Python vision.ImageAnnotatorClient方法代码示例

# 需要导入模块: from google.cloud import vision [as 别名]
# 或者: from google.cloud.vision import ImageAnnotatorClient [as 别名]
def annotate(path):
    """Returns web annotations given the path to an image."""
    # [START vision_web_detection_tutorial_annotate]
    client = vision.ImageAnnotatorClient()

    if path.startswith('http') or path.startswith('gs:'):
        image = types.Image()
        image.source.image_uri = path

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

        image = types.Image(content=content)

    web_detection = client.web_detection(image=image).web_detection
    # [END vision_web_detection_tutorial_annotate]

    return web_detection 

# 需要导入模块: from google.cloud import vision [as 别名]
# 或者: from google.cloud.vision import ImageAnnotatorClient [as 别名]
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.types.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] 

# 需要导入模块: from google.cloud import vision [as 别名]
# 或者: from google.cloud.vision import ImageAnnotatorClient [as 别名]
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.types.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] 

# 需要导入模块: from google.cloud import vision [as 别名]
# 或者: from google.cloud.vision import ImageAnnotatorClient [as 别名]
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.types.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] 

# 需要导入模块: from google.cloud import vision [as 别名]
# 或者: from google.cloud.vision import ImageAnnotatorClient [as 别名]
def detect_face(face_file, max_results=4):
    """Uses the Vision API to detect faces in the given file.

    Args:
        face_file: A file-like object containing an image with faces.

    Returns:
        An array of Face objects with information about the picture.
    """
    # [START vision_face_detection_tutorial_client]
    client = vision.ImageAnnotatorClient()
    # [END vision_face_detection_tutorial_client]

    content = face_file.read()
    image = types.Image(content=content)

    return client.face_detection(
        image=image, max_results=max_results).face_annotations
# [END vision_face_detection_tutorial_send_request]


# [START vision_face_detection_tutorial_process_response] 

# 需要导入模块: from google.cloud import vision [as 别名]
# 或者: from google.cloud.vision import ImageAnnotatorClient [as 别名]
def detect_labels_uri(uri):
    """This Function detects labels in the image file located in Google Cloud Storage or on
    theWeb and returns a comma separated list of labels. This will return an empty string
    if not passed a valid image file

    Args:
        uri: a string link to a photo in gcs or on the web

    (Adapted From:
    https://github.com/GoogleCloudPlatform/python-docs-samples/blob/master/vision/cloud-client/detect/detect.py)
    """
    # Initialize cloud vision api client and image object.
    client = vision.ImageAnnotatorClient()
    image = types.Image()
    image.source.image_uri = uri

    # Send an api call for this image, extract label descriptions
    # and return a comma-space separated string.
    response = client.label_detection(image=image)
    labels = response.label_annotations
    label_list = [l.description for l in labels]
    return ', '.join(label_list) 

# 需要导入模块: from google.cloud import vision [as 别名]
# 或者: from google.cloud.vision import ImageAnnotatorClient [as 别名]
def getImageTags(bucketName, fileName):
  client = vision.ImageAnnotatorClient()
  image_uri = "gs://" + bucketName + "/" + fileName
  request = {
    "image": {"source": {"image_uri": image_uri}},
    "features": [
      {
        "type": vision.enums.Feature.Type.LABEL_DETECTION,
        "max_results": 6
      },
      {
        "type": vision.enums.Feature.Type.LANDMARK_DETECTION,
        "max_results": 3
      }
    ],
  }
  response = client.annotate_image(request)
  return getTagsFromResponse(response) 

# 需要导入模块: from google.cloud import vision [as 别名]
# 或者: from google.cloud.vision import ImageAnnotatorClient [as 别名]
def __init__(
            self,
            bounds=(0, 255),
            channel_axis=3,
            preprocessing=(0, 1)):

        from google.cloud import vision
        super(GoogleSafeSearchModel, self).__init__(
            bounds=bounds,
            channel_axis=channel_axis,
            preprocessing=preprocessing)

        self._task = 'cls'
        self.model = vision.ImageAnnotatorClient() 

# 需要导入模块: from google.cloud import vision [as 别名]
# 或者: from google.cloud.vision import ImageAnnotatorClient [as 别名]
def pic_to_text(infile):
    """Detects text in an image file

    ARGS
    infile: path to image file

    RETURNS
    String of text detected in image
    """

    # Instantiates a client
    client = vision.ImageAnnotatorClient()

    # Opens the input image file
    with io.open(infile, 'rb') as image_file:
        content = image_file.read()

    image = vision.types.Image(content=content)

    # For dense text, use document_text_detection
    # For less dense text, use text_detection
    response = client.document_text_detection(image=image)
    text = response.full_text_annotation.text

    return text
    # [END translate_hybrid_vision]


# [START translate_hybrid_create_glossary] 

# 需要导入模块: from google.cloud import vision [as 别名]
# 或者: from google.cloud.vision import ImageAnnotatorClient [as 别名]
def run_quickstart():
    # [START vision_quickstart]
    import io
    import os

    # Imports the Google Cloud client library
    # [START vision_python_migration_import]
    from google.cloud import vision
    from google.cloud.vision import types
    # [END vision_python_migration_import]

    # Instantiates a client
    # [START vision_python_migration_client]
    client = vision.ImageAnnotatorClient()
    # [END vision_python_migration_client]

    # The name of the image file to annotate
    file_name = os.path.abspath('resources/wakeupcat.jpg')

    # Loads the image into memory
    with io.open(file_name, 'rb') as image_file:
        content = image_file.read()

    image = types.Image(content=content)

    # Performs label detection on the image file
    response = client.label_detection(image=image)
    labels = response.label_annotations

    print('Labels:')
    for label in labels:
        print(label.description)
    # [END vision_quickstart] 

# 需要导入模块: from google.cloud import vision [as 别名]
# 或者: from google.cloud.vision import ImageAnnotatorClient [as 别名]
def set_endpoint():
    """Change your endpoint"""
    # [START vision_set_endpoint]
    from google.cloud import vision

    client_options = {'api_endpoint': 'eu-vision.googleapis.com'}

    client = vision.ImageAnnotatorClient(client_options=client_options)
    # [END vision_set_endpoint]
    image_source = vision.types.ImageSource(
        image_uri='gs://cloud-samples-data/vision/text/screen.jpg')
    image = vision.types.Image(source=image_source)

    response = client.text_detection(image=image)

    print('Texts:')
    for text in response.text_annotations:
        print('{}'.format(text.description))

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

        print('bounds: {}\n'.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)) 

# 需要导入模块: from google.cloud import vision [as 别名]
# 或者: from google.cloud.vision import ImageAnnotatorClient [as 别名]
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.types.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] 

# 需要导入模块: from google.cloud import vision [as 别名]
# 或者: from google.cloud.vision import ImageAnnotatorClient [as 别名]
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.types.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] 

# 需要导入模块: from google.cloud import vision [as 别名]
# 或者: from google.cloud.vision import ImageAnnotatorClient [as 别名]
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.types.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] 

# 需要导入模块: from google.cloud import vision [as 别名]
# 或者: from google.cloud.vision import ImageAnnotatorClient [as 别名]
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.types.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]