detect.py

#!/usr/bin/env python

# Copyright 2017 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
#
#     http://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.

"""This application demonstrates how to perform basic operations with the
Google Cloud Vision API.

Example Usage:
python detect.py text ./resources/wakeupcat.jpg
python detect.py labels ./resources/landmark.jpg
python detect.py web ./resources/landmark.jpg
python detect.py web-uri http://wheresgus.com/dog.JPG
python detect.py web-geo ./resources/city.jpg
python detect.py faces-uri gs://your-bucket/file.jpg
python detect.py ocr-uri gs://python-docs-samples-tests/HodgeConj.pdf \
gs://BUCKET_NAME/PREFIX/
python detect.py object-localization ./resources/puppies.jpg
python detect.py object-localization-uri gs://...

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

import argparse


# [START vision_face_detection]
def detect_faces(path):
    """Detects faces in an image."""
    from google.cloud import vision
    import io
    client = vision.ImageAnnotatorClient()

    # [START vision_python_migration_face_detection]
    # [START vision_python_migration_image_file]
    with io.open(path, 'rb') as image_file:
        content = image_file.read()

    image = vision.Image(content=content)
    # [END vision_python_migration_image_file]

    response = client.face_detection(image=image)
    faces = response.face_annotations

    # Names of likelihood from google.cloud.vision.enums
    likelihood_name = ('UNKNOWN', 'VERY_UNLIKELY', 'UNLIKELY', 'POSSIBLE',
                       'LIKELY', 'VERY_LIKELY')
    print('Faces:')

    for face in faces:
        print('anger: {}'.format(likelihood_name[face.anger_likelihood]))
        print('joy: {}'.format(likelihood_name[face.joy_likelihood]))
        print('surprise: {}'.format(likelihood_name[face.surprise_likelihood]))

        vertices = (['({},{})'.format(vertex.x, vertex.y)
                    for vertex in face.bounding_poly.vertices])

        print('face bounds: {}'.format(','.join(vertices)))

    if response.error.message:
        raise Exception(
            '{}\nFor more info on error messages, check: '
            'https://cloud.google.com/apis/design/errors'.format(
                response.error.message))
    # [END vision_python_migration_face_detection]
# [END vision_face_detection]


# [START vision_face_detection_gcs]
def detect_faces_uri(uri):
    """Detects faces in the file located in Google Cloud Storage or the web."""
    from google.cloud import vision
    client = vision.ImageAnnotatorClient()
    # [START vision_python_migration_image_uri]
    image = vision.Image()
    image.source.image_uri = uri
    # [END vision_python_migration_image_uri]

    response = client.face_detection(image=image)
    faces = response.face_annotations

    # Names of likelihood from google.cloud.vision.enums
    likelihood_name = ('UNKNOWN', 'VERY_UNLIKELY', 'UNLIKELY', 'POSSIBLE',
                       'LIKELY', 'VERY_LIKELY')
    print('Faces:')

    for face in faces:
        print('anger: {}'.format(likelihood_name[face.anger_likelihood]))
        print('joy: {}'.format(likelihood_name[face.joy_likelihood]))
        print('surprise: {}'.format(likelihood_name[face.surprise_likelihood]))

        vertices = (['({},{})'.format(vertex.x, vertex.y)
                    for vertex in face.bounding_poly.vertices])

        print('face bounds: {}'.format(','.join(vertices)))

    if response.error.message:
        raise Exception(
            '{}\nFor more info on error messages, check: '
            'https://cloud.google.com/apis/design/errors'.format(
                response.error.message))
# [END vision_face_detection_gcs]


# [START vision_label_detection]
def detect_labels(path):
    """Detects labels in the file."""
    from google.cloud import vision
    import io
    client = vision.ImageAnnotatorClient()

    # [START vision_python_migration_label_detection]
    with io.open(path, 'rb') as image_file:
        content = image_file.read()

    image = vision.Image(content=content)

    response = client.label_detection(image=image)
    labels = response.label_annotations
    print('Labels:')

    for label in labels:
        print(label.description)

    if response.error.message:
        raise Exception(
            '{}\nFor more info on error messages, check: '
            'https://cloud.google.com/apis/design/errors'.format(
                response.error.message))
    # [END vision_python_migration_label_detection]
# [END vision_label_detection]


