rotation = boxes3d_decompose(boxes3d[:, :, :])
#todo: remove it after gtbox is ok
size[:,1:3] = size[:,1:3]/cfg.TRACKLET_GTBOX_LENGTH_SCALE
for j in range(len(translation)):
if 0<translation[j,1]<8:
tracklet.add_tracklet(frame_num, size[j], translation[j], rotation[j])
resize_scale=top_image.shape[0]/rgb_image.shape[0]
rgb_image = cv2.resize(rgb_image,(int(rgb_image.shape[1]*resize_scale), top_image.shape[0]))
rgb_image = cv2.cvtColor(rgb_image, cv2.COLOR_BGR2RGB)
new_image = np.concatenate((top_image, rgb_image), axis = 1)
cv2.imwrite(os.path.join(log_dir,'image','%5d_image.jpg'%i), new_image)
if generate_video:
vid_in.writeFrame(new_image)
vid_in.close()
tracklet.write_tracklet()
predict.dump_weigths(os.path.join(log_dir, 'pretrained_model'))
if cfg.TRACKING_TIMER:
predict.log_msg.write('It takes %0.2f secs for inferring the whole test dataset. \n' % \
(time_it.total_time()))
print("tracklet file named tracklet_labels.xml is written successfully.")
return tracklet.path
def pred_and_save(tracklet_pred_dir,
dataset,
frame_offset=0,
log_tag=None,
weights_tag=None):
top_shape, front_shape, rgb_shape = dataset.get_shape()
predict = mv3d.Predictor(top_shape,
front_shape,
rgb_shape,
log_tag=log_tag,
weights_tag=weights_tag)
queue = deque(maxlen=1)
# timer
timer_step = 100
if cfg.TRACKING_TIMER:
time_it = timer()
# dataset.size - 1 for in dataset.get_shape(), a frame is used. So it'll omit first frame for prediction,
# fix this if has more time
for i in range(dataset.size - 1 if fast_test == False else frame_offset +
1):
rgb, top, front, _, _, frame_id = dataset.load()
# handling multiple bags.
current_bag = frame_id.split('/')[1]
current_frame_num = int(frame_id.split('/')[2])
if i == 0:
prev_tag_bag = None
else:
prev_tag_bag = queue[0]
if current_bag != prev_tag_bag:
# print('current bag name: ', current_bag, '. previous bag name ', prev_tag_bag)
if i != 0:
tracklet.write_tracklet()
tracklet = Tracklet_saver(tracklet_pred_dir,
current_bag,
exist_ok=True)
# print('frame counter reset to 0. ')
queue.append(current_bag)
# frame_num = i - frame_offset
# if frame_num < 0:
# continue
# detection
boxes3d, probs = predict(top, front, rgb)
# predict.dump_log(log_subdir=os.path.join('tracking',log_tag), n_frame=i, frame_tag=frame_id)
# time timer_step iterations. Turn it on/off in config.py
if cfg.TRACKING_TIMER and i % timer_step == 0 and i != 0:
predict.track_log.write('It takes %0.2f secs for inferring %d frames. \n' % \
(time_it.time_diff_per_n_loops(), timer_step))
if len(boxes3d) != 0:
translation, size, rotation = boxes3d_decompose(boxes3d[:, :, :])
# print(translation)
# print(len(translation))
# add to tracklets
for j in range(len(translation)):
# if 0 < translation[j, 1] < 8:
# print('pose wrote. '
tracklet.add_tracklet(current_frame_num, size[j],
translation[j], rotation[j], probs[j],
boxes3d[j])
# print('frame_counter is here: ', current_frame_num, ' and i is here: ', i, 'frame id is here: ', frame_id)
tracklet.write_tracklet()
predict.save_weights(dir=os.path.join(log_dir, 'pretrained_model'))
if cfg.TRACKING_TIMER:
predict.log_msg.write('It takes %0.2f secs for inferring the whole test dataset. \n' % \
(time_it.total_time()))
print("tracklet file named tracklet_labels.xml is written successfully.")
