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removed C API in the following modules: photo, video, imgcodecs, videoio (#13060)

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* removed C API in the following modules: photo, video, imgcodecs, videoio

* trying to fix various compile errors and warnings on Windows and Linux

* continue to fix compile errors and warnings

* continue to fix compile errors, warnings, as well as the test failures

* trying to resolve compile warnings on Android

* Update cap_dc1394_v2.cpp

fix warning from the new GCC
This commit is contained in:
Vadim Pisarevsky
2018-11-09 00:52:09 +03:00
committed by GitHub
parent 5087ff0814
commit 11eafca3e2
34 changed files with 389 additions and 1403 deletions
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modules/video/include/opencv2/video/tracking.hpp
+8
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@@ -47,6 +47,14 @@
#include "opencv2/core.hpp"
#include "opencv2/imgproc.hpp"
enum
{
CV_LKFLOW_PYR_A_READY = 1,
CV_LKFLOW_PYR_B_READY = 2,
CV_LKFLOW_INITIAL_GUESSES = 4,
CV_LKFLOW_GET_MIN_EIGENVALS = 8
};
namespace cv
{
modules/video/include/opencv2/video/tracking_c.h
-228
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@@ -1,228 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef OPENCV_TRACKING_C_H
#define OPENCV_TRACKING_C_H
#include "opencv2/imgproc/types_c.h"
#ifdef __cplusplus
extern "C" {
#endif
/** @addtogroup video_c
@{
*/
/****************************************************************************************\
* Motion Analysis *
\****************************************************************************************/
/************************************ optical flow ***************************************/
#define CV_LKFLOW_PYR_A_READY 1
#define CV_LKFLOW_PYR_B_READY 2
#define CV_LKFLOW_INITIAL_GUESSES 4
#define CV_LKFLOW_GET_MIN_EIGENVALS 8
/* It is Lucas & Kanade method, modified to use pyramids.
Also it does several iterations to get optical flow for
every point at every pyramid level.
Calculates optical flow between two images for certain set of points (i.e.
it is a "sparse" optical flow, which is opposite to the previous 3 methods) */
CVAPI(void) cvCalcOpticalFlowPyrLK( const CvArr* prev, const CvArr* curr,
CvArr* prev_pyr, CvArr* curr_pyr,
const CvPoint2D32f* prev_features,
CvPoint2D32f* curr_features,
int count,
CvSize win_size,
int level,
char* status,
float* track_error,
CvTermCriteria criteria,
int flags );
/* Modification of a previous sparse optical flow algorithm to calculate
affine flow */
CVAPI(void) cvCalcAffineFlowPyrLK( const CvArr* prev, const CvArr* curr,
CvArr* prev_pyr, CvArr* curr_pyr,
const CvPoint2D32f* prev_features,
CvPoint2D32f* curr_features,
float* matrices, int count,
CvSize win_size, int level,
char* status, float* track_error,
CvTermCriteria criteria, int flags );
/* Estimate optical flow for each pixel using the two-frame G. Farneback algorithm */
CVAPI(void) cvCalcOpticalFlowFarneback( const CvArr* prev, const CvArr* next,
CvArr* flow, double pyr_scale, int levels,
int winsize, int iterations, int poly_n,
double poly_sigma, int flags );
/********************************* motion templates *************************************/
/****************************************************************************************\
* All the motion template functions work only with single channel images. *
