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Added the sample for async image batch annotation #2045

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20 changes: 14 additions & 6 deletions vision/cloud-client/detect/README.rst
Original file line number Diff line number Diff line change
Expand Up @@ -165,7 +165,7 @@ To run this sample:
$ python beta_snippets.py

usage: beta_snippets.py [-h]
{object-localization,object-localization-uri,handwritten-ocr,handwritten-ocr-uri,doc-features,doc-features-uri}
{object-localization,object-localization-uri,handwritten-ocr,handwritten-ocr-uri,batch-annotate-files,batch-annotate-files-uri,batch-annotate-images-uri}
...

Google Cloud Vision API Python Beta Snippets
Expand All @@ -176,14 +176,15 @@ To run this sample:
python beta_snippets.py object-localization-uri gs://...
python beta_snippets.py handwritten-ocr INPUT_IMAGE
python beta_snippets.py handwritten-ocr-uri gs://...
python beta_snippets.py doc-features INPUT_PDF
python beta_snippets.py doc-features_uri gs://...
python beta_snippets.py batch-annotate-files INPUT_PDF
python beta_snippets.py batch-annotate-files-uri gs://...
python beta_snippets.py batch-annotate-images-uri gs://... gs://...

For more information, the documentation at
https://cloud.google.com/vision/docs.

positional arguments:
{object-localization,object-localization-uri,handwritten-ocr,handwritten-ocr-uri,doc-features,doc-features-uri}
{object-localization,object-localization-uri,handwritten-ocr,handwritten-ocr-uri,batch-annotate-files,batch-annotate-files-uri,batch-annotate-images-uri}
object-localization
Localize objects in the local image. Args: path: The
path to the local file.
Expand All @@ -197,14 +198,21 @@ To run this sample:
Detects handwritten characters in the file located in
Google Cloud Storage. Args: uri: The path to the file
in Google Cloud Storage (gs://...)
doc-features Detects document features in a PDF/TIFF/GIF file.
batch-annotate-files
Detects document features in a PDF/TIFF/GIF file.
While your PDF file may have several pages, this API
can process up to 5 pages only. Args: path: The path
to the local file.
doc-features-uri Detects document features in a PDF/TIFF/GIF file.
batch-annotate-files-uri
Detects document features in a PDF/TIFF/GIF file.
While your PDF file may have several pages, this API
can process up to 5 pages only. Args: uri: The path to
the file in Google Cloud Storage (gs://...)
batch-annotate-images-uri
Batch annotation of images on Google Cloud Storage
asynchronously. Args: image_uri: The path to the image
in Google Cloud Storage (gs://...) gcs_uri: The path
to the output path in Google Cloud Storage (gs://...)

optional arguments:
-h, --help show this help message and exit
Expand Down
116 changes: 97 additions & 19 deletions vision/cloud-client/detect/beta_snippets.py
Original file line number Diff line number Diff line change
Expand Up @@ -23,8 +23,9 @@
python beta_snippets.py object-localization-uri gs://...
python beta_snippets.py handwritten-ocr INPUT_IMAGE
python beta_snippets.py handwritten-ocr-uri gs://...
python beta_snippets.py doc-features INPUT_PDF
python beta_snippets.py doc-features_uri gs://...
python beta_snippets.py batch-annotate-files INPUT_PDF
python beta_snippets.py batch-annotate-files-uri gs://...
python beta_snippets.py batch-annotate-images-uri gs://... gs://...


For more information, the documentation at
Expand Down Expand Up @@ -176,8 +177,8 @@ def detect_handwritten_ocr_uri(uri):
# [END vision_handwritten_ocr_gcs_beta]


# [START vision_fulltext_detection_pdf_beta]
def detect_document_features(path):
# [START detect_batch_annotate_files_beta]
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def detect_batch_annotate_files(path):
"""Detects document features in a PDF/TIFF/GIF file.

While your PDF file may have several pages,
Expand Down Expand Up @@ -224,12 +225,12 @@ def detect_document_features(path):
for symbol in word.symbols:
print('\t\t\tSymbol: {} (confidence: {})'.format(
symbol.text, symbol.confidence))
# [END vision_fulltext_detection_pdf_beta]
# [END detect_batch_annotate_files_beta]


# [START vision_fulltext_detection_pdf_gcs_beta]
def detect_document_features_uri(gcs_uri):
"""Detects document features in a PDF/TIFF/GIF file.
# [START detect_batch_annotate_files_gcs_beta]
def detect_batch_annotate_files_uri(gcs_uri):
"""Detects document features in a PDF/TIFF/GIF file.

While your PDF file may have several pages,
this API can process up to 5 pages only.
Expand Down Expand Up @@ -272,7 +273,75 @@ def detect_document_features_uri(gcs_uri):
for symbol in word.symbols:
print('\t\t\tSymbol: {} (confidence: {})'.format(
symbol.text, symbol.confidence))
# [END vision_fulltext_detection_pdf_gcs_beta]
# [END detect_batch_annotate_files_gcs_beta]


# [START detect_async_batch_annotate_images_beta]
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def async_batch_annotate_images_uri(image_uri, gcs_uri):
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"""Batch annotation of images on Google Cloud Storage asynchronously.

