visionai-data-format 1.3.2

converter tool for visionai format

visionai-data-format

VisionAI format is Dataverse 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 given Ontology information.

For a more in-depth understanding of the VisionAI format, please visit this URL: https://linkervision.gitbook.io/dataverse/visionai-format/visionai-data-format.

Package (PyPi) | Source code

Getting started

(WIP)

Install the package

pip3 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
• Validate VisionAI data with given Ontology

Validate VisionAI schema

Example

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()

Explanation

To begin, we define our custom VisionAI data. Subsequently, we employ the VisionAI(**custom_visionai_data).dict() to ensure the conformity of our custom data with the VisionAI schema. If there are any missing required fields or if the value types deviate from the defined data types, an error will be raised (prompting a list of VisionAIException exceptions). On the other hand, if the data passes validation, the function will yield a dictionary containing the validated VisionAI data.

Validate VisionAI data with given Ontology

Ontology Schema

Before uploading a dataset to the Dataverse platform, it's advisable to validate VisionAI annotations using the Ontology schema. The Ontology schema serves as a predefined structure for Project Ontology data in Dataverse."

1. contexts : fill this section only if the project ontology is of the classification type.
2. objects : fill this section for project ontologies other than classification, such as bounding_box or semantic_segmentation.
3. streams : this section is mandatory as it contains project sensor-related information.
4. tags : complete this section for semantic_segmentation project ontologies.

Example

Here is an example of the Ontology Schema and how to validate VisionAI data using 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 `VisionAIException` exceptions
# Otherwise, it will return empty list
# example of errors :
# >[visionai_data_format.exceptions.visionai.VisionAIException("frame stream sensors {'lidar2'} doesn't match with visionai streams sensor {'camera1', 'lidar1'}.")]
print(errors)

Explanation

Begin by creating a new Ontology that includes the project ontology. Subsequently, use the validate_with_ontology(ontology=validated_ontology) function to check if the current VisionAI data aligns with the information in the Ontology. The function will return a list of VisionAIException if any issues are detected; otherwise, it returns an empty list.

Converter tools

Convert BDD+ format data to VisionAI format

(Only support box2D and camera sensor data only for now)

python3 visionai_data_format/convert_dataset.py -input_format bddp -output_format vision_ai -image_annotation_type 2d_bounding_box -input_annotation_path ./bdd_test.json -source_data_root ./data_root -output_dest_folder ~/visionai_output_dir -uri_root http://storage_test -n_frame 5 -sequence_idx_start 0 -camera_sensor_name camera1 -annotation_name groundtruth -img_extension .jpg --copy_sensor_data

Arguments :

• -input_format : input format (use bddp for BDD+)
• -output_format : output format (vision_ai)
• -image_annotation_type : label annotation type for image (2d_bounding_box for box2D)
• -input_annotation_path : source annotation path (BDD+ format json file)
• -source_data_root : source data root for sensor data and calibration data (will find and copy image from this root)
• -output_dest_folder : output root folder (VisionAI local root folder)
• -uri_root : uri root for target upload VAI storage i.e: https://azuresorate/vai_dataset
• -n_frame : number of frame to be converted (-1 means all), by default -1
• -sequence_idx_start : sequence start id, by default 0
• -camera_sensor_name : camera sensor name (default: "", specified it if need to convert camera data)
• -lidar_sensor_name : lidar sensor name (default: "", specified it if need to convert lidar data)
• -annotation_name : annotation folder name (default: "groundtruth")
• -img_extension : image file extension (default: ".jpg")
• --copy_sensor_data :enable to copy image/lidar data

Convert VisionAI format data to BDD+ format

(Only support box2D for now) The script below could help convert VisionAI annotation data to BDD+ json file

python3 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 storage1 -container_name dataset1 -annotation_name groundtruth

Arguments :

• -vai_src_folder : VAI root folder contains VAI format json file
• -bdd_dest_file : BDD+ format file save destination
• -company_code : company code
• -storage_name : storage name
• -container_name : container name (dataset name)
• -annotation_name : annotation folder name (default: "groundtruth")

Convert Kitti format data to VisionAI format

(Only support KITTI with one camera and one lidar sensor)

Important:

• image type is not restricted, could be ".jpg" or ".png", but we will convert it into ".jpg" in VisionAI format
• only support for P2 projection matrix calibration information

Currently,only support KITTI dataset with structure folder :

