https://github.com/saksham-jain177/ai-imagepipeline

An AI pipeline that segments images, identifies objects, extracts text, and summarizes results, all through an interactive Streamlit app. It utilizes models like PyTorch and tools such as Tesseract and OpenCV for efficient image processing.
https://github.com/saksham-jain177/ai-imagepipeline

ai image-identification image-processing image-segmentation opencv pipeline python pytorch streamlit tesseract-ocr

Last synced: 13 days ago
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An AI pipeline that segments images, identifies objects, extracts text, and summarizes results, all through an interactive Streamlit app. It utilizes models like PyTorch and tools such as Tesseract and OpenCV for efficient image processing.

• Host: GitHub
• URL: https://github.com/saksham-jain177/ai-imagepipeline
• Owner: saksham-jain177
• Created: 2024-08-17T12:40:30.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-09-02T13:38:13.000Z (2 months ago)
• Last Synced: 2024-10-17T12:38:01.266Z (30 days ago)
• Topics: ai, image-identification, image-processing, image-segmentation, opencv, pipeline, python, pytorch, streamlit, tesseract-ocr
• Language: Python
• Homepage:
• Size: 19.4 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## Project Overview

This project is an AI pipeline for image segmentation, object identification, text extraction, and summarization.

## Installation

1. Clone the repository: 

    `git clone https://github.com/saksham-jain177/AI-ImagePipeline`

2. Install the required dependencies: 

    `pip install -r requirements.txt`

## Project Structure

```

│

├── data/

│   ├── input_images/            # Images to be processed

│   ├── segmented_objects/       # Segmented objects from input images

│   └── output/                  # Final output including summaries

│

├── models/

│   ├── segmentation_model.py    # Segmentation model for image processing

│   ├── identification_model.py  # Model for object identification

│   ├── text_extraction_model.py # Model for extracting text from objects

│   └── summarization_model.py   # Model for summarizing the findings

│

├── utils/

│   ├── preprocessing.py         # Data preprocessing utilities

│   ├── postprocessing.py        # Post-processing utilities

│   ├── data_mapping.py          # Data mapping functions

│   └── visualization.py         # Functions for visualizing results

│

├── streamlit_app/

│   ├── app.py                   # Streamlit app for running the pipeline

│   └── components/              # UI components

│

├── tests/                       # Unit tests for various components

│   ├── test_segmentation.py

│   ├── test_identification.py

│   ├── test_text_extraction.py

│   └── test_summarization.py

│

├── README.md

└── requirements.txt

```

## Usage

1. Run the pipeline:

    `streamlit run streamlit_app/app.py`

This will launch a web interface where you can upload images, segment them, extract text, and view summaries.

2. Input Data:

Place your input images in the data/input_images/ directory. These images will be processed by the pipeline.

3. Output Data:

The segmented objects, extracted text, and final summaries will be saved in the data/output/ directory.

4. Clearing Previous Data:

Before running the pipeline, ensure that the segmented_objects/ and output/ directories are cleared if you want a fresh run.

## Testing

Run unit tests to ensure that each module is working as expected:

    `pytest tests/`