# [START vision_label_detection_gcs]
def detect_labels_uri(uri):
    """Detects labels in the file located in Google Cloud Storage or on the
    Web."""
    from google.cloud import vision
    client = vision.ImageAnnotatorClient()
    image = vision.Image()
    image.source.image_uri = uri

    response = client.label_detection(image=image)
    labels = response.label_annotations
    print('Labels:')

    for label in labels:
        print(label.description)

    if response.error.message:
        raise Exception(
            '{}\nFor more info on error messages, check: '
            'https://cloud.google.com/apis/design/errors'.format(
                response.error.message))
# [END vision_label_detection_gcs]


# [START vision_landmark_detection]
def detect_landmarks(path):
    """Detects landmarks in the file."""
    from google.cloud import vision
    import io
    client = vision.ImageAnnotatorClient()

    # [START vision_python_migration_landmark_detection]
    with io.open(path, 'rb') as image_file:
        content = image_file.read()

    image = vision.Image(content=content)

    response = client.landmark_detection(image=image)
    landmarks = response.landmark_annotations
    print('Landmarks:')

    for landmark in landmarks:
        print(landmark.description)
        for location in landmark.locations:
            lat_lng = location.lat_lng
            print('Latitude {}'.format(lat_lng.latitude))
            print('Longitude {}'.format(lat_lng.longitude))

    if response.error.message:
        raise Exception(
            '{}\nFor more info on error messages, check: '
            'https://cloud.google.com/apis/design/errors'.format(
                response.error.message))
    # [END vision_python_migration_landmark_detection]
# [END vision_landmark_detection]


# [START vision_landmark_detection_gcs]
def detect_landmarks_uri(uri):
    """Detects landmarks in the file located in Google Cloud Storage or on the
    Web."""
    from google.cloud import vision
    client = vision.ImageAnnotatorClient()
    image = vision.Image()
    image.source.image_uri = uri

    response = client.landmark_detection(image=image)
    landmarks = response.landmark_annotations
    print('Landmarks:')

    for landmark in landmarks:
        print(landmark.description)

    if response.error.message:
        raise Exception(
            '{}\nFor more info on error messages, check: '
            'https://cloud.google.com/apis/design/errors'.format(
                response.error.message))
# [END vision_landmark_detection_gcs]


# [START vision_logo_detection]
def detect_logos(path):
    """Detects logos in the file."""
    from google.cloud import vision
    import io
    client = vision.ImageAnnotatorClient()

    # [START vision_python_migration_logo_detection]
    with io.open(path, 'rb') as image_file:
        content = image_file.read()

    image = vision.Image(content=content)

    response = client.logo_detection(image=image)
    logos = response.logo_annotations
    print('Logos:')

    for logo in logos:
        print(logo.description)

    if response.error.message:
        raise Exception(
            '{}\nFor more info on error messages, check: '
            'https://cloud.google.com/apis/design/errors'.format(
                response.error.message))
    # [END vision_python_migration_logo_detection]
# [END vision_logo_detection]


# [START vision_logo_detection_gcs]
def detect_logos_uri(uri):
    """Detects logos in the file located in Google Cloud Storage or on the Web.
    """
    from google.cloud import vision
    client = vision.ImageAnnotatorClient()
    image = vision.Image()
    image.source.image_uri = uri

    response = client.logo_detection(image=image)
    logos = response.logo_annotations
    print('Logos:')

    for logo in logos:
        print(logo.description)

    if response.error.message:
        raise Exception(
            '{}\nFor more info on error messages, check: '
            'https://cloud.google.com/apis/design/errors'.format(
                response.error.message))
# [END vision_logo_detection_gcs]


# [START vision_safe_search_detection]
def detect_safe_search(path):
    """Detects unsafe features in the file."""
    from google.cloud import vision
    import io
    client = vision.ImageAnnotatorClient()

    # [START vision_python_migration_safe_search_detection]
    with io.open(path, 'rb') as image_file:
        content = image_file.read()

    image = vision.Image(content=content)

    response = client.safe_search_detection(image=image)
    safe = response.safe_search_annotation

