Added KAZE and AKAZE wrappers

modules/features2d/include/opencv2/features2d.hpp

@@ -887,7 +887,73 @@ protected:
     PixelTestFn test_fn_;
 };
 
+/*!
+KAZE implementation
+*/
+class CV_EXPORTS_W KAZE : public Feature2D
+{
+public:
+    CV_WRAP explicit KAZE(bool _extended = false);
+
+    virtual ~KAZE();
+
+    // returns the descriptor size in bytes
+    int descriptorSize() const;
+    // returns the descriptor type
+    int descriptorType() const;
+    // returns the default norm type
+    int defaultNorm() const;
 
+    AlgorithmInfo* info() const;
+
+    void operator()(InputArray image, InputArray mask,
+        std::vector<KeyPoint>& keypoints,
+        OutputArray descriptors,
+        bool useProvidedKeypoints) const;
+
+protected:
+    void detectImpl(InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask) const;
+    void computeImpl(InputArray image, std::vector<KeyPoint>& keypoints, OutputArray descriptors) const;
+
+    CV_PROP bool extended;
+};
+
+/*!
+AKAZE implementation
+*/
+class CV_EXPORTS_W AKAZE : public Feature2D
+{
+public:
+    CV_WRAP explicit AKAZE(int _descriptor = 5, int _descriptor_size = 0, int _descriptor_channels = 3);
+
+    virtual ~AKAZE();
+
+    // returns the descriptor size in bytes
+    int descriptorSize() const;
+    // returns the descriptor type
+    int descriptorType() const;
+    // returns the default norm type
+    int defaultNorm() const;
+
+    // Compute the AKAZE features on an image
+    void operator()(InputArray image, InputArray mask, std::vector<KeyPoint>& keypoints) const;
+
+    // Compute the BRISK features and descriptors on an image
+    void operator()(InputArray image, InputArray mask, std::vector<KeyPoint>& keypoints,
+        OutputArray descriptors, bool useProvidedKeypoints = false) const;
+
+    AlgorithmInfo* info() const;
+
+protected:
+
+    void computeImpl(InputArray image, std::vector<KeyPoint>& keypoints, OutputArray descriptors) const;
+    void detectImpl(InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask = noArray()) const;
+
+    CV_PROP int descriptor_channels;
+    CV_PROP int descriptor;
+    CV_PROP int descriptor_size;
+
+};
 /****************************************************************************************\
 *                                      Distance                                          *
 \****************************************************************************************/

