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attempt to add 0d/1d mat support (#3511)

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* fixed BIMEF test failures and very slightly optimized (there is a _lot_ of room for the further optimization)

* dummy commit to rerun tests

* fixed contrib tests

* seem to fixed one more opencv_face test

* maybe adding some whitespaces will help? )

* hopefully fixed the remaining test failures
This commit is contained in:
Vadim Pisarevsky
2023-09-21 18:25:19 +03:00
committed by GitHub
parent f23a567f0d
commit 0200571aa3
10 changed files with 64 additions and 53 deletions
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1
modules/cudaimgproc/test/test_histogram.cpp
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@@ -107,7 +107,6 @@ CUDA_TEST_P(HistEven, Accuracy)
Mat srcRoi = src(roi);
cv::calcHist(&srcRoi, 1, channels, cv::Mat(), hist_gold, 1, histSize, ranges);
hist_gold = hist_gold.t();
hist_gold.convertTo(hist_gold, CV_32S);
EXPECT_MAT_NEAR(hist_gold, hist, 0.0);

6
modules/cudev/test/test_nd.cu
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@@ -41,12 +41,6 @@ public:
else
ret.push_back(Range::all());
if (dims == 1)
{
// Mat expects two ranges even in this case
ret.push_back(Range::all());
}
return ret;
}

57
modules/face/src/facemarkAAM.cpp
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@@ -243,7 +243,7 @@ void FacemarkAAMImpl::training(void* parameters){
/* get PCA Projection vectors */
Mat S;
getProjection(M.t(),S,param_max_n);
getProjection(M.t(), S, param_max_n);
/* Create similarity eig*/
Mat shape_S,shape_Q;
calcSimilarityEig(AAM.s0,S,AAM.Q,AAM.S);
@@ -259,7 +259,7 @@ void FacemarkAAMImpl::training(void* parameters){
/*get the min and max of x and y coordinate*/
double min_x, max_x, min_y, max_y;
s0_scaled_m = s0_scaled_m.reshape(1);
s0_scaled_m = s0_scaled_m.reshape(1, (int)s0_scaled_m.total());
Mat s0_scaled_x = s0_scaled_m.col(0);
Mat s0_scaled_y = s0_scaled_m.col(1);
minMaxIdx(s0_scaled_x, &min_x, &max_x);
@@ -287,7 +287,7 @@ void FacemarkAAMImpl::training(void* parameters){
if(params.verbose) printf("extract features from image #%i/%i\n", (int)(i+1), (int)images.size());
warped = warpImage(images[i],base_shape, facePoints[i], AAM.triangles, AAM.textures[scale].resolution,AAM.textures[scale].textureIdx);
feat = getFeature<uchar>(warped, AAM.textures[scale].ind1);
texture_feats.push_back(feat.t());
texture_feats.push_back(feat.reshape(feat.channels(), 1));
}
Mat T= texture_feats.t();
@@ -307,7 +307,7 @@ void FacemarkAAMImpl::training(void* parameters){
for(int i =0;i<AAM.textures[scale].A.cols;i++){
Mat c = AAM.textures[scale].A.col(i);
ud = getFeature<float>(c,fe_map);
U_data.push_back(ud.t());
U_data.push_back(ud.reshape(ud.channels(), 1));
}
Mat U = U_data.t();
AAM.textures[scale].AA = orthonormal(U);
@@ -413,7 +413,7 @@ bool FacemarkAAMImpl::fitImpl( const Mat image, std::vector<Point2f>& landmarks,
Mat Wx_dp, Wy_dp;
createWarpJacobian(S, AAM.Q, AAM.triangles, AAM.textures[scl],Wx_dp, Wy_dp, Tp);
std::vector<Point2f> s0_init = Mat(Mat(R*scale*AAM.scales[scl]*Mat(Mat(s0).reshape(1)).t()).t()).reshape(2);
std::vector<Point2f> s0_init = Mat(Mat(R*scale*AAM.scales[scl]*Mat(Mat(s0).reshape(1, (int)s0.size())).t()).t()).reshape(2);
std::vector<Point2f> curr_shape = Mat(Mat(s0_init)+Scalar(T.x,T.y));
curr_shape = Mat(1.0/scale*Mat(curr_shape)).reshape(2);
@@ -444,8 +444,8 @@ bool FacemarkAAMImpl::fitImpl( const Mat image, std::vector<Point2f>& landmarks,
AAM.textures[scl].resolution ,
AAM.textures[scl].textureIdx);
I = getFeature<uchar>(warped, AAM.textures[scl].ind1);
II = getFeature<uchar>(warped, AAM.textures[scl].ind2);
I = getFeature<uchar>(warped, AAM.textures[scl].ind1).t();
II = getFeature<uchar>(warped, AAM.textures[scl].ind2).t();
if(t==0){
c = A.t()*(I-AAM.textures[scl].A0); //little bit different to matlab, probably due to datatype
@@ -480,8 +480,8 @@ bool FacemarkAAMImpl::fitImpl( const Mat image, std::vector<Point2f>& landmarks,
invert(Hfsic, iHfsic);
/*compute dp dq and dc*/
Mat dqp = iHfsic*Jfsic.t()*(II-AAM.textures[scl].AA0);
dc = AA.t()*(II-Mat(Irec_vec)-J*dqp);
Mat dqp = iHfsic*Jfsic.t()*(II-AAM.textures[scl].AA0.t());
dc = AA.t()*(II-Mat(Irec_vec).t()-J*dqp);
warpUpdate(curr_shape, dqp, s0,S, AAM.Q, AAM.triangles,Tp);
}
landmarks = Mat(scale*Mat(curr_shape)).reshape(2);
@@ -602,16 +602,17 @@ Mat FacemarkAAMImpl::procrustes(std::vector<Point2f> P, std::vector<Point2f> Q,
reduce(Ys,sumYs, 0, REDUCE_SUM);
//calculate the normrnd
double normX = sqrt(Mat(sumXs.reshape(1)).at<float>(0)+Mat(sumXs.reshape(1)).at<float>(1));
double normY = sqrt(Mat(sumYs.reshape(1)).at<float>(0)+Mat(sumYs.reshape(1)).at<float>(1));
Point2f sumXs0 = sumXs.at<Point2f>(0), sumYs0 = sumYs.at<Point2f>(0);
double normX = sqrt(sumXs0.x + sumXs0.y);
double normY = sqrt(sumYs0.x + sumYs0.y);
//normalization
X0 = X0/normX;
Y0 = Y0/normY;
//reshape, convert to 2D Matrix
Mat Xn=X0.reshape(1);
Mat Yn=Y0.reshape(1);
Mat Xn=X0.reshape(1, (int)X0.total());
Mat Yn=Y0.reshape(1, (int)Y0.total());
