Azure Image Analysis client library for Python - version 1.0.0b3
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 in your Azure subscription.
- You will need the key and endpoint from this resource to authenticate against the service.
- Note that in order to run Image Analysis with the
CAPTION
orDENSE_CAPTIONS
features, the Azure resource needs to be from a GPU-supported region. See the note here for a list of supported regions.
pip install azure-ai-vision-imageanalysis
To authenticate the ImageAnalysisClient
using api key, you will need the endpoint and api key from your Azure Computer Vision resource in the Azure Portal. The code snippet below assumes these values are stored in environment variables:
Set the environment variable
VISION_ENDPOINT
to the endpoint URL. It has the formhttps://your-resource-name.cognitiveservices.azure.com
, whereyour-resource-name
is your unique Azure Computer Vision resource name.Set the environment variable
VISION_KEY
to the key. The key is a 32-character Hexadecimal number.
Note: The client library does not directly read these environment variable at run time. The endpoint and key must be provided to the constructor of
ImageAnalysisClient
in your code. The code snippet below reads environment variables to promote the practice of not hard-coding secrets in your source code.
Once you define the 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)
)
You can also authenticate ImageAnalysisClient
with Entra ID using the Azure Identity library. To use the DefaultAzureCredential provider shown below, or other credential providers in this library, 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 theazure.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 importDefaultAzureCredential
fromazure.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:
VisualFeatures.CAPTION
(Examples | Samples): Generate a human-readable sentence that describes the content of an image.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.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.VisualFeatures.TAGS
(Samples): Extract content tags for thousands of recognizable objects, living beings, scenery, and actions that appear in images.VisualFeatures.OBJECTS
(Samples): Object detection. This is similar to tagging, but focused on detecting physical objects in the image and returning their location.VisualFeatures.SMART_CROPS
(Samples): Used to find a representative sub-region of the image for thumbnail generation, with priority given to include faces.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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