Type Annotations for boto3 Rekognition

1.42.3 · active · verified Sat Apr 11

This library provides comprehensive type annotations for the `boto3` AWS Rekognition service, generated by `mypy-boto3-builder`. It enhances developer experience by enabling static type checking, autocompletion, and improved error detection for `boto3` users working with Rekognition, ensuring type safety in AWS SDK interactions. The current version is 1.42.3 and it is actively maintained through regular updates from the `mypy-boto3-builder` project.

Warnings

breaking Support for Python 3.8 was removed in `mypy-boto3-builder` version 8.12.0. Consequently, `mypy-boto3-rekognition` versions 1.42.3 and newer require Python 3.9 or higher.
Fix: Upgrade your Python environment to 3.9 or newer.
breaking TypeDef naming conventions changed in `mypy-boto3-builder` 8.9.0. Specifically, argument TypeDefs were shortened (e.g., `CreateDistributionRequestRequestTypeDef` became `CreateDistributionRequestTypeDef`) and conflicting `Extra` postfixes were moved (e.g., `CreateDistributionExtraRequestTypeDef` became `CreateDistributionRequestExtraTypeDef`).
Fix: Update your code to use the new, shorter TypeDef names for request and response parameters.
gotcha This library provides *only* type hints (stubs). It does not include the functional `boto3` library itself. You must install `boto3` separately (`pip install boto3`) for your code to run.
Fix: Ensure `boto3` is installed alongside `mypy-boto3-rekognition`.
gotcha The `mypy-boto3` packages are designed to be version-matched with the `boto3` library. Using `mypy-boto3-rekognition` 1.42.3 implies you should be using `boto3` version 1.42.3. Mismatched versions can lead to incorrect or missing type hints.
Fix: Keep `mypy-boto3-rekognition` and `boto3` versions synchronized. For example, `pip install 'boto3==1.42.3' 'mypy-boto3-rekognition==1.42.3'`.
gotcha Users of PyCharm might experience slow performance with `mypy-boto3` due to issues with Literal overloads (PyCharm issue PY-40997). In such cases, it's recommended to either use `boto3-stubs-lite` (if explicit type annotations are acceptable) or disable PyCharm's internal type checker and rely on external tools like `mypy` or `pyright`.
Fix: Consider `pip install boto3-stubs-lite[rekognition]` for PyCharm, or configure PyCharm to use an external type checker.

Install

pip install mypy-boto3-rekognition Standalone installation
pip install 'boto3-stubs[rekognition]' Install as part of boto3-stubs meta-package

Imports

RekognitionClient

from mypy_boto3_rekognition.client import RekognitionClient

DetectLabelsResponseTypeDef
```
from mypy_boto3_rekognition.type_defs import DetectLabelsResponseTypeDef
```
Type definitions (TypedDicts) for API responses and request parameters.
AttributeType
```
from mypy_boto3_rekognition.literals import AttributeType
```
Literals for string-based enum values used in API calls.
ProjectVersionRunningWaiter
```
from mypy_boto3_rekognition.waiter import ProjectVersionRunningWaiter
```
Type hints for waiters, used to pause execution until a resource enters a desired state.
DetectFacesPaginator
```
from mypy_boto3_rekognition.paginator import DetectFacesPaginator
```
Type hints for paginators, used to iterate through large result sets.

Quickstart

This quickstart demonstrates how to initialize a Rekognition client with type annotations and use it to detect labels in an image stored in an S3 bucket. It shows explicit type hints for the client object and the response, leveraging the benefits of static type checking. Ensure `boto3` is installed and AWS credentials are configured for the example to run.

import boto3
from mypy_boto3_rekognition.client import RekognitionClient
from mypy_boto3_rekognition.type_defs import DetectLabelsResponseTypeDef
import os

# Configure AWS region from environment variable or default
aws_region = os.environ.get("AWS_REGION", "us-east-1")

# Create a Rekognition client with type hints
# boto3 will automatically pick up credentials from environment variables (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY)
# or ~/.aws/credentials, or IAM roles.
rekognition_client: RekognitionClient = boto3.client("rekognition", region_name=aws_region)

# Example: Detect labels in an image from an S3 bucket
try:
    # Replace with your S3 bucket and object key for testing
    s3_bucket_name = os.environ.get("REKOGNITION_TEST_BUCKET", "your-rekognition-test-bucket")
    s3_object_key = os.environ.get("REKOGNITION_TEST_OBJECT_KEY", "your-image.jpg")

    if s3_bucket_name == "your-rekognition-test-bucket" or s3_object_key == "your-image.jpg":
        print("Please set REKOGNITION_TEST_BUCKET and REKOGNITION_TEST_OBJECT_KEY environment variables or replace placeholders.")
    else:
        response: DetectLabelsResponseTypeDef = rekognition_client.detect_labels(
            Image={
                'S3Object': {
                    'Bucket': s3_bucket_name,
                    'Name': s3_object_key,
                }
            },
            MaxLabels=10,
            MinConfidence=75
        )

        print(f"Detected labels in s3://{s3_bucket_name}/{s3_object_key}:")
        for label in response.get("Labels", []):
            print(f"  {label['Name']} (Confidence: {label['Confidence']:.2f}%)")

except rekognition_client.exceptions.InvalidImageFormatException:
    print("Error: Image format not supported by Rekognition.")
except rekognition_client.exceptions.ClientError as e:
    print(f"AWS Client Error: {e}")
except Exception as e:
    print(f"An unexpected error occurred: {e}")

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