    # Names of likelihood from google.cloud.vision.enums
    likelihood_name = ('UNKNOWN', 'VERY_UNLIKELY', 'UNLIKELY', 'POSSIBLE',
                       'LIKELY', 'VERY_LIKELY')
    print('Safe search:')

    print('adult: {}'.format(likelihood_name[safe.adult]))
    print('medical: {}'.format(likelihood_name[safe.medical]))
    print('spoofed: {}'.format(likelihood_name[safe.spoof]))
    print('violence: {}'.format(likelihood_name[safe.violence]))
    print('racy: {}'.format(likelihood_name[safe.racy]))

    if response.error.message:
        raise Exception(
            '{}\nFor more info on error messages, check: '
            'https://cloud.google.com/apis/design/errors'.format(
                response.error.message))
    # [END vision_python_migration_safe_search_detection]
# [END vision_safe_search_detection]


# [START vision_safe_search_detection_gcs]
def detect_safe_search_uri(uri):
    """Detects unsafe features in the file located in Google Cloud Storage or
    on the Web."""
    from google.cloud import vision
    client = vision.ImageAnnotatorClient()
    image = vision.Image()
    image.source.image_uri = uri

    response = client.safe_search_detection(image=image)
    safe = response.safe_search_annotation

    # Names of likelihood from google.cloud.vision.enums
    likelihood_name = ('UNKNOWN', 'VERY_UNLIKELY', 'UNLIKELY', 'POSSIBLE',
                       'LIKELY', 'VERY_LIKELY')
    print('Safe search:')

    print('adult: {}'.format(likelihood_name[safe.adult]))
    print('medical: {}'.format(likelihood_name[safe.medical]))
    print('spoofed: {}'.format(likelihood_name[safe.spoof]))
    print('violence: {}'.format(likelihood_name[safe.violence]))
    print('racy: {}'.format(likelihood_name[safe.racy]))

    if response.error.message:
        raise Exception(
            '{}\nFor more info on error messages, check: '
            'https://cloud.google.com/apis/design/errors'.format(
                response.error.message))
# [END vision_safe_search_detection_gcs]


# [START vision_text_detection]
def detect_text(path):
    """Detects text in the file."""
    from google.cloud import vision
    import io
    client = vision.ImageAnnotatorClient()

    # [START vision_python_migration_text_detection]
    with io.open(path, 'rb') as image_file:
        content = image_file.read()

    image = vision.Image(content=content)

    response = client.text_detection(image=image)
    texts = response.text_annotations
    print('Texts:')

    for text in texts:
        print('\n"{}"'.format(text.description))

        vertices = (['({},{})'.format(vertex.x, vertex.y)
                    for vertex in text.bounding_poly.vertices])

        print('bounds: {}'.format(','.join(vertices)))

    if response.error.message:
        raise Exception(
            '{}\nFor more info on error messages, check: '
            'https://cloud.google.com/apis/design/errors'.format(
                response.error.message))
    # [END vision_python_migration_text_detection]
# [END vision_text_detection]


# [START vision_text_detection_gcs]
def detect_text_uri(uri):
    """Detects text in the file located in Google Cloud Storage or on the Web.
    """
    from google.cloud import vision
    client = vision.ImageAnnotatorClient()
    image = vision.Image()
    image.source.image_uri = uri

    response = client.text_detection(image=image)
    texts = response.text_annotations
    print('Texts:')

    for text in texts:
        print('\n"{}"'.format(text.description))

        vertices = (['({},{})'.format(vertex.x, vertex.y)
                    for vertex in text.bounding_poly.vertices])

        print('bounds: {}'.format(','.join(vertices)))

    if response.error.message:
        raise Exception(
            '{}\nFor more info on error messages, check: '
            'https://cloud.google.com/apis/design/errors'.format(
                response.error.message))
# [END vision_text_detection_gcs]


# [START vision_image_property_detection]
def detect_properties(path):
    """Detects image properties in the file."""
    from google.cloud import vision
    import io
    client = vision.ImageAnnotatorClient()

