Seam Carving Algorithm Implementation

A C implementation of the seam carving algorithm for content-aware image resizing, based on the seminal paper "Seam Carving for Content-Aware Image Resizing" by Avidan and Shamir (SIGGRAPH 2007).

Overview

Seam carving is an algorithm for content-aware image resizing that removes (or adds) pixels in a way that preserves the most important visual content. Instead of traditional scaling that can distort images, seam carving identifies and removes the least noticeable pixel paths (seams) from the image.

Features

• Energy Calculation: Computes pixel importance using gradient-based energy functions
• Dynamic Programming: Efficiently finds optimal seams using DP algorithms
• Vertical & Horizontal Seams: Supports both width and height reduction
• **P3 PPM Support: Handles P3 (ASCII) portable pixmap format for image I/O
• Performance Optimized: Includes both debug and optimized builds
• Comprehensive Testing: Full test suite with various image scenarios

Building

make

This creates:

• bin/carve_debug - Debug version with sanitizers and debugging symbols
• bin/carve_opt - Optimized version for performance
• bin/testrunner - Unit test executable

Usage

# Basic usage - shows image statistics
./bin/carve_opt input.ppm

# Resize image (example - check argparser.h for full options)
./bin/carve_opt -w 400 -h 300 input.ppm

# Run with debug version for development
./bin/carve_debug input.ppm

Testing

Run the comprehensive test suite:

# Run all tests
make check

# Run custom tests
make test_all

# List available tests
python3 test/run_tests.py -l

# Run specific test
python3 test/run_tests.py -f [TESTNAME]

📋 For detailed testing instructions and troubleshooting, see TESTING.md

Algorithm Overview

1. Energy Calculation: For each pixel, calculate energy based on color gradients
2. Seam Detection: Use dynamic programming to find minimum energy vertical/horizontal path
3. Seam Removal: Remove the seam and shift remaining pixels
4. Iteration: Repeat until desired dimensions are reached

Project Structure

src/
├── main.c          # Main program and CLI interface
├── image.c/.h      # Image loading, saving, and basic operations
├── energy.c/.h     # Energy calculation algorithms
├── argparser.c/.h  # Command line argument parsing
├── indexing.c/.h   # 2D array indexing utilities
└── util.h          # Common definitions and utilities

test/
├── run_tests.py    # Python test runner
├── data/           # Test images (PPM format)
├── ref_output/     # Expected outputs for validation
└── custom_tests/   # Additional C test files

Implementation Highlights

• Memory Management: Careful allocation and deallocation in C
• Performance: Optimized algorithms with O(n*m) complexity for seam finding
• Robustness: Comprehensive error handling and edge case management
• Code Quality: Clean, well-documented C code following best practices
• Testing: Extensive test coverage including boundary conditions

Technical Details

Energy Functions

The implementation uses gradient-based energy functions that measure the rate of color change at each pixel. High-energy pixels (edges, textures) are preserved while low-energy pixels (uniform areas) are candidates for removal.

Dynamic Programming Approach

Seam detection uses dynamic programming to efficiently find the minimum energy path through the image, ensuring optimal seam selection in linear time.

Image Format

Works with P3 PPM (Portable Pixmap) format - ASCII text-based variant with human-readable RGB values.

Academic Context

This project demonstrates understanding of:

• Advanced Algorithms: Dynamic programming and optimization
• Image Processing: Digital image manipulation and analysis
• Systems Programming: Memory management and performance optimization in C
• Software Engineering: Testing, documentation, and code organization

References

• Avidan, S., & Shamir, A. (2007). Seam carving for content-aware image resizing. ACM SIGGRAPH 2007 papers.

License

This project was developed for educational purposes as part of a computer graphics course.