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Doxygen documentation: more fixes and cleanups

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This commit is contained in:
Maksim Shabunin
2014-11-21 11:28:14 +03:00
parent f9a83c28e5
commit 1523fdcc1c
41 changed files with 894 additions and 859 deletions
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208
modules/calib3d/include/opencv2/calib3d.hpp
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@@ -127,19 +127,19 @@ pattern (every view is described by several 3D-2D point correspondences).
@note
- A calibration sample for 3 cameras in horizontal position can be found at
opencv\_source\_code/samples/cpp/3calibration.cpp
opencv_source_code/samples/cpp/3calibration.cpp
- A calibration sample based on a sequence of images can be found at
opencv\_source\_code/samples/cpp/calibration.cpp
opencv_source_code/samples/cpp/calibration.cpp
- A calibration sample in order to do 3D reconstruction can be found at
opencv\_source\_code/samples/cpp/build3dmodel.cpp
opencv_source_code/samples/cpp/build3dmodel.cpp
- A calibration sample of an artificially generated camera and chessboard patterns can be
found at opencv\_source\_code/samples/cpp/calibration\_artificial.cpp
found at opencv_source_code/samples/cpp/calibration_artificial.cpp
- A calibration example on stereo calibration can be found at
opencv\_source\_code/samples/cpp/stereo\_calib.cpp
opencv_source_code/samples/cpp/stereo_calib.cpp
- A calibration example on stereo matching can be found at
opencv\_source\_code/samples/cpp/stereo\_match.cpp
opencv_source_code/samples/cpp/stereo_match.cpp
- (Python) A camera calibration sample can be found at
opencv\_source\_code/samples/python2/calibrate.py
opencv_source_code/samples/python2/calibrate.py
@{
@defgroup calib3d_fisheye Fisheye camera model
@@ -257,9 +257,9 @@ CV_EXPORTS_W void Rodrigues( InputArray src, OutputArray dst, OutputArray jacobi
/** @brief Finds a perspective transformation between two planes.
@param srcPoints Coordinates of the points in the original plane, a matrix of the type CV\_32FC2
@param srcPoints Coordinates of the points in the original plane, a matrix of the type CV_32FC2
or vector\<Point2f\> .
@param dstPoints Coordinates of the points in the target plane, a matrix of the type CV\_32FC2 or
@param dstPoints Coordinates of the points in the target plane, a matrix of the type CV_32FC2 or
a vector\<Point2f\> .
@param method Method used to computed a homography matrix. The following methods are possible:
- **0** - a regular method using all the points
@@ -315,7 +315,7 @@ cannot be estimated, an empty one will be returned.
@note
- A example on calculating a homography for image matching can be found at
opencv\_source\_code/samples/cpp/video\_homography.cpp
opencv_source_code/samples/cpp/video_homography.cpp
*/
CV_EXPORTS_W Mat findHomography( InputArray srcPoints, InputArray dstPoints,
@@ -485,22 +485,22 @@ the vector is NULL/empty, the zero distortion coefficients are assumed.
@param rvec Output rotation vector (see Rodrigues ) that, together with tvec , brings points from
the model coordinate system to the camera coordinate system.
@param tvec Output translation vector.
@param useExtrinsicGuess Parameter used for SOLVEPNP\_ITERATIVE. If true (1), the function uses
@param useExtrinsicGuess Parameter used for SOLVEPNP_ITERATIVE. If true (1), the function uses
the provided rvec and tvec values as initial approximations of the rotation and translation
vectors, respectively, and further optimizes them.
@param flags Method for solving a PnP problem:
- **SOLVEPNP\_ITERATIVE** Iterative method is based on Levenberg-Marquardt optimization. In
- **SOLVEPNP_ITERATIVE** Iterative method is based on Levenberg-Marquardt optimization. In
this case the function finds such a pose that minimizes reprojection error, that is the sum
of squared distances between the observed projections imagePoints and the projected (using
projectPoints ) objectPoints .
- **SOLVEPNP\_P3P** Method is based on the paper of X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang
- **SOLVEPNP_P3P** Method is based on the paper of X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang
"Complete Solution Classification for the Perspective-Three-Point Problem". In this case the
function requires exactly four object and image points.
- **SOLVEPNP\_EPNP** Method has been introduced by F.Moreno-Noguer, V.Lepetit and P.Fua in the
- **SOLVEPNP_EPNP** Method has been introduced by F.Moreno-Noguer, V.Lepetit and P.Fua in the
paper "EPnP: Efficient Perspective-n-Point Camera Pose Estimation".
- **SOLVEPNP\_DLS** Method is based on the paper of Joel A. Hesch and Stergios I. Roumeliotis.
- **SOLVEPNP_DLS** Method is based on the paper of Joel A. Hesch and Stergios I. Roumeliotis.
"A Direct Least-Squares (DLS) Method for PnP".
