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Showing content from https://learn.microsoft.com/en-us/python/api/overview/azure/ai-vision-imageanalysis-readme below:

Azure Image Analysis client library for Python

The Image Analysis service provides AI algorithms for processing images and returning information about their content. In a single service call, you can extract one or more visual features from the image simultaneously, including getting a caption for the image, extracting text shown in the image (OCR) and detecting objects. For more information on the service and the supported visual features, see Image Analysis overview, and the Concepts page.

Use the Image Analysis client library to:

• Authenticate against the service
• Set what features you would like to extract
• Upload an image for analysis, or send an image URL
• Get the analysis result

Product documentation | Samples | Vision Studio | API reference documentation | Package (Pypi) | SDK source code

• Python 3.8 or later installed, including pip.
• An Azure subscription.
• A Computer Vision resource deployed to your Azure subscription. Note that in order to run Image Analysis with the Caption or Dense Captions features, the Computer Vision resource needs to be from a GPU-supported region. See this document for a list of supported regions.
• An endpoint URL. It can be found in the "overview" tab of your Computer Vision resource in the Azure portal, and has the form https://your-resource-name.cognitiveservices.azure.com where your-resource-name is your unique Computer Vision resource name. The samples below assume the environment variable VISION_ENDPOINT has been set to this value.
• For API key authentication, you will need the key. It can be found in the "overview" tab of your Computer Vision resource in the Azure portal. It's a 32-character Hexadecimal number. The samples below assume the environment variable VISION_KEY has been set to this value.
• For Entra ID authentication, your application needs an object that implements the TokenCredential interface. Samples below use DefaultAzureCredential. To get that working, you will need:
  • The role Cognitive Services User assigned to you. Role assigned can be done via the "Access Control (IAM)" tab of your Computer Vision resource in the Azure portal.
  • Azure CLI installed.
  • You are logged into your Azure account by running az login.
  • Note that if you have multiple Azure subscriptions, the subscription that contains your Computer Vision resource must be your default subscription. Run az account list --output table to list all you subscription and see which one is the default. Run az account set --subscription "Your Subscription ID or Name" to change your default subscription.

Also note that the client library does not directly read the VISION_ENDPOINT and VISION_KEYenvironment variables mentioned above at run time. The endpoint and key (for API key authentication) must be provided to the constructor of the ImageAnalysisClient in your code. The sample code below reads environment variables to promote the practice of not hard-coding secrets in your source code.

pip install azure-ai-vision-imageanalysis

Once you defined the two environment variables, this Python code will create and authenticate a synchronous ImageAnalysisClient using key:

import os
from azure.ai.vision.imageanalysis import ImageAnalysisClient
from azure.ai.vision.imageanalysis.models import VisualFeatures
from azure.core.credentials import AzureKeyCredential

# Set the values of your computer vision endpoint and computer vision key
# as environment variables:
try:
    endpoint = os.environ["VISION_ENDPOINT"]
    key = os.environ["VISION_KEY"]
except KeyError:
    print("Missing environment variable 'VISION_ENDPOINT' or 'VISION_KEY'")
    print("Set them before running this sample.")
    exit()

# Create an Image Analysis client for synchronous operations,
# using API key authentication
client = ImageAnalysisClient(
    endpoint=endpoint,
    credential=AzureKeyCredential(key)
)

To use the DefaultAzureCredential provider shown below, or other credential providers, install the azure-identity package:

pip install azure.identity

Assuming you defined the environment variable VISION_ENDPOINT mentioned above, this Python code will create and authenticate a synchronous ImageAnalysisClient using Entra ID:

import os
from azure.ai.vision.imageanalysis import ImageAnalysisClient
from azure.ai.vision.imageanalysis.models import VisualFeatures
from azure.identity import DefaultAzureCredential

# Set the value of your computer vision endpoint as environment variable:
try:
    endpoint = os.environ["VISION_ENDPOINT"]
except KeyError:
    print("Missing environment variable 'VISION_ENDPOINT'.")
    print("Set it before running this sample.")
    exit()

# Create an Image Analysis client for synchronous operations,
# using Entra ID authentication
client = ImageAnalysisClient(
    endpoint=endpoint,
    credential=DefaultAzureCredential(exclude_interactive_browser_credential=False),
)

A synchronous client supports synchronous analysis methods, meaning they will block until the service responds with analysis results. The code snippets below all use synchronous methods because it's easier for a getting-started guide. The SDK offers equivalent asynchronous APIs which are often preferred. To create an asynchronous client, do the following:

• Install the additional package aiohttp:

  pip install aiohttp
  

• Update the above code to import ImageAnalysisClient from the azure.ai.vision.imageanalysis.aio:

  from azure.ai.vision.imageanalysis.aio import ImageAnalysisClient
  

• If you are using Entra ID authentication with DefaultAzureCredential, update the above code to import DefaultAzureCredential from azure.identity.aio:

  from azure.identity.aio import DefaultAzureCredential
  

Once you've initialized an ImageAnalysisClient, you need to select one or more visual features to analyze. The options are specified by the enum class VisualFeatures. The following features are supported:

1. VisualFeatures.CAPTION (Examples | Samples): Generate a human-readable sentence that describes the content of an image.
2. VisualFeatures.READ (Examples | Samples): Also known as Optical Character Recognition (OCR). Extract printed or handwritten text from images. Note: For extracting text from PDF, Office, and HTML documents and document images, use the Document Intelligence service with the Read model. This model is optimized for text-heavy digital and scanned documents with an asynchronous REST API that makes it easy to power your intelligent document processing scenarios. This service is separate from the Image Analysis service and has its own SDK.
3. VisualFeatures.DENSE_CAPTIONS (Samples): Dense Captions provides more details by generating one-sentence captions for up to 10 different regions in the image, including one for the whole image.
4. VisualFeatures.TAGS (Samples): Extract content tags for thousands of recognizable objects, living beings, scenery, and actions that appear in images.
5. VisualFeatures.OBJECTS (Samples): Object detection. This is similar to tagging, but focused on detecting physical objects in the image and returning their location.
6. VisualFeatures.SMART_CROPS (Samples): Used to find a representative sub-region of the image for thumbnail generation, with priority given to include faces.
7. VisualFeatures.PEOPLE (Samples): Detect people in the image and return their location.

For more information about these features, see Image Analysis overview, and the Concepts page.

The ImageAnalysisClient has two overloads for the method analyze:

• Analyze an image from an input bytes object. The client will upload the image to the service as part of the REST request.
• Analyze an image from a publicly-accessible URL. The client will send the image URL to the service. The service will fetch the image.

The examples below show how to do both. The analyze from an input bytes object examples populate the bytes object by loading an image from a file on disk.

Image Analysis works on images that meet the following requirements:

• The image must be presented in JPEG, PNG, GIF, BMP, WEBP, ICO, TIFF, or MPO format
• The file size of the image must be less than 20 megabytes (MB)
• The dimensions of the image must be greater than 50 x 50 pixels and less than 16,000 x 16,000 pixels

The following sections provide code snippets covering these common Image Analysis scenarios:

• Generate an image caption for an image file
• Generate an image caption for an image URL
• Extract text (OCR) from an image file
• Extract text (OCR) from an image URL

These snippets use the synchronous client from Create and authenticate the client.

See the Samples folder for fully working samples for all visual features, including asynchronous clients.

This example demonstrates how to generate a one-sentence caption for the image file sample.jpg using the ImageAnalysisClient. The synchronous (blocking) analyze method call returns an ImageAnalysisResult object with a caption property of type CaptionResult. It contains the generated caption and its confidence score in the range [0, 1]. By default the caption may contain gender terms such as "man", "woman", or "boy", "girl". You have the option to request gender-neutral terms such as "person" or "child" by setting gender_neutral_caption = True when calling analyze.

Notes:

• Caption is only available in some Azure regions. See Prerequisites.
• Caption is only supported in English at the moment.

# Load image to analyze into a 'bytes' object
with open("sample.jpg", "rb") as f:
    image_data = f.read()

# Get a caption for the image. This will be a synchronously (blocking) call.
result = client.analyze(
    image_data=image_data,
    visual_features=[VisualFeatures.CAPTION],
    gender_neutral_caption=True,  # Optional (default is False)
)

# Print caption results to the console
print("Image analysis results:")
print(" Caption:")
if result.caption is not None:
    print(f"   '{result.caption.text}', Confidence {result.caption.confidence:.4f}")

To generate captions for additional images, simply call analyze multiple times. You can use the same ImageAnalysisClient do to multiple analysis calls.

This example is similar to the above, expect it calls the analyze method and provides a publicly accessible image URL instead of a file name.

# Get a caption for the image. This will be a synchronously (blocking) call.
result = client.analyze_from_url(
    image_url="https://aka.ms/azsdk/image-analysis/sample.jpg",
    visual_features=[VisualFeatures.CAPTION],
    gender_neutral_caption=True,  # Optional (default is False)
)

# Print caption results to the console
print("Image analysis results:")
print(" Caption:")
if result.caption is not None:
    print(f"   '{result.caption.text}', Confidence {result.caption.confidence:.4f}")

This example demonstrates how to extract printed or hand-written text for the image file sample.jpg using the ImageAnalysisClient. The synchronous (blocking) analyze method call returns an ImageAnalysisResult object with a read property of type ReadResult. It includes a list of text lines and a bounding polygon surrounding each text line. For each line, it also returns a list of words in the text line and a bounding polygon surrounding each word.