* Silhouette image must have depth IPL_DEPTH_8U or IPL_DEPTH_8S *
* Motion history image must have depth IPL_DEPTH_32F, *
* Gradient mask - IPL_DEPTH_8U or IPL_DEPTH_8S, *
* Motion orientation image - IPL_DEPTH_32F *
* Segmentation mask - IPL_DEPTH_32F *
* All the angles are in degrees, all the times are in milliseconds *
\****************************************************************************************/
/* Updates motion history image given motion silhouette */
CVAPI(void) cvUpdateMotionHistory( const CvArr* silhouette, CvArr* mhi,
double timestamp, double duration );
/* Calculates gradient of the motion history image and fills
a mask indicating where the gradient is valid */
CVAPI(void) cvCalcMotionGradient( const CvArr* mhi, CvArr* mask, CvArr* orientation,
double delta1, double delta2,
int aperture_size CV_DEFAULT(3));
/* Calculates average motion direction within a selected motion region
(region can be selected by setting ROIs and/or by composing a valid gradient mask
with the region mask) */
CVAPI(double) cvCalcGlobalOrientation( const CvArr* orientation, const CvArr* mask,
const CvArr* mhi, double timestamp,
double duration );
/* Splits a motion history image into a few parts corresponding to separate independent motions
(e.g. left hand, right hand) */
CVAPI(CvSeq*) cvSegmentMotion( const CvArr* mhi, CvArr* seg_mask,
CvMemStorage* storage,
double timestamp, double seg_thresh );
/****************************************************************************************\
* Tracking *
\****************************************************************************************/
/* Implements CAMSHIFT algorithm - determines object position, size and orientation
from the object histogram back project (extension of meanshift) */
CVAPI(int) cvCamShift( const CvArr* prob_image, CvRect window,
CvTermCriteria criteria, CvConnectedComp* comp,
CvBox2D* box CV_DEFAULT(NULL) );
/* Implements MeanShift algorithm - determines object position
from the object histogram back project */
CVAPI(int) cvMeanShift( const CvArr* prob_image, CvRect window,
CvTermCriteria criteria, CvConnectedComp* comp );
/*
standard Kalman filter (in G. Welch' and G. Bishop's notation):
x(k)=A*x(k-1)+B*u(k)+w(k) p(w)~N(0,Q)
z(k)=H*x(k)+v(k), p(v)~N(0,R)
*/
typedef struct CvKalman
{
int MP; /* number of measurement vector dimensions */
int DP; /* number of state vector dimensions */
int CP; /* number of control vector dimensions */
/* backward compatibility fields */
#if 1
float* PosterState; /* =state_pre->data.fl */
float* PriorState; /* =state_post->data.fl */
float* DynamMatr; /* =transition_matrix->data.fl */
float* MeasurementMatr; /* =measurement_matrix->data.fl */
float* MNCovariance; /* =measurement_noise_cov->data.fl */
float* PNCovariance; /* =process_noise_cov->data.fl */
float* KalmGainMatr; /* =gain->data.fl */
float* PriorErrorCovariance;/* =error_cov_pre->data.fl */
float* PosterErrorCovariance;/* =error_cov_post->data.fl */
float* Temp1; /* temp1->data.fl */
float* Temp2; /* temp2->data.fl */
#endif
CvMat* state_pre; /* predicted state (x'(k)):
x(k)=A*x(k-1)+B*u(k) */
CvMat* state_post; /* corrected state (x(k)):
x(k)=x'(k)+K(k)*(z(k)-H*x'(k)) */
CvMat* transition_matrix; /* state transition matrix (A) */
CvMat* control_matrix; /* control matrix (B)
(it is not used if there is no control)*/
