Class cv::RgbdNormals#

#include <opencv2/ptcloud/depth.hpp>

Collaboration diagram for cv::RgbdNormals:

Public Types#

enum RgbdNormalsMethod {
    RGBD_NORMALS_METHOD_FALS = 0,
    RGBD_NORMALS_METHOD_LINEMOD = 1,
    RGBD_NORMALS_METHOD_SRI = 2,
    RGBD_NORMALS_METHOD_CROSS_PRODUCT = 3
}

Detailed Description#

Object that can compute the normals in an image. It is an object as it can cache data for speed efficiency The implemented methods are either:

FALS (the fastest) and SRI from Fast and Accurate Computation of Surface Normals from Range Images by H. Badino, D. Huber, Y. Park and T. Kanade
the normals with bilateral filtering on a depth image from Gradient Response Maps for Real-Time Detection of Texture-Less Objects by S. Hinterstoisser, C. Cagniart, S. Ilic, P. Sturm, N. Navab, P. Fua, and V. Lepetit

Member Enumeration Documentation#

enum RgbdNormalsMethod

RGBD_NORMALS_METHOD_FALS
Python: cv.RgbdNormals_RGBD_NORMALS_METHOD_FALS

RGBD_NORMALS_METHOD_LINEMOD
Python: cv.RgbdNormals_RGBD_NORMALS_METHOD_LINEMOD

RGBD_NORMALS_METHOD_SRI
Python: cv.RgbdNormals_RGBD_NORMALS_METHOD_SRI

RGBD_NORMALS_METHOD_CROSS_PRODUCT
Python: cv.RgbdNormals_RGBD_NORMALS_METHOD_CROSS_PRODUCT

Constructor & Destructor Documentation#

RgbdNormals()#

cv::RgbdNormals::RgbdNormals()

~RgbdNormals()#

cv::RgbdNormals::~RgbdNormals()

Member Function Documentation#

apply()#

void cv::RgbdNormals::apply(
InputArray points,
OutputArray normals )

Python:

cv.RgbdNormals.apply(points[, normals]) -> normals

Given a set of 3d points in a depth image, compute the normals at each point.

Parameters

points — a rows x cols x 3 matrix of CV_32F/CV64F or a rows x cols x 1 CV_U16S
normals — a rows x cols x 3 matrix

cache()#

void cv::RgbdNormals::cache()

Python:

cv.RgbdNormals.cache()

Prepares cached data required for calculation If not called by user, called automatically at first calculation

getCols()#

int cv::RgbdNormals::getCols()

Python:

cv.RgbdNormals.getCols() -> retval

getDepth()#

int cv::RgbdNormals::getDepth()

Python:

cv.RgbdNormals.getDepth() -> retval

getK()#

void cv::RgbdNormals::getK(OutputArray val)

Python:

cv.RgbdNormals.getK([, val]) -> val

getMethod()#

RgbdNormals::RgbdNormalsMethod cv::RgbdNormals::getMethod()

Python:

cv.RgbdNormals.getMethod() -> retval

getRows()#

int cv::RgbdNormals::getRows()

Python:

cv.RgbdNormals.getRows() -> retval

getWindowSize()#

int cv::RgbdNormals::getWindowSize()

Python:

cv.RgbdNormals.getWindowSize() -> retval

setCols()#

void cv::RgbdNormals::setCols(int val)

Python:

cv.RgbdNormals.setCols(val)

setK()#

void cv::RgbdNormals::setK(InputArray val)

Python:

cv.RgbdNormals.setK(val)

setRows()#

void cv::RgbdNormals::setRows(int val)

Python:

cv.RgbdNormals.setRows(val)

setWindowSize()#

void cv::RgbdNormals::setWindowSize(int val)

Python:

cv.RgbdNormals.setWindowSize(val)

create()#

static Ptr< RgbdNormals > cv::RgbdNormals::create(
int rows = 0,
int cols = 0,
int depth = 0,
InputArray K = noArray(),
int window_size = 5,
float diff_threshold = 50.f,
RgbdNormals::RgbdNormalsMethod method = RgbdNormals::RgbdNormalsMethod::RGBD_NORMALS_METHOD_FALS )

Python:

cv.RgbdNormals.create([, rows[, cols[, depth[, K[, window_size[, diff_threshold[, method]]]]]]]) -> retval

cv.RgbdNormals_create([, rows[, cols[, depth[, K[, window_size[, diff_threshold[, method]]]]]]]) -> retval

Creates new RgbdNormals object

Parameters

rows — the number of rows of the depth image normals will be computed on
cols — the number of cols of the depth image normals will be computed on
depth — the depth of the normals (only CV_32F or CV_64F)
K — the calibration matrix to use
window_size — the window size to compute the normals: can only be 1,3,5 or 7
diff_threshold — threshold in depth difference, used in LINEMOD algirithm
method — one of the methods to use: RGBD_NORMALS_METHOD_SRI, RGBD_NORMALS_METHOD_FALS

Here is the call graph for this function:

Source file#

The documentation for this class was generated from the following file:

opencv2/ptcloud/depth.hpp