Parallel Image Processing

This project demonstrates a parallel image processing application in C++ using OpenMP for parallelism and OpenCV for image handling. The application allows users to apply different image processing techniques (grayscale, blur, and edge detection) with parallel processing.

Features

• Grayscale Processing: Converts a color image to grayscale using OpenMP.
• Blur: Applies Gaussian blur to the input image.
• Edge Detection: Detects edges in the image using the Canny algorithm.
• Dynamic Thread Management: Automatically adjusts the number of threads based on available CPU cores.
• Performance Measurement: Displays processing time for each operation.

Requirements

• C++17 or higher
• CMake (3.10 or higher)
• OpenCV (4.x)
• OpenMP

Installation and Usage

1. Clone the repository

• git clone https://github.com/ardarvas/parallel-image-processing.git
• cd parallel-image-processing

2. Build the project

• mkdir build
• cd build
• cmake ..
• make

3. Run the application

• ./ParallelImageProcessing <input_file> <output_file> <process_type>

Example:

./ParallelImageProcessing image.jpg output.jpg grayscale

Process Types

• grayscale: Converts the image to grayscale.
• blur: Applies Gaussian blur to the image.
• edge: Detects edges in the image using the Canny algorithm.

Performance

• The application leverages parallelism to reduce processing time for large images. Thread count is dynamically adjusted based on available CPU cores.