​
Task

You want to get, decode, and show elements, such as images and tables, that are embedded in a PDF document.

​
Approach

Extract the Base64-encoded representation of specific elements, such as images and tables, in the document. For each of these extracted elements, decode the Base64-encoded representation of the element into its original visual representation and then show it.

​
To run this example

You will need a document that is one of the document types supported by the extract_image_block_types argument. See the extract_image_block_types entry in API Parameters. This example uses a PDF file with embedded images and tables.

​
Code

For the Unstructured Ingest Python library, you can use the standard Python json.load function to load into a Python dictionary the contents of a JSON file that the Ingest Python library outputs after the processing is complete.

Python
import json, base64, io
from PIL import Image

def get_image_block_types(input_json_file_path: str):
    with open(input_json_file_path, 'r') as file:
        file_elements = json.load(file)

    for element in file_elements:
        if "image_base64" in element["metadata"]:
            # Decode the Base64-encoded representation of the 
            # processed "Image" or "Table" element into its original
            # visual representation, and then show it.
            image_data = base64.b64decode(element["metadata"]["image_base64"])
            image = Image.open(io.BytesIO(image_data))
            image.show()

if __name__ == "__main__":
    # Source: https://github.com/Unstructured-IO/unstructured-ingest/blob/main/example-docs/pdf/embedded-images-tables.pdf

    # Specify where to get the local file, relative to this .py file.
    get_image_block_types(
        input_json_file_path="local-ingest-output/embedded-images-tables.json"
    )

Was this page helpful?

Suggest edits