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calib3d: move undistort files from imgproc

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pull/12728/head
Alexander Alekhin 6 years ago
parent a9c8a526cf
commit 8f1f4273a2
15 changed files with 989 additions and 694 deletions
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  1. 203
      modules/calib3d/include/opencv2/calib3d.hpp
  2. 33
      modules/calib3d/include/opencv2/calib3d/calib3d_c.h
  3. 137
      modules/calib3d/misc/java/test/Calib3dTest.java
  4. 2
      modules/calib3d/src/calibration.cpp
  5. 0
      modules/calib3d/src/distortion_model.hpp
  6. 0
      modules/calib3d/src/undistort.avx2.cpp
  7. 53
      modules/calib3d/src/undistort.cpp
  8. 6
      modules/calib3d/src/undistort.hpp
  9. 581
      modules/calib3d/test/test_undistort.cpp
  10. 2
      modules/calib3d/test/test_undistort_badarg.cpp
  11. 193
      modules/imgproc/include/opencv2/imgproc.hpp
  12. 33
      modules/imgproc/include/opencv2/imgproc/imgproc_c.h
  13. 127
      modules/imgproc/misc/java/test/ImgprocTest.java
  14. 308
      modules/imgproc/test/test_imgwarp.cpp
  15. 5
      samples/android/camera-calibration/src/org/opencv/samples/cameracalibration/OnCameraFrameRender.java

203
modules/calib3d/include/opencv2/calib3d.hpp
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@ -2222,6 +2222,209 @@ public:
int mode = StereoSGBM::MODE_SGBM);
};
//! 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 cvInitUndistortMap 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);
//! 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 Returns the default new camera matrix.
The function returns the camera matrix that is either an exact copy of the input cameraMatrix (when
centerPrinicipalPoint=false ), or the modified one (when centerPrincipalPoint=true).
In the latter case, the new camera matrix will be:
\f[\begin{bmatrix} f_x && 0 && ( \texttt{imgSize.width} -1)*0.5 \\ 0 && f_y && ( \texttt{imgSize.height} -1)*0.5 \\ 0 && 0 && 1 \end{bmatrix} ,\f]
where \f$f_x\f$ and \f$f_y\f$ are \f$(0,0)\f$ and \f$(1,1)\f$ elements of cameraMatrix, respectively.
By default, the undistortion functions in OpenCV (see #initUndistortRectifyMap, #undistort) do not
move the principal point. However, when you work with stereo, it is important to move the principal
points in both views to the same y-coordinate (which is required by most of stereo correspondence
algorithms), and may be to the same x-coordinate too. So, you can form the new camera matrix for
each view where the principal points are located at the center.
@param cameraMatrix Input camera matrix.
@param imgsize Camera view image size in pixels.
@param centerPrincipalPoint Location of the principal point in the new camera matrix. The
parameter indicates whether this location should be at the image center or not.
*/
CV_EXPORTS_W
Mat getDefaultNewCameraMatrix(InputArray cameraMatrix, Size imgsize = Size(),
bool centerPrincipalPoint = false);
/** @brief Computes the ideal point coordinates from the observed point coordinates.
The function is similar to #undistort and #initUndistortRectifyMap but it operates on a
sparse set of points instead of a raster image. Also the function performs a reverse transformation
to projectPoints. In case of a 3D object, it does not reconstruct its 3D coordinates, but for a
planar object, it does, up to a translation vector, if the proper R is specified.
For each observed point coordinate \f$(u, v)\f$ the function computes:
\f[
\begin{array}{l}
x^{"} \leftarrow (u - c_x)/f_x \\
y^{"} \leftarrow (v - c_y)/f_y \\
(x',y') = undistort(x^{"},y^{"}, \texttt{distCoeffs}) \\
{[X\,Y\,W]} ^T \leftarrow R*[x' \, y' \, 1]^T \\
x \leftarrow X/W \\
y \leftarrow Y/W \\
\text{only performed if P is specified:} \\
u' \leftarrow x {f'}_x + {c'}_x \\
v' \leftarrow y {f'}_y + {c'}_y
\end{array}
\f]
where *undistort* is an approximate iterative algorithm that estimates the normalized original
point coordinates out of the normalized distorted point coordinates ("normalized" means that the
coordinates do not depend on the camera matrix).
The function can be used for both a stereo camera head or a monocular camera (when R is empty).
@param src Observed point coordinates, 1xN or Nx1 2-channel (CV_32FC2 or CV_64FC2).
@param dst Output ideal point coordinates after undistortion and reverse perspective
transformation. If matrix P is identity or omitted, dst will contain normalized point coordinates.
@param cameraMatrix Camera matrix \f$\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ .
@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 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 used.
@param P New camera matrix (3x3) or new projection matrix (3x4) \f$\begin{bmatrix} {f'}_x & 0 & {c'}_x & t_x \\ 0 & {f'}_y & {c'}_y & t_y \\ 0 & 0 & 1 & t_z \end{bmatrix}\f$. P1 or P2 computed by
#stereoRectify can be passed here. If the matrix is empty, the identity new camera matrix is used.
*/
CV_EXPORTS_W
void undistortPoints(InputArray src, OutputArray dst,
InputArray cameraMatrix, InputArray distCoeffs,
InputArray R = noArray(), InputArray P = noArray());
/** @overload
@note Default version of #undistortPoints does 5 iterations to compute undistorted points.
*/
CV_EXPORTS_AS(undistortPointsIter)
void undistortPoints(InputArray src, OutputArray dst,
InputArray cameraMatrix, InputArray distCoeffs,
InputArray R, InputArray P, TermCriteria criteria);
//! @} calib3d
/** @brief The methods in this namespace use a so-called fisheye camera model.

33
modules/calib3d/include/opencv2/calib3d/calib3d_c.h
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@ -379,6 +379,39 @@ CVAPI(void) cvReprojectImageTo3D( const CvArr* disparityImage,
CvArr* _3dImage, const CvMat* Q,
int handleMissingValues CV_DEFAULT(0) );
/** @brief Transforms the input image to compensate lens distortion
@see cv::undistort
*/
CVAPI(void) cvUndistort2( const CvArr* src, CvArr* dst,
const CvMat* camera_matrix,
const CvMat* distortion_coeffs,
const CvMat* new_camera_matrix CV_DEFAULT(0) );
/** @brief Computes transformation map from intrinsic camera parameters
that can used by cvRemap
*/
CVAPI(void) cvInitUndistortMap( const CvMat* camera_matrix,
const CvMat* distortion_coeffs,
CvArr* mapx, CvArr* mapy );
/** @brief Computes undistortion+rectification map for a head of stereo camera
@see cv::initUndistortRectifyMap
*/
CVAPI(void) cvInitUndistortRectifyMap( const CvMat* camera_matrix,
const CvMat* dist_coeffs,
const CvMat *R, const CvMat* new_camera_matrix,
CvArr* mapx, CvArr* mapy );
/** @brief Computes the original (undistorted) feature coordinates
from the observed (distorted) coordinates
@see cv::undistortPoints
*/
CVAPI(void) cvUndistortPoints( const CvMat* src, CvMat* dst,
const CvMat* camera_matrix,
const CvMat* dist_coeffs,
const CvMat* R CV_DEFAULT(0),
const CvMat* P CV_DEFAULT(0));
/** @} calib3d_c */
#ifdef __cplusplus