CvMat* measurement_matrix; /* measurement matrix (H) */
CvMat* process_noise_cov; /* process noise covariance matrix (Q) */
CvMat* measurement_noise_cov; /* measurement noise covariance matrix (R) */
CvMat* error_cov_pre; /* priori error estimate covariance matrix (P'(k)):
P'(k)=A*P(k-1)*At + Q)*/
CvMat* gain; /* Kalman gain matrix (K(k)):
K(k)=P'(k)*Ht*inv(H*P'(k)*Ht+R)*/
CvMat* error_cov_post; /* posteriori error estimate covariance matrix (P(k)):
P(k)=(I-K(k)*H)*P'(k) */
CvMat* temp1; /* temporary matrices */
CvMat* temp2;
CvMat* temp3;
CvMat* temp4;
CvMat* temp5;
} CvKalman;
/* Creates Kalman filter and sets A, B, Q, R and state to some initial values */
CVAPI(CvKalman*) cvCreateKalman( int dynam_params, int measure_params,
int control_params CV_DEFAULT(0));
/* Releases Kalman filter state */
CVAPI(void) cvReleaseKalman( CvKalman** kalman);
/* Updates Kalman filter by time (predicts future state of the system) */
CVAPI(const CvMat*) cvKalmanPredict( CvKalman* kalman,
const CvMat* control CV_DEFAULT(NULL));
/* Updates Kalman filter by measurement
(corrects state of the system and internal matrices) */
CVAPI(const CvMat*) cvKalmanCorrect( CvKalman* kalman, const CvMat* measurement );
#define cvKalmanUpdateByTime cvKalmanPredict
#define cvKalmanUpdateByMeasurement cvKalmanCorrect
/** @} video_c */
#ifdef __cplusplus
} // extern "C"
#endif
#endif // OPENCV_TRACKING_C_H
modules/video/src/compat_video.cpp
-301
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@@ -1,301 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
#include "opencv2/video/tracking_c.h"
/////////////////////////// Meanshift & CAMShift ///////////////////////////
CV_IMPL int
cvMeanShift( const void* imgProb, CvRect windowIn,
CvTermCriteria criteria, CvConnectedComp* comp )
{
cv::Mat img = cv::cvarrToMat(imgProb);
cv::Rect window = windowIn;
int iters = cv::meanShift(img, window, criteria);
if( comp )
{
comp->rect = cvRect(window);
comp->area = cvRound(cv::sum(img(window))[0]);
}
return iters;
}
CV_IMPL int
cvCamShift( const void* imgProb, CvRect windowIn,
CvTermCriteria criteria,
CvConnectedComp* comp,
CvBox2D* box )
{
cv::Mat img = cv::cvarrToMat(imgProb);
cv::Rect window = windowIn;
cv::RotatedRect rr = cv::CamShift(img, window, criteria);
if( comp )
{
comp->rect = cvRect(window);
cv::Rect roi = rr.boundingRect() & cv::Rect(0, 0, img.cols, img.rows);
comp->area = cvRound(cv::sum(img(roi))[0]);
}
if( box )
*box = cvBox2D(rr);
return rr.size.width*rr.size.height > 0.f ? 1 : -1;
}
///////////////////////////////// Kalman ///////////////////////////////
CV_IMPL CvKalman*
cvCreateKalman( int DP, int MP, int CP )
{
CvKalman *kalman = 0;
if( DP <= 0 || MP <= 0 )
CV_Error( CV_StsOutOfRange,
"state and measurement vectors must have positive number of dimensions" );
if( CP < 0 )
CP = DP;
/* allocating memory for the structure */
kalman = (CvKalman *)cvAlloc( sizeof( CvKalman ));
memset( kalman, 0, sizeof(*kalman));
kalman->DP = DP;
kalman->MP = MP;
kalman->CP = CP;
kalman->state_pre = cvCreateMat( DP, 1, CV_32FC1 );
cvZero( kalman->state_pre );
kalman->state_post = cvCreateMat( DP, 1, CV_32FC1 );
cvZero( kalman->state_post );
kalman->transition_matrix = cvCreateMat( DP, DP, CV_32FC1 );
cvSetIdentity( kalman->transition_matrix );
kalman->process_noise_cov = cvCreateMat( DP, DP, CV_32FC1 );
cvSetIdentity( kalman->process_noise_cov );
