Merge pull request #18857 from OrestChura:oc/kmeans

[G-API]: kmeans() Standard Kernel Implementation * cv::gapi::kmeans kernel implementation - 4 overloads: - standard GMat - for any dimensionality - GMat without bestLabels initialization - GArray<Point2f> - for 2D - GArray<Point3f> - for 3D - Accuracy tests: - for every input - 2 tests 1) without initializing. In this case, no comparison with cv::kmeans is done as kmeans uses random auto-initialization 2) with initialization - in both cases, only 1 attempt is done as after first attempt kmeans initializes bestLabels randomly * Addressing comments - bestLabels is returned to its original place among parameters - checkVector and isPointsVector functions are merged into one, shared between core.hpp & imgproc.hpp by placing it into gmat.hpp (and implementation - to gmat.cpp) - typos corrected * addressing comments - unified names in tests - const added - typos * Addressing comments - fixed the doc note - ddepth -> expectedDepth, `< 0 ` -> `== -1` * Fix unsupported cases of input Mat - supported: multiple channels, reversed width - added test cases for those - added notes in docs - refactored checkVector to return dimentionality along with quantity * Addressing comments - makes chackVector smaller and (maybe) clearer * Addressing comments * Addressing comments - cv::checkVector -> cv::gapi::detail * Addressing comments - Changed checkVector: returns bool, quantity & dimensionality as references * Addressing comments - Polishing checkVector - FIXME added * Addressing discussion - checkVector: added overload, separate two different functionalities - depth assert - out of the function * Addressing comments - quantity -> amount, dimensionality -> dim - Fix typos * Addressing comments - fix docs - use 2 variable's definitions instead of one (for all non-trivial variables)
2020-11-30 16:18:43 +03:00
parent 95ce8f45ea
commit 986ad4ff06
10 changed files with 599 additions and 15 deletions
@@ -26,6 +26,7 @@
    @defgroup gapi_transform Graph API: Image and channel composition functions
@}
 */
+
 namespace cv { namespace gapi {
 namespace core {
    using GMat2 = std::tuple<GMat,GMat>;
@@ -508,6 +509,77 @@ namespace core {
            return in.withType(in.depth, in.chan).withSize(dsize);
        }
    };
+
+    G_TYPED_KERNEL(
+        GKMeansND,
+        <std::tuple<GOpaque<double>,GMat,GMat>(GMat,int,GMat,TermCriteria,int,KmeansFlags)>,
+        "org.opencv.core.kmeansND") {
+
+        static std::tuple<GOpaqueDesc,GMatDesc,GMatDesc>
+        outMeta(const GMatDesc& in, int K, const GMatDesc& bestLabels, const TermCriteria&, int,
+                KmeansFlags flags) {
+            GAPI_Assert(in.depth == CV_32F);
+            std::vector<int> amount_n_dim = detail::checkVector(in);
+            int amount = amount_n_dim[0], dim = amount_n_dim[1];
+            if (amount == -1)   // Mat with height != 1, width != 1, channels != 1 given
+            {                   // which means that kmeans will consider the following:
+                amount = in.size.height;
+                dim    = in.size.width * in.chan;
+            }
+            // kmeans sets these labels' sizes when no bestLabels given:
+            GMatDesc out_labels(CV_32S, 1, Size{1, amount});
+            // kmeans always sets these centers' sizes:
+            GMatDesc centers   (CV_32F, 1, Size{dim, K});
+            if (flags & KMEANS_USE_INITIAL_LABELS)
+            {
+                GAPI_Assert(bestLabels.depth == CV_32S);
+                int labels_amount = detail::checkVector(bestLabels, 1u);
+                GAPI_Assert(labels_amount == amount);
+                out_labels = bestLabels;  // kmeans preserves bestLabels' sizes if given
+            }
+            return std::make_tuple(empty_gopaque_desc(), out_labels, centers);
+        }
+    };
+
+    G_TYPED_KERNEL(
+        GKMeansNDNoInit,
+        <std::tuple<GOpaque<double>,GMat,GMat>(GMat,int,TermCriteria,int,KmeansFlags)>,
+        "org.opencv.core.kmeansNDNoInit") {
+
+        static std::tuple<GOpaqueDesc,GMatDesc,GMatDesc>
+        outMeta(const GMatDesc& in, int K, const TermCriteria&, int, KmeansFlags flags) {
+            GAPI_Assert( !(flags & KMEANS_USE_INITIAL_LABELS) );
+            GAPI_Assert(in.depth == CV_32F);
+            std::vector<int> amount_n_dim = detail::checkVector(in);
+            int amount = amount_n_dim[0], dim = amount_n_dim[1];
+            if (amount == -1) // Mat with height != 1, width != 1, channels != 1 given
+            {                   // which means that kmeans will consider the following:
+                amount = in.size.height;
+                dim    = in.size.width * in.chan;
+            }
+            GMatDesc out_labels(CV_32S, 1, Size{1, amount});
+            GMatDesc centers   (CV_32F, 1, Size{dim, K});
+            return std::make_tuple(empty_gopaque_desc(), out_labels, centers);
+        }
+    };
+
+    G_TYPED_KERNEL(GKMeans2D, <std::tuple<GOpaque<double>,GArray<int>,GArray<Point2f>>
+                               (GArray<Point2f>,int,GArray<int>,TermCriteria,int,KmeansFlags)>,
+                   "org.opencv.core.kmeans2D") {
+        static std::tuple<GOpaqueDesc,GArrayDesc,GArrayDesc>
+        outMeta(const GArrayDesc&,int,const GArrayDesc&,const TermCriteria&,int,KmeansFlags) {
+            return std::make_tuple(empty_gopaque_desc(), empty_array_desc(), empty_array_desc());
+        }
+    };
+
+    G_TYPED_KERNEL(GKMeans3D, <std::tuple<GOpaque<double>,GArray<int>,GArray<Point3f>>
+                               (GArray<Point3f>,int,GArray<int>,TermCriteria,int,KmeansFlags)>,
+                   "org.opencv.core.kmeans3D") {
+        static std::tuple<GOpaqueDesc,GArrayDesc,GArrayDesc>
+        outMeta(const GArrayDesc&,int,const GArrayDesc&,const TermCriteria&,int,KmeansFlags) {
+            return std::make_tuple(empty_gopaque_desc(), empty_array_desc(), empty_array_desc());
+        }
+    };
 } // namespace core

