工具箱 - 将外部注释转换为文档格式

将外部注释转换为 Document AI Workbench 用于训练的 Document 格式。

深入探索

如需查看包含此代码示例的详细文档,请参阅以下内容:

代码示例

Python

如需了解详情,请参阅 Document AI Python API 参考文档

如需向 Document AI 进行身份验证,请设置应用默认凭据。 如需了解详情,请参阅为本地开发环境设置身份验证


from google.cloud.documentai_toolbox import converter

# TODO(developer): Uncomment these variables before running the sample.
# This sample will convert external annotations to the Document.json format used by Document AI Workbench for training.
# To process this the external annotation must have these type of objects:
#       1) Type
#       2) Text
#       3) Bounding Box (bounding boxes must be 1 of the 3 optional types)
#
# This is the bare minimum requirement to convert the annotations but for better accuracy you will need to also have:
#       1) Document width & height
#
# Bounding Box Types:
#   Type 1:
#       bounding_box:[{"x":1,"y":2},{"x":2,"y":2},{"x":2,"y":3},{"x":1,"y":3}]
#   Type 2:
#       bounding_box:{ "Width": 1, "Height": 1, "Left": 1, "Top": 1}
#   Type 3:
#       bounding_box: [1,2,2,2,2,3,1,3]
#
#   Note: If these types are not sufficient you can propose a feature request or contribute the new type and conversion functionality.
#
# Given a folders in gcs_input_path with the following structure :
#
# gs://path/to/input/folder
#   ├──test_annotations.json
#   ├──test_config.json
#   └──test.pdf
#
# An example of the config is in sample-converter-configs/Azure/form-config.json
#
# location = "us",
# processor_id = "my_processor_id"
# gcs_input_path = "gs://path/to/input/folder"
# gcs_output_path = "gs://path/to/input/folder"


def convert_external_annotations_sample(
    location: str,
    processor_id: str,
    project_id: str,
    gcs_input_path: str,
    gcs_output_path: str,
) -> None:
    converter.convert_from_config(
        project_id=project_id,
        location=location,
        processor_id=processor_id,
        gcs_input_path=gcs_input_path,
        gcs_output_path=gcs_output_path,
    )

后续步骤

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