Cameras have been around for a long-long time. However, with the introduction of the cheap *pinhole* cameras in the late 20th century, they became a common occurrence in our everyday life. Unfortunately, this cheapness comes with its price: significant distortion. Luckily, these are constants and with a calibration and some remapping we can correct this. Furthermore, with calibration you may also determinate the relation between the camera's natural units (pixels) and the real world units (for example millimeters).
Cameras have been around for a long-long time. However, with the introduction of the cheap *pinhole* cameras in the late 20th century, they became a common occurrence in our everyday life. Unfortunately, this cheapness comes with its price: significant distortion. Luckily, these are constants and with a calibration and some remapping we can correct this. Furthermore, with calibration you may also determine the relation between the camera's natural units (pixels) and the real world units (for example millimeters).
Theory
======
For the distortion OpenCV takes into account the radial and tangential factors. For the radial one uses the following formula:
For the distortion OpenCV takes into account the radial and tangential factors. For the radial factor one uses the following formula:
So for an old pixel point at :math:`(x,y)` coordinate in the input image, for a corrected output image its position will be :math:`(x_{corrected} y_{corrected})`. The presence of the radial distortion manifests in form of the "barrel" or "fish-eye" effect.
So for an old pixel point at :math:`(x,y)` coordinates in the input image, its position on the corrected output image will be :math:`(x_{corrected} y_{corrected})`. The presence of the radial distortion manifests in form of the "barrel" or "fish-eye" effect.
Tangential distortion occurs because the image taking lenses are not perfectly parallel to the imaging plane. Correcting this is made via the formulas:
Tangential distortion occurs because the image taking lenses are not perfectly parallel to the imaging plane. It can be corrected via the formulas:
..math::
x_{corrected} = x + [ 2p_1xy + p_2(r^2+2x^2)] \\
y_{corrected} = y + [ p_1(r^2+ 2y^2)+ 2p_2xy]
So we have five distortion parameters, which in OpenCV are organized in a 5 column one row matrix:
So we have five distortion parameters which in OpenCV are presented as one row matrix with 5 columns:
Now for the unit conversion, we use the following formula:
Now for the unit conversion we use the following formula:
..math::
\left [ \begin{matrix} x \\ y \\ w \end{matrix} \right ] = \left [ \begin{matrix} f_x & 0 & c_x \\ 0 & f_y & c_y \\ 0 & 0 & 1 \end{matrix} \right ] \left [ \begin{matrix} X \\ Y \\ Z \end{matrix} \right ]
Here the presence of the :math:`w` is cause we use a homography coordinate system (and :math:`w=Z`). The unknown parameters are :math:`f_x` and :math:`f_y` (camera focal lengths) and :math:`(c_x, c_y)` what are the optical centers expressed in pixels coordinates. If for both axes a common focal length is used with a given :math:`a` aspect ratio (usually 1), then :math:`f_y=f_x*a` and in the upper formula we will have a single :math:`f` focal length. The matrix containing these four parameters is referred to as the *camera matrix*. While the distortion coefficients are the same regardless of the camera resolutions used, these should be scaled along with the current resolution from the calibrated resolution.
Here the presence of :math:`w` is explained by the use of homography coordinate system (and :math:`w=Z`). The unknown parameters are :math:`f_x` and :math:`f_y` (camera focal lengths) and :math:`(c_x, c_y)` which are the optical centers expressed in pixels coordinates. If for both axes a common focal length is used with a given :math:`a` aspect ratio (usually 1), then :math:`f_y=f_x*a` and in the upper formula we will have a single focal length :math:`f`. The matrix containing these four parameters is referred to as the *camera matrix*. While the distortion coefficients are the same regardless of the camera resolutions used, these should be scaled along with the current resolution from the calibrated resolution.
The process of determining these two matrices is the calibration. Calculating these parameters is done by some basic geometrical equations. The equations used depend on the calibrating objects used. Currently OpenCV supports three types of object for calibration:
The process of determining these two matrices is the calibration. Calculation of these parameters is done through basic geometrical equations. The equations used depend on the chosen calibrating objects. Currently OpenCV supports three types of objects for calibration:
..container:: enumeratevisibleitemswithsquare
@ -46,7 +46,7 @@ The process of determining these two matrices is the calibration. Calculating th
+ Symmetrical circle pattern
+ Asymmetrical circle pattern
Basically, you need to take snapshots of these patterns with your camera and let OpenCV find them. Each found pattern equals in a new equation. To solve the equation you need at least a predetermined number of pattern snapshots to form a well-posed equation system. This number is higher for the chessboard pattern and less for the circle ones. For example, in theory the chessboard one requires at least two. However, in practice we have a good amount of noise present in our input images, so for good results you will probably want at least 10 good snapshots of the input pattern in different position.
Basically, you need to take snapshots of these patterns with your camera and let OpenCV find them. Each found pattern results in a new equation. To solve the equation you need at least a predetermined number of pattern snapshots to form a well-posed equation system. This number is higher for the chessboard pattern and less for the circle ones. For example, in theory the chessboard pattern requires at least two snapshots. However, in practice we have a good amount of noise present in our input images, so for good results you will probably need at least 10 good snapshots of the input pattern in different positions.
Goal
====
@ -55,19 +55,19 @@ The sample application will:
..container:: enumeratevisibleitemswithsquare
+ Determinate the distortion matrix
+ Determinate the camera matrix
+ Input from Camera, Video and Image file list
+ Configuration from XML/YAML file
+ Determine the distortion matrix
+ Determine the camera matrix
+ Take input from Camera, Video and Image file list
+ Read configuration from XML/YAML file
+ Save the results into XML/YAML file
+ Calculate re-projection error
Source code
===========
You may also find the source code in the :file:`samples/cpp/tutorial_code/calib3d/camera_calibration/` folder of the OpenCV source library or :download:`download it from here <../../../../samples/cpp/tutorial_code/calib3d/camera_calibration/camera_calibration.cpp>`. The program has a single argument. The name of its configuration file. If none given it will try to open the one named "default.xml". :download:`Here's a sample configuration file <../../../../samples/cpp/tutorial_code/calib3d/camera_calibration/in_VID5.xml>` in XML format. In the configuration file you may choose to use as input a camera, a video file or an image list. If you opt for the later one, you need to create a configuration file where you enumerate the images to use. Here's :download:`an example of this <../../../../samples/cpp/tutorial_code/calib3d/camera_calibration/VID5.xml>`. The important part to remember is that the images needs to be specified using the absolute path or the relative one from your applications working directory. You may find all this in the beforehand mentioned directory.
You may also find the source code in the :file:`samples/cpp/tutorial_code/calib3d/camera_calibration/` folder of the OpenCV source library or :download:`download it from here <../../../../samples/cpp/tutorial_code/calib3d/camera_calibration/camera_calibration.cpp>`. The program has a single argument: the name of its configuration file. If none is given then it will try to open the one named "default.xml". :download:`Here's a sample configuration file <../../../../samples/cpp/tutorial_code/calib3d/camera_calibration/in_VID5.xml>` in XML format. In the configuration file you may choose to use camera as an input, a video file or an image list. If you opt for the last one, you will need to create a configuration file where you enumerate the images to use. Here's :download:`an example of this <../../../../samples/cpp/tutorial_code/calib3d/camera_calibration/VID5.xml>`. The important part to remember is that the images need to be specified using the absolute path or the relative one from your application's working directory. You may find all this in the samples directory mentioned above.
The application starts up with reading the settings from the configuration file. Although, this is an important part of it, it has nothing to do with the subject of this tutorial: *camera calibration*. Therefore, I've chosen to do not post here the code part for that. The technical background on how to do this you can find in the :ref:`fileInputOutputXMLYAML` tutorial.
The application starts up with reading the settings from the configuration file. Although, this is an important part of it, it has nothing to do with the subject of this tutorial: *camera calibration*. Therefore, I've chosen not to post the code for that part here. Technical background on how to do this you can find in the :ref:`fileInputOutputXMLYAML` tutorial.
Explanation
===========
@ -93,9 +93,9 @@ Explanation
return -1;
}
For this I've used simple OpenCV class input operation. After reading the file I've an additional post-process function that checks for the validity of the input. Only if all of them are good will be the *goodInput* variable true.
For this I've used simple OpenCV class input operation. After reading the file I've an additional post-processing function that checks validity of the input. Only if all inputs are good then *goodInput* variable will be true.
#. **Get next input, if it fails or we have enough of them calibrate**. After this we have a big loop where we do the following operations: get the next image from the image list, camera or video file. If this fails or we have enough images we run the calibration process. In case of image we step out of the loop and otherwise the remaining frames will be undistorted (if the option is set) via changing from *DETECTION* mode to *CALIBRATED* one.
#. **Get next input, if it fails or we have enough of them - calibrate**. After this we have a big loop where we do the following operations: get the next image from the image list, camera or video file. If this fails or we have enough images then we run the calibration process. In case of image we step out of the loop and otherwise the remaining frames will be undistorted (if the option is set) via changing from *DETECTION* mode to the*CALIBRATED* one.
..code-block:: cpp
@ -125,7 +125,7 @@ Explanation
For some cameras we may need to flip the input image. Here we do this too.
#. **Find the pattern in the current input**. The formation of the equations I mentioned above consists of finding the major patterns in the input: in case of the chessboard this is their corners of the squares and for the circles, well, the circles itself. The position of these will form the result and is collected into the *pointBuf* vector.
#. **Find the pattern in the current input**. The formation of the equations I mentioned above aims to finding major patterns in the input: in case of the chessboard this are corners of the squares and for the circles, well, the circles themselves. The position of these will form the result which will be written into the *pointBuf* vector.
..code-block:: cpp
@ -146,9 +146,9 @@ Explanation
break;
}
Depending on the type of the input pattern you use either the :calib3d:`findChessboardCorners <findchessboardcorners>` or the :calib3d:`findCirclesGrid <findcirclesgrid>` function. For both of them you pass on the current image, the size of the board and you'll get back the positions of the patterns. Furthermore, they return a boolean variable that states if in the input we could find or not the pattern (we only need to take into account images where this is true!).
Depending on the type of the input pattern you use either the :calib3d:`findChessboardCorners <findchessboardcorners>` or the :calib3d:`findCirclesGrid <findcirclesgrid>` function. For both of them you pass the current image and the size of the board and you'll get the positions of the patterns. Furthermore, they return a boolean variable which states if the pattern was found in the input (we only need to take into account those images where this is true!).
Then again in case of cameras we only take camera images after an input delay time passed. This is in order to allow for the user to move the chessboard around and as getting different images. Same images mean same equations, and same equations at the calibration will form an ill-posed problem, so the calibration will fail. For square images the position of the corners are only approximate. We may improve this by calling the :feature2d:`cornerSubPix <cornersubpix>` function. This way will get a better calibration result. After this we add a valid inputs result to the *imagePoints* vector to collect all of the equations into a single container. Finally, for visualization feedback purposes we will draw the found points on the input image with the:calib3d:`findChessboardCorners <drawchessboardcorners>` function.
Then again in case of cameras we only take camera images when an input delay time is passed. This is done in order to allow user moving the chessboard around and getting different images. Similar images result in similar equations, and similar equations at the calibration step will form an ill-posed problem, so the calibration will fail. For square images the positions of the corners are only approximate. We may improve this by calling the :feature2d:`cornerSubPix <cornersubpix>` function. It will produce better calibration result. After this we add a valid inputs result to the *imagePoints* vector to collect all of the equations into a single container. Finally, for visualization feedback purposes we will draw the found points on the input image using:calib3d:`findChessboardCorners <drawchessboardcorners>` function.
..code-block:: cpp
@ -175,7 +175,7 @@ Explanation
drawChessboardCorners( view, s.boardSize, Mat(pointBuf), found );
}
#. **Show state and result for the user, plus command line control of the application**. The showing part consists of a text output on the live feed, and for video or camera input to show the "capturing" frame we simply bitwise negate the input image.
#. **Show state and result to the user, plus command line control of the application**. This part shows text output on the image.
..code-block:: cpp
@ -199,7 +199,7 @@ Explanation
if( blinkOutput )
bitwise_not(view, view);
If we only ran the calibration and got the camera matrix plus the distortion coefficients we may just as correct the image with the :imgproc_geometric:`undistort <undistort>` function:
If we ran calibration and got camera's matrix with the distortion coefficients we may want to correct the image using :imgproc_geometric:`undistort <undistort>` function:
..code-block:: cpp
@ -212,7 +212,7 @@ Explanation
//------------------------------ Show image and check for input commands -------------------
imshow("Image View", view);
Then we wait for an input key and if this is *u* we toggle the distortion removal, if it is *g* we start all over the detection process (or simply start it), and finally for the *ESC* key quit the application:
Then we wait for an input key and if this is *u* we toggle the distortion removal, if it is *g* we start again the detection process, and finally for the *ESC* key we quit the application:
..code-block:: cpp
@ -229,7 +229,7 @@ Explanation
imagePoints.clear();
}
#. **Show the distortion removal for the images too**. When you work with an image list it is not possible to remove the distortion inside the loop. Therefore, you must append this after the loop. Taking advantage of this now I'll expand the :imgproc_geometric:`undistort <undistort>` function, which is in fact first a call of the :imgproc_geometric:`initUndistortRectifyMap <initundistortrectifymap>` to find out the transformation matrices and then doing the transformation with the :imgproc_geometric:`remap <remap>` function. Because, after a successful calibration the map calculation needs to be done only once, by using this expanded form you may speed up your application:
#. **Show the distortion removal for the images too**. When you work with an image list it is not possible to remove the distortion inside the loop. Therefore, you must do this after the loop. Taking advantage of this now I'll expand the :imgproc_geometric:`undistort <undistort>` function, which is in fact first calls :imgproc_geometric:`initUndistortRectifyMap <initundistortrectifymap>` to find transformation matrices and then performs transformation using :imgproc_geometric:`remap <remap>` function. Because, after successful calibration map calculation needs to be done only once, by using this expanded form you may speed up your application:
..code-block:: cpp
@ -256,7 +256,7 @@ Explanation
The calibration and save
========================
Because the calibration needs to be only once per camera it makes sense to save them after a successful calibration. This way later on you can just load these values into your program. Due to this we first make the calibration, and if it succeeds we save the result into an OpenCV style XML or YAML file, depending on the extension you give in the configuration file.
Because the calibration needs to be done only once per camera, it makes sense to save it after a successful calibration. This way later on you can just load these values into your program. Due to this we first make the calibration, and if it succeeds we save the result into an OpenCV style XML or YAML file, depending on the extension you give in the configuration file.
Therefore in the first function we just split up these two processes. Because we want to save many of the calibration variables we'll create these variables here and pass on both of them to the calibration and saving function. Again, I'll not show the saving part as that has little in common with the calibration. Explore the source file in order to find out how and what:
@ -280,7 +280,7 @@ Therefore in the first function we just split up these two processes. Because we
return ok;
}
We do the calibration with the help of the :calib3d:`calibrateCamera <calibratecamera>` function. This has the following parameters:
We do the calibration with the help of the :calib3d:`calibrateCamera <calibratecamera>` function. It has the following parameters:
..container:: enumeratevisibleitemswithsquare
@ -318,11 +318,11 @@ We do the calibration with the help of the :calib3d:`calibrateCamera <calibratec
+ The image points. This is a vector of *Point2f* vector that for each input image contains where the important points (corners for chessboard, and center of circles for the circle patterns) were found. We already collected this from what the:calib3d:`findChessboardCorners <findchessboardcorners>` or the :calib3d:`findCirclesGrid <findcirclesgrid>` function returned. We just need to pass it on.
+ The image points. This is a vector of *Point2f* vector which for each input image contains coordinates of the important points (corners for chessboard and centers of the circles for the circle pattern). We have already collected this from:calib3d:`findChessboardCorners <findchessboardcorners>` or :calib3d:`findCirclesGrid <findcirclesgrid>` function. We just need to pass it on.
+ The size of the image acquired from the camera, video file or the images.
+ The camera matrix. If we used the fix aspect ratio option we need to set the :math:`f_x` to zero:
+ The camera matrix. If we used the fixed aspect ratio option we need to set the :math:`f_x` to zero:
..code-block:: cpp
@ -336,16 +336,16 @@ We do the calibration with the help of the :calib3d:`calibrateCamera <calibratec
distCoeffs = Mat::zeros(8, 1, CV_64F);
+ The function will calculate for all the views the rotation and translation vector that transform the object points (given in the model coordinate space) to the image points (given in the world coordinate space). The 7th and 8th parameters are an output vector of matrices containing in the ith position the rotation and translation vector for the ith object point to the ith image point.
+ For all the views the function will calculate rotation and translation vectors which transform the object points (given in the model coordinate space) to the image points (given in the world coordinate space). The 7-th and 8-th parameters are the output vector of matrices containing in the i-th position the rotation and translation vector for the i-th object point to the i-th image point.
+ The final argument is a flag. You need to specify here options like fix the aspect ratio for the focal length, assume zero tangential distortion or to fix the principal point.
+ The final argument is the flag. You need to specify here options like fix the aspect ratio for the focal length, assume zero tangential distortion or to fix the principal point.
+ The function returns the average re-projection error. This number gives a good estimation of just how exact is the found parameters. This should be as close to zero as possible. Given the intrinsic, distortion, rotation and translation matrices we may calculate the error for one view by using the :calib3d:`projectPoints <projectpoints>` to first transform the object point to image point. Then we calculate the absolute norm between what we got with our transformation and the corner/circle finding algorithm. To find the average error we calculate the arithmetical mean of the errors calculate for all the calibration images.
+ The function returns the average re-projection error. This number gives a good estimation of precision of the found parameters. This should be as close to zero as possible. Given the intrinsic, distortion, rotation and translation matrices we may calculate the error for one view by using the :calib3d:`projectPoints <projectpoints>` to first transform the object point to image point. Then we calculate the absolute norm between what we got with our transformation and the corner/circle finding algorithm. To find the average error we calculate the arithmetical mean of the errors calculated for all the calibration images.
..code-block:: cpp
@ -378,25 +378,25 @@ We do the calibration with the help of the :calib3d:`calibrateCamera <calibratec
Results
=======
Let there be :download:`this input chessboard pattern <../../../pattern.png>`that has a size of 9 X 6. I've used an AXIS IP camera to create a couple of snapshots of the board and saved it into a VID5 directory. I've put this inside the :file:`images/CameraCalibraation` folder of my working directory and created the following :file:`VID5.XML` file that describes which images to use:
Let there be :download:`this input chessboard pattern <../../../pattern.png>`which has a size of 9 X 6. I've used an AXIS IP camera to create a couple of snapshots of the board and saved it into VID5 directory. I've put this inside the :file:`images/CameraCalibration` folder of my working directory and created the following :file:`VID5.XML` file that describes which images to use:
..code-block:: xml
<?xml version="1.0"?>
<opencv_storage>
<images>
images/CameraCalibraation/VID5/xx1.jpg
images/CameraCalibraation/VID5/xx2.jpg
images/CameraCalibraation/VID5/xx3.jpg
images/CameraCalibraation/VID5/xx4.jpg
images/CameraCalibraation/VID5/xx5.jpg
images/CameraCalibraation/VID5/xx6.jpg
images/CameraCalibraation/VID5/xx7.jpg
images/CameraCalibraation/VID5/xx8.jpg
images/CameraCalibration/VID5/xx1.jpg
images/CameraCalibration/VID5/xx2.jpg
images/CameraCalibration/VID5/xx3.jpg
images/CameraCalibration/VID5/xx4.jpg
images/CameraCalibration/VID5/xx5.jpg
images/CameraCalibration/VID5/xx6.jpg
images/CameraCalibration/VID5/xx7.jpg
images/CameraCalibration/VID5/xx8.jpg
</images>
</opencv_storage>
Then specified the :file:`images/CameraCalibraation/VID5/VID5.XML` as input in the configuration file. Here's a chessboard pattern found during the runtime of the application:
Then passed :file:`images/CameraCalibration/VID5/VID5.XML` as an input in the configuration file. Here's a chessboard pattern found during the runtime of the application:
..image:: images/fileListImage.jpg
:alt:A found chessboard
@ -433,7 +433,7 @@ In both cases in the specified output XML/YAML file you'll find the camera and d
Add these values as constants to your program, call the :imgproc_geometric:`initUndistortRectifyMap <initundistortrectifymap>` and the :imgproc_geometric:`remap <remap>` function to remove distortion and enjoy distortion free inputs with cheap and low quality cameras.
Add these values as constants to your program, call the :imgproc_geometric:`initUndistortRectifyMap <initundistortrectifymap>` and the :imgproc_geometric:`remap <remap>` function to remove distortion and enjoy distortion free inputs for cheap and low quality cameras.
You may observe a runtime instance of this on the `YouTube here <https://www.youtube.com/watch?v=ViPN810E0SU>`_.
Template class for specifying the size of an image or rectangle. The class includes two members called ``width`` and ``height``. The structure can be converted to and from the old OpenCV structures
``CvSize`` and ``CvSize2D32f`` . The same set of arithmetic and comparison operations as for ``Point_`` is available.
//! checks whether the rectangle contains the point
bool contains(const Point_<_Tp>& pt) const;
_Tp x, y, width, height; //< the top-left corner, as well as width and height of the rectangle
};
Template class for 2D rectangles, described by the following parameters:
* Coordinates of the top-left corner. This is a default interpretation of ``Rect_::x`` and ``Rect_::y`` in OpenCV. Though, in your algorithms you may count ``x`` and ``y`` from the bottom-left corner.
@ -171,6 +307,28 @@ RotatedRect
-----------
..ocv:class:: RotatedRect
::
class CV_EXPORTS RotatedRect
{
public:
//! various constructors
RotatedRect();
RotatedRect(const Point2f& center, const Size2f& size, float angle);
RotatedRect(const CvBox2D& box);
//! returns 4 vertices of the rectangle
void points(Point2f pts[]) const;
//! returns the minimal up-right rectangle containing the rotated rectangle
Rect boundingRect() const;
//! conversion to the old-style CvBox2D structure
operator CvBox2D() const;
Point2f center; //< the rectangle mass center
Size2f size; //< width and height of the rectangle
float angle; //< the rotation angle. When the angle is 0, 90, 180, 270 etc., the rectangle becomes an up-right rectangle.
};
The class represents rotated (i.e. not up-right) rectangles on a plane. Each rectangle is specified by the center point (mass center), length of each side (represented by cv::Size2f structure) and the rotation angle in degrees.
..ocv:function:: RotatedRect::RotatedRect()
@ -217,7 +375,33 @@ TermCriteria
------------
..ocv:class:: TermCriteria
The class defining termination criteria for iterative algorithms. You can initialize it by default constructor and then override any parameters, or the structure may be fully initialized using the advanced variant of the constructor.
::
class CV_EXPORTS TermCriteria
{
public:
enum
{
COUNT=1, //!< the maximum number of iterations or elements to compute
MAX_ITER=COUNT, //!< ditto
EPS=2 //!< the desired accuracy or change in parameters at which the iterative algorithm stops
};
//! default constructor
TermCriteria();
//! full constructor
TermCriteria(int type, int maxCount, double epsilon);
//! conversion from CvTermCriteria
TermCriteria(const CvTermCriteria& criteria);
//! conversion to CvTermCriteria
operator CvTermCriteria() const;
int type; //!< the type of termination criteria: COUNT, EPS or COUNT + EPS
int maxCount; // the maximum number of iterations/elements
double epsilon; // the desired accuracy
};
The class defining termination criteria for iterative algorithms. You can initialize it by default constructor and then override any parameters, or the structure may be fully initialized using the advanced variant of the constructor.
TermCriteria::TermCriteria
--------------------------
@ -321,9 +505,36 @@ Scalar\_
--------
..ocv:class:: Scalar_
Template class for a 4-element vector derived from Vec. ::
Template class for a 4-element vector derived from Vec.
::
template<typename _Tp> class CV_EXPORTS Scalar_ : public Vec<_Tp, 4>
@ -664,7 +885,6 @@ OpenCV C++ n-dimensional dense array class ::
...
};
The class ``Mat`` represents an n-dimensional dense numerical single-channel or multi-channel array. It can be used to store real or complex-valued vectors and matrices, grayscale or color images, voxel volumes, vector fields, point clouds, tensors, histograms (though, very high-dimensional histograms may be better stored in a ``SparseMat`` ). The data layout of the array
:math:`M` is defined by the array ``M.step[]``, so that the address of element
virtual AlgorithmInfo* info() const /* TODO: make it = 0;*/ { return 0; }
};
This is a base class for all more or less complex algorithms in OpenCV, especially for classes of algorithms, for which there can be multiple implementations. The examples are stereo correspondence (for which there are algorithms like block matching, semi-global block matching, graph-cut etc.), background subtraction (which can be done using mixture-of-gaussians models, codebook-based algorithm etc.), optical flow (block matching, Lucas-Kanade, Horn-Schunck etc.).
The class provides the following features for all derived classes:
__constantfloat4c_DXY[5]={(float4)(1,5,1,5),(float4)(1,1,5,5),(float4)(4,8,4,8),(float4)(4,4,8,8),(float4)(1,-1,-1,1)};// Use integral image to calculate haar wavelets.
Roman Donchenko
11 years ago
committed by
OpenCV Buildbot