def setUpClass(cls):
cls.system = mock_types.MockSystem()
cls.image_source = mock_types.MockImageSource()
image_data = [
np.random.normal(128, 20, size=(10, 10)).astype(np.uint8)
for _ in range(cls.num_images)
]
cls.images = [
Image(
pixels=pixels,
metadata=make_metadata(
pixels=pixels,
source_type=ImageSourceType.SYNTHETIC,
camera_pose=Transform(
(idx * 15, idx, 0),
tf3d.quaternions.axangle2quat((1, 2, 3), 5 * idx * np.pi / (2 * cls.num_images)), w_first=True
),
intrinsics=CameraIntrinsics(
width=10, height=10,
fx=5, fy=5,
cx=5, cy=5
)
)
)
for idx, pixels in enumerate(image_data)
]
def make_image_sequence(timestep, width, height, length):
images = []
for _ in range(length):
pixels = np.random.randint(0, 255, size=(height, width, 3), dtype=np.uint8)
image = Image(
pixels=pixels,
metadata=imeta.make_metadata(
pixels,
source_type=imeta.ImageSourceType.SYNTHETIC,
intrinsics=CameraIntrinsics(800, 600, 550.2, 750.2, 400, 300),
),
additional_metadata={'test': True}
)
image.save()
images.append(image)
sequence = ImageCollection(
images=images,
timestamps=[idx * timestep for idx in range(length)],
sequence_type=ImageSequenceType.SEQUENTIAL
)
sequence.save()
return sequence
def import_sequence(root_folder: Path, left_path: Path, right_path: Path,
depth_quality: DepthNoiseQuality = DepthNoiseQuality.KINECT_NOISE) -> ImageCollection:
"""
Import the sequence, as a bunch of stereo images, and then organised into an ImageCollection.
ImageCollection and StereoImage objects are saved.
:param root_folder: The root folder to import from, containing the timestamps and settings files
:param left_path: The path to the left image sequences
:param right_path: The path to the right image sequences
:param depth_quality: Noisy depth is generated as we go, the quality to use when doing so
:return: The imported image collection
"""
# Read the timestamps and the generator settings
# These are saved from python, so need for special loading
with (root_folder / 'settings.json').open('r') as fp:
settings = json_load(fp)
with (root_folder / 'timestamps.json').open('r') as fp:
timestamps = json_load(fp)
# Read the camera settings from file
left_camera_intrinsics = read_camera_intrinsics(left_path / '_camera_settings.json')
right_camera_intrinsics = read_camera_intrinsics(right_path / '_camera_settings.json')
# left_object_labels = read_object_classes(left_path / '_object_settings.json')
# right_object_labels = read_object_classes(right_path / '_object_settings.json')
max_img_id = min(
find_max_img_id(lambda idx: left_path / IMG_TEMPLATE.format(idx)),
find_max_img_id(lambda idx: right_path / IMG_TEMPLATE.format(idx)),
)
if len(timestamps) != max_img_id + 1:
raise RuntimeError(f"Maximum image id {max_img_id} didn't match the number "
f"of available timestamps ({len(timestamps)}), cannot associate.")
# Read meta-information from the generator, including the timestamps
sequence_name = root_folder.name
(
trajectory_id, environment_type, light_level, time_of_day, simulation_world,
lighting_model, texture_mipmap_bias, normal_maps_enabled, roughness_enabled, min_object_size,
geometry_decimation
) = parse_settings(settings)
# Import all the images
images = []
image_group = sequence_name
origin = None
# Open the image manager for writing once, so that we're not constantly opening and closing it with each image
with arvet.database.image_manager.get().get_group(image_group, allow_write=True):
for img_idx in range(max_img_id + 1):
logging.getLogger(__name__).info(f"Loading image {img_idx}...")
