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Merge pull request #372 from cuda-geek:gpu-cascade-fixes

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cuda-geek 2013-01-31 20:13:30 +04:00 committed by OpenCV Buildbot
commit d874d93e24
7 changed files with 182 additions and 213 deletions
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8
modules/gpu/include/opencv2/gpu/gpu.hpp
View File

@ -1556,7 +1556,7 @@ protected:
ChannelsProcessor();
};
// Implementation of soft (stageless) cascaded detector.
// Implementation of soft (stage-less) cascaded detector.
class CV_EXPORTS SCascade : public Algorithm
{
public:
@ -1577,8 +1577,8 @@ public:
enum { NO_REJECT = 1, DOLLAR = 2, /*PASCAL = 4,*/ DEFAULT = NO_REJECT, NMS_MASK = 0xF};
// An empty cascade will be created.
// Param minScale is a minimum scale relative to the original size of the image on which cascade will be applyed.
// Param minScale is a maximum scale relative to the original size of the image on which cascade will be applyed.
// Param minScale is a minimum scale relative to the original size of the image on which cascade will be applied.
// Param minScale is a maximum scale relative to the original size of the image on which cascade will be applied.
// Param scales is a number of scales from minScale to maxScale.
// Param flags is an extra tuning flags.
SCascade(const double minScale = 0.4, const double maxScale = 5., const int scales = 55,
@ -1595,7 +1595,7 @@ public:
// Load cascade config.
virtual void read(const FileNode& fn);
// Return the matrix of of detectioned objects.
// Return the matrix of of detected objects.
// Param image is a frame on which detector will be applied.
// Param rois is a regions of interests mask generated by genRoi.
// Only the objects that fall into one of the regions will be returned.

