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fix:make zerocolor edge

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lennlouisgeek
2026-02-04 03:18:20 +08:00
parent ca3545b8b0
commit 001685b633
157 changed files with 31832 additions and 32681 deletions
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31
3rdpart/OpenCV/include/opencv2/video/background_segm.hpp
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@@ -71,21 +71,6 @@ public:
*/
CV_WRAP virtual void apply(InputArray image, OutputArray fgmask, double learningRate=-1) = 0;
/** @brief Computes a foreground mask with known foreground mask input.
@param image Next video frame. Floating point frame will be used without scaling and should be in range \f$[0,255]\f$.
@param fgmask The output foreground mask as an 8-bit binary image.
@param knownForegroundMask The mask for inputting already known foreground, allows model to ignore pixels.
@param learningRate The value between 0 and 1 that indicates how fast the background model is
learnt. Negative parameter value makes the algorithm to use some automatically chosen learning
rate. 0 means that the background model is not updated at all, 1 means that the background model
is completely reinitialized from the last frame.
@note This method has a default virtual implementation that throws a "not impemented" error.
Foreground masking may not be supported by all background subtractors.
*/
CV_WRAP virtual void apply(InputArray image, InputArray knownForegroundMask, OutputArray fgmask, double learningRate=-1) = 0;
/** @brief Computes a background image.
@param backgroundImage The output background image.
@@ -117,7 +102,7 @@ public:
CV_WRAP virtual int getNMixtures() const = 0;
/** @brief Sets the number of gaussian components in the background model.
The model needs to be reinitialized to reserve memory.
The model needs to be reinitalized to reserve memory.
*/
CV_WRAP virtual void setNMixtures(int nmixtures) = 0;//needs reinitialization!
@@ -221,18 +206,6 @@ public:
is completely reinitialized from the last frame.
*/
CV_WRAP virtual void apply(InputArray image, OutputArray fgmask, double learningRate=-1) CV_OVERRIDE = 0;
/** @brief Computes a foreground mask and skips known foreground in evaluation.
@param image Next video frame. Floating point frame will be used without scaling and should be in range \f$[0,255]\f$.
@param fgmask The output foreground mask as an 8-bit binary image.
@param knownForegroundMask The mask for inputting already known foreground, allows model to ignore pixels.
@param learningRate The value between 0 and 1 that indicates how fast the background model is
learnt. Negative parameter value makes the algorithm to use some automatically chosen learning
rate. 0 means that the background model is not updated at all, 1 means that the background model
is completely reinitialized from the last frame.
*/
CV_WRAP virtual void apply(InputArray image, InputArray knownForegroundMask, OutputArray fgmask, double learningRate=-1) CV_OVERRIDE = 0;
};
/** @brief Creates MOG2 Background Subtractor
@@ -268,7 +241,7 @@ public:
CV_WRAP virtual int getNSamples() const = 0;
/** @brief Sets the number of data samples in the background model.
The model needs to be reinitialized to reserve memory.
The model needs to be reinitalized to reserve memory.
*/
CV_WRAP virtual void setNSamples(int _nN) = 0;//needs reinitialization!

145
3rdpart/OpenCV/include/opencv2/video/tracking.hpp
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@@ -46,9 +46,6 @@
#include "opencv2/core.hpp"
#include "opencv2/imgproc.hpp"
#ifdef HAVE_OPENCV_DNN
# include "opencv2/dnn.hpp"
#endif
namespace cv
{
@@ -268,43 +265,17 @@ enum
MOTION_HOMOGRAPHY = 3
};
/**
@brief Computes the Enhanced Correlation Coefficient (ECC) value between two images
/** @brief Computes the Enhanced Correlation Coefficient value between two images @cite EP08 .
The Enhanced Correlation Coefficient (ECC) is a normalized measure of similarity between two images @cite EP08.
The result lies in the range [-1, 1], where 1 corresponds to perfect similarity (modulo affine shift and scale),
0 indicates no correlation, and -1 indicates perfect negative correlation.
@param templateImage single-channel template image; CV_8U or CV_32F array.
@param inputImage single-channel input image to be warped to provide an image similar to
templateImage, same type as templateImage.
@param inputMask An optional mask to indicate valid values of inputImage.