kalman->measurement_matrix = cvCreateMat( MP, DP, CV_32FC1 );
cvZero( kalman->measurement_matrix );
kalman->measurement_noise_cov = cvCreateMat( MP, MP, CV_32FC1 );
cvSetIdentity( kalman->measurement_noise_cov );
kalman->error_cov_pre = cvCreateMat( DP, DP, CV_32FC1 );
kalman->error_cov_post = cvCreateMat( DP, DP, CV_32FC1 );
cvZero( kalman->error_cov_post );
kalman->gain = cvCreateMat( DP, MP, CV_32FC1 );
if( CP > 0 )
{
kalman->control_matrix = cvCreateMat( DP, CP, CV_32FC1 );
cvZero( kalman->control_matrix );
}
kalman->temp1 = cvCreateMat( DP, DP, CV_32FC1 );
kalman->temp2 = cvCreateMat( MP, DP, CV_32FC1 );
kalman->temp3 = cvCreateMat( MP, MP, CV_32FC1 );
kalman->temp4 = cvCreateMat( MP, DP, CV_32FC1 );
kalman->temp5 = cvCreateMat( MP, 1, CV_32FC1 );
#if 1
kalman->PosterState = kalman->state_pre->data.fl;
kalman->PriorState = kalman->state_post->data.fl;
kalman->DynamMatr = kalman->transition_matrix->data.fl;
kalman->MeasurementMatr = kalman->measurement_matrix->data.fl;
kalman->MNCovariance = kalman->measurement_noise_cov->data.fl;
kalman->PNCovariance = kalman->process_noise_cov->data.fl;
kalman->KalmGainMatr = kalman->gain->data.fl;
kalman->PriorErrorCovariance = kalman->error_cov_pre->data.fl;
kalman->PosterErrorCovariance = kalman->error_cov_post->data.fl;
#endif
return kalman;
}
CV_IMPL void
cvReleaseKalman( CvKalman** _kalman )
{
CvKalman *kalman;
if( !_kalman )
CV_Error( CV_StsNullPtr, "" );
kalman = *_kalman;
if( !kalman )
return;
/* freeing the memory */
cvReleaseMat( &kalman->state_pre );
cvReleaseMat( &kalman->state_post );
cvReleaseMat( &kalman->transition_matrix );
cvReleaseMat( &kalman->control_matrix );
cvReleaseMat( &kalman->measurement_matrix );
cvReleaseMat( &kalman->process_noise_cov );
cvReleaseMat( &kalman->measurement_noise_cov );
cvReleaseMat( &kalman->error_cov_pre );
cvReleaseMat( &kalman->gain );
cvReleaseMat( &kalman->error_cov_post );
cvReleaseMat( &kalman->temp1 );
cvReleaseMat( &kalman->temp2 );
cvReleaseMat( &kalman->temp3 );
cvReleaseMat( &kalman->temp4 );
cvReleaseMat( &kalman->temp5 );
memset( kalman, 0, sizeof(*kalman));
/* deallocating the structure */
cvFree( _kalman );
}
CV_IMPL const CvMat*
cvKalmanPredict( CvKalman* kalman, const CvMat* control )
{
if( !kalman )
CV_Error( CV_StsNullPtr, "" );
/* update the state */
/* x'(k) = A*x(k) */
cvMatMulAdd( kalman->transition_matrix, kalman->state_post, 0, kalman->state_pre );
if( control && kalman->CP > 0 )
/* x'(k) = x'(k) + B*u(k) */
cvMatMulAdd( kalman->control_matrix, control, kalman->state_pre, kalman->state_pre );
/* update error covariance matrices */
/* temp1 = A*P(k) */
cvMatMulAdd( kalman->transition_matrix, kalman->error_cov_post, 0, kalman->temp1 );
/* P'(k) = temp1*At + Q */
cvGEMM( kalman->temp1, kalman->transition_matrix, 1, kalman->process_noise_cov, 1,
kalman->error_cov_pre, CV_GEMM_B_T );
/* handle the case when there will be measurement before the next predict */
cvCopy(kalman->state_pre, kalman->state_post);
return kalman->state_pre;
}
CV_IMPL const CvMat*
cvKalmanCorrect( CvKalman* kalman, const CvMat* measurement )
{
if( !kalman || !measurement )
CV_Error( CV_StsNullPtr, "" );
/* temp2 = H*P'(k) */
cvMatMulAdd( kalman->measurement_matrix, kalman->error_cov_pre, 0, kalman->temp2 );
/* temp3 = temp2*Ht + R */
cvGEMM( kalman->temp2, kalman->measurement_matrix, 1,
kalman->measurement_noise_cov, 1, kalman->temp3, CV_GEMM_B_T );
