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Merge pull request #13267 from LaurentBerger:StitchPython

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* Python wrapper for detail

* hide pyrotationwrapper

* copy code in pyopencv_rotationwarper.hpp

* move ImageFeatures MatchInfo and CameraParams in core/misc/

* add python test for detail

* move test_detail in test_stitching

* rename
This commit is contained in:
LaurentBerger 2018-12-18 19:49:16 +01:00 committed by Alexander Alekhin
parent fd27d5ea00
commit 2fb409b286
20 changed files with 1000 additions and 208 deletions
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8
modules/core/misc/python/pyopencv_rotationwarper.hpp Normal file
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@ -0,0 +1,8 @@
#ifdef HAVE_OPENCV_STITCHING
typedef std::vector<detail::ImageFeatures> vector_ImageFeatures;
typedef std::vector<detail::MatchesInfo> vector_MatchesInfo;
typedef std::vector<detail::CameraParams> vector_CameraParams;
#endif

55
modules/python/src2/cv2.cpp
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@ -197,6 +197,7 @@ typedef std::vector<size_t> vector_size_t;
typedef std::vector<Point> vector_Point; typedef std::vector<Point> vector_Point;
typedef std::vector<Point2f> vector_Point2f; typedef std::vector<Point2f> vector_Point2f;
typedef std::vector<Point3f> vector_Point3f; typedef std::vector<Point3f> vector_Point3f;
typedef std::vector<Size> vector_Size;
typedef std::vector<Vec2f> vector_Vec2f; typedef std::vector<Vec2f> vector_Vec2f;
typedef std::vector<Vec3f> vector_Vec3f; typedef std::vector<Vec3f> vector_Vec3f;
typedef std::vector<Vec4f> vector_Vec4f; typedef std::vector<Vec4f> vector_Vec4f;
@ -1338,6 +1339,19 @@ template<> struct pyopencvVecConverter<Mat>
} }
}; };
template<> struct pyopencvVecConverter<UMat>
{
static bool to(PyObject* obj, std::vector<UMat>& value, const ArgInfo info)
{
return pyopencv_to_generic_vec(obj, value, info);
}
static PyObject* from(const std::vector<UMat>& value)
{
return pyopencv_from_generic_vec(value);
}
};
template<> struct pyopencvVecConverter<KeyPoint> template<> struct pyopencvVecConverter<KeyPoint>
{ {
static bool to(PyObject* obj, std::vector<KeyPoint>& value, const ArgInfo info) static bool to(PyObject* obj, std::vector<KeyPoint>& value, const ArgInfo info)
@ -1364,6 +1378,47 @@ template<> struct pyopencvVecConverter<DMatch>
} }
}; };
template<> struct pyopencvVecConverter<detail::ImageFeatures>
{
static bool to(PyObject* obj, std::vector<detail::ImageFeatures>& value, const ArgInfo info)
{
return pyopencv_to_generic_vec(obj, value, info);
}
static PyObject* from(const std::vector<detail::ImageFeatures>& value)
{
return pyopencv_from_generic_vec(value);
}
};
template<> struct pyopencvVecConverter<detail::MatchesInfo>
{
static bool to(PyObject* obj, std::vector<detail::MatchesInfo>& value, const ArgInfo info)
{
return pyopencv_to_generic_vec(obj, value, info);
}
static PyObject* from(const std::vector<detail::MatchesInfo>& value)
{
return pyopencv_from_generic_vec(value);
}
};
template<> struct pyopencvVecConverter<detail::CameraParams>
{
static bool to(PyObject* obj, std::vector<detail::CameraParams>& value, const ArgInfo info)
{
return pyopencv_to_generic_vec(obj, value, info);
}
static PyObject* from(const std::vector<detail::CameraParams>& value)
{
return pyopencv_from_generic_vec(value);
}
};
template<> struct pyopencvVecConverter<String> template<> struct pyopencvVecConverter<String>
{ {
static bool to(PyObject* obj, std::vector<String>& value, const ArgInfo info) static bool to(PyObject* obj, std::vector<String>& value, const ArgInfo info)

63
modules/python/test/test_stitching.py
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@ -19,5 +19,68 @@ class stitching_test(NewOpenCVTests):
self.assertAlmostEqual(pano.shape[0], 685, delta=100, msg="rows: %r" % list(pano.shape)) self.assertAlmostEqual(pano.shape[0], 685, delta=100, msg="rows: %r" % list(pano.shape))
self.assertAlmostEqual(pano.shape[1], 1025, delta=100, msg="cols: %r" % list(pano.shape)) self.assertAlmostEqual(pano.shape[1], 1025, delta=100, msg="cols: %r" % list(pano.shape))
class stitching_detail_test(NewOpenCVTests):
def test_simple(self):
img = self.get_sample('stitching/a1.png')
finder= cv.ORB.create()
imgFea = cv.detail.computeImageFeatures2(finder,img)
self.assertIsNotNone(imgFea)
matcher = cv.detail_BestOf2NearestMatcher(False, 0.3)
self.assertIsNotNone(matcher)
matcher = cv.detail_AffineBestOf2NearestMatcher(False, False, 0.3)
self.assertIsNotNone(matcher)
matcher = cv.detail_BestOf2NearestRangeMatcher(2, False, 0.3)
self.assertIsNotNone(matcher)
estimator = cv.detail_AffineBasedEstimator()
self.assertIsNotNone(estimator)
estimator = cv.detail_HomographyBasedEstimator()
self.assertIsNotNone(estimator)
adjuster = cv.detail_BundleAdjusterReproj()
self.assertIsNotNone(adjuster)
adjuster = cv.detail_BundleAdjusterRay()
self.assertIsNotNone(adjuster)
adjuster = cv.detail_BundleAdjusterAffinePartial()
self.assertIsNotNone(adjuster)
adjuster = cv.detail_NoBundleAdjuster()
self.assertIsNotNone(adjuster)
compensator=cv.detail.ExposureCompensator_createDefault(cv.detail.ExposureCompensator_NO)
self.assertIsNotNone(compensator)
compensator=cv.detail.ExposureCompensator_createDefault(cv.detail.ExposureCompensator_GAIN)
self.assertIsNotNone(compensator)
compensator=cv.detail.ExposureCompensator_createDefault(cv.detail.ExposureCompensator_GAIN_BLOCKS)
self.assertIsNotNone(compensator)
seam_finder = cv.detail.SeamFinder_createDefault(cv.detail.SeamFinder_NO)
self.assertIsNotNone(seam_finder)
seam_finder = cv.detail.SeamFinder_createDefault(cv.detail.SeamFinder_NO)
self.assertIsNotNone(seam_finder)
seam_finder = cv.detail.SeamFinder_createDefault(cv.detail.SeamFinder_VORONOI_SEAM)
self.assertIsNotNone(seam_finder)
seam_finder = cv.detail_GraphCutSeamFinder("COST_COLOR")
self.assertIsNotNone(seam_finder)
seam_finder = cv.detail_GraphCutSeamFinder("COST_COLOR_GRAD")
self.assertIsNotNone(seam_finder)
seam_finder = cv.detail_DpSeamFinder("COLOR")
self.assertIsNotNone(seam_finder)
seam_finder = cv.detail_DpSeamFinder("COLOR_GRAD")
self.assertIsNotNone(seam_finder)
blender = cv.detail.Blender_createDefault(cv.detail.Blender_NO)
self.assertIsNotNone(blender)
blender = cv.detail.Blender_createDefault(cv.detail.Blender_FEATHER)
self.assertIsNotNone(blender)
blender = cv.detail.Blender_createDefault(cv.detail.Blender_MULTI_BAND)
self.assertIsNotNone(blender)
timelapser = cv.detail.Timelapser_createDefault(cv.detail.Timelapser_AS_IS);
self.assertIsNotNone(timelapser)
timelapser = cv.detail.Timelapser_createDefault(cv.detail.Timelapser_CROP);
self.assertIsNotNone(timelapser)
if __name__ == '__main__': if __name__ == '__main__':
NewOpenCVTests.bootstrap() NewOpenCVTests.bootstrap()

4
modules/stitching/include/opencv2/stitching/detail/autocalib.hpp
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@ -64,7 +64,7 @@ undergoes rotations around its centre only.
See "Construction of Panoramic Image Mosaics with Global and Local Alignment" See "Construction of Panoramic Image Mosaics with Global and Local Alignment"
by Heung-Yeung Shum and Richard Szeliski. by Heung-Yeung Shum and Richard Szeliski.
*/ */
void CV_EXPORTS focalsFromHomography(const Mat &H, double &f0, double &f1, bool &f0_ok, bool &f1_ok); void CV_EXPORTS_W focalsFromHomography(const Mat &H, double &f0, double &f1, bool &f0_ok, bool &f1_ok);
/** @brief Estimates focal lengths for each given camera. /** @brief Estimates focal lengths for each given camera.
@ -76,7 +76,7 @@ void CV_EXPORTS estimateFocal(const std::vector<ImageFeatures> &features,
const std::vector<MatchesInfo> &pairwise_matches, const std::vector<MatchesInfo> &pairwise_matches,
std::vector<double> &focals); std::vector<double> &focals);
bool CV_EXPORTS calibrateRotatingCamera(const std::vector<Mat> &Hs, Mat &K); bool CV_EXPORTS_W calibrateRotatingCamera(const std::vector<Mat> &Hs,CV_OUT Mat &K);
//! @} stitching_autocalib //! @} stitching_autocalib

