adding python bindings

modules/sfm/CMakeLists.txt

@@ -69,6 +69,7 @@ ocv_add_module(sfm
   opencv_calib3d
   opencv_features2d
   opencv_xfeatures2d
+  WRAP python
 )
 
 

modules/sfm/include/opencv2/sfm/conditioning.hpp

@@ -54,7 +54,7 @@ namespace sfm
   forming an approximately symmetric circular cloud of points of radius 1 about the origin.\n
   Reference: @cite HartleyZ00 4.4.4 pag.109
 */
-CV_EXPORTS
+CV_EXPORTS_W
 void
 preconditionerFromPoints( InputArray points,
                           OutputArray T );
@@ -67,7 +67,7 @@ preconditionerFromPoints( InputArray points,
   bringing the centroid to the origin with an average centroid \f$(1,1,1)^T\f$.\n
   Reference: @cite HartleyZ00 4.4.4 pag.107.
 */
-CV_EXPORTS
+CV_EXPORTS_W
 void
 isotropicPreconditionerFromPoints( InputArray points,
                                    OutputArray T );
@@ -77,7 +77,7 @@ isotropicPreconditionerFromPoints( InputArray points,
   @param T Input 3x3 transformation matrix such that \f$x = T*X\f$, where \f$X\f$ are the points to transform and \f$x\f$ the transformed points.
   @param transformed_points Output vector of N-dimensional transformed points.
 */
-CV_EXPORTS
+CV_EXPORTS_W
 void
 applyTransformationToPoints( InputArray points,
                              InputArray T,
@@ -92,7 +92,7 @@ applyTransformationToPoints( InputArray points,
   This operation is an essential step before applying the DLT algorithm in order to consider the result as optimal.\n
   Reference: @cite HartleyZ00 4.4.4 pag.109
 */
-CV_EXPORTS
+CV_EXPORTS_W
 void
 normalizePoints( InputArray points,
                  OutputArray normalized_points,
@@ -107,7 +107,7 @@ normalizePoints( InputArray points,
   This operation is an essential step before applying the DLT algorithm in order to consider the result as optimal.\n
   Reference: @cite HartleyZ00 4.4.4 pag.107.
 */
-CV_EXPORTS
+CV_EXPORTS_W
 void
 normalizeIsotropicPoints( InputArray points,
                           OutputArray normalized_points,

modules/sfm/include/opencv2/sfm/fundamental.hpp

@@ -53,7 +53,7 @@ namespace sfm
   @param P1 Output 3x4 one possible projection matrix.
   @param P2 Output 3x4 another possible projection matrix.
  */
-CV_EXPORTS
+CV_EXPORTS_W
 void
 projectionsFromFundamental( InputArray F,
                             OutputArray P1,
@@ -64,7 +64,7 @@ projectionsFromFundamental( InputArray F,
   @param P2 Input 3x4 second projection matrix.
   @param F Output 3x3 fundamental matrix.
  */
-CV_EXPORTS
+CV_EXPORTS_W
 void
 fundamentalFromProjections( InputArray P1,
                             InputArray P2,
@@ -78,7 +78,7 @@ fundamentalFromProjections( InputArray P1,
   Uses the normalized 8-point fundamental matrix solver.
   Reference: @cite HartleyZ00 11.2 pag.281 (x1 = x, x2 = x')
  */
-CV_EXPORTS
+CV_EXPORTS_W
 void
 normalizedEightPointSolver( InputArray x1,
                             InputArray x2,
@@ -96,7 +96,7 @@ normalizedEightPointSolver( InputArray x1,
   of the second one assuming the first one to be at the origin.
   If T1 and T2 are the camera motions, the computed relative motion is \f$T = T_2 T_1^{-1}\f$
  */
-CV_EXPORTS
+CV_EXPORTS_W
 void
 relativeCameraMotion( InputArray R1,
                       InputArray t1,
@@ -112,7 +112,7 @@ relativeCameraMotion( InputArray R1,
 
   Reference: @cite HartleyZ00 9.6 pag 259 (Result 9.19)
  */
-CV_EXPORTS
+CV_EXPORTS_W
 void
 motionFromEssential( InputArray E,
                      OutputArrayOfArrays Rs,
@@ -131,7 +131,7 @@ motionFromEssential( InputArray E,
 
