def calibrate(args):
np.set_printoptions(precision=4, suppress=True)
# Use image path if not explicity specified
output_path = args.paths.image_path or args.paths.output_path
ws = workspace.Workspace(output_path, args.paths.name)
setup_logging(args.runtime.log_level, [ws.log_handler],
log_file=path.join(output_path, f"{args.paths.name}.txt"))
boards = find_board_config(args.paths.image_path,
board_file=args.paths.boards)
camera_images = find_camera_images(args.paths.image_path,
args.paths.cameras,
args.paths.camera_pattern)
initialise_with_images(ws, boards, camera_images, args.camera,
args.runtime)
optimize(ws, args.optimizer)
ws.export()
ws.dump()
if args.vis:
visualize_ws(ws)
def calibrate_intrinsic(args):
paths = setup_paths(args.paths)
setup_logging(args.runtime.log_level, [], log_file=paths.log_file)
info(pformat_struct(args))
image_path = os.path.expanduser(args.paths.image_path)
info(f"Finding images in {image_path}")
camera_images = find_camera_images(image_path,
args.paths.cameras,
args.paths.camera_pattern,
matching=False)
image_counts = {
k: len(files)
for k, files in zip(camera_images.cameras, camera_images.filenames)
}
info("Found camera directories with images {}".format(image_counts))
board_names, boards = split_dict(
find_board_config(image_path, args.paths.boards))
info("Loading images..")
images = image.detect.load_images(camera_images.filenames,
prefix=camera_images.image_path,
j=args.runtime.num_threads)
image_sizes = map_list(common_image_size, images)
info({
k: image_size
for k, image_size in zip(camera_images.cameras, image_sizes)
})
cache_key = struct(boards=boards,
image_sizes=image_sizes,
filenames=camera_images.filenames)
detected_points = detect_boards_cached(boards,
images,
paths.detections,
cache_key,
j=args.runtime.num_threads)
cameras, errs = calibrate_cameras(boards,
detected_points,
image_sizes,
model=args.camera.distortion_model,
fix_aspect=args.camera.fix_aspect,
has_skew=args.camera.allow_skew,
max_images=args.camera.limit_intrinsic)
for name, camera, err in zip(camera_images.cameras, cameras, errs):
info(f"Calibrated {name}, with RMS={err:.2f}")
info(camera)
info("")
info(f"Writing single calibrations to {paths.calibration_file}")
export_single(paths.calibration_file, cameras, camera_images.cameras,
camera_images.filenames)
def show_result(args):
np.set_printoptions(precision=4, suppress=True)
filename = args.workspace_file
if path.isdir(filename):
filename = path.join(filename, "calibration.pkl")
ws = Workspace.load(filename)
setup_logging('INFO', [ws.log_handler])
ws.load_images()
visualize(ws)
def initialise(args, paths):
ws = workspace.Workspace()
setup_logging(args.log_level, [ws.log_handler], log_file=paths.log_file)
info(args)
board_file = args.boards or path.join(args.image_path, "boards.yaml")
if args.boards is None:
assert path.isfile(board_file),\
f"either specify boards description file with --boards or add boards.yaml to image path"
else:
assert path.isfile(args.boards), f"board file {args.boards} not found"
boards = board.load_config(board_file)
info("Using boards:")
for name, b in boards.items():
info(f"{name} {b}")
cameras = map_none(str.split, args.cameras, ",")
ws.find_images_matching(args.image_path, cameras, args.camera_pattern, master = args.master)
ws.load_images(j=args.j)
ws.detect_boards(boards, cache_file=paths.detection_cache,
load_cache=not args.no_cache, j=args.j)
ws.calibrate_single(args.distortion_model, fix_aspect=args.fix_aspect,
has_skew=args.allow_skew, max_images=args.intrinsic_images)
motion_model = None
if args.motion_model == "rolling":
motion_model = RollingFrames
elif args.motion_model == "static":
motion_model = StaticFrames
else:
assert False, f"unknown motion model {args.motion_model}, (static|rolling)"
ws.initialise_poses(motion_model=motion_model)
return ws