56
modules/stitching/include/opencv2/stitching/detail/blenders.hpp
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@ -60,35 +60,35 @@ namespace detail {
Simple blender which puts one image over another Simple blender which puts one image over another
*/ */
class CV_EXPORTS Blender class CV_EXPORTS_W Blender
{ {
public: public:
virtual ~Blender() {} virtual ~Blender() {}
enum { NO, FEATHER, MULTI_BAND }; enum { NO, FEATHER, MULTI_BAND };
static Ptr<Blender> createDefault(int type, bool try_gpu = false); CV_WRAP static Ptr<Blender> createDefault(int type, bool try_gpu = false);
/** @brief Prepares the blender for blending. /** @brief Prepares the blender for blending.
@param corners Source images top-left corners @param corners Source images top-left corners
@param sizes Source image sizes @param sizes Source image sizes
*/ */
void prepare(const std::vector<Point> &corners, const std::vector<Size> &sizes); CV_WRAP void prepare(const std::vector<Point> &corners, const std::vector<Size> &sizes);
/** @overload */ /** @overload */
virtual void prepare(Rect dst_roi); CV_WRAP virtual void prepare(Rect dst_roi);
/** @brief Processes the image. /** @brief Processes the image.
@param img Source image @param img Source image
@param mask Source image mask @param mask Source image mask
@param tl Source image top-left corners @param tl Source image top-left corners
*/ */
virtual void feed(InputArray img, InputArray mask, Point tl); CV_WRAP virtual void feed(InputArray img, InputArray mask, Point tl);
/** @brief Blends and returns the final pano. /** @brief Blends and returns the final pano.
@param dst Final pano @param dst Final pano
@param dst_mask Final pano mask @param dst_mask Final pano mask
*/ */
virtual void blend(InputOutputArray dst, InputOutputArray dst_mask); CV_WRAP virtual void blend(CV_IN_OUT InputOutputArray dst,CV_IN_OUT InputOutputArray dst_mask);
protected: protected:
UMat dst_, dst_mask_; UMat dst_, dst_mask_;
@ -97,22 +97,22 @@ protected:
/** @brief Simple blender which mixes images at its borders. /** @brief Simple blender which mixes images at its borders.
*/ */
class CV_EXPORTS FeatherBlender : public Blender class CV_EXPORTS_W FeatherBlender : public Blender
{ {
public: public:
FeatherBlender(float sharpness = 0.02f); CV_WRAP FeatherBlender(float sharpness = 0.02f);
float sharpness() const { return sharpness_; } CV_WRAP float sharpness() const { return sharpness_; }
void setSharpness(float val) { sharpness_ = val; } CV_WRAP void setSharpness(float val) { sharpness_ = val; }
void prepare(Rect dst_roi) CV_OVERRIDE; CV_WRAP void prepare(Rect dst_roi) CV_OVERRIDE;
void feed(InputArray img, InputArray mask, Point tl) CV_OVERRIDE; CV_WRAP void feed(InputArray img, InputArray mask, Point tl) CV_OVERRIDE;
void blend(InputOutputArray dst, InputOutputArray dst_mask) CV_OVERRIDE; CV_WRAP void blend(InputOutputArray dst, InputOutputArray dst_mask) CV_OVERRIDE;
//! Creates weight maps for fixed set of source images by their masks and top-left corners. //! Creates weight maps for fixed set of source images by their masks and top-left corners.
//! Final image can be obtained by simple weighting of the source images. //! Final image can be obtained by simple weighting of the source images.
Rect createWeightMaps(const std::vector<UMat> &masks, const std::vector<Point> &corners, CV_WRAP Rect createWeightMaps(const std::vector<UMat> &masks, const std::vector<Point> &corners,
std::vector<UMat> &weight_maps); CV_IN_OUT std::vector<UMat> &weight_maps);
private: private:
float sharpness_; float sharpness_;
@ -124,17 +124,17 @@ inline FeatherBlender::FeatherBlender(float _sharpness) { setSharpness(_sharpnes
/** @brief Blender which uses multi-band blending algorithm (see @cite BA83). /** @brief Blender which uses multi-band blending algorithm (see @cite BA83).
*/ */
class CV_EXPORTS MultiBandBlender : public Blender class CV_EXPORTS_W MultiBandBlender : public Blender
{ {
public: public:
MultiBandBlender(int try_gpu = false, int num_bands = 5, int weight_type = CV_32F); CV_WRAP MultiBandBlender(int try_gpu = false, int num_bands = 5, int weight_type = CV_32F);
int numBands() const { return actual_num_bands_; } CV_WRAP int numBands() const { return actual_num_bands_; }
void setNumBands(int val) { actual_num_bands_ = val; } CV_WRAP void setNumBands(int val) { actual_num_bands_ = val; }
void prepare(Rect dst_roi) CV_OVERRIDE; CV_WRAP void prepare(Rect dst_roi) CV_OVERRIDE;
void feed(InputArray img, InputArray mask, Point tl) CV_OVERRIDE; CV_WRAP void feed(InputArray img, InputArray mask, Point tl) CV_OVERRIDE;
void blend(InputOutputArray dst, InputOutputArray dst_mask) CV_OVERRIDE; CV_WRAP void blend(CV_IN_OUT InputOutputArray dst, CV_IN_OUT InputOutputArray dst_mask) CV_OVERRIDE;
private: private:
int actual_num_bands_, num_bands_; int actual_num_bands_, num_bands_;
@ -165,16 +165,16 @@ private:
////////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////////
// Auxiliary functions // Auxiliary functions
void CV_EXPORTS normalizeUsingWeightMap(InputArray weight, InputOutputArray src); void CV_EXPORTS_W normalizeUsingWeightMap(InputArray weight, CV_IN_OUT InputOutputArray src);
void CV_EXPORTS createWeightMap(InputArray mask, float sharpness, InputOutputArray weight); void CV_EXPORTS_W createWeightMap(InputArray mask, float sharpness, CV_IN_OUT InputOutputArray weight);
void CV_EXPORTS createLaplacePyr(InputArray img, int num_levels, std::vector<UMat>& pyr); void CV_EXPORTS_W createLaplacePyr(InputArray img, int num_levels, CV_IN_OUT std::vector<UMat>& pyr);
void CV_EXPORTS createLaplacePyrGpu(InputArray img, int num_levels, std::vector<UMat>& pyr); void CV_EXPORTS_W createLaplacePyrGpu(InputArray img, int num_levels, CV_IN_OUT std::vector<UMat>& pyr);
// Restores source image // Restores source image
void CV_EXPORTS restoreImageFromLaplacePyr(std::vector<UMat>& pyr); void CV_EXPORTS_W restoreImageFromLaplacePyr(CV_IN_OUT std::vector<UMat>& pyr);
void CV_EXPORTS restoreImageFromLaplacePyrGpu(std::vector<UMat>& pyr); void CV_EXPORTS_W restoreImageFromLaplacePyrGpu(CV_IN_OUT std::vector<UMat>& pyr);
//! @} //! @}

16
modules/stitching/include/opencv2/stitching/detail/camera.hpp
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@ -55,19 +55,19 @@ namespace detail {
@note Translation is assumed to be zero during the whole stitching pipeline. : @note Translation is assumed to be zero during the whole stitching pipeline. :
*/ */
struct CV_EXPORTS CameraParams struct CV_EXPORTS_W_SIMPLE CameraParams
{ {
CameraParams(); CameraParams();
CameraParams(const CameraParams& other); CameraParams(const CameraParams& other);
CameraParams& operator =(const CameraParams& other); CameraParams& operator =(const CameraParams& other);
Mat K() const; CV_WRAP Mat K() const;
double focal; // Focal length CV_PROP_RW double focal; // Focal length
double aspect; // Aspect ratio CV_PROP_RW double aspect; // Aspect ratio
double ppx; // Principal point X CV_PROP_RW double ppx; // Principal point X
double ppy; // Principal point Y CV_PROP_RW double ppy; // Principal point Y
Mat R; // Rotation CV_PROP_RW Mat R; // Rotation
Mat t; // Translation CV_PROP_RW Mat t; // Translation
}; };
//! @} //! @}

38
modules/stitching/include/opencv2/stitching/detail/exposure_compensate.hpp
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@ -57,13 +57,13 @@ namespace detail {
/** @brief Base class for all exposure compensators. /** @brief Base class for all exposure compensators.
*/ */
class CV_EXPORTS ExposureCompensator class CV_EXPORTS_W ExposureCompensator
{ {
public: public:
virtual ~ExposureCompensator() {} virtual ~ExposureCompensator() {}
enum { NO, GAIN, GAIN_BLOCKS }; enum { NO, GAIN, GAIN_BLOCKS };
static Ptr<ExposureCompensator> createDefault(int type); CV_WRAP static Ptr<ExposureCompensator> createDefault(int type);
/** /**
@param corners Source image top-left corners @param corners Source image top-left corners
@ -71,11 +71,11 @@ public:
@param masks Image masks to update (second value in pair specifies the value which should be used @param masks Image masks to update (second value in pair specifies the value which should be used
to detect where image is) to detect where image is)
*/ */
void feed(const std::vector<Point> &corners, const std::vector<UMat> &images, CV_WRAP void feed(const std::vector<Point> &corners, const std::vector<UMat> &images,
const std::vector<UMat> &masks); const std::vector<UMat> &masks);
/** @overload */ /** @overload */
virtual void feed(const std::vector<Point> &corners, const std::vector<UMat> &images, virtual void feed(const std::vector<Point> &corners, const std::vector<UMat> &images,
const std::vector<std::pair<UMat,uchar> > &masks) = 0; const std::vector<std::pair<UMat, uchar> > &masks) = 0;
/** @brief Compensate exposure in the specified image. /** @brief Compensate exposure in the specified image.
@param index Image index @param index Image index
@ -83,28 +83,38 @@ public:
@param image Image to process @param image Image to process
@param mask Image mask @param mask Image mask
*/ */
virtual void apply(int index, Point corner, InputOutputArray image, InputArray mask) = 0; CV_WRAP virtual void apply(int index, Point corner, InputOutputArray image, InputArray mask) = 0;
CV_WRAP virtual void getMatGains(CV_OUT std::vector<Mat>& ) {CV_Error(Error::StsInternal, "");};
CV_WRAP virtual void setMatGains(std::vector<Mat>& ) { CV_Error(Error::StsInternal, ""); };
CV_WRAP void setUpdateGain(bool b) { updateGain = b; };
CV_WRAP bool getUpdateGain() { return updateGain; };
protected :
bool updateGain;
}; };
/** @brief Stub exposure compensator which does nothing. /** @brief Stub exposure compensator which does nothing.
*/ */
class CV_EXPORTS NoExposureCompensator : public ExposureCompensator class CV_EXPORTS_W NoExposureCompensator : public ExposureCompensator
{ {
public: public:
void feed(const std::vector<Point> &/*corners*/, const std::vector<UMat> &/*images*/, void feed(const std::vector<Point> &/*corners*/, const std::vector<UMat> &/*images*/,
const std::vector<std::pair<UMat,uchar> > &/*masks*/) CV_OVERRIDE { } const std::vector<std::pair<UMat,uchar> > &/*masks*/) CV_OVERRIDE { }
void apply(int /*index*/, Point /*corner*/, InputOutputArray /*image*/, InputArray /*mask*/) CV_OVERRIDE { } CV_WRAP void apply(int /*index*/, Point /*corner*/, InputOutputArray /*image*/, InputArray /*mask*/) CV_OVERRIDE { }
CV_WRAP void getMatGains(CV_OUT std::vector<Mat>& umv) CV_OVERRIDE { umv.clear(); return; };
CV_WRAP void setMatGains(std::vector<Mat>& umv) CV_OVERRIDE { umv.clear(); return; };
}; };
/** @brief Exposure compensator which tries to remove exposure related artifacts by adjusting image /** @brief Exposure compensator which tries to remove exposure related artifacts by adjusting image
intensities, see @cite BL07 and @cite WJ10 for details. intensities, see @cite BL07 and @cite WJ10 for details.
*/ */
class CV_EXPORTS GainCompensator : public ExposureCompensator class CV_EXPORTS_W GainCompensator : public ExposureCompensator
{ {
public: public:
void feed(const std::vector<Point> &corners, const std::vector<UMat> &images, void feed(const std::vector<Point> &corners, const std::vector<UMat> &images,
const std::vector<std::pair<UMat,uchar> > &masks) CV_OVERRIDE; const std::vector<std::pair<UMat,uchar> > &masks) CV_OVERRIDE;
void apply(int index, Point corner, InputOutputArray image, InputArray mask) CV_OVERRIDE; CV_WRAP void apply(int index, Point corner, InputOutputArray image, InputArray mask) CV_OVERRIDE;
CV_WRAP void getMatGains(CV_OUT std::vector<Mat>& umv) CV_OVERRIDE ;
CV_WRAP void setMatGains(std::vector<Mat>& umv) CV_OVERRIDE ;
std::vector<double> gains() const; std::vector<double> gains() const;
private: private:
@ -114,14 +124,16 @@ private:
/** @brief Exposure compensator which tries to remove exposure related artifacts by adjusting image block /** @brief Exposure compensator which tries to remove exposure related artifacts by adjusting image block
intensities, see @cite UES01 for details. intensities, see @cite UES01 for details.
*/ */
class CV_EXPORTS BlocksGainCompensator : public ExposureCompensator class CV_EXPORTS_W BlocksGainCompensator : public ExposureCompensator
{ {
public: public:
BlocksGainCompensator(int bl_width = 32, int bl_height = 32) CV_WRAP BlocksGainCompensator(int bl_width = 32, int bl_height = 32)
: bl_width_(bl_width), bl_height_(bl_height) {} : bl_width_(bl_width), bl_height_(bl_height) {setUpdateGain(true);}
void feed(const std::vector<Point> &corners, const std::vector<UMat> &images, void feed(const std::vector<Point> &corners, const std::vector<UMat> &images,
const std::vector<std::pair<UMat,uchar> > &masks) CV_OVERRIDE; const std::vector<std::pair<UMat,uchar> > &masks) CV_OVERRIDE;
void apply(int index, Point corner, InputOutputArray image, InputArray mask) CV_OVERRIDE; CV_WRAP void apply(int index, Point corner, InputOutputArray image, InputArray mask) CV_OVERRIDE;
CV_WRAP void getMatGains(CV_OUT std::vector<Mat>& umv) CV_OVERRIDE;
CV_WRAP void setMatGains(std::vector<Mat>& umv) CV_OVERRIDE;
private: private:
int bl_width_, bl_height_; int bl_width_, bl_height_;

