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init: 64 bits version of the webcam

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Nicolas RACOT
2026-07-13 16:31:52 +02:00
parent c0f3eeb00d
commit 07e526544d
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include/opencv2/video/tracking.hpp
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@ -41,8 +41,8 @@
//
//M*/
#ifndef __OPENCV_TRACKING_HPP__
#define __OPENCV_TRACKING_HPP__
#ifndef OPENCV_TRACKING_HPP
#define OPENCV_TRACKING_HPP
#include "opencv2/core.hpp"
#include "opencv2/imgproc.hpp"
@ -74,10 +74,13 @@ See the OpenCV sample camshiftdemo.c that tracks colored objects.
@note
- (Python) A sample explaining the camshift tracking algorithm can be found at
opencv_source_code/samples/python2/camshift.py
opencv_source_code/samples/python/camshift.py
*/
CV_EXPORTS_W RotatedRect CamShift( InputArray probImage, CV_IN_OUT Rect& window,
TermCriteria criteria );
/** @example samples/cpp/camshiftdemo.cpp
An example using the mean-shift tracking algorithm
*/
/** @brief Finds an object on a back projection image.
@ -97,8 +100,6 @@ projection and remove the noise. For example, you can do this by retrieving conn
with findContours , throwing away contours with small area ( contourArea ), and rendering the
remaining contours with drawContours.
@note
- A mean-shift tracking sample can be found at opencv_source_code/samples/cpp/camshiftdemo.cpp
*/
CV_EXPORTS_W int meanShift( InputArray probImage, CV_IN_OUT Rect& window, TermCriteria criteria );
@ -123,6 +124,10 @@ CV_EXPORTS_W int buildOpticalFlowPyramid( InputArray img, OutputArrayOfArrays py
int derivBorder = BORDER_CONSTANT,
bool tryReuseInputImage = true );
/** @example samples/cpp/lkdemo.cpp
An example using the Lucas-Kanade optical flow algorithm
*/
/** @brief Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with
pyramids.
@ -166,9 +171,9 @@ The function implements a sparse iterative version of the Lucas-Kanade optical f
- An example using the Lucas-Kanade optical flow algorithm can be found at
opencv_source_code/samples/cpp/lkdemo.cpp
- (Python) An example using the Lucas-Kanade optical flow algorithm can be found at
opencv_source_code/samples/python2/lk_track.py
opencv_source_code/samples/python/lk_track.py
- (Python) An example using the Lucas-Kanade tracker for homography matching can be found at
opencv_source_code/samples/python2/lk_homography.py
opencv_source_code/samples/python/lk_homography.py
*/
CV_EXPORTS_W void calcOpticalFlowPyrLK( InputArray prevImg, InputArray nextImg,
InputArray prevPts, InputOutputArray nextPts,
@ -213,7 +218,7 @@ The function finds an optical flow for each prev pixel using the @cite Farneback
- An example using the optical flow algorithm described by Gunnar Farneback can be found at
opencv_source_code/samples/cpp/fback.cpp
- (Python) An example using the optical flow algorithm described by Gunnar Farneback can be
found at opencv_source_code/samples/python2/opt_flow.py
found at opencv_source_code/samples/python/opt_flow.py
*/
CV_EXPORTS_W void calcOpticalFlowFarneback( InputArray prev, InputArray next, InputOutputArray flow,
double pyr_scale, int levels, int winsize,
@ -226,7 +231,7 @@ CV_EXPORTS_W void calcOpticalFlowFarneback( InputArray prev, InputArray next, In
@param dst Second input 2D point set of the same size and the same type as A, or another image.
@param fullAffine If true, the function finds an optimal affine transformation with no additional
restrictions (6 degrees of freedom). Otherwise, the class of transformations to choose from is
limited to combinations of translation, rotation, and uniform scaling (5 degrees of freedom).
limited to combinations of translation, rotation, and uniform scaling (4 degrees of freedom).
The function finds an optimal affine transform *[A|b]* (a 2 x 3 floating-point matrix) that
approximates best the affine transformation between:
@ -245,9 +250,11 @@ where src[i] and dst[i] are the i-th points in src and dst, respectively
when fullAffine=false.
