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authorDaniel Baumann <daniel.baumann@progress-linux.org>2024-04-19 02:57:58 +0000
committerDaniel Baumann <daniel.baumann@progress-linux.org>2024-04-19 02:57:58 +0000
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tree9754ff1ca740f6346cf8483ec915d4054bc5da2d /ml/dlib/examples/hough_transform_ex.cpp
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Adding upstream version 1.44.3.upstream/1.44.3upstream
Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>
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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));
+ }
+}
+