//calculate the covariance matrix
Mat M = Xn.t()*Yn;
@@ -634,7 +635,7 @@ Mat FacemarkAAMImpl::procrustes(std::vector<Point2f> P, std::vector<Point2f> Q,
trans[1] = t.at<float>(1);
// calculate the recovered form
Mat Qmat = Mat(Q).reshape(1);
Mat Qmat = Mat(Q).reshape(1, (int)Q.size());
return Mat(scale*Qmat*rot+trans).clone();
}
@@ -777,7 +778,7 @@ Mat FacemarkAAMImpl::orthonormal(Mat Mo){
return O.colRange(0,k).clone();
}
void FacemarkAAMImpl::calcSimilarityEig(std::vector<Point2f> s0,Mat S, Mat & Q_orth, Mat & S_orth){
void FacemarkAAMImpl::calcSimilarityEig(std::vector<Point2f> s0, Mat S, Mat & Q_orth, Mat & S_orth){
int npts = (int)s0.size();
Mat Q = Mat::zeros(2*npts,4,CV_32FC1);
@@ -792,7 +793,7 @@ void FacemarkAAMImpl::calcSimilarityEig(std::vector<Point2f> s0,Mat S, Mat & Q_o
w.copyTo(c0);
/*c1 = [-s0(npts:2*npts); s0(0:npts-1)]*/
Mat s0_mat = Mat(s0).reshape(1);
Mat s0_mat = Mat(s0).reshape(1, (int)s0.size());
// s0_mat.convertTo(s0_mat, CV_64FC1);
Mat swapper = Mat::zeros(2,npts,CV_32FC1);
Mat s00 = s0_mat.col(0);
@@ -826,11 +827,15 @@ void FacemarkAAMImpl::calcSimilarityEig(std::vector<Point2f> s0,Mat S, Mat & Q_o
Mat allOrth = orthonormal(all.t());
Q_orth = allOrth.colRange(0,4).clone();
S_orth = allOrth.colRange(4,allOrth.cols).clone();
}
inline Mat FacemarkAAMImpl::linearize(Mat s){ // all x values and then all y values
return Mat(s.reshape(1).t()).reshape(1,2*s.rows);
int npoints = s.rows;
if (s.channels() > 1) {
npoints = (int)s.total();
s = s.reshape(1, npoints);
}
return Mat(s.t()).reshape(1, 2*npoints);
}
inline Mat FacemarkAAMImpl::linearize(std::vector<Point2f> s){ // all x values and then all y values
return linearize(Mat(s));
@@ -843,7 +848,7 @@ void FacemarkAAMImpl::delaunay(std::vector<Point2f> s, std::vector<Vec3i> & tria
std::vector<Vec6f> tp;
double min_x, max_x, min_y, max_y;
Mat S = Mat(s).reshape(1);
Mat S = Mat(s).reshape(1, (int)s.size());
Mat s_x = S.col(0);
Mat s_y = S.col(1);
minMaxIdx(s_x, &min_x, &max_x);
@@ -954,8 +959,8 @@ Mat FacemarkAAMImpl::warpImage(
source[1] = curr_shape[triangles[i][1]];
source[2] = curr_shape[triangles[i][2]];
Mat target_mtx = Mat(target).reshape(1)-1.0;
Mat source_mtx = Mat(source).reshape(1)-1.0;
Mat target_mtx = Mat(target).reshape(1,(int)target.size())-1.0;
Mat source_mtx = Mat(source).reshape(1,(int)source.size())-1.0;
Mat U = target_mtx.col(0);
Mat V = target_mtx.col(1);
Mat X = source_mtx.col(0);
@@ -979,7 +984,7 @@ Mat FacemarkAAMImpl::warpImage(
R=A.colRange(0,2);
t=A.colRange(2,3);
Mat pts_ori = Mat(textureIdx[i]).reshape(1);
Mat pts_ori = Mat(textureIdx[i]).reshape(1, (int)textureIdx[i].size());
Mat pts = pts_ori.t(); //matlab
Mat bx = pts_ori.col(0);
Mat by = pts_ori.col(1);
@@ -1081,8 +1086,9 @@ void FacemarkAAMImpl::warpUpdate(std::vector<Point2f> & shape, Mat delta, std::v
Mat(ds0, Range((int)s0.size(),(int)s0.size()*2)).copyTo(c1);
Mat s_new = computeWarpParts(shape,s0,ds0_mat, triangles, Tp);
s_new -= Mat(s0).reshape(1, (int)s0.size());
Mat diff =linearize(Mat(s_new - Mat(s0).reshape(1)));
Mat diff = linearize(s_new);
Mat r = Q.t()*diff;
Mat p = S.t()*diff;
@@ -1118,8 +1124,9 @@ Mat FacemarkAAMImpl::computeWarpParts(std::vector<Point2f> curr_shape,std::vecto
source[2] = curr_shape[triangles[idx][2]];
A = getAffineTransform(target,source);
Mat p_m = Mat(p).reshape(1, (int)p.size());
Mat(A*Mat(p)).reshape(2).copyTo(v);
Mat(A*p_m).reshape(2).copyTo(v);
vx.push_back(v[0].x);
vy.push_back(v[0].y);
}// j
@@ -1134,7 +1141,7 @@ Mat FacemarkAAMImpl::computeWarpParts(std::vector<Point2f> curr_shape,std::vecto
new_shape.push_back(Point2f(mx,my));
} // s0.size()
return Mat(new_shape).reshape(1).clone();
return Mat(new_shape).reshape(1, (int)new_shape.size()).clone();
}
void FacemarkAAMImpl::gradient(const Mat M, Mat & gx, Mat & gy){