75
modules/stitching/include/opencv2/stitching/detail/matchers.hpp
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@ -55,24 +55,38 @@ namespace detail {
//! @{ //! @{
/** @brief Structure containing image keypoints and descriptors. */ /** @brief Structure containing image keypoints and descriptors. */
struct CV_EXPORTS ImageFeatures struct CV_EXPORTS_W_SIMPLE ImageFeatures
{ {
int img_idx; CV_PROP_RW int img_idx;
Size img_size; CV_PROP_RW Size img_size;
std::vector<KeyPoint> keypoints; std::vector<KeyPoint> keypoints;
UMat descriptors; CV_PROP_RW UMat descriptors;
CV_WRAP std::vector<KeyPoint> getKeypoints() { return keypoints; };
}; };
/** @brief
CV_EXPORTS void computeImageFeatures( @param featuresFinder
@param images
@param features
@param masks
*/
CV_EXPORTS_W void computeImageFeatures(
const Ptr<Feature2D> &featuresFinder, const Ptr<Feature2D> &featuresFinder,
InputArrayOfArrays images, InputArrayOfArrays images,
std::vector<ImageFeatures> &features, CV_OUT std::vector<ImageFeatures> &features,
InputArrayOfArrays masks = noArray()); InputArrayOfArrays masks = noArray());
CV_EXPORTS void computeImageFeatures( /** @brief
@param featuresFinder
@param image
@param features
@param mask
*/
CV_EXPORTS_AS(computeImageFeatures2) void computeImageFeatures(
const Ptr<Feature2D> &featuresFinder, const Ptr<Feature2D> &featuresFinder,
InputArray image, InputArray image,
ImageFeatures &features, CV_OUT ImageFeatures &features,
InputArray mask = noArray()); InputArray mask = noArray());
/** @brief Structure containing information about matches between two images. /** @brief Structure containing information about matches between two images.
@ -82,33 +96,36 @@ homography or affine transformation based on selected matcher.
@sa detail::FeaturesMatcher @sa detail::FeaturesMatcher
*/ */
struct CV_EXPORTS MatchesInfo struct CV_EXPORTS_W_SIMPLE MatchesInfo
{ {
MatchesInfo(); MatchesInfo();
MatchesInfo(const MatchesInfo &other); MatchesInfo(const MatchesInfo &other);
MatchesInfo& operator =(const MatchesInfo &other); MatchesInfo& operator =(const MatchesInfo &other);
int src_img_idx, dst_img_idx; //!< Images indices (optional) CV_PROP_RW int src_img_idx;
CV_PROP_RW int dst_img_idx; //!< Images indices (optional)
std::vector<DMatch> matches; std::vector<DMatch> matches;
std::vector<uchar> inliers_mask; //!< Geometrically consistent matches mask std::vector<uchar> inliers_mask; //!< Geometrically consistent matches mask
int num_inliers; //!< Number of geometrically consistent matches CV_PROP_RW int num_inliers; //!< Number of geometrically consistent matches
Mat H; //!< Estimated transformation CV_PROP_RW Mat H; //!< Estimated transformation
double confidence; //!< Confidence two images are from the same panorama CV_PROP_RW double confidence; //!< Confidence two images are from the same panorama
CV_WRAP std::vector<DMatch> getMatches() { return matches; };
CV_WRAP std::vector<uchar> getInliers() { return inliers_mask; };
}; };
/** @brief Feature matchers base class. */ /** @brief Feature matchers base class. */
class CV_EXPORTS FeaturesMatcher class CV_EXPORTS_W FeaturesMatcher
{ {
public: public:
virtual ~FeaturesMatcher() {} CV_WRAP virtual ~FeaturesMatcher() {}
/** @overload /** @overload
@param features1 First image features @param features1 First image features
@param features2 Second image features @param features2 Second image features
@param matches_info Found matches @param matches_info Found matches
*/ */
void operator ()(const ImageFeatures &features1, const ImageFeatures &features2, CV_WRAP_AS(apply) void operator ()(const ImageFeatures &features1, const ImageFeatures &features2,
MatchesInfo& matches_info) { match(features1, features2, matches_info); } CV_OUT MatchesInfo& matches_info) { match(features1, features2, matches_info); }
/** @brief Performs images matching. /** @brief Performs images matching.
@ -120,16 +137,16 @@ public:
@sa detail::MatchesInfo @sa detail::MatchesInfo
*/ */
void operator ()(const std::vector<ImageFeatures> &features, std::vector<MatchesInfo> &pairwise_matches, CV_WRAP_AS(apply2) void operator ()(const std::vector<ImageFeatures> &features, CV_OUT std::vector<MatchesInfo> &pairwise_matches,
const cv::UMat &mask = cv::UMat()); const cv::UMat &mask = cv::UMat());
/** @return True, if it's possible to use the same matcher instance in parallel, false otherwise /** @return True, if it's possible to use the same matcher instance in parallel, false otherwise
*/ */
bool isThreadSafe() const { return is_thread_safe_; } CV_WRAP bool isThreadSafe() const { return is_thread_safe_; }
/** @brief Frees unused memory allocated before if there is any. /** @brief Frees unused memory allocated before if there is any.
*/ */
virtual void collectGarbage() {} CV_WRAP virtual void collectGarbage() {}
protected: protected:
FeaturesMatcher(bool is_thread_safe = false) : is_thread_safe_(is_thread_safe) {} FeaturesMatcher(bool is_thread_safe = false) : is_thread_safe_(is_thread_safe) {}
@ -152,7 +169,7 @@ ratio between descriptor distances is greater than the threshold match_conf
@sa detail::FeaturesMatcher @sa detail::FeaturesMatcher
*/ */
class CV_EXPORTS BestOf2NearestMatcher : public FeaturesMatcher class CV_EXPORTS_W BestOf2NearestMatcher : public FeaturesMatcher
{ {
public: public:
/** @brief Constructs a "best of 2 nearest" matcher. /** @brief Constructs a "best of 2 nearest" matcher.
@ -164,23 +181,25 @@ public:
@param num_matches_thresh2 Minimum number of matches required for the 2D projective transform @param num_matches_thresh2 Minimum number of matches required for the 2D projective transform
re-estimation on inliers re-estimation on inliers
*/ */
BestOf2NearestMatcher(bool try_use_gpu = false, float match_conf = 0.3f, int num_matches_thresh1 = 6, CV_WRAP BestOf2NearestMatcher(bool try_use_gpu = false, float match_conf = 0.3f, int num_matches_thresh1 = 6,
int num_matches_thresh2 = 6); int num_matches_thresh2 = 6);
void collectGarbage() CV_OVERRIDE; CV_WRAP void collectGarbage() CV_OVERRIDE;
CV_WRAP static Ptr<BestOf2NearestMatcher> create(bool try_use_gpu = false, float match_conf = 0.3f, int num_matches_thresh1 = 6,
int num_matches_thresh2 = 6);
protected: protected:
void match(const ImageFeatures &features1, const ImageFeatures &features2, MatchesInfo &matches_info) CV_OVERRIDE;
void match(const ImageFeatures &features1, const ImageFeatures &features2, MatchesInfo &matches_info) CV_OVERRIDE;
int num_matches_thresh1_; int num_matches_thresh1_;
int num_matches_thresh2_; int num_matches_thresh2_;
Ptr<FeaturesMatcher> impl_; Ptr<FeaturesMatcher> impl_;
}; };
class CV_EXPORTS BestOf2NearestRangeMatcher : public BestOf2NearestMatcher class CV_EXPORTS_W BestOf2NearestRangeMatcher : public BestOf2NearestMatcher
{ {
public: public:
BestOf2NearestRangeMatcher(int range_width = 5, bool try_use_gpu = false, float match_conf = 0.3f, CV_WRAP BestOf2NearestRangeMatcher(int range_width = 5, bool try_use_gpu = false, float match_conf = 0.3f,
int num_matches_thresh1 = 6, int num_matches_thresh2 = 6); int num_matches_thresh1 = 6, int num_matches_thresh2 = 6);
void operator ()(const std::vector<ImageFeatures> &features, std::vector<MatchesInfo> &pairwise_matches, void operator ()(const std::vector<ImageFeatures> &features, std::vector<MatchesInfo> &pairwise_matches,
@ -200,7 +219,7 @@ transformation (affine trasformation estimate will be placed in matches_info).
@sa cv::detail::FeaturesMatcher cv::detail::BestOf2NearestMatcher @sa cv::detail::FeaturesMatcher cv::detail::BestOf2NearestMatcher
*/ */
class CV_EXPORTS AffineBestOf2NearestMatcher : public BestOf2NearestMatcher class CV_EXPORTS_W AffineBestOf2NearestMatcher : public BestOf2NearestMatcher
{ {
public: public:
/** @brief Constructs a "best of 2 nearest" matcher that expects affine trasformation /** @brief Constructs a "best of 2 nearest" matcher that expects affine trasformation
@ -215,7 +234,7 @@ public:
@sa cv::estimateAffine2D cv::estimateAffinePartial2D @sa cv::estimateAffine2D cv::estimateAffinePartial2D
*/ */
AffineBestOf2NearestMatcher(bool full_affine = false, bool try_use_gpu = false, CV_WRAP AffineBestOf2NearestMatcher(bool full_affine = false, bool try_use_gpu = false,
float match_conf = 0.3f, int num_matches_thresh1 = 6) : float match_conf = 0.3f, int num_matches_thresh1 = 6) :
BestOf2NearestMatcher(try_use_gpu, match_conf, num_matches_thresh1, num_matches_thresh1), BestOf2NearestMatcher(try_use_gpu, match_conf, num_matches_thresh1, num_matches_thresh1),
full_affine_(full_affine) {} full_affine_(full_affine) {}

58
modules/stitching/include/opencv2/stitching/detail/motion_estimators.hpp
View File

