PixelForge
PixelForge is a comprehensive web-based platform developed for Digital Image Processing coursework. Built with React and leveraging HTML5 Canvas, it provides an interactive environment to perform advanced image manipulations, transformations, and analysis.
Contents
🛠️ Technologies
Built with:
🚀 Getting Started
Prerequisites
Before running the application, ensure you have:
Live Demo
The application is hosted on Vercel and can be accessed directly:
https://pixelforge-riadoliveira.vercel.app.
Local Setup
# Clone the repository
$ git clone https://github.com/RiadOliveira/PixelForge.git
# Navigate to project directory
$ cd PixelForge
# Install dependencies
$ npm install
# Start the development server
$ npm start
⚙️ Features
Basic Image Operations
- Multi-format Support: Load and display images in JPEG, PNG, GIF, TIFF, PGM, and BMP formats.
- Arithmetic Operations: Pixel-level addition, subtraction, multiplication, and division between images.
- Logical Operations: Bitwise AND, OR, and XOR operations between images.
- Image Comparison: Side-by-side comparison tools for analyzing operation results.
- Basic Transformations: Rotation, scaling, translation, reflection (horizontal/vertical), and shearing (X/Y axes).
- Composite Transformations: Custom transformation sequences with user-defined order of operations.
- Zoom Capabilities:
- Zoom IN with replication and interpolation techniques.
- Zoom OUT with exclusion and mean-value approaches.
Color Space Manipulation
- Color Space Conversion: RGB to HSB/HSV, YUV and CMYK.
- Component Isolation: Display individual channels of each color space.
- Color Plane Decomposition: Separate color planes for analysis.
Pseudocoloring Techniques
- Density Slicing: Assign specific colors to grayscale intensity ranges.
- Color Redistribution: Recolorize images by redistributing existing color values.
- Histogram-based Coloring: Automatic color assignment based on image statistics.
- Linear Transformations:
- Full-range contrast stretching.
- Piecewise linear transformations with custom breakpoints.
- Non-linear Transformations:
- Logarithmic, exponential, square, square root, and inverse operations.
- Binary thresholding with adjustable levels.
Image Enhancement
- Histogram Equalization: Global and adaptive histogram equalization.
- Gamma Correction: Adjust brightness with non-linear gamma values.
- Bit-plane Slicing: Visualize and manipulate individual bit planes.
Filtering Techniques
- Low-pass Filters:
- (3×3, 5×5) Mean, median, mode, min, max.
- Edge-preserving variants (Kawahara, Tomita-Tsuji, etc.).
- High-pass Filters:
- Basic kernels (H1, H2, M1-M3).
- High-boost filtering with adjustable amplification.
- Halftoning:
- Ordered dithering (2×2, 2x3, 3×3 patterns).
- Error diffusion algorithms (Floyd-Steinberg, Jarvis-Judice-Ninke, etc.).
Image Segmentation
- Edge Detection:
- Operators: Roberts, Prewitt, Sobel, Kirsch, Robinson, Laplacian.
- Directional detection (0°, 45°, 90°, 135°).
- Thresholding:
- Global threshold selection.
- Local adaptive methods (mean, Niblack).
- Region-based:
- Region growing with seed selection.
- Watershed algorithm with marker control.
- Feature Extraction: Point, line, and contour detection.
📷 Application Screenshots
📝 License
This project is MIT Licensed. See
LICENSE file for more details.
👨💻 Author
Ríad Oliveira
🌐 Socials