4
modules/face/src/facemarkLBF.cpp
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@@ -536,7 +536,7 @@ void FacemarkLBFImpl::prepareTrainingData(Mat img, std::vector<Point2f> facePoin
std::vector<Mat> & cropped, std::vector<Mat> & shapes, std::vector<BBox> &boxes)
{
Mat shape;
Mat _shape = Mat(facePoints).reshape(1);
Mat _shape = Mat(facePoints).reshape(1, (int)facePoints.size());
Rect box = getBBox(img, _shape);
if(img.channels()>1){
@@ -1359,7 +1359,7 @@ Mat FacemarkLBFImpl::Regressor::supportVectorRegression(
if(verbose) printf("Objective value = %lf\n", v);
if(verbose) printf("nSV = %d\n",nSV);
return Mat(Mat(w).t()).clone();
return Mat(w).reshape(2, 1).clone();
}//end

2
modules/face/test/test_facemark_aam.cpp
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@@ -108,7 +108,7 @@ TEST(CV_Face_FacemarkAAM, test_workflow) {
Mat image;
std::vector<Point2f> landmarks;
for(size_t i=0;i<images_train.size();i++)
for(size_t i = 0; i < images_train.size(); i++)
{
image = imread(images_train[i].c_str());
EXPECT_TRUE(loadFacePoints(points_train[i].c_str(),landmarks));

23
modules/intensity_transform/src/bimef.cpp
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@@ -265,9 +265,13 @@ static Mat_<float> applyK(const Mat_<float>& I, float k, float a=-0.3293f, float
float gamma = std::pow(k, a);
Mat_<float> J(I.size());
pow(I, gamma, J);
J = J*beta;
CV_Assert(I.isContinuous());
size_t i, npix = I.total();
const float* Iptr = I.ptr<float>();
float* Jptr = J.ptr<float>();
for (i = 0; i < npix; i++) {
Jptr[i] = pow(Iptr[i], gamma)*beta;
}
return J;
}
@@ -293,15 +297,18 @@ static float entropy(const Mat_<float>& I)
float range[] = { 0, 256 };
const float* histRange = { range };
calcHist(&I_uchar, 1, NULL, Mat(), hist, 1, &histSize, &histRange);
double histsum = cv::sum(hist)[0];
Mat_<float> hist_norm = hist / cv::sum(hist)[0];
Mat_<float> hist_norm = hist / histsum;
int i, nbins = (int)hist_norm.total();
float E = 0;
for (int i = 0; i < hist_norm.rows; i++)
for (i = 0; i < nbins; i++)
{
if (hist_norm(i,0) > 0)
float v = hist_norm(i);
if (v > 0)
{
E += hist_norm(i,0) * std::log2(hist_norm(i,0));
E += v * std::log2(v);
}
}
@@ -487,7 +494,7 @@ static void BIMEF_impl(InputArray input_, OutputArray output_, float mu, float *
// t: scene illumination map
Mat_<float> t_b(imgDouble.size());
t_b.forEach(
[&](float &pixel, const int * position) -> void
[&](float &pixel, const int* position) -> void
{
pixel = std::max(std::max(imgDouble(position[0], position[1])[0],
imgDouble(position[0], position[1])[1]),

7
modules/mcc/src/colorspace.cpp
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@@ -120,13 +120,14 @@ void RGBBase_::calM()
XYZg = Mat(xyY2XYZ({ xg, yg }), true);
XYZb = Mat(xyY2XYZ({ xb, yb }), true);
merge(std::vector<Mat> { XYZr, XYZg, XYZb }, XYZ_rgbl);
XYZ_rgbl = XYZ_rgbl.reshape(1, XYZ_rgbl.rows);
XYZ_rgbl = XYZ_rgbl.reshape(1, (int)XYZ_rgbl.total());
Mat XYZw = Mat(getIlluminants(io), true);
XYZw = XYZw.reshape(1, (int)XYZw.total());
solve(XYZ_rgbl, XYZw, Srgb);
merge(std::vector<Mat> { Srgb.at<double>(0) * XYZr, Srgb.at<double>(1) * XYZg,
Srgb.at<double>(2) * XYZb },
M_to);
M_to = M_to.reshape(1, M_to.rows);
M_to = M_to.reshape(1, (int)M_to.total());
M_from = M_to.inv();
};
@@ -382,6 +383,8 @@ Mat XYZ::cam_(IO sio, IO dio, CAM method) const
// Function from http://www.brucelindbloom.com/index.html?ColorCheckerRGB.html.
Mat XYZws = Mat(getIlluminants(dio));
Mat XYZWd = Mat(getIlluminants(sio));
XYZws = XYZws.reshape(1, (int)XYZws.total());
XYZWd = XYZWd.reshape(1, (int)XYZWd.total());
Mat MA = MAs.at(method)[0];
Mat MA_inv = MAs.at(method)[1];
Mat M = MA_inv * Mat::diag((MA * XYZws) / (MA * XYZWd)) * MA;