@sa
getAffineTransform, getPerspectiveTransform, findHomography
estimateAffine2D, estimateAffinePartial2D, getAffineTransform, getPerspectiveTransform, findHomography
*/
CV_EXPORTS_W Mat estimateRigidTransform( InputArray src, InputArray dst, bool fullAffine );
CV_EXPORTS_W Mat estimateRigidTransform( InputArray src, InputArray dst, bool fullAffine);
CV_EXPORTS_W Mat estimateRigidTransform( InputArray src, InputArray dst, bool fullAffine, int ransacMaxIters, double ransacGoodRatio,
int ransacSize0);
enum
@ -258,6 +265,10 @@ enum
MOTION_HOMOGRAPHY = 3
};
/** @example samples/cpp/image_alignment.cpp
An example using the image alignment ECC algorithm
*/
/** @brief Finds the geometric transform (warp) between two images in terms of the ECC criterion @cite EP08 .
@param templateImage single-channel template image; CV_8U or CV_32F array.
@ -297,7 +308,7 @@ row is ignored.
Unlike findHomography and estimateRigidTransform, the function findTransformECC implements an
area-based alignment that builds on intensity similarities. In essence, the function updates the
initial transformation that roughly aligns the images. If this information is missing, the identity
warp (unity matrix) should be given as input. Note that if images undergo strong
warp (unity matrix) is used as an initialization. Note that if images undergo strong
displacements/rotations, an initial transformation that roughly aligns the images is necessary
(e.g., a simple euclidean/similarity transform that allows for the images showing the same image
content approximately). Use inverse warping in the second image to take an image close to the first
@ -306,32 +317,28 @@ sample image_alignment.cpp that demonstrates the use of the function. Note that
an exception if algorithm does not converges.
@sa
estimateRigidTransform, findHomography
estimateAffine2D, estimateAffinePartial2D, findHomography
*/
CV_EXPORTS_W double findTransformECC( InputArray templateImage, InputArray inputImage,
InputOutputArray warpMatrix, int motionType = MOTION_AFFINE,
TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 50, 0.001),
InputArray inputMask = noArray());
/** @example samples/cpp/kalman.cpp
An example using the standard Kalman filter
*/
/** @brief Kalman filter class.
The class implements a standard Kalman filter <http://en.wikipedia.org/wiki/Kalman_filter>,
@cite Welch95 . However, you can modify transitionMatrix, controlMatrix, and measurementMatrix to get
an extended Kalman filter functionality. See the OpenCV sample kalman.cpp.
@note
- An example using the standard Kalman filter can be found at
opencv_source_code/samples/cpp/kalman.cpp
an extended Kalman filter functionality.
@note In C API when CvKalman\* kalmanFilter structure is not needed anymore, it should be released
with cvReleaseKalman(&kalmanFilter)
*/
class CV_EXPORTS_W KalmanFilter
{
public:
/** @brief The constructors.
@note In C API when CvKalman\* kalmanFilter structure is not needed anymore, it should be released
with cvReleaseKalman(&kalmanFilter)
*/
CV_WRAP KalmanFilter();
/** @overload
@param dynamParams Dimensionality of the state.
@ -397,6 +404,27 @@ public:
CV_WRAP virtual void collectGarbage() = 0;
};
/** @brief Base interface for sparse optical flow algorithms.
*/
class CV_EXPORTS_W SparseOpticalFlow : public Algorithm
{
public:
/** @brief Calculates a sparse optical flow.
@param prevImg First input image.
@param nextImg Second input image of the same size and the same type as prevImg.
@param prevPts Vector of 2D points for which the flow needs to be found.
@param nextPts Output vector of 2D points containing the calculated new positions of input features in the second image.
@param status Output status vector. Each element of the vector is set to 1 if the
flow for the corresponding features has been found. Otherwise, it is set to 0.
@param err Optional output vector that contains error response for each point (inverse confidence).
*/
CV_WRAP virtual void calc(InputArray prevImg, InputArray nextImg,
InputArray prevPts, InputOutputArray nextPts,
OutputArray status,
OutputArray err = cv::noArray()) = 0;
};
/** @brief "Dual TV L1" Optical Flow Algorithm.