For single-channel images, the ECC is defined as:
@sa
findTransformECC
*/
\f[
\mathrm{ECC}(I, T) = \frac{\sum_{x} (I(x) - \mu_I)(T(x) - \mu_T)}
{\sqrt{\sum_{x} (I(x) - \mu_I)^2} \cdot \sqrt{\sum_{x} (T(x) - \mu_T)^2}}
\f]
For multi-channel images (e.g., 3-channel RGB), the formula generalizes to:
\f[
\mathrm{ECC}(I, T) =
\frac{\sum_{x} \sum_{c=1}^{C} (I_c(x) - \mu_{I_c})(T_c(x) - \mu_{T_c})}
{\sqrt{\sum_{x} \sum_{c=1}^{C} (I_c(x) - \mu_{I_c})^2} \cdot
\sqrt{\sum_{x} \sum_{c=1}^{C} (T_c(x) - \mu_{T_c})^2}}
\f]
Where:
- \f$I_c(x), T_c(x)\f$ are the values of channel \f$c\f$ at spatial location \f$x\f$,
- \f$\mu_{I_c}, \mu_{T_c}\f$ are the mean values of channel \f$c\f$ over the masked region (if provided),
- \f$C\f$ is the number of channels (only 1 and 3 are currently supported),
- The sums run over all pixels \f$x\f$ in the image domain (optionally restricted by mask).
@param templateImage Input template image; must have either 1 or 3 channels and be of type CV_8U, CV_16U, CV_32F, or CV_64F.
@param inputImage Input image to be compared with the template; must have the same type and number of channels as templateImage.
@param inputMask Optional single-channel mask to specify the valid region of interest in inputImage and templateImage.
@return The ECC similarity coefficient in the range [-1, 1].
@sa findTransformECC
*/
CV_EXPORTS_W double computeECC(InputArray templateImage, InputArray inputImage, InputArray inputMask = noArray());
/** @example samples/cpp/image_alignment.cpp
@@ -313,8 +284,8 @@ 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 1 or 3 channel template image; CV_8U, CV_16U, CV_32F, CV_64F type.
@param inputImage input image which should be warped with the final warpMatrix in
@param templateImage single-channel template image; CV_8U or CV_32F array.
@param inputImage single-channel input image which should be warped with the final warpMatrix in
order to provide an image similar to templateImage, same type as templateImage.
@param warpMatrix floating-point \f$2\times 3\f$ or \f$3\times 3\f$ mapping matrix (warp).
@param motionType parameter, specifying the type of motion:
@@ -331,7 +302,7 @@ order to provide an image similar to templateImage, same type as templateImage.
criteria.epsilon defines the threshold of the increment in the correlation coefficient between two
iterations (a negative criteria.epsilon makes criteria.maxcount the only termination criterion).
Default values are shown in the declaration above.
@param inputMask An optional single channel mask to indicate valid values of inputImage.
@param inputMask An optional mask to indicate valid values of inputImage.
@param gaussFiltSize An optional value indicating size of gaussian blur filter; (DEFAULT: 5)
The function estimates the optimum transformation (warpMatrix) with respect to ECC criterion
@@ -374,51 +345,6 @@ double findTransformECC(InputArray templateImage, InputArray inputImage,
TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 50, 0.001),
InputArray inputMask = noArray());
/** @brief Finds the geometric transform (warp) between two images in terms of the ECC criterion @cite EP08
using validity masks for both the template and the input images.
This function extends findTransformECC() by adding a mask for the template image.
The Enhanced Correlation Coefficient is evaluated only over pixels that are valid in both images:
on each iteration inputMask is warped into the template frame and combined with templateMask, and
only the intersection of these masks contributes to the objective function.
@param templateImage 1 or 3 channel template image; CV_8U, CV_16U, CV_32F, CV_64F type.
@param inputImage input image which should be warped with the final warpMatrix in
order to provide an image similar to templateImage, same type as templateImage.
@param templateMask single-channel 8-bit mask for templateImage indicating valid pixels
to be used in the alignment. Must have the same size as templateImage.
@param inputMask single-channel 8-bit mask for inputImage indicating valid pixels
before warping. Must have the same size as inputImage.
@param warpMatrix floating-point \f$2\times 3\f$ or \f$3\times 3\f$ mapping matrix (warp).
@param motionType parameter, specifying the type of motion:
- **MOTION_TRANSLATION** sets a translational motion model; warpMatrix is \f$2\times 3\f$ with
the first \f$2\times 2\f$ part being the unity matrix and the rest two parameters being
estimated.
- **MOTION_EUCLIDEAN** sets a Euclidean (rigid) transformation as motion model; three
parameters are estimated; warpMatrix is \f$2\times 3\f$.
- **MOTION_AFFINE** sets an affine motion model (DEFAULT); six parameters are estimated;
warpMatrix is \f$2\times 3\f$.
- **MOTION_HOMOGRAPHY** sets a homography as a motion model; eight parameters are
estimated; warpMatrix is \f$3\times 3\f$.