6
modules/rapid/src/rapid.cpp
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@@ -174,7 +174,7 @@ void extractLineBundle(int len, InputArray ctl2d, InputArray img, OutputArray bu
CV_Assert(ctl2d.getMat().checkVector(2, CV_32F) > 0);
Mat_<Point2f> contour = ctl2d.getMat();
const int N = contour.rows;
const int N = (int)contour.total();
const int W = len * 2 + 1;
srcLocations.create(N, W, CV_16SC2);
@@ -305,8 +305,8 @@ void convertCorrespondencies(InputArray _cols, InputArray _srcLocations, OutputA
Mat opts3d;
if(!_pts3d.empty())
{
CV_Assert(_cols.rows() == _pts3d.rows());
pts3d = _pts3d.getMat();
pts3d = _pts3d.getMat().t();
CV_Assert(cols.rows == pts3d.rows);
opts3d.create(0, 1, pts3d.type());
opts3d.reserve(cols.rows);
}

4
modules/wechat_qrcode/test/test_qrcode.cpp
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@@ -321,7 +321,7 @@ TEST(Objdetect_QRCode_points_position, rotate45) {
auto decoded_info1 = detector.detectAndDecode(image, points1);
ASSERT_EQ(1ull, decoded_info1.size());
ASSERT_EQ(expect_msg, decoded_info1[0]);
EXPECT_NEAR(0, cvtest::norm(Mat(goldCorners), points1[0].reshape(1, 8), NORM_INF), 8.);
EXPECT_NEAR(0, cvtest::norm(Mat(goldCorners).reshape(1, (int)goldCorners.size()), points1[0].reshape(1, 8), NORM_INF), 8.);
const double angle = 45;
Point2f pc(image.cols/2.f, image.rows/2.f);
@@ -338,7 +338,7 @@ TEST(Objdetect_QRCode_points_position, rotate45) {
auto decoded_info2 = detector.detectAndDecode(image, points2);
ASSERT_EQ(1ull, decoded_info2.size());
ASSERT_EQ(expect_msg, decoded_info2[0]);
EXPECT_NEAR(0, cvtest::norm(Mat(rotateGoldCorners), points2[0].reshape(1, 8), NORM_INF), 11.);
EXPECT_NEAR(0, cvtest::norm(Mat(rotateGoldCorners).reshape(1, (int)rotateGoldCorners.size()), points2[0].reshape(1, 8), NORM_INF), 11.);
}
INSTANTIATE_TEST_CASE_P(/**/, Objdetect_QRCode, testing::ValuesIn(qrcode_images_name));

7
modules/xphoto/test/test_oil_painting.cpp
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@@ -41,7 +41,7 @@ Mat testOilPainting(Mat imgSrc, int halfSize, int dynRatio, int colorSpace)
double maxVal = 0;
Point pMin, pMax;
minMaxLoc(hist, 0, &maxVal, &pMin, &pMax);
mask.setTo(0, lum != static_cast<int>(pMax.y));
mask.setTo(0, lum != static_cast<int>(pMax.x));
Scalar v = mean(imgSrc, mask);
*vDst = Vec3b(static_cast<uchar>(v[0]), static_cast<uchar>(v[1]), static_cast<uchar>(v[2]));
}
@@ -65,7 +65,7 @@ Mat testOilPainting(Mat imgSrc, int halfSize, int dynRatio, int colorSpace)
double maxVal = 0;
Point pMin, pMax;
minMaxLoc(hist, 0, &maxVal, &pMin, &pMax);
mask.setTo(0, lum != static_cast<int>(pMax.y));
mask.setTo(0, lum != static_cast<int>(pMax.x));
Scalar v = mean(imgSrc, mask);
*vDst = static_cast<uchar>(v[0]);
}
@@ -90,7 +90,8 @@ TEST(xphoto_oil_painting, regression)
double maxVal;
Point pIdx;
minMaxLoc(p, NULL, &maxVal, NULL, &pIdx);
ASSERT_LE(p.at<uchar>(pIdx), 2);
int v = p.at<uchar>(pIdx);
ASSERT_LE(v, 2);
}
Mat orig2 = imread(folder + "exp1.png",IMREAD_GRAYSCALE);
ASSERT_TRUE(!orig2.empty());