/* temp4 = inv(temp3)*temp2 = Kt(k) */
cvSolve( kalman->temp3, kalman->temp2, kalman->temp4, CV_SVD );
/* K(k) */
cvTranspose( kalman->temp4, kalman->gain );
/* temp5 = z(k) - H*x'(k) */
cvGEMM( kalman->measurement_matrix, kalman->state_pre, -1, measurement, 1, kalman->temp5 );
/* x(k) = x'(k) + K(k)*temp5 */
cvMatMulAdd( kalman->gain, kalman->temp5, kalman->state_pre, kalman->state_post );
/* P(k) = P'(k) - K(k)*temp2 */
cvGEMM( kalman->gain, kalman->temp2, -1, kalman->error_cov_pre, 1,
kalman->error_cov_post, 0 );
return kalman->state_post;
}
///////////////////////////////////// Optical Flow ////////////////////////////////
CV_IMPL void
cvCalcOpticalFlowPyrLK( const void* arrA, const void* arrB,
void* /*pyrarrA*/, void* /*pyrarrB*/,
const CvPoint2D32f * featuresA,
CvPoint2D32f * featuresB,
int count, CvSize winSize, int level,
char *status, float *error,
CvTermCriteria criteria, int flags )
{
if( count <= 0 )
return;
CV_Assert( featuresA && featuresB );
cv::Mat A = cv::cvarrToMat(arrA), B = cv::cvarrToMat(arrB);
cv::Mat ptA(count, 1, CV_32FC2, (void*)featuresA);
cv::Mat ptB(count, 1, CV_32FC2, (void*)featuresB);
cv::Mat st, err;
if( status )
st = cv::Mat(count, 1, CV_8U, (void*)status);
if( error )
err = cv::Mat(count, 1, CV_32F, (void*)error);
cv::calcOpticalFlowPyrLK( A, B, ptA, ptB, st,
error ? cv::_OutputArray(err) : (cv::_OutputArray)cv::noArray(),
winSize, level, criteria, flags);
}
CV_IMPL void cvCalcOpticalFlowFarneback(
const CvArr* _prev, const CvArr* _next,
CvArr* _flow, double pyr_scale, int levels,
int winsize, int iterations, int poly_n,
double poly_sigma, int flags )
{
cv::Mat prev = cv::cvarrToMat(_prev), next = cv::cvarrToMat(_next);
cv::Mat flow = cv::cvarrToMat(_flow);
CV_Assert( flow.size() == prev.size() && flow.type() == CV_32FC2 );
cv::calcOpticalFlowFarneback( prev, next, flow, pyr_scale, levels,
winsize, iterations, poly_n, poly_sigma, flags );
}
modules/video/test/test_camshift.cpp
+34 -80
View File
@@ -40,10 +40,12 @@
//M*/
#include "test_precomp.hpp"
#include "opencv2/video/tracking_c.h"
#include "opencv2/video/tracking.hpp"
namespace opencv_test { namespace {
using namespace cv;
class CV_TrackBaseTest : public cvtest::BaseTest
{
public:
@@ -59,10 +61,10 @@ protected:
void generate_object();
int min_log_size, max_log_size;
CvMat* img;
CvBox2D box0;
CvSize img_size;
CvTermCriteria criteria;
Mat img;
RotatedRect box0;
Size img_size;
TermCriteria criteria;
int img_type;
};
@@ -84,7 +86,7 @@ CV_TrackBaseTest::~CV_TrackBaseTest()
void CV_TrackBaseTest::clear()
{
cvReleaseMat( &img );
img.release();
cvtest::BaseTest::clear();
}
@@ -103,8 +105,7 @@ int CV_TrackBaseTest::read_params( const cv::FileStorage& fs )
max_log_size = cvtest::clipInt( max_log_size, 1, 10 );
if( min_log_size > max_log_size )
{
int t;
CV_SWAP( min_log_size, max_log_size, t );
std::swap( min_log_size, max_log_size );
}
return 0;
@@ -122,13 +123,12 @@ void CV_TrackBaseTest::generate_object()
double a = sin(angle), b = -cos(angle);
double inv_ww = 1./(width*width), inv_hh = 1./(height*height);
img = cvCreateMat( img_size.height, img_size.width, img_type );
cvZero( img );
img = Mat::zeros( img_size.height, img_size.width, img_type );
// use the straightforward algorithm: for every pixel check if it is inside the ellipse
for( y = 0; y < img_size.height; y++ )
{
uchar* ptr = img->data.ptr + img->step*y;
uchar* ptr = img.ptr(y);