    # [START vision_python_migration_image_properties]
    with io.open(path, 'rb') as image_file:
        content = image_file.read()

    image = vision.Image(content=content)

    response = client.image_properties(image=image)
    props = response.image_properties_annotation
    print('Properties:')

    for color in props.dominant_colors.colors:
        print('fraction: {}'.format(color.pixel_fraction))
        print('\tr: {}'.format(color.color.red))
        print('\tg: {}'.format(color.color.green))
        print('\tb: {}'.format(color.color.blue))
        print('\ta: {}'.format(color.color.alpha))

    if response.error.message:
        raise Exception(
            '{}\nFor more info on error messages, check: '
            'https://cloud.google.com/apis/design/errors'.format(
                response.error.message))
    # [END vision_python_migration_image_properties]
# [END vision_image_property_detection]


# [START vision_image_property_detection_gcs]
def detect_properties_uri(uri):
    """Detects image properties in the file located in Google Cloud Storage or
    on the Web."""
    from google.cloud import vision
    client = vision.ImageAnnotatorClient()
    image = vision.Image()
    image.source.image_uri = uri

    response = client.image_properties(image=image)
    props = response.image_properties_annotation
    print('Properties:')

    for color in props.dominant_colors.colors:
        print('frac: {}'.format(color.pixel_fraction))
        print('\tr: {}'.format(color.color.red))
        print('\tg: {}'.format(color.color.green))
        print('\tb: {}'.format(color.color.blue))
        print('\ta: {}'.format(color.color.alpha))

    if response.error.message:
        raise Exception(
            '{}\nFor more info on error messages, check: '
            'https://cloud.google.com/apis/design/errors'.format(
                response.error.message))
# [END vision_image_property_detection_gcs]


# [START vision_web_detection]
def detect_web(path):
    """Detects web annotations given an image."""
    from google.cloud import vision
    import io
    client = vision.ImageAnnotatorClient()

    # [START vision_python_migration_web_detection]
    with io.open(path, 'rb') as image_file:
        content = image_file.read()

    image = vision.Image(content=content)

    response = client.web_detection(image=image)
    annotations = response.web_detection

    if annotations.best_guess_labels:
        for label in annotations.best_guess_labels:
            print('\nBest guess label: {}'.format(label.label))

    if annotations.pages_with_matching_images:
        print('\n{} Pages with matching images found:'.format(
            len(annotations.pages_with_matching_images)))

        for page in annotations.pages_with_matching_images:
            print('\n\tPage url   : {}'.format(page.url))

            if page.full_matching_images:
                print('\t{} Full Matches found: '.format(
                       len(page.full_matching_images)))

                for image in page.full_matching_images:
                    print('\t\tImage url  : {}'.format(image.url))

            if page.partial_matching_images:
                print('\t{} Partial Matches found: '.format(
                       len(page.partial_matching_images)))

                for image in page.partial_matching_images:
                    print('\t\tImage url  : {}'.format(image.url))

    if annotations.web_entities:
        print('\n{} Web entities found: '.format(
            len(annotations.web_entities)))

        for entity in annotations.web_entities:
            print('\n\tScore      : {}'.format(entity.score))
            print(u'\tDescription: {}'.format(entity.description))

    if annotations.visually_similar_images:
        print('\n{} visually similar images found:\n'.format(
            len(annotations.visually_similar_images)))

        for image in annotations.visually_similar_images:
            print('\tImage url    : {}'.format(image.url))

    if response.error.message:
        raise Exception(
            '{}\nFor more info on error messages, check: '
            'https://cloud.google.com/apis/design/errors'.format(
                response.error.message))
    # [END vision_python_migration_web_detection]
# [END vision_web_detection]


# [START vision_web_detection_gcs]
def detect_web_uri(uri):
    """Detects web annotations in the file located in Google Cloud Storage."""
    from google.cloud import vision
    client = vision.ImageAnnotatorClient()
    image = vision.Image()
    image.source.image_uri = uri

    response = client.web_detection(image=image)
    annotations = response.web_detection

    if annotations.best_guess_labels:
        for label in annotations.best_guess_labels:
            print('\nBest guess label: {}'.format(label.label))

    if annotations.pages_with_matching_images:
        print('\n{} Pages with matching images found:'.format(
            len(annotations.pages_with_matching_images)))

        for page in annotations.pages_with_matching_images:
            print('\n\tPage url   : {}'.format(page.url))