 namespace streaming {
@@ -1757,6 +1829,79 @@ GAPI_EXPORTS GMat warpAffine(const GMat& src, const Mat& M, const Size& dsize, i
                             int borderMode = cv::BORDER_CONSTANT, const Scalar& borderValue = Scalar());
 //! @} gapi_transform

+/** @brief Finds centers of clusters and groups input samples around the clusters.
+
+The function kmeans implements a k-means algorithm that finds the centers of K clusters
+and groups the input samples around the clusters. As an output, \f$\texttt{bestLabels}_i\f$
+contains a 0-based cluster index for the \f$i^{th}\f$ sample.
+
+@note
+ - Function textual ID is "org.opencv.core.kmeansND"
+ - In case of an N-dimentional points' set given, input GMat can have the following traits:
+2 dimensions, a single row or column if there are N channels,
+or N columns if there is a single channel. Mat should have @ref CV_32F depth.
+ - Although, if GMat with height != 1, width != 1, channels != 1 given as data, n-dimensional
+samples are considered given in amount of A, where A = height, n = width * channels.
+ - In case of GMat given as data:
+     - the output labels are returned as 1-channel GMat with sizes
+width = 1, height = A, where A is samples amount, or width = bestLabels.width,
+height = bestLabels.height if bestLabels given;
+     - the cluster centers are returned as 1-channel GMat with sizes
+width = n, height = K, where n is samples' dimentionality and K is clusters' amount.
+ - As one of possible usages, if you want to control the initial labels for each attempt
+by yourself, you can utilize just the core of the function. To do that, set the number
+of attempts to 1, initialize labels each time using a custom algorithm, pass them with the
+( flags = #KMEANS_USE_INITIAL_LABELS ) flag, and then choose the best (most-compact) clustering.
+
+@param data Data for clustering. An array of N-Dimensional points with float coordinates is needed.
+Function can take GArray<Point2f>, GArray<Point3f> for 2D and 3D cases or GMat for any
+dimentionality and channels.
+@param K Number of clusters to split the set by.
+@param bestLabels Optional input integer array that can store the supposed initial cluster indices
+for every sample. Used when ( flags = #KMEANS_USE_INITIAL_LABELS ) flag is set.
+@param criteria The algorithm termination criteria, that is, the maximum number of iterations
+and/or the desired accuracy. The accuracy is specified as criteria.epsilon. As soon as each of
+the cluster centers moves by less than criteria.epsilon on some iteration, the algorithm stops.
+@param attempts Flag to specify the number of times the algorithm is executed using different
+initial labellings. The algorithm returns the labels that yield the best compactness (see the first
+function return value).
+@param flags Flag that can take values of cv::KmeansFlags .
+
+@return
+ - Compactness measure that is computed as
+\f[\sum _i  \| \texttt{samples} _i -  \texttt{centers} _{ \texttt{labels} _i} \| ^2\f]
+after every attempt. The best (minimum) value is chosen and the corresponding labels and the
+compactness value are returned by the function.
+ - Integer array that stores the cluster indices for every sample.
+ - Array of the cluster centers.
+*/
+GAPI_EXPORTS std::tuple<GOpaque<double>,GMat,GMat>
+kmeans(const GMat& data, const int K, const GMat& bestLabels,
+       const TermCriteria& criteria, const int attempts, const KmeansFlags flags);
+
+/** @overload
+@note
+ - Function textual ID is "org.opencv.core.kmeansNDNoInit"
+ - #KMEANS_USE_INITIAL_LABELS flag must not be set while using this overload.
+ */
+GAPI_EXPORTS std::tuple<GOpaque<double>,GMat,GMat>
+kmeans(const GMat& data, const int K, const TermCriteria& criteria, const int attempts,
+       const KmeansFlags flags);
+
+/** @overload
+@note Function textual ID is "org.opencv.core.kmeans2D"
+ */
+GAPI_EXPORTS std::tuple<GOpaque<double>,GArray<int>,GArray<Point2f>>
+kmeans(const GArray<Point2f>& data, const int K, const GArray<int>& bestLabels,
+       const TermCriteria& criteria, const int attempts, const KmeansFlags flags);
+
+/** @overload
+@note Function textual ID is "org.opencv.core.kmeans3D"
+ */
+GAPI_EXPORTS std::tuple<GOpaque<double>,GArray<int>,GArray<Point3f>>
+kmeans(const GArray<Point3f>& data, const int K, const GArray<int>& bestLabels,
+       const TermCriteria& criteria, const int attempts, const KmeansFlags flags);
+
 namespace streaming {
 /** @brief Gets dimensions from Mat.

@@ -203,6 +203,27 @@ struct GAPI_EXPORTS GMatDesc

 static inline GMatDesc empty_gmat_desc() { return GMatDesc{-1,-1,{-1,-1}}; }

+namespace gapi { namespace detail {
+/** Checks GMatDesc fields if the passed matrix is a set of n-dimentional points.
+@param in GMatDesc to check.
+@param n expected dimensionality.
+@return the amount of points. In case input matrix can't be described as vector of points
+of expected dimensionality, returns -1.
+ */
+int checkVector(const GMatDesc& in, const size_t n);
+
+/** @overload
+
+Checks GMatDesc fields if the passed matrix can be described as a set of points of any
+dimensionality.
+
+@return array of two elements in form of std::vector<int>: the amount of points
+and their calculated dimensionality. In case input matrix can't be described as vector of points,
+returns {-1, -1}.
+ */
+std::vector<int> checkVector(const GMatDesc& in);
+}} // namespace gapi::detail
+
 #if !defined(GAPI_STANDALONE)
 GAPI_EXPORTS GMatDesc descr_of(const cv::UMat &mat);
 #endif // !defined(GAPI_STANDALONE)
@@ -43,15 +43,6 @@ void validateFindingContoursMeta(const int depth, const int chan, const int mode
        break;
    }
 }
-
-// Checks if the passed mat is a set of n-dimentional points of the given depth
-bool isPointsVector(const int chan, const cv::Size &size, const int depth,
-                    const int n, const int ddepth = -1)
-{
-    return (ddepth == depth || ddepth < 0) &&
-           ((chan == n && (size.height == 1 || size.width == 1)) ||
-            (chan == 1 && size.width == n));
-}
 } // anonymous namespace

 namespace cv { namespace gapi {
@@ -212,10 +203,17 @@ namespace imgproc {
    G_TYPED_KERNEL(GBoundingRectMat, <GOpaque<Rect>(GMat)>,
                   "org.opencv.imgproc.shape.boundingRectMat") {
        static GOpaqueDesc outMeta(GMatDesc in) {
-            GAPI_Assert((in.depth == CV_8U && in.chan == 1) ||
-                        (isPointsVector(in.chan, in.size, in.depth, 2, CV_32S) ||
-                         isPointsVector(in.chan, in.size, in.depth, 2, CV_32F)));
-
+            if (in.depth == CV_8U)
+            {
+                GAPI_Assert(in.chan == 1);
+            }
+            else
+            {
+                GAPI_Assert (in.depth == CV_32S || in.depth == CV_32F);
+                int amount = detail::checkVector(in, 2u);
+                GAPI_Assert(amount != -1 &&
+                            "Input Mat can't be described as vector of 2-dimentional points");
+            }
            return empty_gopaque_desc();
        }
    };
@@ -237,7 +235,9 @@ namespace imgproc {
    G_TYPED_KERNEL(GFitLine2DMat, <GOpaque<Vec4f>(GMat,DistanceTypes,double,double,double)>,
                   "org.opencv.imgproc.shape.fitLine2DMat") {
        static GOpaqueDesc outMeta(GMatDesc in,DistanceTypes,double,double,double) {
-            GAPI_Assert(isPointsVector(in.chan, in.size, in.depth, 2, -1));
+            int amount = detail::checkVector(in, 2u);
+            GAPI_Assert(amount != -1 &&
+                        "Input Mat can't be described as vector of 2-dimentional points");
            return empty_gopaque_desc();
        }
    };
@@ -269,7 +269,9 @@ namespace imgproc {
    G_TYPED_KERNEL(GFitLine3DMat, <GOpaque<Vec6f>(GMat,DistanceTypes,double,double,double)>,
                   "org.opencv.imgproc.shape.fitLine3DMat") {
        static GOpaqueDesc outMeta(GMatDesc in,int,double,double,double) {
-            GAPI_Assert(isPointsVector(in.chan, in.size, in.depth, 3, -1));
+            int amount = detail::checkVector(in, 3u);
+            GAPI_Assert(amount != -1 &&
+                        "Input Mat can't be described as vector of 3-dimentional points");
            return empty_gopaque_desc();
        }
    };
@@ -36,6 +36,38 @@ const cv::GOrigin& cv::GMat::priv() const
    return *m_priv;
 }