137
modules/calib3d/misc/java/test/Calib3dTest.java
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@ -1,6 +1,7 @@
package org.opencv.test.calib3d;
import org.opencv.calib3d.Calib3d;
import org.opencv.core.Core;
import org.opencv.core.CvType;
import org.opencv.core.Mat;
import org.opencv.core.MatOfDouble;
@ -14,6 +15,15 @@ import org.opencv.imgproc.Imgproc;
public class Calib3dTest extends OpenCVTestCase {
Size size;
@Override
protected void setUp() throws Exception {
super.setUp();
size = new Size(3, 3);
}
public void testCalibrateCameraListOfMatListOfMatSizeMatMatListOfMatListOfMat() {
fail("Not yet implemented");
}
@ -602,4 +612,131 @@ public class Calib3dTest extends OpenCVTestCase {
Calib3d.computeCorrespondEpilines(left, 1, fundamental, lines);
assertMatEqual(truth, lines, EPS);
}
public void testGetDefaultNewCameraMatrixMat() {
Mat mtx = Calib3d.getDefaultNewCameraMatrix(gray0);
assertFalse(mtx.empty());
assertEquals(0, Core.countNonZero(mtx));
}
public void testGetDefaultNewCameraMatrixMatSizeBoolean() {
Mat mtx = Calib3d.getDefaultNewCameraMatrix(gray0, size, true);
assertFalse(mtx.empty());
assertFalse(0 == Core.countNonZero(mtx));
// TODO_: write better test
}
public void testInitUndistortRectifyMap() {
fail("Not yet implemented");
Mat cameraMatrix = new Mat(3, 3, CvType.CV_32F);
cameraMatrix.put(0, 0, 1, 0, 1);
cameraMatrix.put(1, 0, 0, 1, 1);
cameraMatrix.put(2, 0, 0, 0, 1);
Mat R = new Mat(3, 3, CvType.CV_32F, new Scalar(2));
Mat newCameraMatrix = new Mat(3, 3, CvType.CV_32F, new Scalar(3));
Mat distCoeffs = new Mat();
Mat map1 = new Mat();
Mat map2 = new Mat();
// TODO: complete this test
Calib3d.initUndistortRectifyMap(cameraMatrix, distCoeffs, R, newCameraMatrix, size, CvType.CV_32F, map1, map2);
}
public void testInitWideAngleProjMapMatMatSizeIntIntMatMat() {
fail("Not yet implemented");
Mat cameraMatrix = new Mat(3, 3, CvType.CV_32F);
Mat distCoeffs = new Mat(1, 4, CvType.CV_32F);
// Size imageSize = new Size(2, 2);
cameraMatrix.put(0, 0, 1, 0, 1);
cameraMatrix.put(1, 0, 0, 1, 2);
cameraMatrix.put(2, 0, 0, 0, 1);
distCoeffs.put(0, 0, 1, 3, 2, 4);
truth = new Mat(3, 3, CvType.CV_32F);
truth.put(0, 0, 0, 0, 0);
truth.put(1, 0, 0, 0, 0);
truth.put(2, 0, 0, 3, 0);
// TODO: No documentation for this function
// Calib3d.initWideAngleProjMap(cameraMatrix, distCoeffs, imageSize,
// 5, m1type, truthput1, truthput2);
}
public void testInitWideAngleProjMapMatMatSizeIntIntMatMatInt() {
fail("Not yet implemented");
}
public void testInitWideAngleProjMapMatMatSizeIntIntMatMatIntDouble() {
fail("Not yet implemented");
}
public void testUndistortMatMatMatMat() {
Mat src = new Mat(3, 3, CvType.CV_32F, new Scalar(3));
Mat cameraMatrix = new Mat(3, 3, CvType.CV_32F) {
{
put(0, 0, 1, 0, 1);
put(1, 0, 0, 1, 2);
put(2, 0, 0, 0, 1);
}
};
Mat distCoeffs = new Mat(1, 4, CvType.CV_32F) {
{
put(0, 0, 1, 3, 2, 4);
}
};
Calib3d.undistort(src, dst, cameraMatrix, distCoeffs);
truth = new Mat(3, 3, CvType.CV_32F) {
{
put(0, 0, 0, 0, 0);
put(1, 0, 0, 0, 0);
put(2, 0, 0, 3, 0);
}
};
assertMatEqual(truth, dst, EPS);
}
public void testUndistortMatMatMatMatMat() {
Mat src = new Mat(3, 3, CvType.CV_32F, new Scalar(3));
Mat cameraMatrix = new Mat(3, 3, CvType.CV_32F) {
{
put(0, 0, 1, 0, 1);
put(1, 0, 0, 1, 2);
put(2, 0, 0, 0, 1);
}
};
Mat distCoeffs = new Mat(1, 4, CvType.CV_32F) {
{
put(0, 0, 2, 1, 4, 5);
}
};
Mat newCameraMatrix = new Mat(3, 3, CvType.CV_32F, new Scalar(1));
Calib3d.undistort(src, dst, cameraMatrix, distCoeffs, newCameraMatrix);
truth = new Mat(3, 3, CvType.CV_32F, new Scalar(3));
assertMatEqual(truth, dst, EPS);
}
//undistortPoints(List<Point> src, List<Point> dst, Mat cameraMatrix, Mat distCoeffs)
public void testUndistortPointsListOfPointListOfPointMatMat() {
MatOfPoint2f src = new MatOfPoint2f(new Point(1, 2), new Point(3, 4), new Point(-1, -1));
MatOfPoint2f dst = new MatOfPoint2f();
Mat cameraMatrix = Mat.eye(3, 3, CvType.CV_64FC1);
Mat distCoeffs = new Mat(8, 1, CvType.CV_64FC1, new Scalar(0));
Calib3d.undistortPoints(src, dst, cameraMatrix, distCoeffs);
assertEquals(src.size(), dst.size());
for(int i=0; i<src.toList().size(); i++) {
//Log.d("UndistortPoints", "s="+src.get(i)+", d="+dst.get(i));
assertTrue(src.toList().get(i).equals(dst.toList().get(i)));
}
}
}

2
modules/calib3d/src/calibration.cpp
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@ -42,7 +42,7 @@
#include "precomp.hpp"
#include "opencv2/imgproc/imgproc_c.h"
#include "opencv2/imgproc/detail/distortion_model.hpp"
#include "distortion_model.hpp"
#include "opencv2/calib3d/calib3d_c.h"
#include <stdio.h>
#include <iterator>

0
modules/imgproc/include/opencv2/imgproc/detail/distortion_model.hpp → modules/calib3d/src/distortion_model.hpp
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0
modules/imgproc/src/undistort.avx2.cpp → modules/calib3d/src/undistort.avx2.cpp
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53
modules/imgproc/src/undistort.cpp → modules/calib3d/src/undistort.cpp
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@ -41,9 +41,11 @@
//M*/
#include "precomp.hpp"
#include "opencv2/imgproc/detail/distortion_model.hpp"
#include "distortion_model.hpp"
#include "undistort.hpp"
#include "opencv2/calib3d/calib3d_c.h"
cv::Mat cv::getDefaultNewCameraMatrix( InputArray _cameraMatrix, Size imgsize,
bool centerPrincipalPoint )
{
@ -534,29 +536,31 @@ static void cvUndistortPointsInternal( const CvMat* _src, CvMat* _dst, const CvM
}
}
void cvUndistortPoints( const CvMat* _src, CvMat* _dst, const CvMat* _cameraMatrix,
const CvMat* _distCoeffs,
const CvMat* matR, const CvMat* matP )
void cvUndistortPoints(const CvMat* _src, CvMat* _dst, const CvMat* _cameraMatrix,
const CvMat* _distCoeffs,
const CvMat* matR, const CvMat* matP)
{
cvUndistortPointsInternal(_src, _dst, _cameraMatrix, _distCoeffs, matR, matP,
cv::TermCriteria(cv::TermCriteria::COUNT, 5, 0.01));
}
void cv::undistortPoints( InputArray _src, OutputArray _dst,
InputArray _cameraMatrix,
InputArray _distCoeffs,
InputArray _Rmat,
InputArray _Pmat )
namespace cv {
void undistortPoints(InputArray _src, OutputArray _dst,
InputArray _cameraMatrix,
InputArray _distCoeffs,
InputArray _Rmat,
InputArray _Pmat)
{
undistortPoints(_src, _dst, _cameraMatrix, _distCoeffs, _Rmat, _Pmat, TermCriteria(TermCriteria::MAX_ITER, 5, 0.01));
}
void cv::undistortPoints( InputArray _src, OutputArray _dst,
InputArray _cameraMatrix,
InputArray _distCoeffs,
InputArray _Rmat,
InputArray _Pmat,
TermCriteria criteria)
void undistortPoints(InputArray _src, OutputArray _dst,
InputArray _cameraMatrix,
InputArray _distCoeffs,
InputArray _Rmat,
InputArray _Pmat,
TermCriteria criteria)
{
Mat src = _src.getMat(), cameraMatrix = _cameraMatrix.getMat();
Mat distCoeffs = _distCoeffs.getMat(), R = _Rmat.getMat(), P = _Pmat.getMat();
@ -578,10 +582,7 @@ void cv::undistortPoints( InputArray _src, OutputArray _dst,
cvUndistortPointsInternal(&_csrc, &_cdst, &_ccameraMatrix, pD, pR, pP, criteria);
}
namespace cv
{
static Point2f mapPointSpherical(const Point2f& p, float alpha, Vec4d* J, int projType)
static Point2f mapPointSpherical(const Point2f& p, float alpha, Vec4d* J, enum UndistortTypes projType)
{
double x = p.x, y = p.y;
double beta = 1 + 2*alpha;
@ -613,11 +614,11 @@ static Point2f mapPointSpherical(const Point2f& p, float alpha, Vec4d* J, int pr
}
return Point2f((float)asin(x1), (float)asin(y1));
}
CV_Error(CV_StsBadArg, "Unknown projection type");
CV_Error(Error::StsBadArg, "Unknown projection type");
}
static Point2f invMapPointSpherical(Point2f _p, float alpha, int projType)
static Point2f invMapPointSpherical(Point2f _p, float alpha, enum UndistortTypes projType)
{
double eps = 1e-12;
Vec2d p(_p.x, _p.y), q(_p.x, _p.y), err;
@ -646,11 +647,10 @@ static Point2f invMapPointSpherical(Point2f _p, float alpha, int projType)
return i < maxiter ? Point2f((float)q[0], (float)q[1]) : Point2f(-FLT_MAX, -FLT_MAX);
}
}
float cv::initWideAngleProjMap( InputArray _cameraMatrix0, InputArray _distCoeffs0,
Size imageSize, int destImageWidth, int m1type,
OutputArray _map1, OutputArray _map2, int projType, double _alpha )
float initWideAngleProjMap(InputArray _cameraMatrix0, InputArray _distCoeffs0,
Size imageSize, int destImageWidth, int m1type,
OutputArray _map1, OutputArray _map2,
enum UndistortTypes projType, double _alpha)
{
Mat cameraMatrix0 = _cameraMatrix0.getMat(), distCoeffs0 = _distCoeffs0.getMat();
double k[14] = {0,0,0,0,0,0,0,0,0,0,0,0,0,0}, M[9]={0,0,0,0,0,0,0,0,0};
@ -735,4 +735,5 @@ float cv::initWideAngleProjMap( InputArray _cameraMatrix0, InputArray _distCoeff
return scale;
}
} // namespace
/* End of file */

