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//! # Binary descriptors for lines extracted from an image //! //! Introduction //! ------------ //! //! One of the most challenging activities in computer vision is the extraction of useful information //! from a given image. Such information, usually comes in the form of points that preserve some kind of //! property (for instance, they are scale-invariant) and are actually representative of input image. //! //! The goal of this module is seeking a new kind of representative information inside an image and //! providing the functionalities for its extraction and representation. In particular, differently from //! previous methods for detection of relevant elements inside an image, lines are extracted in place of //! points; a new class is defined ad hoc to summarize a line's properties, for reuse and plotting //! purposes. //! //! Computation of binary descriptors //! --------------------------------- //! //! To obtatin a binary descriptor representing a certain line detected from a certain octave of an //! image, we first compute a non-binary descriptor as described in [LBD](https://docs.opencv.org/3.4.8/d0/de3/citelist.html#CITEREF_LBD) . Such algorithm works on //! lines extracted using EDLine detector, as explained in [EDL](https://docs.opencv.org/3.4.8/d0/de3/citelist.html#CITEREF_EDL) . Given a line, we consider a //! rectangular region centered at it and called *line support region (LSR)*. Such region is divided //! into a set of bands , whose length equals the one of line. //! //! If we indicate with  the direction of line, the orthogonal and clockwise direction to line //!  can be determined; these two directions, are used to construct a reference frame //! centered in the middle point of line. The gradients of pixels  inside LSR can be projected //! to the newly determined frame, obtaining their local equivalent //! . //! //! Later on, a Gaussian function is applied to all LSR's pixels along  direction; first, //! we assign a global weighting coefficient  to //! *i*-th row in LSR, where  is the distance of *i*-th row from the center row in LSR, //!  and  is the width of bands (the same for every band). Secondly, //! considering a band  and its neighbor bands , we assign a local weighting //! , where  is the distance of *k*-th //! row from the center row in  and . Using the global and local weights, we obtain, //! at the same time, the reduction of role played by gradients far from line and of boundary effect, //! respectively. //! //! Each band  in LSR has an associated *band descriptor(BD)* which is computed considering //! previous and next band (top and bottom bands are ignored when computing descriptor for first and //! last band). Once each band has been assignen its BD, the LBD descriptor of line is simply given by //! //!  //! //! To compute a band descriptor , each *k*-th row in it is considered and the gradients in such //! row are accumulated: //! //!  //! //! with . //! //! By stacking previous results, we obtain the *band description matrix (BDM)* //! //!  //! //! with  the number of rows in band : //! //!  //! //! Each  can be obtained using the standard deviation vector  and mean vector  of //! . Thus, finally: //! //!  //! //! Once the LBD has been obtained, it must be converted into a binary form. For such purpose, we //! consider 32 possible pairs of BD inside it; each couple of BD is compared bit by bit and comparison //! generates an 8 bit string. Concatenating 32 comparison strings, we get the 256-bit final binary //! representation of a single LBD. use std::os::raw::{c_char, c_void}; use libc::{ptrdiff_t, size_t}; use crate::{Error, Result, core, sys, types}; use crate::core::{_InputArrayTrait, _OutputArrayTrait}; pub const MLN10: f64 = 2.30258509299404568402; pub const RELATIVE_ERROR_FACTOR: f64 = 100.0; /// A class to represent a line /// /// As aformentioned, it is been necessary to design a class that fully stores the information needed to /// characterize completely a line and plot it on image it was extracted from, when required. /// /// *KeyLine* class has been created for such goal; it is mainly inspired to Feature2d's KeyPoint class, /// since KeyLine shares some of *KeyPoint*'s fields, even if a part of them assumes a different /// meaning, when speaking about lines. In particular: /// /// * the *class_id* field is used to gather lines extracted from different octaves which refer to /// same line inside original image (such lines and the one they represent in original image share /// the same *class_id* value) /// * the *angle* field represents line's slope with respect to (positive) X axis /// * the *pt* field represents line's midpoint /// * the *response* field is computed as the ratio between the line's length and maximum between /// image's width and height /// * the *size* field is the area of the smallest rectangle containing line /// /// Apart from fields inspired to KeyPoint class, KeyLines stores information about extremes of line in /// original image and in octave it was extracted from, about line's length and number of pixels it /// covers. #[repr(C)] #[derive(Copy, Clone, Debug, PartialEq)] pub struct KeyLine { pub angle: f32, pub class_id: i32, pub octave: i32, pub pt: core::Point2f, pub response: f32, pub size: f32, pub start_point_x: f32, pub start_point_y: f32, pub end_point_x: f32, pub end_point_y: f32, pub s_point_in_octave_x: f32, pub s_point_in_octave_y: f32, pub e_point_in_octave_x: f32, pub e_point_in_octave_y: f32, pub line_length: f32, pub num_of_pixels: i32, } /// Lines extraction methodology /// ---------------------------- /// /// The lines extraction methodology described in the following is mainly based on [EDL](https://docs.opencv.org/3.4.8/d0/de3/citelist.html#CITEREF_EDL) . The /// extraction starts with a Gaussian pyramid generated from an original image, downsampled N-1 times, /// blurred N times, to obtain N layers (one for each octave), with layer 0 corresponding to input /// image. Then, from each layer (octave) in the pyramid, lines are extracted using LSD algorithm. /// /// Differently from EDLine lines extractor used in original article, LSD furnishes information only /// about lines extremes; thus, additional information regarding slope and equation of line are computed /// via analytic methods. The number of pixels is obtained using *LineIterator*. Extracted lines are /// returned in the form of KeyLine objects, but since extraction is based on a method different from /// the one used in *BinaryDescriptor* class, data associated to a line's extremes in original image and /// in octave it was extracted from, coincide. KeyLine's field *class_id* is used as an index to /// indicate the order of extraction of a line inside a single octave. #[repr(C)] #[derive(Copy, Clone, Debug, PartialEq)] pub struct LSDParam { pub scale: f64, pub sigma_scale: f64, pub quant: f64, pub ang_th: f64, pub log_eps: f64, pub density_th: f64, pub n_bins: i32, } /// struct for drawing options #[repr(C)] #[derive(Copy, Clone, Debug, PartialEq)] pub struct DrawLinesMatchesFlags { __rust_private: [u8; 0], } /// Draws keylines. /// /// ## Parameters /// * image: input image /// * keylines: keylines to be drawn /// * outImage: output image to draw on /// * color: color of lines to be drawn (if set to defaul value, color is chosen randomly) /// * flags: drawing flags /// /// ## C++ default parameters /// * color: Scalar::all( -1 ) /// * flags: DrawLinesMatchesFlags::DEFAULT pub fn draw_keylines(image: &core::Mat, keylines: &types::VectorOfKeyLine, out_image: &mut core::Mat, color: core::Scalar, flags: i32) -> Result<()> { unsafe { sys::cv_line_descriptor_drawKeylines_Mat_VectorOfKeyLine_Mat_Scalar_int(image.as_raw_Mat(), keylines.as_raw_VectorOfKeyLine(), out_image.as_raw_Mat(), color, flags) }.into_result() } /// Draws the found matches of keylines from two images. /// /// ## Parameters /// * img1: first image /// * keylines1: keylines extracted from first image /// * img2: second image /// * keylines2: keylines extracted from second image /// * matches1to2: vector of matches /// * outImg: output matrix to draw on /// * matchColor: drawing color for matches (chosen randomly in case of default value) /// * singleLineColor: drawing color for keylines (chosen randomly in case of default value) /// * matchesMask: mask to indicate which matches must be drawn /// * flags: drawing flags, see DrawLinesMatchesFlags /// /// /// Note: If both *matchColor* and *singleLineColor* are set to their default values, function draws /// matched lines and line connecting them with same color /// /// ## C++ default parameters /// * match_color: Scalar::all( -1 ) /// * single_line_color: Scalar::all( -1 ) /// * matches_mask: std::vector<char>() /// * flags: DrawLinesMatchesFlags::DEFAULT pub fn draw_line_matches(img1: &core::Mat, keylines1: &types::VectorOfKeyLine, img2: &core::Mat, keylines2: &types::VectorOfKeyLine, matches1to2: &types::VectorOfDMatch, out_img: &mut core::Mat, match_color: core::Scalar, single_line_color: core::Scalar, matches_mask: &types::VectorOfchar, flags: i32) -> Result<()> { unsafe { sys::cv_line_descriptor_drawLineMatches_Mat_VectorOfKeyLine_Mat_VectorOfKeyLine_VectorOfDMatch_Mat_Scalar_Scalar_VectorOfchar_int(img1.as_raw_Mat(), keylines1.as_raw_VectorOfKeyLine(), img2.as_raw_Mat(), keylines2.as_raw_VectorOfKeyLine(), matches1to2.as_raw_VectorOfDMatch(), out_img.as_raw_Mat(), match_color, single_line_color, matches_mask.as_raw_VectorOfchar(), flags) }.into_result() } // boxed class cv::line_descriptor::BinaryDescriptor /// Class implements both functionalities for detection of lines and computation of their /// binary descriptor. /// /// Class' interface is mainly based on the ones of classical detectors and extractors, such as /// Feature2d's @ref features2d_main and @ref features2d_match. Retrieved information about lines is /// stored in line_descriptor::KeyLine objects. pub struct BinaryDescriptor { #[doc(hidden)] pub(crate) ptr: *mut c_void } impl Drop for BinaryDescriptor { fn drop(&mut self) { unsafe { sys::cv_BinaryDescriptor_delete(self.ptr) }; } } impl BinaryDescriptor { #[inline(always)] pub fn as_raw_BinaryDescriptor(&self) -> *mut c_void { self.ptr } pub unsafe fn from_raw_ptr(ptr: *mut c_void) -> Self { Self { ptr } } } unsafe impl Send for BinaryDescriptor {} impl core::AlgorithmTrait for BinaryDescriptor { #[inline(always)] fn as_raw_Algorithm(&self) -> *mut c_void { self.ptr } } impl BinaryDescriptor { /// Constructor /// /// ## Parameters /// * parameters: configuration parameters BinaryDescriptor::Params /// /// If no argument is provided, constructor sets default values (see comments in the code snippet in /// previous section). Default values are strongly reccomended. /// /// ## C++ default parameters /// * parameters: BinaryDescriptor::Params() pub fn new(parameters: &crate::line_descriptor::BinaryDescriptor_Params) -> Result<crate::line_descriptor::BinaryDescriptor> { unsafe { sys::cv_line_descriptor_BinaryDescriptor_BinaryDescriptor_Params(parameters.as_raw_BinaryDescriptor_Params()) }.into_result().map(|ptr| crate::line_descriptor::BinaryDescriptor { ptr }) } /// Create a BinaryDescriptor object with default parameters (or with the ones provided) /// and return a smart pointer to it pub fn create_binary_descriptor() -> Result<types::PtrOfBinaryDescriptor> { unsafe { sys::cv_line_descriptor_BinaryDescriptor_createBinaryDescriptor() }.into_result().map(|ptr| types::PtrOfBinaryDescriptor { ptr }) } pub fn create_binary_descriptor_1(parameters: &crate::line_descriptor::BinaryDescriptor_Params) -> Result<types::PtrOfBinaryDescriptor> { unsafe { sys::cv_line_descriptor_BinaryDescriptor_createBinaryDescriptor_Params(parameters.as_raw_BinaryDescriptor_Params()) }.into_result().map(|ptr| types::PtrOfBinaryDescriptor { ptr }) } /// Get current number of octaves pub fn get_num_of_octaves(&mut self) -> Result<i32> { unsafe { sys::cv_line_descriptor_BinaryDescriptor_getNumOfOctaves(self.as_raw_BinaryDescriptor()) }.into_result() } /// Set number of octaves /// ## Parameters /// * octaves: number of octaves pub fn set_num_of_octaves(&mut self, octaves: i32) -> Result<()> { unsafe { sys::cv_line_descriptor_BinaryDescriptor_setNumOfOctaves_int(self.as_raw_BinaryDescriptor(), octaves) }.into_result() } /// Get current width of bands pub fn get_width_of_band(&mut self) -> Result<i32> { unsafe { sys::cv_line_descriptor_BinaryDescriptor_getWidthOfBand(self.as_raw_BinaryDescriptor()) }.into_result() } /// Set width of bands /// ## Parameters /// * width: width of bands pub fn set_width_of_band(&mut self, width: i32) -> Result<()> { unsafe { sys::cv_line_descriptor_BinaryDescriptor_setWidthOfBand_int(self.as_raw_BinaryDescriptor(), width) }.into_result() } /// Get current reduction ratio (used in Gaussian pyramids) pub fn get_reduction_ratio(&mut self) -> Result<i32> { unsafe { sys::cv_line_descriptor_BinaryDescriptor_getReductionRatio(self.as_raw_BinaryDescriptor()) }.into_result() } /// Set reduction ratio (used in Gaussian pyramids) /// ## Parameters /// * rRatio: reduction ratio pub fn set_reduction_ratio(&mut self, r_ratio: i32) -> Result<()> { unsafe { sys::cv_line_descriptor_BinaryDescriptor_setReductionRatio_int(self.as_raw_BinaryDescriptor(), r_ratio) }.into_result() } /// Read parameters from a FileNode object and store them /// /// ## Parameters /// * fn: source FileNode file pub fn read(&mut self, _fn: &core::FileNode) -> Result<()> { unsafe { sys::cv_line_descriptor_BinaryDescriptor_read_FileNode(self.as_raw_BinaryDescriptor(), _fn.as_raw_FileNode()) }.into_result() } /// Store parameters to a FileStorage object /// /// ## Parameters /// * fs: output FileStorage file pub fn write(&self, fs: &mut core::FileStorage) -> Result<()> { unsafe { sys::cv_line_descriptor_BinaryDescriptor_write_const_FileStorage(self.as_raw_BinaryDescriptor(), fs.as_raw_FileStorage()) }.into_result() } /// Requires line detection /// /// ## Parameters /// * image: input image /// * keypoints: vector that will store extracted lines for one or more images /// * mask: mask matrix to detect only KeyLines of interest /// /// ## C++ default parameters /// * mask: Mat() pub fn detect(&mut self, image: &core::Mat, keypoints: &mut types::VectorOfKeyLine, mask: &core::Mat) -> Result<()> { unsafe { sys::cv_line_descriptor_BinaryDescriptor_detect_Mat_VectorOfKeyLine_Mat(self.as_raw_BinaryDescriptor(), image.as_raw_Mat(), keypoints.as_raw_VectorOfKeyLine(), mask.as_raw_Mat()) }.into_result() } /// ## Parameters /// * images: input images /// * keylines: set of vectors that will store extracted lines for one or more images /// * masks: vector of mask matrices to detect only KeyLines of interest from each input image /// /// ## C++ default parameters /// * masks: std::vector<Mat>() pub fn detect_1(&self, images: &types::VectorOfMat, keylines: &mut types::VectorOfVectorOfKeyLine, masks: &types::VectorOfMat) -> Result<()> { unsafe { sys::cv_line_descriptor_BinaryDescriptor_detect_const_VectorOfMat_VectorOfVectorOfKeyLine_VectorOfMat(self.as_raw_BinaryDescriptor(), images.as_raw_VectorOfMat(), keylines.as_raw_VectorOfVectorOfKeyLine(), masks.as_raw_VectorOfMat()) }.into_result() } /// Requires descriptors computation /// /// ## Parameters /// * image: input image /// * keylines: vector containing lines for which descriptors must be computed /// * descriptors: /// * returnFloatDescr: flag (when set to true, original non-binary descriptors are returned) /// /// ## C++ default parameters /// * return_float_descr: false pub fn compute(&self, image: &core::Mat, keylines: &mut types::VectorOfKeyLine, descriptors: &mut core::Mat, return_float_descr: bool) -> Result<()> { unsafe { sys::cv_line_descriptor_BinaryDescriptor_compute_const_Mat_VectorOfKeyLine_Mat_bool(self.as_raw_BinaryDescriptor(), image.as_raw_Mat(), keylines.as_raw_VectorOfKeyLine(), descriptors.as_raw_Mat(), return_float_descr) }.into_result() } /// ## Parameters /// * images: input images /// * keylines: set of vectors containing lines for which descriptors must be computed /// * descriptors: /// * returnFloatDescr: flag (when set to true, original non-binary descriptors are returned) /// /// ## C++ default parameters /// * return_float_descr: false pub fn compute_1(&self, images: &types::VectorOfMat, keylines: &mut types::VectorOfVectorOfKeyLine, descriptors: &mut types::VectorOfMat, return_float_descr: bool) -> Result<()> { unsafe { sys::cv_line_descriptor_BinaryDescriptor_compute_const_VectorOfMat_VectorOfVectorOfKeyLine_VectorOfMat_bool(self.as_raw_BinaryDescriptor(), images.as_raw_VectorOfMat(), keylines.as_raw_VectorOfVectorOfKeyLine(), descriptors.as_raw_VectorOfMat(), return_float_descr) }.into_result() } /// Return descriptor size pub fn descriptor_size(&self) -> Result<i32> { unsafe { sys::cv_line_descriptor_BinaryDescriptor_descriptorSize_const(self.as_raw_BinaryDescriptor()) }.into_result() } /// Return data type pub fn descriptor_type(&self) -> Result<i32> { unsafe { sys::cv_line_descriptor_BinaryDescriptor_descriptorType_const(self.as_raw_BinaryDescriptor()) }.into_result() } /// returns norm mode pub fn default_norm(&self) -> Result<i32> { unsafe { sys::cv_line_descriptor_BinaryDescriptor_defaultNorm_const(self.as_raw_BinaryDescriptor()) }.into_result() } } // boxed class cv::line_descriptor::BinaryDescriptor::Params /// List of BinaryDescriptor parameters: pub struct BinaryDescriptor_Params { #[doc(hidden)] pub(crate) ptr: *mut c_void } impl Drop for BinaryDescriptor_Params { fn drop(&mut self) { unsafe { sys::cv_BinaryDescriptor_Params_delete(self.ptr) }; } } impl BinaryDescriptor_Params { #[inline(always)] pub fn as_raw_BinaryDescriptor_Params(&self) -> *mut c_void { self.ptr } pub unsafe fn from_raw_ptr(ptr: *mut c_void) -> Self { Self { ptr } } } unsafe impl Send for BinaryDescriptor_Params {} impl BinaryDescriptor_Params { pub fn default() -> Result<crate::line_descriptor::BinaryDescriptor_Params> { unsafe { sys::cv_line_descriptor_BinaryDescriptor_Params_Params() }.into_result().map(|ptr| crate::line_descriptor::BinaryDescriptor_Params { ptr }) } /// read parameters from a FileNode object and store them (struct function) pub fn read(&mut self, _fn: &core::FileNode) -> Result<()> { unsafe { sys::cv_line_descriptor_BinaryDescriptor_Params_read_FileNode(self.as_raw_BinaryDescriptor_Params(), _fn.as_raw_FileNode()) }.into_result() } /// store parameters to a FileStorage object (struct function) pub fn write(&self, fs: &mut core::FileStorage) -> Result<()> { unsafe { sys::cv_line_descriptor_BinaryDescriptor_Params_write_const_FileStorage(self.as_raw_BinaryDescriptor_Params(), fs.as_raw_FileStorage()) }.into_result() } } // boxed class cv::line_descriptor::BinaryDescriptorMatcher /// furnishes all functionalities for querying a dataset provided by user or internal to /// class (that user must, anyway, populate) on the model of @ref features2d_match /// /// /// Once descriptors have been extracted from an image (both they represent lines and points), it /// becomes interesting to be able to match a descriptor with another one extracted from a different /// image and representing the same line or point, seen from a differente perspective or on a different /// scale. In reaching such goal, the main headache is designing an efficient search algorithm to /// associate a query descriptor to one extracted from a dataset. In the following, a matching modality /// based on *Multi-Index Hashing (MiHashing)* will be described. /// /// Multi-Index Hashing /// ------------------- /// /// The theory described in this section is based on [MIH](https://docs.opencv.org/3.4.8/d0/de3/citelist.html#CITEREF_MIH) . Given a dataset populated with binary /// codes, each code is indexed *m* times into *m* different hash tables, according to *m* substrings it /// has been divided into. Thus, given a query code, all the entries close to it at least in one /// substring are returned by search as *neighbor candidates*. Returned entries are then checked for /// validity by verifying that their full codes are not distant (in Hamming space) more than *r* bits /// from query code. In details, each binary code **h** composed of *b* bits is divided into *m* /// disjoint substrings , each with length ///  or  bits. Formally, when two codes **h** and **g** differ /// by at the most *r* bits, in at the least one of their *m* substrings they differ by at the most ///  bits. In particular, when  (where  /// is the Hamming norm), there must exist a substring *k* (with ) such that /// ///  /// /// That means that if Hamming distance between each of the *m* substring is strictly greater than /// , then  must be larger that *r* and that is a /// contradiction. If the codes in dataset are divided into *m* substrings, then *m* tables will be /// built. Given a query **q** with substrings , *i*-th hash table is /// searched for entries distant at the most  from  and a set of /// candidates  is obtained. The union of sets ///  is a superset of the *r*-neighbors /// of **q**. Then, last step of algorithm is computing the Hamming distance between **q** and each /// element in , deleting the codes that are distant more that *r* from **q**. pub struct BinaryDescriptorMatcher { #[doc(hidden)] pub(crate) ptr: *mut c_void } impl Drop for BinaryDescriptorMatcher { fn drop(&mut self) { unsafe { sys::cv_BinaryDescriptorMatcher_delete(self.ptr) }; } } impl BinaryDescriptorMatcher { #[inline(always)] pub fn as_raw_BinaryDescriptorMatcher(&self) -> *mut c_void { self.ptr } pub unsafe fn from_raw_ptr(ptr: *mut c_void) -> Self { Self { ptr } } } unsafe impl Send for BinaryDescriptorMatcher {} impl core::AlgorithmTrait for BinaryDescriptorMatcher { #[inline(always)] fn as_raw_Algorithm(&self) -> *mut c_void { self.ptr } } impl BinaryDescriptorMatcher { /// For every input query descriptor, retrieve the best matching one from a dataset provided from user /// or from the one internal to class /// /// ## Parameters /// * queryDescriptors: query descriptors /// * trainDescriptors: dataset of descriptors furnished by user /// * matches: vector to host retrieved matches /// * mask: mask to select which input descriptors must be matched to one in dataset /// /// ## C++ default parameters /// * mask: Mat() pub fn _match(&self, query_descriptors: &core::Mat, train_descriptors: &core::Mat, matches: &mut types::VectorOfDMatch, mask: &core::Mat) -> Result<()> { unsafe { sys::cv_line_descriptor_BinaryDescriptorMatcher_match_const_Mat_Mat_VectorOfDMatch_Mat(self.as_raw_BinaryDescriptorMatcher(), query_descriptors.as_raw_Mat(), train_descriptors.as_raw_Mat(), matches.as_raw_VectorOfDMatch(), mask.as_raw_Mat()) }.into_result() } /// ## Parameters /// * queryDescriptors: query descriptors /// * matches: vector to host retrieved matches /// * masks: vector of masks to select which input descriptors must be matched to one in dataset /// (the *i*-th mask in vector indicates whether each input query can be matched with descriptors in /// dataset relative to *i*-th image) /// /// ## C++ default parameters /// * masks: std::vector<Mat>() pub fn _match_1(&mut self, query_descriptors: &core::Mat, matches: &mut types::VectorOfDMatch, masks: &types::VectorOfMat) -> Result<()> { unsafe { sys::cv_line_descriptor_BinaryDescriptorMatcher_match_Mat_VectorOfDMatch_VectorOfMat(self.as_raw_BinaryDescriptorMatcher(), query_descriptors.as_raw_Mat(), matches.as_raw_VectorOfDMatch(), masks.as_raw_VectorOfMat()) }.into_result() } /// For every input query descriptor, retrieve the best *k* matching ones from a dataset provided from /// user or from the one internal to class /// /// ## Parameters /// * queryDescriptors: query descriptors /// * trainDescriptors: dataset of descriptors furnished by user /// * matches: vector to host retrieved matches /// * k: number of the closest descriptors to be returned for every input query /// * mask: mask to select which input descriptors must be matched to ones in dataset /// * compactResult: flag to obtain a compact result (if true, a vector that doesn't contain any /// matches for a given query is not inserted in final result) /// /// ## C++ default parameters /// * mask: Mat() /// * compact_result: false pub fn knn_match(&self, query_descriptors: &core::Mat, train_descriptors: &core::Mat, matches: &mut types::VectorOfVectorOfDMatch, k: i32, mask: &core::Mat, compact_result: bool) -> Result<()> { unsafe { sys::cv_line_descriptor_BinaryDescriptorMatcher_knnMatch_const_Mat_Mat_VectorOfVectorOfDMatch_int_Mat_bool(self.as_raw_BinaryDescriptorMatcher(), query_descriptors.as_raw_Mat(), train_descriptors.as_raw_Mat(), matches.as_raw_VectorOfVectorOfDMatch(), k, mask.as_raw_Mat(), compact_result) }.into_result() } /// ## Parameters /// * queryDescriptors: query descriptors /// * matches: vector to host retrieved matches /// * k: number of the closest descriptors to be returned for every input query /// * masks: vector of masks to select which input descriptors must be matched to ones in dataset /// (the *i*-th mask in vector indicates whether each input query can be matched with descriptors in /// dataset relative to *i*-th image) /// * compactResult: flag to obtain a compact result (if true, a vector that doesn't contain any /// matches for a given query is not inserted in final result) /// /// ## C++ default parameters /// * masks: std::vector<Mat>() /// * compact_result: false pub fn knn_match_1(&mut self, query_descriptors: &core::Mat, matches: &mut types::VectorOfVectorOfDMatch, k: i32, masks: &types::VectorOfMat, compact_result: bool) -> Result<()> { unsafe { sys::cv_line_descriptor_BinaryDescriptorMatcher_knnMatch_Mat_VectorOfVectorOfDMatch_int_VectorOfMat_bool(self.as_raw_BinaryDescriptorMatcher(), query_descriptors.as_raw_Mat(), matches.as_raw_VectorOfVectorOfDMatch(), k, masks.as_raw_VectorOfMat(), compact_result) }.into_result() } /// For every input query descriptor, retrieve, from a dataset provided from user or from the one /// internal to class, all the descriptors that are not further than *maxDist* from input query /// /// ## Parameters /// * queryDescriptors: query descriptors /// * trainDescriptors: dataset of descriptors furnished by user /// * matches: vector to host retrieved matches /// * maxDistance: search radius /// * mask: mask to select which input descriptors must be matched to ones in dataset /// * compactResult: flag to obtain a compact result (if true, a vector that doesn't contain any /// matches for a given query is not inserted in final result) /// /// ## C++ default parameters /// * mask: Mat() /// * compact_result: false pub fn radius_match(&self, query_descriptors: &core::Mat, train_descriptors: &core::Mat, matches: &mut types::VectorOfVectorOfDMatch, max_distance: f32, mask: &core::Mat, compact_result: bool) -> Result<()> { unsafe { sys::cv_line_descriptor_BinaryDescriptorMatcher_radiusMatch_const_Mat_Mat_VectorOfVectorOfDMatch_float_Mat_bool(self.as_raw_BinaryDescriptorMatcher(), query_descriptors.as_raw_Mat(), train_descriptors.as_raw_Mat(), matches.as_raw_VectorOfVectorOfDMatch(), max_distance, mask.as_raw_Mat(), compact_result) }.into_result() } /// ## Parameters /// * queryDescriptors: query descriptors /// * matches: vector to host retrieved matches /// * maxDistance: search radius /// * masks: vector of masks to select which input descriptors must be matched to ones in dataset /// (the *i*-th mask in vector indicates whether each input query can be matched with descriptors in /// dataset relative to *i*-th image) /// * compactResult: flag to obtain a compact result (if true, a vector that doesn't contain any /// matches for a given query is not inserted in final result) /// /// ## C++ default parameters /// * masks: std::vector<Mat>() /// * compact_result: false pub fn radius_match_1(&mut self, query_descriptors: &core::Mat, matches: &mut types::VectorOfVectorOfDMatch, max_distance: f32, masks: &types::VectorOfMat, compact_result: bool) -> Result<()> { unsafe { sys::cv_line_descriptor_BinaryDescriptorMatcher_radiusMatch_Mat_VectorOfVectorOfDMatch_float_VectorOfMat_bool(self.as_raw_BinaryDescriptorMatcher(), query_descriptors.as_raw_Mat(), matches.as_raw_VectorOfVectorOfDMatch(), max_distance, masks.as_raw_VectorOfMat(), compact_result) }.into_result() } /// Store locally new descriptors to be inserted in dataset, without updating dataset. /// /// ## Parameters /// * descriptors: matrices containing descriptors to be inserted into dataset /// /// /// Note: Each matrix *i* in **descriptors** should contain descriptors relative to lines extracted from /// *i*-th image. pub fn add(&mut self, descriptors: &types::VectorOfMat) -> Result<()> { unsafe { sys::cv_line_descriptor_BinaryDescriptorMatcher_add_VectorOfMat(self.as_raw_BinaryDescriptorMatcher(), descriptors.as_raw_VectorOfMat()) }.into_result() } /// Update dataset by inserting into it all descriptors that were stored locally by *add* function. /// /// /// Note: Every time this function is invoked, current dataset is deleted and locally stored descriptors /// are inserted into dataset. The locally stored copy of just inserted descriptors is then removed. pub fn train(&mut self) -> Result<()> { unsafe { sys::cv_line_descriptor_BinaryDescriptorMatcher_train(self.as_raw_BinaryDescriptorMatcher()) }.into_result() } /// Create a BinaryDescriptorMatcher object and return a smart pointer to it. pub fn create_binary_descriptor_matcher() -> Result<types::PtrOfBinaryDescriptorMatcher> { unsafe { sys::cv_line_descriptor_BinaryDescriptorMatcher_createBinaryDescriptorMatcher() }.into_result().map(|ptr| types::PtrOfBinaryDescriptorMatcher { ptr }) } /// Clear dataset and internal data pub fn clear(&mut self) -> Result<()> { unsafe { sys::cv_line_descriptor_BinaryDescriptorMatcher_clear(self.as_raw_BinaryDescriptorMatcher()) }.into_result() } /// Constructor. /// /// The BinaryDescriptorMatcher constructed is able to store and manage 256-bits long entries. pub fn default() -> Result<crate::line_descriptor::BinaryDescriptorMatcher> { unsafe { sys::cv_line_descriptor_BinaryDescriptorMatcher_BinaryDescriptorMatcher() }.into_result().map(|ptr| crate::line_descriptor::BinaryDescriptorMatcher { ptr }) } } impl KeyLine { /// Returns the start point of the line in the original image pub fn get_start_point(self) -> Result<core::Point2f> { unsafe { sys::cv_line_descriptor_KeyLine_getStartPoint_const(self) }.into_result() } /// Returns the end point of the line in the original image pub fn get_end_point(self) -> Result<core::Point2f> { unsafe { sys::cv_line_descriptor_KeyLine_getEndPoint_const(self) }.into_result() } /// Returns the start point of the line in the octave it was extracted from pub fn get_start_point_in_octave(self) -> Result<core::Point2f> { unsafe { sys::cv_line_descriptor_KeyLine_getStartPointInOctave_const(self) }.into_result() } /// Returns the end point of the line in the octave it was extracted from pub fn get_end_point_in_octave(self) -> Result<core::Point2f> { unsafe { sys::cv_line_descriptor_KeyLine_getEndPointInOctave_const(self) }.into_result() } /// constructor pub fn default() -> Result<crate::line_descriptor::KeyLine> { unsafe { sys::cv_line_descriptor_KeyLine_KeyLine() }.into_result() } } // boxed class cv::line_descriptor::LSDDetector pub struct LSDDetector { #[doc(hidden)] pub(crate) ptr: *mut c_void } impl Drop for LSDDetector { fn drop(&mut self) { unsafe { sys::cv_LSDDetector_delete(self.ptr) }; } } impl LSDDetector { #[inline(always)] pub fn as_raw_LSDDetector(&self) -> *mut c_void { self.ptr } pub unsafe fn from_raw_ptr(ptr: *mut c_void) -> Self { Self { ptr } } } unsafe impl Send for LSDDetector {} impl core::AlgorithmTrait for LSDDetector { #[inline(always)] fn as_raw_Algorithm(&self) -> *mut c_void { self.ptr } } impl LSDDetector { pub fn default() -> Result<crate::line_descriptor::LSDDetector> { unsafe { sys::cv_line_descriptor_LSDDetector_LSDDetector() }.into_result().map(|ptr| crate::line_descriptor::LSDDetector { ptr }) } pub fn new(_params: crate::line_descriptor::LSDParam) -> Result<crate::line_descriptor::LSDDetector> { unsafe { sys::cv_line_descriptor_LSDDetector_LSDDetector_LSDParam(_params) }.into_result().map(|ptr| crate::line_descriptor::LSDDetector { ptr }) } /// Creates ad LSDDetector object, using smart pointers. pub fn create_lsd_detector() -> Result<types::PtrOfLSDDetector> { unsafe { sys::cv_line_descriptor_LSDDetector_createLSDDetector() }.into_result().map(|ptr| types::PtrOfLSDDetector { ptr }) } pub fn create_lsd_detector_1(params: crate::line_descriptor::LSDParam) -> Result<types::PtrOfLSDDetector> { unsafe { sys::cv_line_descriptor_LSDDetector_createLSDDetector_LSDParam(params) }.into_result().map(|ptr| types::PtrOfLSDDetector { ptr }) } /// Detect lines inside an image. /// /// ## Parameters /// * image: input image /// * keypoints: vector that will store extracted lines for one or more images /// * scale: scale factor used in pyramids generation /// * numOctaves: number of octaves inside pyramid /// * mask: mask matrix to detect only KeyLines of interest /// /// ## C++ default parameters /// * mask: Mat() pub fn detect(&mut self, image: &core::Mat, keypoints: &mut types::VectorOfKeyLine, scale: i32, num_octaves: i32, mask: &core::Mat) -> Result<()> { unsafe { sys::cv_line_descriptor_LSDDetector_detect_Mat_VectorOfKeyLine_int_int_Mat(self.as_raw_LSDDetector(), image.as_raw_Mat(), keypoints.as_raw_VectorOfKeyLine(), scale, num_octaves, mask.as_raw_Mat()) }.into_result() } /// ## Parameters /// * images: input images /// * keylines: set of vectors that will store extracted lines for one or more images /// * scale: scale factor used in pyramids generation /// * numOctaves: number of octaves inside pyramid /// * masks: vector of mask matrices to detect only KeyLines of interest from each input image /// /// ## C++ default parameters /// * masks: std::vector<Mat>() pub fn detect_multiple(&self, images: &types::VectorOfMat, keylines: &mut types::VectorOfVectorOfKeyLine, scale: i32, num_octaves: i32, masks: &types::VectorOfMat) -> Result<()> { unsafe { sys::cv_line_descriptor_LSDDetector_detect_const_VectorOfMat_VectorOfVectorOfKeyLine_int_int_VectorOfMat(self.as_raw_LSDDetector(), images.as_raw_VectorOfMat(), keylines.as_raw_VectorOfVectorOfKeyLine(), scale, num_octaves, masks.as_raw_VectorOfMat()) }.into_result() } } impl LSDParam { pub fn default() -> Result<crate::line_descriptor::LSDParam> { unsafe { sys::cv_line_descriptor_LSDParam_LSDParam() }.into_result() } }