sample_image_embeddings.py

# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------
"""
DESCRIPTION:
 This sample demonstrates how to get image embeddings vectors for 
 two input images, using a synchronous client.

USAGE:
 python sample_image_embeddings.py

 Set these two environment variables before running the sample:
 1) AZURE_AI_IMAGE_EMBEDDINGS_ENDPOINT - Your endpoint URL, in the form 
 https://<your-deployment-name>.<your-azure-region>.inference.ai.azure.com
 where `your-deployment-name` is your unique AI Model deployment name, and
 `your-azure-region` is the Azure region where your model is deployed.
 2) AZURE_AI_IMAGE_EMBEDDINGS_KEY - Your model key (a 32-character string). Keep it secret.
"""


defsample_image_embeddings():
importos
importbase64

try:
endpoint=os.environ["AZURE_AI_IMAGE_EMBEDDINGS_ENDPOINT"]
key=os.environ["AZURE_AI_IMAGE_EMBEDDINGS_KEY"]
exceptKeyError:
print("Missing environment variable 'AZURE_AI_IMAGE_EMBEDDINGS_ENDPOINT' or 'AZURE_AI_IMAGE_EMBEDDINGS_KEY'")
print("Set them before running this sample.")
exit()

# [START image_embeddings]
fromazure.ai.inferenceimportImageEmbeddingsClient
fromazure.ai.inference.modelsimportEmbeddingInput
fromazure.core.credentialsimportAzureKeyCredential

withopen("sample1.png", "rb") asf:
image1: str=base64.b64encode(f.read()).decode("utf-8")
withopen("sample2.png", "rb") asf:
image2: str=base64.b64encode(f.read()).decode("utf-8")

client=ImageEmbeddingsClient(endpoint=endpoint, credential=AzureKeyCredential(key))

response=client.embed(input=[EmbeddingInput(image=image1), EmbeddingInput(image=image2)])

foriteminresponse.data:
length=len(item.embedding)
print(
f"data[{item.index}]: length={length}, [{item.embedding[0]}, {item.embedding[1]}, "
f"..., {item.embedding[length-2]}, {item.embedding[length-1]}]"
 )
# [END image_embeddings]


if__name__=="__main__":
sample_image_embeddings()