6
modules/imgproc/src/undistort.hpp → modules/calib3d/src/undistort.hpp
Unescape View File

@ -40,8 +40,8 @@
//
//M*/
#ifndef OPENCV_IMGPROC_UNDISTORT_HPP
#define OPENCV_IMGPROC_UNDISTORT_HPP
#ifndef OPENCV_CALIB3D_UNDISTORT_HPP
#define OPENCV_CALIB3D_UNDISTORT_HPP
namespace cv
{
@ -54,6 +54,6 @@ namespace cv
#endif
}
#endif
#endif // OPENCV_CALIB3D_UNDISTORT_HPP
/* End of file */

581
modules/calib3d/test/test_undistort.cpp
Unescape View File

@ -42,6 +42,7 @@
#include "test_precomp.hpp"
#include "opencv2/imgproc/imgproc_c.h"
#include "opencv2/calib3d/calib3d_c.h"
namespace opencv_test { namespace {
@ -938,4 +939,584 @@ TEST(Calib3d_DefaultNewCameraMatrix, accuracy) { CV_DefaultNewCameraMatrixTest t
TEST(Calib3d_UndistortPoints, accuracy) { CV_UndistortPointsTest test; test.safe_run(); }
TEST(Calib3d_InitUndistortRectifyMap, accuracy) { CV_InitUndistortRectifyMapTest test; test.safe_run(); }
////////////////////////////// undistort /////////////////////////////////
static void test_remap( const Mat& src, Mat& dst, const Mat& mapx, const Mat& mapy,
Mat* mask=0, int interpolation=CV_INTER_LINEAR )
{
int x, y, k;
int drows = dst.rows, dcols = dst.cols;
int srows = src.rows, scols = src.cols;
const uchar* sptr0 = src.ptr();
int depth = src.depth(), cn = src.channels();
int elem_size = (int)src.elemSize();
int step = (int)(src.step / CV_ELEM_SIZE(depth));
int delta;
if( interpolation != CV_INTER_CUBIC )
{
delta = 0;
scols -= 1; srows -= 1;
}
else
{
delta = 1;
scols = MAX(scols - 3, 0);
srows = MAX(srows - 3, 0);
}
int scols1 = MAX(scols - 2, 0);
int srows1 = MAX(srows - 2, 0);
if( mask )
*mask = Scalar::all(0);
for( y = 0; y < drows; y++ )
{
uchar* dptr = dst.ptr(y);
const float* mx = mapx.ptr<float>(y);
const float* my = mapy.ptr<float>(y);
uchar* m = mask ? mask->ptr(y) : 0;
for( x = 0; x < dcols; x++, dptr += elem_size )
{
float xs = mx[x];
float ys = my[x];
int ixs = cvFloor(xs);
int iys = cvFloor(ys);
if( (unsigned)(ixs - delta - 1) >= (unsigned)scols1 ||
(unsigned)(iys - delta - 1) >= (unsigned)srows1 )
{
if( m )
m[x] = 1;
if( (unsigned)(ixs - delta) >= (unsigned)scols ||
(unsigned)(iys - delta) >= (unsigned)srows )
continue;
}
xs -= ixs;
ys -= iys;
switch( depth )
{
case CV_8U:
{
const uchar* sptr = sptr0 + iys*step + ixs*cn;
for( k = 0; k < cn; k++ )
{
float v00 = sptr[k];
float v01 = sptr[cn + k];
float v10 = sptr[step + k];
float v11 = sptr[step + cn + k];
v00 = v00 + xs*(v01 - v00);
v10 = v10 + xs*(v11 - v10);
v00 = v00 + ys*(v10 - v00);
dptr[k] = (uchar)cvRound(v00);
}
}
break;
case CV_16U:
{
const ushort* sptr = (const ushort*)sptr0 + iys*step + ixs*cn;
for( k = 0; k < cn; k++ )
{
float v00 = sptr[k];
float v01 = sptr[cn + k];
float v10 = sptr[step + k];
float v11 = sptr[step + cn + k];
v00 = v00 + xs*(v01 - v00);
v10 = v10 + xs*(v11 - v10);
v00 = v00 + ys*(v10 - v00);
((ushort*)dptr)[k] = (ushort)cvRound(v00);
}
}
break;
case CV_32F:
{
const float* sptr = (const float*)sptr0 + iys*step + ixs*cn;
for( k = 0; k < cn; k++ )
{
float v00 = sptr[k];
float v01 = sptr[cn + k];
float v10 = sptr[step + k];
float v11 = sptr[step + cn + k];
v00 = v00 + xs*(v01 - v00);
v10 = v10 + xs*(v11 - v10);
v00 = v00 + ys*(v10 - v00);
((float*)dptr)[k] = (float)v00;
}
}
break;
default:
assert(0);
}
}
}
}
class CV_ImgWarpBaseTest : public cvtest::ArrayTest
{
public:
CV_ImgWarpBaseTest( bool warp_matrix );
protected:
int read_params( CvFileStorage* fs );
int prepare_test_case( int test_case_idx );
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
void get_minmax_bounds( int i, int j, int type, Scalar& low, Scalar& high );
void fill_array( int test_case_idx, int i, int j, Mat& arr );
int interpolation;
int max_interpolation;
double spatial_scale_zoom, spatial_scale_decimate;
};
CV_ImgWarpBaseTest::CV_ImgWarpBaseTest( bool warp_matrix )
{
test_array[INPUT].push_back(NULL);
if( warp_matrix )
test_array[INPUT].push_back(NULL);
test_array[INPUT_OUTPUT].push_back(NULL);
test_array[REF_INPUT_OUTPUT].push_back(NULL);
max_interpolation = 5;
interpolation = 0;
element_wise_relative_error = false;
spatial_scale_zoom = 0.01;
spatial_scale_decimate = 0.005;
}
int CV_ImgWarpBaseTest::read_params( CvFileStorage* fs )
{
int code = cvtest::ArrayTest::read_params( fs );
return code;
}
void CV_ImgWarpBaseTest::get_minmax_bounds( int i, int j, int type, Scalar& low, Scalar& high )
{
cvtest::ArrayTest::get_minmax_bounds( i, j, type, low, high );
if( CV_MAT_DEPTH(type) == CV_32F )
{
low = Scalar::all(-10.);
high = Scalar::all(10);
}
}
void CV_ImgWarpBaseTest::get_test_array_types_and_sizes( int test_case_idx,
vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
RNG& rng = ts->get_rng();
int depth = cvtest::randInt(rng) % 3;
int cn = cvtest::randInt(rng) % 3 + 1;
cvtest::ArrayTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
depth = depth == 0 ? CV_8U : depth == 1 ? CV_16U : CV_32F;
cn += cn == 2;
types[INPUT][0] = types[INPUT_OUTPUT][0] = types[REF_INPUT_OUTPUT][0] = CV_MAKETYPE(depth, cn);
if( test_array[INPUT].size() > 1 )
types[INPUT][1] = cvtest::randInt(rng) & 1 ? CV_32FC1 : CV_64FC1;
interpolation = cvtest::randInt(rng) % max_interpolation;
}
void CV_ImgWarpBaseTest::fill_array( int test_case_idx, int i, int j, Mat& arr )
{
if( i != INPUT || j != 0 )
cvtest::ArrayTest::fill_array( test_case_idx, i, j, arr );
}
int CV_ImgWarpBaseTest::prepare_test_case( int test_case_idx )
{
int code = cvtest::ArrayTest::prepare_test_case( test_case_idx );
Mat& img = test_mat[INPUT][0];
int i, j, cols = img.cols;
int type = img.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
double scale = depth == CV_16U ? 1000. : 255.*0.5;
double space_scale = spatial_scale_decimate;
vector<float> buffer(img.cols*cn);
if( code <= 0 )
return code;
if( test_mat[INPUT_OUTPUT][0].cols >= img.cols &&
test_mat[INPUT_OUTPUT][0].rows >= img.rows )
space_scale = spatial_scale_zoom;
for( i = 0; i < img.rows; i++ )
{
uchar* ptr = img.ptr(i);
switch( cn )
{
case 1:
for( j = 0; j < cols; j++ )
buffer[j] = (float)((sin((i+1)*space_scale)*sin((j+1)*space_scale)+1.)*scale);
break;
case 2:
for( j = 0; j < cols; j++ )
{
buffer[j*2] = (float)((sin((i+1)*space_scale)+1.)*scale);
buffer[j*2+1] = (float)((sin((i+j)*space_scale)+1.)*scale);
}
break;
case 3:
for( j = 0; j < cols; j++ )
{
buffer[j*3] = (float)((sin((i+1)*space_scale)+1.)*scale);
buffer[j*3+1] = (float)((sin(j*space_scale)+1.)*scale);
buffer[j*3+2] = (float)((sin((i+j)*space_scale)+1.)*scale);
}
break;
case 4:
for( j = 0; j < cols; j++ )
{
buffer[j*4] = (float)((sin((i+1)*space_scale)+1.)*scale);
buffer[j*4+1] = (float)((sin(j*space_scale)+1.)*scale);
buffer[j*4+2] = (float)((sin((i+j)*space_scale)+1.)*scale);
buffer[j*4+3] = (float)((sin((i-j)*space_scale)+1.)*scale);
}
break;
default:
assert(0);
}
/*switch( depth )
{
case CV_8U:
for( j = 0; j < cols*cn; j++ )
ptr[j] = (uchar)cvRound(buffer[j]);
break;
case CV_16U:
for( j = 0; j < cols*cn; j++ )
((ushort*)ptr)[j] = (ushort)cvRound(buffer[j]);
break;
case CV_32F:
for( j = 0; j < cols*cn; j++ )
((float*)ptr)[j] = (float)buffer[j];
break;
default:
assert(0);
}*/
cv::Mat src(1, cols*cn, CV_32F, &buffer[0]);
cv::Mat dst(1, cols*cn, depth, ptr);
src.convertTo(dst, dst.type());
}
return code;
}
class CV_UndistortTest : public CV_ImgWarpBaseTest
{
public:
CV_UndistortTest();
protected:
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
void run_func();
int prepare_test_case( int test_case_idx );
void prepare_to_validation( int /*test_case_idx*/ );
double get_success_error_level( int test_case_idx, int i, int j );
void fill_array( int test_case_idx, int i, int j, Mat& arr );
private:
bool useCPlus;
cv::Mat input0;
cv::Mat input1;
cv::Mat input2;
cv::Mat input_new_cam;
cv::Mat input_output;
