Utilities

Converting annotation format

After exporting project annotations (in SuperAnnotate format), it is possible to convert them to other annotation formats:

sa.export_annotation("<input_folder>", "<output_folder>", "<dataset_format>", "<dataset_name>",
"<project_type>", "<task>")

Note

Right now we support only SuperAnnotate annotation format to COCO annotation format conversion, but you can convert from “COCO”, “Pascal VOC”, “DataLoop”, “LabelBox”, “SageMaker”, “Supervisely”, “VGG”, “VoTT” or “YOLO” annotation formats to SuperAnnotate annotation format.

You can find more information annotation format conversion here. We provide some examples in our GitHub repository. In the root folder of our github repository, you can run following commands to do conversions.

from superannotate import export_annotation
from superannotate import import_annotation

 # From SA format to COCO panoptic format
 export_annotation(
    "tests/converter_test/COCO/input/fromSuperAnnotate/cats_dogs_panoptic_segm",
    "tests/converter_test/COCO/output/panoptic",
    "COCO", "panoptic_test", "Pixel","panoptic_segmentation"
 )

 # From COCO keypoints detection format to SA annotation format
 import_annotation(
    "tests/converter_test/COCO/input/toSuperAnnotate/keypoint_detection",
    "tests/converter_test/COCO/output/keypoints",
    "COCO", "person_keypoints_test", "Vector", "keypoint_detection"
 )

 # Pascal VOC annotation format to SA annotation format
 import_annotation(
    "tests/converter_test/VOC/input/fromPascalVOCToSuperAnnotate/VOC2012",
    "tests/converter_test/VOC/output/instances",
    "VOC", "instances_test", "Pixel", "instance_segmentation"
 )

 # YOLO annotation format to SA annotation format
 import_annotation(
   'tests/converter_test/YOLO/input/toSuperAnnotate',
   'tests/converter_test/YOLO/output',
   'YOLO', '', 'Vector', 'object_detection'
   )

 # LabelBox annotation format to SA annotation format
 import_annotation(
    "tests/converter_test/LabelBox/input/toSuperAnnotate/",
    "tests/converter_test/LabelBox/output/objects/",
    "LabelBox", "labelbox_example", "Vector", "object_detection"
 )

 # Supervisely annotation format to SA annotation format
 import_annotation(
    "tests/converter_test/Supervisely/input/toSuperAnnotate",
    "tests/converter_test/Supervisely/output",
    "Supervisely", "", "Vector", "vector_annotation"
 )

 # DataLoop annotation format to SA annotation format
 import_annotation(
    "tests/converter_test/DataLoop/input/toSuperAnnotate",
    "tests/converter_test/DataLoop/output",
    "DataLoop", "", "Vector", "vector_annotation"
 )

 # VGG annotation format to SA annotation format
 import_annotation(
    "tests/converter_test/VGG/input/toSuperAnnotate",
    "tests/converter_test/VGG/output",
    "VGG", "vgg_test", "Vector", "instance_segmentation"
 )

 # VoTT annotation format to SA annotation format
 import_annotation(
    "tests/converter_test/VoTT/input/toSuperAnnotate",
    "tests/converter_test/VoTT/output",
    "VoTT", "", "Vector", "vector_annotation"
 )

 # GoogleCloud annotation format to SA annotation format
 import_annotation(
    "tests/converter_test/GoogleCloud/input/toSuperAnnotate",
    "tests/converter_test/GoogleCloud/output",
    "GoogleCloud", "image_object_detection", "Vector", "object_detection"
 )

 # GoogleCloud annotation format to SA annotation format
 import_annotation(
    "tests/converter_test/SageMaker/input/toSuperAnnotate",
    "tests/converter_test/SageMaker/output",
    "SageMaker", "test-obj-detect", "Vector", "object_detection"
 )

pandas DataFrame out of project annotations and annotation instance filtering

To create a pandas DataFrame from project SuperAnnotate format annotations:

df = sa.aggregate_annotations_as_df("<path_to_project_folder>")

The created DataFrame will have columns specified at aggregate_annotations_as_df.

Example of created DataFrame:

Each row represents annotation information. One full annotation with multiple attribute groups can be grouped under instanceId field.