modules/features2d/src/akaze.cpp

@@ -0,0 +1,149 @@
+#include "precomp.hpp"
+#include "akaze/AKAZE.h"
+
+namespace cv
+{
+
+    AKAZE::AKAZE(int _descriptor, int _descriptor_size, int _descriptor_channels)
+        : descriptor_channels(_descriptor_channels)
+        , descriptor(_descriptor)
+        , descriptor_size(_descriptor_size)
+    {
+
+    }
+
+    AKAZE::~AKAZE()
+    {
+
+    }
+
+    // returns the descriptor size in bytes
+    int AKAZE::descriptorSize() const
+    {
+        if (descriptor < MLDB_UPRIGHT)
+        {
+            return 64;
+        }
+        else
+        {
+            // We use the full length binary descriptor -> 486 bits
+            if (descriptor_size == 0)
+            {
+                int t = (6 + 36 + 120) * descriptor_channels;
+                return ceil(t / 8.);
+            }
+            else
+            {
+                // We use the random bit selection length binary descriptor
+                return ceil(descriptor_size / 8.);
+            }
+        }
+    }
+
+    // returns the descriptor type
+    int AKAZE::descriptorType() const
+    {
+        if (descriptor < MLDB_UPRIGHT)
+        {
+            return CV_32FC1;
+        }
+        else
+        {
+            return CV_8UC1;
+        }
+    }
+
+    // returns the default norm type
+    int AKAZE::defaultNorm() const
+    {
+        if (descriptor < MLDB_UPRIGHT)
+        {
+            return NORM_L2;
+        }
+        else
+        {
+            return NORM_HAMMING;
+        }
+    }
+
+
+    void AKAZE::operator()(InputArray image, InputArray mask,
+        std::vector<KeyPoint>& keypoints,
+        OutputArray descriptors,
+        bool useProvidedKeypoints) const
+    {
+        cv::Mat img = image.getMat();
+        if (img.type() != CV_8UC1)
+            cvtColor(image, img, COLOR_BGR2GRAY);
+
+        Mat img1_32;
+        img.convertTo(img1_32, CV_32F, 1.0 / 255.0, 0);
+
+        cv::Mat& desc = descriptors.getMatRef();
+
+        AKAZEOptions options;
+        options.img_width = img.cols;
+        options.img_height = img.rows;
+
+        AKAZEFeatures impl(options);
+        impl.Create_Nonlinear_Scale_Space(img1_32);
+
+        if (!useProvidedKeypoints)
+        {
+            impl.Feature_Detection(keypoints);
+        }
+
+        if (!mask.empty())
+        {
+            cv::KeyPointsFilter::runByPixelsMask(keypoints, mask.getMat());
+        }
+
+        impl.Compute_Descriptors(keypoints, desc);
+    }
+
+    void AKAZE::detectImpl(InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask) const
+    {
+        cv::Mat img = image.getMat();
+        if (img.type() != CV_8UC1)
+            cvtColor(image, img, COLOR_BGR2GRAY);
+
+        Mat img1_32;
+        img.convertTo(img1_32, CV_32F, 1.0 / 255.0, 0);
+
+        AKAZEOptions options;
+        options.img_width = img.cols;
+        options.img_height = img.rows;
+
+        AKAZEFeatures impl(options);
+        impl.Create_Nonlinear_Scale_Space(img1_32);
+        impl.Feature_Detection(keypoints);
+
+        if (!mask.empty())
+        {
+            cv::KeyPointsFilter::runByPixelsMask(keypoints, mask.getMat());
+        }
+    }
+
+    void AKAZE::computeImpl(InputArray image, std::vector<KeyPoint>& keypoints, OutputArray descriptors) const
+    {
+        cv::Mat img = image.getMat();
+        if (img.type() != CV_8UC1)
+            cvtColor(image, img, COLOR_BGR2GRAY);
+
+        Mat img1_32;
+        img.convertTo(img1_32, CV_32F, 1.0 / 255.0, 0);
+
+        cv::Mat& desc = descriptors.getMatRef();
+
+        AKAZEOptions options;
+        options.img_width = img.cols;
+        options.img_height = img.rows;
+
+        AKAZEFeatures impl(options);
+        impl.Create_Nonlinear_Scale_Space(img1_32);
+        impl.Compute_Descriptors(keypoints, desc);
+
+        CV_Assert(!desc.rows || desc.cols == descriptorSize() && "Descriptor size does not match expected");
+        CV_Assert(!desc.rows || (desc.type() & descriptorType()) && "Descriptor type does not match expected");
+    }
+}

modules/features2d/src/features2d_init.cpp

@@ -125,6 +125,20 @@ CV_INIT_ALGORITHM(GFTTDetector, "Feature2D.GFTT",
 
 ///////////////////////////////////////////////////////////////////////////////////////////////////////////
 
+CV_INIT_ALGORITHM(KAZE, "Feature2D.KAZE",
+                  obj.info()->addParam(obj, "extended", obj.extended))
+
+///////////////////////////////////////////////////////////////////////////////////////////////////////////
+
+CV_INIT_ALGORITHM(AKAZE, "Feature2D.AKAZE",
+                  obj.info()->addParam(obj, "descriptor_channels", obj.descriptor_channels);
+                  obj.info()->addParam(obj, "descriptor", obj.descriptor);
+                  obj.info()->addParam(obj, "descriptor_size", obj.descriptor_size))
+
+///////////////////////////////////////////////////////////////////////////////////////////////////////////
+
+
+
 CV_INIT_ALGORITHM(SimpleBlobDetector, "Feature2D.SimpleBlob",
                   obj.info()->addParam(obj, "thresholdStep",    obj.params.thresholdStep);
                   obj.info()->addParam(obj, "minThreshold",     obj.params.minThreshold);
@@ -202,6 +216,8 @@ bool cv::initModule_features2d(void)
     all &= !FREAK_info_auto.name().empty();
     all &= !ORB_info_auto.name().empty();
     all &= !GFTTDetector_info_auto.name().empty();
+    all &= !KAZE_info_auto.name().empty();
+    all &= !AKAZE_info_auto.name().empty();
         all &= !HarrisDetector_info_auto.name().empty();
     all &= !DenseFeatureDetector_info_auto.name().empty();
     all &= !GridAdaptedFeatureDetector_info_auto.name().empty();