float* fl = (float*)ptr;
double x_ = (y - cy)*b, y_ = (y - cy)*a;
@@ -163,8 +163,7 @@ int CV_TrackBaseTest::prepare_test_case( int test_case_idx )
if( box0.size.width > box0.size.height )
{
float t;
CV_SWAP( box0.size.width, box0.size.height, t );
std::swap( box0.size.width, box0.size.height );
}
m = MAX( box0.size.width, box0.size.height );
@@ -176,7 +175,7 @@ int CV_TrackBaseTest::prepare_test_case( int test_case_idx )
box0.center.x = (float)(img_size.width*0.5 + (cvtest::randReal(rng)-0.5)*(img_size.width - m));
box0.center.y = (float)(img_size.height*0.5 + (cvtest::randReal(rng)-0.5)*(img_size.height - m));
criteria = cvTermCriteria( CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 10, 0.1 );
criteria = TermCriteria( TermCriteria::EPS + TermCriteria::MAX_ITER, 10, 0.1 );
generate_object();
@@ -209,9 +208,8 @@ protected:
int validate_test_results( int test_case_idx );
void generate_object();
CvBox2D box;
CvRect init_rect;
CvConnectedComp comp;
RotatedRect box;
Rect init_rect;
int area0;
};
@@ -231,12 +229,10 @@ int CV_CamShiftTest::prepare_test_case( int test_case_idx )
if( code <= 0 )
return code;
area0 = cvCountNonZero(img);
area0 = countNonZero(img);
for(i = 0; i < 100; i++)
{
CvMat temp;
m = MAX(box0.size.width,box0.size.height)*0.8;
init_rect.x = cvFloor(box0.center.x - m*(0.45 + cvtest::randReal(rng)*0.2));
init_rect.y = cvFloor(box0.center.y - m*(0.45 + cvtest::randReal(rng)*0.2));
@@ -248,8 +244,8 @@ int CV_CamShiftTest::prepare_test_case( int test_case_idx )
init_rect.y + init_rect.height >= img_size.height )
continue;
cvGetSubRect( img, &temp, init_rect );
area = cvCountNonZero( &temp );
Mat temp = img(init_rect);
area = countNonZero( temp );
if( area >= 0.1*area0 )
break;
@@ -261,7 +257,7 @@ int CV_CamShiftTest::prepare_test_case( int test_case_idx )
void CV_CamShiftTest::run_func(void)
{
cvCamShift( img, init_rect, criteria, &comp, &box );
box = CamShift( img, init_rect, criteria );
}
@@ -269,15 +265,14 @@ int CV_CamShiftTest::validate_test_results( int /*test_case_idx*/ )
{
int code = cvtest::TS::OK;
double m = MAX(box0.size.width, box0.size.height), delta;
double m = MAX(box0.size.width, box0.size.height);
double diff_angle;
if( cvIsNaN(box.size.width) || cvIsInf(box.size.width) || box.size.width <= 0 ||
cvIsNaN(box.size.height) || cvIsInf(box.size.height) || box.size.height <= 0 ||
cvIsNaN(box.center.x) || cvIsInf(box.center.x) ||
cvIsNaN(box.center.y) || cvIsInf(box.center.y) ||
cvIsNaN(box.angle) || cvIsInf(box.angle) || box.angle < -180 || box.angle > 180 ||
cvIsNaN(comp.area) || cvIsInf(comp.area) || comp.area <= 0 )
cvIsNaN(box.angle) || cvIsInf(box.angle) || box.angle < -180 || box.angle > 180 )
{
ts->printf( cvtest::TS::LOG, "Invalid CvBox2D or CvConnectedComp was returned by cvCamShift\n" );
code = cvtest::TS::FAIL_INVALID_OUTPUT;
@@ -318,29 +313,6 @@ int CV_CamShiftTest::validate_test_results( int /*test_case_idx*/ )
goto _exit_;
}
delta = m*0.7;
if( comp.rect.x < box0.center.x - delta ||
comp.rect.y < box0.center.y - delta ||
comp.rect.x + comp.rect.width > box0.center.x + delta ||
comp.rect.y + comp.rect.height > box0.center.y + delta )
{
ts->printf( cvtest::TS::LOG,
"Incorrect CvConnectedComp ((%d,%d,%d,%d) is not within (%.1f,%.1f,%.1f,%.1f))\n",
comp.rect.x, comp.rect.y, comp.rect.x + comp.rect.width, comp.rect.y + comp.rect.height,
box0.center.x - delta, box0.center.y - delta, box0.center.x + delta, box0.center.y + delta );
