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Merge pull request #29230 from asmorkalov:as/move_undistort

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Inverted imgproc-geometry dependency and moved more functions to geometry #29230

Fixes: https://github.com/opencv/opencv/issues/20267
Continues: https://github.com/opencv/opencv/pull/29175

OpenCV Contrib: https://github.com/opencv/opencv_contrib/pull/4137
OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1377

Summary:
- LSD returned back to imgproc
- drawing functions moved to imgproc
- undistort image and related perf-pixel functions moved to imgproc
- moments moved to geometry
- estimateXXXtransform moved to geometry

After the patch the geometry module depends on code and Flann and may be used everywhere without potential circular dependencies

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
This commit is contained in:
Alexander Smorkalov
2026-06-05 10:13:45 +03:00
committed by GitHub
parent ada32973b4
commit 40ddc700aa
58 changed files with 2021 additions and 1612 deletions
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modules/geometry/include/opencv2/geometry/2d.hpp
+148 -90
View File
@@ -20,12 +20,17 @@ enum RectanglesIntersectTypes {
INTERSECT_FULL = 2 //!< One of the rectangle is fully enclosed in the other
};
//! Variants of Line Segment %Detector
enum LineSegmentDetectorModes {
LSD_REFINE_NONE = 0, //!< No refinement applied
LSD_REFINE_STD = 1, //!< Standard refinement is applied. E.g. breaking arches into smaller straighter line approximations.
LSD_REFINE_ADV = 2 //!< Advanced refinement. Number of false alarms is calculated, lines are
//!< refined through increase of precision, decrement in size, etc.
//! Distance types for Distance Transform and M-estimators
//! @see distanceTransform, fitLine
enum DistanceTypes {
DIST_USER = -1, //!< User defined distance
DIST_L1 = 1, //!< distance = |x1-x2| + |y1-y2|
DIST_L2 = 2, //!< the simple euclidean distance
DIST_C = 3, //!< distance = max(|x1-x2|,|y1-y2|)
DIST_L12 = 4, //!< L1-L2 metric: distance = 2(sqrt(1+x*x/2) - 1))
DIST_FAIR = 5, //!< distance = c^2(|x|/c-log(1+|x|/c)), c = 1.3998
DIST_WELSCH = 6, //!< distance = c^2/2(1-exp(-(x/c)^2)), c = 2.9846
DIST_HUBER = 7 //!< distance = |x|<c ? x^2/2 : c(|x|-c/2), c=1.345
};
//! @addtogroup geometry_subdiv2d
@@ -306,90 +311,6 @@ protected:
//! @} geometry_subdiv2d
//! @addtogroup geometry_feature
//! @{
/** @example samples/cpp/snippets/lsd_lines.cpp
An example using the LineSegmentDetector
\image html building_lsd.png "Sample output image" width=434 height=300
*/
/** @brief Line segment detector class
following the algorithm described at @cite Rafael12 .
@note Implementation has been removed from OpenCV version 3.4.6 to 3.4.15 and version 4.1.0 to 4.5.3 due original code license conflict.
restored again after [Computation of a NFA](https://github.com/rafael-grompone-von-gioi/binomial_nfa) code published under the MIT license.
*/
class CV_EXPORTS_W LineSegmentDetector : public Algorithm
{
public:
/** @brief Finds lines in the input image.
This is the output of the default parameters of the algorithm on the above shown image.
![image](pics/building_lsd.png)
@param image A grayscale (CV_8UC1) input image. If only a roi needs to be selected, use:
`lsd_ptr-\>detect(image(roi), lines, ...); lines += Scalar(roi.x, roi.y, roi.x, roi.y);`
@param lines A vector of Vec4f elements specifying the beginning and ending point of a line. Where
Vec4f is (x1, y1, x2, y2), point 1 is the start, point 2 - end. Returned lines are strictly
oriented depending on the gradient.
@param width Vector of widths of the regions, where the lines are found. E.g. Width of line.
@param prec Vector of precisions with which the lines are found.
@param nfa Vector containing number of false alarms in the line region, with precision of 10%. The
bigger the value, logarithmically better the detection.
- -1 corresponds to 10 mean false alarms
- 0 corresponds to 1 mean false alarm
- 1 corresponds to 0.1 mean false alarms
This vector will be calculated only when the objects type is #LSD_REFINE_ADV.
*/
CV_WRAP virtual void detect(InputArray image, OutputArray lines,
OutputArray width = noArray(), OutputArray prec = noArray(),
OutputArray nfa = noArray()) = 0;
/** @brief Draws the line segments on a given image.
@param image The image, where the lines will be drawn. Should be bigger or equal to the image,
where the lines were found.
@param lines A vector of the lines that needed to be drawn.
*/
CV_WRAP virtual void drawSegments(InputOutputArray image, InputArray lines) = 0;
/** @brief Draws two groups of lines in blue and red, counting the non overlapping (mismatching) pixels.
