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Merge pull request #4074 from vpisarev:objdetect_fixes

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This commit is contained in:
Vadim Pisarevsky 2015-05-28 19:43:51 +00:00
commit b46719b093
9 changed files with 331 additions and 31 deletions
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39
modules/calib3d/test/test_homography.cpp
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@ -662,7 +662,7 @@ TEST(Calib3d_Homography, fromImages)
std::vector< DMatch > good_matches;
for( int i = 0; i < descriptors_1.rows; i++ )
{
if( matches[i].distance <= 42 )
if( matches[i].distance <= 100 )
good_matches.push_back( matches[i]);
}
@ -676,13 +676,32 @@ TEST(Calib3d_Homography, fromImages)
pointframe2.push_back( keypoints_2[ good_matches[i].trainIdx ].pt );
}
Mat inliers;
Mat H = findHomography( pointframe1, pointframe2, RANSAC,3.0,inliers);
int ninliers = countNonZero(inliers);
printf("nfeatures1 = %d, nfeatures2=%d, good matches=%d, ninliers=%d\n",
(int)keypoints_1.size(), (int)keypoints_2.size(),
(int)good_matches.size(), ninliers);
ASSERT_TRUE(!H.empty());
ASSERT_GE(ninliers, 80);
Mat H0, H1, inliers0, inliers1;
double min_t0 = DBL_MAX, min_t1 = DBL_MAX;
for( int i = 0; i < 10; i++ )
{
double t = (double)getTickCount();
H0 = findHomography( pointframe1, pointframe2, RANSAC, 3.0, inliers0 );
t = (double)getTickCount() - t;
min_t0 = std::min(min_t0, t);
}
int ninliers0 = countNonZero(inliers0);
for( int i = 0; i < 10; i++ )
{
double t = (double)getTickCount();
H1 = findHomography( pointframe1, pointframe2, RHO, 3.0, inliers1 );
t = (double)getTickCount() - t;
min_t1 = std::min(min_t1, t);
}
int ninliers1 = countNonZero(inliers1);
double freq = getTickFrequency();
printf("nfeatures1 = %d, nfeatures2=%d, matches=%d, ninliers(RANSAC)=%d, "
"time(RANSAC)=%.2fmsec, ninliers(RHO)=%d, time(RHO)=%.2fmsec\n",
(int)keypoints_1.size(), (int)keypoints_2.size(),
(int)good_matches.size(), ninliers0, min_t0*1000./freq, ninliers1, min_t1*1000./freq);
ASSERT_TRUE(!H0.empty());
ASSERT_GE(ninliers0, 80);
ASSERT_TRUE(!H1.empty());
ASSERT_GE(ninliers1, 80);
}