@param criteria parameter, specifying the termination criteria of the ECC algorithm;
criteria.epsilon defines the threshold of the increment in the correlation coefficient between two
iterations (a negative criteria.epsilon makes criteria.maxcount the only termination criterion).
Default values are shown in the declaration above.
@param gaussFiltSize size of the Gaussian blur filter used for smoothing images and masks
before computing the alignment (DEFAULT: 5).
@sa
findTransformECC, computeECC, estimateAffine2D, estimateAffinePartial2D, findHomography
*/
CV_EXPORTS_W double findTransformECCWithMask( InputArray templateImage,
InputArray inputImage,
InputArray templateMask,
InputArray inputMask,
InputOutputArray warpMatrix,
int motionType = MOTION_AFFINE,
TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 50, 1e-6),
int gaussFiltSize = 5 );
/** @example samples/cpp/kalman.cpp
An example using the standard Kalman filter
*/
@@ -678,16 +604,6 @@ public:
/** @copybrief getFinestScale @see getFinestScale */
CV_WRAP virtual void setFinestScale(int val) = 0;
/** @brief Sets the coarsest scale
@param val Coarsest level of the Gaussian pyramid on which the flow is computed.
If set to -1, the auto-computed coarsest scale will be used.
*/
CV_WRAP virtual void setCoarsestScale(int val) = 0;
/** @brief Gets the coarsest scale
*/
CV_WRAP virtual int getCoarsestScale() const = 0;
/** @brief Size of an image patch for matching (in pixels). Normally, default 8x8 patches work well
enough in most cases.
@see setPatchSize */
@@ -910,13 +826,6 @@ public:
static CV_WRAP
Ptr<TrackerGOTURN> create(const TrackerGOTURN::Params& parameters = TrackerGOTURN::Params());
#ifdef HAVE_OPENCV_DNN
/** @brief Constructor
@param model pre-loaded GOTURN model
*/
static CV_WRAP Ptr<TrackerGOTURN> create(const dnn::Net& model);
#endif
//void init(InputArray image, const Rect& boundingBox) CV_OVERRIDE;
//bool update(InputArray image, CV_OUT Rect& boundingBox) CV_OVERRIDE;
};
@@ -944,16 +853,6 @@ public:
static CV_WRAP
Ptr<TrackerDaSiamRPN> create(const TrackerDaSiamRPN::Params& parameters = TrackerDaSiamRPN::Params());
#ifdef HAVE_OPENCV_DNN
/** @brief Constructor
* @param siam_rpn pre-loaded SiamRPN model
* @param kernel_cls1 pre-loaded CLS model
* @param kernel_r1 pre-loaded R1 model
*/
static CV_WRAP
Ptr<TrackerDaSiamRPN> create(const dnn::Net& siam_rpn, const dnn::Net& kernel_cls1, const dnn::Net& kernel_r1);
#endif
/** @brief Return tracking score
*/
CV_WRAP virtual float getTrackingScore() = 0;
@@ -992,15 +891,6 @@ public:
static CV_WRAP
Ptr<TrackerNano> create(const TrackerNano::Params& parameters = TrackerNano::Params());
#ifdef HAVE_OPENCV_DNN
/** @brief Constructor
* @param backbone pre-loaded backbone model
* @param neckhead pre-loaded neckhead model
*/
static CV_WRAP
Ptr<TrackerNano> create(const dnn::Net& backbone, const dnn::Net& neckhead);
#endif
/** @brief Return tracking score
*/
CV_WRAP virtual float getTrackingScore() = 0;
@@ -1030,7 +920,6 @@ public:
CV_PROP_RW int target;
CV_PROP_RW Scalar meanvalue;
CV_PROP_RW Scalar stdvalue;
CV_PROP_RW float tracking_score_threshold;
};
/** @brief Constructor
@@ -1039,18 +928,6 @@ public:
static CV_WRAP
Ptr<TrackerVit> create(const TrackerVit::Params& parameters = TrackerVit::Params());
#ifdef HAVE_OPENCV_DNN
/** @brief Constructor
* @param model pre-loaded DNN model
* @param meanvalue mean value for image preprocessing
* @param stdvalue std value for image preprocessing
* @param tracking_score_threshold threshold for tracking score
*/
static CV_WRAP
Ptr<TrackerVit> create(const dnn::Net& model, Scalar meanvalue = Scalar(0.485, 0.456, 0.406),
Scalar stdvalue = Scalar(0.229, 0.224, 0.225), float tracking_score_threshold = 0.20f);
#endif
/** @brief Return tracking score
*/
CV_WRAP virtual float getTrackingScore() = 0;