Merge remote-tracking branch 'upstream/3.4' into merge-3.4

doc/js_tutorials/js_setup/js_setup/js_setup.markdown

@@ -145,6 +145,7 @@ Building OpenCV.js from Source
     python ./platforms/js/build_js.py build_js --cmake_option="-DOPENCV_EXTRA_MODULES_PATH=opencv_contrib/modules"
     @endcode
 
+
 Running OpenCV.js Tests
 ---------------------------------------
 
@@ -310,6 +311,12 @@ The example uses latest version of emscripten. If the build fails you should try
 docker run --rm -v $(pwd):/src -u $(id -u):$(id -g) emscripten/emsdk:2.0.10 emcmake python3 ./platforms/js/build_js.py build_js
 @endcode
 
+In Windows use the following PowerShell command:
+
+@code{.bash}
+docker run --rm --workdir /src -v "$(get-location):/src" "emscripten/emsdk:2.0.10" emcmake python3 ./platforms/js/build_js.py build_js
+@endcode
+
 ### Building the documentation with Docker
 
 To build the documentation `doxygen` needs to be installed. Create a file named `Dockerfile` with the following content:

modules/calib3d/src/solvepnp.cpp

@@ -334,18 +334,42 @@ bool solvePnPRansac(InputArray _opoints, InputArray _ipoints,
 
     opoints_inliers.resize(npoints1);
     ipoints_inliers.resize(npoints1);
-    result = solvePnP(opoints_inliers, ipoints_inliers, cameraMatrix,
-                      distCoeffs, rvec, tvec, useExtrinsicGuess,
-                      (flags == SOLVEPNP_P3P || flags == SOLVEPNP_AP3P) ? SOLVEPNP_EPNP : flags) ? 1 : -1;
+    try
+    {
+        result = solvePnP(opoints_inliers, ipoints_inliers, cameraMatrix,
+                          distCoeffs, rvec, tvec, useExtrinsicGuess,
+                          (flags == SOLVEPNP_P3P || flags == SOLVEPNP_AP3P) ? SOLVEPNP_EPNP : flags) ? 1 : -1;
+    }
+    catch (const cv::Exception& e)
+    {
+        if (flags == SOLVEPNP_ITERATIVE &&
+            npoints1 == 5 &&
+            e.what() &&
+            std::string(e.what()).find("DLT algorithm needs at least 6 points") != std::string::npos
+        )
+        {
+            CV_LOG_INFO(NULL, "solvePnPRansac(): solvePnP stage to compute the final pose using points "
+                "in the consensus set raised DLT 6 points exception, use result from MSS (Minimal Sample Sets) stage instead.");
+            rvec = _local_model.col(0);    // output rotation vector
+            tvec = _local_model.col(1);    // output translation vector
+            result = 1;
+        }
+        else
+        {
+            // raise other exceptions
+            throw;
+        }
+    }
 
-    if( result <= 0 )
+    if (result <= 0)
     {
         _rvec.assign(_local_model.col(0));    // output rotation vector
         _tvec.assign(_local_model.col(1));    // output translation vector
 
-        if( _inliers.needed() )
+        if (_inliers.needed())
             _inliers.release();
 
+        CV_LOG_DEBUG(NULL, "solvePnPRansac(): solvePnP stage to compute the final pose using points in the consensus set failed. Return false");
         return false;
     }
     else

modules/calib3d/src/stereobm.cpp

@@ -1148,13 +1148,15 @@ class StereoBMImpl CV_FINAL : public StereoBM
 {
 public:
     StereoBMImpl()
+        : params()
     {
-        params = StereoBMParams();
+        // nothing
     }
 
     StereoBMImpl( int _numDisparities, int _SADWindowSize )
+        : params(_numDisparities, _SADWindowSize)
     {
-        params = StereoBMParams(_numDisparities, _SADWindowSize);
+        // nothing
     }
 
     void compute( InputArray leftarr, InputArray rightarr, OutputArray disparr ) CV_OVERRIDE

modules/calib3d/src/stereosgbm.cpp

@@ -2186,19 +2186,21 @@ class StereoSGBMImpl CV_FINAL : public StereoSGBM
 {
 public:
     StereoSGBMImpl()
+        : params()
     {
-        params = StereoSGBMParams();
+        // nothing
     }
 
