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Merge pull request #6956 from mshabunin:fix-chessboard-bug

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This commit is contained in:
Alexander Alekhin 2016-09-27 13:33:42 +00:00
commit 73e1d64ae0
5 changed files with 535 additions and 676 deletions
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887
modules/calib3d/src/calibinit.cpp
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280
modules/calib3d/src/checkchessboard.cpp
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@ -46,28 +46,26 @@
#include <vector>
#include <algorithm>
//#define DEBUG_WINDOWS
using namespace cv;
using namespace std;
#if defined(DEBUG_WINDOWS)
# include "opencv2/opencv_modules.hpp"
# ifdef HAVE_OPENCV_HIGHGUI
# include "opencv2/highgui.hpp"
# else
# undef DEBUG_WINDOWS
# endif
#endif
int cvCheckChessboardBinary(IplImage* src, CvSize size);
static void icvGetQuadrangleHypotheses(CvSeq* contours, std::vector<std::pair<float, int> >& quads, int class_id)
static void icvGetQuadrangleHypotheses(const std::vector<std::vector< cv::Point > > & contours, const std::vector< cv::Vec4i > & hierarchy, std::vector<std::pair<float, int> >& quads, int class_id)
{
const float min_aspect_ratio = 0.3f;
const float max_aspect_ratio = 3.0f;
const float min_box_size = 10.0f;
for(CvSeq* seq = contours; seq != NULL; seq = seq->h_next)
typedef std::vector< std::vector< cv::Point > >::const_iterator iter_t;
iter_t i;
for (i = contours.begin(); i != contours.end(); ++i)
{
CvBox2D box = cvMinAreaRect2(seq);
const iter_t::difference_type idx = i - contours.begin();
if (hierarchy.at(idx)[3] != -1)
continue; // skip holes
const std::vector< cv::Point > & c = *i;
cv::RotatedRect box = cv::minAreaRect(c);
float box_size = MAX(box.size.width, box.size.height);
if(box_size < min_box_size)
{
@ -98,6 +96,64 @@ inline bool less_pred(const std::pair<float, int>& p1, const std::pair<float, in
return p1.first < p2.first;
}
static void fillQuads(Mat & white, Mat & black, double white_thresh, double black_thresh, vector<pair<float, int> > & quads)
{
Mat thresh;
{
vector< vector<Point> > contours;
vector< Vec4i > hierarchy;
threshold(white, thresh, white_thresh, 255, THRESH_BINARY);
findContours(thresh, contours, hierarchy, RETR_CCOMP, CHAIN_APPROX_SIMPLE);
icvGetQuadrangleHypotheses(contours, hierarchy, quads, 1);
}
{
vector< vector<Point> > contours;
vector< Vec4i > hierarchy;
threshold(black, thresh, black_thresh, 255, THRESH_BINARY_INV);
findContours(thresh, contours, hierarchy, RETR_CCOMP, CHAIN_APPROX_SIMPLE);
icvGetQuadrangleHypotheses(contours, hierarchy, quads, 0);
}
}
static bool checkQuads(vector<pair<float, int> > & quads, const cv::Size & size)
{
const size_t min_quads_count = size.width*size.height/2;
std::sort(quads.begin(), quads.end(), less_pred);
// now check if there are many hypotheses with similar sizes
// do this by floodfill-style algorithm
const float size_rel_dev = 0.4f;
for(size_t i = 0; i < quads.size(); i++)
{
size_t j = i + 1;
for(; j < quads.size(); j++)
{
if(quads[j].first/quads[i].first > 1.0f + size_rel_dev)
{
break;
}
}
if(j + 1 > min_quads_count + i)
{
// check the number of black and white squares
std::vector<int> counts;
countClasses(quads, i, j, counts);
const int black_count = cvRound(ceil(size.width/2.0)*ceil(size.height/2.0));