+static std::vector<int> checkVectorImpl(const int width, const int height, const int chan,
+                                        const int n)
+{
+    if (width == 1 && (n == -1 || n == chan))
+    {
+        return {height, chan};
+    }
+    else if (height == 1 && (n == -1 || n == chan))
+    {
+        return {width, chan};
+    }
+    else if (chan == 1 && (n == -1 || n == width))
+    {
+        return {height, width};
+    }
+    else // input Mat can't be described as vector of points of given dimensionality
+    {
+        return {-1, -1};
+    }
+}
+
+int cv::gapi::detail::checkVector(const cv::GMatDesc& in, const size_t n)
+{
+    GAPI_Assert(n != 0u);
+    return checkVectorImpl(in.size.width, in.size.height, in.chan, static_cast<int>(n))[0];
+}
+
+std::vector<int> cv::gapi::detail::checkVector(const cv::GMatDesc& in)
+{
+    return checkVectorImpl(in.size.width, in.size.height, in.chan, -1);
+}
+
 namespace{
    template <typename T> cv::GMetaArgs vec_descr_of(const std::vector<T> &vec)
        {
@@ -388,6 +388,40 @@ GMat warpAffine(const GMat& src, const Mat& M, const Size& dsize, int flags,
    return core::GWarpAffine::on(src, M, dsize, flags, borderMode, borderValue);
 }

+std::tuple<GOpaque<double>,GMat,GMat> kmeans(const GMat& data, const int K, const GMat& bestLabels,
+                                             const TermCriteria& criteria, const int attempts,
+                                             const KmeansFlags flags)
+{
+    return core::GKMeansND::on(data, K, bestLabels, criteria, attempts, flags);
+}
+
+std::tuple<GOpaque<double>,GMat,GMat> kmeans(const GMat& data, const int K,
+                                             const TermCriteria& criteria, const int attempts,
+                                             const KmeansFlags flags)
+{
+    return core::GKMeansNDNoInit::on(data, K, criteria, attempts, flags);
+}
+
+std::tuple<GOpaque<double>,GArray<int>,GArray<Point2f>> kmeans(const GArray<Point2f>& data,
+                                                               const int              K,
+                                                               const GArray<int>&     bestLabels,
+                                                               const TermCriteria&    criteria,
+                                                               const int              attempts,
+                                                               const KmeansFlags      flags)
+{
+    return core::GKMeans2D::on(data, K, bestLabels, criteria, attempts, flags);
+}
+
+std::tuple<GOpaque<double>,GArray<int>,GArray<Point3f>> kmeans(const GArray<Point3f>& data,
+                                                               const int              K,
+                                                               const GArray<int>&     bestLabels,
+                                                               const TermCriteria&    criteria,
+                                                               const int              attempts,
+                                                               const KmeansFlags      flags)
+{
+    return core::GKMeans3D::on(data, K, bestLabels, criteria, attempts, flags);
+}
+
 GOpaque<Size> streaming::size(const GMat& src)
 {
    return streaming::GSize::on(src);
@@ -585,6 +585,63 @@ GAPI_OCV_KERNEL(GCPUWarpAffine, cv::gapi::core::GWarpAffine)
    }
 };

