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Custom annotations unlock additional featuresAdding application context into the search pipeline.Generate unique descriptions from your own custom vision model.Index manual annotations.
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Custom Annotations
Enhance your understanding of videos by using our simple annotation and tagging pipeline. To enable this, you can create a new Scene object. Then, pass your annotations in the description field and index them using index_scenes() function.
VideoDB allows multiple indexes on video objects. This is advantageous as it allows you to attach additional context to each scene, enhancing the search functionality.
Scene:
A Scene object describes a unique event in the video. From a timeline perspective it’s a timestamp range.
video_id : id of the video object
start : seconds
end : seconds
description : string description
Each scene object has another attribute, frames, which contains a list of objects. However, we don't need them here for custom annotation pipelines because we are bringing the description from outside.
Create a new Scene
Create new Scene objects and add your custom annotation as description.
from videodb.scene import Scene
# create scene object and patch your description
scene1 = Scene(
video_id=video.id,
start=0,
end=100,
description="Detective Martin is being interviewed by the police.",)
scene2 = Scene(
video_id=video.id,
start=600,
end=900,
description="A religious gathering. People are praying and singing")
# create a list of scene objects
scenes = [scene1, scene2]
Index and search scenes
Index using the list of scene objects and use the index_id for search
from videodb import IndexType
#create new index and assign it a name
index_id = video.index_scenes(scenes=scenes, name="My Custom Annotations#1")
# search using the index_id
res = video.search(query="religious gathering", index_type=IndexType.scene, index_id=index_id)
res.play()
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