主站 Developer Toolsazure-ai-vision-imageanalysis-py

azure-ai-vision-imageanalysis-py

Name: azure-ai-vision-imageanalysis-py
Author: microsoft

Azure AI Vision Image Analysis SDK for captions, tags, objects, OCR, people detection, and smart cropping. Use for computer vision and image understanding tasks. Triggers: "image analysis", "computer vision", "OCR", "object detection", "ImageAnalysisClient", "image caption".

1.6k Stars0 Installs更新于 192 days agoMIT

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

Installation

pip install azure-ai-vision-imageanalysis

Environment Variables

VISION_ENDPOINT=https://<resource>.cognitiveservices.azure.com  # Required for all auth methods
AZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production
VISION_KEY=<your-api-key>  # Only required for the legacy API-key auth path below

Authentication & Lifecycle

🔑 Two rules apply to every code sample below:

Prefer DefaultAzureCredential. It works locally (Azure CLI / VS Code / Developer CLI) and in Azure (managed identity, workload identity) with no code change. Avoid connection strings, account/API keys — they bypass Entra audit and rotation.

Local dev: DefaultAzureCredential works as-is.

Production: set AZURE_TOKEN_CREDENTIALS=prod (or AZURE_TOKEN_CREDENTIALS=<specific_credential>) to constrain the credential chain to production-safe credentials.

Wrap every client in a context manager so HTTP transports, sockets, and token caches are released deterministically:

Sync: with <Client>(...) as client:

Async: async with <Client>(...) as client: and async with DefaultAzureCredential() as credential: (from azure.identity.aio)

Snippets may abbreviate this setup, but production code should always follow both rules.

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],
    )

Legacy: API Key (existing keyed deployments)

New code should use DefaultAzureCredential above. Use AzureKeyCredential only if you have an existing keyed deployment that hasn't been migrated to Entra ID yet — for example, regulated environments still completing their Entra rollout.

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],
    )

Analyze Image from URL

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

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

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)

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}")

Object Detection

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)

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

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

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

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)

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

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

Pick sync OR async and stay consistent. Do not mix azure.ai.vision.imageanalysis sync clients with azure.ai.vision.imageanalysis.aio async clients in the same call path. Choose one mode per module.
Always use context managers for clients and async credentials. Wrap every client in with ImageAnalysisClient(...) as client: (sync) or async with ImageAnalysisClient(...) as client: (async). For async DefaultAzureCredential from azure.identity.aio, also use async with credential: so tokens and transports are cleaned up.
Select only needed features to optimize latency and cost
Use async client for high-throughput scenarios
Handle HttpResponseError for invalid images or auth issues
Enable gender_neutral_caption for inclusive descriptions
Specify language for localized captions
Use smart_crops_aspect_ratios matching your thumbnail requirements
Cache results when analyzing the same image multiple times

Reference Files

| File | Contents | |------|----------| | references/capabilities.md | Additional non-hero capabilities, operation-group coverage, and production checklists. | | references/non-hero-scenarios.md | Dedicated non-hero examples for secondary/advanced scenarios. |

安装指令

npx skills add microsoft/azure-ai-vision-imageanalysis-py

下载解压包

下载 skill.zip

下载完整 Skill 目录，包含 SKILL.md 及所有相关文件

信息

Authormicrosoft

Categorydeveloper

Version@main

Last Updated192 days ago

LicenseMIT

在 GitHub 中查看

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Category:developer

主站 Developer Toolsazure-ai-vision-imageanalysis-py

azure-ai-vision-imageanalysis-py

1.6k Stars0 Installs更新于 192 days agoMIT

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

Installation

pip install azure-ai-vision-imageanalysis

Environment Variables

VISION_ENDPOINT=https://<resource>.cognitiveservices.azure.com  # Required for all auth methods
AZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production
VISION_KEY=<your-api-key>  # Only required for the legacy API-key auth path below

Authentication & Lifecycle

🔑 Two rules apply to every code sample below:

Prefer DefaultAzureCredential. It works locally (Azure CLI / VS Code / Developer CLI) and in Azure (managed identity, workload identity) with no code change. Avoid connection strings, account/API keys — they bypass Entra audit and rotation.

Local dev: DefaultAzureCredential works as-is.

Production: set AZURE_TOKEN_CREDENTIALS=prod (or AZURE_TOKEN_CREDENTIALS=<specific_credential>) to constrain the credential chain to production-safe credentials.

Wrap every client in a context manager so HTTP transports, sockets, and token caches are released deterministically:

Sync: with <Client>(...) as client:

Async: async with <Client>(...) as client: and async with DefaultAzureCredential() as credential: (from azure.identity.aio)

Snippets may abbreviate this setup, but production code should always follow both rules.

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],
    )

Legacy: API Key (existing keyed deployments)

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],
    )

Analyze Image from URL

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

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

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)

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}")

Object Detection

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)

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

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

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

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)

Visual Features

Error Handling

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

Pick sync OR async and stay consistent. Do not mix azure.ai.vision.imageanalysis sync clients with azure.ai.vision.imageanalysis.aio async clients in the same call path. Choose one mode per module.
Always use context managers for clients and async credentials. Wrap every client in with ImageAnalysisClient(...) as client: (sync) or async with ImageAnalysisClient(...) as client: (async). For async DefaultAzureCredential from azure.identity.aio, also use async with credential: so tokens and transports are cleaned up.
Select only needed features to optimize latency and cost
Use async client for high-throughput scenarios
Handle HttpResponseError for invalid images or auth issues
Enable gender_neutral_caption for inclusive descriptions
Specify language for localized captions
Use smart_crops_aspect_ratios matching your thumbnail requirements
Cache results when analyzing the same image multiple times

Reference Files

安装指令

npx skills add microsoft/azure-ai-vision-imageanalysis-py

下载解压包

下载 skill.zip

下载完整 Skill 目录，包含 SKILL.md 及所有相关文件

信息

Authormicrosoft

Categorydeveloper

Version@main

Last Updated192 days ago

LicenseMIT

在 GitHub 中查看

Category:developer

azure-ai-vision-imageanalysis-py

Installation

Environment Variables

Authentication & Lifecycle

Legacy: API Key (existing keyed deployments)

Analyze Image from URL

Analyze Image from File

Image Caption

Dense Captions (Multiple Regions)

Tags

Object Detection

OCR (Text Extraction)

People Detection

Smart Cropping

Async Client

Visual Features

Error Handling

Image Requirements

Best Practices

Reference Files

Related Skills

local-places

github

skill-creator

voice-call

notion

gemini

azure-ai-vision-imageanalysis-py

Installation

Environment Variables

Authentication & Lifecycle

Legacy: API Key (existing keyed deployments)

Analyze Image from URL

Analyze Image from File

Image Caption

Dense Captions (Multiple Regions)

Tags

Object Detection

OCR (Text Extraction)

People Detection

Smart Cropping

Async Client

Visual Features

Error Handling

Image Requirements

Best Practices

Reference Files

Related Skills

local-places

github

skill-creator

voice-call

notion

gemini