def get_image(self, frame, idx):
'''Get image
'''
camera_name = dataset_pb2.CameraName.FRONT
camera_calibration = utils.get(frame.context.camera_calibrations, camera_name)
camera = utils.get(frame.images, camera_name)
vehicle_to_image = utils.get_image_transform(camera_calibration) # Transformation
img = utils.decode_image(camera)
self.image=img
return img
def display_labels_on_image(camera_calibration, camera, labels, display_time = -1):
# Get the image transformation matrix
vehicle_to_image = utils.get_image_transform(camera_calibration)
# Decode the JPEG image
img = utils.decode_image(camera)
# Draw all the groundtruth labels
for label in labels:
utils.draw_3d_box(img, vehicle_to_image, label)
# Display the image
cv2.imshow("Image", img)
cv2.waitKey(display_time)
def display_labels_on_image(camera_calibration, camera, labels, camera_labels, display_time = -1):
# Get the image transformation matrix
vehicle_to_image = utils.get_image_transform(camera_calibration)
# Decode the JPEG image
img = utils.decode_image(camera)
# Draw all the groundtruth labels
box_3d_to_2d = []
class_labels = [l.type for l in labels]
for label in labels:
x1, y1, x2, y2 = utils.get_3d_boxes_to_2d(img, vehicle_to_image, label)
box_3d_to_2d += [x1, y1, x2, y2]
utils.draw_3d_box(img, vehicle_to_image, label)
utils.draw_3d_box(img, vehicle_to_image, label, draw_2d_bounding_box=True, colour=(0, 255, 0))
for label in camera_labels:
utils.draw_2d_box(img, label, colour=(255, 0, 255))
# Display the image
cv2.imshow("Image", img)
cv2.waitKey(display_time)
# Parse the top laser range image and get the associated projection.
ri, camera_projection, range_image_pose = utils.parse_range_image_and_camera_projection(laser)
# Convert the range image to a point cloud.
pcl, pcl_attr = utils.project_to_pointcloud(frame, ri, camera_projection, range_image_pose, laser_calibration)
# Get the front camera information
camera_name = dataset_pb2.CameraName.FRONT
camera_calibration = utils.get(frame.context.camera_calibrations, camera_name)
camera = utils.get(frame.images, camera_name)
# Get the transformation matrix for the camera.
vehicle_to_image = utils.get_image_transform(camera_calibration)
# Decode the image
img = utils.decode_image(camera)
# Some of the labels might be fully hidden therefore we attempt to compute the label visibility
# by counting the number of LIDAR points inside each label bounding box.
# For each label, compute the transformation matrix from the vehicle space to the box space.
vehicle_to_labels = [np.linalg.inv(utils.get_box_transformation_matrix(label.box)) for label in frame.laser_labels]
vehicle_to_labels = np.stack(vehicle_to_labels)
# Convert the pointcloud to homogeneous coordinates.
pcl1 = np.concatenate((pcl,np.ones_like(pcl[:,0:1])),axis=1)
# Transform the point cloud to the label space for each label.
# proj_pcl shape is [label, LIDAR point, coordinates]
proj_pcl = np.einsum('lij,bj->lbi', vehicle_to_labels, pcl1)
def export(self,
camera_calibration,
camera,
labels,
camera_labels,
frame_id=0,
sequence_id=None):
assert (os.path.exists(self.export_folder))
# Get the image transformation matrix
vehicle_to_image = utils.get_image_transform(camera_calibration)
# Decode the JPEG image
img = utils.decode_image(camera)
# Draw all the groundtruth labels
box_3d_to_2d = []
box_3d_class_labels = []
for label in labels:
obj = utils.get_3d_boxes_to_2d(img, vehicle_to_image, label)
if obj is None:
continue
box_3d_to_2d.append(list(map(int, obj['box'])))
box_3d_class_labels.append(obj['label'])
box_2d = []
box_2d_labels = []
if camera_labels is not None:
for label in camera_labels:
obj = utils.get_2d_boxes(label)
if obj is None:
continue
box_2d.append(obj['box'])
box_2d_labels.append(obj['label'])
boxes = {
'2d_boxes': {
'boxes': box_2d,
'label': box_2d_labels
},
'3d_boxes': {
'boxes': box_3d_to_2d,
'label': box_3d_class_labels
}
}
velocities = {x[0].name: x[1] for x in camera.velocity.ListFields()}
pose_data = {x[0].name: list(x[1]) for x in camera.pose.ListFields()}
camera_info = {
'id': '{}_{:05d}'.format(sequence_id, frame_id),
'name': camera.name,
'velocities': velocities,
'pose': pose_data,
'shutter': camera.shutter,
'pose_timestamp': camera.pose_timestamp,
'camera_trigger_time': camera.camera_trigger_time,
'camera_readout_done_time': camera.camera_readout_done_time,
'width': img.shape[1],
'height': img.shape[0],
'depth': img.shape[2]
}
if not os.path.exists(os.path.join(EXPORT_FOLDER, 'rgb', sequence_id)):
mkdir_p(os.path.join(EXPORT_FOLDER, 'rgb', sequence_id))
if not os.path.exists(os.path.join(EXPORT_FOLDER, 'label',
sequence_id)):
mkdir_p(os.path.join(EXPORT_FOLDER, 'label', sequence_id))
if not os.path.exists(
os.path.join(EXPORT_FOLDER, 'img_info', sequence_id)):
mkdir_p(os.path.join(EXPORT_FOLDER, 'img_info', sequence_id))
with open(
os.path.join(EXPORT_FOLDER, 'label', sequence_id,
'frame_{:05d}.json'.format(frame_id)), 'w') as f:
json.dump(boxes, f)
with open(
os.path.join(EXPORT_FOLDER, 'img_info', sequence_id,
'frame_{:05d}.json'.format(frame_id)), 'w') as f:
json.dump(camera_info, f)
_filename = os.path.join(EXPORT_FOLDER, 'rgb', sequence_id,
'frame_{:05d}.numpy'.format(frame_id))
img.dump(_filename)
self.executor.submit(convert_to_png, _filename)