bool zero_new_cam;
bool zero_distortion;
};
CV_UndistortTest::CV_UndistortTest() : CV_ImgWarpBaseTest( false )
{
//spatial_scale_zoom = spatial_scale_decimate;
test_array[INPUT].push_back(NULL);
test_array[INPUT].push_back(NULL);
test_array[INPUT].push_back(NULL);
spatial_scale_decimate = spatial_scale_zoom;
}
void CV_UndistortTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
RNG& rng = ts->get_rng();
CV_ImgWarpBaseTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
int type = types[INPUT][0];
type = CV_MAKETYPE( CV_8U, CV_MAT_CN(type) );
types[INPUT][0] = types[INPUT_OUTPUT][0] = types[REF_INPUT_OUTPUT][0] = type;
types[INPUT][1] = cvtest::randInt(rng)%2 ? CV_64F : CV_32F;
types[INPUT][2] = cvtest::randInt(rng)%2 ? CV_64F : CV_32F;
sizes[INPUT][1] = cvSize(3,3);
sizes[INPUT][2] = cvtest::randInt(rng)%2 ? cvSize(4,1) : cvSize(1,4);
types[INPUT][3] = types[INPUT][1];
sizes[INPUT][3] = sizes[INPUT][1];
interpolation = CV_INTER_LINEAR;
}
void CV_UndistortTest::fill_array( int test_case_idx, int i, int j, Mat& arr )
{
if( i != INPUT )
CV_ImgWarpBaseTest::fill_array( test_case_idx, i, j, arr );
}
void CV_UndistortTest::run_func()
{
if (!useCPlus)
{
CvMat a = cvMat(test_mat[INPUT][1]), k = cvMat(test_mat[INPUT][2]);
cvUndistort2( test_array[INPUT][0], test_array[INPUT_OUTPUT][0], &a, &k);
}
else
{
if (zero_distortion)
{
cv::undistort(input0,input_output,input1,cv::Mat());
}
else
{
cv::undistort(input0,input_output,input1,input2);
}
}
}
double CV_UndistortTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
{
int depth = test_mat[INPUT][0].depth();
return depth == CV_8U ? 16 : depth == CV_16U ? 1024 : 5e-2;
}
int CV_UndistortTest::prepare_test_case( int test_case_idx )
{
RNG& rng = ts->get_rng();
int code = CV_ImgWarpBaseTest::prepare_test_case( test_case_idx );
const Mat& src = test_mat[INPUT][0];
double k[4], a[9] = {0,0,0,0,0,0,0,0,1};
double new_cam[9] = {0,0,0,0,0,0,0,0,1};
double sz = MAX(src.rows, src.cols);
Mat& _new_cam0 = test_mat[INPUT][3];
Mat _new_cam(test_mat[INPUT][3].rows,test_mat[INPUT][3].cols,CV_64F,new_cam);
Mat& _a0 = test_mat[INPUT][1];
Mat _a(3,3,CV_64F,a);
Mat& _k0 = test_mat[INPUT][2];
Mat _k(_k0.rows,_k0.cols, CV_MAKETYPE(CV_64F,_k0.channels()),k);
if( code <= 0 )
return code;
double aspect_ratio = cvtest::randReal(rng)*0.6 + 0.7;
a[2] = (src.cols - 1)*0.5 + cvtest::randReal(rng)*10 - 5;
a[5] = (src.rows - 1)*0.5 + cvtest::randReal(rng)*10 - 5;
a[0] = sz/(0.9 - cvtest::randReal(rng)*0.6);
a[4] = aspect_ratio*a[0];
k[0] = cvtest::randReal(rng)*0.06 - 0.03;
k[1] = cvtest::randReal(rng)*0.06 - 0.03;
if( k[0]*k[1] > 0 )
k[1] = -k[1];
if( cvtest::randInt(rng)%4 != 0 )
{
k[2] = cvtest::randReal(rng)*0.004 - 0.002;
k[3] = cvtest::randReal(rng)*0.004 - 0.002;
}
else
k[2] = k[3] = 0;
new_cam[0] = a[0] + (cvtest::randReal(rng) - (double)0.5)*0.2*a[0]; //10%
new_cam[4] = a[4] + (cvtest::randReal(rng) - (double)0.5)*0.2*a[4]; //10%
new_cam[2] = a[2] + (cvtest::randReal(rng) - (double)0.5)*0.3*test_mat[INPUT][0].rows; //15%
new_cam[5] = a[5] + (cvtest::randReal(rng) - (double)0.5)*0.3*test_mat[INPUT][0].cols; //15%
_a.convertTo(_a0, _a0.depth());
zero_distortion = (cvtest::randInt(rng)%2) == 0 ? false : true;
_k.convertTo(_k0, _k0.depth());
zero_new_cam = (cvtest::randInt(rng)%2) == 0 ? false : true;
_new_cam.convertTo(_new_cam0, _new_cam0.depth());
//Testing C++ code
useCPlus = ((cvtest::randInt(rng) % 2)!=0);
if (useCPlus)
{
input0 = test_mat[INPUT][0];
input1 = test_mat[INPUT][1];
input2 = test_mat[INPUT][2];
input_new_cam = test_mat[INPUT][3];
}
return code;
}
void CV_UndistortTest::prepare_to_validation( int /*test_case_idx*/ )
{
if (useCPlus)
{
Mat& output = test_mat[INPUT_OUTPUT][0];
input_output.convertTo(output, output.type());
}
Mat& src = test_mat[INPUT][0];
Mat& dst = test_mat[REF_INPUT_OUTPUT][0];
Mat& dst0 = test_mat[INPUT_OUTPUT][0];
Mat mapx, mapy;
cvtest::initUndistortMap( test_mat[INPUT][1], test_mat[INPUT][2], dst.size(), mapx, mapy );
Mat mask( dst.size(), CV_8U );
test_remap( src, dst, mapx, mapy, &mask, interpolation );
dst.setTo(Scalar::all(0), mask);
dst0.setTo(Scalar::all(0), mask);
}
class CV_UndistortMapTest : public cvtest::ArrayTest
{
public:
CV_UndistortMapTest();
protected:
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
void run_func();
int prepare_test_case( int test_case_idx );
void prepare_to_validation( int /*test_case_idx*/ );
double get_success_error_level( int test_case_idx, int i, int j );
void fill_array( int test_case_idx, int i, int j, Mat& arr );
private:
bool dualChannel;
};
CV_UndistortMapTest::CV_UndistortMapTest()
{
test_array[INPUT].push_back(NULL);
test_array[INPUT].push_back(NULL);
test_array[OUTPUT].push_back(NULL);
test_array[OUTPUT].push_back(NULL);
test_array[REF_OUTPUT].push_back(NULL);
test_array[REF_OUTPUT].push_back(NULL);
element_wise_relative_error = false;
}
void CV_UndistortMapTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
RNG& rng = ts->get_rng();
cvtest::ArrayTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
int depth = cvtest::randInt(rng)%2 ? CV_64F : CV_32F;
Size sz = sizes[OUTPUT][0];
types[INPUT][0] = types[INPUT][1] = depth;
dualChannel = cvtest::randInt(rng)%2 == 0;
types[OUTPUT][0] = types[OUTPUT][1] =
types[REF_OUTPUT][0] = types[REF_OUTPUT][1] = dualChannel ? CV_32FC2 : CV_32F;
sizes[INPUT][0] = cvSize(3,3);
sizes[INPUT][1] = cvtest::randInt(rng)%2 ? cvSize(4,1) : cvSize(1,4);
sz.width = MAX(sz.width,16);
sz.height = MAX(sz.height,16);
sizes[OUTPUT][0] = sizes[OUTPUT][1] =
sizes[REF_OUTPUT][0] = sizes[REF_OUTPUT][1] = sz;
}
void CV_UndistortMapTest::fill_array( int test_case_idx, int i, int j, Mat& arr )
{
if( i != INPUT )
cvtest::ArrayTest::fill_array( test_case_idx, i, j, arr );
}
void CV_UndistortMapTest::run_func()
{
CvMat a = cvMat(test_mat[INPUT][0]), k = cvMat(test_mat[INPUT][1]);
if (!dualChannel )
cvInitUndistortMap( &a, &k, test_array[OUTPUT][0], test_array[OUTPUT][1] );
else
cvInitUndistortMap( &a, &k, test_array[OUTPUT][0], 0 );
}
double CV_UndistortMapTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
{
return 1e-3;
}
int CV_UndistortMapTest::prepare_test_case( int test_case_idx )
{
RNG& rng = ts->get_rng();
int code = cvtest::ArrayTest::prepare_test_case( test_case_idx );
const Mat& mapx = test_mat[OUTPUT][0];
double k[4], a[9] = {0,0,0,0,0,0,0,0,1};
double sz = MAX(mapx.rows, mapx.cols);
Mat& _a0 = test_mat[INPUT][0], &_k0 = test_mat[INPUT][1];
Mat _a(3,3,CV_64F,a);
Mat _k(_k0.rows,_k0.cols, CV_MAKETYPE(CV_64F,_k0.channels()),k);
if( code <= 0 )
return code;
double aspect_ratio = cvtest::randReal(rng)*0.6 + 0.7;
a[2] = (mapx.cols - 1)*0.5 + cvtest::randReal(rng)*10 - 5;
a[5] = (mapx.rows - 1)*0.5 + cvtest::randReal(rng)*10 - 5;
a[0] = sz/(0.9 - cvtest::randReal(rng)*0.6);
a[4] = aspect_ratio*a[0];
k[0] = cvtest::randReal(rng)*0.06 - 0.03;
k[1] = cvtest::randReal(rng)*0.06 - 0.03;
if( k[0]*k[1] > 0 )
k[1] = -k[1];
k[2] = cvtest::randReal(rng)*0.004 - 0.002;
k[3] = cvtest::randReal(rng)*0.004 - 0.002;
_a.convertTo(_a0, _a0.depth());
_k.convertTo(_k0, _k0.depth());
if (dualChannel)
{
test_mat[REF_OUTPUT][1] = Scalar::all(0);
test_mat[OUTPUT][1] = Scalar::all(0);
}
return code;
}
void CV_UndistortMapTest::prepare_to_validation( int )
{
Mat mapx, mapy;
cvtest::initUndistortMap( test_mat[INPUT][0], test_mat[INPUT][1], test_mat[REF_OUTPUT][0].size(), mapx, mapy );
if( !dualChannel )
{
mapx.copyTo(test_mat[REF_OUTPUT][0]);
mapy.copyTo(test_mat[REF_OUTPUT][1]);
}
else
{
Mat p[2] = {mapx, mapy};
cv::merge(p, 2, test_mat[REF_OUTPUT][0]);
}
}
TEST(Calib3d_Undistort, accuracy) { CV_UndistortTest test; test.safe_run(); }
TEST(Calib3d_InitUndistortMap, accuracy) { CV_UndistortMapTest test; test.safe_run(); }
}} // namespace