            if page.full_matching_images:
                print('\t{} Full Matches found: '.format(
                       len(page.full_matching_images)))

                for image in page.full_matching_images:
                    print('\t\tImage url  : {}'.format(image.url))

            if page.partial_matching_images:
                print('\t{} Partial Matches found: '.format(
                       len(page.partial_matching_images)))

                for image in page.partial_matching_images:
                    print('\t\tImage url  : {}'.format(image.url))

    if annotations.web_entities:
        print('\n{} Web entities found: '.format(
            len(annotations.web_entities)))

        for entity in annotations.web_entities:
            print('\n\tScore      : {}'.format(entity.score))
            print(u'\tDescription: {}'.format(entity.description))

    if annotations.visually_similar_images:
        print('\n{} visually similar images found:\n'.format(
            len(annotations.visually_similar_images)))

        for image in annotations.visually_similar_images:
            print('\tImage url    : {}'.format(image.url))

    if response.error.message:
        raise Exception(
            '{}\nFor more info on error messages, check: '
            'https://cloud.google.com/apis/design/errors'.format(
                response.error.message))
# [END vision_web_detection_gcs]


# [START vision_web_detection_include_geo]
def web_entities_include_geo_results(path):
    """Detects web annotations given an image, using the geotag metadata
    in the image to detect web entities."""
    from google.cloud import vision
    import io
    client = vision.ImageAnnotatorClient()

    with io.open(path, 'rb') as image_file:
        content = image_file.read()

    image = vision.Image(content=content)

    web_detection_params = vision.WebDetectionParams(
        include_geo_results=True)
    image_context = vision.ImageContext(
        web_detection_params=web_detection_params)

    response = client.web_detection(image=image, image_context=image_context)

    for entity in response.web_detection.web_entities:
        print('\n\tScore      : {}'.format(entity.score))
        print(u'\tDescription: {}'.format(entity.description))

    if response.error.message:
        raise Exception(
            '{}\nFor more info on error messages, check: '
            'https://cloud.google.com/apis/design/errors'.format(
                response.error.message))
# [END vision_web_detection_include_geo]


# [START vision_web_detection_include_geo_gcs]
def web_entities_include_geo_results_uri(uri):
    """Detects web annotations given an image in the file located in
    Google Cloud Storage., using the geotag metadata in the image to
    detect web entities."""
    from google.cloud import vision
    client = vision.ImageAnnotatorClient()

    image = vision.Image()
    image.source.image_uri = uri

    web_detection_params = vision.WebDetectionParams(
        include_geo_results=True)
    image_context = vision.ImageContext(
        web_detection_params=web_detection_params)

    response = client.web_detection(image=image, image_context=image_context)

    for entity in response.web_detection.web_entities:
        print('\n\tScore      : {}'.format(entity.score))
        print(u'\tDescription: {}'.format(entity.description))

    if response.error.message:
        raise Exception(
            '{}\nFor more info on error messages, check: '
            'https://cloud.google.com/apis/design/errors'.format(
                response.error.message))
# [END vision_web_detection_include_geo_gcs]


# [START vision_crop_hint_detection]
def detect_crop_hints(path):
    """Detects crop hints in an image."""
    from google.cloud import vision
    import io
    client = vision.ImageAnnotatorClient()

    # [START vision_python_migration_crop_hints]
    with io.open(path, 'rb') as image_file:
        content = image_file.read()
    image = vision.Image(content=content)

    crop_hints_params = vision.CropHintsParams(aspect_ratios=[1.77])
    image_context = vision.ImageContext(
        crop_hints_params=crop_hints_params)

    response = client.crop_hints(image=image, image_context=image_context)
    hints = response.crop_hints_annotation.crop_hints

    for n, hint in enumerate(hints):
        print('\nCrop Hint: {}'.format(n))

        vertices = (['({},{})'.format(vertex.x, vertex.y)
                    for vertex in hint.bounding_poly.vertices])

        print('bounds: {}'.format(','.join(vertices)))

    if response.error.message:
        raise Exception(
            '{}\nFor more info on error messages, check: '
            'https://cloud.google.com/apis/design/errors'.format(
                response.error.message))
    # [END vision_python_migration_crop_hints]
# [END vision_crop_hint_detection]