87
modules/gpu/perf/perf_softcascade.cpp
View File

@ -1,6 +1,6 @@
#include "perf_precomp.hpp"
#define PERF_TEST_P1(fixture, name, params) \
#define SC_PERF_TEST_P(fixture, name, params) \
class fixture##_##name : public fixture {\
public:\
fixture##_##name() {}\
@ -28,7 +28,7 @@ namespace {
bool operator()(const cv::gpu::SCascade::Detection& a,
const cv::gpu::SCascade::Detection& b) const
{
if (a.x != b.x) return a.x < b.x;
if (a.x != b.x) return a.x < b.x;
else if (a.y != b.y) return a.y < b.y;
else if (a.w != b.w) return a.w < b.w;
else return a.h < b.h;
@ -52,10 +52,11 @@ namespace {
typedef std::tr1::tuple<std::string, std::string> fixture_t;
typedef perf::TestBaseWithParam<fixture_t> SCascadeTest;
PERF_TEST_P1(SCascadeTest, detect,
SC_PERF_TEST_P(SCascadeTest, detect,
testing::Combine(
testing::Values(std::string("cv/cascadeandhog/sc_cvpr_2012_to_opencv.xml")),
testing::Values(std::string("cv/cascadeandhog/bahnhof/image_00000000_0.png"))))
testing::Values(std::string("cv/cascadeandhog/cascades/inria_caltech-17.01.2013.xml"),
std::string("cv/cascadeandhog/cascades/sc_cvpr_2012_to_opencv_new_format.xml")),
testing::Values(std::string("cv/cascadeandhog/images/image_00000000_0.png"))))
RUN_GPU(SCascadeTest, detect)
{
@ -108,10 +109,11 @@ static cv::Rect getFromTable(int idx)
typedef std::tr1::tuple<std::string, std::string, int> roi_fixture_t;
typedef perf::TestBaseWithParam<roi_fixture_t> SCascadeTestRoi;
PERF_TEST_P1(SCascadeTestRoi, detectInRoi,
SC_PERF_TEST_P(SCascadeTestRoi, detectInRoi,
testing::Combine(
testing::Values(std::string("cv/cascadeandhog/sc_cvpr_2012_to_opencv.xml")),
testing::Values(std::string("cv/cascadeandhog/bahnhof/image_00000000_0.png")),
testing::Values(std::string("cv/cascadeandhog/cascades/inria_caltech-17.01.2013.xml"),
std::string("cv/cascadeandhog/cascades/sc_cvpr_2012_to_opencv_new_format.xml")),
testing::Values(std::string("cv/cascadeandhog/images/image_00000000_0.png")),
testing::Range(0, 5)))
RUN_GPU(SCascadeTestRoi, detectInRoi)
@ -152,10 +154,11 @@ RUN_GPU(SCascadeTestRoi, detectInRoi)
NO_CPU(SCascadeTestRoi, detectInRoi)
PERF_TEST_P1(SCascadeTestRoi, detectEachRoi,
SC_PERF_TEST_P(SCascadeTestRoi, detectEachRoi,
testing::Combine(
testing::Values(std::string("cv/cascadeandhog/sc_cvpr_2012_to_opencv.xml")),
testing::Values(std::string("cv/cascadeandhog/bahnhof/image_00000000_0.png")),
testing::Values(std::string("cv/cascadeandhog/cascades/inria_caltech-17.01.2013.xml"),
std::string("cv/cascadeandhog/cascades/sc_cvpr_2012_to_opencv_new_format.xml")),
testing::Values(std::string("cv/cascadeandhog/images/image_00000000_0.png")),
testing::Range(0, 10)))
RUN_GPU(SCascadeTestRoi, detectEachRoi)
@ -191,58 +194,11 @@ RUN_GPU(SCascadeTestRoi, detectEachRoi)
NO_CPU(SCascadeTestRoi, detectEachRoi)
PERF_TEST_P1(SCascadeTest, detectOnIntegral,
SC_PERF_TEST_P(SCascadeTest, detectStream,
testing::Combine(
testing::Values(std::string("cv/cascadeandhog/sc_cvpr_2012_to_opencv.xml")),
testing::Values(std::string("cv/cascadeandhog/integrals.xml"))))
static std::string itoa(long i)
{
static char s[65];
sprintf(s, "%ld", i);
return std::string(s);
}
RUN_GPU(SCascadeTest, detectOnIntegral)
{
cv::FileStorage fsi(perf::TestBase::getDataPath(GET_PARAM(1)), cv::FileStorage::READ);
ASSERT_TRUE(fsi.isOpened());
cv::gpu::GpuMat hogluv(121 * 10, 161, CV_32SC1);
for (int i = 0; i < 10; ++i)
{
cv::Mat channel;
fsi[std::string("channel") + itoa(i)] >> channel;
cv::gpu::GpuMat gchannel(hogluv, cv::Rect(0, 121 * i, 161, 121));
gchannel.upload(channel);
}
cv::gpu::SCascade cascade;
cv::FileStorage fs(perf::TestBase::getDataPath(GET_PARAM(0)), cv::FileStorage::READ);
ASSERT_TRUE(fs.isOpened());
ASSERT_TRUE(cascade.load(fs.getFirstTopLevelNode()));
cv::gpu::GpuMat objectBoxes(1, 10000 * sizeof(cv::gpu::SCascade::Detection), CV_8UC1), rois(cv::Size(640, 480), CV_8UC1);
rois.setTo(1);
cascade.detect(hogluv, rois, objectBoxes);
TEST_CYCLE()
{
cascade.detect(hogluv, rois, objectBoxes);
}
SANITY_CHECK(sortDetections(objectBoxes));
}
NO_CPU(SCascadeTest, detectOnIntegral)
PERF_TEST_P1(SCascadeTest, detectStream,
testing::Combine(
testing::Values(std::string("cv/cascadeandhog/sc_cvpr_2012_to_opencv.xml")),
testing::Values(std::string("cv/cascadeandhog/bahnhof/image_00000000_0.png"))))
testing::Values(std::string("cv/cascadeandhog/cascades/inria_caltech-17.01.2013.xml"),
std::string("cv/cascadeandhog/cascades/sc_cvpr_2012_to_opencv_new_format.xml")),
testing::Values(std::string("cv/cascadeandhog/images/image_00000000_0.png"))))
RUN_GPU(SCascadeTest, detectStream)
{
@ -269,11 +225,10 @@ RUN_GPU(SCascadeTest, detectStream)
cascade.detect(colored, rois, objectBoxes, s);
}
#ifdef HAVE_CUDA
cudaDeviceSynchronize();
#endif
s.waitForCompletion();
SANITY_CHECK(sortDetections(objectBoxes));
}
NO_CPU(SCascadeTest, detectStream)
#undef SC_PERF_TEST_P