return tracklet.path
dir = os.path.join(sys.path[0], "../MV3D/data/preprocessed/didi")
rgb_path = os.path.join(dir, "rgb", "1/6_f", "00000.png")
rgb = cv2.imread(rgb_path)
top_path = os.path.join(dir, "top", "1/6_f", "00000.npy")
top = np.load(top_path)
front = np.zeros((1, 1), dtype=np.float32)
np_reshape = lambda np_array: np_array.reshape(1, *(np_array.shape))
top = np_reshape(top)
front = np_reshape(front)
rgb = np_reshape(rgb)
boxes3d, probs = m3.predict(top, front, rgb)
print("predict boxes len=%d" % len(boxes3d))
translation, size, rotation = boxes3d_decompose(boxes3d)
print("test translation: {}".format(translation))
print("test size: {}".format(size))
print("test rotation: {}".format(rotation))
class MioServer(xmlrpc.XMLRPC):
"""
An example object to be published.
"""
def xmlrpc_test0(self):
return np.sqrt(2).tolist()
def xmlrpc_test1(self):
print("call xmlrpc_test1")
return [[1, -1], [2, -2], [3, -3]] #np.sqrt(2)
dir = os.path.join(sys.path[0], "../MV3D/data/preprocessed/didi")
rgb_path = os.path.join(dir, "rgb", "1/6_f", "00000.png")
rgb = cv2.imread(rgb_path)
top_path = os.path.join(dir, "top", "1/6_f", "00000.npy")
top = np.load(top_path)
front = np.zeros((1, 1), dtype=np.float32)
np_reshape = lambda np_array: np_array.reshape(1, *(np_array.shape))
top_view = np_reshape(top)
front_view = np_reshape(front)
rgb_view = np_reshape(rgb)
start = time.time()
np.save(os.path.join(sys.path[0], "../MV3D/data/", "top.npy"), top_view)
np.save(os.path.join(sys.path[0], "../MV3D/data/", "rgb.npy"), rgb_view)
end = time.time()
print("save npy use time={} seconds".format(end-start))
start = time.time()
boxes3d = rpc.predict()
end = time.time()
if len(boxes3d):
translation, size, rotation = boxes3d_decompose(np.array(boxes3d))
print("predict boxes len={} use time={} seconds".format(len(boxes3d),end-start))
print("test translation: {}".format(translation))
print("test size: {}".format(size))
print("test rotation: {}".format(rotation))
def pred_and_save(tracklet_pred_dir,
dataset,
generate_video=False,
frame_offset=16,
log_tag=None,
weights_tag=None):
# Tracklet_saver will check whether the file already exists.
tracklet = Tracklet_saver(tracklet_pred_dir, 'pred')
os.makedirs(os.path.join(log_dir, 'image'), exist_ok=True)
gt_tracklet = Tracklet_saver(tracklet_pred_dir, 'gt')
top_shape, front_shape, rgb_shape = dataset.get_shape()
predict = mv3d.Predictor(top_shape,
front_shape,
rgb_shape,
log_tag=log_tag,
weights_tag=weights_tag)
if generate_video:
vid_in = skvideo.io.FFmpegWriter(os.path.join(log_dir, 'output.mp4'))
# timer
timer_step = 100
if cfg.TRACKING_TIMER:
time_it = timer()
print('dataset.size')
print(dataset.size)
lenght = []
gt_lenght = []
frame_num = 0
for i in range(dataset.size if fast_test == False else frame_offset + 1):
rgb, top, front, _, _, _ = dataset.load(size=1)
frame_num = i - frame_offset
print('frame_num')
print(frame_num)
if frame_num < 0:
continue
gt_boxes3d_tmp = np.load(
'/home/mohsen/Desktop/MV3D/data/preprocessed/kitti/gt_boxes3d/object3d/test/%05d.npy'
% i)
#remove gt boxes with hiegh less than 40
gt_boxes3d_list = []
for gt_box3d_tmp in gt_boxes3d_tmp:
# if gt_box3d_tmp[0,0]>0:
gt_box3d_tmp_list = []
gt_box3d_tmp_list.append(gt_box3d_tmp)
gt_project = box3d.box3d_to_rgb_box(gt_box3d_tmp_list)
if abs(gt_project[0][0, 1] - gt_project[0][4, 1]) >= 40:
gt_box3d = gt_box3d_tmp
gt_boxes3d_list.append(gt_box3d)
gt_boxes3d = np.array(gt_boxes3d_list)
# gt_boxes3d = gt_boxes3d_tmp
#####################################
boxes3d_tmp, probs = predict(top, front, rgb)
predict.dump_log(log_subdir=log_subdir, n_frame=i)
# time timer_step iterations. Turn it on/off in config.py
if cfg.TRACKING_TIMER and i % timer_step == 0 and i != 0:
predict.track_log.write('It takes %0.2f secs for inferring %d frames. \n' % \
(time_it.time_diff_per_n_loops(), timer_step))
