From faos-data-scientist
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Provides UI/UX resources: 50+ styles, color palettes, font pairings, guidelines, charts for web/mobile across React, Next.js, Vue, Svelte, Tailwind, React Native, Flutter. Aids planning, building, reviewing interfaces.
Fetches up-to-date documentation from Context7 for libraries and frameworks like React, Next.js, Prisma. Use for setup questions, API references, and code examples.
Guides Payload CMS config (payload.config.ts), collections, fields, hooks, access control, APIs. Debugs validation errors, security, relationships, queries, transactions, hook behavior.
Client library for Azure AI Vision 4.0 image analysis including captions, tags, objects, OCR, and more.
pip install azure-ai-vision-imageanalysis
VISION_ENDPOINT=https://<resource>.cognitiveservices.azure.com
VISION_KEY=<your-api-key> # If using 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)
)
from azure.ai.vision.imageanalysis import ImageAnalysisClient
from azure.identity import DefaultAzureCredential
client = ImageAnalysisClient(
endpoint=os.environ["VISION_ENDPOINT"],
credential=DefaultAzureCredential()
)
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"
)
with open("image.jpg", "rb") as f:
image_data = f.read()
result = client.analyze(
image_data=image_data,
visual_features=[VisualFeatures.CAPTION, VisualFeatures.TAGS]
)
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}")
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}")
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})")
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}")
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})")
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}")
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}")
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)
| 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 |
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}")