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 pip install azure-ai-vision-imageanalysis Environment Variables VISION_ENDPOINT = https:// < resource

.cognitiveservices.azure.com VISION_KEY = < your-api-key

If using API key

Authentication API Key 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) 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 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 } " ) Tags 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 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 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 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 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 When to Use This skill is applicable to execute the workflow or actions described in the overview.