# Read the raw data for the left image
left_frame_data = read_json(left_path / DATA_TEMPLATE.format(img_idx))
left_pixels = image_utils.read_colour(left_path / IMG_TEMPLATE.format(img_idx))
# left_label_image = image_utils.read_colour(left_path / INSTANCE_TEMPLATE.format(img_idx))
left_true_depth = load_depth_image(left_path / DEPTH_TEMPLATE.format(img_idx))
# Read the raw data for the right image
right_frame_data = read_json(right_path / DATA_TEMPLATE.format(img_idx))
right_pixels = image_utils.read_colour(right_path / IMG_TEMPLATE.format(img_idx))
# right_label_image = image_utils.read_colour(right_path / INSTANCE_TEMPLATE.format(img_idx))
right_true_depth = load_depth_image(right_path / DEPTH_TEMPLATE.format(img_idx))
# Ensure all images are c_contiguous
if not left_pixels.flags.c_contiguous:
left_pixels = np.ascontiguousarray(left_pixels)
# if not left_label_image.flags.c_contiguous:
# left_label_image = np.ascontiguousarray(left_label_image)
if not left_true_depth.flags.c_contiguous:
left_true_depth = np.ascontiguousarray(left_true_depth)
if not right_pixels.flags.c_contiguous:
right_pixels = np.ascontiguousarray(right_pixels)
# if not right_label_image.flags.c_contiguous:
# right_label_image = np.ascontiguousarray(right_label_image)
if not right_true_depth.flags.c_contiguous:
right_true_depth = np.ascontiguousarray(right_true_depth)
# Extract the poses
left_camera_pose = read_camera_pose(left_frame_data)
right_camera_pose = read_camera_pose(right_frame_data)
# If we have object data, extract labels for them as well
# Not working? removed
# if len(left_object_labels) > 0:
# left_labelled_objects = find_labelled_objects(left_label_image, left_frame_data, left_object_labels)
# else:
# left_labelled_objects = []
# if len(right_object_labels) > 0:
# right_labelled_objects = find_labelled_objects(right_label_image, right_frame_data, right_object_labels)
# else:
# right_labelled_objects = []
left_labelled_objects = []
right_labelled_objects = []
# Compute a noisy depth image
noisy_depth = create_noisy_depth_image(
left_true_depth=left_true_depth,
right_true_depth=right_true_depth,
camera_intrinsics=left_camera_intrinsics,
right_camera_relative_pose=left_camera_pose.find_relative(right_camera_pose),
quality_level=depth_quality
)
# Re-centre the camera poses relative to the first frame
if origin is None:
origin = left_camera_pose
left_camera_pose = origin.find_relative(left_camera_pose)
right_camera_pose = origin.find_relative(right_camera_pose)
left_metadata = imeta.make_metadata(
pixels=left_pixels,
depth=left_true_depth,
camera_pose=left_camera_pose,
intrinsics=left_camera_intrinsics,
source_type=imeta.ImageSourceType.SYNTHETIC,
environment_type=environment_type,
light_level=light_level,
time_of_day=time_of_day,
simulation_world=simulation_world,
lighting_model=lighting_model,
texture_mipmap_bias=texture_mipmap_bias,
normal_maps_enabled=normal_maps_enabled,
roughness_enabled=roughness_enabled,
geometry_decimation=geometry_decimation,
minimum_object_volume=min_object_size,
labelled_objects=left_labelled_objects
)
right_metadata = imeta.make_right_metadata(
pixels=right_pixels,
depth=right_true_depth,
camera_pose=right_camera_pose,
intrinsics=right_camera_intrinsics,
labelled_objects=right_labelled_objects,
left_metadata=left_metadata
)
image = StereoImage(
pixels=left_pixels,
right_pixels=right_pixels,
depth=noisy_depth,
true_depth=left_true_depth,
right_true_depth=right_true_depth,
image_group=image_group,
metadata=left_metadata,
right_metadata=right_metadata,
)
try:
image.save()
except KeyError as exp:
logging.getLogger(__name__).info(f"Key error while saving image {img_idx} in sequence {sequence_name}, "
f"read from {left_path / IMG_TEMPLATE.format(img_idx)}")
raise exp
images.append(image)
# Create and save the image collection
collection = ImageCollection(
images=images,
image_group=sequence_name,
timestamps=timestamps,
sequence_type=ImageSequenceType.SEQUENTIAL,
dataset="generated-SLAM-data",
sequence_name=sequence_name,
trajectory_id=trajectory_id
)
collection.save()
return collection
def create_frame(self, time: float) -> Image:
img_shape = (self.height, self.width,
3) if self.colour else (self.height, self.width)
frame = np.zeros(img_shape, dtype=np.uint8)
depth = None
if self.mode is ImageMode.RGBD:
depth = (1000 + 2 * len(self.stars)) * np.ones(
(self.height, self.width), dtype=np.float64)
f = self.focal_length
cx = frame.shape[1] / 2
cy = frame.shape[0] / 2
for star in self.stars:
x, y, z = star['pos']
x -= self.speed * time
if z <= 0:
break # Stars are sorted by z value, so once they're past the camera, stop.