Args:
image_uri: The path to the image in Google Cloud Storage (gs://...)
gcs_uri: The path to the output path in Google Cloud Storage (gs://...)
"""
import re

from google.cloud import storage
from google.protobuf import json_format
from google.cloud import vision_v1p4beta1 as vision
client = vision.ImageAnnotatorClient()

# Construct the request for the image(s) to be annotated:
image_source = vision.types.ImageSource(image_uri=image_uri)
image = vision.types.Image(source=image_source)
features = [
vision.types.Feature(type=vision.enums.Feature.Type.LABEL_DETECTION),
vision.types.Feature(type=vision.enums.Feature.Type.TEXT_DETECTION),
vision.types.Feature(type=vision.enums.Feature.Type.IMAGE_PROPERTIES),
]
requests = [
vision.types.AnnotateImageRequest(image=image, features=features),
]

gcs_destination = vision.types.GcsDestination(uri=gcs_uri)
output_config = vision.types.OutputConfig(
gcs_destination=gcs_destination, batch_size=2)

operation = client.async_batch_annotate_images(
requests=requests, output_config=output_config)

print('Waiting for the operation to finish.')
operation.result(timeout=10000)

# Once the request has completed and the output has been
# written to Google Cloud Storage, we can list all the output files.
storage_client = storage.Client()

match = re.match(r'gs://([^/]+)/(.+)', gcs_uri)
bucket_name = match.group(1)
prefix = match.group(2)

bucket = storage_client.get_bucket(bucket_name=bucket_name)

# Lists objects with the given prefix.
blob_list = list(bucket.list_blobs(prefix=prefix))
print('Output files:')
for blob in blob_list:
print(blob.name)

# Processes the first output file from Google Cloud Storage.
# Since we specified batch_size=2, the first response contains
# annotations for the first two annotate image requests.
output = blob_list[0]

json_string = output.download_as_string()
response = json_format.Parse(json_string,
vision.types.BatchAnnotateImagesResponse())

# Prints the actual response for the first annotate image request.
print(u'The annotation response for the first request: {}'.format(
response.responses[0]))
# [END detect_async_batch_annotate_images_beta]


if __name__ == '__main__':
Expand All @@ -297,13 +366,20 @@ def detect_document_features_uri(gcs_uri):
'handwritten-ocr-uri', help=detect_handwritten_ocr_uri.__doc__)
handwritten_uri_parser.add_argument('uri')

doc_features_parser = subparsers.add_parser(
'doc-features', help=detect_document_features.__doc__)
doc_features_parser.add_argument('path')
batch_annotate_parser = subparsers.add_parser(
'batch-annotate-files', help=detect_batch_annotate_files.__doc__)
batch_annotate_parser.add_argument('path')

batch_annotate_uri_parser = subparsers.add_parser(
'batch-annotate-files-uri',
help=detect_batch_annotate_files_uri.__doc__)
batch_annotate_uri_parser.add_argument('uri')

doc_features_uri_parser = subparsers.add_parser(
'doc-features-uri', help=detect_document_features_uri.__doc__)
doc_features_uri_parser.add_argument('uri')
batch_annotate__image_uri_parser = subparsers.add_parser(
'batch-annotate-images-uri',
help=async_batch_annotate_images_uri.__doc__)
batch_annotate__image_uri_parser.add_argument('uri')
batch_annotate__image_uri_parser.add_argument('destination')

args = parser.parse_args()

Expand All @@ -312,12 +388,14 @@ def detect_document_features_uri(gcs_uri):
localize_objects_uri(args.uri)
elif 'handwritten-ocr-uri' in args.command:
detect_handwritten_ocr_uri(args.uri)
elif 'doc-features' in args.command:
detect_handwritten_ocr_uri(args.uri)
elif 'batch-annotate-files' in args.command:
detect_batch_annotate_files_uri(args.uri)
elif 'batch-annotate-images' in args.command:
async_batch_annotate_images_uri(args.uri, args.destination)
else:
if 'object-localization' in args.command:
localize_objects(args.path)
elif 'handwritten-ocr' in args.command:
detect_handwritten_ocr(args.path)
elif 'doc-features' in args.command:
detect_handwritten_ocr(args.path)
elif 'batch-annotate-files' in args.command:
detect_batch_annotate_files(args.path)
20 changes: 16 additions & 4 deletions vision/cloud-client/detect/beta_snippets_test.py
Original file line number Diff line number Diff line change
Expand Up @@ -18,6 +18,10 @@
RESOURCES = os.path.join(os.path.dirname(__file__), 'resources')
GCS_ROOT = 'gs://cloud-samples-data/vision/'

BUCKET = os.environ['CLOUD_STORAGE_BUCKET']
OUTPUT_PREFIX = 'OCR_PDF_TEST_OUTPUT'
GCS_DESTINATION_URI = 'gs://{}/{}/'.format(BUCKET, OUTPUT_PREFIX)


def test_localize_objects(capsys):
path = os.path.join(RESOURCES, 'puppies.jpg')
Expand Down Expand Up @@ -55,17 +59,25 @@ def test_handwritten_ocr_uri(capsys):
assert 'Cloud Vision API' in out


def test_detect_pdf_document(capsys):
def test_detect_batch_annotate_files(capsys):
file_name = os.path.join(RESOURCES, 'kafka.pdf')
beta_snippets.detect_document_features(file_name)
beta_snippets.detect_batch_annotate_files(file_name)
out, _ = capsys.readouterr()
assert 'Symbol: a' in out
assert 'Word text: evenings' in out


def test_detect_pdf_document_from_gcs(capsys):
def test_detect_batch_annotate_files_uri(capsys):
gcs_uri = GCS_ROOT + 'document_understanding/kafka.pdf'
beta_snippets.detect_document_features_uri(gcs_uri)
beta_snippets.detect_batch_annotate_files_uri(gcs_uri)
out, _ = capsys.readouterr()
assert 'Symbol' in out
assert 'Word text' in out


def test_async_batch_annotate_images(capsys):
gcs_uri = GCS_ROOT + 'landmark/eiffel_tower.jpg'
beta_snippets.async_batch_annotate_images_uri(gcs_uri, GCS_DESTINATION_URI)
out, _ = capsys.readouterr()
assert 'language_code: "en"' in out
assert 'description: "Tower"' in out