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#![allow( unused_parens, clippy::excessive_precision, clippy::missing_safety_doc, clippy::not_unsafe_ptr_arg_deref, clippy::should_implement_trait, clippy::too_many_arguments, clippy::unused_unit, )] //! # Image Processing //! //! This module includes image-processing functions. //! # Image Filtering //! //! Functions and classes described in this section are used to perform various linear or non-linear //! filtering operations on 2D images (represented as Mat's). It means that for each pixel location //!  in the source image (normally, rectangular), its neighborhood is considered and used to //! compute the response. In case of a linear filter, it is a weighted sum of pixel values. In case of //! morphological operations, it is the minimum or maximum values, and so on. The computed response is //! stored in the destination image at the same location . It means that the output image //! will be of the same size as the input image. Normally, the functions support multi-channel arrays, //! in which case every channel is processed independently. Therefore, the output image will also have //! the same number of channels as the input one. //! //! Another common feature of the functions and classes described in this section is that, unlike //! simple arithmetic functions, they need to extrapolate values of some non-existing pixels. For //! example, if you want to smooth an image using a Gaussian  filter, then, when //! processing the left-most pixels in each row, you need pixels to the left of them, that is, outside //! of the image. You can let these pixels be the same as the left-most image pixels ("replicated //! border" extrapolation method), or assume that all the non-existing pixels are zeros ("constant //! border" extrapolation method), and so on. OpenCV enables you to specify the extrapolation method. //! For details, see #BorderTypes //! //! @anchor filter_depths //! ### Depth combinations //! Input depth (src.depth()) | Output depth (ddepth) //! --------------------------|---------------------- //! CV_8U | -1/CV_16S/CV_32F/CV_64F //! CV_16U/CV_16S | -1/CV_32F/CV_64F //! CV_32F | -1/CV_32F/CV_64F //! CV_64F | -1/CV_64F //! //! //! Note: when ddepth=-1, the output image will have the same depth as the source. //! //! # Geometric Image Transformations //! //! The functions in this section perform various geometrical transformations of 2D images. They do not //! change the image content but deform the pixel grid and map this deformed grid to the destination //! image. In fact, to avoid sampling artifacts, the mapping is done in the reverse order, from //! destination to the source. That is, for each pixel  of the destination image, the //! functions compute coordinates of the corresponding "donor" pixel in the source image and copy the //! pixel value: //! //!  //! //! In case when you specify the forward mapping , the OpenCV functions first compute the corresponding inverse mapping //!  and then use the above formula. //! //! The actual implementations of the geometrical transformations, from the most generic remap and to //! the simplest and the fastest resize, need to solve two main problems with the above formula: //! //! - Extrapolation of non-existing pixels. Similarly to the filtering functions described in the //! previous section, for some , either one of , or , or both //! of them may fall outside of the image. In this case, an extrapolation method needs to be used. //! OpenCV provides the same selection of extrapolation methods as in the filtering functions. In //! addition, it provides the method #BORDER_TRANSPARENT. This means that the corresponding pixels in //! the destination image will not be modified at all. //! //! - Interpolation of pixel values. Usually  and  are floating-point //! numbers. This means that  can be either an affine or perspective //! transformation, or radial lens distortion correction, and so on. So, a pixel value at fractional //! coordinates needs to be retrieved. In the simplest case, the coordinates can be just rounded to the //! nearest integer coordinates and the corresponding pixel can be used. This is called a //! nearest-neighbor interpolation. However, a better result can be achieved by using more //! sophisticated [interpolation methods](http://en.wikipedia.org/wiki/Multivariate_interpolation) , //! where a polynomial function is fit into some neighborhood of the computed pixel , and then the value of the polynomial at  is taken as the //! interpolated pixel value. In OpenCV, you can choose between several interpolation methods. See //! resize for details. //! //! //! Note: The geometrical transformations do not work with `CV_8S` or `CV_32S` images. //! //! # Miscellaneous Image Transformations //! # Drawing Functions //! //! Drawing functions work with matrices/images of arbitrary depth. The boundaries of the shapes can be //! rendered with antialiasing (implemented only for 8-bit images for now). All the functions include //! the parameter color that uses an RGB value (that may be constructed with the Scalar constructor ) //! for color images and brightness for grayscale images. For color images, the channel ordering is //! normally *Blue, Green, Red*. This is what imshow, imread, and imwrite expect. So, if you form a //! color using the Scalar constructor, it should look like: //! //!  //! //! If you are using your own image rendering and I/O functions, you can use any channel ordering. The //! drawing functions process each channel independently and do not depend on the channel order or even //! on the used color space. The whole image can be converted from BGR to RGB or to a different color //! space using cvtColor . //! //! If a drawn figure is partially or completely outside the image, the drawing functions clip it. Also, //! many drawing functions can handle pixel coordinates specified with sub-pixel accuracy. This means //! that the coordinates can be passed as fixed-point numbers encoded as integers. The number of //! fractional bits is specified by the shift parameter and the real point coordinates are calculated as //!  . This feature is //! especially effective when rendering antialiased shapes. //! //! //! Note: The functions do not support alpha-transparency when the target image is 4-channel. In this //! case, the color[3] is simply copied to the repainted pixels. Thus, if you want to paint //! semi-transparent shapes, you can paint them in a separate buffer and then blend it with the main //! image. //! //! # Color Space Conversions //! # ColorMaps in OpenCV //! //! The human perception isn't built for observing fine changes in grayscale images. Human eyes are more //! sensitive to observing changes between colors, so you often need to recolor your grayscale images to //! get a clue about them. OpenCV now comes with various colormaps to enhance the visualization in your //! computer vision application. //! //! In OpenCV you only need applyColorMap to apply a colormap on a given image. The following sample //! code reads the path to an image from command line, applies a Jet colormap on it and shows the //! result: //! //! @include snippets/imgproc_applyColorMap.cpp //! ## See also //! #ColormapTypes //! //! # Planar Subdivision //! //! The Subdiv2D class described in this section is used to perform various planar subdivision on //! a set of 2D points (represented as vector of Point2f). OpenCV subdivides a plane into triangles //! using the Delaunay's algorithm, which corresponds to the dual graph of the Voronoi diagram. //! In the figure below, the Delaunay's triangulation is marked with black lines and the Voronoi //! diagram with red lines. //! //!  //! //! The subdivisions can be used for the 3D piece-wise transformation of a plane, morphing, fast //! location of points on the plane, building special graphs (such as NNG,RNG), and so forth. //! //! # Histograms //! # Structural Analysis and Shape Descriptors //! # Motion Analysis and Object Tracking //! # Feature Detection //! # Object Detection //! # C API //! # Hardware Acceleration Layer //! # Functions //! # Interface use crate::{mod_prelude::*, core, sys, types}; pub mod prelude { pub use { super::PCAPriorTrait, super::OpticalFlowPCAFlowTrait, super::GPCPatchDescriptorTrait, super::GPCPatchSampleTrait, super::GPCTrainingSamplesTrait, super::GPCTreeTrait, super::GPCDetailsTrait, super::RLOFOpticalFlowParameterTrait, super::DenseRLOFOpticalFlow, super::SparseRLOFOpticalFlow, super::DualTVL1OpticalFlow }; } /// Better quality but slow pub const GPC_DESCRIPTOR_DCT: i32 = 0; /// Worse quality but much faster pub const GPC_DESCRIPTOR_WHT: i32 = 1; /// < Edge-preserving interpolation using ximgproc::EdgeAwareInterpolator, see [Revaud2015](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Revaud2015),Geistert2016. pub const INTERP_EPIC: i32 = 1; /// < Fast geodesic interpolation, see [Geistert2016](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Geistert2016) pub const INTERP_GEO: i32 = 0; /// < SLIC based robust interpolation using ximgproc::RICInterpolator, see [Hu2017](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Hu2017). pub const INTERP_RIC: i32 = 2; /// < Apply a adaptive support region obtained by cross-based segmentation /// as described in [Senst2014](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2014) pub const SR_CROSS: i32 = 1; /// < Apply a constant support region pub const SR_FIXED: i32 = 0; /// < Apply optimized iterative refinement based bilinear equation solutions /// as described in [Senst2013](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2013) pub const ST_BILINEAR: i32 = 1; /// < Apply standard iterative refinement pub const ST_STANDART: i32 = 0; /// Descriptor types for the Global Patch Collider. #[repr(C)] #[derive(Copy, Clone, Debug, PartialEq)] pub enum GPCDescType { /// Better quality but slow GPC_DESCRIPTOR_DCT = 0, /// Worse quality but much faster GPC_DESCRIPTOR_WHT = 1, } opencv_type_enum! { crate::optflow::GPCDescType } #[repr(C)] #[derive(Copy, Clone, Debug, PartialEq)] pub enum InterpolationType { /// < Fast geodesic interpolation, see [Geistert2016](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Geistert2016) INTERP_GEO = 0, /// < Edge-preserving interpolation using ximgproc::EdgeAwareInterpolator, see [Revaud2015](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Revaud2015),Geistert2016. INTERP_EPIC = 1, /// < SLIC based robust interpolation using ximgproc::RICInterpolator, see [Hu2017](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Hu2017). INTERP_RIC = 2, } opencv_type_enum! { crate::optflow::InterpolationType } #[repr(C)] #[derive(Copy, Clone, Debug, PartialEq)] pub enum SolverType { /// < Apply standard iterative refinement ST_STANDART = 0, /// < Apply optimized iterative refinement based bilinear equation solutions /// as described in [Senst2013](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2013) ST_BILINEAR = 1, } opencv_type_enum! { crate::optflow::SolverType } #[repr(C)] #[derive(Copy, Clone, Debug, PartialEq)] pub enum SupportRegionType { /// < Apply a constant support region SR_FIXED = 0, /// < Apply a adaptive support region obtained by cross-based segmentation /// as described in [Senst2014](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2014) SR_CROSS = 1, } opencv_type_enum! { crate::optflow::SupportRegionType } /// Calculates a global motion orientation in a selected region. /// /// ## Parameters /// * orientation: Motion gradient orientation image calculated by the function calcMotionGradient /// * mask: Mask image. It may be a conjunction of a valid gradient mask, also calculated by /// calcMotionGradient , and the mask of a region whose direction needs to be calculated. /// * mhi: Motion history image calculated by updateMotionHistory . /// * timestamp: Timestamp passed to updateMotionHistory . /// * duration: Maximum duration of a motion track in milliseconds, passed to updateMotionHistory /// /// The function calculates an average motion direction in the selected region and returns the angle /// between 0 degrees and 360 degrees. The average direction is computed from the weighted orientation /// histogram, where a recent motion has a larger weight and the motion occurred in the past has a /// smaller weight, as recorded in mhi . pub fn calc_global_orientation(orientation: &dyn core::ToInputArray, mask: &dyn core::ToInputArray, mhi: &dyn core::ToInputArray, timestamp: f64, duration: f64) -> Result<f64> { input_array_arg!(orientation); input_array_arg!(mask); input_array_arg!(mhi); unsafe { sys::cv_motempl_calcGlobalOrientation_const__InputArrayR_const__InputArrayR_const__InputArrayR_double_double(orientation.as_raw__InputArray(), mask.as_raw__InputArray(), mhi.as_raw__InputArray(), timestamp, duration) }.into_result() } /// Calculates a gradient orientation of a motion history image. /// /// ## Parameters /// * mhi: Motion history single-channel floating-point image. /// * mask: Output mask image that has the type CV_8UC1 and the same size as mhi . Its non-zero /// elements mark pixels where the motion gradient data is correct. /// * orientation: Output motion gradient orientation image that has the same type and the same /// size as mhi . Each pixel of the image is a motion orientation, from 0 to 360 degrees. /// * delta1: Minimal (or maximal) allowed difference between mhi values within a pixel /// neighborhood. /// * delta2: Maximal (or minimal) allowed difference between mhi values within a pixel /// neighborhood. That is, the function finds the minimum (  ) and maximum (  ) mhi /// values over  neighborhood of each pixel and marks the motion orientation at  /// as valid only if ///  /// * apertureSize: Aperture size of the Sobel operator. /// /// The function calculates a gradient orientation at each pixel  as: /// ///  /// /// In fact, fastAtan2 and phase are used so that the computed angle is measured in degrees and covers /// the full range 0..360. Also, the mask is filled to indicate pixels where the computed angle is /// valid. /// /// /// Note: /// * (Python) An example on how to perform a motion template technique can be found at /// opencv_source_code/samples/python2/motempl.py /// /// ## C++ default parameters /// * aperture_size: 3 pub fn calc_motion_gradient(mhi: &dyn core::ToInputArray, mask: &mut dyn core::ToOutputArray, orientation: &mut dyn core::ToOutputArray, delta1: f64, delta2: f64, aperture_size: i32) -> Result<()> { input_array_arg!