#includeusing namespace cv; int main( int argc, char** argv ) { Mat image = imread( "room_damage.jpg", IMREAD_GRAYSCALE ); Mat thresholded; threshold( image, thresholded, 128, 255, THRESH_BINARY ); imshow( "Room Damage", thresholded ); waitKey(); return 0; }
#include#include int main() { double earth_radius = 6371.0; double earthquake_magnitude = 7.5; double affected_area = boost::math::constants::pi () * pow(earth_radius, 2) * pow(10, (0.5 * (earthquake_magnitude - 6.0))); std::cout << "Estimated Affected Area: " << affected_area << " sq. km" << std::endl; }
#includeOverall, these libraries can be used to gain insights into the extent of room damage and predict its outcomes.#include using namespace mlpack; using namespace mlpack::regression; int main(int argc, char** argv) { arma::mat X; // Training data. arma::vec y; // Labels. // Load data. Load("room_damage.csv", X, true); Load("room_damage_labels.csv", y, true); // Train model. LinearRegression lr(X, y); // Predict damage for new data. arma::vec new_data = { 3.5, 6.8, 4.2 }; double predicted_damage = lr.Predict(new_data); std::cout << "Predicted Room Damage: " << predicted_damage << std::endl; return 0; }