def load_img_with_targets(self, index):
"""Load images and targets for the training and validation phase"""
sample_id = int(self.sample_id_list[index])
img_path = os.path.join(self.image_dir, '{:06d}.png'.format(sample_id))
lidarData = self.get_lidar(sample_id)
calib = self.get_calib(sample_id)
labels, has_labels = self.get_label(sample_id)
if has_labels:
labels[:, 1:] = transformation.camera_to_lidar_box(
labels[:, 1:], calib.V2C, calib.R0, calib.P2)
if self.lidar_aug:
lidarData, labels[:, 1:] = self.lidar_aug(lidarData, labels[:, 1:])
lidarData, labels = get_filtered_lidar(lidarData, cnf.boundary, labels)
bev_map = makeBEVMap(lidarData, cnf.boundary)
bev_map = torch.from_numpy(bev_map)
hflipped = False
if np.random.random() < self.hflip_prob:
hflipped = True
# C, H, W
bev_map = torch.flip(bev_map, [-1])
targets = self.build_targets(labels, hflipped)
metadatas = {'img_path': img_path, 'hflipped': hflipped}
return metadatas, bev_map, targets
def load_bevmap_front_vs_back(self, index):
"""Load only image for the testing phase"""
sample_id = int(self.sample_id_list[index])
img_path, img_rgb = self.get_image(sample_id)
lidarData = self.get_lidar(sample_id)
front_lidar = get_filtered_lidar(lidarData, cnf.boundary)
front_bevmap = makeBEVMap(front_lidar, cnf.boundary)
front_bevmap = torch.from_numpy(front_bevmap)
back_lidar = get_filtered_lidar(lidarData, cnf.boundary_back)
back_bevmap = makeBEVMap(back_lidar, cnf.boundary_back)
back_bevmap = torch.from_numpy(back_bevmap)
metadatas = {
'img_path': img_path,
}
return metadatas, front_bevmap, back_bevmap, img_rgb
def load_img_only(self, index):
"""Load only image for the testing phase"""
sample_id = int(self.sample_id_list[index])
img_path, img_rgb = self.get_image(sample_id)
lidarData = self.get_lidar(sample_id)
lidarData = get_filtered_lidar(lidarData, cnf.boundary)
bev_map = makeBEVMap(lidarData, cnf.boundary)
bev_map = torch.from_numpy(bev_map)
metadatas = {
'img_path': img_path,
}
return metadatas, bev_map, img_rgb
def draw_img_with_label(self, index):
sample_id = int(self.sample_id_list[index])
img_path, img_rgb = self.get_image(sample_id)
lidarData = self.get_lidar(sample_id)
calib = self.get_calib(sample_id)
labels, has_labels = self.get_label(sample_id)
if has_labels:
labels[:, 1:] = transformation.camera_to_lidar_box(
labels[:, 1:], calib.V2C, calib.R0, calib.P2)
if self.lidar_aug:
lidarData, labels[:, 1:] = self.lidar_aug(lidarData, labels[:, 1:])
lidarData, labels = get_filtered_lidar(lidarData, cnf.boundary, labels)
bev_map = makeBEVMap(lidarData, cnf.boundary)
return bev_map, labels, img_rgb, img_path
def on_scan(scan):
start = timeit.default_timer()
rospy.loginfo("Got scan")
gen = []
for p in pc2.read_points(scan,
field_names=("x", "y", "z", "intensity"),
skip_nans=True):
gen.append(np.array([p[0], p[1], p[2], p[3] / 100.0]))
gen_numpy = np.array(gen, dtype=np.float32)
front_lidar = get_filtered_lidar(gen_numpy, cnf.boundary)
bev_map = makeBEVMap(front_lidar, cnf.boundary)
bev_map = torch.from_numpy(bev_map)
with torch.no_grad():
detections, bev_map, fps = do_detect(configs,
model,
bev_map,
is_front=True)
print(fps)
objects_msg = DetectedObjectArray()
objects_msg.header.stamp = rospy.Time.now()
objects_msg.header.frame_id = scan.header.frame_id
flag = False
for j in range(configs.num_classes):
class_name = ID_TO_CLASS_NAME[j]
if len(detections[j]) > 0:
flag = True
for det in detections[j]:
_score, _x, _y, _z, _h, _w, _l, _yaw = det
yaw = -_yaw
x = _y / cnf.BEV_HEIGHT * cnf.bound_size_x + cnf.boundary[
'minX']
y = _x / cnf.BEV_WIDTH * cnf.bound_size_y + cnf.boundary['minY']
z = _z + cnf.boundary['minZ']
w = _w / cnf.BEV_WIDTH * cnf.bound_size_y
l = _l / cnf.BEV_HEIGHT * cnf.bound_size_x
obj = DetectedObject()
obj.header.stamp = rospy.Time.now()
obj.header.frame_id = scan.header.frame_id
obj.score = 0.9
obj.pose_reliable = True
obj.space_frame = scan.header.frame_id
obj.label = class_name
obj.score = _score
obj.pose.position.x = x
obj.pose.position.y = y
obj.pose.position.z = z
[qx, qy, qz, qw] = euler_to_quaternion(yaw, 0, 0)
obj.pose.orientation.x = qx
obj.pose.orientation.y = qy
obj.pose.orientation.z = qz
obj.pose.orientation.w = qw
obj.dimensions.x = l
obj.dimensions.y = w
obj.dimensions.z = _h
objects_msg.objects.append(obj)
if flag is True:
pub.publish(objects_msg)
stop = timeit.default_timer()
print('Time: ', stop - start)