@ -62,7 +62,7 @@ cameras.
@note The coordinate system origin is implementation-dependent, but you can always normalize the @note The coordinate system origin is implementation-dependent, but you can always normalize the
rotations in respect to the first camera, for instance. : rotations in respect to the first camera, for instance. :
*/ */
class CV_EXPORTS Estimator class CV_EXPORTS_W Estimator
{ {
public: public:
virtual ~Estimator() {} virtual ~Estimator() {}
@ -74,10 +74,12 @@ public:
@param cameras Estimated camera parameters @param cameras Estimated camera parameters
@return True in case of success, false otherwise @return True in case of success, false otherwise
*/ */
bool operator ()(const std::vector<ImageFeatures> &features, CV_WRAP_AS(apply) bool operator ()(const std::vector<ImageFeatures> &features,
const std::vector<MatchesInfo> &pairwise_matches, const std::vector<MatchesInfo> &pairwise_matches,
std::vector<CameraParams> &cameras) CV_OUT CV_IN_OUT std::vector<CameraParams> &cameras)
{ return estimate(features, pairwise_matches, cameras); } {
return estimate(features, pairwise_matches, cameras);
}
protected: protected:
/** @brief This method must implement camera parameters estimation logic in order to make the wrapper /** @brief This method must implement camera parameters estimation logic in order to make the wrapper
@ -90,15 +92,15 @@ protected:
*/ */
virtual bool estimate(const std::vector<ImageFeatures> &features, virtual bool estimate(const std::vector<ImageFeatures> &features,
const std::vector<MatchesInfo> &pairwise_matches, const std::vector<MatchesInfo> &pairwise_matches,
std::vector<CameraParams> &cameras) = 0; CV_OUT std::vector<CameraParams> &cameras) = 0;
}; };
/** @brief Homography based rotation estimator. /** @brief Homography based rotation estimator.
*/ */
class CV_EXPORTS HomographyBasedEstimator : public Estimator class CV_EXPORTS_W HomographyBasedEstimator : public Estimator
{ {
public: public:
HomographyBasedEstimator(bool is_focals_estimated = false) CV_WRAP HomographyBasedEstimator(bool is_focals_estimated = false)
: is_focals_estimated_(is_focals_estimated) {} : is_focals_estimated_(is_focals_estimated) {}
private: private:
@ -116,7 +118,7 @@ final transformation for each camera.
@sa cv::detail::HomographyBasedEstimator @sa cv::detail::HomographyBasedEstimator
*/ */
class CV_EXPORTS AffineBasedEstimator : public Estimator class CV_EXPORTS_W AffineBasedEstimator : public Estimator
{ {
private: private:
virtual bool estimate(const std::vector<ImageFeatures> &features, virtual bool estimate(const std::vector<ImageFeatures> &features,
@ -126,21 +128,21 @@ private:
/** @brief Base class for all camera parameters refinement methods. /** @brief Base class for all camera parameters refinement methods.
*/ */
class CV_EXPORTS BundleAdjusterBase : public Estimator class CV_EXPORTS_W BundleAdjusterBase : public Estimator
{ {
public: public:
const Mat refinementMask() const { return refinement_mask_.clone(); } CV_WRAP const Mat refinementMask() const { return refinement_mask_.clone(); }
void setRefinementMask(const Mat &mask) CV_WRAP void setRefinementMask(const Mat &mask)
{ {
CV_Assert(mask.type() == CV_8U && mask.size() == Size(3, 3)); CV_Assert(mask.type() == CV_8U && mask.size() == Size(3, 3));
refinement_mask_ = mask.clone(); refinement_mask_ = mask.clone();
} }
double confThresh() const { return conf_thresh_; } CV_WRAP double confThresh() const { return conf_thresh_; }
void setConfThresh(double conf_thresh) { conf_thresh_ = conf_thresh; } CV_WRAP void setConfThresh(double conf_thresh) { conf_thresh_ = conf_thresh; }
TermCriteria termCriteria() { return term_criteria_; } CV_WRAP TermCriteria termCriteria() { return term_criteria_; }
void setTermCriteria(const TermCriteria& term_criteria) { term_criteria_ = term_criteria; } CV_WRAP void setTermCriteria(const TermCriteria& term_criteria) { term_criteria_ = term_criteria; }
protected: protected:
/** @brief Construct a bundle adjuster base instance. /** @brief Construct a bundle adjuster base instance.
@ -214,10 +216,10 @@ protected:
/** @brief Stub bundle adjuster that does nothing. /** @brief Stub bundle adjuster that does nothing.
*/ */
class CV_EXPORTS NoBundleAdjuster : public BundleAdjusterBase class CV_EXPORTS_W NoBundleAdjuster : public BundleAdjusterBase
{ {
public: public:
NoBundleAdjuster() : BundleAdjusterBase(0, 0) {} CV_WRAP NoBundleAdjuster() : BundleAdjusterBase(0, 0) {}
private: private:
bool estimate(const std::vector<ImageFeatures> &, const std::vector<MatchesInfo> &, bool estimate(const std::vector<ImageFeatures> &, const std::vector<MatchesInfo> &,
@ -238,10 +240,10 @@ error squares
It can estimate focal length, aspect ratio, principal point. It can estimate focal length, aspect ratio, principal point.
You can affect only on them via the refinement mask. You can affect only on them via the refinement mask.
*/ */
class CV_EXPORTS BundleAdjusterReproj : public BundleAdjusterBase class CV_EXPORTS_W BundleAdjusterReproj : public BundleAdjusterBase
{ {
public: public:
BundleAdjusterReproj() : BundleAdjusterBase(7, 2) {} CV_WRAP BundleAdjusterReproj() : BundleAdjusterBase(7, 2) {}
private: private:
void setUpInitialCameraParams(const std::vector<CameraParams> &cameras) CV_OVERRIDE; void setUpInitialCameraParams(const std::vector<CameraParams> &cameras) CV_OVERRIDE;
@ -258,10 +260,10 @@ between the rays passing through the camera center and a feature. :
It can estimate focal length. It ignores the refinement mask for now. It can estimate focal length. It ignores the refinement mask for now.
*/ */
class CV_EXPORTS BundleAdjusterRay : public BundleAdjusterBase class CV_EXPORTS_W BundleAdjusterRay : public BundleAdjusterBase
{ {
public: public:
BundleAdjusterRay() : BundleAdjusterBase(4, 3) {} CV_WRAP BundleAdjusterRay() : BundleAdjusterBase(4, 3) {}
private: private:
void setUpInitialCameraParams(const std::vector<CameraParams> &cameras) CV_OVERRIDE; void setUpInitialCameraParams(const std::vector<CameraParams> &cameras) CV_OVERRIDE;
@ -282,10 +284,10 @@ It estimates all transformation parameters. Refinement mask is ignored.
@sa AffineBasedEstimator AffineBestOf2NearestMatcher BundleAdjusterAffinePartial @sa AffineBasedEstimator AffineBestOf2NearestMatcher BundleAdjusterAffinePartial
*/ */
class CV_EXPORTS BundleAdjusterAffine : public BundleAdjusterBase class CV_EXPORTS_W BundleAdjusterAffine : public BundleAdjusterBase
{ {
public: public:
BundleAdjusterAffine() : BundleAdjusterBase(6, 2) {} CV_WRAP BundleAdjusterAffine() : BundleAdjusterBase(6, 2) {}
private: private:
void setUpInitialCameraParams(const std::vector<CameraParams> &cameras) CV_OVERRIDE; void setUpInitialCameraParams(const std::vector<CameraParams> &cameras) CV_OVERRIDE;
@ -306,10 +308,10 @@ It estimates all transformation parameters. Refinement mask is ignored.
@sa AffineBasedEstimator AffineBestOf2NearestMatcher BundleAdjusterAffine @sa AffineBasedEstimator AffineBestOf2NearestMatcher BundleAdjusterAffine
*/ */
class CV_EXPORTS BundleAdjusterAffinePartial : public BundleAdjusterBase class CV_EXPORTS_W BundleAdjusterAffinePartial : public BundleAdjusterBase
{ {
public: public:
BundleAdjusterAffinePartial() : BundleAdjusterBase(4, 2) {} CV_WRAP BundleAdjusterAffinePartial() : BundleAdjusterBase(4, 2) {}
private: private:
void setUpInitialCameraParams(const std::vector<CameraParams> &cameras) CV_OVERRIDE; void setUpInitialCameraParams(const std::vector<CameraParams> &cameras) CV_OVERRIDE;
@ -332,17 +334,17 @@ enum WaveCorrectKind
@param rmats Camera rotation matrices. @param rmats Camera rotation matrices.
@param kind Correction kind, see detail::WaveCorrectKind. @param kind Correction kind, see detail::WaveCorrectKind.
*/ */
void CV_EXPORTS waveCorrect(std::vector<Mat> &rmats, WaveCorrectKind kind); void CV_EXPORTS_W waveCorrect(CV_IN_OUT std::vector<Mat> &rmats, WaveCorrectKind kind);
////////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////////
// Auxiliary functions // Auxiliary functions
// Returns matches graph representation in DOT language // Returns matches graph representation in DOT language
String CV_EXPORTS matchesGraphAsString(std::vector<String> &pathes, std::vector<MatchesInfo> &pairwise_matches, String CV_EXPORTS_W matchesGraphAsString(std::vector<String> &pathes, std::vector<MatchesInfo> &pairwise_matches,
float conf_threshold); float conf_threshold);
std::vector<int> CV_EXPORTS leaveBiggestComponent( CV_EXPORTS_W std::vector<int> leaveBiggestComponent(
std::vector<ImageFeatures> &features, std::vector<ImageFeatures> &features,
std::vector<MatchesInfo> &pairwise_matches, std::vector<MatchesInfo> &pairwise_matches,
float conf_threshold); float conf_threshold);

36
modules/stitching/include/opencv2/stitching/detail/seam_finders.hpp
View File

@ -55,35 +55,37 @@ namespace detail {
/** @brief Base class for a seam estimator. /** @brief Base class for a seam estimator.
*/ */
class CV_EXPORTS SeamFinder class CV_EXPORTS_W SeamFinder
{ {
public: public:
virtual ~SeamFinder() {} CV_WRAP virtual ~SeamFinder() {}
enum { NO, VORONOI_SEAM, DP_SEAM };
/** @brief Estimates seams. /** @brief Estimates seams.
@param src Source images @param src Source images
@param corners Source image top-left corners @param corners Source image top-left corners
@param masks Source image masks to update @param masks Source image masks to update
*/ */
virtual void find(const std::vector<UMat> &src, const std::vector<Point> &corners, CV_WRAP virtual void find(const std::vector<UMat> &src, const std::vector<Point> &corners,
std::vector<UMat> &masks) = 0; CV_IN_OUT std::vector<UMat> &masks) = 0;
CV_WRAP static Ptr<SeamFinder> createDefault(int type);
}; };
/** @brief Stub seam estimator which does nothing. /** @brief Stub seam estimator which does nothing.
*/ */
class CV_EXPORTS NoSeamFinder : public SeamFinder class CV_EXPORTS_W NoSeamFinder : public SeamFinder
{ {
public: public:
void find(const std::vector<UMat>&, const std::vector<Point>&, std::vector<UMat>&) CV_OVERRIDE {} CV_WRAP void find(const std::vector<UMat>&, const std::vector<Point>&, CV_IN_OUT std::vector<UMat>&) CV_OVERRIDE {}
}; };
/** @brief Base class for all pairwise seam estimators. /** @brief Base class for all pairwise seam estimators.
*/ */
class CV_EXPORTS PairwiseSeamFinder : public SeamFinder class CV_EXPORTS_W PairwiseSeamFinder : public SeamFinder
{ {
public: public:
virtual void find(const std::vector<UMat> &src, const std::vector<Point> &corners, CV_WRAP virtual void find(const std::vector<UMat> &src, const std::vector<Point> &corners,
std::vector<UMat> &masks) CV_OVERRIDE; CV_IN_OUT std::vector<UMat> &masks) CV_OVERRIDE;
protected: protected:
void run(); void run();
@ -103,11 +105,11 @@ protected:
/** @brief Voronoi diagram-based seam estimator. /** @brief Voronoi diagram-based seam estimator.
*/ */
class CV_EXPORTS VoronoiSeamFinder : public PairwiseSeamFinder class CV_EXPORTS_W VoronoiSeamFinder : public PairwiseSeamFinder
{ {
public: public:
virtual void find(const std::vector<UMat> &src, const std::vector<Point> &corners, CV_WRAP virtual void find(const std::vector<UMat> &src, const std::vector<Point> &corners,
std::vector<UMat> &masks) CV_OVERRIDE; CV_IN_OUT std::vector<UMat> &masks) CV_OVERRIDE;
virtual void find(const std::vector<Size> &size, const std::vector<Point> &corners, virtual void find(const std::vector<Size> &size, const std::vector<Point> &corners,
std::vector<UMat> &masks); std::vector<UMat> &masks);
private: private:
@ -115,15 +117,17 @@ private:
}; };
class CV_EXPORTS DpSeamFinder : public SeamFinder class CV_EXPORTS_W DpSeamFinder : public SeamFinder
{ {
public: public:
enum CostFunction { COLOR, COLOR_GRAD }; enum CostFunction { COLOR, COLOR_GRAD };
DpSeamFinder(CostFunction costFunc = COLOR); DpSeamFinder(CostFunction costFunc = COLOR);
CV_WRAP DpSeamFinder(String costFunc );
CostFunction costFunction() const { return costFunc_; } CostFunction costFunction() const { return costFunc_; }
void setCostFunction(CostFunction val) { costFunc_ = val; } void setCostFunction(CostFunction val) { costFunc_ = val; }
CV_WRAP void setCostFunction(String val);
virtual void find(const std::vector<UMat> &src, const std::vector<Point> &corners, virtual void find(const std::vector<UMat> &src, const std::vector<Point> &corners,
std::vector<UMat> &masks) CV_OVERRIDE; std::vector<UMat> &masks) CV_OVERRIDE;
@ -233,15 +237,17 @@ public:
/** @brief Minimum graph cut-based seam estimator. See details in @cite V03 . /** @brief Minimum graph cut-based seam estimator. See details in @cite V03 .
*/ */
class CV_EXPORTS GraphCutSeamFinder : public GraphCutSeamFinderBase, public SeamFinder class CV_EXPORTS_W GraphCutSeamFinder : public GraphCutSeamFinderBase, public SeamFinder
{ {
public: public:
GraphCutSeamFinder(int cost_type = COST_COLOR_GRAD, float terminal_cost = 10000.f, GraphCutSeamFinder(int cost_type = COST_COLOR_GRAD, float terminal_cost = 10000.f,
float bad_region_penalty = 1000.f); float bad_region_penalty = 1000.f);
CV_WRAP GraphCutSeamFinder(String cost_type,float terminal_cost = 10000.f,
float bad_region_penalty = 1000.f);
~GraphCutSeamFinder(); ~GraphCutSeamFinder();
void find(const std::vector<UMat> &src, const std::vector<Point> &corners, CV_WRAP void find(const std::vector<UMat> &src, const std::vector<Point> &corners,
std::vector<UMat> &masks) CV_OVERRIDE; std::vector<UMat> &masks) CV_OVERRIDE;
private: private:

12
modules/stitching/include/opencv2/stitching/detail/timelapsers.hpp
View File

@ -54,7 +54,7 @@ namespace detail {
// Base Timelapser class, takes a sequence of images, applies appropriate shift, stores result in dst_. // Base Timelapser class, takes a sequence of images, applies appropriate shift, stores result in dst_.
class CV_EXPORTS Timelapser class CV_EXPORTS_W Timelapser
{ {
public: public:
@ -62,11 +62,11 @@ public:
virtual ~Timelapser() {} virtual ~Timelapser() {}
static Ptr<Timelapser> createDefault(int type); CV_WRAP static Ptr<Timelapser> createDefault(int type);
virtual void initialize(const std::vector<Point> &corners, const std::vector<Size> &sizes); CV_WRAP virtual void initialize(const std::vector<Point> &corners, const std::vector<Size> &sizes);
virtual void process(InputArray img, InputArray mask, Point tl); CV_WRAP virtual void process(InputArray img, InputArray mask, Point tl);
virtual const UMat& getDst() {return dst_;} CV_WRAP virtual const UMat& getDst() {return dst_;}
protected: protected:
@ -77,7 +77,7 @@ protected:
}; };
class CV_EXPORTS TimelapserCrop : public Timelapser class CV_EXPORTS_W TimelapserCrop : public Timelapser
{ {
public: public:
virtual void initialize(const std::vector<Point> &corners, const std::vector<Size> &sizes) CV_OVERRIDE; virtual void initialize(const std::vector<Point> &corners, const std::vector<Size> &sizes) CV_OVERRIDE;

14
modules/stitching/include/opencv2/stitching/detail/util.hpp
View File

@ -100,16 +100,16 @@ private:
////////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////////
// Auxiliary functions // Auxiliary functions
CV_EXPORTS bool overlapRoi(Point tl1, Point tl2, Size sz1, Size sz2, Rect &roi); CV_EXPORTS_W bool overlapRoi(Point tl1, Point tl2, Size sz1, Size sz2, Rect &roi);
CV_EXPORTS Rect resultRoi(const std::vector<Point> &corners, const std::vector<UMat> &images); CV_EXPORTS_W Rect resultRoi(const std::vector<Point> &corners, const std::vector<UMat> &images);
CV_EXPORTS Rect resultRoi(const std::vector<Point> &corners, const std::vector<Size> &sizes); CV_EXPORTS_W Rect resultRoi(const std::vector<Point> &corners, const std::vector<Size> &sizes);
CV_EXPORTS Rect resultRoiIntersection(const std::vector<Point> &corners, const std::vector<Size> &sizes); CV_EXPORTS_W Rect resultRoiIntersection(const std::vector<Point> &corners, const std::vector<Size> &sizes);
CV_EXPORTS Point resultTl(const std::vector<Point> &corners); CV_EXPORTS_W Point resultTl(const std::vector<Point> &corners);
// Returns random 'count' element subset of the {0,1,...,size-1} set // Returns random 'count' element subset of the {0,1,...,size-1} set
CV_EXPORTS void selectRandomSubset(int count, int size, std::vector<int> &subset); CV_EXPORTS_W void selectRandomSubset(int count, int size, std::vector<int> &subset);
CV_EXPORTS int& stitchingLogLevel(); CV_EXPORTS_W int& stitchingLogLevel();
//! @} //! @}

24
modules/stitching/include/opencv2/stitching/detail/warpers.hpp
View File

@ -92,7 +92,7 @@ public:
@return Project image top-left corner @return Project image top-left corner
*/ */
virtual Point warp(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode, virtual Point warp(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode,
OutputArray dst) = 0; CV_OUT OutputArray dst) = 0;
/** @brief Projects the image backward. /** @brief Projects the image backward.
@ -105,7 +105,7 @@ public:
@param dst Backward-projected image @param dst Backward-projected image
*/ */
virtual void warpBackward(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode, virtual void warpBackward(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode,
Size dst_size, OutputArray dst) = 0; Size dst_size, CV_OUT OutputArray dst) = 0;
/** /**
@param src_size Source image bounding box @param src_size Source image bounding box
@ -121,7 +121,7 @@ public:
/** @brief Base class for warping logic implementation. /** @brief Base class for warping logic implementation.
*/ */
struct CV_EXPORTS ProjectorBase struct CV_EXPORTS_W_SIMPLE ProjectorBase
{ {
void setCameraParams(InputArray K = Mat::eye(3, 3, CV_32F), void setCameraParams(InputArray K = Mat::eye(3, 3, CV_32F),
InputArray R = Mat::eye(3, 3, CV_32F), InputArray R = Mat::eye(3, 3, CV_32F),
@ -189,13 +189,13 @@ public:
Point2f warpPoint(const Point2f &pt, InputArray K, InputArray R) CV_OVERRIDE; Point2f warpPoint(const Point2f &pt, InputArray K, InputArray R) CV_OVERRIDE;
Point2f warpPoint(const Point2f &pt, InputArray K, InputArray R, InputArray T); Point2f warpPoint(const Point2f &pt, InputArray K, InputArray R, InputArray T);
virtual Rect buildMaps(Size src_size, InputArray K, InputArray R, InputArray T, OutputArray xmap, OutputArray ymap); virtual Rect buildMaps(Size src_size, InputArray K, InputArray R, InputArray T, CV_OUT OutputArray xmap, CV_OUT OutputArray ymap);
Rect buildMaps(Size src_size, InputArray K, InputArray R, OutputArray xmap, OutputArray ymap) CV_OVERRIDE; Rect buildMaps(Size src_size, InputArray K, InputArray R, CV_OUT OutputArray xmap, CV_OUT OutputArray ymap) CV_OVERRIDE;
Point warp(InputArray src, InputArray K, InputArray R, Point warp(InputArray src, InputArray K, InputArray R,
int interp_mode, int border_mode, OutputArray dst) CV_OVERRIDE; int interp_mode, int border_mode, CV_OUT OutputArray dst) CV_OVERRIDE;
virtual Point warp(InputArray src, InputArray K, InputArray R, InputArray T, int interp_mode, int border_mode, virtual Point warp(InputArray src, InputArray K, InputArray R, InputArray T, int interp_mode, int border_mode,
OutputArray dst); CV_OUT OutputArray dst);
Rect warpRoi(Size src_size, InputArray K, InputArray R) CV_OVERRIDE; Rect warpRoi(Size src_size, InputArray K, InputArray R) CV_OVERRIDE;
Rect warpRoi(Size src_size, InputArray K, InputArray R, InputArray T); Rect warpRoi(Size src_size, InputArray K, InputArray R, InputArray T);
@ -220,9 +220,9 @@ public:
AffineWarper(float scale = 1.f) : PlaneWarper(scale) {} AffineWarper(float scale = 1.f) : PlaneWarper(scale) {}
Point2f warpPoint(const Point2f &pt, InputArray K, InputArray R) CV_OVERRIDE; Point2f warpPoint(const Point2f &pt, InputArray K, InputArray R) CV_OVERRIDE;
Rect buildMaps(Size src_size, InputArray K, InputArray R, OutputArray xmap, OutputArray ymap) CV_OVERRIDE; Rect buildMaps(Size src_size, InputArray K, InputArray R, CV_OUT OutputArray xmap, CV_OUT OutputArray ymap) CV_OVERRIDE;
Point warp(InputArray src, InputArray K, InputArray R, Point warp(InputArray src, InputArray K, InputArray R,
int interp_mode, int border_mode, OutputArray dst) CV_OVERRIDE; int interp_mode, int border_mode, CV_OUT OutputArray dst) CV_OVERRIDE;
Rect warpRoi(Size src_size, InputArray K, InputArray R) CV_OVERRIDE; Rect warpRoi(Size src_size, InputArray K, InputArray R) CV_OVERRIDE;
protected: protected:
@ -233,10 +233,10 @@ protected:
}; };
struct CV_EXPORTS SphericalProjector : ProjectorBase struct CV_EXPORTS_W_SIMPLE SphericalProjector : ProjectorBase
{ {
void mapForward(float x, float y, float &u, float &v); CV_WRAP void mapForward(float x, float y, float &u, float &v);
void mapBackward(float u, float v, float &x, float &y); CV_WRAP void mapBackward(float u, float v, float &x, float &y);
}; };

97
modules/stitching/include/opencv2/stitching/warpers.hpp
View File

@ -44,25 +44,94 @@
#define OPENCV_STITCHING_WARPER_CREATORS_HPP #define OPENCV_STITCHING_WARPER_CREATORS_HPP
#include "opencv2/stitching/detail/warpers.hpp" #include "opencv2/stitching/detail/warpers.hpp"
#include <string>
namespace cv { namespace cv {
class CV_EXPORTS_W PyRotationWarper
{
Ptr<detail::RotationWarper> rw;
public:
CV_WRAP PyRotationWarper(String type, float scale);
CV_WRAP PyRotationWarper() {};
~PyRotationWarper() {}
/** @brief Projects the image point.
@param pt Source point
@param K Camera intrinsic parameters
@param R Camera rotation matrix
@return Projected point
*/
CV_WRAP Point2f warpPoint(const Point2f &pt, InputArray K, InputArray R);
/** @brief Builds the projection maps according to the given camera data.
@param src_size Source image size
@param K Camera intrinsic parameters
@param R Camera rotation matrix
@param xmap Projection map for the x axis
@param ymap Projection map for the y axis
@return Projected image minimum bounding box
*/
CV_WRAP Rect buildMaps(Size src_size, InputArray K, InputArray R, OutputArray xmap, OutputArray ymap);
/** @brief Projects the image.
@param src Source image
@param K Camera intrinsic parameters
@param R Camera rotation matrix
@param interp_mode Interpolation mode
@param border_mode Border extrapolation mode
@param dst Projected image
@return Project image top-left corner
*/
CV_WRAP Point warp(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode,
CV_OUT OutputArray dst);
/** @brief Projects the image backward.
@param src Projected image
@param K Camera intrinsic parameters
@param R Camera rotation matrix
@param interp_mode Interpolation mode
@param border_mode Border extrapolation mode
@param dst_size Backward-projected image size
@param dst Backward-projected image
*/
CV_WRAP void warpBackward(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode,
Size dst_size, CV_OUT OutputArray dst);
/**
@param src_size Source image bounding box
@param K Camera intrinsic parameters
@param R Camera rotation matrix
@return Projected image minimum bounding box
*/
CV_WRAP Rect warpRoi(Size src_size, InputArray K, InputArray R);
CV_WRAP float getScale() const { return 1.f; }
CV_WRAP void setScale(float) {}
};
//! @addtogroup stitching_warp //! @addtogroup stitching_warp
//! @{ //! @{
/** @brief Image warper factories base class. /** @brief Image warper factories base class.
*/ */
class WarperCreator
class CV_EXPORTS_W WarperCreator
{ {
public: public:
virtual ~WarperCreator() {} CV_WRAP virtual ~WarperCreator() {}
virtual Ptr<detail::RotationWarper> create(float scale) const = 0; virtual Ptr<detail::RotationWarper> create(float scale) const = 0;
}; };
/** @brief Plane warper factory class. /** @brief Plane warper factory class.
@sa detail::PlaneWarper @sa detail::PlaneWarper
*/ */
class PlaneWarper : public WarperCreator class CV_EXPORTS PlaneWarper : public WarperCreator
{ {
public: public:
Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::PlaneWarper>(scale); } Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::PlaneWarper>(scale); }
@ -71,7 +140,7 @@ public:
/** @brief Affine warper factory class. /** @brief Affine warper factory class.
@sa detail::AffineWarper @sa detail::AffineWarper
*/ */
class AffineWarper : public WarperCreator class CV_EXPORTS AffineWarper : public WarperCreator
{ {
public: public:
Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::AffineWarper>(scale); } Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::AffineWarper>(scale); }
@ -80,32 +149,32 @@ public:
/** @brief Cylindrical warper factory class. /** @brief Cylindrical warper factory class.
@sa detail::CylindricalWarper @sa detail::CylindricalWarper
*/ */
class CylindricalWarper: public WarperCreator class CV_EXPORTS CylindricalWarper: public WarperCreator
{ {
public: public:
Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::CylindricalWarper>(scale); } Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::CylindricalWarper>(scale); }
}; };
/** @brief Spherical warper factory class */ /** @brief Spherical warper factory class */
class SphericalWarper: public WarperCreator class CV_EXPORTS SphericalWarper: public WarperCreator
{ {
public: public:
Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::SphericalWarper>(scale); } Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::SphericalWarper>(scale); }
}; };
class FisheyeWarper : public WarperCreator class CV_EXPORTS FisheyeWarper : public WarperCreator
{ {
public: public:
Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::FisheyeWarper>(scale); } Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::FisheyeWarper>(scale); }
}; };
class StereographicWarper: public WarperCreator class CV_EXPORTS StereographicWarper: public WarperCreator
{ {
public: public:
Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::StereographicWarper>(scale); } Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::StereographicWarper>(scale); }
}; };
class CompressedRectilinearWarper: public WarperCreator class CV_EXPORTS CompressedRectilinearWarper: public WarperCreator
{ {
float a, b; float a, b;
public: public:
@ -116,7 +185,7 @@ public:
Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::CompressedRectilinearWarper>(scale, a, b); } Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::CompressedRectilinearWarper>(scale, a, b); }
}; };
class CompressedRectilinearPortraitWarper: public WarperCreator class CV_EXPORTS CompressedRectilinearPortraitWarper: public WarperCreator
{ {
float a, b; float a, b;
public: public:
@ -127,7 +196,7 @@ public:
Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::CompressedRectilinearPortraitWarper>(scale, a, b); } Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::CompressedRectilinearPortraitWarper>(scale, a, b); }
}; };
class PaniniWarper: public WarperCreator class CV_EXPORTS PaniniWarper: public WarperCreator
{ {
float a, b; float a, b;
public: public:
@ -138,7 +207,7 @@ public:
Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::PaniniWarper>(scale, a, b); } Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::PaniniWarper>(scale, a, b); }
}; };
class PaniniPortraitWarper: public WarperCreator class CV_EXPORTS PaniniPortraitWarper: public WarperCreator
{ {
float a, b; float a, b;
public: public:
@ -149,13 +218,13 @@ public:
Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::PaniniPortraitWarper>(scale, a, b); } Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::PaniniPortraitWarper>(scale, a, b); }
}; };
class MercatorWarper: public WarperCreator class CV_EXPORTS MercatorWarper: public WarperCreator
{ {
public: public:
Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::MercatorWarper>(scale); } Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::MercatorWarper>(scale); }
}; };
class TransverseMercatorWarper: public WarperCreator class CV_EXPORTS TransverseMercatorWarper: public WarperCreator
{ {
public: public:
Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::TransverseMercatorWarper>(scale); } Ptr<detail::RotationWarper> create(float scale) const CV_OVERRIDE { return makePtr<detail::TransverseMercatorWarper>(scale); }

