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Cameras sequence
1min
A CamerasSeq consists of a sequence of camera images, where each frame can contain between 1-9 images from different sensors. For more documentation on what each field corresponds to in the CamerasSeq object please check the section related to Scene Overview.
Python
1from __future__ import absolute_import
2
3from typing import Optional
4from uuid import uuid4
5
6import kognic.io.model.scene.cameras_sequence as CSM
7from kognic.io.client import KognicIOClient
8from kognic.io.logger import setup_logging
9from kognic.io.model import CreateSceneResponse, Image
10
11
12def run(client: KognicIOClient, dryrun: bool = True, **kwargs) -> Optional[CreateSceneResponse]:
13 print("Creating Cameras Sequence Scene...")
14
15 sensor1 = "RFC01"
16 sensor2 = "RFC02"
17 metadata = {"location-lat": 27.986065, "location-long": 86.922623, "vehicle_id": "abg"}
18
19 scene = CSM.CamerasSequence(
20 external_id=f"camera-seq-images-example-{uuid4()}",
21 frames=[
22 CSM.Frame(
23 frame_id="1",
24 relative_timestamp=0,
25 images=[
26 # JPG Images in Frame 1
27 Image(
28 filename="./examples/resources/img_RFC01.jpg",
29 sensor_name=sensor1,
30 ),
31 Image(
32 filename="./examples/resources/img_RFC02.jpg",
33 sensor_name=sensor2,
34 ),
35 ],
36 metadata={"dut_status": "active"},
37 ),
38 CSM.Frame(
39 frame_id="2",
40 relative_timestamp=500,
41 images=[
42 # PNG Images in Frame 2
43 Image(
44 filename="./examples/resources/img_RFC11.png",
45 sensor_name=sensor1,
46 ),
47 Image(
48 filename="./examples/resources/img_RFC12.png",
49 sensor_name=sensor2,
50 ),
51 ],
52 metadata={"dut_status": "active"},
53 ),
54 CSM.Frame(
55 frame_id="3",
56 relative_timestamp=1000,
57 images=[
58 # WebP VP8 Images in Frame 3
59 Image(
60 filename="./examples/resources/img_RFC21.webp",
61 sensor_name=sensor1,
62 ),
63 Image(
64 filename="./examples/resources/img_RFC22.webp",
65 sensor_name=sensor2,
66 ),
67 ],
68 metadata={"dut_status": "active"},
69 ),
70 CSM.Frame(
71 frame_id="4",
72 relative_timestamp=1500,
73 images=[
74 # WebP VP8L Images in Frame 4
75 Image(
76 filename="./examples/resources/img_RFC31.webp",
77 sensor_name=sensor1,
78 ),
79 Image(
80 filename="./examples/resources/img_RFC32.webp",
81 sensor_name=sensor2,
82 ),
83 ],
84 metadata={"dut_status": "active"},
85 ),
86 CSM.Frame(
87 frame_id="5",
88 relative_timestamp=2000,
89 images=[
90 # WebP VP8X Images in Frame 5
91 Image(
92 filename="./examples/resources/img_RFC41.webp",
93 sensor_name=sensor1,
94 ),
95 Image(
96 filename="./examples/resources/img_RFC42.webp",
97 sensor_name=sensor2,
98 ),
99 ],
100 metadata={"dut_status": "active"},
101 ),
102 CSM.Frame(
103 frame_id="6",
104 relative_timestamp=2500,
105 images=[
106 # AVIF Images in Frame 6
107 Image(
108 filename="./examples/resources/img_RFC51.avif",
109 sensor_name=sensor1,
110 ),
111 Image(
112 filename="./examples/resources/img_RFC52.avif",
113 sensor_name=sensor2,
114 ),
115 ],
116 metadata={"dut_status": "active"},
117 ),
118 ],
119 metadata=metadata,
120 )
121
122 # Create scene
123 return client.cameras_sequence.create(scene, dryrun=dryrun, **kwargs)
124
125
126if __name__ == "__main__":
127 setup_logging(level="INFO")
128 # Project - Available via `client.project.get_projects()`
129 project = "<project-identifier>"
130
131 client = KognicIOClient()
132 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.
Updated 17 Dec 2024
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