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@ -531,14 +531,14 @@ bool findChessboardCorners(InputArray image_, Size pattern_size, |
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const int min_dilations = 0; |
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const int max_dilations = is_plain ? 0 : 7; |
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// Try our standard "1" dilation, but if the pattern is not found, iterate the whole procedure with higher dilations.
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// This is necessary because some squares simply do not separate properly with a single dilation. However,
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// Try our standard "0" and "1" dilations, but if the pattern is not found, iterate the whole procedure with higher dilations.
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// This is necessary because some squares simply do not separate properly without and with a single dilations. However,
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// we want to use the minimum number of dilations possible since dilations cause the squares to become smaller,
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// making it difficult to detect smaller squares.
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for (int dilations = min_dilations; dilations <= max_dilations; dilations++) |
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{ |
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//USE BINARY IMAGE COMPUTED USING icvBinarizationHistogramBased METHOD
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if(!is_plain) |
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if(!is_plain && dilations > 0) |
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dilate( thresh_img_new, thresh_img_new, Mat(), Point(-1, -1), 1 ); |
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// So we can find rectangles that go to the edge, we draw a white line around the image edge.
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@ -596,13 +596,13 @@ bool findChessboardCorners(InputArray image_, Size pattern_size, |
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block_size = block_size | 1; |
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// convert to binary
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adaptiveThreshold( img, thresh_img, 255, ADAPTIVE_THRESH_MEAN_C, THRESH_BINARY, block_size, (k/2)*5 ); |
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if (dilations > 0) |
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dilate( thresh_img, thresh_img, Mat(), Point(-1, -1), dilations-1 ); |
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dilate( thresh_img, thresh_img, Mat(), Point(-1, -1), dilations ); |
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} |
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else |
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{ |
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dilate( thresh_img, thresh_img, Mat(), Point(-1, -1), 1 ); |
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if (dilations > 0) |
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dilate( thresh_img, thresh_img, Mat(), Point(-1, -1), 1 ); |
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} |
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SHOW("Old binarization", thresh_img); |
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