converter tool for visionai format
Project description
visionai-data-format
VisionAI
format is Dataverse["url"] standardized annotation format to label objects and sequences in the context of Autonomous Driving System(ADS). VisionAI
provides consistent and effective driving environment description and categorization in the real-world case.
This tool provides validator of VisionAI
format schema. Currently, the library supports:
- Validate created
VisionAI
data format - Validate
VisionAI
data attributes with givenOntology
information.
Getting started
(WIP)
Install the package
pip install visionai-data-format
Prerequisites: You must have Python 3.7 and above to use this package.
Example
The following sections provide examples for the following:
Validate VisionAI schema
To validate VisionAI
data structure, could follow the example below:
from visionai_data_format.schemas.visionai_schema import VisionAIModel
# your custom visionai data
custom_visionai_data = {
"visionai": {
"frame_intervals": [
{
"frame_start": 0,
"frame_end": 0
}
],
"frames": {
"000000000000": {
"objects": {
"893ac389-7782-4bc3-8f61-09a8e48c819f": {
"object_data": {
"bbox": [
{
"name": "bbox_shape",
"stream":"camera1",
"val": [761.565,225.46,98.33000000000004, 164.92000000000002]
}
],
"cuboid": [
{
"name": "cuboid_shape",
"stream": "lidar1",
"val": [
8.727633224700037,-1.8557590122690717,-0.6544039394148177, 0.0,
0.0,-1.5807963267948966,1.2,0.48,1.89
]
}
]
}
}
},
"frame_properties": {
"streams": {
"camera1": {
"uri": "https://helenmlopsstorageqatest.blob.core.windows.net/vainewformat/kitti/kitti_small/data/000000000000/data/camera1/000000000000.png"
},
"lidar1": {
"uri": "https://helenmlopsstorageqatest.blob.core.windows.net/vainewformat/kitti/kitti_small/data/000000000000/data/lidar1/000000000000.pcd"
}
}
}
}
},
"objects": {
"893ac389-7782-4bc3-8f61-09a8e48c819f": {
"frame_intervals": [
{
"frame_start": 0,
"frame_end": 0
}
],
"name": "pedestrian",
"object_data_pointers": {
"bbox_shape": {
"frame_intervals": [
{
"frame_start": 0,
"frame_end": 0
}
],
"type": "bbox"
},
"cuboid_shape": {
"frame_intervals": [
{
"frame_start": 0,
"frame_end": 0
}
],
"type": "cuboid"
}
},
"type": "pedestrian"
}
},
"coordinate_systems": {
"lidar1": {
"type": "sensor_cs",
"parent": "",
"children": [
"camera1"
]
},
"camera1": {
"type": "sensor_cs",
"parent": "lidar1",
"children": [],
"pose_wrt_parent": {
"matrix4x4": [
-0.00159609942076306,
-0.005270645688933059,
0.999984790046273,
0.3321936949138632,
-0.9999162467477257,
0.012848695454066989,
-0.0015282672486530082,
-0.022106263278130818,
-0.012840436309973332,
-0.9999035522454274,
-0.0052907123281999745,
-0.06171977032225582,
0.0,
0.0,
0.0,
1.0
]
}
}
},
"streams": {
"camera1": {
"type": "camera",
"uri": "https://helenmlopsstorageqatest.blob.core.windows.net/vainewformat/kitti/kitti_small/data/000000000000/data/camera1/000000000000.png",
"description": "Frontal camera",
"stream_properties": {
"intrinsics_pinhole": {
"camera_matrix_3x4": [
-1.1285209781809271,
-706.9900823216068,
-181.46849639413674,
0.2499212908887926,
-3.726606344908137,
9.084661126711246,
-1.8645282480709864,
-0.31027342289053916,
707.0385458128643,
-1.0805602883730354,
603.7910589125847,
45.42556655376811
],
"height_px": 370,
"width_px": 1224
}
}
},
"lidar1": {
"type": "lidar",
"uri": "https://helenmlopsstorageqatest.blob.core.windows.net/vainewformat/kitti/kitti_small/data/000000000000/data/lidar1/000000000000.pcd",
"description": "Central lidar"
}
},
"metadata": {
"schema_version": "1.0.0"
}
}
}
# validate custom data
# If the data structure doesn't meets the VisionAI requirements, it would raise BaseModel error message
# otherwise, it will returns dictionary of validated visionai data
validated_visionai = VisionAIModel(**custom_visionai_data).dict()
First, we declare our custom VisionAI
data, then call VisionAI(**custom_visionai_data).dict()
to validate our custom data visionai schema. It will raise error if any of required fields is missing or the value type doesn't meet with defined data type ( BaseModel
error message). Otherwise, it will return dictionary of validated VisionAI
data
Validate VisionAI data with given Ontology
Before upload dataset into Dataverse
platform, we could try to validate a VisionAI
annotation with Ontology
schema. Ontology
schema works as a predefined Project Ontology
data in Dataverse
.
Ontology
contains contexts
, objects
, streams
, and tags
four main elements similar to VisioniAI
schema. The difference is that Ontology
is the union of all categories and attributes that will be compared with a VisionAI
data.
contexts
need to be filled if only the project ontology isclassification
type.objects
need to be filled for other project ontologies instead ofclassification
, such asbounding_box
orsemantic_segmentation
, etc.streams
required to be filled, since it is the project sensor related information.tags
need to be filled in case ofsemantic_segmentation
project ontology.