left = int(np.round(f * ((x - star['width'] / 2) / z) + cx))
right = int(np.round(f * ((x + star['width'] / 2) / z) + cx))
top = int(np.round(f * ((y - star['height'] / 2) / z) + cy))
bottom = int(np.round(f * ((y + star['height'] / 2) / z) + cy))
left = max(0, min(frame.shape[1], left))
right = max(0, min(frame.shape[1], right))
top = max(0, min(frame.shape[0], top))
bottom = max(0, min(frame.shape[0], bottom))
frame[top:bottom, left:right] = star['colour']
if depth is not None:
depth[top:bottom, left:right] = z
metadata = imeta.make_metadata(
pixels=frame,
depth=depth,
source_type=imeta.ImageSourceType.SYNTHETIC,
camera_pose=Transform(location=[time * self.speed, 0, 0],
rotation=[0, 0, 0, 1]),
intrinsics=CameraIntrinsics(width=frame.shape[1],
height=frame.shape[0],
fx=f,
fy=f,
cx=cx,
cy=cy))
# If we're building a stereo image, make the right image
if self.mode is ImageMode.STEREO:
right_frame = np.zeros(img_shape, dtype=np.uint8)
for star in self.stars:
x, y, z = star['pos']
x -= self.stereo_offset + self.speed * time
if z <= 0:
break
left = int(np.round(f * ((x - star['width'] / 2) / z) + cx))
right = int(np.round(f * ((x + star['width'] / 2) / z) + cx))
top = int(np.round(f * ((y - star['height'] / 2) / z) + cy))
bottom = int(np.round(f * ((y + star['height'] / 2) / z) + cy))
left = max(0, min(frame.shape[1], left))
right = max(0, min(frame.shape[1], right))
top = max(0, min(frame.shape[0], top))
bottom = max(0, min(frame.shape[0], bottom))
right_frame[top:bottom, left:right] = star['colour']
right_metadata = imeta.make_right_metadata(
pixels=right_frame,
left_metadata=metadata,
source_type=imeta.ImageSourceType.SYNTHETIC,
camera_pose=Transform(
location=[time * self.speed, -1 * self.stereo_offset, 0],
rotation=[0, 0, 0, 1]),
intrinsics=CameraIntrinsics(width=frame.shape[1],
height=frame.shape[0],
fx=f,
fy=f,
cx=cx,
cy=cy))
return StereoImage(pixels=frame,
image_group='test',
metadata=metadata,
right_pixels=right_frame,
right_metadata=right_metadata)
if depth is not None:
return Image(pixels=frame,
image_group='test',
depth=depth,
metadata=metadata)
return Image(pixels=frame, image_group='test', metadata=metadata)
def import_dataset(root_folder, sequence_number, **_):
"""
Load a KITTI image sequences into the database.
:return:
"""
sequence_number = int(sequence_number)
if not 0 <= sequence_number < 11:
raise ValueError("Cannot import sequence {0}, it is invalid".format(sequence_number))
root_folder = find_root(root_folder, sequence_number)
data = pykitti.odometry(root_folder, sequence="{0:02}".format(sequence_number))
# dataset.calib: Calibration data are accessible as a named tuple
# dataset.timestamps: Timestamps are parsed into a list of timedelta objects
# dataset.poses: Generator to load ground truth poses T_w_cam0
# dataset.camN: Generator to load individual images from camera N
# dataset.gray: Generator to load monochrome stereo pairs (cam0, cam1)
# dataset.rgb: Generator to load RGB stereo pairs (cam2, cam3)
# dataset.velo: Generator to load velodyne scans as [x,y,z,reflectance]
image_group = f"KITTI_{sequence_number:06}"
images = []
timestamps = []
with arvet.database.image_manager.get().get_group(image_group, allow_write=True):
for left_image, right_image, timestamp, pose in zip(data.cam2, data.cam3, data.timestamps, data.poses):
left_image = np.array(left_image)
right_image = np.array(right_image)
camera_pose = make_camera_pose(pose)
# camera pose is for cam0, we want cam2, which is 6cm (0.06m) to the left
# Except that we don't need to control for that, since we want to be relative to the first pose anyway
# camera_pose = camera_pose.find_independent(tf.Transform(location=(0, 0.06, 0), rotation=(0, 0, 0, 1),
# w_first=False))
# Stereo offset is 0.54m (http://www.cvlibs.net/datasets/kitti/setup.php)
right_camera_pose = camera_pose.find_independent(Transform(location=(0, -0.54, 0), rotation=(0, 0, 0, 1),
w_first=False))
camera_intrinsics = CameraIntrinsics(
height=left_image.shape[0],
width=left_image.shape[1],
fx=data.calib.K_cam2[0, 0],
fy=data.calib.K_cam2[1, 1],
cx=data.calib.K_cam2[0, 2],
cy=data.calib.K_cam2[1, 2])
right_camera_intrinsics = CameraIntrinsics(
height=right_image.shape[0],
width=right_image.shape[1],