@param size The size of the image, where lines1 and lines2 were found.
@param lines1 The first group of lines that needs to be drawn. It is visualized in blue color.
@param lines2 The second group of lines. They visualized in red color.
@param image Optional image, where the lines will be drawn. The image should be color(3-channel)
in order for lines1 and lines2 to be drawn in the above mentioned colors.
*/
CV_WRAP virtual int compareSegments(const Size& size, InputArray lines1, InputArray lines2, InputOutputArray image = noArray()) = 0;
virtual ~LineSegmentDetector() { }
};
/** @brief Creates a smart pointer to a LineSegmentDetector object and initializes it.
The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want
to edit those, as to tailor it for their own application.
@param refine The way found lines will be refined, see #LineSegmentDetectorModes
@param scale The scale of the image that will be used to find the lines. Range (0..1].
@param sigma_scale Sigma for Gaussian filter. It is computed as sigma = sigma_scale/scale.
@param quant Bound to the quantization error on the gradient norm.
@param ang_th Gradient angle tolerance in degrees.
@param log_eps Detection threshold: -log10(NFA) \> log_eps. Used only when advance refinement is chosen.
@param density_th Minimal density of aligned region points in the enclosing rectangle.
@param n_bins Number of bins in pseudo-ordering of gradient modulus.
*/
CV_EXPORTS_W Ptr<LineSegmentDetector> createLineSegmentDetector(
LineSegmentDetectorModes refine = LSD_REFINE_STD, double scale = 0.8,
double sigma_scale = 0.6, double quant = 2.0, double ang_th = 22.5,
double log_eps = 0, double density_th = 0.7, int n_bins = 1024);
//! @} geometry_feature
/** @example samples/python/snippets/squares.py
A n example using approxPolyDP function in python. *
*/
@@ -517,6 +438,59 @@ CV_EXPORTS_W double minEnclosingTriangle( InputArray points, CV_OUT OutputArray
CV_EXPORTS_W double minEnclosingConvexPolygon ( InputArray points, OutputArray polygon, int k );
/** @brief Calculates all of the moments up to the third order of a polygon or rasterized shape.
*
* The function computes moments, up to the 3rd order, of a vector shape or a rasterized shape. The
* results are returned in the structure cv::Moments.
*
* @param array Single channel raster image (CV_8U, CV_16U, CV_16S, CV_32F, CV_64F) or an array (
* \f$1 \times N\f$ or \f$N \times 1\f$ ) of 2D points (Point or Point2f).
* @param binaryImage If it is true, all non-zero image pixels are treated as 1's. The parameter is
* used for images only.
* @returns moments.
*
* @note Only applicable to contour moments calculations from Python bindings: Note that the numpy
* type for the input array should be either np.int32 or np.float32.
*
* @note For contour-based moments, the zeroth-order moment \c m00 represents
* the contour area.
*
* If the input contour is degenerate (for example, a single point or all points
* are collinear), the area is zero and therefore \c m00 == 0.
*
* In this case, the centroid coordinates (\c m10/m00, \c m01/m00) are undefined
* and must be handled explicitly by the caller.
*
* A common workaround is to compute the center using cv::boundingRect() or by
* averaging the input points.
*
* @sa contourArea, arcLength
*/
CV_EXPORTS_W Moments moments( InputArray array, bool binaryImage = false );
/** @brief Calculates seven Hu invariants.
*
* The function calculates seven Hu invariants (introduced in @cite Hu62; see also
* <https://en.wikipedia.org/wiki/Image_moment>) defined as:
*
* \f[\begin{array}{l} hu[0]= \eta _{20}+ \eta _{02} \\ hu[1]=( \eta _{20}- \eta _{02})^{2}+4 \eta _{11}^{2} \\ hu[2]=( \eta _{30}-3 \eta _{12})^{2}+ (3 \eta _{21}- \eta _{03})^{2} \\ hu[3]=( \eta _{30}+ \eta _{12})^{2}+ ( \eta _{21}+ \eta _{03})^{2} \\ hu[4]=( \eta _{30}-3 \eta _{12})( \eta _{30}+ \eta _{12})[( \eta _{30}+ \eta _{12})^{2}-3( \eta _{21}+ \eta _{03})^{2}]+(3 \eta _{21}- \eta _{03})( \eta _{21}+ \eta _{03})[3( \eta _{30}+ \eta _{12})^{2}-( \eta _{21}+ \eta _{03})^{2}] \\ hu[5]=( \eta _{20}- \eta _{02})[( \eta _{30}+ \eta _{12})^{2}- ( \eta _{21}+ \eta _{03})^{2}]+4 \eta _{11}( \eta _{30}+ \eta _{12})( \eta _{21}+ \eta _{03}) \\ hu[6]=(3 \eta _{21}- \eta _{03})( \eta _{21}+ \eta _{03})[3( \eta _{30}+ \eta _{12})^{2}-( \eta _{21}+ \eta _{03})^{2}]-( \eta _{30}-3 \eta _{12})( \eta _{21}+ \eta _{03})[3( \eta _{30}+ \eta _{12})^{2}-( \eta _{21}+ \eta _{03})^{2}] \\ \end{array}\f]
*
* where \f$\eta_{ji}\f$ stands for \f$\texttt{Moments::nu}_{ji}\f$ .