2
modules/stitching/src/blenders.cpp
View File

@ -70,7 +70,7 @@ Ptr<Blender> Blender::createDefault(int type, bool try_gpu)
if (type == NO) if (type == NO)
return makePtr<Blender>(); return makePtr<Blender>();
if (type == FEATHER) if (type == FEATHER)
return makePtr<FeatherBlender>(); return makePtr<FeatherBlender>(try_gpu);
if (type == MULTI_BAND) if (type == MULTI_BAND)
return makePtr<MultiBandBlender>(try_gpu); return makePtr<MultiBandBlender>(try_gpu);
CV_Error(Error::StsBadArg, "unsupported blending method"); CV_Error(Error::StsBadArg, "unsupported blending method");

62
modules/stitching/src/exposure_compensate.cpp
View File

@ -47,12 +47,18 @@ namespace detail {
Ptr<ExposureCompensator> ExposureCompensator::createDefault(int type) Ptr<ExposureCompensator> ExposureCompensator::createDefault(int type)
{ {
Ptr<ExposureCompensator> e;
if (type == NO) if (type == NO)
return makePtr<NoExposureCompensator>(); e = makePtr<NoExposureCompensator>();
if (type == GAIN) else if (type == GAIN)
return makePtr<GainCompensator>(); e = makePtr<GainCompensator>();
if (type == GAIN_BLOCKS) if (type == GAIN_BLOCKS)
return makePtr<BlocksGainCompensator>(); e = makePtr<BlocksGainCompensator>();
if (e.get() != nullptr)
{
e->setUpdateGain(true);
return e;
}
CV_Error(Error::StsBadArg, "unsupported exposure compensation method"); CV_Error(Error::StsBadArg, "unsupported exposure compensation method");
} }
@ -120,7 +126,8 @@ void GainCompensator::feed(const std::vector<Point> &corners, const std::vector<
} }
} }
} }
if (getUpdateGain() || gains_.rows != num_images)
{
double alpha = 0.01; double alpha = 0.01;
double beta = 100; double beta = 100;
@ -139,6 +146,7 @@ void GainCompensator::feed(const std::vector<Point> &corners, const std::vector<
} }
solve(A, b, gains_); solve(A, b, gains_);
}
LOGLN("Exposure compensation, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec"); LOGLN("Exposure compensation, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
} }
@ -160,6 +168,24 @@ std::vector<double> GainCompensator::gains() const
return gains_vec; return gains_vec;
} }
void GainCompensator::getMatGains(std::vector<Mat>& umv)
{
umv.clear();
for (int i = 0; i < gains_.rows; ++i)
umv.push_back(Mat(1,1,CV_64FC1,Scalar(gains_(i, 0))));
}
void GainCompensator::setMatGains(std::vector<Mat>& umv)
{
gains_=Mat_<double>(static_cast<int>(umv.size()),1);
for (int i = 0; i < static_cast<int>(umv.size()); i++)
{
int type = umv[i].type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
CV_CheckType(type, depth == CV_64F && cn == 1, "Only double images are supported for gain");
CV_Assert(umv[i].rows == 1 && umv[i].cols == 1);
gains_(i, 0) = umv[i].at<double>(0, 0);
}
}
void BlocksGainCompensator::feed(const std::vector<Point> &corners, const std::vector<UMat> &images, void BlocksGainCompensator::feed(const std::vector<Point> &corners, const std::vector<UMat> &images,
const std::vector<std::pair<UMat,uchar> > &masks) const std::vector<std::pair<UMat,uchar> > &masks)
@ -197,13 +223,15 @@ void BlocksGainCompensator::feed(const std::vector<Point> &corners, const std::v
} }
} }
if (getUpdateGain())
{
GainCompensator compensator; GainCompensator compensator;
compensator.feed(block_corners, block_images, block_masks); compensator.feed(block_corners, block_images, block_masks);
std::vector<double> gains = compensator.gains(); std::vector<double> gains = compensator.gains();
gain_maps_.resize(num_images); gain_maps_.resize(num_images);
Mat_<float> ker(1, 3); Mat_<float> ker(1, 3);
ker(0,0) = 0.25; ker(0,1) = 0.5; ker(0,2) = 0.25; ker(0, 0) = 0.25; ker(0, 1) = 0.5; ker(0, 2) = 0.25;
int bl_idx = 0; int bl_idx = 0;
for (int img_idx = 0; img_idx < num_images; ++img_idx) for (int img_idx = 0; img_idx < num_images; ++img_idx)
@ -221,6 +249,7 @@ void BlocksGainCompensator::feed(const std::vector<Point> &corners, const std::v
sepFilter2D(gain_maps_[img_idx], gain_maps_[img_idx], CV_32F, ker, ker); sepFilter2D(gain_maps_[img_idx], gain_maps_[img_idx], CV_32F, ker, ker);
sepFilter2D(gain_maps_[img_idx], gain_maps_[img_idx], CV_32F, ker, ker); sepFilter2D(gain_maps_[img_idx], gain_maps_[img_idx], CV_32F, ker, ker);
} }
}
} }
@ -251,5 +280,26 @@ void BlocksGainCompensator::apply(int index, Point /*corner*/, InputOutputArray
} }
} }
void BlocksGainCompensator::getMatGains(std::vector<Mat>& umv)
{
umv.clear();
for (int i = 0; i < static_cast<int>(gain_maps_.size()); ++i)
{
Mat m;
gain_maps_[i].copyTo(m);
umv.push_back(m);
}
}
void BlocksGainCompensator::setMatGains(std::vector<Mat>& umv)
{
for (int i = 0; i < static_cast<int>(umv.size()); i++)
{
UMat m;
umv[i].copyTo(m);
gain_maps_.push_back(m);
}
}
} // namespace detail } // namespace detail
} // namespace cv } // namespace cv

6
modules/stitching/src/matchers.cpp
View File

@ -384,6 +384,12 @@ BestOf2NearestMatcher::BestOf2NearestMatcher(bool try_use_gpu, float match_conf,
num_matches_thresh2_ = num_matches_thresh2; num_matches_thresh2_ = num_matches_thresh2;
} }
Ptr<BestOf2NearestMatcher> BestOf2NearestMatcher::create(bool try_use_gpu, float match_conf, int num_matches_thresh1, int num_matches_thresh2)
{
return makePtr<BestOf2NearestMatcher>(try_use_gpu, match_conf, num_matches_thresh1, num_matches_thresh2);
}
void BestOf2NearestMatcher::match(const ImageFeatures &features1, const ImageFeatures &features2, void BestOf2NearestMatcher::match(const ImageFeatures &features1, const ImageFeatures &features2,
MatchesInfo &matches_info) MatchesInfo &matches_info)

45
modules/stitching/src/seam_finders.cpp
View File

@ -47,6 +47,18 @@
namespace cv { namespace cv {
namespace detail { namespace detail {
Ptr<SeamFinder> SeamFinder::createDefault(int type)
{
if (type == NO)
return makePtr<NoSeamFinder>();
if (type == VORONOI_SEAM)
return makePtr<VoronoiSeamFinder>();
if (type == DP_SEAM)
return makePtr<DpSeamFinder>();
CV_Error(Error::StsBadArg, "unsupported exposure compensation method");
}
void PairwiseSeamFinder::find(const std::vector<UMat> &src, const std::vector<Point> &corners, void PairwiseSeamFinder::find(const std::vector<UMat> &src, const std::vector<Point> &corners,
std::vector<UMat> &masks) std::vector<UMat> &masks)
{ {
@ -165,6 +177,26 @@ void VoronoiSeamFinder::findInPair(size_t first, size_t second, Rect roi)
DpSeamFinder::DpSeamFinder(CostFunction costFunc) : costFunc_(costFunc), ncomps_(0) {} DpSeamFinder::DpSeamFinder(CostFunction costFunc) : costFunc_(costFunc), ncomps_(0) {}
DpSeamFinder::DpSeamFinder(String costFunc)
{
ncomps_ = 0;
if (costFunc == "COLOR")
costFunc_ = COLOR;
else if (costFunc == "COLOR_GRAD")
costFunc_ = COLOR_GRAD;
else
CV_Error(-1, "Unknown cost function");
}
void DpSeamFinder::setCostFunction(String costFunc)
{
if (costFunc == "COLOR")
costFunc_ = COLOR;
else if (costFunc == "COLOR_GRAD")
costFunc_ = COLOR_GRAD;
else
CV_Error(-1, "Unknown cost function");
}
void DpSeamFinder::find(const std::vector<UMat> &src, const std::vector<Point> &corners, std::vector<UMat> &masks) void DpSeamFinder::find(const std::vector<UMat> &src, const std::vector<Point> &corners, std::vector<UMat> &masks)
{ {
@ -1324,6 +1356,19 @@ void GraphCutSeamFinder::Impl::findInPair(size_t first, size_t second, Rect roi)
} }
} }
GraphCutSeamFinder::GraphCutSeamFinder(String cost_type, float terminal_cost, float bad_region_penalty)
{
CostType t;
if (cost_type == "COST_COLOR")
t = COST_COLOR;
else if (cost_type == "COST_COLOR_GRAD")
t = COST_COLOR_GRAD;
else
CV_Error(Error::StsBadFunc, "Unknown cost type function");
impl_ = new Impl(t, terminal_cost, bad_region_penalty);
}
GraphCutSeamFinder::GraphCutSeamFinder(int cost_type, float terminal_cost, float bad_region_penalty) GraphCutSeamFinder::GraphCutSeamFinder(int cost_type, float terminal_cost, float bad_region_penalty)
: impl_(new Impl(cost_type, terminal_cost, bad_region_penalty)) {} : impl_(new Impl(cost_type, terminal_cost, bad_region_penalty)) {}