fx=data.calib.K_cam3[0, 0],
fy=data.calib.K_cam3[1, 1],
cx=data.calib.K_cam3[0, 2],
cy=data.calib.K_cam3[1, 2])
left_metadata = imeta.make_metadata(
pixels=left_image,
camera_pose=camera_pose,
intrinsics=camera_intrinsics,
source_type=imeta.ImageSourceType.REAL_WORLD,
environment_type=imeta.EnvironmentType.OUTDOOR_URBAN,
light_level=imeta.LightingLevel.WELL_LIT,
time_of_day=imeta.TimeOfDay.AFTERNOON,
)
right_metadata = imeta.make_right_metadata(
pixels=right_image,
left_metadata=left_metadata,
camera_pose=right_camera_pose,
intrinsics=right_camera_intrinsics
)
image = StereoImage(
pixels=left_image,
right_pixels=right_image,
image_group=image_group,
metadata=left_metadata,
right_metadata=right_metadata
)
image.save()
images.append(image)
timestamps.append(timestamp.total_seconds())
# Create and save the image collection
collection = ImageCollection(
images=images,
timestamps=timestamps,
sequence_type=ImageSequenceType.SEQUENTIAL,
dataset='KITTI',
sequence_name="{0:02}".format(sequence_number),
trajectory_id="KITTI_{0:02}".format(sequence_number)
)
collection.save()
return collection
def import_dataset(root_folder, dataset_name, **_):
"""
Load a TUM RGB-D sequence into the database.
:return:
"""
root_folder = Path(root_folder)
# Step 0: Check the root folder to see if it needs to be extracted from a tarfile
delete_when_done = None
if not root_folder.is_dir():
if (root_folder.parent / dataset_name).is_dir():
# The root was a tarball, but the extracted data already exists, just use that as the root
root_folder = root_folder.parent / dataset_name
else:
candidate_tar_file = root_folder.parent / (dataset_name + '.tgz')
if candidate_tar_file.is_file() and tarfile.is_tarfile(
candidate_tar_file):
# Root is actually a tarfile, extract it. find_roots with handle folder structures
with tarfile.open(candidate_tar_file) as tar_fp:
tar_fp.extractall(root_folder.parent / dataset_name)
root_folder = root_folder.parent / dataset_name
delete_when_done = root_folder
else:
# Could find neither a dir nor a tarfile to extract from
raise NotADirectoryError(
"'{0}' is not a directory".format(root_folder))
# Step 1: Find the relevant metadata files
root_folder, rgb_path, depth_path, trajectory_path = find_files(
root_folder)
# Step 2: Read the metadata from them
image_files = read_image_filenames(rgb_path)
trajectory = read_trajectory(trajectory_path, image_files.keys())
depth_files = read_image_filenames(depth_path)
# Step 3: Associate the different data types by timestamp
all_metadata = associate_data(image_files, trajectory, depth_files)
# Step 3: Load the images from the metadata
first_timestamp = None
image_group = dataset_name
images = []
timestamps = []
with arvet.database.image_manager.get().get_group(image_group,
allow_write=True):
for timestamp, image_file, camera_pose, depth_file in all_metadata:
# Re-zero the timestamps
if first_timestamp is None:
first_timestamp = timestamp
timestamp = (timestamp - first_timestamp)
rgb_data = image_utils.read_colour(
os.path.join(root_folder, image_file))
depth_data = image_utils.read_depth(
os.path.join(root_folder, depth_file))
depth_data = depth_data / 5000 # Re-scale depth to meters
camera_intrinsics = get_camera_intrinsics(root_folder)
metadata = imeta.make_metadata(
pixels=rgb_data,
depth=depth_data,
camera_pose=camera_pose,
intrinsics=camera_intrinsics,
source_type=imeta.ImageSourceType.REAL_WORLD,
environment_type=environment_types.get(
dataset_name, imeta.EnvironmentType.INDOOR_CLOSE),
light_level=imeta.LightingLevel.WELL_LIT,
time_of_day=imeta.TimeOfDay.DAY,
)
image = Image(pixels=rgb_data,
depth=depth_data,
image_group=image_group,
metadata=metadata)
image.save()
images.append(image)
timestamps.append(timestamp)
# Create and save the image collection
collection = ImageCollection(images=images,
timestamps=timestamps,
sequence_type=ImageSequenceType.SEQUENTIAL,
dataset='TUM RGB-D',
sequence_name=dataset_name,
trajectory_id=dataset_name)
collection.save()
if delete_when_done is not None and delete_when_done.exists():
# We're done and need to clean up after ourselves
shutil.rmtree(delete_when_done)
return collection
def import_dataset(root_folder, dataset_name, **_):
"""
Load an Autonomous Systems Lab dataset into the database.