- **SOLVEPNP\_UPNP** Method is based on the paper of A.Penate-Sanchez, J.Andrade-Cetto,
- **SOLVEPNP_UPNP** Method is based on the paper of A.Penate-Sanchez, J.Andrade-Cetto,
F.Moreno-Noguer. "Exhaustive Linearization for Robust Camera Pose and Focal Length
Estimation". In this case the function also estimates the parameters \f$f_x\f$ and \f$f_y\f$
assuming that both have the same value. Then the cameraMatrix is updated with the estimated
@@ -511,7 +511,7 @@ projections, as well as the camera matrix and the distortion coefficients.
@note
- An example of how to use solvePnP for planar augmented reality can be found at
opencv\_source\_code/samples/python2/plane\_ar.py
opencv_source_code/samples/python2/plane_ar.py
*/
CV_EXPORTS_W bool solvePnP( InputArray objectPoints, InputArray imagePoints,
InputArray cameraMatrix, InputArray distCoeffs,
@@ -531,7 +531,7 @@ the vector is NULL/empty, the zero distortion coefficients are assumed.
@param rvec Output rotation vector (see Rodrigues ) that, together with tvec , brings points from
the model coordinate system to the camera coordinate system.
@param tvec Output translation vector.
@param useExtrinsicGuess Parameter used for SOLVEPNP\_ITERATIVE. If true (1), the function uses
@param useExtrinsicGuess Parameter used for SOLVEPNP_ITERATIVE. If true (1), the function uses
the provided rvec and tvec values as initial approximations of the rotation and translation
vectors, respectively, and further optimizes them.
@param iterationsCount Number of iterations.
@@ -550,7 +550,7 @@ makes the function resistant to outliers.
@note
- An example of how to use solvePNPRansac for object detection can be found at
opencv\_source\_code/samples/cpp/tutorial\_code/calib3d/real\_time\_pose\_estimation/
opencv_source_code/samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/
*/
CV_EXPORTS_W bool solvePnPRansac( InputArray objectPoints, InputArray imagePoints,
InputArray cameraMatrix, InputArray distCoeffs,
@@ -582,16 +582,16 @@ CV_EXPORTS_W Mat initCameraMatrix2D( InputArrayOfArrays objectPoints,
@param image Source chessboard view. It must be an 8-bit grayscale or color image.
@param patternSize Number of inner corners per a chessboard row and column
( patternSize = cvSize(points\_per\_row,points\_per\_colum) = cvSize(columns,rows) ).
( patternSize = cvSize(points_per_row,points_per_colum) = cvSize(columns,rows) ).
@param corners Output array of detected corners.
@param flags Various operation flags that can be zero or a combination of the following values:
- **CV\_CALIB\_CB\_ADAPTIVE\_THRESH** Use adaptive thresholding to convert the image to black
- **CV_CALIB_CB_ADAPTIVE_THRESH** Use adaptive thresholding to convert the image to black
and white, rather than a fixed threshold level (computed from the average image brightness).
- **CV\_CALIB\_CB\_NORMALIZE\_IMAGE** Normalize the image gamma with equalizeHist before
- **CV_CALIB_CB_NORMALIZE_IMAGE** Normalize the image gamma with equalizeHist before
applying fixed or adaptive thresholding.
- **CV\_CALIB\_CB\_FILTER\_QUADS** Use additional criteria (like contour area, perimeter,
- **CV_CALIB_CB_FILTER_QUADS** Use additional criteria (like contour area, perimeter,
square-like shape) to filter out false quads extracted at the contour retrieval stage.
- **CALIB\_CB\_FAST\_CHECK** Run a fast check on the image that looks for chessboard corners,
- **CALIB_CB_FAST_CHECK** Run a fast check on the image that looks for chessboard corners,
and shortcut the call if none is found. This can drastically speed up the call in the
degenerate condition when no chessboard is observed.
@@ -637,7 +637,7 @@ CV_EXPORTS bool find4QuadCornerSubpix( InputArray img, InputOutputArray corners,
@param image Destination image. It must be an 8-bit color image.
@param patternSize Number of inner corners per a chessboard row and column
(patternSize = cv::Size(points\_per\_row,points\_per\_column)).
(patternSize = cv::Size(points_per_row,points_per_column)).
@param corners Array of detected corners, the output of findChessboardCorners.
@param patternWasFound Parameter indicating whether the complete board was found or not. The
return value of findChessboardCorners should be passed here.
@@ -652,12 +652,12 @@ CV_EXPORTS_W void drawChessboardCorners( InputOutputArray image, Size patternSiz
@param image grid view of input circles; it must be an 8-bit grayscale or color image.
@param patternSize number of circles per row and column
( patternSize = Size(points\_per\_row, points\_per\_colum) ).
( patternSize = Size(points_per_row, points_per_colum) ).
@param centers output array of detected centers.
