Advanced Image Processing Pipeline

Project Overview

The Advanced Image Processing Pipeline is an automated system designed for image acquisition, augmentation, processing, and analysis using various computer vision techniques. This pipeline leverages the Pexels API for image retrieval and performs tasks such as edge detection, object detection, and feature extraction, offering a complete suite for image analysis and machine learning projects.

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Table of Contents

1. Features
2. Technologies Used
3. Project Structure
4. Installation
5. Configuration
6. Usage
7. Running the Scripts
8. Data Directories
9. Contributing
10. License
11. Contact

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Features

• Image Acquisition: Fetches high-quality images based on predefined queries using the Pexels API.
• Image Augmentation: Applies transformations (rotation, flipping) to diversify the dataset.
• Edge Detection: Uses the Canny algorithm to detect and highlight significant edges.
• Object Detection: Identifies objects like faces using Haar Cascade classifiers.
• Feature Extraction: Extracts image keypoints and descriptors using ORB for further analysis.
• Feature Visualization: Visualizes extracted features to verify processing accuracy.

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Technologies Used

• Programming Language: Python 3.9
• Libraries & Frameworks: OpenCV, NumPy, PyYAML, Scikit-Image, Matplotlib, Requests, Pillow
• Version Control: Git & GitHub

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Project Structure

advanced-image-processing/
├── src/
│   ├── __init__.py
│   ├── config.py
│   ├── fetch_images.py
│   ├── augmentation.py
│   ├── edge_detection.py
│   ├── object_detection.py
│   ├── feature_extraction.py
│   ├── view_features.py
│   └── main.py
├── configs/
│   └── config.yaml
├── data/
│   ├── raw/
│   ├── augmented/
│   └── processed/
├── tests/
│   └── test_config.py
├── .gitignore
├── requirements.txt
└── README.md

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Installation

1. Clone the Repository:

   git clone https://github.com/your_username/advanced-image-processing.git
   cd advanced-image-processing

2. Create a Virtual Environment:

   python3 -m venv venv
   source venv/bin/activate
   pip install --upgrade pip

3. Install Dependencies:

   pip install -r requirements.txt

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Configuration

1. Configure API Keys:
   Create a .env file in the root directory to securely store your API keys:

   PEXELS_API_KEY=your_pexels_api_key_here
   

2. Update config.yaml:
   Edit configs/config.yaml to set desired configurations, including data directories, augmentation parameters, and API query settings.

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Usage

1. Fetch Images: Run the script to download images based on configured search queries:

   python -m src.fetch_images

2. Process Images: Execute the main processing pipeline:

   python -m src.main

3. View Extracted Features: Inspect extracted features like keypoints and descriptors:

   python -m src.view_features

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Running the Scripts

1. fetch_images.py: Fetches images from Pexels.
2. main.py: Orchestrates the entire pipeline (augmentation, edge detection, object detection, feature extraction).
3. view_features.py: Displays the extracted features.

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Data Directories

• data/raw/: Original images from Pexels.
• data/augmented/: Images after transformations.
• data/processed/: Images after edge detection, object detection, and feature extraction.

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Contributing

1. Fork the repository
2. Create a new branch
3. Commit your changes
4. Push to the branch
5. Open a pull request

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License

This project is licensed under the MIT License.

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Contact

For inquiries or support:

• Email: chandansatwani422@gmail.com
• GitHub: https://github.com/Chandan062311

Importance of This Project

The Advanced Image Processing Pipeline plays a crucial role in the fields of computer vision and machine learning by providing an automated and efficient system for handling the entire lifecycle of image data. In today’s data-driven landscape, the ability to process and analyze large volumes of images swiftly and accurately is indispensable for developing robust machine learning models. This pipeline streamlines the process of image acquisition through the Pexels API, ensuring access to a diverse and high-quality dataset essential for training models that generalize well across various real-world scenarios.

Beyond acquisition, the pipeline's capabilities in image augmentation—such as rotation and flipping—enhance dataset diversity, mitigating issues like overfitting and improving model resilience. Advanced processing techniques like edge detection and object detection using Haar Cascade classifiers enable the extraction of meaningful features from images, which are fundamental for tasks like image classification, object recognition, and pattern analysis. Furthermore, the feature extraction component utilizing the ORB algorithm facilitates the identification of keypoints and descriptors, providing valuable information for image matching and tracking applications.

By automating these complex processes, the pipeline significantly reduces the time and effort required by researchers and developers, allowing them to focus more on model development and innovation rather than manual data processing. Additionally, the integration of feature visualization tools aids in the validation and interpretation of extracted features, ensuring the accuracy and reliability of the analysis. Overall, this project serves as a foundational tool that enhances the efficiency, scalability, and effectiveness of image-based machine learning projects, contributing to advancements in areas such as autonomous systems, medical imaging, and multimedia content analysis.