*
* These values are proved to be invariants to the image scale, rotation, and reflection except the
* seventh one, whose sign is changed by reflection. This invariance is proved with the assumption of
* infinite image resolution. In case of raster images, the computed Hu invariants for the original and
* transformed images are a bit different.
*
* @param moments Input moments computed with moments .
* @param hu Output Hu invariants.
*
* @sa matchShapes
*/
CV_EXPORTS void HuMoments( const Moments& moments, double hu[7] );
/** @overload */
CV_EXPORTS_W void HuMoments( const Moments& m, OutputArray hu );
/** @brief Compares two shapes.
*
@@ -856,6 +830,90 @@ CV_EXPORTS_W double contourArea( InputArray contour, bool oriented = false );
*/
CV_EXPORTS_W Rect boundingRect( InputArray array );
/** @brief Calculates an affine matrix of 2D rotation.
The function calculates the following matrix:
\f[\begin{bmatrix} \alpha & \beta & (1- \alpha ) \cdot \texttt{center.x} - \beta \cdot \texttt{center.y} \\ - \beta & \alpha & \beta \cdot \texttt{center.x} + (1- \alpha ) \cdot \texttt{center.y} \end{bmatrix}\f]
where
\f[\begin{array}{l} \alpha = \texttt{scale} \cdot \cos \texttt{angle} , \\ \beta = \texttt{scale} \cdot \sin \texttt{angle} \end{array}\f]
The transformation maps the rotation center to itself. If this is not the target, adjust the shift.
@param center Center of the rotation in the source image.
@param angle Rotation angle in degrees. Positive values mean counter-clockwise rotation (the
coordinate origin is assumed to be the top-left corner).
@param scale Isotropic scale factor.
@sa getAffineTransform, warpAffine, transform
*/
CV_EXPORTS_W Mat getRotationMatrix2D(Point2f center, double angle, double scale);
/** @sa getRotationMatrix2D */
CV_EXPORTS Matx23d getRotationMatrix2D_(Point2f center, double angle, double scale);
inline
Mat getRotationMatrix2D(Point2f center, double angle, double scale)
{
return Mat(getRotationMatrix2D_(center, angle, scale), true);
}
/** @brief Calculates an affine transform from three pairs of the corresponding points.
*
* The function calculates the \f$2 \times 3\f$ matrix of an affine transform so that:
*
* \f[\begin{bmatrix} x'_i \\ y'_i \end{bmatrix} = \texttt{map_matrix} \cdot \begin{bmatrix} x_i \\ y_i \\ 1 \end{bmatrix}\f]
*
* where
*
* \f[dst(i)=(x'_i,y'_i), src(i)=(x_i, y_i), i=0,1,2\f]
*
* @param src Coordinates of triangle vertices in the source image.
* @param dst Coordinates of the corresponding triangle vertices in the destination image.
*
* @sa warpAffine, transform
*/
CV_EXPORTS Mat getAffineTransform( const Point2f src[], const Point2f dst[] );
/** @brief Inverts an affine transformation.
*
* The function computes an inverse affine transformation represented by \f$2 \times 3\f$ matrix M:
*
* \f[\begin{bmatrix} a_{11} & a_{12} & b_1 \\ a_{21} & a_{22} & b_2 \end{bmatrix}\f]
*
* The result is also a \f$2 \times 3\f$ matrix of the same type as M.
*
* @param M Original affine transformation.
* @param iM Output reverse affine transformation.
*/
CV_EXPORTS_W void invertAffineTransform( InputArray M, OutputArray iM );
/** @brief Calculates a perspective transform from four pairs of the corresponding points.
*
* The function calculates the \f$3 \times 3\f$ matrix of a perspective transform so that:
*
* \f[\begin{bmatrix} t_i x'_i \\ t_i y'_i \\ t_i \end{bmatrix} = \texttt{map_matrix} \cdot \begin{bmatrix} x_i \\ y_i \\ 1 \end{bmatrix}\f]
*
* where
*
* \f[dst(i)=(x'_i,y'_i), src(i)=(x_i, y_i), i=0,1,2,3\f]
*
* @param src Coordinates of quadrangle vertices in the source image.
* @param dst Coordinates of the corresponding quadrangle vertices in the destination image.
