🖼️ Interactive Image Processing Tool (PyQt5 & OpenCV)

(Real-time Computer Vision Desktop Application)

📌 Overview

This is a professional PyQt5-based desktop application that bridges the gap between raw OpenCV algorithms and a user-friendly interface. It allows users to perform complex Image Processing tasks interactively with real-time parameter tuning using sliders.

This tool is designed for experimenting with computer vision filters, transformations, and segmentation techniques without writing a single line of code during execution.

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🎯 Key Features

1. Edge Detection Suite

• Advanced Algorithms: Implementation of Canny, Sobel, Prewitt, Roberts, and Laplacian of Gaussian (LoG).
• Real-time Tuning: Dynamic sliders to adjust gradient sensitivity and thresholds.

2. Smoothing & Denoising

• Comprehensive spatial filters including Gaussian Blur, Median Filtering, Mean (Averaging), and Bilateral Filtering (Edge-preserving smoothing).

3. Segmentation & Thresholding

• Adaptive Logic: Includes Otsu's Binarization, Adaptive Thresholding, and Histogram-based segmentation (essential for OCR and object detection preprocessing).

4. Geometric & Morphological Operations

• Transformations: Interactive Rotation and Translation.
• Morphology: Erosion, Dilation, Opening, and Closing for noise removal and shape analysis.

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🏗 Engineering Challenges & Solutions

• Sequential State Management: * Challenge: Applying multiple filters sequentially without losing the previous effect.

  • Solution: Architected a State Buffer System (last_processed_image) that allows users to stack effects (e.g., Blur → Canny) seamlessly.

• UI-to-Algorithm Synchronization: * Challenge: Mapping continuous slider values to discrete/odd OpenCV kernel sizes.

  • Solution: Developed a middleware logic to validate and map GUI inputs to valid mathematical parameters in real-time.

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🚀🛠 Technologies Used

• Python: Core programming logic.
• PyQt5: Professional GUI development and Event-driven architecture.
• OpenCV: High-performance computer vision library.
• NumPy: Numerical matrix computations for image arrays.

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📖 How to Use

1. Load Image: Click the button to upload any JPG/PNG.
2. Experiment: Adjust sliders to apply filters and transformations instantly.
3. Reset: Revert to the original image at any time.
4. Save: Export your final processed image.

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⚙️ Installation

# Clone the repository
git clone [https://github.com/markwael/PyQt5-Image-Tool.git](https://github.com/markwael/PyQt5-Image-Tool.git)

# Install dependencies
pip install PyQt5 opencv-python numpy

# Run the application
python main.py