.kitti_folder
├── calib
│   ├── 000000.txt
│   ├── 000001.txt
│   ├── 000002.txt
│   ├── 000003.txt
│   └── 000004.txt
├── data
│   ├── 000000.png
│   ├── 000001.png
│   ├── 000002.png
│   ├── 000003.png
│   └── 000004.png
├── labels
│   ├── 000000.txt
│   ├── 000001.txt
│   ├── 000002.txt
│   ├── 000003.txt
│   └── 000004.txt
└── pcd
    ├── 000000.pcd
    ├── 000001.pcd
    ├── 000002.pcd
    ├── 000003.pcd
    └── 000004.pcd

Command :

python3 visionai_data_format/convert_dataset.py -input_format kitti -output_format vision_ai -image_annotation_type 2d_bounding_box -source_data_root ./data_root -output_dest_folder ~/visionai_output_dir -uri_root http://storage_test -n_frame 5 -sequence_idx_start 0 -camera_sensor_name camera1 -lidar_sensor_name lidar1 -annotation_name groundtruth -img_extension .jpg --copy_sensor_data

Arguments :

• -input_format : input format (use kitti for KITTI)
• -output_format : output format (vision_ai)
• -image_annotation_type : label annotation type for image (2d_bounding_box for box2D)
• -source_data_root : source data root for sensor data and calibration data (will find and copy image from this root)
• -output_dest_folder : output root folder (VisionAI local root folder)
• -uri_root : uri root for target upload VAI storage i.e: https://azuresorate/vai_dataset
• -n_frame : number of frame to be converted (-1 means all), by default -1
• -sequence_idx_start : sequence start id, by default 0
• -camera_sensor_name : camera sensor name (default: "", specified it if need to convert camera data)
• -lidar_sensor_name : lidar sensor name (default: "", specified it if need to convert lidar data)
• -annotation_name : annotation folder name (default: "groundtruth")
• -img_extension : image file extension (default: ".jpg")
• --copy_sensor_data : enable to copy image/lidar data

Convert COCO format data to VisionAI format

python3 visionai_data_format/convert_dataset.py -input_format coco -output_format vision_ai -image_annotation_type 2d_bounding_box -input_annotation_path ./coco_instance.json -source_data_root ./coco_images/ -output_dest_folder ~/visionai_output_dir -uri_root http://storage_test -n_frame 5 -sequence_idx_start 0 -camera_sensor_name camera1 -annotation_name groundtruth -img_extension .jpg --copy_sensor_data

Arguments :

• -input_format : input format (use coco for COCO format)
• -output_format : output format (vision_ai)
• -image_annotation_type : label annotation type for image (2d_bounding_box for box2D)
• -source_data_root : image data folder
• -output_dest_folder : output root folder (VisionAI local root folder)
• -uri_root : uri root for target upload VisionAI storage i.e: https://azuresorate/vai_dataset
• -n_frame : number of frame to be converted (-1 means all), by default -1
• -sequence_idx_start : sequence start id, by default 0
• -camera_sensor_name : camera sensor name (default: "", specified it if need to convert camera data)
• -annotation_name : VisionAI annotation folder name (default: "groundtruth")
• -img_extension : image file extension (default: ".jpg")
• --copy_sensor_data : enable to copy image data

Convert VisionAI format data to COCO format

python3 visionai_data_format/convert_dataset.py -input_format vision_ai -output_format coco -image_annotation_type 2d_bounding_box -source_data_root ./visionai_data_root -output_dest_folder ~/coco_output_dir -uri_root http://storage_test -n_frame 5 -camera_sensor_name camera1 -annotation_name groundtruth -img_extension .jpg --copy_sensor_data

Arguments :

• -input_format : input format (vision_ai)
• -output_format : output format (use coco for COCO format)
• -image_annotation_type : label annotation type for image (2d_bounding_box for box2D)
• -source_data_root : visionai local data root folder
• -output_dest_folder : output root folder (COCO local root folder)
• -uri_root : uri root for target upload for coco i.e: https://azuresorate/coco_dataset
• -n_frame : number of frame to be converted (-1 means all), by default -1
• -camera_sensor_name : camera sensor name (required for getting the target camera sensor data)
• -annotation_name : VisionAI annotation folder name (default: "groundtruth")
• -img_extension : image file extension (default: ".jpg")
• --copy_sensor_data : enable to copy image data

Troubleshooting

(WIP)

Next steps

(WIP)

Contributing

(WIP)

Links to language repos

(WIP)

Python Readme