* @param solveMethod method passed to cv::solve (#DecompTypes)
*
* @sa findHomography, warpPerspective, perspectiveTransform
*/
CV_EXPORTS_W Mat getPerspectiveTransform(InputArray src, InputArray dst, int solveMethod = DECOMP_LU);
/** @overload */
CV_EXPORTS Mat getPerspectiveTransform(const Point2f src[], const Point2f dst[], int solveMethod = DECOMP_LU);
CV_EXPORTS_W Mat getAffineTransform( InputArray src, InputArray dst );
} // namespace cv
#endif // OPENCV_2D_HPP
modules/geometry/include/opencv2/geometry/3d.hpp
-263
View File
@@ -1280,25 +1280,6 @@ CV_EXPORTS_W int solvePnPGeneric( InputArray objectPoints, InputArray imagePoint
InputArray rvec = noArray(), InputArray tvec = noArray(),
OutputArray reprojectionError = noArray() );
/** @brief Draw axes of the world/object coordinate system from pose estimation. @sa solvePnP
@param image Input/output image. It must have 1 or 3 channels. The number of channels is not altered.
@param cameraMatrix Input 3x3 floating-point matrix of camera intrinsic parameters.
\f$\cameramatrix{A}\f$
@param distCoeffs Input vector of distortion coefficients
\f$\distcoeffs\f$. If the vector is empty, the zero distortion coefficients are assumed.
@param rvec Rotation vector (see @ref Rodrigues ) that, together with tvec, brings points from
the model coordinate system to the camera coordinate system.
@param tvec Translation vector.
@param length Length of the painted axes in the same unit than tvec (usually in meters).
@param thickness Line thickness of the painted axes.
This function draws the axes of the world/object coordinate system w.r.t. to the camera frame.
OX is drawn in red, OY in green and OZ in blue.
*/
CV_EXPORTS_W void drawFrameAxes(InputOutputArray image, InputArray cameraMatrix, InputArray distCoeffs,
InputArray rvec, InputArray tvec, float length, int thickness=3);
/** @brief Converts points from Euclidean to homogeneous space.
@param src Input vector of N-dimensional points.
@@ -2216,201 +2197,6 @@ CV_EXPORTS_W void filterHomographyDecompByVisibleRefpoints(InputArrayOfArrays ro
OutputArray possibleSolutions,
InputArray pointsMask = noArray());
//! cv::undistort mode
enum UndistortTypes
{
PROJ_SPHERICAL_ORTHO = 0,
PROJ_SPHERICAL_EQRECT = 1
};
/** @brief Transforms an image to compensate for lens distortion.
The function transforms an image to compensate radial and tangential lens distortion.
The function is simply a combination of #initUndistortRectifyMap (with unity R ) and #remap
(with bilinear interpolation). See the former function for details of the transformation being
performed.
Those pixels in the destination image, for which there is no correspondent pixels in the source
image, are filled with zeros (black color).
A particular subset of the source image that will be visible in the corrected image can be regulated
by newCameraMatrix. You can use #getOptimalNewCameraMatrix to compute the appropriate
newCameraMatrix depending on your requirements.
The camera matrix and the distortion parameters can be determined using #calibrateCamera. If
the resolution of images is different from the resolution used at the calibration stage, \f$f_x,
f_y, c_x\f$ and \f$c_y\f$ need to be scaled accordingly, while the distortion coefficients remain
the same.
@param src Input (distorted) image.
@param dst Output (corrected) image that has the same size and type as src .
@param cameraMatrix Input camera matrix \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ .
@param distCoeffs Input vector of distortion coefficients
\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$
of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
@param newCameraMatrix Camera matrix of the distorted image. By default, it is the same as
cameraMatrix but you may additionally scale and shift the result by using a different matrix.
*/
CV_EXPORTS_W void undistort( InputArray src, OutputArray dst,
InputArray cameraMatrix,
InputArray distCoeffs,
InputArray newCameraMatrix = noArray() );
/** @brief Computes the undistortion and rectification transformation map.
The function computes the joint undistortion and rectification transformation and represents the
result in the form of maps for #remap. The undistorted image looks like original, as if it is
captured with a camera using the camera matrix =newCameraMatrix and zero distortion. In case of a
monocular camera, newCameraMatrix is usually equal to cameraMatrix, or it can be computed by
#getOptimalNewCameraMatrix for a better control over scaling. In case of a stereo camera,
newCameraMatrix is normally set to P1 or P2 computed by #stereoRectify .
Also, this new camera is oriented differently in the coordinate space, according to R. That, for
example, helps to align two heads of a stereo camera so that the epipolar lines on both images
become horizontal and have the same y- coordinate (in case of a horizontally aligned stereo camera).
The function actually builds the maps for the inverse mapping algorithm that is used by #remap. That
is, for each pixel \f$(u, v)\f$ in the destination (corrected and rectified) image, the function
computes the corresponding coordinates in the source image (that is, in the original image from
camera). The following process is applied:
\f[
\begin{array}{l}
x \leftarrow (u - {c'}_x)/{f'}_x \\
y \leftarrow (v - {c'}_y)/{f'}_y \\
{[X\,Y\,W]} ^T \leftarrow R^{-1}*[x \, y \, 1]^T \\
x' \leftarrow X/W \\
y' \leftarrow Y/W \\
r^2 \leftarrow x'^2 + y'^2 \\
x'' \leftarrow x' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6}
+ 2p_1 x' y' + p_2(r^2 + 2 x'^2) + s_1 r^2 + s_2 r^4\\
y'' \leftarrow y' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6}
+ p_1 (r^2 + 2 y'^2) + 2 p_2 x' y' + s_3 r^2 + s_4 r^4 \\
s\vecthree{x'''}{y'''}{1} =
\vecthreethree{R_{33}(\tau_x, \tau_y)}{0}{-R_{13}((\tau_x, \tau_y)}
{0}{R_{33}(\tau_x, \tau_y)}{-R_{23}(\tau_x, \tau_y)}
{0}{0}{1} R(\tau_x, \tau_y) \vecthree{x''}{y''}{1}\\
map_x(u,v) \leftarrow x''' f_x + c_x \\
map_y(u,v) \leftarrow y''' f_y + c_y
\end{array}
\f]
where \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$
are the distortion coefficients.
In case of a stereo camera, this function is called twice: once for each camera head, after
#stereoRectify, which in its turn is called after #stereoCalibrate. But if the stereo camera
was not calibrated, it is still possible to compute the rectification transformations directly from
the fundamental matrix using #stereoRectifyUncalibrated. For each camera, the function computes
homography H as the rectification transformation in a pixel domain, not a rotation matrix R in 3D
space. R can be computed from H as
\f[\texttt{R} = \texttt{cameraMatrix} ^{-1} \cdot \texttt{H} \cdot \texttt{cameraMatrix}\f]
where cameraMatrix can be chosen arbitrarily.
@param cameraMatrix Input camera matrix \f$A=\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ .
@param distCoeffs Input vector of distortion coefficients
\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$
of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
@param R Optional rectification transformation in the object space (3x3 matrix). R1 or R2 ,
computed by #stereoRectify can be passed here. If the matrix is empty, the identity transformation
is assumed. In #initUndistortRectifyMap R assumed to be an identity matrix.
@param newCameraMatrix New camera matrix \f$A'=\vecthreethree{f_x'}{0}{c_x'}{0}{f_y'}{c_y'}{0}{0}{1}\f$.
@param size Undistorted image size.
@param m1type Type of the first output map that can be CV_32FC1, CV_32FC2 or CV_16SC2, see #convertMaps
@param map1 The first output map.
@param map2 The second output map.
*/
CV_EXPORTS_W
void initUndistortRectifyMap(InputArray cameraMatrix, InputArray distCoeffs,
InputArray R, InputArray newCameraMatrix,
Size size, int m1type, OutputArray map1, OutputArray map2);
/** @brief Computes the projection and inverse-rectification transformation map. In essense, this is the inverse of
#initUndistortRectifyMap to accomodate stereo-rectification of projectors ('inverse-cameras') in projector-camera pairs.
The function computes the joint projection and inverse rectification transformation and represents the
result in the form of maps for #remap. The projected image looks like a distorted version of the original which,
once projected by a projector, should visually match the original. In case of a monocular camera, newCameraMatrix
is usually equal to cameraMatrix, or it can be computed by
#getOptimalNewCameraMatrix for a better control over scaling. In case of a projector-camera pair,
newCameraMatrix is normally set to P1 or P2 computed by #stereoRectify .
The projector is oriented differently in the coordinate space, according to R. In case of projector-camera pairs,
this helps align the projector (in the same manner as #initUndistortRectifyMap for the camera) to create a stereo-rectified pair. This
allows epipolar lines on both images to become horizontal and have the same y-coordinate (in case of a horizontally aligned projector-camera pair).
The function builds the maps for the inverse mapping algorithm that is used by #remap. That
is, for each pixel \f$(u, v)\f$ in the destination (projected and inverse-rectified) image, the function
computes the corresponding coordinates in the source image (that is, in the original digital image). The following process is applied:
\f[
\begin{array}{l}
\text{newCameraMatrix}\\
x \leftarrow (u - {c'}_x)/{f'}_x \\
y \leftarrow (v - {c'}_y)/{f'}_y \\
\\\text{Undistortion}
\\\scriptsize{\textit{though equation shown is for radial undistortion, function implements cv::undistortPoints()}}\\
r^2 \leftarrow x^2 + y^2 \\
\theta \leftarrow \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6}\\
x' \leftarrow \frac{x}{\theta} \\
y' \leftarrow \frac{y}{\theta} \\
\\\text{Rectification}\\
{[X\,Y\,W]} ^T \leftarrow R*[x' \, y' \, 1]^T \\
x'' \leftarrow X/W \\
y'' \leftarrow Y/W \\
\\\text{cameraMatrix}\\
map_x(u,v) \leftarrow x'' f_x + c_x \\
map_y(u,v) \leftarrow y'' f_y + c_y
\end{array}
\f]
where \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$
are the distortion coefficients vector distCoeffs.