     StereoSGBMImpl( int _minDisparity, int _numDisparities, int _SADWindowSize,
                     int _P1, int _P2, int _disp12MaxDiff, int _preFilterCap,
                     int _uniquenessRatio, int _speckleWindowSize, int _speckleRange,
                     int _mode )
+        : params(_minDisparity, _numDisparities, _SADWindowSize,
+                 _P1, _P2, _disp12MaxDiff, _preFilterCap,
+                 _uniquenessRatio, _speckleWindowSize, _speckleRange,
+                 _mode)
     {
-        params = StereoSGBMParams( _minDisparity, _numDisparities, _SADWindowSize,
-                                   _P1, _P2, _disp12MaxDiff, _preFilterCap,
-                                   _uniquenessRatio, _speckleWindowSize, _speckleRange,
-                                   _mode );
+        // nothing
     }
 
     void compute( InputArray leftarr, InputArray rightarr, OutputArray disparr ) CV_OVERRIDE

modules/calib3d/test/test_solvepnp_ransac.cpp

@@ -837,6 +837,43 @@ TEST(Calib3d_SolvePnPRansac, double_support)
     EXPECT_LE(cvtest::norm(t, Mat_<double>(tF), NORM_INF), 1e-3);
 }
 
+TEST(Calib3d_SolvePnPRansac, bad_input_points_19253)
+{
+    // with this specific data
+    // when computing the final pose using points in the consensus set with SOLVEPNP_ITERATIVE and solvePnP()
+    // an exception is thrown from solvePnP because there are 5 non-coplanar 3D points and the DLT algorithm needs at least 6 non-coplanar 3D points
+    // with PR #19253 we choose to return true, with the pose estimated from the MSS stage instead of throwing the exception
+
+    float pts2d_[] = {
+        -5.38358629e-01f, -5.09638414e-02f,
+        -5.07192254e-01f, -2.20743284e-01f,
+        -5.43107152e-01f, -4.90474701e-02f,
+        -5.54325163e-01f, -1.86715424e-01f,
+        -5.59334219e-01f, -4.01909500e-02f,
+        -5.43504596e-01f, -4.61776406e-02f
+    };
+    Mat pts2d(6, 2, CV_32FC1, pts2d_);
+
+    float pts3d_[] = {
+        -3.01153604e-02f, -1.55665115e-01f, 4.50000018e-01f,
+        4.27827090e-01f, 4.28645730e-01f, 1.08600008e+00f,
+        -3.14165242e-02f, -1.52656138e-01f, 4.50000018e-01f,
+        -1.46217480e-01f, 5.57961613e-02f, 7.17000008e-01f,
+        -4.89348806e-02f, -1.38795510e-01f, 4.47000027e-01f,
+        -3.13065052e-02f, -1.52636901e-01f, 4.51000035e-01f
+    };
+    Mat pts3d(6, 3, CV_32FC1, pts3d_);
+
+    Mat camera_mat = Mat::eye(3, 3, CV_64FC1);
+    Mat rvec, tvec;
+    vector<int> inliers;
+
+    // solvePnPRansac will return true with 5 inliers, which means the result is from MSS stage.
+    bool result = solvePnPRansac(pts3d, pts2d, camera_mat, noArray(), rvec, tvec, false, 100, 4.f / 460.f, 0.99, inliers);
+    EXPECT_EQ(inliers.size(), size_t(5));
+    EXPECT_TRUE(result);
+}
+
 TEST(Calib3d_SolvePnP, input_type)
 {
     Matx33d intrinsics(5.4794130238156129e+002, 0., 2.9835545700043139e+002, 0.,

modules/core/include/opencv2/core/fast_math.hpp

@@ -76,6 +76,9 @@
   #if defined __PPC64__ && defined __GNUC__ && defined _ARCH_PWR8 \
       && !defined(OPENCV_SKIP_INCLUDE_ALTIVEC_H)
     #include <altivec.h>
+    #undef vector
+    #undef bool
+    #undef pixel
   #endif
 
   #if defined(CV_INLINE_ROUND_FLT)

modules/core/include/opencv2/core/vsx_utils.hpp

@@ -497,11 +497,13 @@ VSX_IMPL_CONV_EVEN_2_4(vec_uint4,  vec_double2, vec_ctu, vec_ctuo)
     VSX_FINLINE(rt) fnm(const rg& a, int only_truncate) \
     {                                                   \
         assert(only_truncate == 0);                     \
-        CV_UNUSED(only_truncate);                            \
+        CV_UNUSED(only_truncate);                       \
         return fn2(a);                                  \
     }
     VSX_IMPL_CONV_2VARIANT(vec_int4,   vec_float4,  vec_cts, vec_cts)
+    VSX_IMPL_CONV_2VARIANT(vec_uint4,  vec_float4,  vec_ctu, vec_ctu)
     VSX_IMPL_CONV_2VARIANT(vec_float4, vec_int4,    vec_ctf, vec_ctf)
+    VSX_IMPL_CONV_2VARIANT(vec_float4, vec_uint4,   vec_ctf, vec_ctf)
     // define vec_cts for converting double precision to signed doubleword
     // which isn't compatible with xlc but its okay since Eigen only uses it for gcc
     VSX_IMPL_CONV_2VARIANT(vec_dword2, vec_double2, vec_cts, vec_ctsl)

modules/core/src/system.cpp

@@ -128,11 +128,14 @@ void* allocSingletonNewBuffer(size_t size) { return malloc(size); }
 #endif
 
 
-#if CV_VSX && defined __linux__
+#if (defined __ppc64__ || defined __PPC64__) && defined __linux__
 # include "sys/auxv.h"
 # ifndef AT_HWCAP2
 #   define AT_HWCAP2 26
 # endif
+# ifndef PPC_FEATURE2_ARCH_2_07
+#   define PPC_FEATURE2_ARCH_2_07 0x80000000
+# endif
 # ifndef PPC_FEATURE2_ARCH_3_00
 #   define PPC_FEATURE2_ARCH_3_00 0x00800000
 # endif
@@ -588,14 +591,25 @@ struct HWFeatures
     #ifdef __mips_msa
         have[CV_CPU_MSA] = true;
     #endif
-    // there's no need to check VSX availability in runtime since it's always available on ppc64le CPUs
-    have[CV_CPU_VSX] = (CV_VSX);
-    // TODO: Check VSX3 availability in runtime for other platforms
-    #if CV_VSX && defined __linux__
-        uint64 hwcap2 = getauxval(AT_HWCAP2);
-        have[CV_CPU_VSX3] = (hwcap2 & PPC_FEATURE2_ARCH_3_00);
+
+    #if (defined __ppc64__ || defined __PPC64__) && defined __linux__
+        unsigned int hwcap = getauxval(AT_HWCAP);
+        if (hwcap & PPC_FEATURE_HAS_VSX) {
+            hwcap = getauxval(AT_HWCAP2);
+            if (hwcap & PPC_FEATURE2_ARCH_3_00) {
+                have[CV_CPU_VSX] = have[CV_CPU_VSX3] = true;
+            } else {
+                have[CV_CPU_VSX] = (hwcap & PPC_FEATURE2_ARCH_2_07) != 0;
+            }
+        }
     #else
-        have[CV_CPU_VSX3] = (CV_VSX3);
+        // TODO: AIX, FreeBSD
+        #if CV_VSX || defined _ARCH_PWR8 || defined __POWER9_VECTOR__
+            have[CV_CPU_VSX] = true;
+        #endif
+        #if CV_VSX3 || defined __POWER9_VECTOR__
+            have[CV_CPU_VSX3] = true;
+        #endif
     #endif
 