code = cvtest::TS::FAIL_BAD_ACCURACY;
goto _exit_;
}
if( fabs(comp.area - area0) > area0*0.15 )
{
ts->printf( cvtest::TS::LOG,
"Incorrect CvConnectedComp area (=%.1f, should be %d)\n", comp.area, area0 );
code = cvtest::TS::FAIL_BAD_ACCURACY;
goto _exit_;
}
_exit_:
if( code < 0 )
@@ -377,8 +349,7 @@ protected:
int validate_test_results( int test_case_idx );
void generate_object();
CvRect init_rect;
CvConnectedComp comp;
Rect init_rect, rect;
int area0, area;
};
@@ -398,12 +369,10 @@ int CV_MeanShiftTest::prepare_test_case( int test_case_idx )
if( code <= 0 )
return code;
area0 = cvCountNonZero(img);
area0 = countNonZero(img);
for(i = 0; i < 100; i++)
{
CvMat temp;
m = (box0.size.width + box0.size.height)*0.5;
init_rect.x = cvFloor(box0.center.x - m*(0.4 + cvtest::randReal(rng)*0.2));
init_rect.y = cvFloor(box0.center.y - m*(0.4 + cvtest::randReal(rng)*0.2));
@@ -415,8 +384,8 @@ int CV_MeanShiftTest::prepare_test_case( int test_case_idx )
init_rect.y + init_rect.height >= img_size.height )
continue;
cvGetSubRect( img, &temp, init_rect );
area = cvCountNonZero( &temp );
Mat temp = img(init_rect);
area = countNonZero( temp );
if( area >= 0.5*area0 )
break;
@@ -428,7 +397,8 @@ int CV_MeanShiftTest::prepare_test_case( int test_case_idx )
void CV_MeanShiftTest::run_func(void)
{
cvMeanShift( img, init_rect, criteria, &comp );
rect = init_rect;
meanShift( img, rect, criteria );
}
@@ -438,15 +408,8 @@ int CV_MeanShiftTest::validate_test_results( int /*test_case_idx*/ )
Point2f c;
double m = MAX(box0.size.width, box0.size.height), delta;
if( cvIsNaN(comp.area) || cvIsInf(comp.area) || comp.area <= 0 )
{
ts->printf( cvtest::TS::LOG, "Invalid CvConnectedComp was returned by cvMeanShift\n" );
code = cvtest::TS::FAIL_INVALID_OUTPUT;
goto _exit_;
}
c.x = (float)(comp.rect.x + comp.rect.width*0.5);
c.y = (float)(comp.rect.y + comp.rect.height*0.5);
c.x = (float)(rect.x + rect.width*0.5);
c.y = (float)(rect.y + rect.height*0.5);
if( fabs(c.x - box0.center.x) > m*0.1 ||
fabs(c.y - box0.center.y) > m*0.1 )
@@ -459,25 +422,16 @@ int CV_MeanShiftTest::validate_test_results( int /*test_case_idx*/ )
delta = m*0.7;
if( comp.rect.x < box0.center.x - delta ||
comp.rect.y < box0.center.y - delta ||
comp.rect.x + comp.rect.width > box0.center.x + delta ||
comp.rect.y + comp.rect.height > box0.center.y + delta )
if( rect.x < box0.center.x - delta ||
rect.y < box0.center.y - delta ||
rect.x + rect.width > box0.center.x + delta ||
rect.y + rect.height > box0.center.y + delta )
{
ts->printf( cvtest::TS::LOG,
"Incorrect CvConnectedComp ((%d,%d,%d,%d) is not within (%.1f,%.1f,%.1f,%.1f))\n",
comp.rect.x, comp.rect.y, comp.rect.x + comp.rect.width, comp.rect.y + comp.rect.height,
rect.x, rect.y, rect.x + rect.width, rect.y + rect.height,
box0.center.x - delta, box0.center.y - delta, box0.center.x + delta, box0.center.y + delta );
code = cvtest::TS::FAIL_BAD_ACCURACY;
goto _exit_;
}
if( fabs((double)(comp.area - area0)) > fabs((double)(area - area0)) + area0*0.05 )
{
ts->printf( cvtest::TS::LOG,
"Incorrect CvConnectedComp area (=%.1f, should be %d)\n", comp.area, area0 );
code = cvtest::TS::FAIL_BAD_ACCURACY;
goto _exit_;
}
_exit_:
modules/video/test/test_kalman.cpp
+22 -35
View File
@@ -40,7 +40,7 @@
//M*/
#include "test_precomp.hpp"
#include "opencv2/video/tracking_c.h"
#include "opencv2/video/tracking.hpp"