   Reference: See @cite HartleyZ00 9.6 pag 259 (9.6.3 Geometrical interpretation of the 4 solutions).
  */
-CV_EXPORTS
+CV_EXPORTS_W
 int motionFromEssentialChooseSolution( InputArrayOfArrays Rs,
                                        InputArrayOfArrays ts,
                                        InputArray K1,
@@ -147,7 +147,7 @@ int motionFromEssentialChooseSolution( InputArrayOfArrays Rs,
 
   Reference: @cite HartleyZ00 9.6 pag 257 (formula 9.12) or http://ai.stanford.edu/~birch/projective/node20.html
  */
-CV_EXPORTS
+CV_EXPORTS_W
 void
 fundamentalFromEssential( InputArray E,
                           InputArray K1,
@@ -162,7 +162,7 @@ fundamentalFromEssential( InputArray E,
 
   Reference: @cite HartleyZ00 9.6 pag 257 (formula 9.12)
  */
-CV_EXPORTS
+CV_EXPORTS_W
 void
 essentialFromFundamental( InputArray F,
                           InputArray K1,
@@ -178,7 +178,7 @@ essentialFromFundamental( InputArray F,
 
   Reference: @cite HartleyZ00 9.6 pag 257 (formula 9.12)
  */
-CV_EXPORTS
+CV_EXPORTS_W
 void
 essentialFromRt( InputArray R1,
                  InputArray t1,
@@ -192,7 +192,7 @@ essentialFromRt( InputArray R1,
 
   By default divides the fundamental matrix by its L2 norm.
  */
-CV_EXPORTS
+CV_EXPORTS_W
 void
 normalizeFundamental( InputArray F,
                       OutputArray F_normalized );
@@ -207,7 +207,7 @@ normalizeFundamental( InputArray F,
   Find the best transformation such that xp=projection*(s*R*x+t) (same as Pose Estimation, ePNP).
   The routines below are only for the orthographic case for now.
  */
-CV_EXPORTS
+CV_EXPORTS_W
 void
 computeOrientation( InputArrayOfArrays x1,
                     InputArrayOfArrays x2,

modules/sfm/include/opencv2/sfm/numeric.hpp

@@ -55,7 +55,7 @@ namespace sfm
 
   It computes in the same way as woud do @ref reduce but with \a Variance function.
 */
-CV_EXPORTS
+CV_EXPORTS_W
 void
 meanAndVarianceAlongRows( InputArray A,
                           OutputArray mean,
@@ -66,7 +66,7 @@ meanAndVarianceAlongRows( InputArray A,
 
   Reference: @cite HartleyZ00, p581, equation (A4.5).
 */
-CV_EXPORTS
+CV_EXPORTS_W
 Mat
 skew( InputArray x );
 

modules/sfm/include/opencv2/sfm/projection.hpp

@@ -50,7 +50,7 @@ namespace sfm
   @param src Input vector of N-dimensional points.
   @param dst Output vector of N-1-dimensional points.
 */
-CV_EXPORTS
+CV_EXPORTS_W
 void
 homogeneousToEuclidean(InputArray src, OutputArray dst);
 
@@ -58,7 +58,7 @@ homogeneousToEuclidean(InputArray src, OutputArray dst);
   @param src Input vector of N-dimensional points.
   @param dst Output vector of N+1-dimensional points.
 */
-CV_EXPORTS
+CV_EXPORTS_W
 void
 euclideanToHomogeneous(InputArray src, OutputArray dst);
 
@@ -71,7 +71,7 @@ euclideanToHomogeneous(InputArray src, OutputArray dst);
   This function estimate the projection matrix by solving the following equation: \f$P = K * [R|t]\f$
 
  */
-CV_EXPORTS
+CV_EXPORTS_W
 void
 projectionFromKRt(InputArray K, InputArray R, InputArray t, OutputArray P);
 
@@ -83,7 +83,7 @@ projectionFromKRt(InputArray K, InputArray R, InputArray t, OutputArray P);
 
   Reference: @cite HartleyZ00 A4.1.1 pag.579
  */
-CV_EXPORTS
+CV_EXPORTS_W
 void
 KRtFromProjection( InputArray P, OutputArray K, OutputArray R, OutputArray t );
 