195
modules/core/test/test_hal_core.cpp Normal file
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@ -0,0 +1,195 @@
/*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) 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 OpenCV Foundation 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 "test_precomp.hpp"
#include "opencv2/hal.hpp"
using namespace cv;
enum
{
HAL_EXP = 0,
HAL_LOG = 1,
HAL_SQRT = 2
};
TEST(Core_HAL, mathfuncs)
{
for( int hcase = 0; hcase < 6; hcase++ )
{
int depth = hcase % 2 == 0 ? CV_32F : CV_64F;
double eps = depth == CV_32F ? 1e-5 : 1e-10;
int nfunc = hcase / 2;
int n = 100;
Mat src(1, n, depth), dst(1, n, depth), dst0(1, n, depth);
randu(src, 1, 10);
double min_hal_t = DBL_MAX, min_ocv_t = DBL_MAX;
for( int iter = 0; iter < 10; iter++ )
{
double t = (double)getTickCount();
switch (nfunc)
{
case HAL_EXP:
if( depth == CV_32F )
hal::exp(src.ptr<float>(), dst.ptr<float>(), n);
else
hal::exp(src.ptr<double>(), dst.ptr<double>(), n);
break;
case HAL_LOG:
if( depth == CV_32F )
hal::log(src.ptr<float>(), dst.ptr<float>(), n);
else
hal::log(src.ptr<double>(), dst.ptr<double>(), n);
break;
case HAL_SQRT:
if( depth == CV_32F )
hal::sqrt(src.ptr<float>(), dst.ptr<float>(), n);
else
hal::sqrt(src.ptr<double>(), dst.ptr<double>(), n);
break;
default:
CV_Error(Error::StsBadArg, "unknown function");
}
t = (double)getTickCount() - t;
min_hal_t = std::min(min_hal_t, t);
t = (double)getTickCount();
switch (nfunc)
{
case HAL_EXP:
exp(src, dst0);
break;
case HAL_LOG:
log(src, dst0);
break;
case HAL_SQRT:
pow(src, 0.5, dst0);
break;
default:
CV_Error(Error::StsBadArg, "unknown function");
}
t = (double)getTickCount() - t;
min_ocv_t = std::min(min_ocv_t, t);
}
EXPECT_LE(norm(dst, dst0, NORM_INF | NORM_RELATIVE), eps);
double freq = getTickFrequency();
printf("%s (N=%d, %s): hal time=%.2fusec, ocv time=%.2fusec\n",
(nfunc == HAL_EXP ? "exp" : nfunc == HAL_LOG ? "log" : nfunc == HAL_SQRT ? "sqrt" : "???"),
n, (depth == CV_32F ? "f32" : "f64"), min_hal_t*1e6/freq, min_ocv_t*1e6/freq);
}
}
enum
{
HAL_LU = 0,
HAL_CHOL = 1
};
TEST(Core_HAL, mat_decomp)
{
for( int hcase = 0; hcase < 16; hcase++ )
{
int depth = hcase % 2 == 0 ? CV_32F : CV_64F;
int size = (hcase / 2) % 4;
size = size == 0 ? 3 : size == 1 ? 4 : size == 2 ? 6 : 15;
int nfunc = (hcase / 8);
double eps = depth == CV_32F ? 1e-5 : 1e-10;
if( size == 3 )
continue;
Mat a0(size, size, depth), a(size, size, depth), b(size, 1, depth), x(size, 1, depth), x0(size, 1, depth);
randu(a0, -1, 1);
a0 = a0*a0.t();
randu(b, -1, 1);
double min_hal_t = DBL_MAX, min_ocv_t = DBL_MAX;
size_t asize = size*size*a.elemSize();
size_t bsize = size*b.elemSize();
for( int iter = 0; iter < 10; iter++ )
{
memcpy(x.ptr(), b.ptr(), bsize);
memcpy(a.ptr(), a0.ptr(), asize);
double t = (double)getTickCount();
switch (nfunc)
{
case HAL_LU:
if( depth == CV_32F )
hal::LU(a.ptr<float>(), a.step, size, x.ptr<float>(), x.step, 1);
else
hal::LU(a.ptr<double>(), a.step, size, x.ptr<double>(), x.step, 1);
break;
case HAL_CHOL:
if( depth == CV_32F )
hal::Cholesky(a.ptr<float>(), a.step, size, x.ptr<float>(), x.step, 1);
else
hal::Cholesky(a.ptr<double>(), a.step, size, x.ptr<double>(), x.step, 1);
break;
default:
CV_Error(Error::StsBadArg, "unknown function");
}
t = (double)getTickCount() - t;
min_hal_t = std::min(min_hal_t, t);
t = (double)getTickCount();
solve(a0, b, x0, (nfunc == HAL_LU ? DECOMP_LU : DECOMP_CHOLESKY));
t = (double)getTickCount() - t;
min_ocv_t = std::min(min_ocv_t, t);
}
//std::cout << "x: " << Mat(x.t()) << std::endl;
//std::cout << "x0: " << Mat(x0.t()) << std::endl;
EXPECT_LE(norm(x, x0, NORM_INF | NORM_RELATIVE), eps);
double freq = getTickFrequency();
printf("%s (%d x %d, %s): hal time=%.2fusec, ocv time=%.2fusec\n",
(nfunc == HAL_LU ? "LU" : nfunc == HAL_CHOL ? "Cholesky" : "???"),
size, size,
(depth == CV_32F ? "f32" : "f64"),
min_hal_t*1e6/freq, min_ocv_t*1e6/freq);
}
}

3
modules/imgproc/src/sumpixels.cpp
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@ -318,6 +318,7 @@ static void integral_##suffix( T* src, size_t srcstep, ST* sum, size_t sumstep,
{ integral_(src, srcstep, sum, sumstep, sqsum, sqsumstep, tilted, tiltedstep, size, cn); }
DEF_INTEGRAL_FUNC(8u32s, uchar, int, double)
DEF_INTEGRAL_FUNC(8u32s32s, uchar, int, int)
DEF_INTEGRAL_FUNC(8u32f64f, uchar, float, double)
DEF_INTEGRAL_FUNC(8u64f64f, uchar, double, double)
DEF_INTEGRAL_FUNC(16u64f64f, ushort, double, double)
@ -505,6 +506,8 @@ void cv::integral( InputArray _src, OutputArray _sum, OutputArray _sqsum, Output
func = (IntegralFunc)GET_OPTIMIZED(integral_8u32s);
else if( depth == CV_8U && sdepth == CV_32S && sqdepth == CV_32F )
func = (IntegralFunc)integral_8u32s32f;
else if( depth == CV_8U && sdepth == CV_32S && sqdepth == CV_32S )
func = (IntegralFunc)integral_8u32s32s;
else if( depth == CV_8U && sdepth == CV_32F && sqdepth == CV_64F )
func = (IntegralFunc)integral_8u32f64f;
else if( depth == CV_8U && sdepth == CV_32F && sqdepth == CV_32F )