@param flags various operation flags that can be one of the following values:
- **CALIB\_CB\_SYMMETRIC\_GRID** uses symmetric pattern of circles.
- **CALIB\_CB\_ASYMMETRIC\_GRID** uses asymmetric pattern of circles.
- **CALIB\_CB\_CLUSTERING** uses a special algorithm for grid detection. It is more robust to
- **CALIB_CB_SYMMETRIC_GRID** uses symmetric pattern of circles.
- **CALIB_CB_ASYMMETRIC_GRID** uses asymmetric pattern of circles.
- **CALIB_CB_CLUSTERING** uses a special algorithm for grid detection. It is more robust to
perspective distortions but much more sensitive to background clutter.
@param blobDetector feature detector that finds blobs like dark circles on light background.
@@ -703,7 +703,7 @@ together.
@param imageSize Size of the image used only to initialize the intrinsic camera matrix.
@param cameraMatrix Output 3x3 floating-point camera matrix
\f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . If CV\_CALIB\_USE\_INTRINSIC\_GUESS
and/or CV\_CALIB\_FIX\_ASPECT\_RATIO are specified, some or all of fx, fy, cx, cy must be
and/or CV_CALIB_FIX_ASPECT_RATIO are specified, some or all of fx, fy, cx, cy must be
initialized before calling the function.
@param distCoeffs Output vector of distortion coefficients
\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\f$ of 4, 5, 8 or 12 elements.
@@ -714,33 +714,33 @@ space (in which object points are specified) to the world coordinate space, that
position of the calibration pattern in the k-th pattern view (k=0.. *M* -1).
@param tvecs Output vector of translation vectors estimated for each pattern view.
@param flags Different flags that may be zero or a combination of the following values:
- **CV\_CALIB\_USE\_INTRINSIC\_GUESS** cameraMatrix contains valid initial values of
- **CV_CALIB_USE_INTRINSIC_GUESS** cameraMatrix contains valid initial values of
fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image
center ( imageSize is used), and focal distances are computed in a least-squares fashion.
Note, that if intrinsic parameters are known, there is no need to use this function just to
estimate extrinsic parameters. Use solvePnP instead.
- **CV\_CALIB\_FIX\_PRINCIPAL\_POINT** The principal point is not changed during the global
- **CV_CALIB_FIX_PRINCIPAL_POINT** The principal point is not changed during the global
optimization. It stays at the center or at a different location specified when
CV\_CALIB\_USE\_INTRINSIC\_GUESS is set too.
- **CV\_CALIB\_FIX\_ASPECT\_RATIO** The functions considers only fy as a free parameter. The
CV_CALIB_USE_INTRINSIC_GUESS is set too.
- **CV_CALIB_FIX_ASPECT_RATIO** The functions considers only fy as a free parameter. The
ratio fx/fy stays the same as in the input cameraMatrix . When
CV\_CALIB\_USE\_INTRINSIC\_GUESS is not set, the actual input values of fx and fy are
CV_CALIB_USE_INTRINSIC_GUESS is not set, the actual input values of fx and fy are
ignored, only their ratio is computed and used further.
- **CV\_CALIB\_ZERO\_TANGENT\_DIST** Tangential distortion coefficients \f$(p_1, p_2)\f$ are set
- **CV_CALIB_ZERO_TANGENT_DIST** Tangential distortion coefficients \f$(p_1, p_2)\f$ are set
to zeros and stay zero.
- **CV\_CALIB\_FIX\_K1,...,CV\_CALIB\_FIX\_K6** The corresponding radial distortion
coefficient is not changed during the optimization. If CV\_CALIB\_USE\_INTRINSIC\_GUESS is
- **CV_CALIB_FIX_K1,...,CV_CALIB_FIX_K6** The corresponding radial distortion
coefficient is not changed during the optimization. If CV_CALIB_USE_INTRINSIC_GUESS is
set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
- **CV\_CALIB\_RATIONAL\_MODEL** Coefficients k4, k5, and k6 are enabled. To provide the
- **CV_CALIB_RATIONAL_MODEL** Coefficients k4, k5, and k6 are enabled. To provide the
backward compatibility, this extra flag should be explicitly specified to make the
calibration function use the rational model and return 8 coefficients. If the flag is not
set, the function computes and returns only 5 distortion coefficients.
- **CALIB\_THIN\_PRISM\_MODEL** Coefficients s1, s2, s3 and s4 are enabled. To provide the
- **CALIB_THIN_PRISM_MODEL** Coefficients s1, s2, s3 and s4 are enabled. To provide the
backward compatibility, this extra flag should be explicitly specified to make the
calibration function use the thin prism model and return 12 coefficients. If the flag is not
set, the function computes and returns only 5 distortion coefficients.
- **CALIB\_FIX\_S1\_S2\_S3\_S4** The thin prism distortion coefficients are not changed during
the optimization. If CV\_CALIB\_USE\_INTRINSIC\_GUESS is set, the coefficient from the
- **CALIB_FIX_S1_S2_S3_S4** The thin prism distortion coefficients are not changed during
the optimization. If CV_CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the
supplied distCoeffs matrix is used. Otherwise, it is set to 0.
@param criteria Termination criteria for the iterative optimization algorithm.
@@ -750,7 +750,7 @@ points and their corresponding 2D projections in each view must be specified. Th
by using an object with a known geometry and easily detectable feature points. Such an object is
called a calibration rig or calibration pattern, and OpenCV has built-in support for a chessboard as
a calibration rig (see findChessboardCorners ). Currently, initialization of intrinsic parameters
(when CV\_CALIB\_USE\_INTRINSIC\_GUESS is not set) is only implemented for planar calibration
(when CV_CALIB_USE_INTRINSIC_GUESS is not set) is only implemented for planar calibration
patterns (where Z-coordinates of the object points must be all zeros). 3D calibration rigs can also
be used as long as initial cameraMatrix is provided.
@@ -758,7 +758,7 @@ The algorithm performs the following steps:
- Compute the initial intrinsic parameters (the option only available for planar calibration
patterns) or read them from the input parameters. The distortion coefficients are all set to
zeros initially unless some of CV\_CALIB\_FIX\_K? are specified.
zeros initially unless some of CV_CALIB_FIX_K? are specified.
- Estimate the initial camera pose as if the intrinsic parameters have been already known. This is
done using solvePnP .
@@ -822,8 +822,8 @@ observed by the first camera.
observed by the second camera.
@param cameraMatrix1 Input/output first camera matrix:
\f$\vecthreethree{f_x^{(j)}}{0}{c_x^{(j)}}{0}{f_y^{(j)}}{c_y^{(j)}}{0}{0}{1}\f$ , \f$j = 0,\, 1\f$ . If
any of CV\_CALIB\_USE\_INTRINSIC\_GUESS , CV\_CALIB\_FIX\_ASPECT\_RATIO ,
CV\_CALIB\_FIX\_INTRINSIC , or CV\_CALIB\_FIX\_FOCAL\_LENGTH are specified, some or all of the
any of CV_CALIB_USE_INTRINSIC_GUESS , CV_CALIB_FIX_ASPECT_RATIO ,
CV_CALIB_FIX_INTRINSIC , or CV_CALIB_FIX_FOCAL_LENGTH are specified, some or all of the
matrix components must be initialized. See the flags description for details.
@param distCoeffs1 Input/output vector of distortion coefficients
\f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\f$ of 4, 5, 8 ot 12 elements. The
@@ -837,30 +837,30 @@ is similar to distCoeffs1 .
@param E Output essential matrix.
@param F Output fundamental matrix.
@param flags Different flags that may be zero or a combination of the following values:
- **CV\_CALIB\_FIX\_INTRINSIC** Fix cameraMatrix? and distCoeffs? so that only R, T, E , and F
- **CV_CALIB_FIX_INTRINSIC** Fix cameraMatrix? and distCoeffs? so that only R, T, E , and F
matrices are estimated.
- **CV\_CALIB\_USE\_INTRINSIC\_GUESS** Optimize some or all of the intrinsic parameters
- **CV_CALIB_USE_INTRINSIC_GUESS** Optimize some or all of the intrinsic parameters
according to the specified flags. Initial values are provided by the user.
- **CV\_CALIB\_FIX\_PRINCIPAL\_POINT** Fix the principal points during the optimization.
- **CV\_CALIB\_FIX\_FOCAL\_LENGTH** Fix \f$f^{(j)}_x\f$ and \f$f^{(j)}_y\f$ .
- **CV\_CALIB\_FIX\_ASPECT\_RATIO** Optimize \f$f^{(j)}_y\f$ . Fix the ratio \f$f^{(j)}_x/f^{(j)}_y\f$
- **CV_CALIB_FIX_PRINCIPAL_POINT** Fix the principal points during the optimization.
- **CV_CALIB_FIX_FOCAL_LENGTH** Fix \f$f^{(j)}_x\f$ and \f$f^{(j)}_y\f$ .
- **CV_CALIB_FIX_ASPECT_RATIO** Optimize \f$f^{(j)}_y\f$ . Fix the ratio \f$f^{(j)}_x/f^{(j)}_y\f$
.
- **CV\_CALIB\_SAME\_FOCAL\_LENGTH** Enforce \f$f^{(0)}_x=f^{(1)}_x\f$ and \f$f^{(0)}_y=f^{(1)}_y\f$ .
- **CV\_CALIB\_ZERO\_TANGENT\_DIST** Set tangential distortion coefficients for each camera to
- **CV_CALIB_SAME_FOCAL_LENGTH** Enforce \f$f^{(0)}_x=f^{(1)}_x\f$ and \f$f^{(0)}_y=f^{(1)}_y\f$ .
- **CV_CALIB_ZERO_TANGENT_DIST** Set tangential distortion coefficients for each camera to
zeros and fix there.
- **CV\_CALIB\_FIX\_K1,...,CV\_CALIB\_FIX\_K6** Do not change the corresponding radial
distortion coefficient during the optimization. If CV\_CALIB\_USE\_INTRINSIC\_GUESS is set,
- **CV_CALIB_FIX_K1,...,CV_CALIB_FIX_K6** Do not change the corresponding radial
distortion coefficient during the optimization. If CV_CALIB_USE_INTRINSIC_GUESS is set,
the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
- **CV\_CALIB\_RATIONAL\_MODEL** Enable coefficients k4, k5, and k6. To provide the backward
- **CV_CALIB_RATIONAL_MODEL** Enable coefficients k4, k5, and k6. To provide the backward
compatibility, this extra flag should be explicitly specified to make the calibration
function use the rational model and return 8 coefficients. If the flag is not set, the
function computes and returns only 5 distortion coefficients.
- **CALIB\_THIN\_PRISM\_MODEL** Coefficients s1, s2, s3 and s4 are enabled. To provide the
- **CALIB_THIN_PRISM_MODEL** Coefficients s1, s2, s3 and s4 are enabled. To provide the
backward compatibility, this extra flag should be explicitly specified to make the
calibration function use the thin prism model and return 12 coefficients. If the flag is not
set, the function computes and returns only 5 distortion coefficients.
- **CALIB\_FIX\_S1\_S2\_S3\_S4** The thin prism distortion coefficients are not changed during
the optimization. If CV\_CALIB\_USE\_INTRINSIC\_GUESS is set, the coefficient from the
- **CALIB_FIX_S1_S2_S3_S4** The thin prism distortion coefficients are not changed during
the optimization. If CV_CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the
supplied distCoeffs matrix is used. Otherwise, it is set to 0.
@param criteria Termination criteria for the iterative optimization algorithm.
@@ -888,10 +888,10 @@ Besides the stereo-related information, the function can also perform a full cal
two cameras. However, due to the high dimensionality of the parameter space and noise in the input
data, the function can diverge from the correct solution. If the intrinsic parameters can be
estimated with high accuracy for each of the cameras individually (for example, using
calibrateCamera ), you are recommended to do so and then pass CV\_CALIB\_FIX\_INTRINSIC flag to the
calibrateCamera ), you are recommended to do so and then pass CV_CALIB_FIX_INTRINSIC flag to the
function along with the computed intrinsic parameters. Otherwise, if all the parameters are
estimated at once, it makes sense to restrict some parameters, for example, pass
CV\_CALIB\_SAME\_FOCAL\_LENGTH and CV\_CALIB\_ZERO\_TANGENT\_DIST flags, which is usually a
CV_CALIB_SAME_FOCAL_LENGTH and CV_CALIB_ZERO_TANGENT_DIST flags, which is usually a
reasonable assumption.
Similarly to calibrateCamera , the function minimizes the total re-projection error for all the
@@ -923,7 +923,7 @@ camera.
@param P2 Output 3x4 projection matrix in the new (rectified) coordinate systems for the second
camera.
@param Q Output \f$4 \times 4\f$ disparity-to-depth mapping matrix (see reprojectImageTo3D ).
@param flags Operation flags that may be zero or CV\_CALIB\_ZERO\_DISPARITY . If the flag is set,
@param flags Operation flags that may be zero or CV_CALIB_ZERO_DISPARITY . If the flag is set,
the function makes the principal points of each camera have the same pixel coordinates in the
rectified views. And if the flag is not set, the function may still shift the images in the
horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the
@@ -936,7 +936,7 @@ pixels from the original images from the cameras are retained in the rectified i
image pixels are lost). Obviously, any intermediate value yields an intermediate result between
those two extreme cases.
@param newImageSize New image resolution after rectification. The same size should be passed to
initUndistortRectifyMap (see the stereo\_calib.cpp sample in OpenCV samples directory). When (0,0)
initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0)
is passed (default), it is set to the original imageSize . Setting it to larger value can help you
preserve details in the original image, especially when there is a big radial distortion.
@param validPixROI1 Optional output rectangles inside the rectified images where all the pixels
@@ -962,7 +962,7 @@ coordinates. The function distinguishes the following two cases:
\f[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x*f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\f]
where \f$T_x\f$ is a horizontal shift between the cameras and \f$cx_1=cx_2\f$ if
CV\_CALIB\_ZERO\_DISPARITY is set.
CV_CALIB_ZERO_DISPARITY is set.
- **Vertical stereo**: the first and the second camera views are shifted relative to each other
mainly in vertical direction (and probably a bit in the horizontal direction too). The epipolar
@@ -972,14 +972,14 @@ coordinates. The function distinguishes the following two cases:
\f[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y*f \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\f]
where \f$T_y\f$ is a vertical shift between the cameras and \f$cy_1=cy_2\f$ if CALIB\_ZERO\_DISPARITY is
where \f$T_y\f$ is a vertical shift between the cameras and \f$cy_1=cy_2\f$ if CALIB_ZERO_DISPARITY is
set.
As you can see, the first three columns of P1 and P2 will effectively be the new "rectified" camera
matrices. The matrices, together with R1 and R2 , can then be passed to initUndistortRectifyMap to
initialize the rectification map for each camera.
See below the screenshot from the stereo\_calib.cpp sample. Some red horizontal lines pass through
See below the screenshot from the stereo_calib.cpp sample. Some red horizontal lines pass through
the corresponding image regions. This means that the images are well rectified, which is what most
stereo correspondence algorithms rely on. The green rectangles are roi1 and roi2 . You see that
their interiors are all valid pixels.
@@ -1057,7 +1057,7 @@ undistorted image. See roi1, roi2 description in stereoRectify .
@param centerPrincipalPoint Optional flag that indicates whether in the new camera matrix the
principal point should be at the image center or not. By default, the principal point is chosen to
best fit a subset of the source image (determined by alpha) to the corrected image.
@return new\_camera\_matrix Output new camera matrix.
@return new_camera_matrix Output new camera matrix.
The function computes and returns the optimal new camera matrix based on the free scaling parameter.
By varying this parameter, you may retrieve only sensible pixels alpha=0 , keep all the original
@@ -1111,10 +1111,10 @@ CV_EXPORTS void convertPointsHomogeneous( InputArray src, OutputArray dst );
floating-point (single or double precision).
@param points2 Array of the second image points of the same size and format as points1 .
@param method Method for computing a fundamental matrix.
- **CV\_FM\_7POINT** for a 7-point algorithm. \f$N = 7\f$
- **CV\_FM\_8POINT** for an 8-point algorithm. \f$N \ge 8\f$
- **CV\_FM\_RANSAC** for the RANSAC algorithm. \f$N \ge 8\f$
- **CV\_FM\_LMEDS** for the LMedS algorithm. \f$N \ge 8\f$
- **CV_FM_7POINT** for a 7-point algorithm. \f$N = 7\f$
- **CV_FM_8POINT** for an 8-point algorithm. \f$N \ge 8\f$
- **CV_FM_RANSAC** for the RANSAC algorithm. \f$N \ge 8\f$
- **CV_FM_LMEDS** for the LMedS algorithm. \f$N \ge 8\f$
@param param1 Parameter used for RANSAC. It is the maximum distance from a point to an epipolar
line in pixels, beyond which the point is considered an outlier and is not used for computing the
final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the
@@ -1269,7 +1269,7 @@ CV_EXPORTS_W int recoverPose( InputArray E, InputArray points1, InputArray point
/** @brief For points in an image of a stereo pair, computes the corresponding epilines in the other image.
@param points Input points. \f$N \times 1\f$ or \f$1 \times N\f$ matrix of type CV\_32FC2 or
@param points Input points. \f$N \times 1\f$ or \f$1 \times N\f$ matrix of type CV_32FC2 or
vector\<Point2f\> .
@param whichImage Index of the image (1 or 2) that contains the points .
@param F Fundamental matrix that can be estimated using findFundamentalMat or stereoRectify .
@@ -1364,16 +1364,16 @@ CV_EXPORTS_W void validateDisparity( InputOutputArray disparity, InputArray cost
@param disparity Input single-channel 8-bit unsigned, 16-bit signed, 32-bit signed or 32-bit
floating-point disparity image.
@param \_3dImage Output 3-channel floating-point image of the same size as disparity . Each
element of \_3dImage(x,y) contains 3D coordinates of the point (x,y) computed from the disparity
@param _3dImage Output 3-channel floating-point image of the same size as disparity . Each
element of _3dImage(x,y) contains 3D coordinates of the point (x,y) computed from the disparity
map.
@param Q \f$4 \times 4\f$ perspective transformation matrix that can be obtained with stereoRectify.
@param handleMissingValues Indicates, whether the function should handle missing values (i.e.
points where the disparity was not computed). If handleMissingValues=true, then pixels with the
minimal disparity that corresponds to the outliers (see StereoMatcher::compute ) are transformed
to 3D points with a very large Z value (currently set to 10000).
@param ddepth The optional output array depth. If it is -1, the output image will have CV\_32F
depth. ddepth can also be set to CV\_16S, CV\_32S or CV\_32F.
@param ddepth The optional output array depth. If it is -1, the output image will have CV_32F
depth. ddepth can also be set to CV_16S, CV_32S or CV_32F.
The function transforms a single-channel disparity map to a 3-channel image representing a 3D
surface. That is, for each pixel (x,y) andthe corresponding disparity d=disparity(x,y) , it
@@ -1523,19 +1523,19 @@ public:
one as follows:
- By default, the algorithm is single-pass, which means that you consider only 5 directions
instead of 8. Set mode=StereoSGBM::MODE\_HH in createStereoSGBM to run the full variant of the
instead of 8. Set mode=StereoSGBM::MODE_HH in createStereoSGBM to run the full variant of the
algorithm but beware that it may consume a lot of memory.
- The algorithm matches blocks, not individual pixels. Though, setting blockSize=1 reduces the
blocks to single pixels.
- Mutual information cost function is not implemented. Instead, a simpler Birchfield-Tomasi
sub-pixel metric from @cite BT98 is used. Though, the color images are supported as well.
- Some pre- and post- processing steps from K. Konolige algorithm StereoBM are included, for
example: pre-filtering (StereoBM::PREFILTER\_XSOBEL type) and post-filtering (uniqueness
example: pre-filtering (StereoBM::PREFILTER_XSOBEL type) and post-filtering (uniqueness
check, quadratic interpolation and speckle filtering).
@note
- (Python) An example illustrating the use of the StereoSGBM matching algorithm can be found
at opencv\_source\_code/samples/python2/stereo\_match.py
at opencv_source_code/samples/python2/stereo_match.py
*/
class CV_EXPORTS_W StereoSGBM : public StereoMatcher
{
@@ -1573,9 +1573,9 @@ public:
@param P2 The second parameter controlling the disparity smoothness. The larger the values are,
the smoother the disparity is. P1 is the penalty on the disparity change by plus or minus 1
between neighbor pixels. P2 is the penalty on the disparity change by more than 1 between neighbor
pixels. The algorithm requires P2 \> P1 . See stereo\_match.cpp sample where some reasonably good
P1 and P2 values are shown (like 8\*number\_of\_image\_channels\*SADWindowSize\*SADWindowSize and
32\*number\_of\_image\_channels\*SADWindowSize\*SADWindowSize , respectively).
pixels. The algorithm requires P2 \> P1 . See stereo_match.cpp sample where some reasonably good
P1 and P2 values are shown (like 8\*number_of_image_channels\*SADWindowSize\*SADWindowSize and
32\*number_of_image_channels\*SADWindowSize\*SADWindowSize , respectively).
@param disp12MaxDiff Maximum allowed difference (in integer pixel units) in the left-right
disparity check. Set it to a non-positive value to disable the check.
@param preFilterCap Truncation value for the prefiltered image pixels. The algorithm first
@@ -1590,7 +1590,7 @@ public:
@param speckleRange Maximum disparity variation within each connected component. If you do speckle
filtering, set the parameter to a positive value, it will be implicitly multiplied by 16.
Normally, 1 or 2 is good enough.
@param mode Set it to StereoSGBM::MODE\_HH to run the full-scale two-pass dynamic programming
@param mode Set it to StereoSGBM::MODE_HH to run the full-scale two-pass dynamic programming
algorithm. It will consume O(W\*H\*numDisparities) bytes, which is large for 640x480 stereo and
huge for HD-size pictures. By default, it is set to false .
@@ -1687,7 +1687,7 @@ namespace fisheye
1-channel or 1x1 3-channel
@param P New camera 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()
@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.
@@ -1712,10 +1712,10 @@ namespace fisheye
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 fisheye::undistortImage of fisheye camera model (all possible coefficients (k\_1, k\_2,
k\_3, k\_4) of fisheye distortion were optimized under calibration)
- 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 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
@@ -1749,10 +1749,10 @@ namespace fisheye
@param imagePoints vector of vectors of the projections of calibration pattern points.
imagePoints.size() and objectPoints.size() and imagePoints[i].size() must be equal to
objectPoints[i].size() for each i.
@param image\_size Size of the image used only to initialize the intrinsic camera matrix.
@param image_size Size of the image used only to initialize the intrinsic camera matrix.
@param K Output 3x3 floating-point camera matrix
\f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . If
fisheye::CALIB\_USE\_INTRINSIC\_GUESS/ is specified, some or all of fx, fy, cx, cy must be
fisheye::CALIB_USE_INTRINSIC_GUESS/ is specified, some or all of fx, fy, cx, cy must be
initialized before calling the function.
@param D Output vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$.
@param rvecs Output vector of rotation vectors (see Rodrigues ) estimated for each pattern view.
@@ -1762,14 +1762,14 @@ namespace fisheye
position of the calibration pattern in the k-th pattern view (k=0.. *M* -1).
@param tvecs Output vector of translation vectors estimated for each pattern view.
@param flags Different flags that may be zero or a combination of the following values:
- **fisheye::CALIB\_USE\_INTRINSIC\_GUESS** cameraMatrix contains valid initial values of
- **fisheye::CALIB_USE_INTRINSIC_GUESS** cameraMatrix contains valid initial values of
fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image
center ( imageSize is used), and focal distances are computed in a least-squares fashion.
- **fisheye::CALIB\_RECOMPUTE\_EXTRINSIC** Extrinsic will be recomputed after each iteration
- **fisheye::CALIB_RECOMPUTE_EXTRINSIC** Extrinsic will be recomputed after each iteration
of intrinsic optimization.
- **fisheye::CALIB\_CHECK\_COND** The functions will check validity of condition number.
- **fisheye::CALIB\_FIX\_SKEW** Skew coefficient (alpha) is set to zero and stay zero.
- **fisheye::CALIB\_FIX\_K1..4** Selected distortion coefficients are set to zeros and stay
- **fisheye::CALIB_CHECK_COND** The functions will check validity of condition number.
- **fisheye::CALIB_FIX_SKEW** Skew coefficient (alpha) is set to zero and stay zero.
- **fisheye::CALIB_FIX_K1..4** Selected distortion coefficients are set to zeros and stay
zero.
@param criteria Termination criteria for the iterative optimization algorithm.
*/
@@ -1794,13 +1794,13 @@ namespace fisheye
@param P2 Output 3x4 projection matrix in the new (rectified) coordinate systems for the second
camera.
@param Q Output \f$4 \times 4\f$ disparity-to-depth mapping matrix (see reprojectImageTo3D ).
@param flags Operation flags that may be zero or CV\_CALIB\_ZERO\_DISPARITY . If the flag is set,
@param flags Operation flags that may be zero or CV_CALIB_ZERO_DISPARITY . If the flag is set,
the function makes the principal points of each camera have the same pixel coordinates in the
rectified views. And if the flag is not set, the function may still shift the images in the
horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the
useful image area.
@param newImageSize New image resolution after rectification. The same size should be passed to
initUndistortRectifyMap (see the stereo\_calib.cpp sample in OpenCV samples directory). When (0,0)
initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0)
is passed (default), it is set to the original imageSize . Setting it to larger value can help you
preserve details in the original image, especially when there is a big radial distortion.
@param balance Sets the new focal length in range between the min focal length and the max focal
@@ -1820,7 +1820,7 @@ namespace fisheye
observed by the second camera.
@param K1 Input/output first camera matrix:
\f$\vecthreethree{f_x^{(j)}}{0}{c_x^{(j)}}{0}{f_y^{(j)}}{c_y^{(j)}}{0}{0}{1}\f$ , \f$j = 0,\, 1\f$ . If
any of fisheye::CALIB\_USE\_INTRINSIC\_GUESS , fisheye::CV\_CALIB\_FIX\_INTRINSIC are specified,
any of fisheye::CALIB_USE_INTRINSIC_GUESS , fisheye::CV_CALIB_FIX_INTRINSIC are specified,
some or all of the matrix components must be initialized.
@param D1 Input/output vector of distortion coefficients \f$(k_1, k_2, k_3, k_4)\f$ of 4 elements.
@param K2 Input/output second camera matrix. The parameter is similar to K1 .
@@ -1830,16 +1830,16 @@ namespace fisheye
@param R Output rotation matrix between the 1st and the 2nd camera coordinate systems.
@param T Output translation vector between the coordinate systems of the cameras.
@param flags Different flags that may be zero or a combination of the following values:
- **fisheye::CV\_CALIB\_FIX\_INTRINSIC** Fix K1, K2? and D1, D2? so that only R, T matrices
- **fisheye::CV_CALIB_FIX_INTRINSIC** Fix K1, K2? and D1, D2? so that only R, T matrices
are estimated.
- **fisheye::CALIB\_USE\_INTRINSIC\_GUESS** K1, K2 contains valid initial values of
- **fisheye::CALIB_USE_INTRINSIC_GUESS** K1, K2 contains valid initial values of
fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image
center (imageSize is used), and focal distances are computed in a least-squares fashion.
- **fisheye::CALIB\_RECOMPUTE\_EXTRINSIC** Extrinsic will be recomputed after each iteration
- **fisheye::CALIB_RECOMPUTE_EXTRINSIC** Extrinsic will be recomputed after each iteration
of intrinsic optimization.
- **fisheye::CALIB\_CHECK\_COND** The functions will check validity of condition number.
- **fisheye::CALIB\_FIX\_SKEW** Skew coefficient (alpha) is set to zero and stay zero.
- **fisheye::CALIB\_FIX\_K1..4** Selected distortion coefficients are set to zeros and stay
- **fisheye::CALIB_CHECK_COND** The functions will check validity of condition number.
- **fisheye::CALIB_FIX_SKEW** Skew coefficient (alpha) is set to zero and stay zero.
- **fisheye::CALIB_FIX_K1..4** Selected distortion coefficients are set to zeros and stay
zero.
@param criteria Termination criteria for the iterative optimization algorithm.
*/