This project is a modular GUI application for image processing, supporting both spatial and frequency domain operations. It allows users to upload and manipulate images through a user-friendly interface divided into three main tabs.
- Add Noise: Uniform, Gaussian, Salt & Pepper (with adjustable intensity).
- Apply Filters: Average, Gaussian, Median (custom kernel size).
- Edge Detection: Prewitt, Robert, Sobel.
- Thresholding: Global and Local methods.
- Histogram Operations: Equalization and normalization.
Figure: Demonstration of the Tab1
- Convert RGB images to grayscale.
- Display histograms and distribution curves for each RGB channel.
- Combine two images to create a hybrid image using frequency-based filtering.
- Apply low-pass or high-pass filters with adjustable cutoff radius.
The system is modularized into 9 main classes, each handling a specific processing component to promote reusability and clarity:
| Class | Description |
|---|---|
| NoiseAdder | Adds Uniform, Gaussian, or Salt & Pepper noise to test filter robustness. |
| NoiseFilter | Applies Average, Gaussian, and Median filters for noise reduction. |
| HistogramOperations | Computes, visualizes, equalizes, and normalizes image histograms. |
| ImageViewer | Handles image loading, displaying, and double-click interactions within the GUI. |
| ColoredImg | Manages RGB histograms and converts color images to grayscale. |
| EdgeDetectors | Implements Sobel, Roberts, Prewitt, and Canny edge detection methods. |
| FrequencyFilter | Performs Fourier-based filtering (low-pass/high-pass) with adjustable cutoffs. |
| HybridImage | Blends two images using frequency-based hybrid image generation. |
| SignalManager | Manages global PyQt signals to synchronize events between UI components. |
- Noise Manipulation: Add and remove noise interactively.
- Histogram Tools: Equalization, normalization, and thresholding.
- Edge Detection: Compare multiple algorithms visually.
- Frequency Analysis: Visualize frequency filters and results.
- Hybrid Image Creation: Combine low and high-frequency details from two images.
- Language: Python
- Framework: PyQt5
- Libraries: NumPy, OpenCV, Matplotlib (for visualization)
Make sure you have Python 3.8+ installed.
Install dependencies using:
pip install numpy opencv-python PyQt5 matplotlib- Upload an Image: Double-click on the input image area in the GUI.
- Visual Feedback: Each step's output is displayed directly in the interface.
- Dynamic Updates: Histograms and curves update according to the selected operations.