Following is the example of Ontology
Schema and how to validate VisionAI
data with it:
from visionai_data_format.schemas.ontology import Ontology
custom_ontology = {
"objects": {
"pedestrian": {
"attributes": {
"bbox_shape": {
"type": "bbox",
"value": None
},
"cuboid_shape": {
"type": "cuboid",
"value": None
},
"activity": {
"type": "text",
"value": []
}
}
},
"truck": {
"attributes": {
"bbox_shape": {
"type": "bbox",
"value": None
},
"cuboid_shape": {
"type": "cuboid",
"value": None
},
"color": {
"type": "text",
"value": []
},
"new": {
"type": "boolean",
"value": []
},
"year": {
"type": "num",
"value": []
},
"status": {
"type": "vec",
"value": [
"stop",
"run",
"small",
"large"
]
}
}
},
"car": {
"attributes": {
"bbox_shape": {
"type": "bbox",
"value": None
},
"cuboid_shape": {
"type": "cuboid",
"value": None
},
"color": {
"type": "text",
"value": []
},
"new": {
"type": "boolean",
"value": []
},
"year": {
"type": "num",
"value": []
},
"status": {
"type": "vec",
"value": [
"stop",
"run",
"small",
"large"
]
}
}
},
"cyclist": {
"attributes": {
"bbox_shape": {
"type": "bbox",
"value": None
},
"cuboid_shape": {
"type": "cuboid",
"value": None
}
}
},
"dontcare": {
"attributes": {
"bbox_shape": {
"type": "bbox",
"value": None
},
"cuboid_shape": {
"type": "cuboid",
"value": None
}
}
},
"misc": {
"attributes": {
"bbox_shape": {
"type": "bbox",
"value": None
},
"cuboid_shape": {
"type": "cuboid",
"value": None
},
"color": {
"type": "text",
"value": []
},
"info": {
"type": "vec",
"value": [
"toyota",
"new"
]
}
}
},
"van": {
"attributes": {
"bbox_shape": {
"type": "bbox",
"value": None
},
"cuboid_shape": {
"type": "cuboid",
"value": None
}
}
},
"tram": {
"attributes": {
"bbox_shape": {
"type": "bbox",
"value": None
},
"cuboid_shape": {
"type": "cuboid",
"value": None
}
}
},
"person_sitting": {
"attributes": {
"bbox_shape": {
"type": "bbox",
"value": None
},
"cuboid_shape": {
"type": "cuboid",
"value": None
}
}
}
},
"contexts":{
"*tagging": {
"attributes":{
"profession": {
"type": "text",
"value": []
},
"roadname": {
"type": "text",
"value": []
},
"name": {
"type": "text",
"value": []
},
"unknown_object": {
"type": "vec",
"value": [
"sky",
"leaves",
"wheel_vehicle",
"fire",
"water"
]
},
"static_status": {
"type": "boolean",
"value": [
"true",
"false"
]
},
"year": {
"type": "num",
"value": []
},
"weather": {
"type": "text",
"value": []
}
}
}
},
"streams": {
"camera1": {
"type": "camera"
},
"lidar1": {
"type": "lidar"
}
},
"tags": None
}
# Validate your custom ontology
validated_ontology = Ontology(**custom_ontology).dict()
# Validate VisionAI data with our ontology, custom_visionai_data is the custom data from upper example
errors = VisionAIModel(**custom_visionai_data).validate_with_ontology(ontology=validated_ontology)
# Shows the errors
# If there is any error occurred, it will returns list of error messages
# Otherwise, it will return empty list
# example of errors :
# >["validate objects error: Missing attributes from data pointers : {('893ac389-7782-4bc3-8f61-09a8e48c819f', 'bbox_shape'), ('893ac389-7782-4bc3-8f61-09a8e48c819f', 'cuboid_shape')} \n"]
print(errors)
First, create a new Ontology
that contains the project ontology. Then, call validate_with_ontology(ontology=validated_ontology)
to validate whether current VisionAI
data meets the Ontology
data information. It will returns list of error messages if any error occured, otherwise it returns empty list.
Tools
Convert VisionAI
format data to BDD
format
The script below could help convert VisionAI
annotation data to BDD
json file
python visionai_data_format/vai_to_bdd.py -vai_src_folder /path_for_visionai_root_folder -bdd_dest_file /dest_path/bdd.json -company_code 99 -storage_name storge1 -container_name dataset1 -annotation_name groundtruth
Troubleshooting
(WIP)
Next steps
(WIP)
Contributing
(WIP)
Links to language repos
(WIP)
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for visionai-data-format-1.0.5.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | 5062533d460c3c9b72076da6165197629f99102c58a72b55f05277df1a165f65 |
|
MD5 | 739a9908bb369931c64b7ff2a18ac394 |
|
BLAKE2b-256 | 6cd720ed8d49f9635567deda13e6202a58e07aad9c3612b15de4e430b2d92241 |
Hashes for visionai_data_format-1.0.5-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 2c3aa82ac4d9744591389b2f81f994f212757803666c630690392942c7ec5b5e |
|
MD5 | cec2091966c640954b724c6cd1eadfa9 |
|
BLAKE2b-256 | d59ce41a23e2a01e703168990d6dd0d7ad731da5073f3057f6bddd7cc6066d18 |