Difference between revisions of "Kifu: Go game record (kifu) generator"

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1. The input image is filtered and thresholded and a big quadrilateral shape containing many smaller quadrilaterals is selected, sorting contours detected with OpenCV.
 
1. The input image is filtered and thresholded and a big quadrilateral shape containing many smaller quadrilaterals is selected, sorting contours detected with OpenCV.
  
2. The selection is redressed using a perspective correction matrix that OpenCV can also compute,
+
2. The selection is rectified using a perspective correction matrix that OpenCV can also compute,
  
3. OpenCV find lines in the redressed selection and we compute the intersections for each detected line segment.
+
3. OpenCV find lines in the rectified selection and we compute the intersections for each detected line segment.
  
 
4. After, we round the results to group close neighbour points, and compute or approximate the center of each group (ie: intersection).
 
4. After, we round the results to group close neighbour points, and compute or approximate the center of each group (ie: intersection).
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Using vertical and horizontal RGB components sums of a thresholded difference between a reference image and the current one,
 
Using vertical and horizontal RGB components sums of a thresholded difference between a reference image and the current one,
  
it is easy to compute the screen coordinates of a played stone
+
it is easy to compute the image coordinates of a played stone
  
  

Revision as of 14:12, 18 August 2008

Goban and Grid Detection:

1. The input image is filtered and thresholded and a big quadrilateral shape containing many smaller quadrilaterals is selected, sorting contours detected with OpenCV.

2. The selection is rectified using a perspective correction matrix that OpenCV can also compute,

3. OpenCV find lines in the rectified selection and we compute the intersections for each detected line segment.

4. After, we round the results to group close neighbour points, and compute or approximate the center of each group (ie: intersection).

5. Finally we can verify the intersection count and relative alignment to validate the region or restart at step 1 with another threshold


Image change detection:

Using vertical and horizontal RGB components sums of a thresholded difference between a reference image and the current one,

it is easy to compute the image coordinates of a played stone


Stone detection:

When a stone is played it overlaps 1 grid square on a corner, 2 on the borders and 4 in the center.

Computing horizontal and vertical pixel sums for each grid cell or around each intersection can tell where the stone is played and reveal the color of the stone, being darker or brighter than the empty intersection region.

The intersection can also be seen on the difference image when the stone is white, that could also be used to detect the stone color.




Methods for mapping the coordinates:

"2.2 Perspective transformation with two vanishing points" (pages 2 and 3, equations 7 and 10)

http://cipa.icomos.org/fileadmin/papers/potsdam/2001-21-gf01a.pdf


Inverse homography and plane image rectification (page 14)

http://www-prima.imag.fr/jlc/Courses/2002/DEA-IVR.VO/DEA-IVR.VO.S2.pdf


"Inferring Projective Mappings" (page 3)

http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.9.7803

with related java and C source code here: http://www.developpez.net/forums/showthread.php?t=591698




Links:

irc://irc.freenode.net/#kifu

http://www.sourceforge.net/projects/kifu

http://opencvlibrary.sourceforge.net/

Photointerpretation and Small Scale Stereoplotting with Digitally Rectified Photographs with Geometrical constraints: http://cipa.icomos.org/fileadmin/papers/potsdam/2001-21-gf01a.pdf

Vision par Ordinateur: http://www-prima.imag.fr/jlc/Courses/2002/DEA-IVR.VO/DEA-IVR.VO.S2.pdf

Projective Mappings for Image Warping: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.9.7803

http://sciences.ch/htmlfr/geometrie/geometrieprojective01.php