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- Wrap computeNormalsPC3d()
for python
- Update its usage
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@@ -89,7 +89,7 @@ Mat samplePCUniformInd(Mat PC, int sampleStep, std::vector<int>& indices);
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* @param [in] zrange Z components (min and max) of the bounding box of the model
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* @param [in] sample_step_relative The point cloud is sampled such that all points
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* have a certain minimum distance. This minimum distance is determined relatively using
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* the parameter sample_step_relative.
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* the parameter sample_step_relative.
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* @param [in] weightByCenter The contribution of the quantized data points can be weighted
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* by the distance to the origin. This parameter enables/disables the use of weighting.
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* @return Sampled point cloud
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@@ -135,7 +135,7 @@ CV_EXPORTS void getRandomPose(double Pose[16]);
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/**
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* Adds a uniform noise in the given scale to the input point cloud
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* @param [in] pc Input point cloud (CV_32F family).
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* @param [in] pc Input point cloud (CV_32F family).
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* @param [in] scale Input scale of the noise. The larger the scale, the more noisy the output
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*/
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CV_EXPORTS Mat addNoisePC(Mat pc, double scale);
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@@ -148,13 +148,13 @@ CV_EXPORTS Mat addNoisePC(Mat pc, double scale);
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* If PCNormals is provided to be an Nx6 matrix, then no new allocation
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* is made, instead the existing memory is overwritten.
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* @param [in] PC Input point cloud to compute the normals for.
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* @param [in] PCNormals Output point cloud
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* @param [out] PCNormals Output point cloud
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* @param [in] NumNeighbors Number of neighbors to take into account in a local region
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* @param [in] FlipViewpoint Should normals be flipped to a viewing direction?
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* @param [in] viewpoint
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* @return Returns 0 on success
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*/
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CV_EXPORTS int computeNormalsPC3d(const Mat& PC, Mat& PCNormals, const int NumNeighbors, const bool FlipViewpoint, const double viewpoint[3]);
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CV_EXPORTS_W int computeNormalsPC3d(const Mat& PC, CV_OUT Mat& PCNormals, const int NumNeighbors, const bool FlipViewpoint, const Vec3d& viewpoint);
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//! @}
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