const int white_count = cvRound(floor(size.width/2.0)*floor(size.height/2.0));
if(counts[0] < black_count*0.75 ||
counts[1] < white_count*0.75)
{
continue;
}
return true;
}
}
return false;
}
// does a fast check if a chessboard is in the input image. This is a workaround to
// a problem of cvFindChessboardCorners being slow on images with no chessboard
// - src: input image
@ -106,105 +162,32 @@ inline bool less_pred(const std::pair<float, int>& p1, const std::pair<float, in
// 0 if there is no chessboard, -1 in case of error
int cvCheckChessboard(IplImage* src, CvSize size)
{
if(src->nChannels > 1)
{
cvError(CV_BadNumChannels, "cvCheckChessboard", "supports single-channel images only",
__FILE__, __LINE__);
}
cv::Mat img = cv::cvarrToMat(src);
return checkChessboard(img, size);
}
if(src->depth != 8)
{
cvError(CV_BadDepth, "cvCheckChessboard", "supports depth=8 images only",
__FILE__, __LINE__);
}
int checkChessboard(const cv::Mat & img, const cv::Size & size)
{
CV_Assert(img.channels() == 1 && img.depth() == CV_8U);
const int erosion_count = 1;
const float black_level = 20.f;
const float white_level = 130.f;
const float black_white_gap = 70.f;
#if defined(DEBUG_WINDOWS)
cvNamedWindow("1", 1);
cvShowImage("1", src);
cvWaitKey(0);
#endif //DEBUG_WINDOWS
CvMemStorage* storage = cvCreateMemStorage();
IplImage* white = cvCloneImage(src);
IplImage* black = cvCloneImage(src);
cvErode(white, white, NULL, erosion_count);
cvDilate(black, black, NULL, erosion_count);
IplImage* thresh = cvCreateImage(cvGetSize(src), IPL_DEPTH_8U, 1);
Mat white;
Mat black;
erode(img, white, Mat(), Point(-1, -1), erosion_count);
dilate(img, black, Mat(), Point(-1, -1), erosion_count);
int result = 0;
for(float thresh_level = black_level; thresh_level < white_level && !result; thresh_level += 20.0f)
{
cvThreshold(white, thresh, thresh_level + black_white_gap, 255, CV_THRESH_BINARY);
#if defined(DEBUG_WINDOWS)
cvShowImage("1", thresh);
cvWaitKey(0);
#endif //DEBUG_WINDOWS
CvSeq* first = 0;
std::vector<std::pair<float, int> > quads;
cvFindContours(thresh, storage, &first, sizeof(CvContour), CV_RETR_CCOMP);
icvGetQuadrangleHypotheses(first, quads, 1);
cvThreshold(black, thresh, thresh_level, 255, CV_THRESH_BINARY_INV);
#if defined(DEBUG_WINDOWS)
cvShowImage("1", thresh);
cvWaitKey(0);
#endif //DEBUG_WINDOWS
cvFindContours(thresh, storage, &first, sizeof(CvContour), CV_RETR_CCOMP);
icvGetQuadrangleHypotheses(first, quads, 0);
const size_t min_quads_count = size.width*size.height/2;
std::sort(quads.begin(), quads.end(), less_pred);
// now check if there are many hypotheses with similar sizes
// do this by floodfill-style algorithm
const float size_rel_dev = 0.4f;
for(size_t i = 0; i < quads.size(); i++)
{
size_t j = i + 1;
for(; j < quads.size(); j++)
{
if(quads[j].first/quads[i].first > 1.0f + size_rel_dev)
{
break;
}
}
if(j + 1 > min_quads_count + i)
{
// check the number of black and white squares
std::vector<int> counts;
countClasses(quads, i, j, counts);
const int black_count = cvRound(ceil(size.width/2.0)*ceil(size.height/2.0));
const int white_count = cvRound(floor(size.width/2.0)*floor(size.height/2.0));
if(counts[0] < black_count*0.75 ||
counts[1] < white_count*0.75)
{
continue;
}
result = 1;
break;
}
}
vector<pair<float, int> > quads;
