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diff --git a/ml/dlib/examples/hough_transform_ex.cpp b/ml/dlib/examples/hough_transform_ex.cpp
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-// 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));
-    }
-}
-