     #if defined __riscv && defined __riscv_vector
@@ -1861,7 +1875,7 @@ class ParseError
 {
     std::string bad_value;
 public:
-    ParseError(const std::string bad_value_) :bad_value(bad_value_) {}
+    ParseError(const std::string &bad_value_) :bad_value(bad_value_) {}
     std::string toString(const std::string &param) const
     {
         std::ostringstream out;

modules/core/test/ocl/test_opencl.cpp

@@ -120,6 +120,11 @@ TEST(OpenCL, support_SPIR_programs)
         cv::ocl::ProgramSource src = cv::ocl::ProgramSource::fromSPIR(module_name, "simple_spir", (uchar*)&program_binary_code[0], program_binary_code.size(), "");
         cv::String errmsg;
         cv::ocl::Program program(src, "", errmsg);
+        if (program.ptr() == NULL && device.isAMD())
+        {
+            // https://community.amd.com/t5/opencl/spir-support-in-new-drivers-lost/td-p/170165
+            throw cvtest::SkipTestException("Bypass AMD OpenCL runtime bug: 'cl_khr_spir' extension is declared, but it doesn't really work");
+        }
         ASSERT_TRUE(program.ptr() != NULL);
         k.create("test_kernel", program);
     }

modules/dnn/src/layers/detection_output_layer.cpp

@@ -138,6 +138,12 @@ public:
 
     typedef std::map<int, std::vector<util::NormalizedBBox> > LabelBBox;
 
+    inline int getNumOfTargetClasses() {
+        unsigned numBackground =
+            (_backgroundLabelId >= 0 && _backgroundLabelId < _numClasses) ? 1 : 0;
+        return (_numClasses - numBackground);
+    }
+
     bool getParameterDict(const LayerParams &params,
                           const std::string &parameterName,
                           DictValue& result)
@@ -590,12 +596,13 @@ public:
             LabelBBox::const_iterator label_bboxes = decodeBBoxes.find(label);
             if (label_bboxes == decodeBBoxes.end())
                 CV_Error_(cv::Error::StsError, ("Could not find location predictions for label %d", label));
+            int limit = (getNumOfTargetClasses() == 1) ? _keepTopK : std::numeric_limits<int>::max();
             if (_bboxesNormalized)
                 NMSFast_(label_bboxes->second, scores, _confidenceThreshold, _nmsThreshold, 1.0, _topK,
-                         indices[c], util::caffe_norm_box_overlap);
+                         indices[c], util::caffe_norm_box_overlap, limit);
             else
                 NMSFast_(label_bboxes->second, scores, _confidenceThreshold, _nmsThreshold, 1.0, _topK,
-                         indices[c], util::caffe_box_overlap);
+                         indices[c], util::caffe_box_overlap, limit);
             numDetections += indices[c].size();
         }
         if (_keepTopK > -1 && numDetections > (size_t)_keepTopK)

modules/dnn/src/layers/mvn_layer.cpp

@@ -403,7 +403,15 @@ public:
                                         const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
     {
         auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
+#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2021_2)
         auto mvn = std::make_shared<ngraph::op::MVN>(ieInpNode, acrossChannels, normVariance, eps);
+#else
+        int64_t start_axis = acrossChannels ? 1 : 2;
+        std::vector<int64_t> axes_v(ieInpNode->get_shape().size() - start_axis);
+        std::iota(axes_v.begin(), axes_v.end(), start_axis);
+        auto axes = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{axes_v.size()}, axes_v.data());
+        auto mvn = std::make_shared<ngraph::op::v6::MVN>(ieInpNode, axes, normVariance, eps, ngraph::op::MVNEpsMode::INSIDE_SQRT);
+#endif
         return Ptr<BackendNode>(new InfEngineNgraphNode(mvn));
     }
 #endif  // HAVE_DNN_NGRAPH

modules/dnn/src/layers/resize_layer.cpp

@@ -267,6 +267,7 @@ public:
     {
         auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
 