The class implements the "Dual TV L1" optical flow algorithm described in @cite Zach2007 and
@ -444,70 +472,160 @@ class CV_EXPORTS_W DualTVL1OpticalFlow : public DenseOpticalFlow
public:
//! @brief Time step of the numerical scheme
/** @see setTau */
virtual double getTau() const = 0;
CV_WRAP virtual double getTau() const = 0;
/** @copybrief getTau @see getTau */
virtual void setTau(double val) = 0;
CV_WRAP virtual void setTau(double val) = 0;
//! @brief Weight parameter for the data term, attachment parameter
/** @see setLambda */
virtual double getLambda() const = 0;
CV_WRAP virtual double getLambda() const = 0;
/** @copybrief getLambda @see getLambda */
virtual void setLambda(double val) = 0;
CV_WRAP virtual void setLambda(double val) = 0;
//! @brief Weight parameter for (u - v)^2, tightness parameter
/** @see setTheta */
virtual double getTheta() const = 0;
CV_WRAP virtual double getTheta() const = 0;
/** @copybrief getTheta @see getTheta */
virtual void setTheta(double val) = 0;
CV_WRAP virtual void setTheta(double val) = 0;
//! @brief coefficient for additional illumination variation term
/** @see setGamma */
virtual double getGamma() const = 0;
CV_WRAP virtual double getGamma() const = 0;
/** @copybrief getGamma @see getGamma */
virtual void setGamma(double val) = 0;
CV_WRAP virtual void setGamma(double val) = 0;
//! @brief Number of scales used to create the pyramid of images
/** @see setScalesNumber */
virtual int getScalesNumber() const = 0;
CV_WRAP virtual int getScalesNumber() const = 0;
/** @copybrief getScalesNumber @see getScalesNumber */
virtual void setScalesNumber(int val) = 0;
CV_WRAP virtual void setScalesNumber(int val) = 0;
//! @brief Number of warpings per scale
/** @see setWarpingsNumber */
virtual int getWarpingsNumber() const = 0;
CV_WRAP virtual int getWarpingsNumber() const = 0;
/** @copybrief getWarpingsNumber @see getWarpingsNumber */
virtual void setWarpingsNumber(int val) = 0;
CV_WRAP virtual void setWarpingsNumber(int val) = 0;
//! @brief Stopping criterion threshold used in the numerical scheme, which is a trade-off between precision and running time
/** @see setEpsilon */
virtual double getEpsilon() const = 0;
CV_WRAP virtual double getEpsilon() const = 0;
/** @copybrief getEpsilon @see getEpsilon */
virtual void setEpsilon(double val) = 0;
CV_WRAP virtual void setEpsilon(double val) = 0;
//! @brief Inner iterations (between outlier filtering) used in the numerical scheme
/** @see setInnerIterations */
virtual int getInnerIterations() const = 0;
CV_WRAP virtual int getInnerIterations() const = 0;
/** @copybrief getInnerIterations @see getInnerIterations */
virtual void setInnerIterations(int val) = 0;
CV_WRAP virtual void setInnerIterations(int val) = 0;
//! @brief Outer iterations (number of inner loops) used in the numerical scheme
/** @see setOuterIterations */
virtual int getOuterIterations() const = 0;
CV_WRAP virtual int getOuterIterations() const = 0;
/** @copybrief getOuterIterations @see getOuterIterations */
virtual void setOuterIterations(int val) = 0;
CV_WRAP virtual void setOuterIterations(int val) = 0;
//! @brief Use initial flow
/** @see setUseInitialFlow */
virtual bool getUseInitialFlow() const = 0;
CV_WRAP virtual bool getUseInitialFlow() const = 0;
/** @copybrief getUseInitialFlow @see getUseInitialFlow */
virtual void setUseInitialFlow(bool val) = 0;
CV_WRAP virtual void setUseInitialFlow(bool val) = 0;