# for debugging: save image and show image.
top_image = draw_top_image(top[0])
rgb_image = rgb[0]
if len(gt_boxes3d) != 0:
gt_lenght.append(len(gt_boxes3d))
gt_translation, gt_size, gt_rotation = boxes3d_decompose(
gt_boxes3d[:, :, :])
# todo: remove it after gtbox is ok
gt_size[:, 1:3] = gt_size[:, 1:3] / cfg.TRACKLET_GTBOX_LENGTH_SCALE
for j in range(len(gt_translation)):
gt_tracklet.add_tracklet(frame_num, gt_size[j],
gt_translation[j], gt_rotation[j])
#remove predicted boxes with hiegh less than 40
boxes3d_list = []
for box3d_tmp in boxes3d_tmp:
# if box3d_tmp[0, 0] > 0:
box3d_tmp_list = []
box3d_tmp_list.append(box3d_tmp)
project = box3d.box3d_to_rgb_box(box3d_tmp_list)
if abs(project[0][0, 1] - project[0][4, 1]) >= 40:
print(project[0][0, 1] - project[0][4, 1])
pred_box3d = box3d_tmp
boxes3d_list.append(pred_box3d)
boxes3d = np.array(boxes3d_list)
# boxes3d = boxes3d_tmp
#####################################
print('sizes')
print(np.size(boxes3d))
print(gt_boxes3d)
print(np.size(gt_boxes3d))
if len(boxes3d) != 0:
lenght.append(len(boxes3d))
top_image = draw_box3d_on_top(top_image,
boxes3d[:, :, :],
color=(80, 80, 0),
thickness=3)
rgb_image = draw_box3d_on_camera(rgb_image,
boxes3d[:, :, :],
color=(0, 0, 80),
thickness=3)
if len(gt_boxes3d) != 0:
rgb_image = draw_box3d_on_camera(rgb_image,
gt_boxes3d[:, :, :],
color=(0, 80, 0),
thickness=3)
translation, size, rotation = boxes3d_decompose(boxes3d[:, :, :])
# todo: remove it after gtbox is ok
size[:, 1:3] = size[:, 1:3] / cfg.TRACKLET_GTBOX_LENGTH_SCALE
for j in range(len(translation)):
tracklet.add_tracklet(frame_num, size[j], translation[j],
rotation[j])
resize_scale = top_image.shape[0] / rgb_image.shape[0]
rgb_image = cv2.resize(
rgb_image,
(int(rgb_image.shape[1] * resize_scale), top_image.shape[0]))
rgb_image = cv2.cvtColor(rgb_image, cv2.COLOR_BGR2RGB)
new_image = np.concatenate((top_image, rgb_image), axis=1)
cv2.imwrite(os.path.join(log_dir, 'image', '%5d_image.jpg' % i),
new_image)
if generate_video:
vid_in.writeFrame(new_image)
vid_in.close()
print(lenght)
print(sum(lenght))
tracklet.write_tracklet()
predict.dump_weigths(os.path.join(log_dir, 'pretrained_model'))
print(gt_lenght)
print(sum(gt_lenght))
gt_tracklet.write_tracklet()
if cfg.TRACKING_TIMER:
predict.log_msg.write('It takes %0.2f secs for inferring the whole test dataset. \n' % \
(time_it.total_time()))
print("tracklet file named tracklet_labels.xml is written successfully.")
