if __name__ == '__main__':
CLI = argparse.ArgumentParser()
CLI.add_argument("--data", nargs='?', type=str)
CLI.add_argument("--frame_folder", nargs='?', type=str)
CLI.add_argument("--frame_name", nargs='?', type=str)
CLI.add_argument("--frame_format", nargs='?', type=str)
CLI.add_argument("--frame_rate", nargs='?', type=none_or_float)
CLI.add_argument("--colormap", nargs='?', type=str)
CLI.add_argument("--event", nargs='?', type=none_or_str, default=None)
CLI.add_argument("--markercolor", nargs='?', type=str, default='k')
args = CLI.parse_args()
blk = load_neo(args.data)
blk = AnalogSignal2ImageSequence(blk)
# get data
imgseq = blk.segments[0].imagesequences[0]
times = blk.segments[0].analogsignals[0].times # to be replaced
t_start = blk.segments[0].analogsignals[0].t_start # to be replaced
t_stop = blk.segments[0].analogsignals[0].t_stop # to be replaced
dim_t, dim_x, dim_y = imgseq.shape
optical_flow = get_opticalflow(blk.segments[0].imagesequences)
if args.event is not None:
up_coords = get_events(blk.segments[0].events,
frame_times=times,
event_name=args.event)
help="path of output image file")
CLI.add_argument("--alpha", nargs='?', type=float, default=0.001,
help='regularization parameter')
CLI.add_argument("--max_Niter", nargs='?', type=int, default=50,
help='maximum number of iterations')
CLI.add_argument("--convergence_limit", nargs='?', type=float, default=10e-3,
help='absolute change at which consider optimization converged')
CLI.add_argument("--gaussian_sigma", nargs='+', type=float, default=[0,3,3],
help='sigma of gaussian filter in each dimension')
CLI.add_argument("--derivative_filter", nargs='?', type=str, default='Simple',
help='Filter kernel to use for calculating spatial derivatives')
args = CLI.parse_args()
block = load_neo(args.data)
block = AnalogSignal2ImageSequence(block)
imgseq = block.segments[0].imagesequences[-1]
asig = block.segments[0].analogsignals[-1]
frames = imgseq.as_array()
# frames /= np.nanmax(np.abs(frames))
kernelHS = np.array([[1, 2, 1],
[2, 0, 2],
[1, 2, 1]], dtype=np.float) * 1/12
kernelT = np.ones((2, 2), dtype=np.float) * .25
kernelX, kernelY = get_derviation_kernels(args.derivative_filter)
vector_frames = horn_schunck(frames=frames,
alpha=args.alpha,
max_Niter=args.max_Niter,
CLI.add_argument("--node_data",
nargs='?',
type=str,
required=True,
help="path to input data in neo format")
CLI.add_argument("--output",
nargs='?',
type=str,
required=True,
help="path of output file")
args = CLI.parse_args()
trigger_block = load_neo(args.trigger_data)
node_block = load_neo(args.node_data)
block = AnalogSignal2ImageSequence(node_block)
wavefront_evt = [
evt for evt in trigger_block.segments[0].events
if evt.name == "Wavefronts"
]
if wavefront_evt:
wavefront_evt = wavefront_evt[0]
else:
raise ValueError("Input does not contain an event with name " \
+ "'Wavefronts'!")
block.segments[0].events.append(wavefront_evt)
write_neo(args.output, block)