(mhi); output_array_arg!(mask); output_array_arg!(orientation); unsafe { sys::cv_motempl_calcMotionGradient_const__InputArrayR_const__OutputArrayR_const__OutputArrayR_double_double_int(mhi.as_raw__InputArray(), mask.as_raw__OutputArray(), orientation.as_raw__OutputArray(), delta1, delta2, aperture_size) }.into_result() } /// Splits a motion history image into a few parts corresponding to separate independent motions (for /// example, left hand, right hand). /// /// ## Parameters /// * mhi: Motion history image. /// * segmask: Image where the found mask should be stored, single-channel, 32-bit floating-point. /// * boundingRects: Vector containing ROIs of motion connected components. /// * timestamp: Current time in milliseconds or other units. /// * segThresh: Segmentation threshold that is recommended to be equal to the interval between /// motion history "steps" or greater. /// /// The function finds all of the motion segments and marks them in segmask with individual values /// (1,2,...). It also computes a vector with ROIs of motion connected components. After that the motion /// direction for every component can be calculated with calcGlobalOrientation using the extracted mask /// of the particular component. pub fn segment_motion(mhi: &dyn core::ToInputArray, segmask: &mut dyn core::ToOutputArray, bounding_rects: &mut core::Vector::<core::Rect>, timestamp: f64, seg_thresh: f64) -> Result<()> { input_array_arg!(mhi); output_array_arg!(segmask); unsafe { sys::cv_motempl_segmentMotion_const__InputArrayR_const__OutputArrayR_vector_Rect_R_double_double(mhi.as_raw__InputArray(), segmask.as_raw__OutputArray(), bounding_rects.as_raw_mut_VectorOfRect(), timestamp, seg_thresh) }.into_result() } /// Updates the motion history image by a moving silhouette. /// /// ## Parameters /// * silhouette: Silhouette mask that has non-zero pixels where the motion occurs. /// * mhi: Motion history image that is updated by the function (single-channel, 32-bit /// floating-point). /// * timestamp: Current time in milliseconds or other units. /// * duration: Maximal duration of the motion track in the same units as timestamp . /// /// The function updates the motion history image as follows: /// ///  /// /// That is, MHI pixels where the motion occurs are set to the current timestamp , while the pixels /// where the motion happened last time a long time ago are cleared. /// /// The function, together with calcMotionGradient and calcGlobalOrientation , implements a motion /// templates technique described in [Davis97](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Davis97) and [Bradski00](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Bradski00) . pub fn update_motion_history(silhouette: &dyn core::ToInputArray, mhi: &mut dyn core::ToInputOutputArray, timestamp: f64, duration: f64) -> Result<()> { input_array_arg!(silhouette); input_output_array_arg!(mhi); unsafe { sys::cv_motempl_updateMotionHistory_const__InputArrayR_const__InputOutputArrayR_double_double(silhouette.as_raw__InputArray(), mhi.as_raw__InputOutputArray(), timestamp, duration) }.into_result() } /// Fast dense optical flow computation based on robust local optical flow (RLOF) algorithms and sparse-to-dense interpolation scheme. /// /// The RLOF is a fast local optical flow approach described in [Senst2012](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2012) [Senst2013](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2013) [Senst2014](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2014) /// and [Senst2016](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2016) similar to the pyramidal iterative Lucas-Kanade method as /// proposed by [Bouguet00](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Bouguet00). More details and experiments can be found in the following thesis [Senst2019](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2019). /// The implementation is derived from optflow::calcOpticalFlowPyrLK(). /// /// The sparse-to-dense interpolation scheme allows for fast computation of dense optical flow using RLOF (see [Geistert2016](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Geistert2016)). /// For this scheme the following steps are applied: /// -# motion vector seeded at a regular sampled grid are computed. The sparsity of this grid can be configured with setGridStep /// -# (optinally) errornous motion vectors are filter based on the forward backward confidence. The threshold can be configured /// with setForwardBackward. The filter is only applied if the threshold >0 but than the runtime is doubled due to the estimation /// of the backward flow. /// -# Vector field interpolation is applied to the motion vector set to obtain a dense vector field. /// /// ## Parameters /// * I0: first 8-bit input image. If The cross-based RLOF is used (by selecting optflow::RLOFOpticalFlowParameter::supportRegionType /// = SupportRegionType::SR_CROSS) image has to be a 8-bit 3 channel image. /// * I1: second 8-bit input image. If The cross-based RLOF is used (by selecting optflow::RLOFOpticalFlowParameter::supportRegionType /// = SupportRegionType::SR_CROSS) image has to be a 8-bit 3 channel image. /// * flow: computed flow image that has the same size as I0 and type CV_32FC2. /// * rlofParam: see optflow::RLOFOpticalFlowParameter /// * forwardBackwardThreshold: Threshold for the forward backward confidence check. /// For each grid point  a motion vector  is computed. /// If the forward backward error  /// is larger than threshold given by this function then the motion vector will not be used by the following /// vector field interpolation.  denotes the backward flow. Note, the forward backward test /// will only be applied if the threshold > 0. This may results into a doubled runtime for the motion estimation. /// * gridStep: Size of the grid to spawn the motion vectors. For each grid point a motion vector is computed. /// Some motion vectors will be removed due to the forwatd backward threshold (if set >0). The rest will be the /// base of the vector field interpolation. /// * interp_type: interpolation method used to compute the dense optical flow. Two interpolation algorithms are /// supported: /// - **INTERP_GEO** applies the fast geodesic interpolation, see [Geistert2016](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Geistert2016). /// - **INTERP_EPIC_RESIDUAL** applies the edge-preserving interpolation, see [Revaud2015](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Revaud2015),Geistert2016. /// * epicK: see ximgproc::EdgeAwareInterpolator sets the respective parameter. /// * epicSigma: see ximgproc::EdgeAwareInterpolator sets the respective parameter. /// * epicLambda: see ximgproc::EdgeAwareInterpolator sets the respective parameter. /// * ricSPSize: see ximgproc::RICInterpolator sets the respective parameter. /// * ricSLICType: see ximgproc::RICInterpolator sets the respective parameter. /// * use_post_proc: enables ximgproc::fastGlobalSmootherFilter() parameter. /// * fgsLambda: sets the respective ximgproc::fastGlobalSmootherFilter() parameter. /// * fgsSigma: sets the respective ximgproc::fastGlobalSmootherFilter() parameter. /// * use_variational_refinement: enables VariationalRefinement /// /// Parameters have been described in [Senst2012](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2012), [Senst2013](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2013), [Senst2014](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2014), [Senst2016](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2016). /// For the RLOF configuration see optflow::RLOFOpticalFlowParameter for further details. /// /// Note: If the grid size is set to (1,1) and the forward backward threshold <= 0 that the dense optical flow field is purely /// computed with the RLOF. /// /// /// Note: SIMD parallelization is only available when compiling with SSE4.1. /// ## See also /// optflow::DenseRLOFOpticalFlow, optflow::RLOFOpticalFlowParameter /// /// ## C++ default parameters /// * rlof_param: Ptr<RLOFOpticalFlowParameter>() /// * forward_backward_threshold: 0 /// * grid_step: Size(6,6) /// * interp_type: InterpolationType::INTERP_EPIC /// * epic_k: 128 /// * epic_sigma: 0.05f /// * epic_lambda: 100.f /// * ric_sp_size: 15 /// * ric_slic_type: 100 /// * use_post_proc: true /// * fgs_lambda: 500.0f /// * fgs_sigma: 1.5f /// * use_variational_refinement: false pub fn calc_optical_flow_dense_rlof(i0: &dyn core::ToInputArray, i1: &dyn core::ToInputArray, flow: &mut dyn core::ToInputOutputArray, mut rlof_param: core::Ptr::<crate::optflow::RLOFOpticalFlowParameter>, forward_backward_threshold: f32, grid_step: core::Size, interp_type: crate::optflow::InterpolationType, epic_k: i32, epic_sigma: f32, epic_lambda: f32, ric_sp_size: i32, ric_slic_type: i32, use_post_proc: bool, fgs_lambda: f32, fgs_sigma: f32, use_variational_refinement: bool) -> Result<()> { input_array_arg!(i0); input_array_arg!(i1); input_output_array_arg!(flow); unsafe { sys::cv_optflow_calcOpticalFlowDenseRLOF_const__InputArrayR_const__InputArrayR_const__InputOutputArrayR_Ptr_RLOFOpticalFlowParameter__float_Size_InterpolationType_int_float_float_int_int_bool_float_float_bool(i0.as_raw__InputArray(), i1.as_raw__InputArray(), flow.as_raw__InputOutputArray(), rlof_param.as_raw_mut_PtrOfRLOFOpticalFlowParameter(), forward_backward_threshold, grid_step.opencv_as_extern(), interp_type, epic_k, epic_sigma, epic_lambda, ric_sp_size, ric_slic_type, use_post_proc, fgs_lambda, fgs_sigma, use_variational_refinement) }.into_result() } /// Calculate an optical flow using "SimpleFlow" algorithm. /// /// ## Parameters /// * from: First 8-bit 3-channel image. /// * to: Second 8-bit 3-channel image of the same size as prev /// * flow: computed flow image that has the same size as prev and type CV_32FC2 /// * layers: Number of layers /// * averaging_block_size: Size of block through which we sum up when calculate cost function /// for pixel /// * max_flow: maximal flow that we search at each level /// * sigma_dist: vector smooth spatial sigma parameter /// * sigma_color: vector smooth color sigma parameter /// * postprocess_window: window size for postprocess cross bilateral filter /// * sigma_dist_fix: spatial sigma for postprocess cross bilateralf filter /// * sigma_color_fix: color sigma for postprocess cross bilateral filter /// * occ_thr: threshold for detecting occlusions /// * upscale_averaging_radius: window size for bilateral upscale operation /// * upscale_sigma_dist: spatial sigma for bilateral upscale operation /// * upscale_sigma_color: color sigma for bilateral upscale operation /// * speed_up_thr: threshold to detect point with irregular flow - where flow should be /// recalculated after upscale /// /// See [Tao2012](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Tao2012) . And site of project - <http://graphics.berkeley.edu/papers/Tao-SAN-2012-05/>. /// /// /// Note: /// * An example using the simpleFlow algorithm can be found at samples/simpleflow_demo.cpp /// /// ## Overloaded parameters pub fn calc_optical_flow_sf(from: &dyn core::ToInputArray, to: &dyn core::ToInputArray, flow: &mut dyn core::ToOutputArray, layers: i32, averaging_block_size: i32, max_flow: i32) -> Result<()> { input_array_arg!(from); input_array_arg!(to); output_array_arg!(flow); unsafe { sys::cv_optflow_calcOpticalFlowSF_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int_int_int(from.as_raw__InputArray(), to.as_raw__InputArray(), flow.as_raw__OutputArray(), layers, averaging_block_size, max_flow) }.into_result() } /// Calculate an optical flow using "SimpleFlow" algorithm. /// /// ## Parameters /// * from: First 8-bit 3-channel image. /// * to: Second 8-bit 3-channel image of the same size as prev /// * flow: computed flow image that has the same size as prev and type CV_32FC2 /// * layers: Number of layers /// * averaging_block_size: Size of block through which we sum up when calculate cost function /// for pixel /// * max_flow: maximal flow that we search at each level /// * sigma_dist: vector smooth spatial sigma parameter /// * sigma_color: vector smooth color sigma parameter /// * postprocess_window: window size for postprocess cross bilateral filter /// * sigma_dist_fix: spatial sigma for postprocess cross bilateralf filter /// * sigma_color_fix: color sigma for postprocess cross bilateral filter /// * occ_thr: threshold for detecting occlusions /// * upscale_averaging_radius: window size for bilateral upscale operation /// * upscale_sigma_dist: spatial sigma for bilateral upscale operation /// * upscale_sigma_color: color sigma for bilateral upscale operation /// * speed_up_thr: threshold to detect point with irregular flow - where flow should be /// recalculated after upscale /// /// See [Tao2012](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Tao2012) . And site of project - <http://graphics.berkeley.edu/papers/Tao-SAN-2012-05/>. /// /// /// Note: /// * An example using the simpleFlow algorithm can be found at samples/simpleflow_demo.cpp pub fn calc_optical_flow_sf_1(from: &dyn core::ToInputArray, to: &dyn core::ToInputArray, flow: &mut dyn core::ToOutputArray, layers: i32, averaging_block_size: i32, max_flow: i32, sigma_dist: f64, sigma_color: f64, postprocess_window: i32, sigma_dist_fix: f64, sigma_color_fix: f64, occ_thr: f64, upscale_averaging_radius: i32, upscale_sigma_dist: f64, upscale_sigma_color: f64, speed_up_thr: f64) -> Result<()> { input_array_arg!