# [START vision_crop_hint_detection_gcs]
def detect_crop_hints_uri(uri):
    """Detects crop hints in the file located in Google Cloud Storage."""
    from google.cloud import vision
    client = vision.ImageAnnotatorClient()
    image = vision.Image()
    image.source.image_uri = uri

    crop_hints_params = vision.CropHintsParams(aspect_ratios=[1.77])
    image_context = vision.ImageContext(
        crop_hints_params=crop_hints_params)

    response = client.crop_hints(image=image, image_context=image_context)
    hints = response.crop_hints_annotation.crop_hints

    for n, hint in enumerate(hints):
        print('\nCrop Hint: {}'.format(n))

        vertices = (['({},{})'.format(vertex.x, vertex.y)
                    for vertex in hint.bounding_poly.vertices])

        print('bounds: {}'.format(','.join(vertices)))

    if response.error.message:
        raise Exception(
            '{}\nFor more info on error messages, check: '
            'https://cloud.google.com/apis/design/errors'.format(
                response.error.message))
# [END vision_crop_hint_detection_gcs]


# [START vision_fulltext_detection]
def detect_document(path):
    """Detects document features in an image."""
    from google.cloud import vision
    import io
    client = vision.ImageAnnotatorClient()

    # [START vision_python_migration_document_text_detection]
    with io.open(path, 'rb') as image_file:
        content = image_file.read()

    image = vision.Image(content=content)

    response = client.document_text_detection(image=image)

    for page in response.full_text_annotation.pages:
        for block in page.blocks:
            print('\nBlock confidence: {}\n'.format(block.confidence))

            for paragraph in block.paragraphs:
                print('Paragraph confidence: {}'.format(
                    paragraph.confidence))

                for word in paragraph.words:
                    word_text = ''.join([
                        symbol.text for symbol in word.symbols
                    ])
                    print('Word text: {} (confidence: {})'.format(
                        word_text, word.confidence))

                    for symbol in word.symbols:
                        print('\tSymbol: {} (confidence: {})'.format(
                            symbol.text, symbol.confidence))

    if response.error.message:
        raise Exception(
            '{}\nFor more info on error messages, check: '
            'https://cloud.google.com/apis/design/errors'.format(
                response.error.message))
    # [END vision_python_migration_document_text_detection]
# [END vision_fulltext_detection]


# [START vision_fulltext_detection_gcs]
def detect_document_uri(uri):
    """Detects document features in the file located in Google Cloud
    Storage."""
    from google.cloud import vision
    client = vision.ImageAnnotatorClient()
    image = vision.Image()
    image.source.image_uri = uri

    response = client.document_text_detection(image=image)

    for page in response.full_text_annotation.pages:
        for block in page.blocks:
            print('\nBlock confidence: {}\n'.format(block.confidence))

            for paragraph in block.paragraphs:
                print('Paragraph confidence: {}'.format(
                    paragraph.confidence))

                for word in paragraph.words:
                    word_text = ''.join([
                        symbol.text for symbol in word.symbols
                    ])
                    print('Word text: {} (confidence: {})'.format(
                        word_text, word.confidence))

                    for symbol in word.symbols:
                        print('\tSymbol: {} (confidence: {})'.format(
                            symbol.text, symbol.confidence))

    if response.error.message:
        raise Exception(
            '{}\nFor more info on error messages, check: '
            'https://cloud.google.com/apis/design/errors'.format(
                response.error.message))
# [END vision_fulltext_detection_gcs]


# [START vision_text_detection_pdf_gcs]
def async_detect_document(gcs_source_uri, gcs_destination_uri):
    """OCR with PDF/TIFF as source files on GCS"""
    import json
    import re
    from google.cloud import vision
    from google.cloud import storage

    # Supported mime_types are: 'application/pdf' and 'image/tiff'
    mime_type = 'application/pdf'

    # How many pages should be grouped into each json output file.
    batch_size = 2

    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=420)

    # 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('/')]
    print('Output files:')
    for blob in blob_list:
        print(blob.name)