2
modules/calib3d/test/test_undistort_badarg.cpp
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@ -40,7 +40,7 @@
//M*/
#include "test_precomp.hpp"
#include "opencv2/imgproc/imgproc_c.h"
#include "opencv2/calib3d/calib3d_c.h"
namespace opencv_test { namespace {

193
modules/imgproc/include/opencv2/imgproc.hpp
Unescape View File

@ -329,12 +329,6 @@ enum AdaptiveThresholdTypes {
ADAPTIVE_THRESH_GAUSSIAN_C = 1
};
//! cv::undistort mode
enum UndistortTypes {
PROJ_SPHERICAL_ORTHO = 0,
PROJ_SPHERICAL_EQRECT = 1
};
//! class of the pixel in GrabCut algorithm
enum GrabCutClasses {
GC_BGD = 0, //!< an obvious background pixels
@ -2977,193 +2971,6 @@ CV_EXPORTS void buildPyramid( InputArray src, OutputArrayOfArrays dst,
//! @} imgproc_filter
//! @addtogroup imgproc_transform
//! @{
/** @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 cvInitUndistortMap 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 );
//! initializes maps for #remap for wide-angle
CV_EXPORTS_W float initWideAngleProjMap( InputArray cameraMatrix, InputArray distCoeffs,
Size imageSize, int destImageWidth,
int m1type, OutputArray map1, OutputArray map2,
int projType = PROJ_SPHERICAL_EQRECT, double alpha = 0);
/** @brief Returns the default new camera matrix.
The function returns the camera matrix that is either an exact copy of the input cameraMatrix (when
centerPrinicipalPoint=false ), or the modified one (when centerPrincipalPoint=true).
In the latter case, the new camera matrix will be:
\f[\begin{bmatrix} f_x && 0 && ( \texttt{imgSize.width} -1)*0.5 \\ 0 && f_y && ( \texttt{imgSize.height} -1)*0.5 \\ 0 && 0 && 1 \end{bmatrix} ,\f]
where \f$f_x\f$ and \f$f_y\f$ are \f$(0,0)\f$ and \f$(1,1)\f$ elements of cameraMatrix, respectively.
By default, the undistortion functions in OpenCV (see #initUndistortRectifyMap, #undistort) do not
move the principal point. However, when you work with stereo, it is important to move the principal
points in both views to the same y-coordinate (which is required by most of stereo correspondence
algorithms), and may be to the same x-coordinate too. So, you can form the new camera matrix for
each view where the principal points are located at the center.
@param cameraMatrix Input camera matrix.
@param imgsize Camera view image size in pixels.
@param centerPrincipalPoint Location of the principal point in the new camera matrix. The
parameter indicates whether this location should be at the image center or not.
*/
CV_EXPORTS_W Mat getDefaultNewCameraMatrix( InputArray cameraMatrix, Size imgsize = Size(),
bool centerPrincipalPoint = false );
/** @brief Computes the ideal point coordinates from the observed point coordinates.
The function is similar to #undistort and #initUndistortRectifyMap but it operates on a
sparse set of points instead of a raster image. Also the function performs a reverse transformation
to projectPoints. In case of a 3D object, it does not reconstruct its 3D coordinates, but for a
planar object, it does, up to a translation vector, if the proper R is specified.
For each observed point coordinate \f$(u, v)\f$ the function computes:
\f[
\begin{array}{l}
x^{"} \leftarrow (u - c_x)/f_x \\
y^{"} \leftarrow (v - c_y)/f_y \\
(x',y') = undistort(x^{"},y^{"}, \texttt{distCoeffs}) \\
{[X\,Y\,W]} ^T \leftarrow R*[x' \, y' \, 1]^T \\
x \leftarrow X/W \\
y \leftarrow Y/W \\
\text{only performed if P is specified:} \\
u' \leftarrow x {f'}_x + {c'}_x \\
v' \leftarrow y {f'}_y + {c'}_y
\end{array}
\f]
where *undistort* is an approximate iterative algorithm that estimates the normalized original
point coordinates out of the normalized distorted point coordinates ("normalized" means that the
coordinates do not depend on the camera matrix).
The function can be used for both a stereo camera head or a monocular camera (when R is empty).
@param src Observed point coordinates, 1xN or Nx1 2-channel (CV_32FC2 or CV_64FC2).
@param dst Output ideal point coordinates after undistortion and reverse perspective
transformation. If matrix P is identity or omitted, dst will contain normalized point coordinates.
@param cameraMatrix Camera matrix \f$\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ .
@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 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 used.
@param P New camera matrix (3x3) or new projection matrix (3x4) \f$\begin{bmatrix} {f'}_x & 0 & {c'}_x & t_x \\ 0 & {f'}_y & {c'}_y & t_y \\ 0 & 0 & 1 & t_z \end{bmatrix}\f$. P1 or P2 computed by
#stereoRectify can be passed here. If the matrix is empty, the identity new camera matrix is used.
*/
CV_EXPORTS_W void undistortPoints( InputArray src, OutputArray dst,
InputArray cameraMatrix, InputArray distCoeffs,
InputArray R = noArray(), InputArray P = noArray());
/** @overload
@note Default version of #undistortPoints does 5 iterations to compute undistorted points.
*/
CV_EXPORTS_AS(undistortPointsIter) void undistortPoints( InputArray src, OutputArray dst,
InputArray cameraMatrix, InputArray distCoeffs,
InputArray R, InputArray P, TermCriteria criteria);
//! @} imgproc_transform
//! @addtogroup imgproc_hist
//! @{

33
modules/imgproc/include/opencv2/imgproc/imgproc_c.h
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@ -273,39 +273,6 @@ CVAPI(void) cvLinearPolar( const CvArr* src, CvArr* dst,
CvPoint2D32f center, double maxRadius,
int flags CV_DEFAULT(CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS));
/** @brief Transforms the input image to compensate lens distortion