@@ -92,7 +92,7 @@ KRtFromProjection( InputArray P, OutputArray K, OutputArray R, OutputArray t );
   @param t Input 3x1 translation vector.
   @param X Input 3x1 or 4x1 vector with the 3d point.
  */
-CV_EXPORTS
+CV_EXPORTS_W
 double
 depth( InputArray R, InputArray t, InputArray X);
 

modules/sfm/include/opencv2/sfm/reconstruct.hpp

@@ -69,7 +69,7 @@ namespace sfm
    @note
     - Tracks must be as precise as possible. It does not handle outliers and is very sensible to them.
 */
-CV_EXPORTS
+/* CV_EXPORTS_W */ // error: ‘reconstruct’ is not a member of ‘cv::sfm’
 void
 reconstruct(InputArrayOfArrays points2d, OutputArray Ps, OutputArray points3d, InputOutputArray K,
             bool is_projective = false);
@@ -88,7 +88,7 @@ reconstruct(InputArrayOfArrays points2d, OutputArray Ps, OutputArray points3d, I
     - Tracks must be as precise as possible. It does not handle outliers and is very sensible to them.
     - To see a working example for camera motion reconstruction, check the following tutorial: @ref tutorial_sfm_trajectory_estimation.
 */
-CV_EXPORTS
+/* CV_EXPORTS_W */ // error: ‘reconstruct’ is not a member of ‘cv::sfm’
 void
 reconstruct(InputArrayOfArrays points2d, OutputArray Rs, OutputArray Ts, InputOutputArray K,
             OutputArray points3d, bool is_projective = false);
@@ -106,7 +106,7 @@ reconstruct(InputArrayOfArrays points2d, OutputArray Rs, OutputArray Ts, InputOu
     - The images must be ordered as they were an image sequence. Additionally, each frame should be as close as posible to the previous and posterior.
     - For now DAISY features are used in order to compute the 2d points tracks and it only works for 3-4 images.
 */
-CV_EXPORTS
+/* CV_EXPORTS_W */ // error: ‘vector_string’ was not declared in this scope
 void
 reconstruct(const std::vector<std::string> images, OutputArray Ps, OutputArray points3d,
             InputOutputArray K, bool is_projective = false);
@@ -126,7 +126,7 @@ reconstruct(const std::vector<std::string> images, OutputArray Ps, OutputArray p
     - For now DAISY features are used in order to compute the 2d points tracks and it only works for 3-4 images.
     - To see a working example for scene reconstruction, check the following tutorial: @ref tutorial_sfm_scene_reconstruction.
 */
-CV_EXPORTS
+/* CV_EXPORTS_W */ // error: ‘vector_string’ was not declared in this scope
 void
 reconstruct(const std::vector<std::string> images, OutputArray Rs, OutputArray Ts,
             InputOutputArray K, OutputArray points3d, bool is_projective = false);

modules/sfm/include/opencv2/sfm/robust.hpp

@@ -62,7 +62,7 @@ namespace sfm
 
 The fundamental solver relies on the 8 point solution. Returns the best error (in pixels), associated to the solution F.
  */
-CV_EXPORTS
+CV_EXPORTS_W
 double
 fundamentalFromCorrespondences8PointRobust( InputArray x1,
                                             InputArray x2,
@@ -85,7 +85,7 @@ fundamentalFromCorrespondences8PointRobust( InputArray x1,
 
 The fundamental solver relies on the 7 point solution. Returns the best error (in pixels), associated to the solution F.
  */
-CV_EXPORTS
+CV_EXPORTS_W
 double
 fundamentalFromCorrespondences7PointRobust( InputArray x1,
                                             InputArray x2,

modules/sfm/include/opencv2/sfm/simple_pipeline.hpp

@@ -68,7 +68,10 @@ enum {
   In case that the camera model was SFM_DISTORTION_MODEL_DIVISION, it's only needed to provide
   _polynomial_k1 and _polynomial_k2 which will be assigned as division distortion parameters.
  */
-typedef struct libmv_CameraIntrinsicsOptions {
+class CV_EXPORTS_W_SIMPLE libmv_CameraIntrinsicsOptions
+{
+public:
+  CV_WRAP
   libmv_CameraIntrinsicsOptions(const int _distortion_model=0,
                                 const double _focal_length=0,
                                 const double _principal_point_x=0,
@@ -87,8 +90,8 @@ typedef struct libmv_CameraIntrinsicsOptions {
       polynomial_k1(_polynomial_k1),
       polynomial_k2(_polynomial_k2),
       polynomial_k3(_polynomial_k3),
-      division_k1(0),
+      division_k1(_polynomial_p1),
-      division_k2(0)
+      division_k2(_polynomial_p2)
   {
     if ( _distortion_model == SFM_DISTORTION_MODEL_DIVISION )
     {
@@ -98,18 +101,18 @@ typedef struct libmv_CameraIntrinsicsOptions {
   }
 