77
modules/objdetect/src/cascadedetect.cpp
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@ -627,33 +627,33 @@ void HaarEvaluator::computeChannels(int scaleIdx, InputArray img)
int sqy = sy + (sqofs / sbufSize.width);
UMat sum(usbuf, Rect(sx, sy, s.szi.width, s.szi.height));
UMat sqsum(usbuf, Rect(sx, sqy, s.szi.width, s.szi.height));
sqsum.flags = (sqsum.flags & ~UMat::DEPTH_MASK) | CV_32F;
sqsum.flags = (sqsum.flags & ~UMat::DEPTH_MASK) | CV_32S;
if (hasTiltedFeatures)
{
int sty = sy + (tofs / sbufSize.width);
UMat tilted(usbuf, Rect(sx, sty, s.szi.width, s.szi.height));
integral(img, sum, sqsum, tilted, CV_32S, CV_32F);
integral(img, sum, sqsum, tilted, CV_32S, CV_32S);
}
else
{
UMatData* u = sqsum.u;
integral(img, sum, sqsum, noArray(), CV_32S, CV_32F);
CV_Assert(sqsum.u == u && sqsum.size() == s.szi && sqsum.type()==CV_32F);
integral(img, sum, sqsum, noArray(), CV_32S, CV_32S);
CV_Assert(sqsum.u == u && sqsum.size() == s.szi && sqsum.type()==CV_32S);
}
}
else
{
Mat sum(s.szi, CV_32S, sbuf.ptr<int>() + s.layer_ofs, sbuf.step);
Mat sqsum(s.szi, CV_32F, sum.ptr<int>() + sqofs, sbuf.step);
Mat sqsum(s.szi, CV_32S, sum.ptr<int>() + sqofs, sbuf.step);
if (hasTiltedFeatures)
{
Mat tilted(s.szi, CV_32S, sum.ptr<int>() + tofs, sbuf.step);
integral(img, sum, sqsum, tilted, CV_32S, CV_32F);
integral(img, sum, sqsum, tilted, CV_32S, CV_32S);
}
else
integral(img, sum, sqsum, noArray(), CV_32S, CV_32F);
integral(img, sum, sqsum, noArray(), CV_32S, CV_32S);
}
}
@ -689,18 +689,23 @@ bool HaarEvaluator::setWindow( Point pt, int scaleIdx )
return false;
pwin = &sbuf.at<int>(pt) + s.layer_ofs;
const float* pq = (const float*)(pwin + sqofs);
const int* pq = (const int*)(pwin + sqofs);
int valsum = CALC_SUM_OFS(nofs, pwin);
float valsqsum = CALC_SUM_OFS(nofs, pq);
unsigned valsqsum = (unsigned)(CALC_SUM_OFS(nofs, pq));
double nf = (double)normrect.area() * valsqsum - (double)valsum * valsum;
double area = normrect.area();
double nf = area * valsqsum - (double)valsum * valsum;
if( nf > 0. )
{
nf = std::sqrt(nf);
else
nf = 1.;
varianceNormFactor = (float)(1./nf);
return true;
return area*varianceNormFactor < 1e-1;
}
else
{
varianceNormFactor = 1.f;
return false;
}
}
@ -1402,8 +1407,10 @@ bool CascadeClassifierImpl::Data::read(const FileNode &root)
else if( featureTypeStr == CC_LBP )
featureType = FeatureEvaluator::LBP;
else if( featureTypeStr == CC_HOG )
{
featureType = FeatureEvaluator::HOG;
CV_Error(Error::StsNotImplemented, "HOG cascade is not supported in 3.0");
}
else
return false;
@ -1580,6 +1587,43 @@ bool CascadeClassifier::read(const FileNode &root)
return ok;
}
void clipObjects(Size sz, std::vector<Rect>& objects,
std::vector<int>* a, std::vector<double>* b)
{
size_t i, j = 0, n = objects.size();
Rect win0 = Rect(0, 0, sz.width, sz.height);
if(a)
{
CV_Assert(a->size() == n);
}
if(b)
{
CV_Assert(b->size() == n);
}
for( i = 0; i < n; i++ )
{
Rect r = win0 & objects[i];
if( r.area() > 0 )
{
objects[j] = r;
if( i > j )
{
if(a) a->at(j) = a->at(i);
if(b) b->at(j) = b->at(i);
}
j++;
}
}
if( j < n )
{
objects.resize(j);
if(a) a->resize(j);
if(b) b->resize(j);
}
}
void CascadeClassifier::detectMultiScale( InputArray image,
CV_OUT std::vector<Rect>& objects,
double scaleFactor,
@ -1589,6 +1633,7 @@ void CascadeClassifier::detectMultiScale( InputArray image,
{
CV_Assert(!empty());
cc->detectMultiScale(image, objects, scaleFactor, minNeighbors, flags, minSize, maxSize);
clipObjects(image.size(), objects, 0, 0);
}
void CascadeClassifier::detectMultiScale( InputArray image,
@ -1601,6 +1646,7 @@ void CascadeClassifier::detectMultiScale( InputArray image,
CV_Assert(!empty());
cc->detectMultiScale(image, objects, numDetections,
scaleFactor, minNeighbors, flags, minSize, maxSize);
clipObjects(image.size(), objects, &numDetections, 0);
}
void CascadeClassifier::detectMultiScale( InputArray image,
@ -1616,6 +1662,7 @@ void CascadeClassifier::detectMultiScale( InputArray image,
cc->detectMultiScale(image, objects, rejectLevels, levelWeights,
scaleFactor, minNeighbors, flags,
minSize, maxSize, outputRejectLevels);
clipObjects(image.size(), objects, &rejectLevels, &levelWeights);
}
bool CascadeClassifier::isOldFormatCascade() const