@see cv::undistort
*/
CVAPI(void) cvUndistort2( const CvArr* src, CvArr* dst,
const CvMat* camera_matrix,
const CvMat* distortion_coeffs,
const CvMat* new_camera_matrix CV_DEFAULT(0) );
/** @brief Computes transformation map from intrinsic camera parameters
that can used by cvRemap
*/
CVAPI(void) cvInitUndistortMap( const CvMat* camera_matrix,
const CvMat* distortion_coeffs,
CvArr* mapx, CvArr* mapy );
/** @brief Computes undistortion+rectification map for a head of stereo camera
@see cv::initUndistortRectifyMap
*/
CVAPI(void) cvInitUndistortRectifyMap( const CvMat* camera_matrix,
const CvMat* dist_coeffs,
const CvMat *R, const CvMat* new_camera_matrix,
CvArr* mapx, CvArr* mapy );
/** @brief Computes the original (undistorted) feature coordinates
from the observed (distorted) coordinates
@see cv::undistortPoints
*/
CVAPI(void) cvUndistortPoints( const CvMat* src, CvMat* dst,
const CvMat* camera_matrix,
const CvMat* dist_coeffs,
const CvMat* R CV_DEFAULT(0),
const CvMat* P CV_DEFAULT(0));
/** @brief Returns a structuring element of the specified size and shape for morphological operations.
@note the created structuring element IplConvKernel\* element must be released in the end using

127
modules/imgproc/misc/java/test/ImgprocTest.java
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@ -891,21 +891,6 @@ public class ImgprocTest extends OpenCVTestCase {
assertMatEqual(truth, transform, EPS);
}
public void testGetDefaultNewCameraMatrixMat() {
Mat mtx = Imgproc.getDefaultNewCameraMatrix(gray0);
assertFalse(mtx.empty());
assertEquals(0, Core.countNonZero(mtx));
}
public void testGetDefaultNewCameraMatrixMatSizeBoolean() {
Mat mtx = Imgproc.getDefaultNewCameraMatrix(gray0, size, true);
assertFalse(mtx.empty());
assertFalse(0 == Core.countNonZero(mtx));
// TODO_: write better test
}
public void testGetDerivKernelsMatMatIntIntInt() {
Mat kx = new Mat(imgprocSz, imgprocSz, CvType.CV_32F);
Mat ky = new Mat(imgprocSz, imgprocSz, CvType.CV_32F);
@ -1139,52 +1124,6 @@ public class ImgprocTest extends OpenCVTestCase {
fail("Not yet implemented");
}
public void testInitUndistortRectifyMap() {
fail("Not yet implemented");
Mat cameraMatrix = new Mat(3, 3, CvType.CV_32F);
cameraMatrix.put(0, 0, 1, 0, 1);
cameraMatrix.put(1, 0, 0, 1, 1);
cameraMatrix.put(2, 0, 0, 0, 1);
Mat R = new Mat(3, 3, CvType.CV_32F, new Scalar(2));
Mat newCameraMatrix = new Mat(3, 3, CvType.CV_32F, new Scalar(3));
Mat distCoeffs = new Mat();
Mat map1 = new Mat();
Mat map2 = new Mat();
// TODO: complete this test
Imgproc.initUndistortRectifyMap(cameraMatrix, distCoeffs, R, newCameraMatrix, size, CvType.CV_32F, map1, map2);
}
public void testInitWideAngleProjMapMatMatSizeIntIntMatMat() {
fail("Not yet implemented");
Mat cameraMatrix = new Mat(3, 3, CvType.CV_32F);
Mat distCoeffs = new Mat(1, 4, CvType.CV_32F);
// Size imageSize = new Size(2, 2);
cameraMatrix.put(0, 0, 1, 0, 1);
cameraMatrix.put(1, 0, 0, 1, 2);
cameraMatrix.put(2, 0, 0, 0, 1);
distCoeffs.put(0, 0, 1, 3, 2, 4);
truth = new Mat(3, 3, CvType.CV_32F);
truth.put(0, 0, 0, 0, 0);
truth.put(1, 0, 0, 0, 0);
truth.put(2, 0, 0, 3, 0);
// TODO: No documentation for this function
// Imgproc.initWideAngleProjMap(cameraMatrix, distCoeffs, imageSize,
// 5, m1type, truthput1, truthput2);
}
public void testInitWideAngleProjMapMatMatSizeIntIntMatMatInt() {
fail("Not yet implemented");
}
public void testInitWideAngleProjMapMatMatSizeIntIntMatMatIntDouble() {
fail("Not yet implemented");
}
public void testIntegral2MatMatMat() {
Mat src = new Mat(3, 3, CvType.CV_32F, new Scalar(3));
Mat expSum = new Mat(4, 4, CvType.CV_64F);
@ -1748,72 +1687,6 @@ public class ImgprocTest extends OpenCVTestCase {
assertMatEqual(makeMask(gray255.clone(), 0), dst);
}
public void testUndistortMatMatMatMat() {
Mat src = new Mat(3, 3, CvType.CV_32F, new Scalar(3));
Mat cameraMatrix = new Mat(3, 3, CvType.CV_32F) {
{
put(0, 0, 1, 0, 1);
put(1, 0, 0, 1, 2);
put(2, 0, 0, 0, 1);
}
};
Mat distCoeffs = new Mat(1, 4, CvType.CV_32F) {
{
put(0, 0, 1, 3, 2, 4);
}
};
Imgproc.undistort(src, dst, cameraMatrix, distCoeffs);
truth = new Mat(3, 3, CvType.CV_32F) {
{
put(0, 0, 0, 0, 0);
put(1, 0, 0, 0, 0);
put(2, 0, 0, 3, 0);
}
};
assertMatEqual(truth, dst, EPS);
}
public void testUndistortMatMatMatMatMat() {
Mat src = new Mat(3, 3, CvType.CV_32F, new Scalar(3));
Mat cameraMatrix = new Mat(3, 3, CvType.CV_32F) {
{
put(0, 0, 1, 0, 1);
put(1, 0, 0, 1, 2);
put(2, 0, 0, 0, 1);
}
};
Mat distCoeffs = new Mat(1, 4, CvType.CV_32F) {
{
put(0, 0, 2, 1, 4, 5);
}
};
Mat newCameraMatrix = new Mat(3, 3, CvType.CV_32F, new Scalar(1));
Imgproc.undistort(src, dst, cameraMatrix, distCoeffs, newCameraMatrix);
truth = new Mat(3, 3, CvType.CV_32F, new Scalar(3));
assertMatEqual(truth, dst, EPS);
}
//undistortPoints(List<Point> src, List<Point> dst, Mat cameraMatrix, Mat distCoeffs)
public void testUndistortPointsListOfPointListOfPointMatMat() {
MatOfPoint2f src = new MatOfPoint2f(new Point(1, 2), new Point(3, 4), new Point(-1, -1));
MatOfPoint2f dst = new MatOfPoint2f();
Mat cameraMatrix = Mat.eye(3, 3, CvType.CV_64FC1);
Mat distCoeffs = new Mat(8, 1, CvType.CV_64FC1, new Scalar(0));
Imgproc.undistortPoints(src, dst, cameraMatrix, distCoeffs);
assertEquals(src.size(), dst.size());
for(int i=0; i<src.toList().size(); i++) {
//Log.d("UndistortPoints", "s="+src.get(i)+", d="+dst.get(i));
assertTrue(src.toList().get(i).equals(dst.toList().get(i)));
}
}
public void testWarpAffineMatMatMatSize() {
Mat src = new Mat(3, 3, CvType.CV_32F) {
{