fillQuads(white, black, thresh_level + black_white_gap, thresh_level, quads);
if (checkQuads(quads, size))
result = 1;
}
cvReleaseImage(&thresh);
cvReleaseImage(&white);
cvReleaseImage(&black);
cvReleaseMemStorage(&storage);
return result;
}
@ -214,90 +197,29 @@ int cvCheckChessboard(IplImage* src, CvSize size)
// - size: chessboard size
// Returns 1 if a chessboard can be in this image and findChessboardCorners should be called,
// 0 if there is no chessboard, -1 in case of error
int cvCheckChessboardBinary(IplImage* src, CvSize size)
int checkChessboardBinary(const cv::Mat & img, const cv::Size & size)
{
if(src->nChannels > 1)
{
cvError(CV_BadNumChannels, "cvCheckChessboard", "supports single-channel images only",
__FILE__, __LINE__);
}
CV_Assert(img.channels() == 1 && img.depth() == CV_8U);
if(src->depth != 8)
{
cvError(CV_BadDepth, "cvCheckChessboard", "supports depth=8 images only",
__FILE__, __LINE__);
}
CvMemStorage* storage = cvCreateMemStorage();
IplImage* white = cvCloneImage(src);
IplImage* black = cvCloneImage(src);
IplImage* thresh = cvCreateImage(cvGetSize(src), IPL_DEPTH_8U, 1);
Mat white = img.clone();
Mat black = img.clone();
int result = 0;
for ( int erosion_count = 0; erosion_count <= 3; erosion_count++ )
{
if ( 1 == result )
break;
if ( 1 == result )
break;
if ( 0 != erosion_count ) // first iteration keeps original images
{
cvErode(white, white, NULL, 1);
cvDilate(black, black, NULL, 1);
}
if ( 0 != erosion_count ) // first iteration keeps original images
{
erode(white, white, Mat(), Point(-1, -1), 1);
dilate(black, black, Mat(), Point(-1, -1), 1);
}
cvThreshold(white, thresh, 128, 255, CV_THRESH_BINARY);
CvSeq* first = 0;
std::vector<std::pair<float, int> > quads;
cvFindContours(thresh, storage, &first, sizeof(CvContour), CV_RETR_CCOMP);
icvGetQuadrangleHypotheses(first, quads, 1);
cvThreshold(black, thresh, 128, 255, CV_THRESH_BINARY_INV);
cvFindContours(thresh, storage, &first, sizeof(CvContour), CV_RETR_CCOMP);
icvGetQuadrangleHypotheses(first, quads, 0);
const size_t min_quads_count = size.width*size.height/2;
std::sort(quads.begin(), quads.end(), less_pred);
// now check if there are many hypotheses with similar sizes
// do this by floodfill-style algorithm
const float size_rel_dev = 0.4f;
for(size_t i = 0; i < quads.size(); i++)
{
size_t j = i + 1;
for(; j < quads.size(); j++)
{
if(quads[j].first/quads[i].first > 1.0f + size_rel_dev)
{
break;
}
}
if(j + 1 > min_quads_count + i)
{
// check the number of black and white squares
std::vector<int> counts;
countClasses(quads, i, j, counts);
const int black_count = cvRound(ceil(size.width/2.0)*ceil(size.height/2.0));
const int white_count = cvRound(floor(size.width/2.0)*floor(size.height/2.0));
if(counts[0] < black_count*0.75 ||
counts[1] < white_count*0.75)
{
continue;
}
result = 1;
break;
}
}
vector<pair<float, int> > quads;
fillQuads(white, black, 128, 128, quads);
if (checkQuads(quads, size))
result = 1;
}
cvReleaseImage(&thresh);
cvReleaseImage(&white);
cvReleaseImage(&black);
cvReleaseMemStorage(&storage);
return result;
}
}

3
modules/calib3d/src/precomp.hpp
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@ -117,4 +117,7 @@ template<typename T> inline int compressElems( T* ptr, const uchar* mask, int ms
}
int checkChessboard(const cv::Mat & img, const cv::Size & size);