# Load image to analyze into a 'bytes' object
with open("sample.jpg", "rb") as f:
    image_data = f.read()

# Extract text (OCR) from an image stream. This will be a synchronously (blocking) call.
result = client.analyze(
    image_data=image_data,
    visual_features=[VisualFeatures.READ]
)

# Print text (OCR) analysis results to the console
print("Image analysis results:")
print(" Read:")
if result.read is not None:
    for line in result.read.blocks[0].lines:
        print(f"   Line: '{line.text}', Bounding box {line.bounding_polygon}")
        for word in line.words:
            print(f"     Word: '{word.text}', Bounding polygon {word.bounding_polygon}, Confidence {word.confidence:.4f}")

To extract text for additional images, simply call analyze multiple times. You can use the same ImageAnalysisClient do to multiple analysis calls.

Note: For extracting text from PDF, Office, and HTML documents and document images, use the Document Intelligence service with the Read model. This model is optimized for text-heavy digital and scanned documents with an asynchronous REST API that makes it easy to power your intelligent document processing scenarios. This service is separate from the Image Analysis service and has its own SDK.

This example is similar to the above, expect it calls the analyze method and provides a publicly accessible image URL instead of a file name.

# Extract text (OCR) from an image stream. This will be a synchronously (blocking) call.
result = client.analyze_from_url(
    image_url="https://aka.ms/azsdk/image-analysis/sample.jpg",
    visual_features=[VisualFeatures.READ]
)

# Print text (OCR) analysis results to the console
print("Image analysis results:")
print(" Read:")
if result.read is not None:
    for line in result.read.blocks[0].lines:
        print(f"   Line: '{line.text}', Bounding box {line.bounding_polygon}")
        for word in line.words:
            print(f"     Word: '{word.text}', Bounding polygon {word.bounding_polygon}, Confidence {word.confidence:.4f}")

The analyze methods raise an HttpResponseError exception for a non-success HTTP status code response from the service. The exception's status_code will be the HTTP response status code. The exception's error.message contains a detailed message that will allow you to diagnose the issue:

try:
    result = client.analyze( ... )
except HttpResponseError as e:
    print(f"Status code: {e.status_code}")
    print(f"Reason: {e.reason}")
    print(f"Message: {e.error.message}")

For example, when you provide a wrong authentication key:

Status code: 401
Reason: PermissionDenied
Message: Access denied due to invalid subscription key or wrong API endpoint. Make sure to provide a valid key for an active subscription and use a correct regional API endpoint for your resource.

Or when you provide an image URL that does not exist or not accessible:

Status code: 400
Reason: Bad Request
Message: The provided image url is not accessible.

The client uses the standard Python logging library. The SDK logs HTTP request and response details, which may be useful in troubleshooting. To log to stdout, add the following:

import sys
import logging

# Acquire the logger for this client library. Use 'azure' to affect both
# 'azure.core` and `azure.ai.vision.imageanalysis' libraries.
logger = logging.getLogger("azure")

# Set the desired logging level. logging.INFO or logging.DEBUG are good options.
logger.setLevel(logging.INFO)

# Direct logging output to stdout (the default):
handler = logging.StreamHandler(stream=sys.stdout)
# Or direct logging output to a file:
# handler = logging.FileHandler(filename = 'sample.log')
logger.addHandler(handler)

# Optional: change the default logging format. Here we add a timestamp.
formatter = logging.Formatter("%(asctime)s:%(levelname)s:%(name)s:%(message)s")
handler.setFormatter(formatter)

By default logs redact the values of URL query strings, the values of some HTTP request and response headers (including Ocp-Apim-Subscription-Key which holds the key), and the request and response payloads. To create logs without redaction, set the method argument logging_enable = True when you create ImageAnalysisClient, or when you call analyze on the client.

# Create an Image Analysis client with none redacted log
client = ImageAnalysisClient(
    endpoint=endpoint,
    credential=AzureKeyCredential(key),
    logging_enable=True
)

None redacted logs are generated for log level logging.DEBUG only. Be sure to protect none redacted logs to avoid compromising security. For more information see Configure logging in the Azure libraries for Python

• Have a look at the Samples folder, containing fully runnable Python code for Image Analysis (all visual features, synchronous and asynchronous clients, from image file or URL).

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