sipa 0.2.0

Simple Image Processing Application - A GUI-based image processing tool

SIPA - Simple Image Processing Application

A PyQt5-based GUI application for educational image processing operations. This package provides basic image processing functionality implemented from scratch using only NumPy (no advanced image processing libraries).

Features

Color Operations

• RGB to Grayscale conversion
• Binary thresholding (single and double)
• RGB channel transformations
• Contrast adjustment

Geometric Operations

• 90-degree rotation
• Image cropping
• Zoom in/out functionality

Filtering Operations

• Mean filter (blur)
• Median filter (noise reduction)
• Salt and pepper noise addition/removal
• Unsharp masking (sharpening)
• Prewitt edge detection

Morphological Operations

• Erosion
• Dilation
• Opening
• Closing

Histogram Operations

• Histogram calculation (grayscale and RGB)
• Histogram equalization
• Histogram stretching
• Histogram visualization

Arithmetic Operations

• Weighted image addition
• Image division

Installation

From PyPI

pip install sipa

From Source

git clone https://github.com/haydarkadioglu/simple-image-processing-application.git
cd simple-image-processing-application
pip install -e .

Usage

SIPA provides two ways to use the image processing functionality:

1. GUI Application

sipa  # Launch the graphical interface

2. Python Library

Modern Import Style

import sipa
import numpy as np

# Load your image as a numpy array (height, width, 3) for RGB
image = np.array(...)  

# Convert to grayscale
gray_image = sipa.Colors.convert_to_gray(image)

# Apply filters
filtered_image = sipa.Filters.mean_filter(gray_image, kernel_size=5)

# Detect edges
edges = sipa.Filters.detect_edge_prewitt(gray_image)

# Apply morphological operations
binary = sipa.Colors.convert_to_binary(gray_image, threshold=128)
eroded = sipa.Histogram.erode(binary, kernel_size=3)

Legacy Import Style (Backward Compatibility)

# For existing code using the old structure
from Functions import SIP as sip

gray_image = sip.Colors.convert_to_gray(image)
filtered_image = sip.Filters.mean_filter(gray_image, 5)

Quick Example

import sipa
import numpy as np

# Create test image
image = np.random.randint(0, 255, (100, 100, 3), dtype=np.uint8)

# Process image
gray = sipa.Colors.convert_to_gray(image)
binary = sipa.Colors.convert_to_binary(gray, 128)
edges = sipa.Filters.detect_edge_prewitt(gray)
hist = sipa.Histogram.calculate_gray_histogram(gray)

Requirements

• Python >= 3.7
• NumPy >= 1.19.0
• PyQt5 >= 5.15.0
• matplotlib >= 3.3.0
• opencv-python >= 4.5.0

Development

Installing Development Dependencies

pip install -e ".[dev]"

Running Tests

pytest

Code Formatting

black sipa/

Linting

flake8 sipa/

Educational Purpose

This project was created for educational purposes to help people understand basic image processing concepts. All image processing operations are implemented from scratch using only NumPy, making it easy to understand the underlying algorithms.

Screenshots

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Author

Haydar Kadıoğlu

• GitHub: @haydarkadioglu

Acknowledgments

• This project is designed for educational purposes
• All image processing algorithms are implemented from scratch for learning
• Special thanks to the computer vision and image processing community