308
modules/imgproc/test/test_imgwarp.cpp
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@ -812,312 +812,6 @@ void CV_RemapTest::prepare_to_validation( int /*test_case_idx*/ )
dst0.setTo(Scalar::all(0), mask);
}
////////////////////////////// undistort /////////////////////////////////
class CV_UndistortTest : public CV_ImgWarpBaseTest
{
public:
CV_UndistortTest();
protected:
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
void run_func();
int prepare_test_case( int test_case_idx );
void prepare_to_validation( int /*test_case_idx*/ );
double get_success_error_level( int test_case_idx, int i, int j );
void fill_array( int test_case_idx, int i, int j, Mat& arr );
private:
bool useCPlus;
cv::Mat input0;
cv::Mat input1;
cv::Mat input2;
cv::Mat input_new_cam;
cv::Mat input_output;
bool zero_new_cam;
bool zero_distortion;
};
CV_UndistortTest::CV_UndistortTest() : CV_ImgWarpBaseTest( false )
{
//spatial_scale_zoom = spatial_scale_decimate;
test_array[INPUT].push_back(NULL);
test_array[INPUT].push_back(NULL);
test_array[INPUT].push_back(NULL);
spatial_scale_decimate = spatial_scale_zoom;
}
void CV_UndistortTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
RNG& rng = ts->get_rng();
CV_ImgWarpBaseTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
int type = types[INPUT][0];
type = CV_MAKETYPE( CV_8U, CV_MAT_CN(type) );
types[INPUT][0] = types[INPUT_OUTPUT][0] = types[REF_INPUT_OUTPUT][0] = type;
types[INPUT][1] = cvtest::randInt(rng)%2 ? CV_64F : CV_32F;
types[INPUT][2] = cvtest::randInt(rng)%2 ? CV_64F : CV_32F;
sizes[INPUT][1] = cvSize(3,3);
sizes[INPUT][2] = cvtest::randInt(rng)%2 ? cvSize(4,1) : cvSize(1,4);
types[INPUT][3] = types[INPUT][1];
sizes[INPUT][3] = sizes[INPUT][1];
interpolation = CV_INTER_LINEAR;
}
void CV_UndistortTest::fill_array( int test_case_idx, int i, int j, Mat& arr )
{
if( i != INPUT )
CV_ImgWarpBaseTest::fill_array( test_case_idx, i, j, arr );
}
void CV_UndistortTest::run_func()
{
if (!useCPlus)
{
CvMat a = cvMat(test_mat[INPUT][1]), k = cvMat(test_mat[INPUT][2]);
cvUndistort2( test_array[INPUT][0], test_array[INPUT_OUTPUT][0], &a, &k);
}
else
{
if (zero_distortion)
{
cv::undistort(input0,input_output,input1,cv::Mat());
}
else
{
cv::undistort(input0,input_output,input1,input2);
}
}
}
double CV_UndistortTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
{
int depth = test_mat[INPUT][0].depth();
return depth == CV_8U ? 16 : depth == CV_16U ? 1024 : 5e-2;
}
int CV_UndistortTest::prepare_test_case( int test_case_idx )
{
RNG& rng = ts->get_rng();
int code = CV_ImgWarpBaseTest::prepare_test_case( test_case_idx );
const Mat& src = test_mat[INPUT][0];
double k[4], a[9] = {0,0,0,0,0,0,0,0,1};
double new_cam[9] = {0,0,0,0,0,0,0,0,1};
double sz = MAX(src.rows, src.cols);
Mat& _new_cam0 = test_mat[INPUT][3];
Mat _new_cam(test_mat[INPUT][3].rows,test_mat[INPUT][3].cols,CV_64F,new_cam);
Mat& _a0 = test_mat[INPUT][1];
Mat _a(3,3,CV_64F,a);
Mat& _k0 = test_mat[INPUT][2];
Mat _k(_k0.rows,_k0.cols, CV_MAKETYPE(CV_64F,_k0.channels()),k);
if( code <= 0 )
return code;
double aspect_ratio = cvtest::randReal(rng)*0.6 + 0.7;
a[2] = (src.cols - 1)*0.5 + cvtest::randReal(rng)*10 - 5;
a[5] = (src.rows - 1)*0.5 + cvtest::randReal(rng)*10 - 5;
a[0] = sz/(0.9 - cvtest::randReal(rng)*0.6);
a[4] = aspect_ratio*a[0];
k[0] = cvtest::randReal(rng)*0.06 - 0.03;
k[1] = cvtest::randReal(rng)*0.06 - 0.03;
if( k[0]*k[1] > 0 )
k[1] = -k[1];
if( cvtest::randInt(rng)%4 != 0 )
{
k[2] = cvtest::randReal(rng)*0.004 - 0.002;
k[3] = cvtest::randReal(rng)*0.004 - 0.002;
}
else
k[2] = k[3] = 0;
new_cam[0] = a[0] + (cvtest::randReal(rng) - (double)0.5)*0.2*a[0]; //10%
new_cam[4] = a[4] + (cvtest::randReal(rng) - (double)0.5)*0.2*a[4]; //10%
new_cam[2] = a[2] + (cvtest::randReal(rng) - (double)0.5)*0.3*test_mat[INPUT][0].rows; //15%
new_cam[5] = a[5] + (cvtest::randReal(rng) - (double)0.5)*0.3*test_mat[INPUT][0].cols; //15%
_a.convertTo(_a0, _a0.depth());
zero_distortion = (cvtest::randInt(rng)%2) == 0 ? false : true;
_k.convertTo(_k0, _k0.depth());
zero_new_cam = (cvtest::randInt(rng)%2) == 0 ? false : true;
_new_cam.convertTo(_new_cam0, _new_cam0.depth());
//Testing C++ code
useCPlus = ((cvtest::randInt(rng) % 2)!=0);
if (useCPlus)
{
input0 = test_mat[INPUT][0];
input1 = test_mat[INPUT][1];
input2 = test_mat[INPUT][2];
input_new_cam = test_mat[INPUT][3];
}
return code;
}
void CV_UndistortTest::prepare_to_validation( int /*test_case_idx*/ )
{
if (useCPlus)
{
Mat& output = test_mat[INPUT_OUTPUT][0];
input_output.convertTo(output, output.type());
}
Mat& src = test_mat[INPUT][0];
Mat& dst = test_mat[REF_INPUT_OUTPUT][0];
Mat& dst0 = test_mat[INPUT_OUTPUT][0];
Mat mapx, mapy;
cvtest::initUndistortMap( test_mat[INPUT][1], test_mat[INPUT][2], dst.size(), mapx, mapy );
Mat mask( dst.size(), CV_8U );
test_remap( src, dst, mapx, mapy, &mask, interpolation );
dst.setTo(Scalar::all(0), mask);
dst0.setTo(Scalar::all(0), mask);
}
class CV_UndistortMapTest : public cvtest::ArrayTest
{
public:
CV_UndistortMapTest();
protected:
void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
void run_func();
int prepare_test_case( int test_case_idx );
void prepare_to_validation( int /*test_case_idx*/ );
double get_success_error_level( int test_case_idx, int i, int j );
void fill_array( int test_case_idx, int i, int j, Mat& arr );
private:
bool dualChannel;
};
CV_UndistortMapTest::CV_UndistortMapTest()
{
test_array[INPUT].push_back(NULL);
test_array[INPUT].push_back(NULL);
test_array[OUTPUT].push_back(NULL);
test_array[OUTPUT].push_back(NULL);
test_array[REF_OUTPUT].push_back(NULL);
test_array[REF_OUTPUT].push_back(NULL);
element_wise_relative_error = false;
}
void CV_UndistortMapTest::get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types )
{
RNG& rng = ts->get_rng();
cvtest::ArrayTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
int depth = cvtest::randInt(rng)%2 ? CV_64F : CV_32F;
Size sz = sizes[OUTPUT][0];
types[INPUT][0] = types[INPUT][1] = depth;
dualChannel = cvtest::randInt(rng)%2 == 0;
types[OUTPUT][0] = types[OUTPUT][1] =
types[REF_OUTPUT][0] = types[REF_OUTPUT][1] = dualChannel ? CV_32FC2 : CV_32F;
sizes[INPUT][0] = cvSize(3,3);
sizes[INPUT][1] = cvtest::randInt(rng)%2 ? cvSize(4,1) : cvSize(1,4);
sz.width = MAX(sz.width,16);
sz.height = MAX(sz.height,16);
sizes[OUTPUT][0] = sizes[OUTPUT][1] =
sizes[REF_OUTPUT][0] = sizes[REF_OUTPUT][1] = sz;
}
void CV_UndistortMapTest::fill_array( int test_case_idx, int i, int j, Mat& arr )
{
if( i != INPUT )
cvtest::ArrayTest::fill_array( test_case_idx, i, j, arr );
}
void CV_UndistortMapTest::run_func()
{
CvMat a = cvMat(test_mat[INPUT][0]), k = cvMat(test_mat[INPUT][1]);
if (!dualChannel )
cvInitUndistortMap( &a, &k, test_array[OUTPUT][0], test_array[OUTPUT][1] );
else
cvInitUndistortMap( &a, &k, test_array[OUTPUT][0], 0 );
}
double CV_UndistortMapTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
{
return 1e-3;
}
int CV_UndistortMapTest::prepare_test_case( int test_case_idx )
{
RNG& rng = ts->get_rng();
int code = cvtest::ArrayTest::prepare_test_case( test_case_idx );
const Mat& mapx = test_mat[OUTPUT][0];
double k[4], a[9] = {0,0,0,0,0,0,0,0,1};
double sz = MAX(mapx.rows, mapx.cols);
Mat& _a0 = test_mat[INPUT][0], &_k0 = test_mat[INPUT][1];
Mat _a(3,3,CV_64F,a);
Mat _k(_k0.rows,_k0.cols, CV_MAKETYPE(CV_64F,_k0.channels()),k);
if( code <= 0 )
return code;
double aspect_ratio = cvtest::randReal(rng)*0.6 + 0.7;
a[2] = (mapx.cols - 1)*0.5 + cvtest::randReal(rng)*10 - 5;
a[5] = (mapx.rows - 1)*0.5 + cvtest::randReal(rng)*10 - 5;
a[0] = sz/(0.9 - cvtest::randReal(rng)*0.6);
a[4] = aspect_ratio*a[0];
k[0] = cvtest::randReal(rng)*0.06 - 0.03;
k[1] = cvtest::randReal(rng)*0.06 - 0.03;
if( k[0]*k[1] > 0 )
k[1] = -k[1];
k[2] = cvtest::randReal(rng)*0.004 - 0.002;
k[3] = cvtest::randReal(rng)*0.004 - 0.002;
_a.convertTo(_a0, _a0.depth());
_k.convertTo(_k0, _k0.depth());
if (dualChannel)
{
test_mat[REF_OUTPUT][1] = Scalar::all(0);
test_mat[OUTPUT][1] = Scalar::all(0);
}
return code;
}
void CV_UndistortMapTest::prepare_to_validation( int )
{
Mat mapx, mapy;
cvtest::initUndistortMap( test_mat[INPUT][0], test_mat[INPUT][1], test_mat[REF_OUTPUT][0].size(), mapx, mapy );
if( !dualChannel )
{
mapx.copyTo(test_mat[REF_OUTPUT][0]);
mapy.copyTo(test_mat[REF_OUTPUT][1]);
}
else
{
Mat p[2] = {mapx, mapy};
cv::merge(p, 2, test_mat[REF_OUTPUT][0]);
}
}
////////////////////////////// GetRectSubPix /////////////////////////////////
static void
@ -1616,8 +1310,6 @@ TEST(Imgproc_ResizeExact, accuracy) { CV_ResizeExactTest test; test.safe_run();
TEST(Imgproc_WarpAffine, accuracy) { CV_WarpAffineTest test; test.safe_run(); }
TEST(Imgproc_WarpPerspective, accuracy) { CV_WarpPerspectiveTest test; test.safe_run(); }
TEST(Imgproc_Remap, accuracy) { CV_RemapTest test; test.safe_run(); }
TEST(Imgproc_Undistort, accuracy) { CV_UndistortTest test; test.safe_run(); }
TEST(Imgproc_InitUndistortMap, accuracy) { CV_UndistortMapTest test; test.safe_run(); }
TEST(Imgproc_GetRectSubPix, accuracy) { CV_GetRectSubPixTest test; test.safe_run(); }
TEST(Imgproc_GetQuadSubPix, accuracy) { CV_GetQuadSubPixTest test; test.safe_run(); }

