GPU621/Group 3

Optimizing Image Processing using Intel's Data Analytics Library for Parallel computing and Vectorization

Introduction:

In this project we will be comparing Intel's Data Analytics Acceleration Library and OpenMP API to optimize image processing using parallel computing and vectorization. We selected two tasks for this project image sharpening and brightening. The run-time of each task is recorded and able to be compared by our demo program. We will also be comparing the implementation for each library.

In order to be able to more easily engage with image files, we will be utilizing the OpenCV library, leaning especially on the Mat class therein. The Mat class allows us to access the image as a n-dimensional array. Furthermore with our implementation we are able to rely on our parellelization choices instead of that built in to the OpenCV library.

OpenMP Implementation

OpenMP provides extremely simple implementation, especially the process which we are using in our code. In this process we were able to simply use a #pragma parallel for declaration for the OpenMP API to parallelize the process. With this we saw at the operations being performed at a quarter of the time it took the serial version of these processes.

<syntaxhighlight lang="cpp"> void openMP_imgProcessor::sharpenImg(cv::Mat& image) {

   //supressing OpenCV messages
   std::streambuf* coutbuf = std::cout.rdbuf();
   std::cout.rdbuf(nullptr);
   //
   // Convert the image to grayscale
   cv::Mat grayscale;
   cv::cvtColor(image, grayscale, cv::COLOR_BGR2GRAY);
   //
   // Apply the kernel to the grayscale image
   //finds areas with quick jumps from dark to light, increases contrast there
   #pragma omp parallel for
   for (int x = 1; x < image.cols - 1; x++) {
       for (int y = 1; y < image.rows - 1; y++) {
           double sum = 0.0;
           for (int i = -1; i <= 1; i++) {
               for (int j = -1; j <= 1; j++) {
                   sum += grayscale.at<uchar>(y + j, x + i) * LapKernel_[i + 1][j + 1];
               }
           }
           for (int c = 0; c < 3; c++) {
               image.at<cv::Vec3b>(y, x)[c] = cv::saturate_cast<uchar>(image.at<cv::Vec3b>(y, x)[c] + sum * 0.99);
           }
       }
   }
   //
   //stop supressing
   std::cout.rdbuf(coutbuf);

} </syntaxhighlight lang="cpp">

<syntaxhighlight lang="cpp"> void openMP_imgProcessor::brightenImg(cv::Mat& image, int brightnessLvl) {

   //supressing OpenCV messages
   std::streambuf* coutbuf = std::cout.rdbuf();
   std::cout.rdbuf(nullptr);
   
   int width = image.cols;
   int height = image.rows;
   int channels = image.channels();
   
   #pragma omp parallel for
   for (int row = 0; row < height; row++) {
       for (int col = 0; col < width; col++) {
           for (int c = 0; c < channels; c++) {
               uchar& pixel = image.at<cv::Vec3b>(row, col)[c];
               pixel = cv::saturate_cast<uchar>(pixel + brightnessLvl);
           }
       }
   }

   //stop supressing
   std::cout.rdbuf(coutbuf);

} </syntaxhighlight lang="cpp">

Data Analytics Library Overview:

Intel's Data Analytics Library offers a robust collection of tools and algorithms that can assist programmers in building high-performance applications tailored for Intel chips. These tools are designed to interact with various data sources, such as data stored in memory, hard disc, or distributed systems. These functions available in Intel's Data Analytics Library are usable by a broad range of developers because it supports various programming languages, such as C++, Python, and Java. Data Analytics Library offers functionalities for: • Parallel computing. • Vectorization. • Machine learning. • Graph analytics. • Statistical analysis. • Data visualization.