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".
apm install @microsoft/azure-ai-vision-imageanalysis-py---
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.
Triggers: "image analysis", "computer vision", "OCR", "object detection", "ImageAnalysisClient", "image caption".
package: azure-ai-vision-imageanalysis
---
# 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