def vis_example2d(
example,
num_frames: int = 20,
fps: int = 4,
reverse_xy: bool = False,
flip_up_down: bool = False,
class_names: Optional[Sequence[str]] = None,
):
"""
Visualize 2d example as an animated sequence of frames.
Args:
example: element from unbatched image stream, tuple of (features, label),
where features is a dict with "coords", "time" and "polarity" keys.
num_frames: total number of frames in resulting animation.
fps: frame-rate
reverse_xy: if True, transposes coordinates.
flip_up_down: if true, flips up/down (after possible transposition from
`reverse_xy`).
class_names: if provided, prints `class_names[label]` rather than `label`.
"""
features, label = example
coords = features["coords"]
time = features["time"]
time = (time - tf.reduce_min(time)).numpy()
polarity = features["polarity"].numpy()
if class_names is not None:
print(class_names[label.numpy()])
else:
print(label.numpy())
coords = (coords - tf.reduce_min(coords, axis=0)).numpy()
print(f"{time.shape[0]} events over {time[-1] - time[0]} dt")
if reverse_xy:
coords = coords[:, -1::-1]
if num_frames == 1:
frame = as_frame(coords, polarity)
plt.imshow(frame)
plt.show()
else:
frames = as_frames(
coords, time, polarity, num_frames=num_frames, flip_up_down=flip_up_down
)
anim.animate_frames(frames, fps=fps, tight_layout=True)
import os
from events_tfds.utils import make_monotonic
from events_tfds.vis import image
from events_tfds.vis import anim
folder = "/home/rslsync/Resilio Sync/DHP19/DVS_movies/S1/session1"
filename = "mov8.aedat"
path = os.path.join(folder, filename)
with tf.io.gfile.GFile(path, "rb") as fp:
timestamp, x, y, pol, camera = load_dhp19_events(fp)
timestamp = make_monotonic(timestamp, dtype=np.uint64)
assert np.all(timestamp[1:] >= timestamp[:-1])
print("camera", np.min(camera), np.max(camera))
print("polarity", np.min(pol), np.max(pol))
print("x", np.min(x), np.max(x))
print("y", np.min(y), np.max(y))
cam_mask = camera == 1
pol = pol[cam_mask]
x = x[cam_mask]
y = y[cam_mask]
timestamp = timestamp[cam_mask]
print("num_events: {}".format(timestamp.size))
coords = np.stack((x, np.max(y) - y), axis=-1)
print("Creating animation...")
frames = image.as_frames(coords, timestamp, pol, num_frames=200)
anim.animate_frames(frames, fps=20)
if __name__ == "__main__":
save_path = "/tmp/asl-dvs.gif"
# from scipy.io import loadmat
# path = '/home/jackd/Downloads/y_4200.mat'
# loadmat(path)
download_config = None
# download_config = tfds.core.download.DownloadConfig(register_checksums=True)
builder = AslDvs()
builder.download_and_prepare(download_config=download_config)
from events_tfds.vis.image import as_frames
from events_tfds.vis.anim import animate_frames
for events, labels in builder.as_dataset(split="train",
as_supervised=True):
frames = as_frames(
**{k: v.numpy()
for k, v in events.items()},
num_frames=24,
flip_up_down=True,
)
print(tf.reduce_max(events["coords"], axis=0).numpy() + 1)
t = events["time"].numpy()
print(f"{t.size} events over dt = {t[-1] - t[0]}")
print(f"class = {CLASSES[labels.numpy()]}")
animate_frames(frames, fps=8, save_path=save_path)
if save_path is not None:
print(f"Animation saved to {save_path}")
break
continue
_, label, filename = path.split("/")
example_id = int(filename[:-4])
time, coords, polarity = load_neuro_events(fobj)
features = dict(
events=dict(time=time, coords=coords, polarity=polarity),
label=int(label),
example_id=example_id,
)
yield example_id, features
if __name__ == "__main__":
download_config = None
# download_config = tfds.core.download.DownloadConfig(
# register_checksums=True)
builder = NMNIST()
builder.download_and_prepare(download_config=download_config)
from events_tfds.vis.image import as_frames
from events_tfds.vis.anim import animate_frames
for events, labels in builder.as_dataset(split="train",
as_supervised=True):
frames = as_frames(**{k: v.numpy()
for k, v in events.items()},
num_frames=20)
print(labels.numpy())
t = events["time"].numpy()
print(t[-1] - t[0], len(t))
animate_frames(frames, fps=4)
events=dict(
time=time.astype(np.int64),
coords=coords.astype(np.int64),
polarity=polarity,
),
label=label,
example_id=example_id,
)
yield (label, example_id), features
if __name__ == "__main__":
from events_tfds.vis.image import as_frames
from events_tfds.vis.anim import animate_frames
download_config = None
# download_config = tfds.core.download.DownloadConfig(register_checksums=True)
builder = MnistDVS(config=SCALE16)
builder.download_and_prepare(download_config=download_config)
for events, labels in builder.as_dataset(split="train",
as_supervised=True):
frames = as_frames(**{k: v.numpy()
for k, v in events.items()},
num_frames=40)
print(labels.numpy())
print(tf.reduce_max(events["coords"], axis=0).numpy() + 1)
time = events["time"].numpy()
print(f"{time.shape[0]} events over {time[-1] - time[0]} dt")
animate_frames(frames, fps=8)
timestamp, addr = load_raw_events(
fp,
filter_dvs=filter_dvs,
# bytes_skip=bytes_skip,
# bytes_trim=bytes_trim,
# times_first=times_first
)
x, y, polarity = parse_raw_address(addr, **kwargs)
return timestamp, x, y, polarity
if __name__ == "__main__":
from events_tfds.utils import make_monotonic
folder = "/home/rslsync/Resilio Sync/RoShamBoNPP/recordings/aedat/"
# filename = 'background_10.aedat'
filename = "paper_tobi_front.aedat"
path = os.path.join(folder, filename)
with open(path, "rb") as fp:
time, x, y, pol = load_events(fp)
time = make_monotonic(time)
time -= np.min(time)
x -= np.min(x)
y -= np.min(y)
from events_tfds.vis import image
from events_tfds.vis import anim
coords = np.stack((x, y), axis=-1)
frames = image.as_frames(coords, time, pol, num_frames=100)
anim.animate_frames(frames, 20)