int checkChessboardBinary(const cv::Mat & img, const cv::Size & size);
#endif

37
modules/calib3d/test/test_chesscorners.cpp
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@ -51,29 +51,31 @@ using namespace cv;
#define _L2_ERR
void show_points( const Mat& gray, const Mat& u, const vector<Point2f>& v, Size pattern_size, bool was_found )
//#define DEBUG_CHESSBOARD
#ifdef DEBUG_CHESSBOARD
#include "opencv2/highgui.hpp"
void show_points( const Mat& gray, const Mat& expected, const vector<Point2f>& actual, bool was_found )
{
Mat rgb( gray.size(), CV_8U);
merge(vector<Mat>(3, gray), rgb);
for(size_t i = 0; i < v.size(); i++ )
circle( rgb, v[i], 3, Scalar(255, 0, 0), FILLED);
for(size_t i = 0; i < actual.size(); i++ )
circle( rgb, actual[i], 5, Scalar(0, 0, 200), 1, LINE_AA);
if( !u.empty() )
if( !expected.empty() )
{
const Point2f* u_data = u.ptr<Point2f>();
size_t count = u.cols * u.rows;
const Point2f* u_data = expected.ptr<Point2f>();
size_t count = expected.cols * expected.rows;
for(size_t i = 0; i < count; i++ )
circle( rgb, u_data[i], 3, Scalar(0, 255, 0), FILLED);
circle(rgb, u_data[i], 4, Scalar(0, 240, 0), 1, LINE_AA);
}
if (!v.empty())
{
Mat corners((int)v.size(), 1, CV_32FC2, (void*)&v[0]);
drawChessboardCorners( rgb, pattern_size, corners, was_found );
}
//namedWindow( "test", 0 ); imshow( "test", rgb ); waitKey(0);
putText(rgb, was_found ? "FOUND !!!" : "NOT FOUND", Point(5, 20), FONT_HERSHEY_PLAIN, 1, Scalar(0, 240, 0));
imshow( "test", rgb ); while ((uchar)waitKey(0) != 'q') {};
}
#else
#define show_points(...)
#endif
enum Pattern { CHESSBOARD, CIRCLES_GRID, ASYMMETRIC_CIRCLES_GRID };
@ -253,7 +255,6 @@ void CV_ChessboardDetectorTest::run_batch( const string& filename )
result = findCirclesGrid(gray, pattern_size, v, CALIB_CB_ASYMMETRIC_GRID | algorithmFlags);
break;
}
show_points( gray, Mat(), v, pattern_size, result );
if( result ^ doesContatinChessboard || v.size() != count_exp )
{
@ -280,7 +281,7 @@ void CV_ChessboardDetectorTest::run_batch( const string& filename )
if( pattern == CHESSBOARD )
cornerSubPix( gray, v, Size(5, 5), Size(-1,-1), TermCriteria(TermCriteria::EPS|TermCriteria::MAX_ITER, 30, 0.1));
//find4QuadCornerSubpix(gray, v, Size(5, 5));
show_points( gray, expected, v, pattern_size, result );
show_points( gray, expected, v, result );
#ifndef WRITE_POINTS
// printf("called find4QuadCornerSubpix\n");
err = calcError(v, expected);
@ -298,6 +299,10 @@ void CV_ChessboardDetectorTest::run_batch( const string& filename )
max_precise_error = MAX( max_precise_error, err );
#endif
}
else
{
show_points( gray, Mat(), v, result );
}
#ifdef WRITE_POINTS
Mat mat_v(pattern_size, CV_32FC2, (void*)&v[0]);

4
modules/python/test/test_calibration.py
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@ -57,7 +57,7 @@ class calibration_test(NewOpenCVTests):
eps = 0.01
normCamEps = 10.0
normDistEps = 0.001
normDistEps = 0.05
cameraMatrixTest = [[ 532.80992189, 0., 342.4952186 ],
[ 0., 532.93346422, 233.8879292 ],
@ -68,4 +68,4 @@ class calibration_test(NewOpenCVTests):
self.assertLess(abs(rms - 0.196334638034), eps)
self.assertLess(cv2.norm(camera_matrix - cameraMatrixTest, cv2.NORM_L1), normCamEps)
self.assertLess(cv2.norm(dist_coefs - distCoeffsTest, cv2.NORM_L1), normDistEps)
self.assertLess(cv2.norm(dist_coefs - distCoeffsTest, cv2.NORM_L1), normDistEps)