This is an application that takes JSONLines formatted video label dataset as input and builds context dependent knowledge base.
Input data must be JSONLines that contains multiple JSON objects in one of the formats below.
Object
{"type": "object", "class": "person", "label": "Person A", "seconds": 15.0, "coordinates": [100, 200, 20, 30]}{"type": "object", "id": "person_ross_geller", "class": "person", "label": "Ross Geller", "seconds": 15.0, "coordinates": [100, 200, 20, 30]}
Behavior
{"type": "behavior", "class": "stand", "seconds": 15.0, "object": Object}
Emotion
{"type": "emotion", "class": "happy", "seconds": 15.0, "object": Object}
Relation
{"type": "relation", "class": RelationalClass, "subclass": "wear", "seconds": 15.0, "source": Object, "target": Object}
Location
{"type": "location", "class": "central_perk", "seconds": 15.0}
Sound
{"type": "sound", "class": "glass_crashing", "seconds": 15.0}
Object
{"id": "person_ross_geller"}or{"coordinates": [100, 200, 20, 30]}
RelationalClass
"behavior"for subclass"wear","hold","hug", …"emotion"for subclass"love","like", …"position"for subclass"above","below","next_to", …"social"for subclass"son_of","father_of","lover_of","friend_of", …
main.py takes input data as standard input, and prints result knowledge base as
standard output as a JSONLines formatted string. For example:
video-knowledge-builder $ python main.py < test_input.jsonlines > output.jsonlines
The shell command above will provide the contents of test_input.jsonlines as
input for the application and save the result in a file output.jsonlines.