https://github.com/praneethsonu/image-content-detection-pipeline-using-aws-sdks
Focuses on building a complete pipeline for detecting and tagging image content using Amazon Rekognition, S3 Bucket, and Lambda Function.
https://github.com/praneethsonu/image-content-detection-pipeline-using-aws-sdks
aws aws-lambda aws-s3 aws-sdk awsrekognition sdk-python
Last synced: 8 months ago
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Focuses on building a complete pipeline for detecting and tagging image content using Amazon Rekognition, S3 Bucket, and Lambda Function.
- Host: GitHub
- URL: https://github.com/praneethsonu/image-content-detection-pipeline-using-aws-sdks
- Owner: praneethsonu
- License: mit
- Created: 2025-04-07T18:35:34.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-07T19:31:11.000Z (about 1 year ago)
- Last Synced: 2025-04-12T20:54:05.623Z (about 1 year ago)
- Topics: aws, aws-lambda, aws-s3, aws-sdk, awsrekognition, sdk-python
- Homepage:
- Size: 46.9 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Image Content Detection Pipeline Using AWS SDKs with Python
## Description
Highlights the cloud-based approach to recognizing objects in uploaded images using AWS tools.
These titles reflect the integration of key AWS services such as S3, Lambda, and Rekognition, which are aligned with the described workflow.
## We will implement the architecture below using AWS SDKs:
The workflow involves the following steps:
1. Creating an S3 bucket for storing images and a Lambda function for processing those images.
2. Configuring S3 event notifications to invoke our Lambda function when a new image is uploaded to our bucket.
3. We use Amazon Rekognition in our Lambda function to detect what is in the uploaded image.
4. Adding tags to our S3 object based on the labels detected by Amazon Rekognition.
## Some topics before starting the project
- AWS SDKs: A brief overview of the AWS SDKs, including installation, configuration, and authentication.
- Development: Using SDKs to call service APIs and create resources.
- onclusion: Cleaning up the resources we created and summarizing takeaways.
## The Steps involved are:
### 1. Configuration
### 2. SDK Setup
### 3. Development
### 4. Conclusion
### 5. CleanUp
## Getting Started
[Getting Started With Steps](https://github.com/praneethsonu/Image-Content-Detection-Pipeline-Using-AWS-SDKs/tree/main/Getting%20Started%20With%20Steps)
## License
This project is licensed under the MIT License.