   // Common settings of all distortion models.
-  int distortion_model;
+  CV_PROP_RW int distortion_model;
-  int image_width, image_height;
+  CV_PROP_RW int image_width, image_height;
-  double focal_length;
+  CV_PROP_RW double focal_length;
-  double principal_point_x, principal_point_y;
+  CV_PROP_RW double principal_point_x, principal_point_y;
 
   // Radial distortion model.
-  double polynomial_k1, polynomial_k2, polynomial_k3;
+  CV_PROP_RW double polynomial_k1, polynomial_k2, polynomial_k3;
-  double polynomial_p1, polynomial_p2;
+  CV_PROP_RW double polynomial_p1, polynomial_p2;
 
   // Division distortion model.
-  double division_k1, division_k2;
+  CV_PROP_RW double division_k1, division_k2;
-} libmv_CameraIntrinsicsOptions;
+};
 
 
 /** @brief All internal camera parameters that libmv is able to refine.
@@ -128,7 +131,10 @@ enum { SFM_REFINE_FOCAL_LENGTH         = (1 << 0),  // libmv::BUNDLE_FOCAL_LENGT
   @param _select_keyframes allows to select automatically the initial keyframes. If 1 then autoselection is enabled. If 0 then is disabled.
   @param _verbosity_level verbosity logs level for Glog. If -1 then logs are disabled, otherwise the log level will be the input integer.
  */
-typedef struct libmv_ReconstructionOptions {
+class CV_EXPORTS_W_SIMPLE libmv_ReconstructionOptions
+{
+public:
+  CV_WRAP
   libmv_ReconstructionOptions(const int _keyframe1=1,
                               const int _keyframe2=2,
                               const int _refine_intrinsics=1,
@@ -138,21 +144,25 @@ typedef struct libmv_ReconstructionOptions {
       refine_intrinsics(_refine_intrinsics),
       select_keyframes(_select_keyframes),
       verbosity_level(_verbosity_level) {}
-  int keyframe1, keyframe2;
+
-  int refine_intrinsics;
+  CV_PROP_RW int keyframe1, keyframe2;
-  int select_keyframes;
+  CV_PROP_RW int refine_intrinsics;
-  int verbosity_level;
+  CV_PROP_RW int select_keyframes;
-} libmv_ReconstructionOptions;
+  CV_PROP_RW int verbosity_level;
+};
 
 
 /** @brief base class BaseSFM declares a common API that would be used in a typical scene reconstruction scenario
  */
-class CV_EXPORTS BaseSFM
+class CV_EXPORTS_W BaseSFM
 {
 public:
   virtual ~BaseSFM() {};
 
+  CV_WRAP
   virtual void run(InputArrayOfArrays points2d) = 0;
+
+  CV_WRAP
   virtual void run(InputArrayOfArrays points2d, InputOutputArray K, OutputArray Rs,
                    OutputArray Ts, OutputArray points3d) = 0;
 
@@ -160,21 +170,23 @@ public:
   virtual void run(const std::vector <std::string> &images, InputOutputArray K, OutputArray Rs,
                    OutputArray Ts, OutputArray points3d) = 0;
 
-  virtual double getError() const = 0;
+  CV_WRAP virtual double getError() const = 0;
-  virtual void getPoints(OutputArray points3d) = 0;
+  CV_WRAP virtual void getPoints(OutputArray points3d) = 0;
-  virtual cv::Mat getIntrinsics() const = 0;
+  CV_WRAP virtual cv::Mat getIntrinsics() const = 0;
-  virtual void getCameras(OutputArray Rs, OutputArray Ts) = 0;
+  CV_WRAP virtual void getCameras(OutputArray Rs, OutputArray Ts) = 0;
 
+  CV_WRAP
   virtual void
   setReconstructionOptions(const libmv_ReconstructionOptions &libmv_reconstruction_options) = 0;
 
+  CV_WRAP
   virtual void
   setCameraIntrinsicOptions(const libmv_CameraIntrinsicsOptions &libmv_camera_intrinsics_options) = 0;
 };
 