//! @brief Step between scales (<1)
/** @see setScaleStep */
virtual double getScaleStep() const = 0;
CV_WRAP virtual double getScaleStep() const = 0;
/** @copybrief getScaleStep @see getScaleStep */
virtual void setScaleStep(double val) = 0;
CV_WRAP virtual void setScaleStep(double val) = 0;
//! @brief Median filter kernel size (1 = no filter) (3 or 5)
/** @see setMedianFiltering */
virtual int getMedianFiltering() const = 0;
CV_WRAP virtual int getMedianFiltering() const = 0;
/** @copybrief getMedianFiltering @see getMedianFiltering */
virtual void setMedianFiltering(int val) = 0;
CV_WRAP virtual void setMedianFiltering(int val) = 0;
/** @brief Creates instance of cv::DualTVL1OpticalFlow*/
CV_WRAP static Ptr<DualTVL1OpticalFlow> create(
double tau = 0.25,
double lambda = 0.15,
double theta = 0.3,
int nscales = 5,
int warps = 5,
double epsilon = 0.01,
int innnerIterations = 30,
int outerIterations = 10,
double scaleStep = 0.8,
double gamma = 0.0,
int medianFiltering = 5,
bool useInitialFlow = false);
};
/** @brief Creates instance of cv::DenseOpticalFlow
*/
CV_EXPORTS_W Ptr<DualTVL1OpticalFlow> createOptFlow_DualTVL1();
/** @brief Class computing a dense optical flow using the Gunnar Farneback's algorithm.
*/
class CV_EXPORTS_W FarnebackOpticalFlow : public DenseOpticalFlow
{
public:
CV_WRAP virtual int getNumLevels() const = 0;
CV_WRAP virtual void setNumLevels(int numLevels) = 0;
CV_WRAP virtual double getPyrScale() const = 0;
CV_WRAP virtual void setPyrScale(double pyrScale) = 0;
CV_WRAP virtual bool getFastPyramids() const = 0;
CV_WRAP virtual void setFastPyramids(bool fastPyramids) = 0;
CV_WRAP virtual int getWinSize() const = 0;
CV_WRAP virtual void setWinSize(int winSize) = 0;
CV_WRAP virtual int getNumIters() const = 0;
CV_WRAP virtual void setNumIters(int numIters) = 0;
CV_WRAP virtual int getPolyN() const = 0;
CV_WRAP virtual void setPolyN(int polyN) = 0;
CV_WRAP virtual double getPolySigma() const = 0;
CV_WRAP virtual void setPolySigma(double polySigma) = 0;
CV_WRAP virtual int getFlags() const = 0;
CV_WRAP virtual void setFlags(int flags) = 0;
CV_WRAP static Ptr<FarnebackOpticalFlow> create(
int numLevels = 5,
double pyrScale = 0.5,
bool fastPyramids = false,
int winSize = 13,
int numIters = 10,
int polyN = 5,
double polySigma = 1.1,
int flags = 0);
};
/** @brief Class used for calculating a sparse optical flow.
The class can calculate an optical flow for a sparse feature set using the
iterative Lucas-Kanade method with pyramids.
@sa calcOpticalFlowPyrLK
*/
class CV_EXPORTS_W SparsePyrLKOpticalFlow : public SparseOpticalFlow
{
public:
CV_WRAP virtual Size getWinSize() const = 0;
CV_WRAP virtual void setWinSize(Size winSize) = 0;
CV_WRAP virtual int getMaxLevel() const = 0;
CV_WRAP virtual void setMaxLevel(int maxLevel) = 0;
CV_WRAP virtual TermCriteria getTermCriteria() const = 0;
CV_WRAP virtual void setTermCriteria(TermCriteria& crit) = 0;
CV_WRAP virtual int getFlags() const = 0;
CV_WRAP virtual void setFlags(int flags) = 0;
CV_WRAP virtual double getMinEigThreshold() const = 0;
CV_WRAP virtual void setMinEigThreshold(double minEigThreshold) = 0;
CV_WRAP static Ptr<SparsePyrLKOpticalFlow> create(
Size winSize = Size(21, 21),
int maxLevel = 3, TermCriteria crit =
TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01),
int flags = 0,
double minEigThreshold = 1e-4);
};
//! @} video_track
} // cv