Cameras sequence

a camerasseq consists of a sequence of camera images, where each frame can contain between 1 12 images from different sensors for more documentation on what each field corresponds to in the camerasseq object please check the section related to overview docid\ yunpnpwuhzlgg9wb9qnk8 from future import absolute import from typing import optional from uuid import uuid4 import kognic io model scene cameras sequence as csm from kognic io client import kognicioclient from kognic io logger import setup logging from kognic io model import createsceneresponse, image def run(client kognicioclient, dryrun bool = true, kwargs) > optional\[createsceneresponse] print("creating cameras sequence scene ") sensor1 = "rfc01" sensor2 = "rfc02" metadata = {"location lat" 27 986065, "location long" 86 922623, "vehicle id" "abg"} scene = csm camerassequence( external id=f"camera seq images example {uuid4()}", frames=\[ csm frame( frame id="1", relative timestamp=0, images=\[ \# jpg images in frame 1 image( filename=" /examples/resources/img rfc01 jpg", sensor name=sensor1, ), image( filename=" /examples/resources/img rfc02 jpg", sensor name=sensor2, ), ], metadata={"dut status" "active"}, ), csm frame( frame id="2", relative timestamp=500, images=\[ \# png images in frame 2 image( filename=" /examples/resources/img rfc11 png", sensor name=sensor1, ), image( filename=" /examples/resources/img rfc12 png", sensor name=sensor2, ), ], metadata={"dut status" "active"}, ), csm frame( frame id="3", relative timestamp=1000, images=\[ \# webp vp8 images in frame 3 image( filename=" /examples/resources/img rfc21 webp", sensor name=sensor1, ), image( filename=" /examples/resources/img rfc22 webp", sensor name=sensor2, ), ], metadata={"dut status" "active"}, ), csm frame( frame id="4", relative timestamp=1500, images=\[ \# webp vp8l images in frame 4 image( filename=" /examples/resources/img rfc31 webp", sensor name=sensor1, ), image( filename=" /examples/resources/img rfc32 webp", sensor name=sensor2, ), ], metadata={"dut status" "active"}, ), csm frame( frame id="5", relative timestamp=2000, images=\[ \# webp vp8x images in frame 5 image( filename=" /examples/resources/img rfc41 webp", sensor name=sensor1, ), image( filename=" /examples/resources/img rfc42 webp", sensor name=sensor2, ), ], metadata={"dut status" "active"}, ), csm frame( frame id="6", relative timestamp=2500, images=\[ \# avif images in frame 6 image( filename=" /examples/resources/img rfc51 avif", sensor name=sensor1, ), image( filename=" /examples/resources/img rfc52 avif", sensor name=sensor2, ), ], metadata={"dut status" "active"}, ), ], metadata=metadata, ) \# create scene return client cameras sequence create(scene, dryrun=dryrun, kwargs) if name == " main " setup logging(level="info") \# project available via `client project get projects()` project = "\<project identifier>" client = kognicioclient() run(client, project=project) use dryrun to validate scene setting dryrun parameter to true in the method call, will validate the scene using the api but not create it