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

* 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

Author: vpisarev

modules/cudaimgproc/test/test_histogram.cpp

@@ -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);

modules/cudev/test/test_nd.cu

@@ -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;
     }
 

modules/face/src/facemarkAAM.cpp

@@ -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){

modules/face/src/facemarkLBF.cpp

@@ -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
 

modules/face/test/test_facemark_aam.cpp

@@ -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));

modules/intensity_transform/src/bimef.cpp

@@ -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]),

modules/mcc/src/colorspace.cpp

@@ -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;

modules/rapid/src/rapid.cpp

@@ -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);
     }

modules/wechat_qrcode/test/test_qrcode.cpp

@@ -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));

modules/xphoto/test/test_oil_painting.cpp

@@ -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());