11
modules/gpu/src/cuda/icf-sc.cu
View File

@ -352,7 +352,7 @@ namespace icf {
{
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300
#pragma unroll
// scan on shuffl functions
// scan on shuffle functions
for (int i = 1; i < Policy::WARP; i *= 2)
{
const float n = __shfl_up(impact, i, Policy::WARP);
@ -459,7 +459,7 @@ __device void CascadeInvoker<Policy>::detect(Detection* objects, const uint ndet
const int y = blockIdx.y * blockDim.y + threadIdx.y;
const int x = blockIdx.x;
// load Lavel
// load Level
__shared__ Level level;
// check POI
@ -501,11 +501,12 @@ __device void CascadeInvoker<Policy>::detect(Detection* objects, const uint ndet
float impact = leaves[(st + threadIdx.x) * 4 + lShift];
PrefixSum<Policy>::apply(impact);
confidence += impact;
#if __CUDA_ARCH__ >= 120
if(__any((confidence <= stages[(st + threadIdx.x)]))) st += 2048;
if(__any((confidence + impact <= stages[(st + threadIdx.x)]))) st += 2048;
#endif
impact = __shfl(impact, 31);
confidence += impact;
}
if(!threadIdx.x && st == stEnd && ((confidence - FLT_EPSILON) >= 0))
@ -546,7 +547,7 @@ void CascadeInvoker<Policy>::operator()(const PtrStepSzb& roi, const PtrStepSzi&
soft_cascade<Policy, false><<<grid, Policy::block(), 0, stream>>>(inv, det, max_det, ctr, 0);
cudaSafeCall( cudaGetLastError());
grid = dim3(fw, fh / Policy::STA_Y, scales - downscales);
grid = dim3(fw, fh / Policy::STA_Y, min(38, scales) - downscales);
soft_cascade<Policy, true><<<grid, Policy::block(), 0, stream>>>(inv, det, max_det, ctr, downscales);
if (!stream)

