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attempt to add 0d/1d mat support to OpenCV (#23473)

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* attempt to add 0d/1d mat support to OpenCV

* revised the patch; now 1D mat is treated as 1xN 2D mat rather than Nx1.

* a step towards 'green' tests

* another little step towards 'green' tests

* calib test failures seem to be fixed now

* more fixes _core & _dnn

* another step towards green ci; even 0D mat's (a.k.a. scalars) are now partly supported!

* * fixed strange bug in aruco/charuco detector, not sure why it did not work
* also fixed a few remaining failures (hopefully) in dnn & core

* disabled failing GAPI tests - too complex to dig into this compiler pipeline

* hopefully fixed java tests

* trying to fix some more tests

* quick followup fix

* continue to fix test failures and warnings

* quick followup fix

* trying to fix some more tests

* partly fixed support for 0D/scalar UMat's

* use updated parseReduce() from upstream

* trying to fix the remaining test failures

* fixed [ch]aruco tests in Python

* still trying to fix tests

* revert "fix" in dnn's CUDA tensor

* trying to fix dnn+CUDA test failures

* fixed 1D umat creation

* hopefully fixed remaining cuda test failures

* removed training whitespaces
This commit is contained in:
Vadim Pisarevsky
2023-09-21 18:24:38 +03:00
committed by GitHub
parent fdab565711
commit 416bf3253d
80 changed files with 1013 additions and 561 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/calib/src/calibration.cpp
+1 -1
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@@ -334,7 +334,7 @@ static double calibrateCameraInternal( const Mat& objectPoints,
//std::cout << "dist0:" << _k << std::endl;
std::vector<double> param(nparams, 0.0);
Mat paramM(param, false);
Mat paramM = Mat(param, false).reshape(1, nparams);
std::vector<uchar> mask(nparams, (uchar)1);
int solveMethod = DECOMP_EIG;
modules/calib/src/chessboard.cpp
+1 -1
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@@ -272,7 +272,7 @@ void polyfit(const Mat& src_x, const Mat& src_y, Mat& dst, int order)
A.at<double>(y,x) = srcX.at<double>(y)*A.at<double>(y,x-1);
}
cv::Mat w;
solve(A,srcY,w,DECOMP_SVD);
solve(A,srcY.reshape(1, npoints),w,DECOMP_SVD);
w.convertTo(dst, ((src_x.depth() == CV_64F || src_y.depth() == CV_64F) ? CV_64F : CV_32F));
}
modules/calib/src/circlesgrid.cpp
+8 -6
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@@ -204,16 +204,16 @@ void CirclesGridClusterFinder::findCorners(const std::vector<cv::Point2f> &hull2
//corners are the most sharp angles (6)
Mat anglesMat = Mat(angles);
Mat sortedIndices;
sortIdx(anglesMat, sortedIndices, SORT_EVERY_COLUMN + SORT_DESCENDING);
sortIdx(anglesMat, sortedIndices, SORT_EVERY_ROW + SORT_DESCENDING);
CV_Assert(sortedIndices.type() == CV_32SC1);
CV_Assert(sortedIndices.cols == 1);
CV_Assert(sortedIndices.rows == 1);
const int cornersCount = isAsymmetricGrid ? 6 : 4;
Mat cornersIndices;
cv::sort(sortedIndices.rowRange(0, cornersCount), cornersIndices, SORT_EVERY_COLUMN + SORT_ASCENDING);
cv::sort(sortedIndices.colRange(0, cornersCount), cornersIndices, SORT_EVERY_ROW + SORT_ASCENDING);
corners.clear();
for(int i=0; i<cornersCount; i++)
{
corners.push_back(hull2f[cornersIndices.at<int>(i, 0)]);
corners.push_back(hull2f[cornersIndices.at<int>(i)]);
}
}
@@ -427,7 +427,8 @@ void CirclesGridClusterFinder::parsePatternPoints(const std::vector<cv::Point2f>
CV_Error(Error::StsNotImplemented, "The desired functionality requires flann module, which was disabled.");
#else
flann::LinearIndexParams flannIndexParams;
flann::Index flannIndex(Mat(rectifiedPatternPoints).reshape(1), flannIndexParams);
flann::Index flannIndex(Mat(rectifiedPatternPoints).reshape(1,
(int)rectifiedPatternPoints.size()), flannIndexParams);
centers.clear();
for( int i = 0; i < patternSize.height; i++ )
@@ -1126,7 +1127,8 @@ void CirclesGridFinder::findBasis(const std::vector<Point2f> &samples, std::vect
TermCriteria termCriteria;
Mat centers;
const int clustersCount = 4;
kmeans(Mat(samples).reshape(1, 0), clustersCount, bestLabels, termCriteria, parameters.kmeansAttempts,
int nsamples = (int)samples.size();
