azure-ai-vision-imageanalysis-py — Анализ изображений с использованием Azure AI Vision

--- name: azure-ai-vision-imageanalysis-py description: Azure AI Vision Image Analysis SDK for captions, tags, objects, OCR, people detection, and smart cropping. Use for computer vision and image understanding tasks. risk: unknown source: community date_added: '2026-02-27' --- # 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.