+#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2021_2)
         ngraph::op::InterpolateAttrs attrs;
         attrs.pads_begin.push_back(0);
         attrs.pads_end.push_back(0);
@@ -285,6 +286,37 @@ public:
         std::vector<int64_t> shape = {outHeight, outWidth};
         auto out_shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{2}, shape.data());
         auto interp = std::make_shared<ngraph::op::Interpolate>(ieInpNode, out_shape, attrs);
+#else
+        ngraph::op::v4::Interpolate::InterpolateAttrs attrs;
+
+        if (interpolation == "nearest") {
+            attrs.mode = ngraph::op::v4::Interpolate::InterpolateMode::nearest;
+            attrs.coordinate_transformation_mode = ngraph::op::v4::Interpolate::CoordinateTransformMode::half_pixel;
+        } else if (interpolation == "bilinear") {
+            attrs.mode = ngraph::op::v4::Interpolate::InterpolateMode::linear_onnx;
+            attrs.coordinate_transformation_mode = ngraph::op::v4::Interpolate::CoordinateTransformMode::asymmetric;
+        } else {
+            CV_Error(Error::StsNotImplemented, format("Unsupported interpolation: %s", interpolation.c_str()));
+        }
+        attrs.shape_calculation_mode = ngraph::op::v4::Interpolate::ShapeCalcMode::sizes;
+
+        if (alignCorners) {
+            attrs.coordinate_transformation_mode = ngraph::op::v4::Interpolate::CoordinateTransformMode::align_corners;
+        }
+
+        attrs.nearest_mode = ngraph::op::v4::Interpolate::NearestMode::round_prefer_floor;
+
+        std::vector<int64_t> shape = {outHeight, outWidth};
+        auto out_shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{2}, shape.data());
+
+        auto& input_shape = ieInpNode->get_shape();
+        CV_Assert_N(input_shape[2] != 0, input_shape[3] != 0);
+        std::vector<float> scales = {static_cast<float>(outHeight) / input_shape[2], static_cast<float>(outWidth) / input_shape[3]};
+        auto scales_shape = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{2}, scales.data());
+
+        auto axes = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{2}, std::vector<int64_t>{2, 3});
+        auto interp = std::make_shared<ngraph::op::v4::Interpolate>(ieInpNode, out_shape, scales_shape, axes, attrs);
+#endif
         return Ptr<BackendNode>(new InfEngineNgraphNode(interp));
     }
 #endif  // HAVE_DNN_NGRAPH

modules/dnn/src/nms.inl.hpp

@@ -62,12 +62,15 @@ inline void GetMaxScoreIndex(const std::vector<float>& scores, const float thres
 //    score_threshold: a threshold used to filter detection results.
 //    nms_threshold: a threshold used in non maximum suppression.
 //    top_k: if not > 0, keep at most top_k picked indices.
+//    limit: early terminate once the # of picked indices has reached it.
 //    indices: the kept indices of bboxes after nms.
 template <typename BoxType>
 inline void NMSFast_(const std::vector<BoxType>& bboxes,
       const std::vector<float>& scores, const float score_threshold,
       const float nms_threshold, const float eta, const int top_k,
-      std::vector<int>& indices, float (*computeOverlap)(const BoxType&, const BoxType&))
+      std::vector<int>& indices,
+      float (*computeOverlap)(const BoxType&, const BoxType&),
+      int limit = std::numeric_limits<int>::max())
 {
     CV_Assert(bboxes.size() == scores.size());
 
@@ -86,8 +89,12 @@ inline void NMSFast_(const std::vector<BoxType>& bboxes,
             float overlap = computeOverlap(bboxes[idx], bboxes[kept_idx]);
             keep = overlap <= adaptive_threshold;
         }
-        if (keep)
+        if (keep) {
             indices.push_back(idx);
+            if (indices.size() >= limit) {
+                break;
+            }
+        }
         if (keep && eta < 1 && adaptive_threshold > 0.5) {
           adaptive_threshold *= eta;
         }

modules/js/generator/embindgen.py

@@ -119,6 +119,7 @@ type_dict = {
     'InputOutputArray': 'cv::Mat&',
     'InputArrayOfArrays': 'const std::vector<cv::Mat>&',
     'OutputArrayOfArrays': 'std::vector<cv::Mat>&',
+    'string': 'std::string',
     'String': 'std::string',
     'const String&':'const std::string&'
 }
@@ -462,8 +463,7 @@ class JSWrapperGenerator(object):
                     ret_type = type_dict[ptr_type]
             for key in type_dict:
                 if key in ret_type:
-                    ret_type = ret_type.replace(key, type_dict[key])
-
+                    ret_type = re.sub('(^|[^\w])' + key + '($|[^\w])', type_dict[key], ret_type)
             arg_types = []
             unwrapped_arg_types = []
             for arg in variant.args:
@@ -567,7 +567,7 @@ class JSWrapperGenerator(object):
                         # consider the default parameter variants
                         args_num = len(variant.args) - j
                         if args_num in class_info.constructor_arg_num:
-                            # FIXME: workaournd for constructor overload with same args number
+                            # FIXME: workaround for constructor overload with same args number
                             # e.g. DescriptorMatcher
                             continue
                         class_info.constructor_arg_num.add(args_num)
@@ -627,7 +627,6 @@ class JSWrapperGenerator(object):
             ret_type = 'void' if variant.rettype.strip() == '' else variant.rettype
 
             ret_type = ret_type.strip()
-
             if ret_type.startswith('Ptr'): #smart pointer
                 ptr_type = ret_type.replace('Ptr<', '').replace('>', '')
                 if ptr_type in type_dict:

modules/js/test/test_objdetect.js

@@ -159,3 +159,44 @@ QUnit.test('Cascade classification', function(assert) {
         locations.delete();
     }
 });
+QUnit.test('QR code detect and decode', function (assert) {
+    {
+        const detector = new cv.QRCodeDetector();
+        let mat = cv.Mat.ones(800, 600, cv.CV_8U);
+        assert.ok(mat);
+
+        // test detect
+        let points = new cv.Mat();
+        let qrCodeFound = detector.detect(mat, points);
+        assert.equal(points.rows, 0)
+        assert.equal(points.cols, 0)
+        assert.equal(qrCodeFound, false);
+
+        // test detectMult
+        qrCodeFound = detector.detectMulti(mat, points);
+        assert.equal(points.rows, 0)
+        assert.equal(points.cols, 0)
+        assert.equal(qrCodeFound, false);
+
+        // test decode (with random numbers)
+        let decodeTestPoints = cv.matFromArray(1, 4, cv.CV_32FC2, [10, 20, 30, 40, 60, 80, 90, 100]);
+        let qrCodeContent = detector.decode(mat, decodeTestPoints);
+        assert.equal(typeof qrCodeContent, 'string');
+        assert.equal(qrCodeContent, '');
+
+        //test detectAndDecode
+        qrCodeContent = detector.detectAndDecode(mat);
+        assert.equal(typeof qrCodeContent, 'string');
+        assert.equal(qrCodeContent, '');
+
+        // test decodeCurved
+        qrCodeContent = detector.decodeCurved(mat, decodeTestPoints);
+        assert.equal(typeof qrCodeContent, 'string');
+        assert.equal(qrCodeContent, '');
+
+        decodeTestPoints.delete();
+        points.delete();
+        mat.delete();
+
+    }
+});

platforms/js/build_js.py

@@ -115,7 +115,7 @@ class Builder:
                "-DWITH_GPHOTO2=OFF",
                "-DWITH_LAPACK=OFF",
                "-DWITH_ITT=OFF",
-               "-DWITH_QUIRC=OFF",
+               "-DWITH_QUIRC=ON",
                "-DBUILD_ZLIB=ON",
                "-DBUILD_opencv_apps=OFF",
                "-DBUILD_opencv_calib3d=ON",

platforms/js/opencv_js.config.py

@@ -36,7 +36,8 @@ imgproc = {'': ['Canny', 'GaussianBlur', 'Laplacian', 'HoughLines', 'HoughLinesP
 