3
modules/objdetect/src/cascadedetect.hpp
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@ -5,6 +5,9 @@
namespace cv
{
void clipObjects(Size sz, std::vector<Rect>& objects,
std::vector<int>* a, std::vector<double>* b);
class FeatureEvaluator
{
public:

6
modules/objdetect/src/hog.cpp
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@ -41,6 +41,7 @@
//M*/
#include "precomp.hpp"
#include "cascadedetect.hpp"
#include "opencv2/core/core_c.h"
#include "opencl_kernels_objdetect.hpp"
@ -1823,7 +1824,9 @@ static bool ocl_detectMultiScale(InputArray _img, std::vector<Rect> &found_locat
all_candidates.push_back(Rect(Point2d(locations[j]) * scale, scaled_win_size));
}
found_locations.assign(all_candidates.begin(), all_candidates.end());
cv::groupRectangles(found_locations, (int)group_threshold, 0.2);
groupRectangles(found_locations, (int)group_threshold, 0.2);
clipObjects(imgSize, found_locations, 0, 0);
return true;
}
#endif //HAVE_OPENCL
@ -1879,6 +1882,7 @@ void HOGDescriptor::detectMultiScale(
groupRectangles_meanshift(foundLocations, foundWeights, foundScales, finalThreshold, winSize);
else
groupRectangles(foundLocations, foundWeights, (int)finalThreshold, 0.2);
clipObjects(imgSize, foundLocations, 0, &foundWeights);
}
void HOGDescriptor::detectMultiScale(InputArray img, std::vector<Rect>& foundLocations,

2
modules/objdetect/src/opencl/cascadedetect.cl
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@ -160,7 +160,7 @@ void runHaarClassifier(
__global const int* psum = psum1;
#endif
__global const float* psqsum = (__global const float*)(psum1 + sqofs);
__global const int* psqsum = (__global const int*)(psum1 + sqofs);
float sval = (psum[nofs.x] - psum[nofs.y] - psum[nofs.z] + psum[nofs.w])*invarea;
float sqval = (psqsum[nofs0.x] - psqsum[nofs0.y] - psqsum[nofs0.z] + psqsum[nofs0.w])*invarea;
float nf = (float)normarea * sqrt(max(sqval - sval * sval, 0.f));

28
modules/objdetect/test/test_cascadeandhog.cpp
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@ -1361,3 +1361,31 @@ TEST(Objdetect_HOGDetector_Strict, accuracy)
reference_hog.compute(image, descriptors);
}
}
TEST(Objdetect_CascadeDetector, small_img)
{
String root = cvtest::TS::ptr()->get_data_path() + "cascadeandhog/cascades/";
String cascades[] =
{
root + "haarcascade_frontalface_alt.xml",
root + "lbpcascade_frontalface.xml",
String()
};
vector<Rect> objects;
RNG rng((uint64)-1);
for( int i = 0; !cascades[i].empty(); i++ )
{
printf("%d. %s\n", i, cascades[i].c_str());
CascadeClassifier cascade(cascades[i]);
for( int j = 0; j < 100; j++ )
{
int width = rng.uniform(1, 100);
int height = rng.uniform(1, 100);
Mat img(height, width, CV_8U);
randu(img, 0, 256);
cascade.detectMultiScale(img, objects);
}
}
}

1
samples/python2/facedetect.py
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@ -49,6 +49,7 @@ if __name__ == '__main__':
rects = detect(gray, cascade)
vis = img.copy()
draw_rects(vis, rects, (0, 255, 0))
if not nested.empty():
for x1, y1, x2, y2 in rects:
roi = gray[y1:y2, x1:x2]
vis_roi = vis[y1:y2, x1:x2]