namespace opencv_test { namespace {
@@ -67,54 +67,41 @@ void CV_KalmanTest::run( int )
const double EPSILON = 1.000;
RNG& rng = ts->get_rng();
CvKalman* Kalm;
int i, j;
CvMat* Sample = cvCreateMat(Dim,1,CV_32F);
CvMat* Temp = cvCreateMat(Dim,1,CV_32F);
cv::Mat Sample(Dim,1,CV_32F);
cv::Mat Temp(Dim,1,CV_32F);
Kalm = cvCreateKalman(Dim, Dim);
CvMat Dyn = cvMat(Dim,Dim,CV_32F,Kalm->DynamMatr);
CvMat Mes = cvMat(Dim,Dim,CV_32F,Kalm->MeasurementMatr);
CvMat PNC = cvMat(Dim,Dim,CV_32F,Kalm->PNCovariance);
CvMat MNC = cvMat(Dim,Dim,CV_32F,Kalm->MNCovariance);
CvMat PriErr = cvMat(Dim,Dim,CV_32F,Kalm->PriorErrorCovariance);
CvMat PostErr = cvMat(Dim,Dim,CV_32F,Kalm->PosterErrorCovariance);
CvMat PriState = cvMat(Dim,1,CV_32F,Kalm->PriorState);
CvMat PostState = cvMat(Dim,1,CV_32F,Kalm->PosterState);
cvSetIdentity(&PNC);
cvSetIdentity(&PriErr);
cvSetIdentity(&PostErr);
cvSetZero(&MNC);
cvSetZero(&PriState);
cvSetZero(&PostState);
cvSetIdentity(&Mes);
cvSetIdentity(&Dyn);
Mat _Sample = cvarrToMat(Sample);
cvtest::randUni(rng, _Sample, cvScalarAll(-max_init), cvScalarAll(max_init));
cvKalmanCorrect(Kalm, Sample);
cv::KalmanFilter Kalm(Dim, Dim);
Kalm.transitionMatrix = cv::Mat::eye(Dim, Dim, CV_32F);
Kalm.measurementMatrix = cv::Mat::eye(Dim, Dim, CV_32F);
Kalm.processNoiseCov = cv::Mat::eye(Dim, Dim, CV_32F);
Kalm.errorCovPre = cv::Mat::eye(Dim, Dim, CV_32F);
Kalm.errorCovPost = cv::Mat::eye(Dim, Dim, CV_32F);
Kalm.measurementNoiseCov = cv::Mat::zeros(Dim, Dim, CV_32F);
Kalm.statePre = cv::Mat::zeros(Dim, 1, CV_32F);
Kalm.statePost = cv::Mat::zeros(Dim, 1, CV_32F);
cvtest::randUni(rng, Sample, Scalar::all(-max_init), Scalar::all(max_init));
Kalm.correct(Sample);
for(i = 0; i<Steps; i++)
{
cvKalmanPredict(Kalm);
Kalm.predict();
const Mat& Dyn = Kalm.transitionMatrix;
for(j = 0; j<Dim; j++)
{
float t = 0;
for(int k=0; k<Dim; k++)
{
t += Dyn.data.fl[j*Dim+k]*Sample->data.fl[k];
t += Dyn.at<float>(j,k)*Sample.at<float>(k);
}
Temp->data.fl[j]= (float)(t+(cvtest::randReal(rng)*2-1)*max_noise);
Temp.at<float>(j) = (float)(t+(cvtest::randReal(rng)*2-1)*max_noise);
}
cvCopy( Temp, Sample );
cvKalmanCorrect(Kalm,Temp);
Temp.copyTo(Sample);
Kalm.correct(Temp);
}
Mat _state_post = cvarrToMat(Kalm->state_post);
code = cvtest::cmpEps2( ts, _Sample, _state_post, EPSILON, false, "The final estimated state" );
cvReleaseMat(&Sample);
cvReleaseMat(&Temp);
cvReleaseKalman(&Kalm);
Mat _state_post = Kalm.statePost;
code = cvtest::cmpEps2( ts, Sample, _state_post, EPSILON, false, "The final estimated state" );
if( code < 0 )
ts->set_failed_test_info( code );
modules/video/test/test_optflowpyrlk.cpp
+3 -4
View File
@@ -40,7 +40,6 @@
//M*/
#include "test_precomp.hpp"
#include "opencv2/video/tracking_c.h"
namespace opencv_test { namespace {
@@ -71,7 +70,7 @@ void CV_OptFlowPyrLKTest::run( int )
int merr_i = 0, merr_j = 0, merr_k = 0, merr_nan = 0;
char filename[1000];
CvPoint2D32f *v = 0, *v2 = 0;
cv::Point2f *v = 0, *v2 = 0;
cv::Mat _u, _v, _v2;
cv::Mat imgI, imgJ;
@@ -145,8 +144,8 @@ void CV_OptFlowPyrLKTest::run( int )
calcOpticalFlowPyrLK(imgI, imgJ, _u, _v2, status, cv::noArray(), Size( 41, 41 ), 4,
TermCriteria( TermCriteria::MAX_ITER + TermCriteria::EPS, 30, 0.01f ), 0 );
v = (CvPoint2D32f*)_v.ptr();
v2 = (CvPoint2D32f*)_v2.ptr();
v = (cv::Point2f*)_v.ptr();
v2 = (cv::Point2f*)_v2.ptr();
/* compare results */
for( i = 0; i < n; i++ )