SFMLibmvEuclideanReconstruction class provides an interface with the Libmv Structure From Motion pipeline.
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#include <opencv2/sfm/simple_pipeline.hpp>
SFMLibmvEuclideanReconstruction class provides an interface with the Libmv Structure From Motion pipeline.
◆ create()
◆ getCameras()
Python: |
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| Rs, Ts | = | cv.sfm_SFMLibmvEuclideanReconstruction.getCameras( | [, Rs[, Ts]] | ) |
Returns the estimated camera extrinsic parameters.
- Parameters
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Rs | Output vector of 3x3 rotations of the camera. |
Ts | Output vector of 3x1 translations of the camera. |
Implements cv::sfm::BaseSFM.
◆ getError()
virtual double cv::sfm::SFMLibmvEuclideanReconstruction::getError |
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| ) |
const |
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pure virtual |
Python: |
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| retval | = | cv.sfm_SFMLibmvEuclideanReconstruction.getError( | | ) |
◆ getIntrinsics()
virtual cv::Mat cv::sfm::SFMLibmvEuclideanReconstruction::getIntrinsics |
( |
| ) |
const |
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pure virtual |
Python: |
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| retval | = | cv.sfm_SFMLibmvEuclideanReconstruction.getIntrinsics( | | ) |
◆ getPoints()
virtual void cv::sfm::SFMLibmvEuclideanReconstruction::getPoints |
( |
OutputArray |
points3d | ) |
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pure virtual |
Python: |
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| points3d | = | cv.sfm_SFMLibmvEuclideanReconstruction.getPoints( | [, points3d] | ) |
Returns the estimated 3d points.
- Parameters
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points3d | Output array with estimated 3d points. |
Implements cv::sfm::BaseSFM.
◆ run() [1/4]
virtual void cv::sfm::SFMLibmvEuclideanReconstruction::run |
( |
const std::vector< String > & |
images | ) |
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pure virtual |
Python: |
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| None | = | cv.sfm_SFMLibmvEuclideanReconstruction.run( | points2d | ) |
| K, Rs, Ts, points3d | = | cv.sfm_SFMLibmvEuclideanReconstruction.run( | points2d, K[, Rs[, Ts[, points3d]]] | ) |
Calls the pipeline in order to perform Eclidean reconstruction.
- Parameters
-
images | a vector of string with the images paths. |
- Note
- 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.
Implements cv::sfm::BaseSFM.
◆ run() [2/4]
Python: |
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| None | = | cv.sfm_SFMLibmvEuclideanReconstruction.run( | points2d | ) |
| K, Rs, Ts, points3d | = | cv.sfm_SFMLibmvEuclideanReconstruction.run( | points2d, K[, Rs[, Ts[, points3d]]] | ) |
Calls the pipeline in order to perform Eclidean reconstruction.
- Parameters
-
images | a vector of string with the images paths. |
K | Input/Output camera matrix \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\). Input parameters used as initial guess. |
Rs | Output vector of 3x3 rotations of the camera. |
Ts | Output vector of 3x1 translations of the camera. |
points3d | Output array with estimated 3d points. |
- Note
- 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.
Implements cv::sfm::BaseSFM.
◆ run() [3/4]
Python: |
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| None | = | cv.sfm_SFMLibmvEuclideanReconstruction.run( | points2d | ) |
| K, Rs, Ts, points3d | = | cv.sfm_SFMLibmvEuclideanReconstruction.run( | points2d, K[, Rs[, Ts[, points3d]]] | ) |
Calls the pipeline in order to perform Eclidean reconstruction.
- Parameters
-
points2d | Input vector of vectors of 2d points (the inner vector is per image). |
- Note
- Tracks must be as precise as possible. It does not handle outliers and is very sensible to them.
Implements cv::sfm::BaseSFM.
◆ run() [4/4]
Python: |
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| None | = | cv.sfm_SFMLibmvEuclideanReconstruction.run( | points2d | ) |
| K, Rs, Ts, points3d | = | cv.sfm_SFMLibmvEuclideanReconstruction.run( | points2d, K[, Rs[, Ts[, points3d]]] | ) |
Calls the pipeline in order to perform Eclidean reconstruction.
- Parameters
-
points2d | Input vector of vectors of 2d points (the inner vector is per image). |
K | Input/Output camera matrix \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\). Input parameters used as initial guess. |
Rs | Output vector of 3x3 rotations of the camera. |
Ts | Output vector of 3x1 translations of the camera. |
points3d | Output array with estimated 3d points. |
- Note
- Tracks must be as precise as possible. It does not handle outliers and is very sensible to them.
Implements cv::sfm::BaseSFM.
◆ setCameraIntrinsicOptions()
virtual void cv::sfm::SFMLibmvEuclideanReconstruction::setCameraIntrinsicOptions |
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const libmv_CameraIntrinsicsOptions & |
libmv_camera_intrinsics_options | ) |
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pure virtual |
Python: |
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| None | = | cv.sfm_SFMLibmvEuclideanReconstruction.setCameraIntrinsicOptions( | libmv_camera_intrinsics_options | ) |
Setter method for camera intrinsic options.
- Parameters
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libmv_camera_intrinsics_options | struct with camera intrinsic options such as camera model and the internal camera parameters. |
Implements cv::sfm::BaseSFM.
◆ setReconstructionOptions()
virtual void cv::sfm::SFMLibmvEuclideanReconstruction::setReconstructionOptions |
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const libmv_ReconstructionOptions & |
libmv_reconstruction_options | ) |
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pure virtual |
Python: |
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| None | = | cv.sfm_SFMLibmvEuclideanReconstruction.setReconstructionOptions( | libmv_reconstruction_options | ) |
Setter method for reconstruction options.
- Parameters
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libmv_reconstruction_options | struct with reconstruction options such as initial keyframes, automatic keyframe selection, parameters to refine and the verbosity level. |
Implements cv::sfm::BaseSFM.
The documentation for this class was generated from the following file: