https://github.com/sumansuhag/wasserstoff-aiinterntask

Welcome to the AI Pipeline for Image Segmentation and Object Analysis project – a state-of-the-art solution designed to process, segment, identify, and analyze objects within images. This AI-powered pipeline is engineered to deliver precise insights by extracting, mapping, and summarizing data from each segmented object.
https://github.com/sumansuhag/wasserstoff-aiinterntask

artificial-intelligence cdn data data-science modeling pipline

Last synced: over 1 year ago
JSON representation

Welcome to the AI Pipeline for Image Segmentation and Object Analysis project – a state-of-the-art solution designed to process, segment, identify, and analyze objects within images. This AI-powered pipeline is engineered to deliver precise insights by extracting, mapping, and summarizing data from each segmented object.

• Host: GitHub
• URL: https://github.com/sumansuhag/wasserstoff-aiinterntask
• Owner: sumansuhag
• Created: 2024-09-23T02:00:17.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2025-01-24T06:47:55.000Z (over 1 year ago)
• Last Synced: 2025-01-24T07:27:53.862Z (over 1 year ago)
• Topics: artificial-intelligence, cdn, data, data-science, modeling, pipline
• Language: Jupyter Notebook
• Homepage:
• Size: 13.8 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
AI-Pipeline-for-Image-Segmentation-and-Object-Analysis: Revolutionizing Image Processing with Deep Learning:

Welcome to the AI Pipeline for Image Segmentation and Object Analysis** project – a state-of-the-art solution designed to process, segment, identify, and analyze objects within images. This AI-powered pipeline is engineered to deliver precise insights by extracting, mapping, and summarizing data from each segmented object, making it an indispensable tool for industries that require automated image analysis at scale.

🌟 Project Overview

This project implements a cutting-edge AI pipeline capable of performing image segmentation using advanced models like Mask R-CNN, extracting objects, identifying attributes, and generating detailed summaries. The process is designed to transform raw images into structured data, enabling efficient object analysis and decision-making. The pipeline produces:

- Annotated Images with highlighted objects.

- A Summary Table that maps key data to the identified objects.

🚀 Key Features & Capabilities

- Image Segmentation

  Using Mask R-CNN, the pipeline performs precise segmentation, delineating each object within the image for further analysis.

  

- Object Extraction & Storage  

  Identifies and stores each segmented object, allowing for the subsequent processing of their individual attributes.

- Object Identification

  The pipeline uses powerful models to **identify each object** within the image, recognizing and classifying items based on predefined categories.

- Text and Data Extraction 

  Leverage **OCR (Optical Character Recognition)** models like Tesseract or Google Cloud Vision to extract text and relevant data from segmented objects.

- Object Attributes Summarization

  Automatically summarizes key characteristics of each object (size, color, shape, etc.) for downstream analysis.

- Data Mapping 

  Mapped data is linked to the master image, creating a unified structure that pairs visual information with valuable attributes.

- Output Generation

  The pipeline generates two main outputs:

  1. Annotated Image with visual markers on each segmented object.

  2. Summary Table detailing the extracted data and attributes of each object.

     

📁 Project Structure

project_root/

├── data/

│   ├── input_images/        # Store raw input images

│   ├── segmented_objects/   # Store segmented object data

│   └── output/              # Store processed output (images, tables)

├── models/

│   ├── segmentation_model.py      # Mask R-CNN or U-Net for image segmentation

│   ├── identification_model.py    # Object detection & identification models

│   ├── text_extraction_model.py  # OCR model for text extraction

│   └── summarization_model.py    # Summarization and attributes extraction

├── utils/

│   ├── preprocessing.py   # Preprocessing functions for data

│   ├── postprocessing.py  # Postprocessing functions for cleaned data

│   ├── data_mapping.py    # Mapping of extracted data to images

│   └── visualization.py   # Functions for image and data visualizations

├── streamlit_app/

│   └── app.py             # UI for easy image upload and processing

├── tests/

│   ├── test_segmentation.py     # Unit tests for segmentation functionality

│   ├── test_identification.py   # Unit tests for object identification

│   ├── test_text_extraction.py # Unit tests for OCR functions

│   └── test_summarization.py   # Unit tests for summarization logic

├── README.md

└── requirements.txt        # List of project dependencies

🔧 Technical Details

Key Components & Libraries 

This project incorporates deep learning and advanced image processing techniques to create a robust pipeline:

AI Models:

  - Segmentation for accurate image segmentation.

  - Object Detectionfor real-time object identification.

Popular Libraries:

  - OpenCV for computer vision tasks.

  - scikit-image for advanced image processing.

  - pandas and numpy for data manipulation.

💡 How It Works

1. Upload an Image: The **Streamlit UI** allows users to easily upload an image for processing.

2. Segmentation: The uploaded image is passed through a segmentation model (e.g., Mask R-CNN) to identify and segment objects.

3. Object Analysis: Each object is processed to extract key attributes like size, shape, and text using **OCR**.

4. Data Mapping: The extracted data is mapped to the respective objects and saved along with the master image.

5. Output: The system generates a visual output (annotated image) and a summary table with all extracted information.

⚙️ Cloud Integration & Scalability

This project is designed for scalability, using microservices architecture and event-driven patterns. It is optimized for cloud deployment, making it easy to scale as per the needs of different use cases:

- Amazon SageMaker

- Microsoft Azure ML

🔧 Getting Started

To get started, simply clone the repository and install the required dependencies:

https://github.com/sumansuhag/wasserstoff-AiInternTask/blob/main/AI%40model.ipynb

cd AI-Pipeline-for-Image-Segmentation-and-Object-Analysis

python -m venv env

env\Scripts\activate  # Windows

pip install -r requirements.txt

This AI pipeline is a game-changer for industries that rely on image analysis. By automating segmentation and object recognition, it provides:

- Enhanced Object Detection for improved accuracy in analyzing complex images.

- Scalable Solutions for high-throughput environments like manufacturing, healthcare, and surveillance.

- Actionable Insights from the extracted data, empowering businesses to make better decisions.