[−][src]Trait opencv::stereo::QuasiDenseStereo

pub trait QuasiDenseStereo {
    pub fn as_raw_QuasiDenseStereo(&self) -> *const c_void;

    pub fn as_raw_mut_QuasiDenseStereo(&mut self) -> *mut c_void;

    pub fn param(&mut self) -> PropagationParameters { ... }

    pub fn set_param(&mut self, val: PropagationParameters) { ... }

    pub fn load_parameters(&mut self, filepath: &str) -> Result<i32> { ... }

    pub fn save_parameters(&mut self, filepath: &str) -> Result<i32> { ... }

    pub fn get_sparse_matches(
        &mut self, 
        s_matches: &mut Vector<Match>
    ) -> Result<()> { ... }

    pub fn get_dense_matches(
        &mut self, 
        dense_matches: &mut Vector<Match>
    ) -> Result<()> { ... }

    pub fn process(&mut self, img_left: &Mat, img_right: &Mat) -> Result<()> { ... }

    pub fn get_match(&mut self, x: i32, y: i32) -> Result<Point2f> { ... }

    pub fn get_disparity(&mut self, disparity_lvls: u8) -> Result<Mat> { ... }
}

Class containing the methods needed for Quasi Dense Stereo computation.

This module contains the code to perform quasi dense stereo matching. The method initially starts with a sparse 3D reconstruction based on feature matching across a stereo image pair and subsequently propagates the structure into neighboring image regions. To obtain initial seed correspondences, the algorithm locates Shi and Tomashi features in the left image of the stereo pair and then tracks them using pyramidal Lucas-Kanade in the right image. To densify the sparse correspondences, the algorithm computes the zero-mean normalized cross-correlation (ZNCC) in small patches around every seed pair and uses it as a quality metric for each match. In this code, we introduce a custom structure to store the location and ZNCC value of correspondences called "Match". Seed Matches are stored in a priority queue sorted according to their ZNCC value, allowing for the best quality Match to be readily available. The algorithm pops Matches and uses them to extract new matches around them. This is done by considering a small neighboring area around each Seed and retrieving correspondences above a certain texture threshold that are not previously computed. New matches are stored in the seed priority queue and used as seeds. The propagation process ends when no additional matches can be retrieved.

See also

This code represents the work presented in Stoyanov2010. If this code is useful for your work please cite Stoyanov2010.

Also the original growing scheme idea is described in Lhuillier2000

Required methods

pub fn as_raw_QuasiDenseStereo(&self) -> *const c_void[src]

pub fn as_raw_mut_QuasiDenseStereo(&mut self) -> *mut c_void[src]

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Provided methods

pub fn param(&mut self) -> PropagationParameters[src]

pub fn set_param(&mut self, val: PropagationParameters)[src]

pub fn load_parameters(&mut self, filepath: &str) -> Result<i32>[src]

Load a file containing the configuration parameters of the class.

Parameters

  • filepath: The location of the .YAML file containing the configuration parameters.

Note: default value is an empty string in which case the default parameters will be loaded. @retval 1: If the path is not empty and the program loaded the parameters successfully. @retval 0: If the path is empty and the program loaded default parameters. @retval -1: If the file location is not valid or the program could not open the file and loaded default parameters from defaults.hpp.

Note: The method is automatically called in the constructor and configures the class.

Note: Loading different parameters will have an effect on the output. This is useful for tuning in case of video processing.

See also

loadParameters

pub fn save_parameters(&mut self, filepath: &str) -> Result<i32>[src]

Save a file containing all the configuration parameters the class is currently set to.

Parameters

  • filepath: The location to store the parameters file.

Note: Calling this method with no arguments will result in storing class parameters to a file names "qds_parameters.yaml" in the root project folder.

Note: This method can be used to generate a template file for tuning the class.

See also

loadParameters

pub fn get_sparse_matches(
    &mut self,
    s_matches: &mut Vector<Match>
) -> Result<()>[src]

Get The sparse corresponding points.

Parameters

  • sMatches:[out] A vector containing all sparse correspondences.

Note: The method clears the sMatches vector.

Note: The returned Match elements inside the sMatches vector, do not use corr member.

pub fn get_dense_matches(
    &mut self,
    dense_matches: &mut Vector<Match>
) -> Result<()>[src]

Get The dense corresponding points.

Parameters

  • denseMatches:[out] A vector containing all dense matches.

Note: The method clears the denseMatches vector.

Note: The returned Match elements inside the sMatches vector, do not use corr member.

pub fn process(&mut self, img_left: &Mat, img_right: &Mat) -> Result<()>[src]

Main process of the algorithm. This method computes the sparse seeds and then densifies them.

Initially input images are converted to gray-scale and then the sparseMatching method is called to obtain the sparse stereo. Finally quasiDenseMatching is called to densify the corresponding points.

Parameters

  • imgLeft: The left Channel of a stereo image pair.
  • imgRight: The right Channel of a stereo image pair.

Note: If input images are in color, the method assumes that are BGR and converts them to grayscale.

See also

sparseMatching quasiDenseMatching

pub fn get_match(&mut self, x: i32, y: i32) -> Result<Point2f>[src]

Specify pixel coordinates in the left image and get its corresponding location in the right image.

Parameters

  • x: The x pixel coordinate in the left image channel.
  • y: The y pixel coordinate in the left image channel. @retval cv::Point(x, y) The location of the corresponding pixel in the right image. @retval cv::Point(0, 0) (NO_MATCH) if no match is found in the right image for the specified pixel location in the left image.

Note: This method should be always called after process, otherwise the matches will not be correct.

pub fn get_disparity(&mut self, disparity_lvls: u8) -> Result<Mat>[src]

Compute and return the disparity map based on the correspondences found in the "process" method.

Parameters

  • disparityLvls: The level of detail in output disparity image.

Note: Default level is 50

Returns

cv::Mat containing a the disparity image in grayscale.

See also

computeDisparity quantizeDisparity

C++ default parameters

  • disparity_lvls: 50
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Implementations

impl<'_> dyn QuasiDenseStereo + '_[src]

pub fn create(
    mono_img_size: Size,
    param_filepath: &str
) -> Result<Ptr<dyn QuasiDenseStereo>>[src]

C++ default parameters

  • param_filepath: cv::String()

Implementors

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