+GAPI_OCV_KERNEL(GCPUKMeansND, cv::gapi::core::GKMeansND)
+{
+    static void run(const cv::Mat& data, const int K, const cv::Mat& inBestLabels,
+                    const cv::TermCriteria& criteria, const int attempts,
+                    const cv::KmeansFlags flags,
+                    double& compactness, cv::Mat& outBestLabels, cv::Mat& centers)
+    {
+        if (flags & cv::KMEANS_USE_INITIAL_LABELS)
+        {
+            inBestLabels.copyTo(outBestLabels);
+        }
+        compactness = cv::kmeans(data, K, outBestLabels, criteria, attempts, flags, centers);
+    }
+};
+
+GAPI_OCV_KERNEL(GCPUKMeansNDNoInit, cv::gapi::core::GKMeansNDNoInit)
+{
+    static void run(const cv::Mat& data, const int K, const cv::TermCriteria& criteria,
+                    const int attempts, const cv::KmeansFlags flags,
+                    double& compactness, cv::Mat& outBestLabels, cv::Mat& centers)
+    {
+        compactness = cv::kmeans(data, K, outBestLabels, criteria, attempts, flags, centers);
+    }
+};
+
+GAPI_OCV_KERNEL(GCPUKMeans2D, cv::gapi::core::GKMeans2D)
+{
+    static void run(const std::vector<cv::Point2f>& data, const int K,
+                    const std::vector<int>& inBestLabels, const cv::TermCriteria& criteria,
+                    const int attempts, const cv::KmeansFlags flags,
+                    double& compactness, std::vector<int>& outBestLabels,
+                    std::vector<cv::Point2f>& centers)
+    {
+        if (flags & cv::KMEANS_USE_INITIAL_LABELS)
+        {
+            outBestLabels = inBestLabels;
+        }
+        compactness = cv::kmeans(data, K, outBestLabels, criteria, attempts, flags, centers);
+    }
+};
+
+GAPI_OCV_KERNEL(GCPUKMeans3D, cv::gapi::core::GKMeans3D)
+{
+    static void run(const std::vector<cv::Point3f>& data, const int K,
+                    const std::vector<int>& inBestLabels, const cv::TermCriteria& criteria,
+                    const int attempts, const cv::KmeansFlags flags,
+                    double& compactness, std::vector<int>& outBestLabels,
+                    std::vector<cv::Point3f>& centers)
+    {
+        if (flags & cv::KMEANS_USE_INITIAL_LABELS)
+        {
+            outBestLabels = inBestLabels;
+        }
+        compactness = cv::kmeans(data, K, outBestLabels, criteria, attempts, flags, centers);
+    }
+};
+
 GAPI_OCV_KERNEL(GCPUParseSSDBL, cv::gapi::nn::parsers::GParseSSDBL)
 {
    static void run(const cv::Mat&  in_ssd_result,
@@ -714,6 +771,10 @@ cv::gapi::GKernelPackage cv::gapi::core::cpu::kernels()
         , GCPUNormalize
         , GCPUWarpPerspective
         , GCPUWarpAffine
+         , GCPUKMeansND
+         , GCPUKMeansNDNoInit
+         , GCPUKMeans2D
+         , GCPUKMeans3D
         , GCPUParseSSDBL
         , GOCVParseSSD
         , GCPUParseYolo
@@ -151,6 +151,16 @@ GAPI_TEST_FIXTURE(WarpPerspectiveTest, initMatrixRandU,
 GAPI_TEST_FIXTURE(WarpAffineTest, initMatrixRandU,
        FIXTURE_API(CompareMats, double , double, int, int, cv::Scalar),
        6, cmpF, angle, scale, flags, border_mode, border_value)
+GAPI_TEST_FIXTURE(KMeansNDNoInitTest, initMatrixRandU, FIXTURE_API(int, cv::KmeansFlags),
+                  2, K, flags)
+GAPI_TEST_FIXTURE(KMeansNDInitTest, initMatrixRandU,
+                  FIXTURE_API(CompareMats, int, cv::KmeansFlags), 3, cmpF, K, flags)
+GAPI_TEST_FIXTURE(KMeans2DNoInitTest, initNothing, FIXTURE_API(int, cv::KmeansFlags),
+                  2, K, flags)
+GAPI_TEST_FIXTURE(KMeans2DInitTest, initNothing, FIXTURE_API(int, cv::KmeansFlags), 2, K, flags)
+GAPI_TEST_FIXTURE(KMeans3DNoInitTest, initNothing, FIXTURE_API(int, cv::KmeansFlags),
+                  2, K, flags)
+GAPI_TEST_FIXTURE(KMeans3DInitTest, initNothing, FIXTURE_API(int, cv::KmeansFlags), 2, K, flags)

 GAPI_TEST_EXT_BASE_FIXTURE(ParseSSDBLTest, ParserSSDTest, initNothing,
    FIXTURE_API(float, int), 2, confidence_threshold, filter_label)
@@ -15,6 +15,16 @@
 namespace opencv_test
 {

+namespace
+{
+template <typename Elem>
+inline bool compareVectorsAbsExact(const std::vector<Elem>& outOCV,
+                                   const std::vector<Elem>& outGAPI)
+{
+    return AbsExactVector<Elem>().to_compare_f()(outOCV, outGAPI);
+}
+}
+
 TEST_P(MathOpTest, MatricesAccuracyTest)
 {
    // G-API code & corresponding OpenCV code ////////////////////////////////
@@ -1377,6 +1387,187 @@ TEST_P(NormalizeTest, Test)
    }
 }

+TEST_P(KMeansNDNoInitTest, AccuracyTest)
+{
+    const int amount = sz.height != 1 ? sz.height : sz.width,
+              dim    = sz.height != 1 ? sz.width  : (type >> CV_CN_SHIFT) + 1;
+                                                    // amount of channels
+    const cv::TermCriteria criteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 30, 0);
+    const int attempts = 1;
+    double compact_gapi = -1.;
+    cv::Mat labels_gapi, centers_gapi;
+    // G-API code //////////////////////////////////////////////////////////////
+    cv::GMat in;
+    cv::GOpaque<double> compactness;
+    cv::GMat outLabels, centers;
+    std::tie(compactness, outLabels, centers) = cv::gapi::kmeans(in, K, criteria, attempts, flags);
+    cv::GComputation c(cv::GIn(in), cv::GOut(compactness, outLabels, centers));
+    c.apply(cv::gin(in_mat1), cv::gout(compact_gapi, labels_gapi, centers_gapi), getCompileArgs());
+    // Validation //////////////////////////////////////////////////////////////
+    {
+        EXPECT_GE(compact_gapi, 0.);
+        EXPECT_EQ(labels_gapi.cols, 1);
+        EXPECT_EQ(labels_gapi.rows, amount);
+        EXPECT_FALSE(labels_gapi.empty());
+        EXPECT_EQ(centers_gapi.cols, dim);
+        EXPECT_EQ(centers_gapi.rows, K);
+        EXPECT_FALSE(centers_gapi.empty());
+    }
+}
+
+TEST_P(KMeansNDInitTest, AccuracyTest)
+{
+    const int amount = sz.height != 1 ? sz.height : sz.width;
+    const cv::TermCriteria criteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 30, 0);
+    const int attempts = 1;
+    cv::Mat bestLabels(cv::Size{1, amount}, CV_32SC1);
+    double compact_ocv = -1., compact_gapi = -1.;
+    cv::Mat labels_ocv, labels_gapi, centers_ocv, centers_gapi;
+    cv::randu(bestLabels, 0, K);
+    bestLabels.copyTo(labels_ocv);
+    // G-API code //////////////////////////////////////////////////////////////
+    cv::GMat in, inLabels;
+    cv::GOpaque<double> compactness;
+    cv::GMat outLabels, centers;
+    std::tie(compactness, outLabels, centers) =
+        cv::gapi::kmeans(in, K, inLabels, criteria, attempts, flags);
+    cv::GComputation c(cv::GIn(in, inLabels), cv::GOut(compactness, outLabels, centers));
+    c.apply(cv::gin(in_mat1, bestLabels), cv::gout(compact_gapi, labels_gapi, centers_gapi),
+            getCompileArgs());
+    // OpenCV code /////////////////////////////////////////////////////////////
+    compact_ocv = cv::kmeans(in_mat1, K, labels_ocv, criteria, attempts, flags, centers_ocv);
+    // Comparison //////////////////////////////////////////////////////////////
+    {
+        EXPECT_TRUE(compact_gapi == compact_ocv);
+        EXPECT_TRUE(cmpF(labels_gapi, labels_ocv));
+        EXPECT_TRUE(cmpF(centers_gapi, centers_ocv));
+    }
+}
+
+TEST_P(KMeans2DNoInitTest, AccuracyTest)
+{
+    const int amount = sz.height;
+    const cv::TermCriteria criteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 30, 0);
+    const int attempts = 1;
+    std::vector<cv::Point2f> in_vector{};
+    double compact_gapi = -1.;
+    std::vector<int> labels_gapi{};
+    std::vector<cv::Point2f> centers_gapi{};
+    initPointsVectorRandU(amount, in_vector);
+    // G-API code //////////////////////////////////////////////////////////////
+    cv::GArray<cv::Point2f> in;
+    cv::GArray<int> inLabels(std::vector<int>{});
+    cv::GOpaque<double> compactness;
+    cv::GArray<int> outLabels;
+    cv::GArray<cv::Point2f> centers;
+    std::tie(compactness, outLabels, centers) =
+        cv::gapi::kmeans(in, K, inLabels, criteria, attempts, flags);
+    cv::GComputation c(cv::GIn(in), cv::GOut(compactness, outLabels, centers));
+    c.apply(cv::gin(in_vector), cv::gout(compact_gapi, labels_gapi, centers_gapi), getCompileArgs());
+    // Validation //////////////////////////////////////////////////////////////
+    {
+        EXPECT_GE(compact_gapi, 0.);
+        EXPECT_EQ(labels_gapi.size(), static_cast<size_t>(amount));
+        EXPECT_EQ(centers_gapi.size(), static_cast<size_t>(K));
+    }
+}
+
+TEST_P(KMeans2DInitTest, AccuracyTest)
+{
+    const int amount = sz.height;
+    const cv::TermCriteria criteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 30, 0);
+    const int attempts = 1;
+    std::vector<cv::Point2f> in_vector{};
+    std::vector<int> bestLabels(amount);
+    double compact_ocv = -1., compact_gapi = -1.;
+    std::vector<int> labels_ocv{}, labels_gapi{};
+    std::vector<cv::Point2f> centers_ocv{}, centers_gapi{};
+    initPointsVectorRandU(amount, in_vector);
+    cv::randu(bestLabels, 0, K);
+    labels_ocv = bestLabels;
+    // G-API code //////////////////////////////////////////////////////////////
+    cv::GArray<cv::Point2f> in;
+    cv::GArray<int> inLabels;
+    cv::GOpaque<double> compactness;
+    cv::GArray<int> outLabels;
+    cv::GArray<cv::Point2f> centers;
+    std::tie(compactness, outLabels, centers) =
+        cv::gapi::kmeans(in, K, inLabels, criteria, attempts, flags);
+    cv::GComputation c(cv::GIn(in, inLabels), cv::GOut(compactness, outLabels, centers));
+    c.apply(cv::gin(in_vector, bestLabels), cv::gout(compact_gapi, labels_gapi, centers_gapi),
+            getCompileArgs());
+    // OpenCV code /////////////////////////////////////////////////////////////
+    compact_ocv = cv::kmeans(in_vector, K, labels_ocv, criteria, attempts, flags, centers_ocv);
+    // Comparison //////////////////////////////////////////////////////////////
+    {
+        EXPECT_TRUE(compact_gapi == compact_ocv);
+        EXPECT_TRUE(compareVectorsAbsExact(labels_gapi, labels_ocv));
+        EXPECT_TRUE(compareVectorsAbsExact(centers_gapi, centers_ocv));
+    }
+}
+
+TEST_P(KMeans3DNoInitTest, AccuracyTest)
+{
+    const int amount = sz.height;
+    const cv::TermCriteria criteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 30, 0);
+    const int attempts = 1;
+    std::vector<cv::Point3f> in_vector{};
+    double compact_gapi = -1.;
+    std::vector<int> labels_gapi{};
+    std::vector<cv::Point3f> centers_gapi{};
+    initPointsVectorRandU(amount, in_vector);
+    // G-API code //////////////////////////////////////////////////////////////
+    cv::GArray<cv::Point3f> in;
+    cv::GArray<int> inLabels(std::vector<int>{});
+    cv::GOpaque<double> compactness;
+    cv::GArray<int> outLabels;
+    cv::GArray<cv::Point3f> centers;
+    std::tie(compactness, outLabels, centers) =
+        cv::gapi::kmeans(in, K, inLabels, criteria, attempts, flags);
+    cv::GComputation c(cv::GIn(in), cv::GOut(compactness, outLabels, centers));
+    c.apply(cv::gin(in_vector), cv::gout(compact_gapi, labels_gapi, centers_gapi), getCompileArgs());
+    // Validation //////////////////////////////////////////////////////////////
+    {
+        EXPECT_GE(compact_gapi, 0.);
+        EXPECT_EQ(labels_gapi.size(), static_cast<size_t>(amount));
+        EXPECT_EQ(centers_gapi.size(), static_cast<size_t>(K));
+    }
+}
+
+TEST_P(KMeans3DInitTest, AccuracyTest)
+{
+    const int amount = sz.height;
+    const cv::TermCriteria criteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 30, 0);
+    const int attempts = 1;
+    std::vector<cv::Point3f> in_vector{};
+    std::vector<int> bestLabels(amount);
+    double compact_ocv = -1., compact_gapi = -1.;
+    std::vector<int> labels_ocv{}, labels_gapi{};
+    std::vector<cv::Point3f> centers_ocv{}, centers_gapi{};
+    initPointsVectorRandU(amount, in_vector);
+    cv::randu(bestLabels, 0, K);
+    labels_ocv = bestLabels;
+    // G-API code //////////////////////////////////////////////////////////////
+    cv::GArray<cv::Point3f> in;
+    cv::GArray<int> inLabels;
+    cv::GOpaque<double> compactness;
+    cv::GArray<int> outLabels;
+    cv::GArray<cv::Point3f> centers;
+    std::tie(compactness, outLabels, centers) =
+        cv::gapi::kmeans(in, K, inLabels, criteria, attempts, flags);
+    cv::GComputation c(cv::GIn(in, inLabels), cv::GOut(compactness, outLabels, centers));
+    c.apply(cv::gin(in_vector, bestLabels), cv::gout(compact_gapi, labels_gapi, centers_gapi),
+            getCompileArgs());
+    // OpenCV code /////////////////////////////////////////////////////////////
+    compact_ocv = cv::kmeans(in_vector, K, labels_ocv, criteria, attempts, flags, centers_ocv);
+    // Comparison //////////////////////////////////////////////////////////////
+    {
+        EXPECT_TRUE(compact_gapi == compact_ocv);
+        EXPECT_TRUE(compareVectorsAbsExact(labels_gapi, labels_ocv));
+        EXPECT_TRUE(compareVectorsAbsExact(centers_gapi, centers_ocv));
+    }
+}
+
 // PLEASE DO NOT PUT NEW ACCURACY TESTS BELOW THIS POINT! //////////////////////

