From 7f22f33c3ec4d853f53d99bb7ca048f829fb3a49 Mon Sep 17 00:00:00 2001 From: Eric Schmidt Date: Thu, 11 May 2023 14:00:44 -0700 Subject: [PATCH] feat: adds Vision OCR --- src/document_extract.py | 106 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 106 insertions(+) create mode 100644 src/document_extract.py diff --git a/src/document_extract.py b/src/document_extract.py new file mode 100644 index 0000000..1285ac2 --- /dev/null +++ b/src/document_extract.py @@ -0,0 +1,106 @@ +# Copyright 2023 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import json +import re +from google.cloud import vision +from google.cloud import storage + + +def async_document_extract( + bucket: str, + name: str, + timeout: int = 420, +): + """Perform OCR with PDF/TIFF as source files on GCS. + + Original sample is here: + https://github.com/GoogleCloudPlatform/python-docs-samples/blob/main/vision/snippets/detect/detect.py#L806 + + Note: This function can cause the IOPub data rate to be exceeded on a + Jupyter server. This rate can be changed by setting the variable + `--ServerApp.iopub_data_rate_limit + + Args: + bucket (str): GCS URI of the bucket containing the PDF/TIFF files. + name (str): Name of the PDF/TIFF file. + timeout (int): Timeout in seconds for the request. + + + Returns: + tuple: (text, gcs_output_path) + """ + + gcs_source_uri = f'gs://{bucket}/{name}' + file_stem = name.split('.') + gcs_destination_uri = f'gs://{bucket}/{file_stem[-2]}/' + gcs_output_path = f'gs://{bucket}/{file_stem[-2]}/complete_text.txt' + mime_type = 'application/pdf' + batch_size = 2 + + # Perform Vision OCR + client = vision.ImageAnnotatorClient() + + feature = vision.Feature( + type_=vision.Feature.Type.DOCUMENT_TEXT_DETECTION) + + gcs_source = vision.GcsSource(uri=gcs_source_uri) + input_config = vision.InputConfig( + gcs_source=gcs_source, mime_type=mime_type) + + gcs_destination = vision.GcsDestination(uri=gcs_destination_uri) + output_config = vision.OutputConfig( + gcs_destination=gcs_destination, batch_size=batch_size) + + async_request = vision.AsyncAnnotateFileRequest( + features=[feature], input_config=input_config, + output_config=output_config) + + operation = client.async_batch_annotate_files( + requests=[async_request]) + + print('Waiting for the operation to finish.') + operation.result(timeout=timeout) + + # Once the request has completed and the output has been + # written to GCS, we can list all the output files. + storage_client = storage.Client() + + match = re.match(r'gs://([^/]+)/(.+)', gcs_destination_uri) + bucket_name = match.group(1) + prefix = match.group(2) + + bucket = storage_client.get_bucket(bucket_name) + + # List objects with the given prefix, filtering out folders. + blob_list = [blob for blob in list(bucket.list_blobs( + prefix=prefix)) if not blob.name.endswith('/')] + + # Concatenate all text from the blobs + complete_text = "" + for output in blob_list: + + json_string = output.download_as_bytes().decode("utf-8") + response = json.loads(json_string) + + # The actual response for the first page of the input file. + page_response = response['responses'][0] + annotation = page_response['fullTextAnnotation'] + + complete_text = complete_text + annotation['text'] + + blob = bucket.blob(gcs_output_path) + blob.upload_from_string(complete_text) + + return (complete_text, gcs_output_path)