In case of a stereo-rectified projector-camera pair, this function is called for the projector while #initUndistortRectifyMap is called for the camera head.
This is done after #stereoRectify, which in turn is called after #stereoCalibrate. If the projector-camera pair
is not calibrated, it is still possible to compute the rectification transformations directly from
the fundamental matrix using #stereoRectifyUncalibrated. For the projector and camera, the function computes
homography H as the rectification transformation in a pixel domain, not a rotation matrix R in 3D
space. R can be computed from H as
\f[\texttt{R} = \texttt{cameraMatrix} ^{-1} \cdot \texttt{H} \cdot \texttt{cameraMatrix}\f]
where cameraMatrix can be chosen arbitrarily.
@param cameraMatrix Input camera matrix \f$A=\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ .
@param distCoeffs Input vector of distortion coefficients
\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$
of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
@param R Optional rectification transformation in the object space (3x3 matrix). R1 or R2,
computed by #stereoRectify can be passed here. If the matrix is empty, the identity transformation
is assumed.
@param newCameraMatrix New camera matrix \f$A'=\vecthreethree{f_x'}{0}{c_x'}{0}{f_y'}{c_y'}{0}{0}{1}\f$.
@param size Distorted image size.
@param m1type Type of the first output map. Can be CV_32FC1, CV_32FC2 or CV_16SC2, see #convertMaps
@param map1 The first output map for #remap.
@param map2 The second output map for #remap.
*/
CV_EXPORTS_W
void initInverseRectificationMap( InputArray cameraMatrix, InputArray distCoeffs,
InputArray R, InputArray newCameraMatrix,
const Size& size, int m1type, OutputArray map1, OutputArray map2 );
//! initializes maps for #remap for wide-angle
CV_EXPORTS
float initWideAngleProjMap(InputArray cameraMatrix, InputArray distCoeffs,
Size imageSize, int destImageWidth,
int m1type, OutputArray map1, OutputArray map2,
enum UndistortTypes projType = PROJ_SPHERICAL_EQRECT, double alpha = 0);
static inline
float initWideAngleProjMap(InputArray cameraMatrix, InputArray distCoeffs,
Size imageSize, int destImageWidth,
int m1type, OutputArray map1, OutputArray map2,
int projType, double alpha = 0)
{
return initWideAngleProjMap(cameraMatrix, distCoeffs, imageSize, destImageWidth,
m1type, map1, map2, (UndistortTypes)projType, alpha);
}
/** @brief Computes useful camera characteristics from the camera intrinsic matrix.
*
* @param cameraMatrix Input camera intrinsic matrix that can be estimated by #calibrateCamera or
@@ -2701,55 +2487,6 @@ length. Balance is in range of [0, 1].
*/
CV_EXPORTS_W void estimateNewCameraMatrixForUndistortRectify(InputArray K, InputArray D, const Size &image_size, InputArray R,
OutputArray P, double balance = 0.0, const Size& new_size = Size(), double fov_scale = 1.0);
/** @brief Computes undistortion and rectification maps for image transform by cv::remap(). If D is empty zero
distortion is used, if R or P is empty identity matrixes are used.
@param K Camera intrinsic matrix \f$cameramatrix{K}\f$.
@param D Input vector of distortion coefficients \f$\distcoeffsfisheye\f$.
@param R Rectification transformation in the object space: 3x3 1-channel, or vector: 3x1/1x3
1-channel or 1x1 3-channel
@param P New camera intrinsic matrix (3x3) or new projection matrix (3x4)
@param size Undistorted image size.
@param m1type Type of the first output map that can be CV_32FC1 or CV_16SC2 . See convertMaps()
for details.
@param map1 The first output map.
@param map2 The second output map.
*/
CV_EXPORTS_W void initUndistortRectifyMap(InputArray K, InputArray D, InputArray R, InputArray P,
const cv::Size& size, int m1type, OutputArray map1, OutputArray map2);
/** @brief Transforms an image to compensate for fisheye lens distortion.
@param distorted image with fisheye lens distortion.
@param undistorted Output image with compensated fisheye lens distortion.
@param K Camera intrinsic matrix \f$cameramatrix{K}\f$.
@param D Input vector of distortion coefficients \f$\distcoeffsfisheye\f$.
@param Knew Camera intrinsic matrix of the distorted image. By default, it is the identity matrix but you
may additionally scale and shift the result by using a different matrix.
@param new_size the new size
The function transforms an image to compensate radial and tangential lens distortion.
The function is simply a combination of #cv::fisheye::initUndistortRectifyMap (with unity R ) and remap
(with bilinear interpolation). See the former function for details of the transformation being
performed.
See below the results of undistortImage.