(from); input_array_arg!(to); output_array_arg!(flow); unsafe { sys::cv_optflow_calcOpticalFlowSF_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int_int_int_double_double_int_double_double_double_int_double_double_double(from.as_raw__InputArray(), to.as_raw__InputArray(), flow.as_raw__OutputArray(), layers, averaging_block_size, max_flow, sigma_dist, sigma_color, postprocess_window, sigma_dist_fix, sigma_color_fix, occ_thr, upscale_averaging_radius, upscale_sigma_dist, upscale_sigma_color, speed_up_thr) }.into_result() } /// Calculates fast optical flow for a sparse feature set using the robust local optical flow (RLOF) similar /// to optflow::calcOpticalFlowPyrLK(). /// /// The RLOF is a fast local optical flow approach described in [Senst2012](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2012) [Senst2013](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2013) [Senst2014](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2014) /// and [Senst2016](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2016) similar to the pyramidal iterative Lucas-Kanade method as /// proposed by [Bouguet00](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Bouguet00). More details and experiments can be found in the following thesis [Senst2019](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2019). /// The implementation is derived from optflow::calcOpticalFlowPyrLK(). /// /// ## Parameters /// * prevImg: first 8-bit input image. If The cross-based RLOF is used (by selecting optflow::RLOFOpticalFlowParameter::supportRegionType /// = SupportRegionType::SR_CROSS) image has to be a 8-bit 3 channel image. /// * nextImg: second 8-bit input image. If The cross-based RLOF is used (by selecting optflow::RLOFOpticalFlowParameter::supportRegionType /// = SupportRegionType::SR_CROSS) image has to be a 8-bit 3 channel image. /// * prevPts: vector of 2D points for which the flow needs to be found; point coordinates must be single-precision /// floating-point numbers. /// * nextPts: output vector of 2D points (with single-precision floating-point coordinates) containing the calculated /// new positions of input features in the second image; when optflow::RLOFOpticalFlowParameter::useInitialFlow variable is true the vector must /// have the same size as in the input and contain the initialization point correspondences. /// * status: output status vector (of unsigned chars); each element of the vector is set to 1 if the flow for the /// corresponding features has passed the forward backward check. /// * err: output vector of errors; each element of the vector is set to the forward backward error for the corresponding feature. /// * rlofParam: see optflow::RLOFOpticalFlowParameter /// * forwardBackwardThreshold: Threshold for the forward backward confidence check. If forewardBackwardThreshold <=0 the forward /// /// /// Note: SIMD parallelization is only available when compiling with SSE4.1. /// /// Parameters have been described in [Senst2012](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2012), [Senst2013](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2013), [Senst2014](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2014) and [Senst2016](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2016). /// For the RLOF configuration see optflow::RLOFOpticalFlowParameter for further details. /// /// ## C++ default parameters /// * rlof_param: Ptr<RLOFOpticalFlowParameter>() /// * forward_backward_threshold: 0 pub fn calc_optical_flow_sparse_rlof(prev_img: &dyn core::ToInputArray, next_img: &dyn core::ToInputArray, prev_pts: &dyn core::ToInputArray, next_pts: &mut dyn core::ToInputOutputArray, status: &mut dyn core::ToOutputArray, err: &mut dyn core::ToOutputArray, mut rlof_param: core::Ptr::<crate::optflow::RLOFOpticalFlowParameter>, forward_backward_threshold: f32) -> Result<()> { input_array_arg!(prev_img); input_array_arg!(next_img); input_array_arg!(prev_pts); input_output_array_arg!(next_pts); output_array_arg!(status); output_array_arg!(err); unsafe { sys::cv_optflow_calcOpticalFlowSparseRLOF_const__InputArrayR_const__InputArrayR_const__InputArrayR_const__InputOutputArrayR_const__OutputArrayR_const__OutputArrayR_Ptr_RLOFOpticalFlowParameter__float(prev_img.as_raw__InputArray(), next_img.as_raw__InputArray(), prev_pts.as_raw__InputArray(), next_pts.as_raw__InputOutputArray(), status.as_raw__OutputArray(), err.as_raw__OutputArray(), rlof_param.as_raw_mut_PtrOfRLOFOpticalFlowParameter(), forward_backward_threshold) }.into_result() } /// Fast dense optical flow based on PyrLK sparse matches interpolation. /// /// ## Parameters /// * from: first 8-bit 3-channel or 1-channel image. /// * to: second 8-bit 3-channel or 1-channel image of the same size as from /// * flow: computed flow image that has the same size as from and CV_32FC2 type /// * grid_step: stride used in sparse match computation. Lower values usually /// result in higher quality but slow down the algorithm. /// * k: number of nearest-neighbor matches considered, when fitting a locally affine /// model. Lower values can make the algorithm noticeably faster at the cost of /// some quality degradation. /// * sigma: parameter defining how fast the weights decrease in the locally-weighted affine /// fitting. Higher values can help preserve fine details, lower values can help to get rid /// of the noise in the output flow. /// * use_post_proc: defines whether the ximgproc::fastGlobalSmootherFilter() is used /// for post-processing after interpolation /// * fgs_lambda: see the respective parameter of the ximgproc::fastGlobalSmootherFilter() /// * fgs_sigma: see the respective parameter of the ximgproc::fastGlobalSmootherFilter() /// /// ## C++ default parameters /// * grid_step: 8 /// * k: 128 /// * sigma: 0.05f /// * use_post_proc: true /// * fgs_lambda: 500.0f /// * fgs_sigma: 1.5f pub fn calc_optical_flow_sparse_to_dense(from: &dyn core::ToInputArray, to: &dyn core::ToInputArray, flow: &mut dyn core::ToOutputArray, grid_step: i32, k: i32, sigma: f32, use_post_proc: bool, fgs_lambda: f32, fgs_sigma: f32) -> Result<()> { input_array_arg!(from); input_array_arg!(to); output_array_arg!(flow); unsafe { sys::cv_optflow_calcOpticalFlowSparseToDense_const__InputArrayR_const__InputArrayR_const__OutputArrayR_int_int_float_bool_float_float(from.as_raw__InputArray(), to.as_raw__InputArray(), flow.as_raw__OutputArray(), grid_step, k, sigma, use_post_proc, fgs_lambda, fgs_sigma) }.into_result() } /// DeepFlow optical flow algorithm implementation. /// /// The class implements the DeepFlow optical flow algorithm described in [Weinzaepfel2013](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Weinzaepfel2013) . See /// also <http://lear.inrialpes.fr/src/deepmatching/> . /// Parameters - class fields - that may be modified after creating a class instance: /// * member float alpha /// Smoothness assumption weight /// * member float delta /// Color constancy assumption weight /// * member float gamma /// Gradient constancy weight /// * member float sigma /// Gaussian smoothing parameter /// * member int minSize /// Minimal dimension of an image in the pyramid (next, smaller images in the pyramid are generated /// until one of the dimensions reaches this size) /// * member float downscaleFactor /// Scaling factor in the image pyramid (must be \< 1) /// * member int fixedPointIterations /// How many iterations on each level of the pyramid /// * member int sorIterations /// Iterations of Succesive Over-Relaxation (solver) /// * member float omega /// Relaxation factor in SOR pub fn create_opt_flow_deep_flow() -> Result<core::Ptr::<dyn crate::video::DenseOpticalFlow>> { unsafe { sys::cv_optflow_createOptFlow_DeepFlow() }.into_result().map(|r| unsafe { core::Ptr::<dyn crate::video::DenseOpticalFlow>::opencv_from_extern(r) } ) } /// Additional interface to the Dense RLOF algorithm - optflow::calcOpticalFlowDenseRLOF() pub fn create_opt_flow_dense_rlof() -> Result<core::Ptr::<dyn crate::video::DenseOpticalFlow>> { unsafe { sys::cv_optflow_createOptFlow_DenseRLOF() }.into_result().map(|r| unsafe { core::Ptr::<dyn crate::video::DenseOpticalFlow>::opencv_from_extern(r) } ) } /// Creates instance of cv::DenseOpticalFlow pub fn create_opt_flow_dual_tvl1() -> Result<core::Ptr::<dyn crate::optflow::DualTVL1OpticalFlow>> { unsafe { sys::cv_optflow_createOptFlow_DualTVL1() }.into_result().map(|r| unsafe { core::Ptr::<dyn crate::optflow::DualTVL1OpticalFlow>::opencv_from_extern(r) } ) } /// Additional interface to the Farneback's algorithm - calcOpticalFlowFarneback() pub fn create_opt_flow_farneback() -> Result<core::Ptr::<dyn crate::video::DenseOpticalFlow>> { unsafe { sys::cv_optflow_createOptFlow_Farneback() }.into_result().map(|r| unsafe { core::Ptr::<dyn crate::video::DenseOpticalFlow>::opencv_from_extern(r) } ) } /// Creates an instance of PCAFlow pub fn create_opt_flow_pca_flow() -> Result<core::Ptr::<dyn crate::video::DenseOpticalFlow>> { unsafe { sys::cv_optflow_createOptFlow_PCAFlow() }.into_result().map(|r| unsafe { core::Ptr::<dyn crate::video::DenseOpticalFlow>::opencv_from_extern(r) } ) } /// Additional interface to the SimpleFlow algorithm - calcOpticalFlowSF() pub fn create_opt_flow_simple_flow() -> Result<core::Ptr::<dyn crate::video::DenseOpticalFlow>> { unsafe { sys::cv_optflow_createOptFlow_SimpleFlow() }.into_result().map(|r| unsafe { core::Ptr::<dyn crate::video::DenseOpticalFlow>::opencv_from_extern(r) } ) } /// Additional interface to the Sparse RLOF algorithm - optflow::calcOpticalFlowSparseRLOF() pub fn create_opt_flow_sparse_rlof() -> Result<core::Ptr::<dyn crate::video::SparseOpticalFlow>> { unsafe { sys::cv_optflow_createOptFlow_SparseRLOF() }.into_result().map(|r| unsafe { core::Ptr::<dyn crate::video::SparseOpticalFlow>::opencv_from_extern(r) } ) } /// Additional interface to the SparseToDenseFlow algorithm - calcOpticalFlowSparseToDense() pub fn create_opt_flow_sparse_to_dense() -> Result<core::Ptr::<dyn crate::video::DenseOpticalFlow>> { unsafe { sys::cv_optflow_createOptFlow_SparseToDense() }.into_result().map(|r| unsafe { core::Ptr::<dyn crate::video::DenseOpticalFlow>::opencv_from_extern(r) } ) } pub fn read(fn_: &core::FileNode, node: &mut crate::optflow::GPCTree_Node, unnamed: crate::optflow::GPCTree_Node) -> Result<()> { unsafe { sys::cv_read_const_FileNodeR_NodeR_Node(fn_.as_raw_FileNode(), node, unnamed.opencv_as_extern()) }.into_result() } pub fn write(fs: &mut core::FileStorage, name: &str, node: crate::optflow::GPCTree_Node) -> Result<()> { extern_container_arg!(name); unsafe { sys::cv_write_FileStorageR_const_StringR_const_NodeR(fs.as_raw_mut_FileStorage(), name.opencv_as_extern(), &node) }.into_result() } /// Fast dense optical flow computation based on robust local optical flow (RLOF) algorithms and sparse-to-dense interpolation /// scheme. /// /// The RLOF is a fast local optical flow approach described in [Senst2012](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2012) [Senst2013](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2013) [Senst2014](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2014) /// and [Senst2016](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2016) similar to the pyramidal iterative Lucas-Kanade method as /// proposed by [Bouguet00](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Bouguet00). More details and experiments can be found in the following thesis [Senst2019](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2019). /// The implementation is derived from optflow::calcOpticalFlowPyrLK(). /// /// The sparse-to-dense interpolation scheme allows for fast computation of dense optical flow using RLOF (see [Geistert2016](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Geistert2016)). /// For this scheme the following steps are applied: /// -# motion vector seeded at a regular sampled grid are computed. The sparsity of this grid can be configured with setGridStep /// -# (optinally) errornous motion vectors are filter based on the forward backward confidence. The threshold can be configured /// with setForwardBackward. The filter is only applied if the threshold >0 but than the runtime is doubled due to the estimation /// of the backward flow. /// -# Vector field interpolation is applied to the motion vector set to obtain a dense vector field. /// /// For the RLOF configuration see optflow::RLOFOpticalFlowParameter for further details. /// Parameters have been described in [Senst2012](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2012) [Senst2013](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2013) [Senst2014](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2014) and [Senst2016](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2016). /// /// /// Note: If the grid size is set to (1,1) and the forward backward threshold <= 0 than pixelwise dense optical flow field is /// computed by RLOF without using interpolation. /// ## See also /// optflow::calcOpticalFlowDenseRLOF(), optflow::RLOFOpticalFlowParameter pub trait DenseRLOFOpticalFlow: crate::video::DenseOpticalFlow { fn as_raw_DenseRLOFOpticalFlow(&self) -> *const c_void; fn as_raw_mut_DenseRLOFOpticalFlow(&mut self) -> *mut c_void; /// Configuration of the RLOF alogrithm. /// ## See also /// optflow::RLOFOpticalFlowParameter, getRLOFOpticalFlowParameter fn set_rlof_optical_flow_parameter(&mut self, mut val: core::Ptr::<crate::optflow::RLOFOpticalFlowParameter>) -> Result<()> { unsafe { sys::cv_optflow_DenseRLOFOpticalFlow_setRLOFOpticalFlowParameter_Ptr_RLOFOpticalFlowParameter_(self.as_raw_mut_DenseRLOFOpticalFlow(), val.as_raw_mut_PtrOfRLOFOpticalFlowParameter()) }.into_result() } /// Configuration of the RLOF alogrithm. /// ## See also /// optflow::RLOFOpticalFlowParameter, getRLOFOpticalFlowParameter /// optflow::RLOFOpticalFlowParameter, setRLOFOpticalFlowParameter fn get_rlof_optical_flow_parameter(&self) -> Result<core::Ptr::<crate::optflow::RLOFOpticalFlowParameter>> { unsafe { sys::cv_optflow_DenseRLOFOpticalFlow_getRLOFOpticalFlowParameter_const(self.as_raw_DenseRLOFOpticalFlow()) }.into_result().map(|r| unsafe { core::Ptr::<crate::optflow::RLOFOpticalFlowParameter>::opencv_from_extern(r) } ) } /// Threshold for the forward backward confidence check /// For each grid point  a motion vector  is computed. /// * If the forward backward error  /// * is larger than threshold given by this function then the motion vector will not be used by the following /// * vector field interpolation.  denotes the backward flow. Note, the forward backward test /// * will only be applied if the threshold > 0. This may results into a doubled runtime for the motion estimation. /// * see also: getForwardBackward, setGridStep fn set_forward_backward(&mut self, val: f32) -> Result<()> { unsafe { sys::cv_optflow_DenseRLOFOpticalFlow_setForwardBackward_float(self.as_raw_mut_DenseRLOFOpticalFlow(), val) }.into_result() } /// Threshold for the forward backward confidence check /// For each grid point  a motion vector  is computed. /// * If the forward backward error  /// * is larger than threshold given by this function then the motion vector will not be used by the following /// * vector field interpolation.  denotes the backward flow. Note, the forward backward test /// * will only be applied if the threshold > 0. This may results into a doubled runtime for the motion estimation. /// * getForwardBackward, setGridStep /// ## See also /// setForwardBackward fn get_forward_backward(&self) -> Result<f32> { unsafe { sys::cv_optflow_DenseRLOFOpticalFlow_getForwardBackward_const(self.as_raw_DenseRLOFOpticalFlow()) }.into_result() } /// Size of the grid to spawn the motion vectors. /// For each grid point a motion vector is computed. Some motion vectors will be removed due to the forwatd backward /// * threshold (if set >0). The rest will be the base of the vector field interpolation. /// * see also: getForwardBackward, setGridStep fn get_grid_step(&self) -> Result<core::Size> { unsafe { sys::cv_optflow_DenseRLOFOpticalFlow_getGridStep_const(self.as_raw_DenseRLOFOpticalFlow()) }.into_result() } /// Size of the grid to spawn the motion vectors. /// For each grid point a motion vector is computed. Some motion vectors will be removed due to the forwatd backward /// * threshold (if set >0). The rest will be the base of the vector field interpolation. /// * see also: getForwardBackward, setGridStep /// * see also: getGridStep fn set_grid_step(&mut self, val: core::Size) -> Result<()> { unsafe { sys::cv_optflow_DenseRLOFOpticalFlow_setGridStep_Size(self.as_raw_mut_DenseRLOFOpticalFlow(), val.opencv_as_extern()) }.into_result() } /// Interpolation used to compute the dense optical flow. /// Two interpolation algorithms are supported /// * - **INTERP_GEO** applies the fast geodesic interpolation, see [Geistert2016](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Geistert2016). /// * - **INTERP_EPIC_RESIDUAL** applies the edge-preserving interpolation, see [Revaud2015](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Revaud2015),Geistert2016. /// * see also: ximgproc::EdgeAwareInterpolator, getInterpolation fn set_interpolation(&mut self, val: crate::optflow::InterpolationType) -> Result<()> { unsafe { sys::cv_optflow_DenseRLOFOpticalFlow_setInterpolation_InterpolationType(self.as_raw_mut_DenseRLOFOpticalFlow(), val) }.into_result() } /// Interpolation used to compute the dense optical flow. /// Two interpolation algorithms are supported /// * - **INTERP_GEO** applies the fast geodesic interpolation, see [Geistert2016](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Geistert2016). /// * - **INTERP_EPIC_RESIDUAL** applies the edge-preserving interpolation, see [Revaud2015](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Revaud2015),Geistert2016. /// * see also: ximgproc::EdgeAwareInterpolator, getInterpolation /// * see also: ximgproc::EdgeAwareInterpolator, setInterpolation fn get_interpolation(&self) -> Result<crate::optflow::InterpolationType> { unsafe { sys::cv_optflow_DenseRLOFOpticalFlow_getInterpolation_const(self.as_raw_DenseRLOFOpticalFlow()) }.into_result() } /// see ximgproc::EdgeAwareInterpolator() K value. /// K is a number of nearest-neighbor matches considered, when fitting a locally affine /// * model. Usually it should be around 128. However, lower values would make the interpolation noticeably faster. /// * see also: ximgproc::EdgeAwareInterpolator, setEPICK fn get_epick(&self) -> Result<i32> { unsafe { sys::cv_optflow_DenseRLOFOpticalFlow_getEPICK_const(self.as_raw_DenseRLOFOpticalFlow()) }.into_result() } /// see ximgproc::EdgeAwareInterpolator() K value. /// K is a number of nearest-neighbor matches considered, when fitting a locally affine /// * model. Usually it should be around 128. However, lower values would make the interpolation noticeably faster. /// * see also: ximgproc::EdgeAwareInterpolator, setEPICK /// * see also: ximgproc::EdgeAwareInterpolator, getEPICK fn set_epick(&mut self, val: i32) -> Result<()> { unsafe { sys::cv_optflow_DenseRLOFOpticalFlow_setEPICK_int(self.as_raw_mut_DenseRLOFOpticalFlow(), val) }.into_result() } /// see ximgproc::EdgeAwareInterpolator() sigma value. /// Sigma is a parameter defining how fast the weights decrease in the locally-weighted affine /// * fitting. Higher values can help preserve fine details, lower values can help to get rid of noise in the /// * output flow. /// * see also: ximgproc::EdgeAwareInterpolator, setEPICSigma fn get_epic_sigma(&self) -> Result<f32> { unsafe { sys::cv_optflow_DenseRLOFOpticalFlow_getEPICSigma_const(self.as_raw_DenseRLOFOpticalFlow()) }.into_result() } /// see ximgproc::EdgeAwareInterpolator() sigma value. /// Sigma is a parameter defining how fast the weights decrease in the locally-weighted affine /// * fitting. Higher values can help preserve fine details, lower values can help to get rid of noise in the /// * output flow. /// * see also: ximgproc::EdgeAwareInterpolator, setEPICSigma /// * see also: ximgproc::EdgeAwareInterpolator, getEPICSigma fn set_epic_sigma(&mut self, val: f32) -> Result<()> { unsafe { sys::cv_optflow_DenseRLOFOpticalFlow_setEPICSigma_float(self.as_raw_mut_DenseRLOFOpticalFlow(), val) }.into_result() } /// see ximgproc::EdgeAwareInterpolator() lambda value. /// Lambda is a parameter defining the weight of the edge-aware term in geodesic distance, /// * should be in the range of 0 to 1000. /// * see also: ximgproc::EdgeAwareInterpolator, setEPICSigma fn get_epic_lambda(&self) -> Result<f32> { unsafe { sys::cv_optflow_DenseRLOFOpticalFlow_getEPICLambda_const(self.as_raw_DenseRLOFOpticalFlow()) }.into_result() } /// see ximgproc::EdgeAwareInterpolator() lambda value. /// Lambda is a parameter defining the weight of the edge-aware term in geodesic distance, /// * should be in the range of 0 to 1000. /// * see also: ximgproc::EdgeAwareInterpolator, setEPICSigma /// * see also: ximgproc::EdgeAwareInterpolator, getEPICLambda fn set_epic_lambda(&mut self, val: f32) -> Result<()> { unsafe { sys::cv_optflow_DenseRLOFOpticalFlow_setEPICLambda_float(self.as_raw_mut_DenseRLOFOpticalFlow(), val) }.into_result() } /// see ximgproc::EdgeAwareInterpolator(). /// Sets the respective fastGlobalSmootherFilter() parameter. /// * see also: ximgproc::EdgeAwareInterpolator, setFgsLambda fn get_fgs_lambda(&self) -> Result<f32> { unsafe { sys::cv_optflow_DenseRLOFOpticalFlow_getFgsLambda_const(self.as_raw_DenseRLOFOpticalFlow()) }.into_result() } /// see ximgproc::EdgeAwareInterpolator(). /// Sets the respective fastGlobalSmootherFilter() parameter. /// * see also: ximgproc::EdgeAwareInterpolator, setFgsLambda /// * see also: ximgproc::EdgeAwareInterpolator, ximgproc::fastGlobalSmootherFilter, getFgsLambda fn set_fgs_lambda(&mut self, val: f32) -> Result<()> { unsafe { sys::cv_optflow_DenseRLOFOpticalFlow_setFgsLambda_float(self.as_raw_mut_DenseRLOFOpticalFlow(), val) }.into_result() } /// see ximgproc::EdgeAwareInterpolator(). /// Sets the respective fastGlobalSmootherFilter() parameter. /// * see also: ximgproc::EdgeAwareInterpolator, ximgproc::fastGlobalSmootherFilter, setFgsSigma fn get_fgs_sigma(&self) -> Result<f32> { unsafe { sys::cv_optflow_DenseRLOFOpticalFlow_getFgsSigma_const(self.as_raw_DenseRLOFOpticalFlow()) }.into_result() } /// see ximgproc::EdgeAwareInterpolator(). /// Sets the respective fastGlobalSmootherFilter() parameter. /// * see also: ximgproc::EdgeAwareInterpolator, ximgproc::fastGlobalSmootherFilter, setFgsSigma /// * see also: ximgproc::EdgeAwareInterpolator, ximgproc::fastGlobalSmootherFilter, getFgsSigma fn set_fgs_sigma(&mut self, val: f32) -> Result<()> { unsafe { sys::cv_optflow_DenseRLOFOpticalFlow_setFgsSigma_float(self.as_raw_mut_DenseRLOFOpticalFlow(), val) }.into_result() } /// enables ximgproc::fastGlobalSmootherFilter /// /// * see also: getUsePostProc fn set_use_post_proc(&mut self, val: bool) -> Result<()> { unsafe { sys::cv_optflow_DenseRLOFOpticalFlow_setUsePostProc_bool(self.as_raw_mut_DenseRLOFOpticalFlow(), val) }.into_result() } /// enables ximgproc::fastGlobalSmootherFilter /// /// * see also: getUsePostProc /// * see also: ximgproc::fastGlobalSmootherFilter, setUsePostProc fn get_use_post_proc(&self) -> Result<bool> { unsafe { sys::cv_optflow_DenseRLOFOpticalFlow_getUsePostProc_const(self.as_raw_DenseRLOFOpticalFlow()) }.into_result() } /// enables VariationalRefinement /// /// * see also: getUseVariationalRefinement fn set_use_variational_refinement(&mut self, val: bool) -> Result<()> { unsafe { sys::cv_optflow_DenseRLOFOpticalFlow_setUseVariationalRefinement_bool(self.as_raw_mut_DenseRLOFOpticalFlow(), val) }.into_result() } /// enables VariationalRefinement /// /// * see also: getUseVariationalRefinement /// * see also: ximgproc::fastGlobalSmootherFilter, setUsePostProc fn get_use_variational_refinement(&self) -> Result<bool> { unsafe { sys::cv_optflow_DenseRLOFOpticalFlow_getUseVariationalRefinement_const(self.as_raw_DenseRLOFOpticalFlow()) }.into_result() } /// Parameter to tune the approximate size of the superpixel used for oversegmentation. /// /// * see also: cv::ximgproc::createSuperpixelSLIC, cv::ximgproc::RICInterpolator fn set_ricsp_size(&mut self, val: i32) -> Result<()> { unsafe { sys::cv_optflow_DenseRLOFOpticalFlow_setRICSPSize_int(self.as_raw_mut_DenseRLOFOpticalFlow(), val) }.into_result() } /// Parameter to tune the approximate size of the superpixel used for oversegmentation. /// /// * see also: cv::ximgproc::createSuperpixelSLIC, cv::ximgproc::RICInterpolator /// * see also: setRICSPSize fn get_ricsp_size(&self) -> Result<i32> { unsafe { sys::cv_optflow_DenseRLOFOpticalFlow_getRICSPSize_const(self.as_raw_DenseRLOFOpticalFlow()) }.into_result() } /// Parameter to choose superpixel algorithm variant to use: /// - cv::ximgproc::SLICType SLIC segments image using a desired region_size (value: 100) /// - cv::ximgproc::SLICType SLICO will optimize using adaptive compactness factor (value: 101) /// - cv::ximgproc::SLICType MSLIC will optimize using manifold methods resulting in more content-sensitive superpixels (value: 102). /// ## See also /// cv::ximgproc::createSuperpixelSLIC, cv::ximgproc::RICInterpolator fn set_ricslic_type(&mut self, val: i32) -> Result<()> { unsafe { sys::cv_optflow_DenseRLOFOpticalFlow_setRICSLICType_int(self.as_raw_mut_DenseRLOFOpticalFlow(), val) }.into_result() } /// Parameter to choose superpixel algorithm variant to use: /// - cv::ximgproc::SLICType SLIC segments image using a desired region_size (value: 100) /// - cv::ximgproc::SLICType SLICO will optimize using adaptive compactness factor (value: 101) /// - cv::ximgproc::SLICType MSLIC will optimize using manifold methods resulting in more content-sensitive superpixels (value: 102). /// ## See also /// cv::ximgproc::createSuperpixelSLIC, cv::ximgproc::RICInterpolator /// * setRICSLICType fn get_ricslic_type(&self) -> Result<i32> { unsafe { sys::cv_optflow_DenseRLOFOpticalFlow_getRICSLICType_const(self.as_raw_DenseRLOFOpticalFlow()) }.into_result() } } impl dyn DenseRLOFOpticalFlow + '_ { /// Creates instance of optflow::DenseRLOFOpticalFlow /// /// ## Parameters /// * rlofParam: see optflow::RLOFOpticalFlowParameter /// * forwardBackwardThreshold: see setForwardBackward /// * gridStep: see setGridStep /// * interp_type: see setInterpolation /// * epicK: see setEPICK /// * epicSigma: see setEPICSigma /// * epicLambda: see setEPICLambda /// * ricSPSize: see setRICSPSize /// * ricSLICType: see setRICSLICType /// * use_post_proc: see setUsePostProc /// * fgsLambda: see setFgsLambda /// * fgsSigma: see setFgsSigma /// * use_variational_refinement: see setUseVariationalRefinement /// /// ## C++ default parameters /// * rlof_param: Ptr<RLOFOpticalFlowParameter>() /// * forward_backward_threshold: 1.f /// * grid_step: Size(6,6) /// * interp_type: InterpolationType::INTERP_EPIC /// * epic_k: 128 /// * epic_sigma: 0.05f /// * epic_lambda: 999.0f /// * ric_sp_size: 15 /// * ric_slic_type: 100 /// * use_post_proc: true /// * fgs_lambda: 500.0f /// * fgs_sigma: 1.5f /// * use_variational_refinement: false pub fn create(mut rlof_param: core::Ptr::<crate::optflow::RLOFOpticalFlowParameter>, forward_backward_threshold: f32, grid_step: core::Size, interp_type: crate::optflow::InterpolationType, epic_k: i32, epic_sigma: f32, epic_lambda: f32, ric_sp_size: i32, ric_slic_type: i32, use_post_proc: bool, fgs_lambda: f32, fgs_sigma: f32, use_variational_refinement: bool) -> Result<core::Ptr::<dyn crate::optflow::DenseRLOFOpticalFlow>> { unsafe { sys::cv_optflow_DenseRLOFOpticalFlow_create_Ptr_RLOFOpticalFlowParameter__float_Size_InterpolationType_int_float_float_int_int_bool_float_float_bool(rlof_param.as_raw_mut_PtrOfRLOFOpticalFlowParameter(), forward_backward_threshold, grid_step.opencv_as_extern(), interp_type, epic_k, epic_sigma, epic_lambda, ric_sp_size, ric_slic_type, use_post_proc, fgs_lambda, fgs_sigma, use_variational_refinement) }.into_result().map(|r| unsafe { core::Ptr::<dyn crate::optflow::DenseRLOFOpticalFlow>::opencv_from_extern(r) } ) } } /// "Dual TV L1" Optical Flow Algorithm. /// /// The class implements the "Dual TV L1" optical flow algorithm described in [Zach2007](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Zach2007) and /// [Javier2012](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Javier2012) . /// Here are important members of the class that control the algorithm, which you can set after /// constructing the class instance: /// /// * member double tau /// Time step of the numerical scheme. /// /// * member double lambda /// Weight parameter for the data term, attachment parameter. This is the most relevant /// parameter, which determines the smoothness of the output. The smaller this parameter is, /// the smoother the solutions we obtain. It depends on the range of motions of the images, so /// its value should be adapted to each image sequence. /// /// * member double theta /// Weight parameter for (u - v)\^2, tightness parameter. It serves as a link between the /// attachment and the regularization terms. In theory, it should have a small value in order /// to maintain both parts in correspondence. The method is stable for a large range of values /// of this parameter. /// /// * member int nscales /// Number of scales used to create the pyramid of images. /// /// * member int warps /// Number of warpings per scale. Represents the number of times that I1(x+u0) and grad( /// I1(x+u0) ) are computed per scale. This is a parameter that assures the stability of the /// method. It also affects the running time, so it is a compromise between speed and /// accuracy. /// /// * member double epsilon /// Stopping criterion threshold used in the numerical scheme, which is a trade-off between /// precision and running time. A small value will yield more accurate solutions at the /// expense of a slower convergence. /// /// * member int iterations /// Stopping criterion iterations number used in the numerical scheme. /// /// C. Zach, T. Pock and H. Bischof, "A Duality Based Approach for Realtime TV-L1 Optical Flow". /// Javier Sanchez, Enric Meinhardt-Llopis and Gabriele Facciolo. "TV-L1 Optical Flow Estimation". pub trait DualTVL1OpticalFlow: crate::video::DenseOpticalFlow { fn as_raw_DualTVL1OpticalFlow(&self) -> *const c_void; fn as_raw_mut_DualTVL1OpticalFlow(&mut self) -> *mut c_void; /// Time step of the numerical scheme /// ## See also /// setTau fn get_tau(&self) -> Result<f64> { unsafe { sys::cv_optflow_DualTVL1OpticalFlow_getTau_const(self.as_raw_DualTVL1OpticalFlow()) }.into_result() } /// Time step of the numerical scheme /// ## See also /// setTau getTau fn set_tau(&mut self, val: f64) -> Result<()> { unsafe { sys::cv_optflow_DualTVL1OpticalFlow_setTau_double(self.as_raw_mut_DualTVL1OpticalFlow(), val) }.into_result() } /// Weight parameter for the data term, attachment parameter /// ## See also /// setLambda fn get_lambda(&self) -> Result<f64> { unsafe { sys::cv_optflow_DualTVL1OpticalFlow_getLambda_const(self.as_raw_DualTVL1OpticalFlow()) }.into_result() } /// Weight parameter for the data term, attachment parameter /// ## See also /// setLambda getLambda fn set_lambda(&mut self, val: f64) -> Result<()> { unsafe { sys::cv_optflow_DualTVL1OpticalFlow_setLambda_double(self.as_raw_mut_DualTVL1OpticalFlow(), val) }.into_result() } /// Weight parameter for (u - v)^2, tightness parameter /// ## See also /// setTheta fn get_theta(&self) -> Result<f64> { unsafe { sys::cv_optflow_DualTVL1OpticalFlow_getTheta_const(self.as_raw_DualTVL1OpticalFlow()) }.into_result() } /// Weight parameter for (u - v)^2, tightness parameter /// ## See also /// setTheta getTheta fn set_theta(&mut self, val: f64) -> Result<()> { unsafe { sys::cv_optflow_DualTVL1OpticalFlow_setTheta_double(self.as_raw_mut_DualTVL1OpticalFlow(), val) }.into_result() } /// coefficient for additional illumination variation term /// ## See also /// setGamma fn get_gamma(&self) -> Result<f64> { unsafe { sys::cv_optflow_DualTVL1OpticalFlow_getGamma_const(self.as_raw_DualTVL1OpticalFlow()) }.into_result() } /// coefficient for additional illumination variation term /// ## See also /// setGamma getGamma fn set_gamma(&mut self, val: f64) -> Result<()> { unsafe { sys::cv_optflow_DualTVL1OpticalFlow_setGamma_double(self.as_raw_mut_DualTVL1OpticalFlow(), val) }.into_result() } /// Number of scales used to create the pyramid of images /// ## See also /// setScalesNumber fn get_scales_number(&self) -> Result<i32> { unsafe { sys::cv_optflow_DualTVL1OpticalFlow_getScalesNumber_const(self.as_raw_DualTVL1OpticalFlow()) }.into_result() } /// Number of scales used to create the pyramid of images /// ## See also /// setScalesNumber getScalesNumber fn set_scales_number(&mut self, val: i32) -> Result<()> { unsafe { sys::cv_optflow_DualTVL1OpticalFlow_setScalesNumber_int(self.as_raw_mut_DualTVL1OpticalFlow(), val) }.into_result() } /// Number of warpings per scale /// ## See also /// setWarpingsNumber fn get_warpings_number(&self) -> Result<i32> { unsafe { sys::cv_optflow_DualTVL1OpticalFlow_getWarpingsNumber_const(self.as_raw_DualTVL1OpticalFlow()) }.into_result() } /// Number of warpings per scale /// ## See also /// setWarpingsNumber getWarpingsNumber fn set_warpings_number(&mut self, val: i32) -> Result<()> { unsafe { sys::cv_optflow_DualTVL1OpticalFlow_setWarpingsNumber_int(self.as_raw_mut_DualTVL1OpticalFlow(), val) }.into_result() } /// Stopping criterion threshold used in the numerical scheme, which is a trade-off between precision and running time /// ## See also /// setEpsilon fn get_epsilon(&self) -> Result<f64> { unsafe { sys::cv_optflow_DualTVL1OpticalFlow_getEpsilon_const(self.as_raw_DualTVL1OpticalFlow()) }.into_result() } /// Stopping criterion threshold used in the numerical scheme, which is a trade-off between precision and running time /// ## See also /// setEpsilon getEpsilon fn set_epsilon(&mut self, val: f64) -> Result<()> { unsafe { sys::cv_optflow_DualTVL1OpticalFlow_setEpsilon_double(self.as_raw_mut_DualTVL1OpticalFlow(), val) }.into_result() } /// Inner iterations (between outlier filtering) used in the numerical scheme /// ## See also /// setInnerIterations fn get_inner_iterations(&self) -> Result<i32> { unsafe { sys::cv_optflow_DualTVL1OpticalFlow_getInnerIterations_const(self.as_raw_DualTVL1OpticalFlow()) }.into_result() } /// Inner iterations (between outlier filtering) used in the numerical scheme /// ## See also /// setInnerIterations getInnerIterations fn set_inner_iterations(&mut self, val: i32) -> Result<()> { unsafe { sys::cv_optflow_DualTVL1OpticalFlow_setInnerIterations_int(self.as_raw_mut_DualTVL1OpticalFlow(), val) }.into_result() } /// Outer iterations (number of inner loops) used in the numerical scheme /// ## See also /// setOuterIterations fn get_outer_iterations(&self) -> Result<i32> { unsafe { sys::cv_optflow_DualTVL1OpticalFlow_getOuterIterations_const(self.as_raw_DualTVL1OpticalFlow()) }.into_result() } /// Outer iterations (number of inner loops) used in the numerical scheme /// ## See also /// setOuterIterations getOuterIterations fn set_outer_iterations(&mut self, val: i32) -> Result<()> { unsafe { sys::cv_optflow_DualTVL1OpticalFlow_setOuterIterations_int(self.as_raw_mut_DualTVL1OpticalFlow(), val) }.into_result() } /// Use initial flow /// ## See also /// setUseInitialFlow fn get_use_initial_flow(&self) -> Result<bool> { unsafe { sys::cv_optflow_DualTVL1OpticalFlow_getUseInitialFlow_const(self.as_raw_DualTVL1OpticalFlow()) }.into_result() } /// Use initial flow /// ## See also /// setUseInitialFlow getUseInitialFlow fn set_use_initial_flow(&mut self, val: bool) -> Result<()> { unsafe { sys::cv_optflow_DualTVL1OpticalFlow_setUseInitialFlow_bool(self.as_raw_mut_DualTVL1OpticalFlow(), val) }.into_result() } /// Step between scales (<1) /// ## See also /// setScaleStep fn get_scale_step(&self) -> Result<f64> { unsafe { sys::cv_optflow_DualTVL1OpticalFlow_getScaleStep_const(self.as_raw_DualTVL1OpticalFlow()) }.into_result() } /// Step between scales (<1) /// ## See also /// setScaleStep getScaleStep fn set_scale_step(&mut self, val: f64) -> Result<()> { unsafe { sys::cv_optflow_DualTVL1OpticalFlow_setScaleStep_double(self.as_raw_mut_DualTVL1OpticalFlow(), val) }.into_result() } /// Median filter kernel size (1 = no filter) (3 or 5) /// ## See also /// setMedianFiltering fn get_median_filtering(&self) -> Result<i32> { unsafe { sys::cv_optflow_DualTVL1OpticalFlow_getMedianFiltering_const(self.as_raw_DualTVL1OpticalFlow()) }.into_result() } /// Median filter kernel size (1 = no filter) (3 or 5) /// ## See also /// setMedianFiltering getMedianFiltering fn set_median_filtering(&mut self, val: i32) -> Result<()> { unsafe { sys::cv_optflow_DualTVL1OpticalFlow_setMedianFiltering_int(self.as_raw_mut_DualTVL1OpticalFlow(), val) }.into_result() } } impl dyn DualTVL1OpticalFlow + '_ { /// Creates instance of cv::DualTVL1OpticalFlow /// /// ## C++ default parameters /// * tau: 0.25 /// * lambda: 0.15 /// * theta: 0.3 /// * nscales: 5 /// * warps: 5 /// * epsilon: 0.01 /// * innner_iterations: 30 /// * outer_iterations: 10 /// * scale_step: 0.8 /// * gamma: 0.0 /// * median_filtering: 5 /// * use_initial_flow: false pub fn create(tau: f64, lambda: f64, theta: f64, nscales: i32, warps: i32, epsilon: f64, innner_iterations: i32, outer_iterations: i32, scale_step: f64, gamma: f64, median_filtering: i32, use_initial_flow: bool) -> Result<core::Ptr::<dyn crate::optflow::DualTVL1OpticalFlow>> { unsafe { sys::cv_optflow_DualTVL1OpticalFlow_create_double_double_double_int_int_double_int_int_double_double_int_bool(tau, lambda, theta, nscales, warps, epsilon, innner_iterations, outer_iterations, scale_step, gamma, median_filtering, use_initial_flow) }.into_result().map(|r| unsafe { core::Ptr::<dyn crate::optflow::DualTVL1OpticalFlow>::opencv_from_extern(r) } ) } } pub trait GPCDetailsTrait { fn as_raw_GPCDetails(&self) -> *const c_void; fn as_raw_mut_GPCDetails(&mut self) -> *mut c_void; } pub struct GPCDetails { ptr: *mut c_void } opencv_type_boxed! { GPCDetails } impl Drop for GPCDetails { fn drop(&mut self) { extern "C" { fn cv_GPCDetails_delete(instance: *mut c_void); } unsafe { cv_GPCDetails_delete(self.as_raw_mut_GPCDetails()) }; } } impl GPCDetails { #[inline] pub fn as_raw_GPCDetails(&self) -> *const c_void { self.as_raw() } #[inline] pub fn as_raw_mut_GPCDetails(&mut self) -> *mut c_void { self.as_raw_mut() } } unsafe impl Send for GPCDetails {} impl crate::optflow::GPCDetailsTrait for GPCDetails { #[inline] fn as_raw_GPCDetails(&self) -> *const c_void { self.as_raw() } #[inline] fn as_raw_mut_GPCDetails(&mut self) -> *mut c_void { self.as_raw_mut() } } impl GPCDetails { pub fn get_all_descriptors_for_image(img_ch: &core::Mat, descr: &mut core::Vector::<crate::optflow::GPCPatchDescriptor>, mp: crate::optflow::GPCMatchingParams, typ: i32) -> Result<()> { unsafe { sys::cv_optflow_GPCDetails_getAllDescriptorsForImage_const_MatX_vector_GPCPatchDescriptor_R_const_GPCMatchingParamsR_int(img_ch.as_raw_Mat(), descr.as_raw_mut_VectorOfGPCPatchDescriptor(), &mp, typ) }.into_result() } pub fn get_coordinates_from_index(index: size_t, sz: core::Size, x: &mut i32, y: &mut i32) -> Result<()> { unsafe { sys::cv_optflow_GPCDetails_getCoordinatesFromIndex_size_t_Size_intR_intR(index, sz.opencv_as_extern(), x, y) }.into_result() } } /// Class encapsulating matching parameters. #[repr(C)] #[derive(Copy, Clone, Debug, PartialEq)] pub struct GPCMatchingParams { /// Whether to use OpenCL to speed up the matching. pub use_opencl: bool, } opencv_type_simple! { crate::optflow::GPCMatchingParams } impl GPCMatchingParams { /// ## C++ default parameters /// * _use_opencl: false pub fn new(_use_opencl: bool) -> Result<crate::optflow::GPCMatchingParams> { unsafe { sys::cv_optflow_GPCMatchingParams_GPCMatchingParams_bool(_use_opencl) }.into_result() } pub fn copy(params: crate::optflow::GPCMatchingParams) -> Result<crate::optflow::GPCMatchingParams> { unsafe { sys::cv_optflow_GPCMatchingParams_GPCMatchingParams_const_GPCMatchingParamsR(¶ms) }.into_result() } } pub trait GPCPatchDescriptorTrait { fn as_raw_GPCPatchDescriptor(&self) -> *const c_void; fn as_raw_mut_GPCPatchDescriptor(&mut self) -> *mut c_void; fn feature(&self) -> core::Vec18<f64> { unsafe { sys::cv_optflow_GPCPatchDescriptor_getPropFeature_const(self.as_raw_GPCPatchDescriptor()) }.into_result().expect("Infallible function failed: feature") } fn set_feature(&mut self, val: core::Vec18<f64>) -> () { unsafe { sys::cv_optflow_GPCPatchDescriptor_setPropFeature_Vec_double__18_(self.as_raw_mut_GPCPatchDescriptor(), val.opencv_as_extern()) }.into_result().expect("Infallible function failed: set_feature") } fn dot(&self, coef: core::Vec18<f64>) -> Result<f64> { unsafe { sys::cv_optflow_GPCPatchDescriptor_dot_const_const_Vec_double__18_R(self.as_raw_GPCPatchDescriptor(), &coef) }.into_result() } fn mark_as_separated(&mut self) -> Result<()> { unsafe { sys::cv_optflow_GPCPatchDescriptor_markAsSeparated(self.as_raw_mut_GPCPatchDescriptor()) }.into_result() } fn is_separated(&self) -> Result<bool> { unsafe { sys::cv_optflow_GPCPatchDescriptor_isSeparated_const(self.as_raw_GPCPatchDescriptor()) }.into_result() } } pub struct GPCPatchDescriptor { ptr: *mut c_void } opencv_type_boxed! { GPCPatchDescriptor } impl Drop for GPCPatchDescriptor { fn drop(&mut self) { extern "C" { fn cv_GPCPatchDescriptor_delete(instance: *mut c_void); } unsafe { cv_GPCPatchDescriptor_delete(self.as_raw_mut_GPCPatchDescriptor()) }; } } impl GPCPatchDescriptor { #[inline] pub fn as_raw_GPCPatchDescriptor(&self) -> *const c_void { self.as_raw() } #[inline] pub fn as_raw_mut_GPCPatchDescriptor(&mut self) -> *mut c_void { self.as_raw_mut() } } unsafe impl Send for GPCPatchDescriptor {} impl crate::optflow::GPCPatchDescriptorTrait for GPCPatchDescriptor { #[inline] fn as_raw_GPCPatchDescriptor(&self) -> *const c_void { self.as_raw() } #[inline] fn as_raw_mut_GPCPatchDescriptor(&mut self) -> *mut c_void { self.as_raw_mut() } } impl GPCPatchDescriptor { /// number of features in a patch descriptor pub const nFeatures: u32 = 18; } pub trait GPCPatchSampleTrait { fn as_raw_GPCPatchSample(&self) -> *const c_void; fn as_raw_mut_GPCPatchSample(&mut self) -> *mut c_void; fn ref_(&mut self) -> crate::optflow::GPCPatchDescriptor { unsafe { sys::cv_optflow_GPCPatchSample_getPropRef(self.as_raw_mut_GPCPatchSample()) }.into_result().map(|r| unsafe { crate::optflow::GPCPatchDescriptor::opencv_from_extern(r) } ).expect("Infallible function failed: ref_") } fn set_ref(&mut self, mut val: crate::optflow::GPCPatchDescriptor) -> () { unsafe { sys::cv_optflow_GPCPatchSample_setPropRef_GPCPatchDescriptor(self.as_raw_mut_GPCPatchSample(), val.as_raw_mut_GPCPatchDescriptor()) }.into_result().expect("Infallible function failed: set_ref") } fn pos(&mut self) -> crate::optflow::GPCPatchDescriptor { unsafe { sys::cv_optflow_GPCPatchSample_getPropPos(self.as_raw_mut_GPCPatchSample()) }.into_result().map(|r| unsafe { crate::optflow::GPCPatchDescriptor::opencv_from_extern(r) } ).expect("Infallible function failed: pos") } fn set_pos(&mut self, mut val: crate::optflow::GPCPatchDescriptor) -> () { unsafe { sys::cv_optflow_GPCPatchSample_setPropPos_GPCPatchDescriptor(self.as_raw_mut_GPCPatchSample(), val.as_raw_mut_GPCPatchDescriptor()) }.into_result().expect("Infallible function failed: set_pos") } fn neg(&mut self) -> crate::optflow::GPCPatchDescriptor { unsafe { sys::cv_optflow_GPCPatchSample_getPropNeg(self.as_raw_mut_GPCPatchSample()) }.into_result().map(|r| unsafe { crate::optflow::GPCPatchDescriptor::opencv_from_extern(r) } ).expect("Infallible function failed: neg") } fn set_neg(&mut self, mut val: crate::optflow::GPCPatchDescriptor) -> () { unsafe { sys::cv_optflow_GPCPatchSample_setPropNeg_GPCPatchDescriptor(self.as_raw_mut_GPCPatchSample(), val.as_raw_mut_GPCPatchDescriptor()) }.into_result().expect("Infallible function failed: set_neg") } fn get_directions(&self, refdir: &mut bool, posdir: &mut bool, negdir: &mut bool, coef: core::Vec18<f64>, rhs: f64) -> Result<()> { unsafe { sys::cv_optflow_GPCPatchSample_getDirections_const_boolR_boolR_boolR_const_Vec_double__18_R_double(self.as_raw_GPCPatchSample(), refdir, posdir, negdir, &coef, rhs) }.into_result() } } pub struct GPCPatchSample { ptr: *mut c_void } opencv_type_boxed! { GPCPatchSample } impl Drop for GPCPatchSample { fn drop(&mut self) { extern "C" { fn cv_GPCPatchSample_delete(instance: *mut c_void); } unsafe { cv_GPCPatchSample_delete(self.as_raw_mut_GPCPatchSample()) }; } } impl GPCPatchSample { #[inline] pub fn as_raw_GPCPatchSample(&self) -> *const c_void { self.as_raw() } #[inline] pub fn as_raw_mut_GPCPatchSample(&mut self) -> *mut c_void { self.as_raw_mut() } } unsafe impl Send for GPCPatchSample {} impl crate::optflow::GPCPatchSampleTrait for GPCPatchSample { #[inline] fn as_raw_GPCPatchSample(&self) -> *const c_void { self.as_raw() } #[inline] fn as_raw_mut_GPCPatchSample(&mut self) -> *mut c_void { self.as_raw_mut() } } impl GPCPatchSample { } /// Class encapsulating training parameters. #[repr(C)] #[derive(Copy, Clone, Debug, PartialEq)] pub struct GPCTrainingParams { /// Maximum tree depth to stop partitioning. pub max_tree_depth: u32, /// Minimum number of samples in the node to stop partitioning. pub min_number_of_samples: i32, /// Type of descriptors to use. pub descriptor_type: i32, /// Print progress to stdout. pub print_progress: bool, } opencv_type_simple! { crate::optflow::GPCTrainingParams } impl GPCTrainingParams { /// ## C++ default parameters /// * _max_tree_depth: 20 /// * _min_number_of_samples: 3 /// * _descriptor_type: GPC_DESCRIPTOR_DCT /// * _print_progress: true pub fn new(_max_tree_depth: u32, _min_number_of_samples: i32, _descriptor_type: crate::optflow::GPCDescType, _print_progress: bool) -> Result<crate::optflow::GPCTrainingParams> { unsafe { sys::cv_optflow_GPCTrainingParams_GPCTrainingParams_unsigned_int_int_GPCDescType_bool(_max_tree_depth, _min_number_of_samples, _descriptor_type, _print_progress) }.into_result() } pub fn check(self) -> Result<bool> { unsafe { sys::cv_optflow_GPCTrainingParams_check_const(self.opencv_as_extern()) }.into_result() } } /// Class encapsulating training samples. pub trait GPCTrainingSamplesTrait { fn as_raw_GPCTrainingSamples(&self) -> *const c_void; fn as_raw_mut_GPCTrainingSamples(&mut self) -> *mut c_void; fn size(&self) -> Result<size_t> { unsafe { sys::cv_optflow_GPCTrainingSamples_size_const(self.as_raw_GPCTrainingSamples()) }.into_result() } fn typ(&self) -> Result<i32> { unsafe { sys::cv_optflow_GPCTrainingSamples_type_const(self.as_raw_GPCTrainingSamples()) }.into_result() } } /// Class encapsulating training samples. pub struct GPCTrainingSamples { ptr: *mut c_void } opencv_type_boxed! { GPCTrainingSamples } impl Drop for GPCTrainingSamples { fn drop(&mut self) { extern "C" { fn cv_GPCTrainingSamples_delete(instance: *mut c_void); } unsafe { cv_GPCTrainingSamples_delete(self.as_raw_mut_GPCTrainingSamples()) }; } } impl GPCTrainingSamples { #[inline] pub fn as_raw_GPCTrainingSamples(&self) -> *const c_void { self.as_raw() } #[inline] pub fn as_raw_mut_GPCTrainingSamples(&mut self) -> *mut c_void { self.as_raw_mut() } } unsafe impl Send for GPCTrainingSamples {} impl crate::optflow::GPCTrainingSamplesTrait for GPCTrainingSamples { #[inline] fn as_raw_GPCTrainingSamples(&self) -> *const c_void { self.as_raw() } #[inline] fn as_raw_mut_GPCTrainingSamples(&mut self) -> *mut c_void { self.as_raw_mut() } } impl GPCTrainingSamples { /// This function can be used to extract samples from a pair of images and a ground truth flow. /// Sizes of all the provided vectors must be equal. pub fn create(images_from: &core::Vector::<String>, images_to: &core::Vector::<String>, gt: &core::Vector::<String>, descriptor_type: i32) -> Result<core::Ptr::<crate::optflow::GPCTrainingSamples>> { unsafe { sys::cv_optflow_GPCTrainingSamples_create_const_vector_String_R_const_vector_String_R_const_vector_String_R_int(images_from.as_raw_VectorOfString(), images_to.as_raw_VectorOfString(), gt.as_raw_VectorOfString(), descriptor_type) }.into_result().map(|r| unsafe { core::Ptr::<crate::optflow::GPCTrainingSamples>::opencv_from_extern(r) } ) } pub fn create_1(images_from: &dyn core::ToInputArray, images_to: &dyn core::ToInputArray, gt: &dyn core::ToInputArray, descriptor_type: i32) -> Result<core::Ptr::<crate::optflow::GPCTrainingSamples>> { input_array_arg!(images_from); input_array_arg!(images_to); input_array_arg!(gt); unsafe { sys::cv_optflow_GPCTrainingSamples_create_const__InputArrayR_const__InputArrayR_const__InputArrayR_int(images_from.as_raw__InputArray(), images_to.as_raw__InputArray(), gt.as_raw__InputArray(), descriptor_type) }.into_result().map(|r| unsafe { core::Ptr::<crate::optflow::GPCTrainingSamples>::opencv_from_extern(r) } ) } } /// Class for individual tree. pub trait GPCTreeTrait: core::AlgorithmTrait { fn as_raw_GPCTree(&self) -> *const c_void; fn as_raw_mut_GPCTree(&mut self) -> *mut c_void; /// ## C++ default parameters /// * params: GPCTrainingParams() fn train(&mut self, samples: &mut crate::optflow::GPCTrainingSamples, params: crate::optflow::GPCTrainingParams) -> Result<()> { unsafe { sys::cv_optflow_GPCTree_train_GPCTrainingSamplesR_const_GPCTrainingParams(self.as_raw_mut_GPCTree(), samples.as_raw_mut_GPCTrainingSamples(), params.opencv_as_extern()) }.into_result() } fn write(&self, fs: &mut core::FileStorage) -> Result<()> { unsafe { sys::cv_optflow_GPCTree_write_const_FileStorageR(self.as_raw_GPCTree(), fs.as_raw_mut_FileStorage()) }.into_result() } fn read(&mut self, fn_: &core::FileNode) -> Result<()> { unsafe { sys::cv_optflow_GPCTree_read_const_FileNodeR(self.as_raw_mut_GPCTree(), fn_.as_raw_FileNode()) }.into_result() } fn find_leaf_for_patch(&self, descr: &crate::optflow::GPCPatchDescriptor) -> Result<u32> { unsafe { sys::cv_optflow_GPCTree_findLeafForPatch_const_const_GPCPatchDescriptorR(self.as_raw_GPCTree(), descr.as_raw_GPCPatchDescriptor()) }.into_result() } fn get_descriptor_type(&self) -> Result<i32> { unsafe { sys::cv_optflow_GPCTree_getDescriptorType_const(self.as_raw_GPCTree()) }.into_result() } } /// Class for individual tree. pub struct GPCTree { ptr: *mut c_void } opencv_type_boxed! { GPCTree } impl Drop for GPCTree { fn drop(&mut self) { extern "C" { fn cv_GPCTree_delete(instance: *mut c_void); } unsafe { cv_GPCTree_delete(self.as_raw_mut_GPCTree()) }; } } impl GPCTree { #[inline] pub fn as_raw_GPCTree(&self) -> *const c_void { self.as_raw() } #[inline] pub fn as_raw_mut_GPCTree(&mut self) -> *mut c_void { self.as_raw_mut() } } unsafe impl Send for GPCTree {} impl core::AlgorithmTrait for GPCTree { #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } } impl crate::optflow::GPCTreeTrait for GPCTree { #[inline] fn as_raw_GPCTree(&self) -> *const c_void { self.as_raw() } #[inline] fn as_raw_mut_GPCTree(&mut self) -> *mut c_void { self.as_raw_mut() } } impl GPCTree { pub fn create() -> Result<core::Ptr::<crate::optflow::GPCTree>> { unsafe { sys::cv_optflow_GPCTree_create() }.into_result().map(|r| unsafe { core::Ptr::<crate::optflow::GPCTree>::opencv_from_extern(r) } ) } } #[repr(C)] #[derive(Copy, Clone, Debug, PartialEq)] pub struct GPCTree_Node { /// Hyperplane coefficients pub coef: core::Vec18<f64>, /// Bias term of the hyperplane pub rhs: f64, pub left: u32, pub right: u32, } opencv_type_simple! { crate::optflow::GPCTree_Node } impl GPCTree_Node { } /// PCAFlow algorithm. pub trait OpticalFlowPCAFlowTrait: crate::video::DenseOpticalFlow { fn as_raw_OpticalFlowPCAFlow(&self) -> *const c_void; fn as_raw_mut_OpticalFlowPCAFlow(&mut self) -> *mut c_void; fn calc(&mut self, i0: &dyn core::ToInputArray, i1: &dyn core::ToInputArray, flow: &mut dyn core::ToInputOutputArray) -> Result<()> { input_array_arg!(i0); input_array_arg!(i1); input_output_array_arg!(flow); unsafe { sys::cv_optflow_OpticalFlowPCAFlow_calc_const__InputArrayR_const__InputArrayR_const__InputOutputArrayR(self.as_raw_mut_OpticalFlowPCAFlow(), i0.as_raw__InputArray(), i1.as_raw__InputArray(), flow.as_raw__InputOutputArray()) }.into_result() } fn collect_garbage(&mut self) -> Result<()> { unsafe { sys::cv_optflow_OpticalFlowPCAFlow_collectGarbage(self.as_raw_mut_OpticalFlowPCAFlow()) }.into_result() } } /// PCAFlow algorithm. pub struct OpticalFlowPCAFlow { ptr: *mut c_void } opencv_type_boxed! { OpticalFlowPCAFlow } impl Drop for OpticalFlowPCAFlow { fn drop(&mut self) { extern "C" { fn cv_OpticalFlowPCAFlow_delete(instance: *mut c_void); } unsafe { cv_OpticalFlowPCAFlow_delete(self.as_raw_mut_OpticalFlowPCAFlow()) }; } } impl OpticalFlowPCAFlow { #[inline] pub fn as_raw_OpticalFlowPCAFlow(&self) -> *const c_void { self.as_raw() } #[inline] pub fn as_raw_mut_OpticalFlowPCAFlow(&mut self) -> *mut c_void { self.as_raw_mut() } } unsafe impl Send for OpticalFlowPCAFlow {} impl core::AlgorithmTrait for OpticalFlowPCAFlow { #[inline] fn as_raw_Algorithm(&self) -> *const c_void { self.as_raw() } #[inline] fn as_raw_mut_Algorithm(&mut self) -> *mut c_void { self.as_raw_mut() } } impl crate::video::DenseOpticalFlow for OpticalFlowPCAFlow { #[inline] fn as_raw_DenseOpticalFlow(&self) -> *const c_void { self.as_raw() } #[inline] fn as_raw_mut_DenseOpticalFlow(&mut self) -> *mut c_void { self.as_raw_mut() } } impl crate::optflow::OpticalFlowPCAFlowTrait for OpticalFlowPCAFlow { #[inline] fn as_raw_OpticalFlowPCAFlow(&self) -> *const c_void { self.as_raw() } #[inline] fn as_raw_mut_OpticalFlowPCAFlow(&mut self) -> *mut c_void { self.as_raw_mut() } } impl OpticalFlowPCAFlow { /// Creates an instance of PCAFlow algorithm. /// ## Parameters /// * _prior: Learned prior or no prior (default). see also: cv::optflow::PCAPrior /// * _basisSize: Number of basis vectors. /// * _sparseRate: Controls density of sparse matches. /// * _retainedCornersFraction: Retained corners fraction. /// * _occlusionsThreshold: Occlusion threshold. /// * _dampingFactor: Regularization term for solving least-squares. It is not related to the prior regularization. /// * _claheClip: Clip parameter for CLAHE. /// /// ## C++ default parameters /// * _prior: Ptr<constPCAPrior>() /// * _basis_size: Size(18,14) /// * _sparse_rate: 0.024 /// * _retained_corners_fraction: 0.2 /// * _occlusions_threshold: 0.0003 /// * _damping_factor: 0.00002 /// * _clahe_clip: 14 pub fn new(_prior: core::Ptr::<crate::optflow::PCAPrior>, _basis_size: core::Size, _sparse_rate: f32, _retained_corners_fraction: f32, _occlusions_threshold: f32, _damping_factor: f32, _clahe_clip: f32) -> Result<crate::optflow::OpticalFlowPCAFlow> { unsafe { sys::cv_optflow_OpticalFlowPCAFlow_OpticalFlowPCAFlow_Ptr_const_PCAPrior__const_Size_float_float_float_float_float(_prior.as_raw_PtrOfPCAPrior(), _basis_size.opencv_as_extern(), _sparse_rate, _retained_corners_fraction, _occlusions_threshold, _damping_factor, _clahe_clip) }.into_result().map(|r| unsafe { crate::optflow::OpticalFlowPCAFlow::opencv_from_extern(r) } ) } } /// @brief /// This class can be used for imposing a learned prior on the resulting optical flow. /// Solution will be regularized according to this prior. /// You need to generate appropriate prior file with "learn_prior.py" script beforehand. pub trait PCAPriorTrait { fn as_raw_PCAPrior(&self) -> *const c_void; fn as_raw_mut_PCAPrior(&mut self) -> *mut c_void; fn get_padding(&self) -> Result<i32> { unsafe { sys::cv_optflow_PCAPrior_getPadding_const(self.as_raw_PCAPrior()) }.into_result() } fn get_basis_size(&self) -> Result<i32> { unsafe { sys::cv_optflow_PCAPrior_getBasisSize_const(self.as_raw_PCAPrior()) }.into_result() } fn fill_constraints(&self, a1: &mut f32, a2: &mut f32, b1: &mut f32, b2: &mut f32) -> Result<()> { unsafe { sys::cv_optflow_PCAPrior_fillConstraints_const_floatX_floatX_floatX_floatX(self.as_raw_PCAPrior(), a1, a2, b1, b2) }.into_result() } } /// @brief /// This class can be used for imposing a learned prior on the resulting optical flow. /// Solution will be regularized according to this prior. /// You need to generate appropriate prior file with "learn_prior.py" script beforehand. pub struct PCAPrior { ptr: *mut c_void } opencv_type_boxed! { PCAPrior } impl Drop for PCAPrior { fn drop(&mut self) { extern "C" { fn cv_PCAPrior_delete(instance: *mut c_void); } unsafe { cv_PCAPrior_delete(self.as_raw_mut_PCAPrior()) }; } } impl PCAPrior { #[inline] pub fn as_raw_PCAPrior(&self) -> *const c_void { self.as_raw() } #[inline] pub fn as_raw_mut_PCAPrior(&mut self) -> *mut c_void { self.as_raw_mut() } } unsafe impl Send for PCAPrior {} impl crate::optflow::PCAPriorTrait for PCAPrior { #[inline] fn as_raw_PCAPrior(&self) -> *const c_void { self.as_raw() } #[inline] fn as_raw_mut_PCAPrior(&mut self) -> *mut c_void { self.as_raw_mut() } } impl PCAPrior { pub fn new(path_to_prior: &str) -> Result<crate::optflow::PCAPrior> { extern_container_arg!(path_to_prior); unsafe { sys::cv_optflow_PCAPrior_PCAPrior_const_charX(path_to_prior.opencv_as_extern()) }.into_result().map(|r| unsafe { crate::optflow::PCAPrior::opencv_from_extern(r) } ) } } /// This is used store and set up the parameters of the robust local optical flow (RLOF) algoritm. /// /// The RLOF is a fast local optical flow approach described in [Senst2012](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2012) [Senst2013](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2013) [Senst2014](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2014) /// and [Senst2016](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2016) similar to the pyramidal iterative Lucas-Kanade method as /// proposed by [Bouguet00](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Bouguet00). More details and experiments can be found in the following thesis [Senst2019](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2019). /// The implementation is derived from optflow::calcOpticalFlowPyrLK(). /// This RLOF implementation can be seen as an improved pyramidal iterative Lucas-Kanade and includes /// a set of improving modules. The main improvements in respect to the pyramidal iterative Lucas-Kanade /// are: /// - A more robust redecending M-estimator framework (see [Senst2012](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2012)) to improve the accuracy at /// motion boundaries and appearing and disappearing pixels. /// - an adaptive support region strategies to improve the accuracy at motion boundaries to reduce the /// corona effect, i.e oversmoothing of the PLK at motion/object boundaries. The cross-based segementation /// strategy (SR_CROSS) proposed in [Senst2014](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2014) uses a simple segmenation approach to obtain the optimal /// shape of the support region. /// - To deal with illumination changes (outdoor sequences and shadow) the intensity constancy assumption /// based optical flow equation has been adopt with the Gennert and Negahdaripour illumination model /// (see [Senst2016](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2016)). This model can be switched on/off with the useIlluminationModel variable. /// - By using a global motion prior initialization (see [Senst2016](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2016)) of the iterative refinement /// the accuracy could be significantly improved for large displacements. This initialization can be /// switched on and of with useGlobalMotionPrior variable. /// /// The RLOF can be computed with the SparseOpticalFlow class or function interface to track a set of features /// or with the DenseOpticalFlow class or function interface to compute dense optical flow. /// ## See also /// optflow::DenseRLOFOpticalFlow, optflow::calcOpticalFlowDenseRLOF(), optflow::SparseRLOFOpticalFlow, optflow::calcOpticalFlowSparseRLOF() pub trait RLOFOpticalFlowParameterTrait { fn as_raw_RLOFOpticalFlowParameter(&self) -> *const c_void; fn as_raw_mut_RLOFOpticalFlowParameter(&mut self) -> *mut c_void; fn solver_type(&self) -> crate::optflow::SolverType { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_getPropSolverType_const(self.as_raw_RLOFOpticalFlowParameter()) }.into_result().expect("Infallible function failed: solver_type") } fn set_solver_type(&mut self, val: crate::optflow::SolverType) -> () { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_setPropSolverType_SolverType(self.as_raw_mut_RLOFOpticalFlowParameter(), val) }.into_result().expect("Infallible function failed: set_solver_type") } fn support_region_type(&self) -> crate::optflow::SupportRegionType { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_getPropSupportRegionType_const(self.as_raw_RLOFOpticalFlowParameter()) }.into_result().expect("Infallible function failed: support_region_type") } fn set_support_region_type(&mut self, val: crate::optflow::SupportRegionType) -> () { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_setPropSupportRegionType_SupportRegionType(self.as_raw_mut_RLOFOpticalFlowParameter(), val) }.into_result().expect("Infallible function failed: set_support_region_type") } fn norm_sigma0(&self) -> f32 { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_getPropNormSigma0_const(self.as_raw_RLOFOpticalFlowParameter()) }.into_result().expect("Infallible function failed: norm_sigma0") } fn set_norm_sigma0(&mut self, val: f32) -> () { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_setPropNormSigma0_float(self.as_raw_mut_RLOFOpticalFlowParameter(), val) }.into_result().expect("Infallible function failed: set_norm_sigma0") } fn norm_sigma1(&self) -> f32 { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_getPropNormSigma1_const(self.as_raw_RLOFOpticalFlowParameter()) }.into_result().expect("Infallible function failed: norm_sigma1") } fn set_norm_sigma1(&mut self, val: f32) -> () { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_setPropNormSigma1_float(self.as_raw_mut_RLOFOpticalFlowParameter(), val) }.into_result().expect("Infallible function failed: set_norm_sigma1") } fn small_win_size(&self) -> i32 { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_getPropSmallWinSize_const(self.as_raw_RLOFOpticalFlowParameter()) }.into_result().expect("Infallible function failed: small_win_size") } fn set_small_win_size(&mut self, val: i32) -> () { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_setPropSmallWinSize_int(self.as_raw_mut_RLOFOpticalFlowParameter(), val) }.into_result().expect("Infallible function failed: set_small_win_size") } fn large_win_size(&self) -> i32 { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_getPropLargeWinSize_const(self.as_raw_RLOFOpticalFlowParameter()) }.into_result().expect("Infallible function failed: large_win_size") } fn set_large_win_size(&mut self, val: i32) -> () { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_setPropLargeWinSize_int(self.as_raw_mut_RLOFOpticalFlowParameter(), val) }.into_result().expect("Infallible function failed: set_large_win_size") } fn cross_segmentation_threshold(&self) -> i32 { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_getPropCrossSegmentationThreshold_const(self.as_raw_RLOFOpticalFlowParameter()) }.into_result().expect("Infallible function failed: cross_segmentation_threshold") } fn set_cross_segmentation_threshold(&mut self, val: i32) -> () { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_setPropCrossSegmentationThreshold_int(self.as_raw_mut_RLOFOpticalFlowParameter(), val) }.into_result().expect("Infallible function failed: set_cross_segmentation_threshold") } fn max_level(&self) -> i32 { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_getPropMaxLevel_const(self.as_raw_RLOFOpticalFlowParameter()) }.into_result().expect("Infallible function failed: max_level") } fn set_max_level(&mut self, val: i32) -> () { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_setPropMaxLevel_int(self.as_raw_mut_RLOFOpticalFlowParameter(), val) }.into_result().expect("Infallible function failed: set_max_level") } fn use_initial_flow(&self) -> bool { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_getPropUseInitialFlow_const(self.as_raw_RLOFOpticalFlowParameter()) }.into_result().expect("Infallible function failed: use_initial_flow") } fn set_use_initial_flow(&mut self, val: bool) -> () { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_setPropUseInitialFlow_bool(self.as_raw_mut_RLOFOpticalFlowParameter(), val) }.into_result().expect("Infallible function failed: set_use_initial_flow") } fn use_illumination_model(&self) -> bool { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_getPropUseIlluminationModel_const(self.as_raw_RLOFOpticalFlowParameter()) }.into_result().expect("Infallible function failed: use_illumination_model") } fn set_use_illumination_model(&mut self, val: bool) -> () { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_setPropUseIlluminationModel_bool(self.as_raw_mut_RLOFOpticalFlowParameter(), val) }.into_result().expect("Infallible function failed: set_use_illumination_model") } fn use_global_motion_prior(&self) -> bool { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_getPropUseGlobalMotionPrior_const(self.as_raw_RLOFOpticalFlowParameter()) }.into_result().expect("Infallible function failed: use_global_motion_prior") } fn set_use_global_motion_prior(&mut self, val: bool) -> () { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_setPropUseGlobalMotionPrior_bool(self.as_raw_mut_RLOFOpticalFlowParameter(), val) }.into_result().expect("Infallible function failed: set_use_global_motion_prior") } fn max_iteration(&self) -> i32 { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_getPropMaxIteration_const(self.as_raw_RLOFOpticalFlowParameter()) }.into_result().expect("Infallible function failed: max_iteration") } fn set_max_iteration(&mut self, val: i32) -> () { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_setPropMaxIteration_int(self.as_raw_mut_RLOFOpticalFlowParameter(), val) }.into_result().expect("Infallible function failed: set_max_iteration") } fn min_eigen_value(&self) -> f32 { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_getPropMinEigenValue_const(self.as_raw_RLOFOpticalFlowParameter()) }.into_result().expect("Infallible function failed: min_eigen_value") } fn set_min_eigen_value(&mut self, val: f32) -> () { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_setPropMinEigenValue_float(self.as_raw_mut_RLOFOpticalFlowParameter(), val) }.into_result().expect("Infallible function failed: set_min_eigen_value") } fn global_motion_ransac_threshold(&self) -> f32 { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_getPropGlobalMotionRansacThreshold_const(self.as_raw_RLOFOpticalFlowParameter()) }.into_result().expect("Infallible function failed: global_motion_ransac_threshold") } fn set_global_motion_ransac_threshold(&mut self, val: f32) -> () { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_setPropGlobalMotionRansacThreshold_float(self.as_raw_mut_RLOFOpticalFlowParameter(), val) }.into_result().expect("Infallible function failed: set_global_motion_ransac_threshold") } /// Enable M-estimator or disable and use least-square estimator. /// Enables M-estimator by setting sigma parameters to (3.2, 7.0). Disabling M-estimator can reduce /// * runtime, while enabling can improve the accuracy. /// ## Parameters /// * val: If true M-estimator is used. If false least-square estimator is used. /// * see also: setNormSigma0, setNormSigma1 fn set_use_m_estimator(&mut self, val: bool) -> Result<()> { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_setUseMEstimator_bool(self.as_raw_mut_RLOFOpticalFlowParameter(), val) }.into_result() } fn set_solver_type_1(&mut self, val: crate::optflow::SolverType) -> Result<()> { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_setSolverType_SolverType(self.as_raw_mut_RLOFOpticalFlowParameter(), val) }.into_result() } fn get_solver_type(&self) -> Result<crate::optflow::SolverType> { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_getSolverType_const(self.as_raw_RLOFOpticalFlowParameter()) }.into_result() } fn set_support_region_type_1(&mut self, val: crate::optflow::SupportRegionType) -> Result<()> { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_setSupportRegionType_SupportRegionType(self.as_raw_mut_RLOFOpticalFlowParameter(), val) }.into_result() } fn get_support_region_type(&self) -> Result<crate::optflow::SupportRegionType> { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_getSupportRegionType_const(self.as_raw_RLOFOpticalFlowParameter()) }.into_result() } fn set_norm_sigma0_1(&mut self, val: f32) -> Result<()> { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_setNormSigma0_float(self.as_raw_mut_RLOFOpticalFlowParameter(), val) }.into_result() } fn get_norm_sigma0(&self) -> Result<f32> { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_getNormSigma0_const(self.as_raw_RLOFOpticalFlowParameter()) }.into_result() } fn set_norm_sigma1_1(&mut self, val: f32) -> Result<()> { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_setNormSigma1_float(self.as_raw_mut_RLOFOpticalFlowParameter(), val) }.into_result() } fn get_norm_sigma1(&self) -> Result<f32> { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_getNormSigma1_const(self.as_raw_RLOFOpticalFlowParameter()) }.into_result() } fn set_small_win_size_1(&mut self, val: i32) -> Result<()> { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_setSmallWinSize_int(self.as_raw_mut_RLOFOpticalFlowParameter(), val) }.into_result() } fn get_small_win_size(&self) -> Result<i32> { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_getSmallWinSize_const(self.as_raw_RLOFOpticalFlowParameter()) }.into_result() } fn set_large_win_size_1(&mut self, val: i32) -> Result<()> { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_setLargeWinSize_int(self.as_raw_mut_RLOFOpticalFlowParameter(), val) }.into_result() } fn get_large_win_size(&self) -> Result<i32> { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_getLargeWinSize_const(self.as_raw_RLOFOpticalFlowParameter()) }.into_result() } fn set_cross_segmentation_threshold_1(&mut self, val: i32) -> Result<()> { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_setCrossSegmentationThreshold_int(self.as_raw_mut_RLOFOpticalFlowParameter(), val) }.into_result() } fn get_cross_segmentation_threshold(&self) -> Result<i32> { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_getCrossSegmentationThreshold_const(self.as_raw_RLOFOpticalFlowParameter()) }.into_result() } fn set_max_level_1(&mut self, val: i32) -> Result<()> { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_setMaxLevel_int(self.as_raw_mut_RLOFOpticalFlowParameter(), val) }.into_result() } fn get_max_level(&self) -> Result<i32> { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_getMaxLevel_const(self.as_raw_RLOFOpticalFlowParameter()) }.into_result() } fn set_use_initial_flow_1(&mut self, val: bool) -> Result<()> { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_setUseInitialFlow_bool(self.as_raw_mut_RLOFOpticalFlowParameter(), val) }.into_result() } fn get_use_initial_flow(&self) -> Result<bool> { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_getUseInitialFlow_const(self.as_raw_RLOFOpticalFlowParameter()) }.into_result() } fn set_use_illumination_model_1(&mut self, val: bool) -> Result<()> { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_setUseIlluminationModel_bool(self.as_raw_mut_RLOFOpticalFlowParameter(), val) }.into_result() } fn get_use_illumination_model(&self) -> Result<bool> { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_getUseIlluminationModel_const(self.as_raw_RLOFOpticalFlowParameter()) }.into_result() } fn set_use_global_motion_prior_1(&mut self, val: bool) -> Result<()> { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_setUseGlobalMotionPrior_bool(self.as_raw_mut_RLOFOpticalFlowParameter(), val) }.into_result() } fn get_use_global_motion_prior(&self) -> Result<bool> { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_getUseGlobalMotionPrior_const(self.as_raw_RLOFOpticalFlowParameter()) }.into_result() } fn set_max_iteration_1(&mut self, val: i32) -> Result<()> { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_setMaxIteration_int(self.as_raw_mut_RLOFOpticalFlowParameter(), val) }.into_result() } fn get_max_iteration(&self) -> Result<i32> { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_getMaxIteration_const(self.as_raw_RLOFOpticalFlowParameter()) }.into_result() } fn set_min_eigen_value_1(&mut self, val: f32) -> Result<()> { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_setMinEigenValue_float(self.as_raw_mut_RLOFOpticalFlowParameter(), val) }.into_result() } fn get_min_eigen_value(&self) -> Result<f32> { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_getMinEigenValue_const(self.as_raw_RLOFOpticalFlowParameter()) }.into_result() } fn set_global_motion_ransac_threshold_1(&mut self, val: f32) -> Result<()> { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_setGlobalMotionRansacThreshold_float(self.as_raw_mut_RLOFOpticalFlowParameter(), val) }.into_result() } fn get_global_motion_ransac_threshold(&self) -> Result<f32> { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_getGlobalMotionRansacThreshold_const(self.as_raw_RLOFOpticalFlowParameter()) }.into_result() } } /// This is used store and set up the parameters of the robust local optical flow (RLOF) algoritm. /// /// The RLOF is a fast local optical flow approach described in [Senst2012](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2012) [Senst2013](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2013) [Senst2014](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2014) /// and [Senst2016](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2016) similar to the pyramidal iterative Lucas-Kanade method as /// proposed by [Bouguet00](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Bouguet00). More details and experiments can be found in the following thesis [Senst2019](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2019). /// The implementation is derived from optflow::calcOpticalFlowPyrLK(). /// This RLOF implementation can be seen as an improved pyramidal iterative Lucas-Kanade and includes /// a set of improving modules. The main improvements in respect to the pyramidal iterative Lucas-Kanade /// are: /// - A more robust redecending M-estimator framework (see [Senst2012](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2012)) to improve the accuracy at /// motion boundaries and appearing and disappearing pixels. /// - an adaptive support region strategies to improve the accuracy at motion boundaries to reduce the /// corona effect, i.e oversmoothing of the PLK at motion/object boundaries. The cross-based segementation /// strategy (SR_CROSS) proposed in [Senst2014](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2014) uses a simple segmenation approach to obtain the optimal /// shape of the support region. /// - To deal with illumination changes (outdoor sequences and shadow) the intensity constancy assumption /// based optical flow equation has been adopt with the Gennert and Negahdaripour illumination model /// (see [Senst2016](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2016)). This model can be switched on/off with the useIlluminationModel variable. /// - By using a global motion prior initialization (see [Senst2016](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2016)) of the iterative refinement /// the accuracy could be significantly improved for large displacements. This initialization can be /// switched on and of with useGlobalMotionPrior variable. /// /// The RLOF can be computed with the SparseOpticalFlow class or function interface to track a set of features /// or with the DenseOpticalFlow class or function interface to compute dense optical flow. /// ## See also /// optflow::DenseRLOFOpticalFlow, optflow::calcOpticalFlowDenseRLOF(), optflow::SparseRLOFOpticalFlow, optflow::calcOpticalFlowSparseRLOF() pub struct RLOFOpticalFlowParameter { ptr: *mut c_void } opencv_type_boxed! { RLOFOpticalFlowParameter } impl Drop for RLOFOpticalFlowParameter { fn drop(&mut self) { extern "C" { fn cv_RLOFOpticalFlowParameter_delete(instance: *mut c_void); } unsafe { cv_RLOFOpticalFlowParameter_delete(self.as_raw_mut_RLOFOpticalFlowParameter()) }; } } impl RLOFOpticalFlowParameter { #[inline] pub fn as_raw_RLOFOpticalFlowParameter(&self) -> *const c_void { self.as_raw() } #[inline] pub fn as_raw_mut_RLOFOpticalFlowParameter(&mut self) -> *mut c_void { self.as_raw_mut() } } unsafe impl Send for RLOFOpticalFlowParameter {} impl crate::optflow::RLOFOpticalFlowParameterTrait for RLOFOpticalFlowParameter { #[inline] fn as_raw_RLOFOpticalFlowParameter(&self) -> *const c_void { self.as_raw() } #[inline] fn as_raw_mut_RLOFOpticalFlowParameter(&mut self) -> *mut c_void { self.as_raw_mut() } } impl RLOFOpticalFlowParameter { pub fn default() -> Result<crate::optflow::RLOFOpticalFlowParameter> { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_RLOFOpticalFlowParameter() }.into_result().map(|r| unsafe { crate::optflow::RLOFOpticalFlowParameter::opencv_from_extern(r) } ) } /// Creates instance of optflow::RLOFOpticalFlowParameter pub fn create() -> Result<core::Ptr::<crate::optflow::RLOFOpticalFlowParameter>> { unsafe { sys::cv_optflow_RLOFOpticalFlowParameter_create() }.into_result().map(|r| unsafe { core::Ptr::<crate::optflow::RLOFOpticalFlowParameter>::opencv_from_extern(r) } ) } } /// Class used for calculation sparse optical flow and feature tracking with robust local optical flow (RLOF) algorithms. /// /// The RLOF is a fast local optical flow approach described in [Senst2012](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2012) [Senst2013](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2013) [Senst2014](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2014) /// and [Senst2016](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2016) similar to the pyramidal iterative Lucas-Kanade method as /// proposed by [Bouguet00](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Bouguet00). More details and experiments can be found in the following thesis [Senst2019](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2019). /// The implementation is derived from optflow::calcOpticalFlowPyrLK(). /// /// For the RLOF configuration see optflow::RLOFOpticalFlowParameter for further details. /// Parameters have been described in [Senst2012](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2012), [Senst2013](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2013), [Senst2014](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2014) and [Senst2016](https://docs.opencv.org/4.3.0/d0/de3/citelist.html#CITEREF_Senst2016). /// /// /// Note: SIMD parallelization is only available when compiling with SSE4.1. /// ## See also /// optflow::calcOpticalFlowSparseRLOF(), optflow::RLOFOpticalFlowParameter pub trait SparseRLOFOpticalFlow: crate::video::SparseOpticalFlow { fn as_raw_SparseRLOFOpticalFlow(&self) -> *const c_void; fn as_raw_mut_SparseRLOFOpticalFlow(&mut self) -> *mut c_void; /// @copydoc DenseRLOFOpticalFlow::setRLOFOpticalFlowParameter fn set_rlof_optical_flow_parameter(&mut self, mut val: core::Ptr::<crate::optflow::RLOFOpticalFlowParameter>) -> Result<()> { unsafe { sys::cv_optflow_SparseRLOFOpticalFlow_setRLOFOpticalFlowParameter_Ptr_RLOFOpticalFlowParameter_(self.as_raw_mut_SparseRLOFOpticalFlow(), val.as_raw_mut_PtrOfRLOFOpticalFlowParameter()) }.into_result() } /// @copydoc DenseRLOFOpticalFlow::setRLOFOpticalFlowParameter /// ## See also /// setRLOFOpticalFlowParameter fn get_rlof_optical_flow_parameter(&self) -> Result<core::Ptr::<crate::optflow::RLOFOpticalFlowParameter>> { unsafe { sys::cv_optflow_SparseRLOFOpticalFlow_getRLOFOpticalFlowParameter_const(self.as_raw_SparseRLOFOpticalFlow()) }.into_result().map(|r| unsafe { core::Ptr::<crate::optflow::RLOFOpticalFlowParameter>::opencv_from_extern(r) } ) } /// Threshold for the forward backward confidence check /// For each feature point a motion vector  is computed. /// * If the forward backward error  /// * is larger than threshold given by this function then the status will not be used by the following /// * vector field interpolation.  denotes the backward flow. Note, the forward backward test /// * will only be applied if the threshold > 0. This may results into a doubled runtime for the motion estimation. /// * see also: setForwardBackward fn set_forward_backward(&mut self, val: f32) -> Result<()> { unsafe { sys::cv_optflow_SparseRLOFOpticalFlow_setForwardBackward_float(self.as_raw_mut_SparseRLOFOpticalFlow(), val) }.into_result() } /// Threshold for the forward backward confidence check /// For each feature point a motion vector  is computed. /// * If the forward backward error  /// * is larger than threshold given by this function then the status will not be used by the following /// * vector field interpolation.  denotes the backward flow. Note, the forward backward test /// * will only be applied if the threshold > 0. This may results into a doubled runtime for the motion estimation. /// * see also: setForwardBackward /// * see also: setForwardBackward fn get_forward_backward(&self) -> Result<f32> { unsafe { sys::cv_optflow_SparseRLOFOpticalFlow_getForwardBackward_const(self.as_raw_SparseRLOFOpticalFlow()) }.into_result() } } impl dyn SparseRLOFOpticalFlow + '_ { /// Creates instance of SparseRLOFOpticalFlow /// /// ## Parameters /// * rlofParam: see setRLOFOpticalFlowParameter /// * forwardBackwardThreshold: see setForwardBackward /// /// ## C++ default parameters /// * rlof_param: Ptr<RLOFOpticalFlowParameter>() /// * forward_backward_threshold: 1.f pub fn create(mut rlof_param: core::Ptr::<crate::optflow::RLOFOpticalFlowParameter>, forward_backward_threshold: f32) -> Result<core::Ptr::<dyn crate::optflow::SparseRLOFOpticalFlow>> { unsafe { sys::cv_optflow_SparseRLOFOpticalFlow_create_Ptr_RLOFOpticalFlowParameter__float(rlof_param.as_raw_mut_PtrOfRLOFOpticalFlowParameter(), forward_backward_threshold) }.into_result().map(|r| unsafe { core::Ptr::<dyn crate::optflow::SparseRLOFOpticalFlow>::opencv_from_extern(r) } ) } }