ov_core::Grider_DOG class

Does Difference of Gaussian (DoG) in a grid pattern.

Reference
- Public static functions

This does "Difference of Gaussian" detection in a grid pattern to try to get good features. We then pick the top features in each grid, and return the top features collected over the entire image. This class hasn't been tested that much, as we normally use the Grider_FAST class instead.

Public static functions

static auto compare_response(cv::KeyPoint first, cv::KeyPoint second) -> bool: Compare keypoints based on their response value.
static void detect(const cv::Mat& img, std::vector<cv::KeyPoint>& pts, int ksize, float sigma_small, float sigma_big, float threshold): For a given small image region this will do the Difference of Gaussian (DOG) detection.
static void perform_griding(const cv::Mat& img, std::vector<cv::KeyPoint>& pts, int num_features, int grid_x, int grid_y, int ksize, float sigma_small, float sigma_big, float threshold): This function will perform grid extraction using Difference of Gaussian (DOG)

Function documentation

static bool ov_core::Grider_DOG::compare_response(cv::KeyPoint first, cv::KeyPoint second)

Compare keypoints based on their response value.

Parameters
first	First keypoint
second	Second keypoint

We want to have the keypoints with the highest values! See: https://stackoverflow.com/a/10910921

static void ov_core::Grider_DOG::detect(const cv::Mat& img, std::vector<cv::KeyPoint>& pts, int ksize, float sigma_small, float sigma_big, float threshold)

For a given small image region this will do the Difference of Gaussian (DOG) detection.

Will return the vector of keypoints with the averaged response for that given UV. See: https://github.com/jrdi/opencv-examples/blob/master/dog/main.cpp

static void ov_core::Grider_DOG::perform_griding(const cv::Mat& img, std::vector<cv::KeyPoint>& pts, int num_features, int grid_x, int grid_y, int ksize, float sigma_small, float sigma_big, float threshold)

This function will perform grid extraction using Difference of Gaussian (DOG)

Parameters
img	Image we will do FAST extraction on
pts	vector of extracted points we will return
num_features	max number of features we want to extract
grid_x	size of grid in the x-direction / u-direction
grid_y	size of grid in the y-direction / v-direction
ksize	kernel size
sigma_small	small gaussian sigma
sigma_big	big gaussian sigma
threshold	response threshold