 TEST_P(BackendOutputAllocationTest, EmptyOutput)
@@ -1174,6 +1174,28 @@ inline std::ostream& operator<<(std::ostream& os, DistanceTypes op)
 #undef CASE
    return os;
 }
+
+inline std::ostream& operator<<(std::ostream& os, KmeansFlags op)
+{
+    int op_(op);
+    switch (op_)
+    {
+    case KmeansFlags::KMEANS_RANDOM_CENTERS:
+        os << "KMEANS_RANDOM_CENTERS";
+        break;
+    case KmeansFlags::KMEANS_PP_CENTERS:
+        os << "KMEANS_PP_CENTERS";
+        break;
+    case KmeansFlags::KMEANS_RANDOM_CENTERS | KmeansFlags::KMEANS_USE_INITIAL_LABELS:
+        os << "KMEANS_RANDOM_CENTERS | KMEANS_USE_INITIAL_LABELS";
+        break;
+    case KmeansFlags::KMEANS_PP_CENTERS | KmeansFlags::KMEANS_USE_INITIAL_LABELS:
+        os << "KMEANS_PP_CENTERS | KMEANS_USE_INITIAL_LABELS";
+        break;
+    default: GAPI_Assert(false && "unknown KmeansFlags value");
+    }
+    return os;
+}
 }  // namespace cv