kmeans(Mat(samples).reshape(1, nsamples), clustersCount, bestLabels, termCriteria, parameters.kmeansAttempts,
KMEANS_RANDOM_CENTERS, centers);
CV_Assert( centers.type() == CV_32FC1 );
modules/calib/src/fisheye.cpp
+16 -9
View File
@@ -183,8 +183,8 @@ double cv::fisheye::calibrate(InputArrayOfArrays objectPoints, InputArrayOfArray
}
else
{
if (rvecs.needed()) Mat(omc).convertTo(rvecs, rvecs.empty() ? CV_64FC3 : rvecs.type());
if (tvecs.needed()) Mat(Tc).convertTo(tvecs, tvecs.empty() ? CV_64FC3 : tvecs.type());
if (rvecs.needed()) Mat(omc).reshape(3, (int)omc.size()).convertTo(rvecs, rvecs.empty() ? CV_64FC3 : rvecs.type());
if (tvecs.needed()) Mat(Tc).reshape(3, (int)Tc.size()).convertTo(tvecs, tvecs.empty() ? CV_64FC3 : tvecs.type());
}
return rms;
@@ -340,7 +340,9 @@ double cv::fisheye::stereoCalibrate(InputArrayOfArrays objectPoints, InputArrayO
Mat rvec = Mat(rvecs1[image_idx]);
Mat tvec = Mat(tvecs1[image_idx]);
cv::internal::projectPoints(object, projected, rvec, tvec, intrinsicLeft, jacobians);
Mat(Mat((imageLeft - projected).t()).reshape(1, 1).t()).copyTo(ekk.rowRange(0, 2 * n_points));
Mat pt_diff = imageLeft.reshape(1, n_points*2) -
projected.reshape(1, n_points*2);
pt_diff.copyTo(ekk.rowRange(0, 2 * n_points));
jacobians.colRange(8, 11).copyTo(Jkk.colRange(24 + image_idx * 6, 27 + image_idx * 6).rowRange(0, 2 * n_points));
jacobians.colRange(11, 14).copyTo(Jkk.colRange(27 + image_idx * 6, 30 + image_idx * 6).rowRange(0, 2 * n_points));
jacobians.colRange(0, 2).copyTo(Jkk.colRange(0, 2).rowRange(0, 2 * n_points));
@@ -354,7 +356,10 @@ double cv::fisheye::stereoCalibrate(InputArrayOfArrays objectPoints, InputArrayO
tvec = Mat(tvecs2[image_idx]);
cv::internal::projectPoints(object, projected, omr, Tr, intrinsicRight, jacobians);
Mat(Mat((imageRight - projected).t()).reshape(1, 1).t()).copyTo(ekk.rowRange(2 * n_points, 4 * n_points));
pt_diff = imageRight.reshape(1, n_points*2) -
projected.reshape(1, n_points*2);
pt_diff.copyTo(ekk.rowRange(2 * n_points, 4 * n_points));
Mat dxrdom = jacobians.colRange(8, 11) * domrdom + jacobians.colRange(11, 14) * dTrdom;
Mat dxrdT = jacobians.colRange(8, 11) * domrdT + jacobians.colRange(11, 14)* dTrdT;
Mat dxrdomckk = jacobians.colRange(8, 11) * domrdomckk + jacobians.colRange(11, 14) * dTrdomckk;
@@ -580,7 +585,7 @@ void cv::internal::ComputeExtrinsicRefine(const Mat& imagePoints, const Mat& obj
Mat jacobians;
projectPoints(objectPoints, x, rvec, tvec, param, jacobians);
Mat ex = imagePoints - Mat(x).t();
Mat ex = imagePoints - Mat(x);
ex = ex.reshape(1, 2);
J = jacobians.colRange(8, 14).clone();
@@ -826,7 +831,7 @@ void cv::internal::ComputeJacobians(InputArrayOfArrays objectPoints, InputArrayO
objectPoints.getMat(image_idx).convertTo(object, CV_64FC3);
imagePoints.getMat (image_idx).convertTo(image, CV_64FC2);
bool imT = image.rows < image.cols;
bool imT = image.channels() == 1 && image.rows > image.cols;
Mat om(omc.getMat().col(image_idx)), T(Tc.getMat().col(image_idx));
std::vector<Point2d> x;
@@ -850,8 +855,9 @@ void cv::internal::ComputeJacobians(InputArrayOfArrays objectPoints, InputArrayO
JJ2(Rect(9 + 6 * image_idx, 0, 6, 9)) = A * B.t();
JJ2(Rect(0, 9 + 6 * image_idx, 9, 6)) = JJ2(Rect(9 + 6 * image_idx, 0, 6, 9)).t();
ex3.rowRange(0, 9) += A * exkk.reshape(1, 2 * exkk.rows);
ex3.rowRange(9 + 6 * image_idx, 9 + 6 * (image_idx + 1)) = B * exkk.reshape(1, 2 * exkk.rows);
Mat exkk_col = exkk.reshape(1, 2 * (int)exkk.total());
ex3.rowRange(0, 9) += A * exkk_col;
ex3.rowRange(9 + 6 * image_idx, 9 + 6 * (image_idx + 1)) = B * exkk_col;
if (check_cond)
{
@@ -891,13 +897,14 @@ void cv::internal::EstimateUncertainties(InputArrayOfArrays objectPoints, InputA
objectPoints.getMat(image_idx).convertTo(object, CV_64FC3);
imagePoints.getMat (image_idx).convertTo(image, CV_64FC2);
bool imT = image.rows < image.cols;
bool imT = image.channels() == 1 && image.rows > image.cols;
Mat om(omc.getMat().col(image_idx)), T(Tc.getMat().col(image_idx));
std::vector<Point2d> x;
projectPoints(object, x, om, T, params, noArray());
Mat ex_ = (imT ? image.t() : image) - Mat(x);
ex_ = ex_.reshape(2, (int)ex_.total());
ex_.copyTo(ex.rowRange(insert_idx, insert_idx + ex_.rows));
insert_idx += ex_.rows;
}