5
samples/android/camera-calibration/src/org/opencv/samples/cameracalibration/OnCameraFrameRender.java
Unescape View File

@ -4,6 +4,7 @@ import java.util.ArrayList;
import java.util.List;
import org.opencv.android.CameraBridgeViewBase.CvCameraViewFrame;
import org.opencv.calib3d.Calib3d;
import org.opencv.core.Core;
import org.opencv.core.Mat;
import org.opencv.core.MatOfPoint;
@ -50,7 +51,7 @@ class UndistortionFrameRender extends FrameRender {
@Override
public Mat render(CvCameraViewFrame inputFrame) {
Mat renderedFrame = new Mat(inputFrame.rgba().size(), inputFrame.rgba().type());
Imgproc.undistort(inputFrame.rgba(), renderedFrame,
Calib3d.undistort(inputFrame.rgba(), renderedFrame,
mCalibrator.getCameraMatrix(), mCalibrator.getDistortionCoefficients());
return renderedFrame;
@ -71,7 +72,7 @@ class ComparisonFrameRender extends FrameRender {
@Override
public Mat render(CvCameraViewFrame inputFrame) {
Mat undistortedFrame = new Mat(inputFrame.rgba().size(), inputFrame.rgba().type());
Imgproc.undistort(inputFrame.rgba(), undistortedFrame,
Calib3d.undistort(inputFrame.rgba(), undistortedFrame,
mCalibrator.getCameraMatrix(), mCalibrator.getDistortionCoefficients());
Mat comparisonFrame = inputFrame.rgba();

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