    // 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 Hough transform tool in the
        dlib C++ Library.

        In this example we are going to draw a line on an image and then use the
        Hough transform to detect the location of the line.  Moreover, we do this in
        a loop that changes the line's position slightly each iteration, which gives
        a pretty animation of the Hough transform in action.
    */

    #include <dlib/gui_widgets.h>
    #include <dlib/image_transforms.h>

    using namespace dlib;

    int main()
    {
        // First let's make a 400x400 image.  This will form the input to the Hough transform.
        array2d<unsigned char> img(400,400);
        // Now we make a hough_transform object.  The 300 here means that the Hough transform
        // will operate on a 300x300 subwindow of its input image.
        hough_transform ht(300);

        image_window win, win2;
        double angle1 = 0;
        double angle2 = 0;
        while(true)
        {
            // Generate a line segment that is rotating around inside the image.  The line is
            // generated based on the values in angle1 and angle2. So each iteration creates a
            // slightly different line.
            angle1 += pi/130;
            angle2 += pi/400;
            const point cent = center(get_rect(img));
            // A point 90 pixels away from the center of the image but rotated by angle1.
            const point arc = rotate_point(cent, cent + point(90,0), angle1);
            // Now make a line that goes though arc but rotate it by angle2.
            const point l = rotate_point(arc, arc + point(500,0), angle2);
            const point r = rotate_point(arc, arc - point(500,0), angle2);

            // Next, blank out the input image and then draw our line on it.
            assign_all_pixels(img, 0);
            draw_line(img, l, r, 255);

            const point offset(50,50);
            array2d<int> himg;
            // pick the window inside img on which we will run the Hough transform.
            const rectangle box = translate_rect(get_rect(ht),offset);
            // Now let's compute the hough transform for a subwindow in the image.  In
            // particular, we run it on the 300x300 subwindow with an upper left corner at the
            // pixel point(50,50).  The output is stored in himg.
            ht(img, box, himg);
            // Now that we have the transformed image, the Hough image pixel with the largest
            // value should indicate where the line is.  So we find the coordinates of the
            // largest pixel:
            point p = max_point(mat(himg));
            // And then ask the ht object for the line segment in the original image that
            // corresponds to this point in Hough transform space.
            std::pair<point,point> line = ht.get_line(p);

            // Finally, let's display all these things on the screen.  We copy the original
            // input image into a color image and then draw the detected line on top in red.
            array2d<rgb_pixel> temp;
            assign_image(temp, img);
            // Note that we must offset the output line to account for our offset subwindow.
            // We do this by just adding in the offset to the line endpoints.
            draw_line(temp, line.first+offset, line.second+offset, rgb_pixel(255,0,0));
            win.clear_overlay();
            win.set_image(temp);
            // Also show the subwindow we ran the Hough transform on as a green box.  You will
            // see that the detected line is exactly contained within this box and also
            // overlaps the original line.
            win.add_overlay(box, rgb_pixel(0,255,0));

            // We can also display the Hough transform itself using the jet color scheme.
            win2.set_image(jet(himg));
        }
    }