    # Process the first output file from GCS.
    # Since we specified batch_size=2, the first response contains
    # the first two pages of the input file.
    output = blob_list[0]

    json_string = output.download_as_string()
    response = json.loads(json_string)

    # The actual response for the first page of the input file.
    first_page_response = response['responses'][0]
    annotation = first_page_response['fullTextAnnotation']

    # Here we print the full text from the first page.
    # The response contains more information:
    # annotation/pages/blocks/paragraphs/words/symbols
    # including confidence scores and bounding boxes
    print('Full text:\n')
    print(annotation['text'])
# [END vision_text_detection_pdf_gcs]


# [START vision_localize_objects]
def localize_objects(path):
    """Localize objects in the local image.

    Args:
    path: The path to the local file.
    """
    from google.cloud import vision
    client = vision.ImageAnnotatorClient()

    with open(path, 'rb') as image_file:
        content = image_file.read()
    image = vision.Image(content=content)

    objects = client.object_localization(
        image=image).localized_object_annotations

    print('Number of objects found: {}'.format(len(objects)))
    for object_ in objects:
        print('\n{} (confidence: {})'.format(object_.name, object_.score))
        print('Normalized bounding polygon vertices: ')
        for vertex in object_.bounding_poly.normalized_vertices:
            print(' - ({}, {})'.format(vertex.x, vertex.y))
# [END vision_localize_objects]


# [START vision_localize_objects_gcs]
def localize_objects_uri(uri):
    """Localize objects in the image on Google Cloud Storage

    Args:
    uri: The path to the file in Google Cloud Storage (gs://...)
    """
    from google.cloud import vision
    client = vision.ImageAnnotatorClient()

    image = vision.Image()
    image.source.image_uri = uri

    objects = client.object_localization(
        image=image).localized_object_annotations

    print('Number of objects found: {}'.format(len(objects)))
    for object_ in objects:
        print('\n{} (confidence: {})'.format(object_.name, object_.score))
        print('Normalized bounding polygon vertices: ')
        for vertex in object_.bounding_poly.normalized_vertices:
            print(' - ({}, {})'.format(vertex.x, vertex.y))
# [END vision_localize_objects_gcs]


def run_local(args):
    if args.command == 'faces':
        detect_faces(args.path)
    elif args.command == 'labels':
        detect_labels(args.path)
    elif args.command == 'landmarks':
        detect_landmarks(args.path)
    elif args.command == 'text':
        detect_text(args.path)
    elif args.command == 'logos':
        detect_logos(args.path)
    elif args.command == 'safe-search':
        detect_safe_search(args.path)
    elif args.command == 'properties':
        detect_properties(args.path)
    elif args.command == 'web':
        detect_web(args.path)
    elif args.command == 'crophints':
        detect_crop_hints(args.path)
    elif args.command == 'document':
        detect_document(args.path)
    elif args.command == 'web-geo':
        web_entities_include_geo_results(args.path)
    elif args.command == 'object-localization':
        localize_objects(args.path)


def run_uri(args):
    if args.command == 'text-uri':
        detect_text_uri(args.uri)
    elif args.command == 'faces-uri':
        detect_faces_uri(args.uri)
    elif args.command == 'labels-uri':
        detect_labels_uri(args.uri)
    elif args.command == 'landmarks-uri':
        detect_landmarks_uri(args.uri)
    elif args.command == 'logos-uri':
        detect_logos_uri(args.uri)
    elif args.command == 'safe-search-uri':
        detect_safe_search_uri(args.uri)
    elif args.command == 'properties-uri':
        detect_properties_uri(args.uri)
    elif args.command == 'web-uri':
        detect_web_uri(args.uri)
    elif args.command == 'crophints-uri':
        detect_crop_hints_uri(args.uri)
    elif args.command == 'document-uri':
        detect_document_uri(args.uri)
    elif args.command == 'web-geo-uri':
        web_entities_include_geo_results_uri(args.uri)
    elif args.command == 'ocr-uri':
        async_detect_document(args.uri, args.destination_uri)
    elif args.command == 'object-localization-uri':
        localize_objects_uri(args.uri)


if __name__ == '__main__':
    parser = argparse.ArgumentParser(
        description=__doc__,
        formatter_class=argparse.RawDescriptionHelpFormatter)
    subparsers = parser.add_subparsers(dest='command')