See http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets#downloads
Some information drawn from the ethz_asl dataset tools, see: https://github.com/ethz-asl/dataset_tools
:param root_folder: The body folder, containing body.yaml (i.e. the extracted mav0 folder)
:param dataset_name: The name of the dataset, see the manager for the list of valid values.
:return:
"""
if not os.path.isdir(root_folder):
raise NotADirectoryError(
"'{0}' is not a directory".format(root_folder))
# Step 1: Find the various files containing the data (that's a 6-element tuple unpack for the return value)
(root_folder, left_rgb_path, left_camera_intrinsics_path, right_rgb_path,
right_camera_intrinsics_path, trajectory_path) = find_files(root_folder)
# Step 2: Read the meta-information from the files (that's a 6-element tuple unpack for the return value)
left_image_files = read_image_filenames(left_rgb_path)
left_extrinsics, left_intrinsics = get_camera_calibration(
left_camera_intrinsics_path)
right_image_files = read_image_filenames(left_rgb_path)
right_extrinsics, right_intrinsics = get_camera_calibration(
right_camera_intrinsics_path)
trajectory = read_trajectory(trajectory_path, left_image_files.keys())
# Step 3: Create stereo rectification matrices from the intrinsics
left_x, left_y, left_intrinsics, right_x, right_y, right_intrinsics = rectify(
left_extrinsics, left_intrinsics, right_extrinsics, right_intrinsics)
# Change the coordinates correctly on the extrinsics. Has to happen after rectification
left_extrinsics = fix_coordinates(left_extrinsics)
right_extrinsics = fix_coordinates(right_extrinsics)
# Step 4: Associate the different data types by timestamp. Trajectory last because it's bigger than the stereo.
all_metadata = associate_data(left_image_files, right_image_files,
trajectory)
# Step 5: Load the images from the metadata
first_timestamp = None
image_group = dataset_name
images = []
timestamps = []
with arvet.database.image_manager.get().get_group(image_group,
allow_write=True):
for timestamp, left_image_file, right_image_file, robot_pose in all_metadata:
# Timestamps are in POSIX nanoseconds, re-zero them to the start of the dataset, and scale to seconds
if first_timestamp is None:
first_timestamp = timestamp
timestamp = (timestamp - first_timestamp) / 1e9
left_data = image_utils.read_colour(
os.path.join(root_folder, 'cam0', 'data', left_image_file))
right_data = image_utils.read_colour(
os.path.join(root_folder, 'cam1', 'data', right_image_file))
# Error check the loaded image data
if left_data is None or left_data.size is 0:
logging.getLogger(__name__).warning(
"Could not read left image \"{0}\", result is empty. Skipping."
.format(
os.path.join(root_folder, 'cam0', 'data',
left_image_file)))
continue
if right_data is None or right_data.size is 0:
logging.getLogger(__name__).warning(
"Could not read right image \"{0}\", result is empty. Skipping."
.format(
os.path.join(root_folder, 'cam1', 'data',
right_image_file)))
continue
left_data = cv2.remap(left_data, left_x, left_y, cv2.INTER_LINEAR)
right_data = cv2.remap(right_data, right_x, right_y,
cv2.INTER_LINEAR)
left_pose = robot_pose.find_independent(left_extrinsics)
right_pose = robot_pose.find_independent(right_extrinsics)
left_metadata = imeta.make_metadata(
pixels=left_data,
camera_pose=left_pose,
intrinsics=left_intrinsics,
source_type=imeta.ImageSourceType.REAL_WORLD,
environment_type=environment_types.get(
dataset_name, imeta.EnvironmentType.INDOOR_CLOSE),
light_level=imeta.LightingLevel.WELL_LIT,
time_of_day=imeta.TimeOfDay.DAY,
)
right_metadata = imeta.make_right_metadata(
pixels=right_data,
left_metadata=left_metadata,
camera_pose=right_pose,
intrinsics=right_intrinsics)
image = StereoImage(pixels=left_data,
right_pixels=right_data,
image_group=image_group,
metadata=left_metadata,
right_metadata=right_metadata)
image.save()
images.append(image)
timestamps.append(timestamp)
# Create and save the image collection
collection = ImageCollection(images=images,
timestamps=timestamps,
sequence_type=ImageSequenceType.SEQUENTIAL,
dataset='EuRoC MAV',
sequence_name=dataset_name,
trajectory_id=dataset_name)
collection.save()
return collection