74
modules/stitching/src/warpers.cpp
View File

@ -42,8 +42,79 @@
#include "precomp.hpp" #include "precomp.hpp"
#include "opencl_kernels_stitching.hpp" #include "opencl_kernels_stitching.hpp"
#include <iostream>
namespace cv { namespace cv {
PyRotationWarper::PyRotationWarper(String warp_type, float scale)
{
Ptr<WarperCreator> warper_creator;
if (warp_type == "plane")
warper_creator = makePtr<cv::PlaneWarper>();
else if (warp_type == "affine")
warper_creator = makePtr<cv::AffineWarper>();
else if (warp_type == "cylindrical")
warper_creator = makePtr<cv::CylindricalWarper>();
else if (warp_type == "spherical")
warper_creator = makePtr<cv::SphericalWarper>();
else if (warp_type == "fisheye")
warper_creator = makePtr<cv::FisheyeWarper>();
else if (warp_type == "stereographic")
warper_creator = makePtr<cv::StereographicWarper>();
else if (warp_type == "compressedPlaneA2B1")
warper_creator = makePtr<cv::CompressedRectilinearWarper>(2.0f, 1.0f);
else if (warp_type == "compressedPlaneA1.5B1")
warper_creator = makePtr<cv::CompressedRectilinearWarper>(1.5f, 1.0f);
else if (warp_type == "compressedPlanePortraitA2B1")
warper_creator = makePtr<cv::CompressedRectilinearPortraitWarper>(2.0f, 1.0f);
else if (warp_type == "compressedPlanePortraitA1.5B1")
warper_creator = makePtr<cv::CompressedRectilinearPortraitWarper>(1.5f, 1.0f);
else if (warp_type == "paniniA2B1")
warper_creator = makePtr<cv::PaniniWarper>(2.0f, 1.0f);
else if (warp_type == "paniniA1.5B1")
warper_creator = makePtr<cv::PaniniWarper>(1.5f, 1.0f);
else if (warp_type == "paniniPortraitA2B1")
warper_creator = makePtr<cv::PaniniPortraitWarper>(2.0f, 1.0f);
else if (warp_type == "paniniPortraitA1.5B1")
warper_creator = makePtr<cv::PaniniPortraitWarper>(1.5f, 1.0f);
else if (warp_type == "mercator")
warper_creator = makePtr<cv::MercatorWarper>();
else if (warp_type == "transverseMercator")
warper_creator = makePtr<cv::TransverseMercatorWarper>();
if (warper_creator.get() != nullptr)
{
rw = warper_creator->create(scale);
}
else
CV_Error(Error::StsError, "unknown warper :" + warp_type);
}
Point2f PyRotationWarper::warpPoint(const Point2f &pt, InputArray K, InputArray R)
{
return rw.get()->warpPoint(pt, K, R);
}
Rect PyRotationWarper::buildMaps(Size src_size, InputArray K, InputArray R, OutputArray xmap, OutputArray ymap)
{
return rw.get()->buildMaps(src_size, K, R, xmap, ymap);
}
Point PyRotationWarper::warp(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode,
OutputArray dst)
{
if (rw.get() == nullptr)
CV_Error(Error::StsError, "Warper is null");
Point p = rw.get()->warp(src, K, R, interp_mode, border_mode, dst);
return p;
}
void PyRotationWarper::warpBackward(InputArray src, InputArray K, InputArray R, int interp_mode, int border_mode,
Size dst_size, OutputArray dst)
{
return rw.get()->warpBackward(src, K, R, interp_mode, border_mode, dst_size, dst);
}
Rect PyRotationWarper::warpRoi(Size src_size, InputArray K, InputArray R)
{
return rw.get()->warpRoi(src_size, K, R);
}
namespace detail { namespace detail {
void ProjectorBase::setCameraParams(InputArray _K, InputArray _R, InputArray _T) void ProjectorBase::setCameraParams(InputArray _K, InputArray _R, InputArray _T)
@ -157,7 +228,6 @@ Point PlaneWarper::warp(InputArray src, InputArray K, InputArray R, InputArray T
{ {
UMat uxmap, uymap; UMat uxmap, uymap;
Rect dst_roi = buildMaps(src.size(), K, R, T, uxmap, uymap); Rect dst_roi = buildMaps(src.size(), K, R, T, uxmap, uymap);
dst.create(dst_roi.height + 1, dst_roi.width + 1, src.type()); dst.create(dst_roi.height + 1, dst_roi.width + 1, src.type());
remap(src, dst, uxmap, uymap, interp_mode, border_mode); remap(src, dst, uxmap, uymap, interp_mode, border_mode);

