azure-ai-vision-imageanalysis-py

import os
from azure.identity import DefaultAzureCredential, ManagedIdentityCredential
from azure.ai.vision.imageanalysis import ImageAnalysisClient
from azure.ai.vision.imageanalysis.models import VisualFeatures
 
# Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>
credential = DefaultAzureCredential(require_envvar=True)
# Or use a specific credential directly in production:
# See https://learn.microsoft.com/python/api/overview/azure/identity-readme?view=azure-python#credential-classes
# credential = ManagedIdentityCredential()
 
with ImageAnalysisClient(
    endpoint=os.environ["VISION_ENDPOINT"],
    credential=credential,
) as client:
    result = client.analyze_from_url(
        image_url="https://aka.ms/azsdk/image-analysis/sample.jpg",
        visual_features=[VisualFeatures.CAPTION],
    )

import os
from azure.core.credentials import AzureKeyCredential
from azure.ai.vision.imageanalysis import ImageAnalysisClient
from azure.ai.vision.imageanalysis.models import VisualFeatures
 
with ImageAnalysisClient(
    endpoint=os.environ["VISION_ENDPOINT"],
    credential=AzureKeyCredential(os.environ["VISION_KEY"]),
) as client:
    result = client.analyze_from_url(
        image_url="https://aka.ms/azsdk/image-analysis/sample.jpg",
        visual_features=[VisualFeatures.CAPTION],
    )

from azure.ai.vision.imageanalysis.models import VisualFeatures
 
image_url = "https://example.com/image.jpg"
 
result = client.analyze_from_url(
    image_url=image_url,
    visual_features=[
        VisualFeatures.CAPTION,
        VisualFeatures.TAGS,
        VisualFeatures.OBJECTS,
        VisualFeatures.READ,
        VisualFeatures.PEOPLE,
        VisualFeatures.SMART_CROPS,
        VisualFeatures.DENSE_CAPTIONS
    ],
    gender_neutral_caption=True,
    language="en"
)

with open("image.jpg", "rb") as f:
    image_data = f.read()
 
result = client.analyze(
    image_data=image_data,
    visual_features=[VisualFeatures.CAPTION, VisualFeatures.TAGS]
)

result = client.analyze_from_url(
    image_url=image_url,
    visual_features=[VisualFeatures.CAPTION],
    gender_neutral_caption=True
)
 
if result.caption:
    print(f"Caption: {result.caption.text}")
    print(f"Confidence: {result.caption.confidence:.2f}")

result = client.analyze_from_url(
    image_url=image_url,
    visual_features=[VisualFeatures.DENSE_CAPTIONS]
)
 
if result.dense_captions:
    for caption in result.dense_captions.list:
        print(f"Caption: {caption.text}")
        print(f"  Confidence: {caption.confidence:.2f}")
        print(f"  Bounding box: {caption.bounding_box}")

result = client.analyze_from_url(
    image_url=image_url,
    visual_features=[VisualFeatures.TAGS]
)
 
if result.tags:
    for tag in result.tags.list:
        print(f"Tag: {tag.name} (confidence: {tag.confidence:.2f})")

result = client.analyze_from_url(
    image_url=image_url,
    visual_features=[VisualFeatures.OBJECTS]
)
 
if result.objects:
    for obj in result.objects.list:
        print(f"Object: {obj.tags[0].name}")
        print(f"  Confidence: {obj.tags[0].confidence:.2f}")
        box = obj.bounding_box
        print(f"  Bounding box: x={box.x}, y={box.y}, w={box.width}, h={box.height}")

result = client.analyze_from_url(
    image_url=image_url,
    visual_features=[VisualFeatures.READ]
)
 
if result.read:
    for block in result.read.blocks:
        for line in block.lines:
            print(f"Line: {line.text}")
            print(f"  Bounding polygon: {line.bounding_polygon}")
            
            # Word-level details
            for word in line.words:
                print(f"  Word: {word.text} (confidence: {word.confidence:.2f})")

result = client.analyze_from_url(
    image_url=image_url,
    visual_features=[VisualFeatures.PEOPLE]
)
 
if result.people:
    for person in result.people.list:
        print(f"Person detected:")
        print(f"  Confidence: {person.confidence:.2f}")
        box = person.bounding_box
        print(f"  Bounding box: x={box.x}, y={box.y}, w={box.width}, h={box.height}")

result = client.analyze_from_url(
    image_url=image_url,
    visual_features=[VisualFeatures.SMART_CROPS],
    smart_crops_aspect_ratios=[0.9, 1.33, 1.78]  # Portrait, 4:3, 16:9
)
 
if result.smart_crops:
    for crop in result.smart_crops.list:
        print(f"Aspect ratio: {crop.aspect_ratio}")
        box = crop.bounding_box
        print(f"  Crop region: x={box.x}, y={box.y}, w={box.width}, h={box.height}")

from azure.ai.vision.imageanalysis.aio import ImageAnalysisClient
from azure.identity.aio import DefaultAzureCredential
 
async def analyze_image():
    async with DefaultAzureCredential() as credential:
        async with ImageAnalysisClient(
            endpoint=endpoint,
            credential=credential
        ) as client:
            result = await client.analyze_from_url(
                image_url=image_url,
                visual_features=[VisualFeatures.CAPTION]
            )
            print(result.caption.text)

from azure.core.exceptions import HttpResponseError
 
try:
    result = client.analyze_from_url(
        image_url=image_url,
        visual_features=[VisualFeatures.CAPTION]
    )
except HttpResponseError as e:
    print(f"Status code: {e.status_code}")
    print(f"Reason: {e.reason}")
    print(f"Message: {e.error.message}")