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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

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.