 #endif //OPENCV_GAPI_TESTS_COMMON_HPP
@@ -484,6 +484,72 @@ INSTANTIATE_TEST_CASE_P(NormalizeTestCPU, NormalizeTest,
                                Values(NORM_MINMAX, NORM_INF, NORM_L1, NORM_L2),
                                Values(-1, CV_8U, CV_16U, CV_16S, CV_32F)));

+INSTANTIATE_TEST_CASE_P(KMeansNDNoInitTestCPU, KMeansNDNoInitTest,
+                        Combine(Values(CV_32FC1),
+                                Values(cv::Size(2, 20)),
+                                Values(-1),
+                                Values(CORE_CPU),
+                                Values(5),
+                                Values(cv::KMEANS_RANDOM_CENTERS, cv::KMEANS_PP_CENTERS)));
+
+INSTANTIATE_TEST_CASE_P(KMeansNDInitTestCPU, KMeansNDInitTest,
+                        Combine(Values(CV_32FC1, CV_32FC3),
+                                Values(cv::Size(1, 20),
+                                       cv::Size(2, 20),
+                                       cv::Size(5, 720)),
+                                Values(-1),
+                                Values(CORE_CPU),
+                                Values(AbsTolerance(0.01).to_compare_obj()),
+                                Values(5, 15),
+                                Values(cv::KMEANS_RANDOM_CENTERS | cv::KMEANS_USE_INITIAL_LABELS,
+                                       cv::KMEANS_PP_CENTERS     | cv::KMEANS_USE_INITIAL_LABELS)));
+
+INSTANTIATE_TEST_CASE_P(KMeansNDInitReverseTestCPU, KMeansNDInitTest,
+                        Combine(Values(CV_32FC3),
+                                Values(cv::Size(20, 1)),
+                                Values(-1),
+                                Values(CORE_CPU),
+                                Values(AbsTolerance(0.01).to_compare_obj()),
+                                Values(5, 15),
+                                Values(cv::KMEANS_RANDOM_CENTERS | cv::KMEANS_USE_INITIAL_LABELS,
+                                       cv::KMEANS_PP_CENTERS     | cv::KMEANS_USE_INITIAL_LABELS)));
+
+INSTANTIATE_TEST_CASE_P(KMeans2DNoInitTestCPU, KMeans2DNoInitTest,
+                        Combine(Values(-1),
+                                Values(cv::Size(-1, 20)),
+                                Values(-1),
+                                Values(CORE_CPU),
+                                Values(5),
+                                Values(cv::KMEANS_RANDOM_CENTERS, cv::KMEANS_PP_CENTERS)));
+
+INSTANTIATE_TEST_CASE_P(KMeans2DInitTestCPU, KMeans2DInitTest,
+                        Combine(Values(-1),
+                                Values(cv::Size(-1, 720),
+                                       cv::Size(-1, 20)),
+                                Values(-1),
+                                Values(CORE_CPU),
+                                Values(5, 15),
+                                Values(cv::KMEANS_RANDOM_CENTERS | cv::KMEANS_USE_INITIAL_LABELS,
+                                       cv::KMEANS_PP_CENTERS     | cv::KMEANS_USE_INITIAL_LABELS)));
+
+INSTANTIATE_TEST_CASE_P(KMeans3DNoInitTestCPU, KMeans3DNoInitTest,
+                        Combine(Values(-1),
+                                Values(cv::Size(-1, 20)),
+                                Values(-1),
+                                Values(CORE_CPU),
+                                Values(5),
+                                Values(cv::KMEANS_RANDOM_CENTERS, cv::KMEANS_PP_CENTERS)));
+
+INSTANTIATE_TEST_CASE_P(KMeans3DInitTestCPU, KMeans3DInitTest,
+                        Combine(Values(-1),
+                                Values(cv::Size(-1, 720),
+                                       cv::Size(-1, 20)),
+                                Values(-1),
+                                Values(CORE_CPU),
+                                Values(5, 15),
+                                Values(cv::KMEANS_RANDOM_CENTERS | cv::KMEANS_USE_INITIAL_LABELS,
+                                       cv::KMEANS_PP_CENTERS     | cv::KMEANS_USE_INITIAL_LABELS)));
+
 // PLEASE DO NOT PUT NEW ACCURACY TESTS BELOW THIS POINT! //////////////////////

 INSTANTIATE_TEST_CASE_P(BackendOutputAllocationTestCPU, BackendOutputAllocationTest,