    detect_faces_parser = subparsers.add_parser(
        'faces', help=detect_faces.__doc__)
    detect_faces_parser.add_argument('path')

    faces_file_parser = subparsers.add_parser(
        'faces-uri', help=detect_faces_uri.__doc__)
    faces_file_parser.add_argument('uri')

    detect_labels_parser = subparsers.add_parser(
        'labels', help=detect_labels.__doc__)
    detect_labels_parser.add_argument('path')

    labels_file_parser = subparsers.add_parser(
        'labels-uri', help=detect_labels_uri.__doc__)
    labels_file_parser.add_argument('uri')

    detect_landmarks_parser = subparsers.add_parser(
        'landmarks', help=detect_landmarks.__doc__)
    detect_landmarks_parser.add_argument('path')

    landmark_file_parser = subparsers.add_parser(
        'landmarks-uri', help=detect_landmarks_uri.__doc__)
    landmark_file_parser.add_argument('uri')

    detect_text_parser = subparsers.add_parser(
        'text', help=detect_text.__doc__)
    detect_text_parser.add_argument('path')

    text_file_parser = subparsers.add_parser(
        'text-uri', help=detect_text_uri.__doc__)
    text_file_parser.add_argument('uri')

    detect_logos_parser = subparsers.add_parser(
        'logos', help=detect_logos.__doc__)
    detect_logos_parser.add_argument('path')

    logos_file_parser = subparsers.add_parser(
        'logos-uri', help=detect_logos_uri.__doc__)
    logos_file_parser.add_argument('uri')

    safe_search_parser = subparsers.add_parser(
        'safe-search', help=detect_safe_search.__doc__)
    safe_search_parser.add_argument('path')

    safe_search_file_parser = subparsers.add_parser(
        'safe-search-uri',
        help=detect_safe_search_uri.__doc__)
    safe_search_file_parser.add_argument('uri')

    properties_parser = subparsers.add_parser(
        'properties', help=detect_properties.__doc__)
    properties_parser.add_argument('path')

    properties_file_parser = subparsers.add_parser(
        'properties-uri',
        help=detect_properties_uri.__doc__)
    properties_file_parser.add_argument('uri')

    # 1.1 Vision features
    web_parser = subparsers.add_parser(
        'web', help=detect_web.__doc__)
    web_parser.add_argument('path')

    web_uri_parser = subparsers.add_parser(
        'web-uri',
        help=detect_web_uri.__doc__)
    web_uri_parser.add_argument('uri')

    web_geo_parser = subparsers.add_parser(
        'web-geo', help=web_entities_include_geo_results.__doc__)
    web_geo_parser.add_argument('path')

    web_geo_uri_parser = subparsers.add_parser(
        'web-geo-uri',
        help=web_entities_include_geo_results_uri.__doc__)
    web_geo_uri_parser.add_argument('uri')

    crop_hints_parser = subparsers.add_parser(
        'crophints', help=detect_crop_hints.__doc__)
    crop_hints_parser.add_argument('path')

    crop_hints_uri_parser = subparsers.add_parser(
        'crophints-uri', help=detect_crop_hints_uri.__doc__)
    crop_hints_uri_parser.add_argument('uri')

    document_parser = subparsers.add_parser(
        'document', help=detect_document.__doc__)
    document_parser.add_argument('path')

    document_uri_parser = subparsers.add_parser(
        'document-uri', help=detect_document_uri.__doc__)
    document_uri_parser.add_argument('uri')

    ocr_uri_parser = subparsers.add_parser(
        'ocr-uri', help=async_detect_document.__doc__)
    ocr_uri_parser.add_argument('uri')
    ocr_uri_parser.add_argument('destination_uri')

    object_localization_parser = subparsers.add_parser(
        'object-localization', help=async_detect_document.__doc__)
    object_localization_parser.add_argument('path')

    object_localization_uri_parser = subparsers.add_parser(
        'object-localization-uri', help=async_detect_document.__doc__)
    object_localization_uri_parser.add_argument('uri')

    args = parser.parse_args()

    if 'uri' in args.command:
        run_uri(args)
    else:
        run_local(args)