modules/features2d/src/kaze.cpp

@@ -0,0 +1,120 @@
+#include "precomp.hpp"
+#include "kaze/KAZE.h"
+
+namespace cv
+{
+    KAZE::KAZE(bool _extended /* = false */)
+        : extended(_extended)
+    {
+    }
+
+    KAZE::~KAZE()
+    {
+
+    }
+
+    // returns the descriptor size in bytes
+    int KAZE::descriptorSize() const
+    {
+        return extended ? 128 : 64;
+    }
+
+    // returns the descriptor type
+    int KAZE::descriptorType() const
+    {
+        return CV_32F;
+    }
+
+    // returns the default norm type
+    int KAZE::defaultNorm() const
+    {
+        return NORM_L2;
+    }
+
+
+    void KAZE::operator()(InputArray image, InputArray mask,
+        std::vector<KeyPoint>& keypoints,
+        OutputArray descriptors,
+        bool useProvidedKeypoints) const
+    {
+        cv::Mat img = image.getMat();
+        if (img.type() != CV_8UC1)
+            cvtColor(image, img, COLOR_BGR2GRAY);
+
+        Mat img1_32;
+        img.convertTo(img1_32, CV_32F, 1.0 / 255.0, 0);
+
+        cv::Mat& desc = descriptors.getMatRef();
+
+        KAZEOptions options;
+        options.img_width = img.cols;
+        options.img_height = img.rows;
+        options.extended = extended;
+
+        KAZEFeatures impl(options);
+        impl.Create_Nonlinear_Scale_Space(img1_32);
+
+        if (!useProvidedKeypoints)
+        {
+            impl.Feature_Detection(keypoints);
+        }
+
+        if (!mask.empty())
+        {
+            cv::KeyPointsFilter::runByPixelsMask(keypoints, mask.getMat());
+        }
+
+        impl.Feature_Description(keypoints, desc);
+
+        CV_Assert(!desc.rows || desc.cols == descriptorSize() && "Descriptor size does not match expected");
+        CV_Assert(!desc.rows || (desc.type() & descriptorType()) && "Descriptor type does not match expected");
+    }
+
+    void KAZE::detectImpl(InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask) const
+    {
+        Mat img = image.getMat();
+        if (img.type() != CV_8UC1)
+            cvtColor(image, img, COLOR_BGR2GRAY);
+
+        Mat img1_32;
+        img.convertTo(img1_32, CV_32F, 1.0 / 255.0, 0);
+
+        KAZEOptions options;
+        options.img_width = img.cols;
+        options.img_height = img.rows;
+        options.extended = extended;
+
+        KAZEFeatures impl(options);
+        impl.Create_Nonlinear_Scale_Space(img1_32);
+        impl.Feature_Detection(keypoints);
+
+        if (!mask.empty())
+        {
+            cv::KeyPointsFilter::runByPixelsMask(keypoints, mask.getMat());
+        }
+    }
+
+    void KAZE::computeImpl(InputArray image, std::vector<KeyPoint>& keypoints, OutputArray descriptors) const
+    {
+        cv::Mat img = image.getMat();
+        if (img.type() != CV_8UC1)
+            cvtColor(image, img, COLOR_BGR2GRAY);
+
+        Mat img1_32;
+        img.convertTo(img1_32, CV_32F, 1.0 / 255.0, 0);
+
+        cv::Mat& desc = descriptors.getMatRef();
+
+        KAZEOptions options;
+        options.img_width = img.cols;
+        options.img_height = img.rows;
+        options.extended = extended;
+
+        KAZEFeatures impl(options);
+        impl.Create_Nonlinear_Scale_Space(img1_32);
+        impl.Feature_Description(keypoints, desc);
+
+        CV_Assert(!desc.rows || desc.cols == descriptorSize() && "Descriptor size does not match expected");
+        CV_Assert(!desc.rows || (desc.type() & descriptorType()) && "Descriptor type does not match expected");
+    }
+}