387
samples/python/stitching_detailed.py Normal file
View File

@ -0,0 +1,387 @@
"""Rotation model images stitcher.
stitching_detailed img1 img2 [...imgN] [flags]
Flags:
--preview
Run stitching in the preview mode. Works faster than usual mode,
but output image will have lower resolution.
--try_cuda (yes|no)
Try to use CUDA. The default value is 'no'. All default values
are for CPU mode.
\nMotion Estimation Flags:
--work_megapix <float>
Resolution for image registration step. The default is 0.6 Mpx.
--features (surf|orb|sift)
Type of features used for images matching. The default is surf.
--matcher (homography|affine)
Matcher used for pairwise image matching.
--estimator (homography|affine)
Type of estimator used for transformation estimation.
--match_conf <float>
Confidence for feature matching step. The default is 0.65 for surf and 0.3 for orb.
--conf_thresh <float>
Threshold for two images are from the same panorama confidence.
The default is 1.0.
--ba (no|reproj|ray|affine)
Bundle adjustment cost function. The default is ray.
--ba_refine_mask (mask)
Set refinement mask for bundle adjustment. It looks like 'x_xxx',
where 'x' means refine respective parameter and '_' means don't
refine one, and has the following format:
<fx><skew><ppx><aspect><ppy>. The default mask is 'xxxxx'. If bundle
adjustment doesn't support estimation of selected parameter then
the respective flag is ignored.
--wave_correct (no|horiz|vert)
Perform wave effect correction. The default is 'horiz'.
--save_graph <file_name>
Save matches graph represented in DOT language to <file_name> file.
Labels description: Nm is number of matches, Ni is number of inliers,
C is confidence.
\nCompositing Flags:
--warp (affine|plane|cylindrical|spherical|fisheye|stereographic|compressedPlaneA2B1|compressedPlaneA1.5B1|compressedPlanePortraitA2B1|compressedPlanePortraitA1.5B1|paniniA2B1|paniniA1.5B1|paniniPortraitA2B1|paniniPortraitA1.5B1|mercator|transverseMercator)
Warp surface type. The default is 'spherical'.
--seam_megapix <float>
Resolution for seam estimation step. The default is 0.1 Mpx.
--seam (no|voronoi|gc_color|gc_colorgrad)
Seam estimation method. The default is 'gc_color'.
--compose_megapix <float>
Resolution for compositing step. Use -1 for original resolution.
The default is -1.
--expos_comp (no|gain|gain_blocks)
Exposure compensation method. The default is 'gain_blocks'.
--blend (no|feather|multiband)
Blending method. The default is 'multiband'.
--blend_strength <float>
Blending strength from [0,100] range. The default is 5.
--output <result_img>
The default is 'result.jpg'.
--timelapse (as_is|crop)
Output warped images separately as frames of a time lapse movie, with 'fixed_' prepended to input file names.
--rangewidth <int>
uses range_width to limit number of images to match with.\n
"""
import numpy as np
import cv2 as cv
import sys
import argparse
parser = argparse.ArgumentParser(description='stitching_detailed')
parser.add_argument('img_names', nargs='+',help='files to stitch',type=str)
parser.add_argument('--preview',help='Run stitching in the preview mode. Works faster than usual mode but output image will have lower resolution.',type=bool,dest = 'preview' )
parser.add_argument('--try_cuda',action = 'store', default = False,help='Try to use CUDA. The default value is no. All default values are for CPU mode.',type=bool,dest = 'try_cuda' )
parser.add_argument('--work_megapix',action = 'store', default = 0.6,help=' Resolution for image registration step. The default is 0.6 Mpx',type=float,dest = 'work_megapix' )
parser.add_argument('--features',action = 'store', default = 'orb',help='Type of features used for images matching. The default is orb.',type=str,dest = 'features' )
parser.add_argument('--matcher',action = 'store', default = 'homography',help='Matcher used for pairwise image matching.',type=str,dest = 'matcher' )
parser.add_argument('--estimator',action = 'store', default = 'homography',help='Type of estimator used for transformation estimation.',type=str,dest = 'estimator' )
parser.add_argument('--match_conf',action = 'store', default = 0.3,help='Confidence for feature matching step. The default is 0.65 for surf and 0.3 for orb.',type=float,dest = 'match_conf' )
parser.add_argument('--conf_thresh',action = 'store', default = 1.0,help='Threshold for two images are from the same panorama confidence.The default is 1.0.',type=float,dest = 'conf_thresh' )
parser.add_argument('--ba',action = 'store', default = 'ray',help='Bundle adjustment cost function. The default is ray.',type=str,dest = 'ba' )
parser.add_argument('--ba_refine_mask',action = 'store', default = 'xxxxx',help='Set refinement mask for bundle adjustment. mask is "xxxxx"',type=str,dest = 'ba_refine_mask' )
parser.add_argument('--wave_correct',action = 'store', default = 'horiz',help='Perform wave effect correction. The default is "horiz"',type=str,dest = 'wave_correct' )
parser.add_argument('--save_graph',action = 'store', default = None,help='Save matches graph represented in DOT language to <file_name> file.',type=str,dest = 'save_graph' )
parser.add_argument('--warp',action = 'store', default = 'plane',help='Warp surface type. The default is "spherical".',type=str,dest = 'warp' )
parser.add_argument('--seam_megapix',action = 'store', default = 0.1,help=' Resolution for seam estimation step. The default is 0.1 Mpx.',type=float,dest = 'seam_megapix' )
parser.add_argument('--seam',action = 'store', default = 'no',help='Seam estimation method. The default is "gc_color".',type=str,dest = 'seam' )
parser.add_argument('--compose_megapix',action = 'store', default = -1,help='Resolution for compositing step. Use -1 for original resolution.',type=float,dest = 'compose_megapix' )
parser.add_argument('--expos_comp',action = 'store', default = 'no',help='Exposure compensation method. The default is "gain_blocks".',type=str,dest = 'expos_comp' )
parser.add_argument('--blend',action = 'store', default = 'multiband',help='Blending method. The default is "multiband".',type=str,dest = 'blend' )
parser.add_argument('--blend_strength',action = 'store', default = 5,help='Blending strength from [0,100] range.',type=int,dest = 'blend_strength' )
parser.add_argument('--output',action = 'store', default = 'result.jpg',help='The default is "result.jpg"',type=str,dest = 'output' )
parser.add_argument('--timelapse',action = 'store', default = None,help='Output warped images separately as frames of a time lapse movie, with "fixed_" prepended to input file names.',type=str,dest = 'timelapse' )
parser.add_argument('--rangewidth',action = 'store', default = -1,help='uses range_width to limit number of images to match with.',type=int,dest = 'rangewidth' )
args = parser.parse_args()
img_names=args.img_names
print(img_names)
preview = args.preview
try_cuda = args.try_cuda
work_megapix = args.work_megapix
seam_megapix = args.seam_megapix
compose_megapix = args.compose_megapix
conf_thresh = args.conf_thresh
features_type = args.features
matcher_type = args.matcher
estimator_type = args.estimator
ba_cost_func = args.ba
ba_refine_mask = args.ba_refine_mask
wave_correct = args.wave_correct
if wave_correct=='no':
do_wave_correct= False
else:
do_wave_correct=True
if args.save_graph is None:
save_graph = False
else:
save_graph =True
save_graph_to = args.save_graph
warp_type = args.warp
if args.expos_comp=='no':
expos_comp_type = cv.detail.ExposureCompensator_NO
elif args.expos_comp=='gain':
expos_comp_type = cv.detail.ExposureCompensator_GAIN
elif args.expos_comp=='gain_blocks':
expos_comp_type = cv.detail.ExposureCompensator_GAIN_BLOCKS
else:
print("Bad exposure compensation method")
exit
match_conf = args.match_conf
seam_find_type = args.seam
blend_type = args.blend
blend_strength = args.blend_strength
result_name = args.output
if args.timelapse is not None:
timelapse = True
if args.timelapse=="as_is":
timelapse_type = cv.detail.Timelapser_AS_IS
elif args.timelapse=="crop":
timelapse_type = cv.detail.Timelapser_CROP
else:
print("Bad timelapse method")
exit()
else:
timelapse= False
range_width = args.rangewidth
if features_type=='orb':
finder= cv.ORB.create()
elif features_type=='surf':
finder= cv.xfeatures2d_SURF.create()
elif features_type=='sift':
finder= cv.xfeatures2d_SIFT.create()
else:
print ("Unknown descriptor type")
exit()
seam_work_aspect = 1
full_img_sizes=[]
features=[]
images=[]
is_work_scale_set = False
is_seam_scale_set = False
is_compose_scale_set = False;
for name in img_names:
full_img = cv.imread(name)
if full_img is None:
print("Cannot read image ",name)
exit()
full_img_sizes.append((full_img.shape[1],full_img.shape[0]))
if work_megapix < 0:
img = full_img
work_scale = 1
is_work_scale_set = True
else:
if is_work_scale_set is False:
work_scale = min(1.0, np.sqrt(work_megapix * 1e6 / (full_img.shape[0]*full_img.shape[1])))
is_work_scale_set = True
img = cv.resize(src=full_img, dsize=None, fx=work_scale, fy=work_scale, interpolation=cv.INTER_LINEAR_EXACT)
if is_seam_scale_set is False:
seam_scale = min(1.0, np.sqrt(seam_megapix * 1e6 / (full_img.shape[0]*full_img.shape[1])))
seam_work_aspect = seam_scale / work_scale
is_seam_scale_set = True
imgFea= cv.detail.computeImageFeatures2(finder,img)
features.append(imgFea)
img = cv.resize(src=full_img, dsize=None, fx=seam_scale, fy=seam_scale, interpolation=cv.INTER_LINEAR_EXACT)
images.append(img)
if matcher_type== "affine":
matcher = cv.detail.AffineBestOf2NearestMatcher_create(False, try_cuda, match_conf)
elif range_width==-1:
matcher = cv.detail.BestOf2NearestMatcher_create(try_cuda, match_conf)
else:
matcher = cv.detail.BestOf2NearestRangeMatcher_create(range_width, try_cuda, match_conf)
p=matcher.apply2(features)
matcher.collectGarbage()
if save_graph:
f = open(save_graph_to,"w")
# f.write(matchesGraphAsString(img_names, pairwise_matches, conf_thresh))
f.close()
indices=cv.detail.leaveBiggestComponent(features,p,0.3)
img_subset =[]
img_names_subset=[]
full_img_sizes_subset=[]
num_images=len(indices)
for i in range(0,num_images):
img_names_subset.append(img_names[indices[i,0]])
img_subset.append(images[indices[i,0]])
full_img_sizes_subset.append(full_img_sizes[indices[i,0]])
images = img_subset;
img_names = img_names_subset;
full_img_sizes = full_img_sizes_subset;
num_images = len(img_names)
if num_images < 2:
print("Need more images")
exit()
if estimator_type == "affine":
estimator = cv.detail_AffineBasedEstimator()
else:
estimator = cv.detail_HomographyBasedEstimator()
b, cameras =estimator.apply(features,p,None)
if not b:
print("Homography estimation failed.")
exit()
for cam in cameras:
cam.R=cam.R.astype(np.float32)
if ba_cost_func == "reproj":
adjuster = cv.detail_BundleAdjusterReproj()
elif ba_cost_func == "ray":
adjuster = cv.detail_BundleAdjusterRay()
elif ba_cost_func == "affine":
adjuster = cv.detail_BundleAdjusterAffinePartial()
elif ba_cost_func == "no":
adjuster = cv.detail_NoBundleAdjuster()
else:
print( "Unknown bundle adjustment cost function: ", ba_cost_func )
exit()
adjuster.setConfThresh(1)
refine_mask=np.zeros((3,3),np.uint8)
if ba_refine_mask[0] == 'x':
refine_mask[0,0] = 1
if ba_refine_mask[1] == 'x':
refine_mask[0,1] = 1
if ba_refine_mask[2] == 'x':
refine_mask[0,2] = 1
if ba_refine_mask[3] == 'x':
refine_mask[1,1] = 1
if ba_refine_mask[4] == 'x':
refine_mask[1,2] = 1
adjuster.setRefinementMask(refine_mask)
b,cameras = adjuster.apply(features,p,cameras)
if not b:
print("Camera parameters adjusting failed.")
exit()
focals=[]
for cam in cameras:
focals.append(cam.focal)
sorted(focals)
if len(focals)%2==1:
warped_image_scale = focals[len(focals) // 2]
else:
warped_image_scale = (focals[len(focals) // 2]+focals[len(focals) // 2-1])/2
if do_wave_correct:
rmats=[]
for cam in cameras:
rmats.append(np.copy(cam.R))
rmats = cv.detail.waveCorrect( rmats, cv.detail.WAVE_CORRECT_HORIZ)
for idx,cam in enumerate(cameras):
cam.R = rmats[idx]
corners=[]
mask=[]
masks_warped=[]
images_warped=[]
sizes=[]
masks=[]
for i in range(0,num_images):
um=cv.UMat(255*np.ones((images[i].shape[0],images[i].shape[1]),np.uint8))
masks.append(um)
warper = cv.PyRotationWarper(warp_type,warped_image_scale*seam_work_aspect) # warper peut etre nullptr?
for i in range(0,num_images):
K = cameras[i].K().astype(np.float32)
swa = seam_work_aspect
K[0,0] *= swa
K[0,2] *= swa
K[1,1] *= swa
K[1,2] *= swa
corner,image_wp =warper.warp(images[i],K,cameras[i].R,cv.INTER_LINEAR, cv.BORDER_REFLECT)
corners.append(corner)
sizes.append((image_wp.shape[1],image_wp.shape[0]))
images_warped.append(image_wp)
p,mask_wp =warper.warp(masks[i],K,cameras[i].R,cv.INTER_NEAREST, cv.BORDER_CONSTANT)
masks_warped.append(mask_wp)
images_warped_f=[]
for img in images_warped:
imgf=img.astype(np.float32)
images_warped_f.append(imgf)
compensator=cv.detail.ExposureCompensator_createDefault(expos_comp_type)
compensator.feed(corners, images_warped, masks_warped)
if seam_find_type == "no":
seam_finder = cv.detail.SeamFinder_createDefault(cv.detail.SeamFinder_NO)
elif seam_find_type == "voronoi":
seam_finder = cv.detail.SeamFinder_createDefault(cv.detail.SeamFinder_VORONOI_SEAM);
elif seam_find_type == "gc_color":
seam_finder = cv.detail_GraphCutSeamFinder("COST_COLOR")
elif seam_find_type == "gc_colorgrad":
seam_finder = cv.detail_GraphCutSeamFinder("COST_COLOR_GRAD")
elif seam_find_type == "dp_color":
seam_finder = cv.detail_DpSeamFinder("COLOR")
elif seam_find_type == "dp_colorgrad":
seam_finder = cv.detail_DpSeamFinder("COLOR_GRAD")
if seam_finder is None:
print("Can't create the following seam finder ",seam_find_type)
exit()
seam_finder.find(images_warped_f, corners,masks_warped )
imgListe=[]
compose_scale=1
corners=[]
sizes=[]
images_warped=[]
images_warped_f=[]
masks=[]
blender= None
timelapser=None
compose_work_aspect=1
for idx,name in enumerate(img_names): # https://github.com/opencv/opencv/blob/master/samples/cpp/stitching_detailed.cpp#L725 ?
full_img = cv.imread(name)
if not is_compose_scale_set:
if compose_megapix > 0:
compose_scale = min(1.0, np.sqrt(compose_megapix * 1e6 / (full_img.shape[0]*full_img.shape[1])))
is_compose_scale_set = True;
compose_work_aspect = compose_scale / work_scale;
warped_image_scale *= compose_work_aspect
warper = cv.PyRotationWarper(warp_type,warped_image_scale)
for i in range(0,len(img_names)):
cameras[i].focal *= compose_work_aspect
cameras[i].ppx *= compose_work_aspect
cameras[i].ppy *= compose_work_aspect
sz = (full_img.shape[1] * compose_scale,full_img.shape[0] * compose_scale)
K = cameras[i].K().astype(np.float32)
roi = warper.warpRoi(sz, K, cameras[i].R);
corners.append(roi[0:2])
sizes.append(roi[2:4])
if abs(compose_scale - 1) > 1e-1:
img =cv.resize(src=full_img, dsize=None, fx=compose_scale, fy=compose_scale, interpolation=cv.INTER_LINEAR_EXACT)
else:
img = full_img;
img_size = (img.shape[1],img.shape[0]);
K=cameras[idx].K().astype(np.float32)
corner,image_warped =warper.warp(img,K,cameras[idx].R,cv.INTER_LINEAR, cv.BORDER_REFLECT)
mask =255*np.ones((img.shape[0],img.shape[1]),np.uint8)
p,mask_warped =warper.warp(mask,K,cameras[idx].R,cv.INTER_NEAREST, cv.BORDER_CONSTANT)
compensator.apply(idx,corners[idx],image_warped,mask_warped)
image_warped_s = image_warped.astype(np.int16)
image_warped=[]
dilated_mask = cv.dilate(masks_warped[idx],None)
seam_mask = cv.resize(dilated_mask,(mask_warped.shape[1],mask_warped.shape[0]),0,0,cv.INTER_LINEAR_EXACT)
mask_warped = cv.bitwise_and(seam_mask,mask_warped)
if blender==None and not timelapse:
blender = cv.detail.Blender_createDefault(1)
dst_sz = cv.detail.resultRoi(corners,sizes)
blend_strength=1
blend_width = np.sqrt(dst_sz[2]*dst_sz[3]) * blend_strength / 100
if blend_width < 1:
blender = cv.detail.Blender_createDefault(cv.detail.Blender_NO)
elif blend_type == "MULTI_BAND":
blender = cv.detail.Blender_createDefault(cv.detail.Blender_MULTIBAND)
blender.setNumBands((np.log(blend_width)/np.log(2.) - 1.).astype(np.int))
elif blend_type == "FEATHER":
blender = cv.detail.Blender_createDefault(cv.detail.Blender_FEATHER)
blender.setSharpness(1./blend_width)
blender.prepare(corners, sizes)
elif timelapser==None and timelapse:
timelapser = cv.detail.createDefault(timelapse_type);
timelapser.initialize(corners, sizes)
if timelapse:
matones=np.ones((image_warped_s.shape[0],image_warped_s.shape[1]), np.uint8)
timelapser.process(image_warped_s, matones, corners[idx])
pos_s = img_names[idx].rfind("/");
if pos_s == -1:
fixedFileName = "fixed_" + img_names[idx];
else:
fixedFileName = img_names[idx][:pos_s + 1 ]+"fixed_" + img_names[idx][pos_s + 1: ]
cv.imwrite(fixedFileName, timelapser.getDst())
else:
blender.feed(image_warped_s, mask_warped, corners[idx])
if not timelapse:
result=None
result_mask=None
result,result_mask = blender.blend(result,result_mask)
cv.imwrite(result_name,result)