- a\) result of undistort of perspective camera model (all possible coefficients (k_1, k_2, k_3,
k_4, k_5, k_6) of distortion were optimized under calibration)
- b\) result of #cv::fisheye::undistortImage of fisheye camera model (all possible coefficients (k_1, k_2,
k_3, k_4) of fisheye distortion were optimized under calibration)
- c\) original image was captured with fisheye lens
Pictures a) and b) almost the same. But if we consider points of image located far from the center
of image, we can notice that on image a) these points are distorted.
![image](pics/fisheye_undistorted.jpg)
*/
CV_EXPORTS_W void undistortImage(InputArray distorted, OutputArray undistorted,
InputArray K, InputArray D, InputArray Knew = cv::noArray(), const Size& new_size = Size());
/**
@brief Finds an object pose from 3D-2D point correspondences for fisheye camera model.
modules/geometry/include/opencv2/geometry/detail/gcgraph.hpp
+395
View File
@@ -0,0 +1,395 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
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//
//M*/
#ifndef OPENCV_IMGPROC_DETAIL_GCGRAPH_HPP
#define OPENCV_IMGPROC_DETAIL_GCGRAPH_HPP
//! @cond IGNORED
namespace cv { namespace detail {
template <class TWeight> class GCGraph
{
public:
GCGraph();
GCGraph( unsigned int vtxCount, unsigned int edgeCount );
~GCGraph();
void create( unsigned int vtxCount, unsigned int edgeCount );
int addVtx();
void addEdges( int i, int j, TWeight w, TWeight revw );
void addTermWeights( int i, TWeight sourceW, TWeight sinkW );
TWeight maxFlow();
bool inSourceSegment( int i );
private:
class Vtx
{
public:
Vtx *next; // initialized and used in maxFlow() only
int parent;
int first;
int ts;
int dist;
TWeight weight;
uchar t;
};
class Edge
{
public:
int dst;
int next;
TWeight weight;
};
std::vector<Vtx> vtcs;
std::vector<Edge> edges;
TWeight flow;
};
template <class TWeight>
GCGraph<TWeight>::GCGraph()
{
flow = 0;
}
template <class TWeight>
GCGraph<TWeight>::GCGraph( unsigned int vtxCount, unsigned int edgeCount )
{
create( vtxCount, edgeCount );
}
template <class TWeight>
GCGraph<TWeight>::~GCGraph()
{
}
template <class TWeight>
void GCGraph<TWeight>::create( unsigned int vtxCount, unsigned int edgeCount )
{
vtcs.reserve( vtxCount );
edges.reserve( edgeCount + 2 );
flow = 0;
}
template <class TWeight>
int GCGraph<TWeight>::addVtx()
{
Vtx v;
memset( &v, 0, sizeof(Vtx));
vtcs.push_back(v);
return (int)vtcs.size() - 1;
}
template <class TWeight>
void GCGraph<TWeight>::addEdges( int i, int j, TWeight w, TWeight revw )
{
CV_Assert( i>=0 && i<(int)vtcs.size() );
CV_Assert( j>=0 && j<(int)vtcs.size() );
CV_Assert( w>=0 && revw>=0 );
CV_Assert( i != j );
if( !edges.size() )
edges.resize( 2 );
Edge fromI, toI;
fromI.dst = j;
fromI.next = vtcs[i].first;
fromI.weight = w;
vtcs[i].first = (int)edges.size();
edges.push_back( fromI );
toI.dst = i;
toI.next = vtcs[j].first;
toI.weight = revw;
vtcs[j].first = (int)edges.size();
edges.push_back( toI );
}
template <class TWeight>
void GCGraph<TWeight>::addTermWeights( int i, TWeight sourceW, TWeight sinkW )
{
CV_Assert( i>=0 && i<(int)vtcs.size() );
TWeight dw = vtcs[i].weight;
if( dw > 0 )
sourceW += dw;
else
sinkW -= dw;
flow += (sourceW < sinkW) ? sourceW : sinkW;
vtcs[i].weight = sourceW - sinkW;
}
template <class TWeight>
TWeight GCGraph<TWeight>::maxFlow()
{
CV_Assert(!vtcs.empty());
CV_Assert(!edges.empty());
const int TERMINAL = -1, ORPHAN = -2;
Vtx stub, *nilNode = &stub, *first = nilNode, *last = nilNode;
int curr_ts = 0;
stub.next = nilNode;
Vtx *vtxPtr = &vtcs[0];
Edge *edgePtr = &edges[0];
std::vector<Vtx*> orphans;
// initialize the active queue and the graph vertices
for( int i = 0; i < (int)vtcs.size(); i++ )
