dlib C++ Library - image_ex.cpp

// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
/*
 This is an example illustrating the use of the GUI API as well as some 
 aspects of image manipulation from the dlib C++ Library.
 This is a pretty simple example. It takes a BMP file on the command line
 and opens it up, runs a simple edge detection algorithm on it, and 
 displays the results on the screen. 
*/
#include <dlib/gui_widgets.h>
#include <dlib/image_io.h>
#include <dlib/image_transforms.h>
#include <fstream>
using namespace std;
using namespace dlib;
// ----------------------------------------------------------------------------
int main(int argc, char** argv)
{
 try
 {
 // make sure the user entered an argument to this program
 if (argc != 2)
 {
 cout << "error, you have to enter a BMP file as an argument to this program" << endl;
 return 1;
 }
 // Here we declare an image object that can store rgb_pixels. Note that in 
 // dlib there is no explicit image object, just a 2D array and
 // various pixel types. 
 array2d<rgb_pixel> img;
 // Now load the image file into our image. If something is wrong then
 // load_image() will throw an exception. Also, if you linked with libpng
 // and libjpeg then load_image() can load PNG and JPEG files in addition
 // to BMP files.
 load_image(img, argv[1]);
 // Now let's use some image functions. First let's blur the image a little.
 array2d<unsigned char> blurred_img;
 gaussian_blur(img, blurred_img); 
 // Now find the horizontal and vertical gradient images.
 array2d<short> horz_gradient, vert_gradient;
 array2d<unsigned char> edge_image;
 sobel_edge_detector(blurred_img, horz_gradient, vert_gradient);
 // now we do the non-maximum edge suppression step so that our edges are nice and thin
 suppress_non_maximum_edges(horz_gradient, vert_gradient, edge_image); 
 // Now we would like to see what our images look like. So let's use a 
 // window to display them on the screen. (Note that you can zoom into 
 // the window by holding CTRL and scrolling the mouse wheel)
 image_window my_window(edge_image, "Normal Edge Image");
 // We can also easily display the edge_image as a heatmap or using the jet color
 // scheme like so.
 image_window win_hot(heatmap(edge_image));
 image_window win_jet(jet(edge_image));
 // also make a window to display the original image
 image_window my_window2(img, "Original Image");
 // Sometimes you want to get input from the user about which pixels are important
 // for some task. You can do this easily by trapping user clicks as shown below.
 // This loop executes every time the user double clicks on some image pixel and it
 // will terminate once the user closes the window.
 point p;
 while (my_window.get_next_double_click(p))
 {
 cout << "User double clicked on pixel: " << p << endl;
 cout << "edge pixel value at this location is: " << (int)edge_image[p.y()][p.x()] << endl;
 }
 // wait until the user closes the windows before we let the program 
 // terminate.
 win_hot.wait_until_closed();
 my_window2.wait_until_closed();
 // Finally, note that you can access the elements of an image using the normal [row][column]
 // operator like so:
 cout << horz_gradient[0][3] << endl;
 cout << "number of rows in image: " << horz_gradient.nr() << endl;
 cout << "number of columns in image: " << horz_gradient.nc() << endl;
 }
 catch (exception& e)
 {
 cout << "exception thrown: " << e.what() << endl;
 }
}
// ----------------------------------------------------------------------------