 /** @brief SFMLibmvEuclideanReconstruction class provides an interface with the Libmv Structure From Motion pipeline.
  */
-class CV_EXPORTS SFMLibmvEuclideanReconstruction : public BaseSFM
+class CV_EXPORTS_W SFMLibmvEuclideanReconstruction : public BaseSFM
 {
 public:
   /** @brief Calls the pipeline in order to perform Eclidean reconstruction.
@@ -183,6 +195,7 @@ public:
     @note
       - Tracks must be as precise as possible. It does not handle outliers and is very sensible to them.
   */
+  CV_WRAP
   virtual void run(InputArrayOfArrays points2d) = 0;
 
   /** @brief Calls the pipeline in order to perform Eclidean reconstruction.
@@ -195,6 +208,7 @@ public:
     @note
       - Tracks must be as precise as possible. It does not handle outliers and is very sensible to them.
   */
+  CV_WRAP
   virtual void run(InputArrayOfArrays points2d, InputOutputArray K, OutputArray Rs,
                    OutputArray Ts, OutputArray points3d) = 0;
 
@@ -205,6 +219,7 @@ public:
       - The images must be ordered as they were an image sequence. Additionally, each frame should be as close as posible to the previous and posterior.
       - For now DAISY features are used in order to compute the 2d points tracks and it only works for 3-4 images.
   */
+  /* CV_WRAP */ // error: ‘vector_string’ was not declared in this scope
   virtual void run(const std::vector <std::string> &images) = 0;
 
   /** @brief Calls the pipeline in order to perform Eclidean reconstruction.
@@ -218,32 +233,38 @@ public:
       - The images must be ordered as they were an image sequence. Additionally, each frame should be as close as posible to the previous and posterior.
       - For now DAISY features are used in order to compute the 2d points tracks and it only works for 3-4 images.
   */
+  /* CV_WRAP */ // error: ‘vector_string’ was not declared in this scope
   virtual void run(const std::vector <std::string> &images, InputOutputArray K, OutputArray Rs,
                    OutputArray Ts, OutputArray points3d) = 0;
 
   /** @brief Returns the computed reprojection error.
   */
+  CV_WRAP
   virtual double getError() const = 0;
 
   /** @brief Returns the estimated 3d points.
     @param points3d Output array with estimated 3d points.
   */
+  CV_WRAP
   virtual void getPoints(OutputArray points3d) = 0;
 
   /** @brief Returns the refined camera calibration matrix.
   */
+  CV_WRAP
   virtual cv::Mat getIntrinsics() const = 0;
 
   /** @brief Returns the estimated camera extrinsic parameters.
     @param Rs Output vector of 3x3 rotations of the camera.
     @param Ts Output vector of 3x1 translations of the camera.
   */
+  CV_WRAP
   virtual void getCameras(OutputArray Rs, OutputArray Ts) = 0;
 
   /** @brief Setter method for reconstruction options.
     @param libmv_reconstruction_options struct with reconstruction options such as initial keyframes,
       automatic keyframe selection, parameters to refine and the verbosity level.
   */
+  CV_WRAP
   virtual void
   setReconstructionOptions(const libmv_ReconstructionOptions &libmv_reconstruction_options) = 0;
 
@@ -251,14 +272,16 @@ public:
     @param libmv_camera_intrinsics_options struct with camera intrinsic options such as camera model and
       the internal camera parameters.
   */
+  CV_WRAP
   virtual void
   setCameraIntrinsicOptions(const libmv_CameraIntrinsicsOptions &libmv_camera_intrinsics_options) = 0;
 
   /** @brief Creates an instance of the SFMLibmvEuclideanReconstruction class. Initializes Libmv. */
+  /* CV_WRAP */ // error: ‘SFMLibmvEuclideanReruction’ was not declared in this scope
   static Ptr<SFMLibmvEuclideanReconstruction>
     create(const libmv_CameraIntrinsicsOptions &camera_instrinsic_options=libmv_CameraIntrinsicsOptions(),
            const libmv_ReconstructionOptions &reconstruction_options=libmv_ReconstructionOptions());
-};
+  };
 
 //! @} sfm
 

modules/sfm/include/opencv2/sfm/triangulation.hpp

@@ -54,7 +54,7 @@ namespace sfm
   Triangulates the 3d position of 2d correspondences between several images.
   Reference: Internally it uses DLT method @cite HartleyZ00 12.2 pag.312
 */
-CV_EXPORTS
+CV_EXPORTS_W
 void
 triangulatePoints(InputArrayOfArrays points2d, InputArrayOfArrays projection_matrices,
                   OutputArray points3d);