Module vision_models (1.53.0)

Version 1.53.0

• 1.75.0 (latest)
• 1.74.0
• 1.73.0
• 1.72.0
• 1.71.1
• 1.70.0
• 1.69.0
• 1.68.0
• 1.67.1
• 1.66.0
• 1.65.0
• 1.63.0
• 1.62.0
• 1.60.0
• 1.59.0
• 1.58.0
• 1.57.0
• 1.56.0
• 1.55.0
• 1.54.1
• 1.53.0
• 1.52.0
• 1.51.0
• 1.50.0
• 1.49.0
• 1.48.0
• 1.47.0
• 1.46.0
• 1.45.0
• 1.44.0
• 1.43.0
• 1.39.0
• 1.38.1
• 1.37.0
• 1.36.4
• 1.35.0
• 1.34.0
• 1.33.1
• 1.32.0
• 1.31.1
• 1.30.1
• 1.29.0
• 1.28.1
• 1.27.1
• 1.26.1
• 1.25.0
• 1.24.1
• 1.23.0
• 1.22.1
• 1.21.0
• 1.20.0
• 1.19.1
• 1.18.3
• 1.17.1
• 1.16.1
• 1.15.1
• 1.14.0
• 1.13.1
• 1.12.1
• 1.11.0
• 1.10.0
• 1.9.0
• 1.8.1
• 1.7.1
• 1.6.2
• 1.5.0
• 1.4.3
• 1.3.0
• 1.2.0
• 1.1.1
• 1.0.1
• 0.9.0
• 0.8.0
• 0.7.1
• 0.6.0
• 0.5.1
• 0.4.0
• 0.3.1

Classes for working with vision models.

Classes

GeneratedImage

GeneratedImage(
    image_bytes: typing.Optional[bytes],
    generation_parameters: typing.Dict[str, typing.Any],
    gcs_uri: typing.Optional[str] = None,
)

Generated image.

Image

Image(
    image_bytes: typing.Optional[bytes] = None, gcs_uri: typing.Optional[str] = None
)

Image.

ImageCaptioningModel

ImageCaptioningModel(model_id: str, endpoint_name: typing.Optional[str] = None)

Generates captions from image.

Examples::

model = ImageCaptioningModel.from_pretrained("imagetext@001")
image = Image.load_from_file("image.png")
captions = model.get_captions(
    image=image,
    # Optional:
    number_of_results=1,
    language="en",
)

ImageGenerationModel

ImageGenerationModel(model_id: str, endpoint_name: typing.Optional[str] = None)

Generates images from text prompt.

Examples::

model = ImageGenerationModel.from_pretrained("imagegeneration@002")
response = model.generate_images(
    prompt="Astronaut riding a horse",
    # Optional:
    number_of_images=1,
    seed=0,
)
response[0].show()
response[0].save("image1.png")

ImageGenerationResponse

ImageGenerationResponse(images: typing.List[GeneratedImage])

Image generation response.

ImageQnAModel

ImageQnAModel(model_id: str, endpoint_name: typing.Optional[str] = None)

Answers questions about an image.

Examples::

model = ImageQnAModel.from_pretrained("imagetext@001")
image = Image.load_from_file("image.png")
answers = model.ask_question(
    image=image,
    question="What color is the car in this image?",
    # Optional:
    number_of_results=1,
)

ImageTextModel

ImageTextModel(model_id: str, endpoint_name: typing.Optional[str] = None)

Generates text from images.

Examples::

model = ImageTextModel.from_pretrained("imagetext@001")
image = Image.load_from_file("image.png")

captions = model.get_captions(
    image=image,
    # Optional:
    number_of_results=1,
    language="en",
)

answers = model.ask_question(
    image=image,
    question="What color is the car in this image?",
    # Optional:
    number_of_results=1,
)

MultiModalEmbeddingModel

MultiModalEmbeddingModel(model_id: str, endpoint_name: typing.Optional[str] = None)

Generates embedding vectors from images and videos.

Examples::

model = MultiModalEmbeddingModel.from_pretrained("multimodalembedding@001")
image = Image.load_from_file("image.png")
video = Video.load_from_file("video.mp4")

embeddings = model.get_embeddings(
    image=image,
    video=video,
    contextual_text="Hello world",
)
image_embedding = embeddings.image_embedding
video_embeddings = embeddings.video_embeddings
text_embedding = embeddings.text_embedding

MultiModalEmbeddingResponse

MultiModalEmbeddingResponse(
    _prediction_response: typing.Any,
    image_embedding: typing.Optional[typing.List[float]] = None,
    video_embeddings: typing.Optional[
        typing.List[vertexai.vision_models.VideoEmbedding]
    ] = None,
    text_embedding: typing.Optional[typing.List[float]] = None,
)

The multimodal embedding response.

Video

Video(
    video_bytes: typing.Optional[bytes] = None, gcs_uri: typing.Optional[str] = None
)

Video.

VideoEmbedding

VideoEmbedding(
    start_offset_sec: int, end_offset_sec: int, embedding: typing.List[float]
)

Embeddings generated from video with offset times.

VideoSegmentConfig

VideoSegmentConfig(
    start_offset_sec: int = 0, end_offset_sec: int = 120, interval_sec: int = 16
)

The specific video segments (in seconds) the embeddings are generated for.

WatermarkVerificationModel

WatermarkVerificationModel(
    model_id: str, endpoint_name: typing.Optional[str] = None
)

Verifies if an image has a watermark.

WatermarkVerificationResponse

WatermarkVerificationResponse(
    _prediction_response: Any, watermark_verification_result: Optional[str] = None
)

WatermarkVerificationResponse(_prediction_response: Any, watermark_verification_result: Optional[str] = None)