Azure AI Vision Image Analysis SDK for Python

Client library for Azure AI Vision 4.0 image analysis including captions, tags, objects, OCR, and more.

Installation

bash
pip install azure-ai-vision-imageanalysis

Environment Variables

bash
VISION_ENDPOINT=https://<resource>.cognitiveservices.azure.com
VISION_KEY=<your-api-key>  # If using API key

Authentication

API Key

python
import os
from azure.ai.vision.imageanalysis import ImageAnalysisClient
from azure.core.credentials import AzureKeyCredential

endpoint = os.environ["VISION_ENDPOINT"]
key = os.environ["VISION_KEY"]

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

Entra ID (Recommended)

python
from azure.ai.vision.imageanalysis import ImageAnalysisClient
from azure.identity import DefaultAzureCredential

client = ImageAnalysisClient(
    endpoint=os.environ["VISION_ENDPOINT"],
    credential=DefaultAzureCredential()
)

Analyze Image from URL

python
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"
)

Analyze Image from File

python
with open("image.jpg", "rb") as f:
    image_data = f.read()

result = client.analyze(
    image_data=image_data,
    visual_features=[VisualFeatures.CAPTION, VisualFeatures.TAGS]
)

Image Caption

python
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}")

Dense Captions (Multiple Regions)

python
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}")

Tags

python
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})")

Object Detection

python
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}")

OCR (Text Extraction)

python
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})")

People Detection

python
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}")

Smart Cropping

python
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}")

Async Client

python
from azure.ai.vision.imageanalysis.aio import ImageAnalysisClient
from azure.identity.aio import DefaultAzureCredential

async def analyze_image():
    async with ImageAnalysisClient(
        endpoint=endpoint,
        credential=DefaultAzureCredential()
    ) as client:
        result = await client.analyze_from_url(
            image_url=image_url,
            visual_features=[VisualFeatures.CAPTION]
        )
        print(result.caption.text)

Visual Features

| Feature | Description | |---------|-------------| | CAPTION | Single sentence describing the image | | DENSE_CAPTIONS | Captions for multiple regions | | TAGS | Content tags (objects, scenes, actions) | | OBJECTS | Object detection with bounding boxes | | READ | OCR text extraction | | PEOPLE | People detection with bounding boxes | | SMART_CROPS | Suggested crop regions for thumbnails |

Error Handling

python
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}")

Image Requirements

• Formats: JPEG, PNG, GIF, BMP, WEBP, ICO, TIFF, MPO
• Max size: 20 MB
• Dimensions: 50x50 to 16000x16000 pixels

Best Practices

1. Select only needed features to optimize latency and cost
2. Use async client for high-throughput scenarios
3. Handle HttpResponseError for invalid images or auth issues
4. Enable gender_neutral_caption for inclusive descriptions
5. Specify language for localized captions
6. Use smart_crops_aspect_ratios matching your thumbnail requirements
7. Cache results when analyzing the same image multiple times

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.