def visualize_helper(box_list):
vgrid_spec = VGridSpec(
video_meta=video_metadata_intel,
vis_format=VideoBlockFormat(imaps=[(str(i), box)
for i, box in enumerate(box_list)]),
video_endpoint=VIDEO_COLLECTION_BASEURL_INTEL)
return VGridWidget(vgrid_spec=vgrid_spec.to_json_compressed())
def find_clips_for_keyword(keyword, use_only_video=False):
ism = {}
for vm in tqdm(video_metadata):
if not vm["annotation_filename"] or (not vm["only_video"]
and use_only_video):
continue
try:
h5 = eeghdf.Eeghdf(vm["annotation_filename"])
except:
print(vm["annotation_filename"])
os.remove(vm["annotation_filename"])
continue
starts = [start / 10**7 for start in h5._annotation_start100ns]
texts = h5._annotation_text
if not keyword or any(keyword.lower() in text.lower()
for text in texts):
interval_set = IntervalSet([
Interval(
Bounds3D(start, start + 5), # we set the duration
{
'spatial_type':
SpatialType_Caption(">>" + text + "\n"),
'metadata': {}
}) for start, text in zip(starts, texts)
])
ism[vm["id"]] = interval_set
print(
f"Found {len(ism)} videos with keyword {keyword} in the annotations.")
vgrid_spec = VGridSpec(video_meta=video_metadata_wrapper,
vis_format=VideoBlockFormat(imaps=[('bboxes',
ism)]),
video_endpoint='http://localhost:8080')
return VGridWidget(vgrid_spec=vgrid_spec.to_json_compressed())
def IntervalSetMapping_to_vgrid(ism, flat=False):
video_meta = []
if flat:
video_meta_id = 0
ism_final = {}
for video_id, intervalSet in ism.items():
video = Video.objects.filter(id=video_id)[0]
for i in intervalSet.get_intervals():
ism_final[video_meta_id] = IntervalSet([i])
video_meta.append(VideoMetadata(path=video.path, id=video_meta_id))
video_meta_id += 1
else:
for video_id, intervalSet in ism.items():
video = Video.objects.filter(id=video_id)[0]
video_meta.append(VideoMetadata(path=video.path, id=video_id))
ism_final = ism
vgrid_spec = VGridSpec(
video_meta=video_meta,
video_endpoint="http://sora.stanford.edu/system_media/",
vis_format=VideoBlockFormat([('test', ism_final)]),
show_timeline=True)
return VGridWidget(vgrid_spec=vgrid_spec.to_json())
)
)
pose_metadata[video_meta.id] = IntervalSet(pose_intervals)
# In[29]:
pose_annotation_files
# In[31]:
vgrid_spec = VGridSpec(
video_meta = metadata_videos,
vis_format = VideoBlockFormat(imaps = [
('pose', IntervalSetMapping(pose_metadata))
]),
video_endpoint = 'http://localhost:8080'
)
VGridWidget(vgrid_spec = vgrid_spec.to_json_compressed())
# In[ ]:
ism = IntervalSetMapping({
0: IntervalSet([
Interval(
Bounds3D(0, 10),
{
'spatial_type': SpatialType_Temporal(),
'metadata': {
'generic_metadata': Metadata_Generic(
'this will be drawn below the timeline when expanded'
),
'other_generic_metadata': Metadata_Generic({
'key': 'this object will also be stringified'
})
}
}
)
])
})
vgrid_spec = VGridSpec(
video_meta = video_metadata,
vis_format = VideoBlockFormat(imaps = [
('bboxes', ism)
])
)
# Pass this to your Javascript application somehow
json_for_js = vgrid_spec.to_json_compressed()
def vgrid_widget(**kwargs):
return VGridWidget(vgrid_spec=VGridSpec(
video_endpoint='http://{}/system_media'.format(hostname),
frameserver_endpoint='http://{}:7500/fetch'.format(hostname),
use_frameserver=True,
**kwargs).to_json())