return tracklet.path, gt_tracklet.path
last_states_c = np.zeros((1, n_hidden))
last_states_h = np.zeros((1, n_hidden))
for i in range(20000):
# load dataset from didi
train_rgbs, train_tops, train_fronts, train_gt_labels, train_gt_boxes3d,\
frame_id = tra_set_loader.load(1)
#'1/15/00000'.split('/')[2] == '00000':
if frame_id[0].split('/')[2] == '00000':
last_states_c = np.zeros((1, n_hidden))
last_states_h = np.zeros((1, n_hidden))
n_history_saved = 0
trans_gt, size, rotation= boxes3d.boxes3d_decompose(train_gt_boxes3d[0])
trans_history, _ = get_train_data2(n_max_objects_input,trans_gt )
feed={
net_trans_history: trans_history,
net_trans_gt: trans_gt,
net_rnn_states_c: last_states_c,
net_rnn_states_h: last_states_h,
IS_TRAIN_PHASE: True
}
_, total_loss,trans_reg_loss,error_reg_loss,predict_tans,error_predict,last_states = \
sess.run([solver_step,net_loss, net_trans_reg_loss, net_score_reg_loss,
net_trans_predict, net_score_predict, net_states], feed)
def sync_callback(msg1, msg2):
# msg1: /image_raw # msg2: /velodyne_points: velodyne_points
func_start = time.time()
timestamp1 = msg1.header.stamp.to_nsec()
print('image_callback: msg : seq=%d, timestamp=%19d' %
(msg1.header.seq, timestamp1))
timestamp2 = msg2.header.stamp.to_nsec()
print('velodyne_callback: msg : seq=%d, timestamp=%19d' %
(msg2.header.seq, timestamp2))
arr = msg_to_arr(msg2)
lidar = np.array([[item[0], item[1], item[2], item[3]] for item in arr])
camera_image = bridge.imgmsg_to_cv2(msg1, "bgr8")
print("camera_image is {}".format(camera_image.shape))
top_view = point_cloud_2_top(lidar,
res=0.2,
zres=0.5,
side_range=(-45, 45),
fwd_range=(-45, 45),
height_range=(-3, 0.5))
top_image = draw_top_image(top_view[:, :, -1])
if 0: # if show the images
cemara_show_image = cv2.resize(
camera_image,
(camera_image.shape[1] // 2, camera_image.shape[0] // 2))
top_show_image_width = camera_image.shape[0] // 2
top_show_image = cv2.resize(
top_image, (top_show_image_width, top_show_image_width))
show_image = np.concatenate((top_show_image, cemara_show_image),
axis=1)
cv2.imshow("top", show_image)
cv2.waitKey(1)
# use test data until round2 pipeline is ok
np_reshape = lambda np_array: np_array.reshape(1, *(np_array.shape))
top_view = np_reshape(top)
front_view = np_reshape(front)
rgb_view = np_reshape(rgb)
np.save(os.path.join(sys.path[0], "../MV3D/data/", "top.npy"), top_view)
np.save(os.path.join(sys.path[0], "../MV3D/data/", "rgb.npy"), rgb_view)
start = time.time()
boxes3d = rpc.predict()
end = time.time()
print("predict boxes len={} use predict time: {} seconds.".format(
len(boxes3d), end - start))
if len(boxes3d) > 0:
translation, size, rotation = boxes3d_decompose(np.array(boxes3d))
# publish (boxes3d) to tracker_node
markerArray = MarkerArray()
for i in range(len(boxes3d)):
m = Marker()
m.type = Marker.CUBE
m.header.frame_id = "velodyne"
m.header.stamp = msg2.header.stamp
m.scale.x, m.scale.y, m.scale.z = size[i][0], size[i][1], size[i][
2]
m.pose.position.x, m.pose.position.y, m.pose.position.z = \
translation[i][0], translation[i][1], translation[i][2]
m.pose.orientation.x, m.pose.orientation.y, m.pose.orientation.z, m.pose.orientation.w = \
rotation[i][0], rotation[i][1], rotation[i][2], 0.
m.color.a, m.color.r, m.color.g, m.color.b = \
1.0, 0.0, 1.0, 0.0
markerArray.markers.append(m)
pub.publish(markerArray)
func_end = time.time()
print("sync_callback use {} seconds".format(func_end - func_start))