{
Vtx* v = vtxPtr + i;
v->ts = 0;
if( v->weight != 0 )
{
last = last->next = v;
v->dist = 1;
v->parent = TERMINAL;
v->t = v->weight < 0;
}
else
v->parent = 0;
}
first = first->next;
last->next = nilNode;
nilNode->next = 0;
// run the search-path -> augment-graph -> restore-trees loop
for(;;)
{
Vtx* v, *u;
int e0 = -1, ei = 0, ej = 0;
TWeight minWeight, weight;
uchar vt;
// grow S & T search trees, find an edge connecting them
while( first != nilNode )
{
v = first;
if( v->parent )
{
vt = v->t;
for( ei = v->first; ei != 0; ei = edgePtr[ei].next )
{
if( edgePtr[ei^vt].weight == 0 )
continue;
u = vtxPtr+edgePtr[ei].dst;
if( !u->parent )
{
u->t = vt;
u->parent = ei ^ 1;
u->ts = v->ts;
u->dist = v->dist + 1;
if( !u->next )
{
u->next = nilNode;
last = last->next = u;
}
continue;
}
if( u->t != vt )
{
e0 = ei ^ vt;
break;
}
if( u->dist > v->dist+1 && u->ts <= v->ts )
{
// reassign the parent
u->parent = ei ^ 1;
u->ts = v->ts;
u->dist = v->dist + 1;
}
}
if( e0 > 0 )
break;
}
// exclude the vertex from the active list
first = first->next;
v->next = 0;
}
if( e0 <= 0 )
break;
// find the minimum edge weight along the path
minWeight = edgePtr[e0].weight;
CV_Assert( minWeight > 0 );
// k = 1: source tree, k = 0: destination tree
for( int k = 1; k >= 0; k-- )
{
for( v = vtxPtr+edgePtr[e0^k].dst;; v = vtxPtr+edgePtr[ei].dst )
{
if( (ei = v->parent) < 0 )
break;
weight = edgePtr[ei^k].weight;
minWeight = MIN(minWeight, weight);
CV_Assert( minWeight > 0 );
}
weight = fabs(v->weight);
minWeight = MIN(minWeight, weight);
CV_Assert( minWeight > 0 );
}
// modify weights of the edges along the path and collect orphans
edgePtr[e0].weight -= minWeight;
edgePtr[e0^1].weight += minWeight;
flow += minWeight;
// k = 1: source tree, k = 0: destination tree
for( int k = 1; k >= 0; k-- )
{
for( v = vtxPtr+edgePtr[e0^k].dst;; v = vtxPtr+edgePtr[ei].dst )
{
if( (ei = v->parent) < 0 )
break;
edgePtr[ei^(k^1)].weight += minWeight;
if( (edgePtr[ei^k].weight -= minWeight) == 0 )
{
orphans.push_back(v);
v->parent = ORPHAN;
}
}
v->weight = v->weight + minWeight*(1-k*2);
if( v->weight == 0 )
{
orphans.push_back(v);
v->parent = ORPHAN;
}
}
// restore the search trees by finding new parents for the orphans
curr_ts++;
while( !orphans.empty() )
{
Vtx* v2 = orphans.back();
orphans.pop_back();
int d, minDist = INT_MAX;
e0 = 0;
vt = v2->t;
for( ei = v2->first; ei != 0; ei = edgePtr[ei].next )
{
if( edgePtr[ei^(vt^1)].weight == 0 )
continue;
u = vtxPtr+edgePtr[ei].dst;
if( u->t != vt || u->parent == 0 )
continue;
// compute the distance to the tree root
for( d = 0;; )
{
if( u->ts == curr_ts )
{
d += u->dist;
break;
}
ej = u->parent;
d++;
if( ej < 0 )
{
if( ej == ORPHAN )
d = INT_MAX-1;
else
{
u->ts = curr_ts;
u->dist = 1;
}
break;
}
u = vtxPtr+edgePtr[ej].dst;
}
// update the distance
if( ++d < INT_MAX )
{
if( d < minDist )
{
minDist = d;
e0 = ei;
}
for( u = vtxPtr+edgePtr[ei].dst; u->ts != curr_ts; u = vtxPtr+edgePtr[u->parent].dst )
{
u->ts = curr_ts;
u->dist = --d;
}
}
}
if( (v2->parent = e0) > 0 )
{
v2->ts = curr_ts;
v2->dist = minDist;
continue;
}
/* no parent is found */
v2->ts = 0;
for( ei = v2->first; ei != 0; ei = edgePtr[ei].next )
{
u = vtxPtr+edgePtr[ei].dst;
ej = u->parent;
if( u->t != vt || !ej )
continue;
if( edgePtr[ei^(vt^1)].weight && !u->next )
{
u->next = nilNode;
last = last->next = u;
}
if( ej > 0 && vtxPtr+edgePtr[ej].dst == v2 )
{
orphans.push_back(u);
u->parent = ORPHAN;
}
}
}
}
return flow;
}
template <class TWeight>
bool GCGraph<TWeight>::inSourceSegment( int i )
{
CV_Assert( i>=0 && i<(int)vtcs.size() );
return vtcs[i].t == 0;
}
}} // namespace detail, cv
//! @endcond
#endif // OPENCV_IMGPROC_DETAIL_GCGRAPH_HPP