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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.
<code>
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];
}
}
//apply filter
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);
}
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);
}
</code>