 objdetect = {'': ['groupRectangles'],
              'HOGDescriptor': ['load', 'HOGDescriptor', 'getDefaultPeopleDetector', 'getDaimlerPeopleDetector', 'setSVMDetector', 'detectMultiScale'],
-             'CascadeClassifier': ['load', 'detectMultiScale2', 'CascadeClassifier', 'detectMultiScale3', 'empty', 'detectMultiScale']}
+             'CascadeClassifier': ['load', 'detectMultiScale2', 'CascadeClassifier', 'detectMultiScale3', 'empty', 'detectMultiScale'],
+             'QRCodeDetector': ['QRCodeDetector', 'decode', 'decodeCurved', 'detect', 'detectAndDecode', 'detectMulti', 'setEpsX', 'setEpsY']}
 
 video = {'': ['CamShift', 'calcOpticalFlowFarneback', 'calcOpticalFlowPyrLK', 'createBackgroundSubtractorMOG2', \
              'findTransformECC', 'meanShift'],

samples/cpp/tutorial_code/calib3d/camera_calibration/camera_calibration.cpp

@@ -418,7 +418,12 @@ int main(int argc, char* argv[])
         {
             Mat temp = view.clone();
             if (s.useFisheye)
-              cv::fisheye::undistortImage(temp, view, cameraMatrix, distCoeffs);
+            {
+                Mat newCamMat;
+                fisheye::estimateNewCameraMatrixForUndistortRectify(cameraMatrix, distCoeffs, imageSize,
+                                                                    Matx33d::eye(), newCamMat, 1);
+                cv::fisheye::undistortImage(temp, view, cameraMatrix, distCoeffs, newCamMat);
+            }
             else
               undistort(temp, view, cameraMatrix, distCoeffs);
         }
@@ -547,7 +552,7 @@ static bool runCalibration( Settings& s, Size& imageSize, Mat& cameraMatrix, Mat
 {
     //! [fixed_aspect]
     cameraMatrix = Mat::eye(3, 3, CV_64F);
-    if( s.flag & CALIB_FIX_ASPECT_RATIO )
+    if( !s.useFisheye && s.flag & CALIB_FIX_ASPECT_RATIO )
         cameraMatrix.at<double>(0,0) = s.aspectRatio;
     //! [fixed_aspect]
     if (s.useFisheye) {
@@ -630,7 +635,7 @@ static void saveCameraParams( Settings& s, Size& imageSize, Mat& cameraMatrix, M
     fs << "board_height" << s.boardSize.height;
     fs << "square_size" << s.squareSize;
 
-    if( s.flag & CALIB_FIX_ASPECT_RATIO )
+    if( !s.useFisheye && s.flag & CALIB_FIX_ASPECT_RATIO )
         fs << "fix_aspect_ratio" << s.aspectRatio;
 
     if (s.flag)

samples/dnn/virtual_try_on.py

@@ -113,7 +113,7 @@ class BilinearFilter(object):
             out[yy] = np.round(np.sum(img[ymin : ymin + ymax, 0:out.shape[1]] * k[:, np.newaxis], axis=0))
 
     def imaging_resample(self, img, xsize, ysize):
-        height, width, *args = img.shape
+        height, width = img.shape[0:2]
         bounds_horiz, kk_horiz, ksize_horiz = self._precompute_coeffs(width, xsize)
         bounds_vert, kk_vert, ksize_vert    = self._precompute_coeffs(height, ysize)
 
@@ -233,7 +233,6 @@ class CpVton(object):
         return Li
 
     def _prepare_to_transform(self, out_h=256, out_w=192, grid_size=5):
-        grid = np.zeros([out_h, out_w, 3], dtype=np.float32)
         grid_X, grid_Y = np.meshgrid(np.linspace(-1, 1, out_w), np.linspace(-1, 1, out_h))
         grid_X = np.expand_dims(np.expand_dims(grid_X, axis=0), axis=3)
         grid_Y = np.expand_dims(np.expand_dims(grid_Y, axis=0), axis=3)
@@ -398,7 +397,7 @@ class CorrelationLayer(object):
 
     def getMemoryShapes(self, inputs):
         fetureAShape = inputs[0]
-        b, c, h, w = fetureAShape
+        b, _, h, w = fetureAShape
         return [[b, h * w, h, w]]
 
     def forward(self, inputs):