158
modules/gpu/src/softcascade.cpp
View File

@ -103,43 +103,40 @@ struct cv::gpu::SCascade::Fields
{
static const char *const SC_STAGE_TYPE = "stageType";
static const char *const SC_BOOST = "BOOST";
static const char *const SC_FEATURE_TYPE = "featureType";
static const char *const SC_ICF = "ICF";
static const char *const SC_ORIG_W = "width";
static const char *const SC_ORIG_H = "height";
static const char *const SC_FEATURE_FORMAT = "featureFormat";
static const char *const SC_SHRINKAGE = "shrinkage";
static const char *const SC_OCTAVES = "octaves";
static const char *const SC_OCT_SCALE = "scale";
static const char *const SC_OCT_WEAKS = "weaks";
static const char *const SC_TREES = "trees";
static const char *const SC_WEAK_THRESHOLD = "treeThreshold";
static const char *const SC_FEATURES = "features";
static const char *const SC_INTERNAL = "internalNodes";
static const char *const SC_LEAF = "leafValues";
static const char *const SC_F_CHANNEL = "channel";
static const char *const SC_F_RECT = "rect";
// only Ada Boost supported
std::string stageTypeStr = (string)root[SC_STAGE_TYPE];
CV_Assert(stageTypeStr == SC_BOOST);
// only HOG-like integral channel features cupported
// only HOG-like integral channel features supported
string featureTypeStr = (string)root[SC_FEATURE_TYPE];
CV_Assert(featureTypeStr == SC_ICF);
static const char *const SC_ORIG_W = "width";
static const char *const SC_ORIG_H = "height";
int origWidth = (int)root[SC_ORIG_W];
int origHeight = (int)root[SC_ORIG_H];
static const char *const SC_OCTAVES = "octaves";
static const char *const SC_STAGES = "stages";
static const char *const SC_FEATURES = "features";
static const char *const SC_WEEK = "weakClassifiers";
static const char *const SC_INTERNAL = "internalNodes";
static const char *const SC_LEAF = "leafValues";
static const char *const SC_OCT_SCALE = "scale";
static const char *const SC_OCT_STAGES = "stageNum";
static const char *const SC_OCT_SHRINKAGE = "shrinkingFactor";
static const char *const SC_STAGE_THRESHOLD = "stageThreshold";
static const char * const SC_F_CHANNEL = "channel";
static const char * const SC_F_RECT = "rect";
std::string fformat = (string)root[SC_FEATURE_FORMAT];
bool useBoxes = (fformat == "BOX");
ushort shrinkage = cv::saturate_cast<ushort>((int)root[SC_SHRINKAGE]);
FileNode fn = root[SC_OCTAVES];
if (fn.empty()) return false;
if (fn.empty()) return 0;
using namespace device::icf;
@ -149,82 +146,97 @@ struct cv::gpu::SCascade::Fields
std::vector<float> vleaves;
FileNodeIterator it = fn.begin(), it_end = fn.end();
int feature_offset = 0;
ushort octIndex = 0;
ushort shrinkage = 1;
for (; it != it_end; ++it)
for (ushort octIndex = 0; it != it_end; ++it, ++octIndex)
{
FileNode fns = *it;
float scale = (float)fns[SC_OCT_SCALE];
float scale = powf(2.f,saturate_cast<float>((int)fns[SC_OCT_SCALE]));
bool isUPOctave = scale >= 1;
ushort nstages = saturate_cast<ushort>((int)fns[SC_OCT_STAGES]);
ushort nweaks = saturate_cast<ushort>((int)fns[SC_OCT_WEAKS]);
ushort2 size;
size.x = cvRound(origWidth * scale);
size.y = cvRound(origHeight * scale);
shrinkage = saturate_cast<ushort>((int)fns[SC_OCT_SHRINKAGE]);
Octave octave(octIndex, nstages, shrinkage, size, scale);
Octave octave(octIndex, nweaks, shrinkage, size, scale);
CV_Assert(octave.stages > 0);
voctaves.push_back(octave);
FileNode ffs = fns[SC_FEATURES];
if (ffs.empty()) return false;
if (ffs.empty()) return 0;
FileNodeIterator ftrs = ffs.begin();
std::vector<cv::Rect> feature_rects;
std::vector<int> feature_channels;
fns = fns[SC_STAGES];
FileNodeIterator ftrs = ffs.begin(), ftrs_end = ffs.end();
int feature_offset = 0;
for (; ftrs != ftrs_end; ++ftrs, ++feature_offset )
{
cv::FileNode ftn = (*ftrs)[SC_F_RECT];
cv::FileNodeIterator r_it = ftn.begin();
int x = (int)*(r_it++);
int y = (int)*(r_it++);
int w = (int)*(r_it++);
int h = (int)*(r_it++);
if (useBoxes)
{
if (isUPOctave)
{
w -= x;
h -= y;
}
}
else
{
if (!isUPOctave)
{
w += x;
h += y;
}
}
feature_rects.push_back(cv::Rect(x, y, w, h));
feature_channels.push_back((int)(*ftrs)[SC_F_CHANNEL]);
}
fns = fns[SC_TREES];
if (fn.empty()) return false;
// for each stage (~ decision tree with H = 2)
FileNodeIterator st = fns.begin(), st_end = fns.end();
for (; st != st_end; ++st )
{
fns = *st;
vstages.push_back((float)fns[SC_STAGE_THRESHOLD]);
FileNode octfn = *st;
float threshold = (float)octfn[SC_WEAK_THRESHOLD];
vstages.push_back(threshold);
fns = fns[SC_WEEK];
FileNodeIterator ftr = fns.begin(), ft_end = fns.end();
for (; ftr != ft_end; ++ftr)
FileNode intfns = octfn[SC_INTERNAL];
FileNodeIterator inIt = intfns.begin(), inIt_end = intfns.end();
for (; inIt != inIt_end;)
{
fns = (*ftr)[SC_INTERNAL];
FileNodeIterator inIt = fns.begin(), inIt_end = fns.end();
for (; inIt != inIt_end;)
{
// int feature = (int)(*(inIt +=2)) + feature_offset;
inIt +=3;
// extract feature, Todo:check it
unsigned int th = saturate_cast<unsigned int>((float)(*(inIt++)));
cv::FileNode ftn = (*ftrs)[SC_F_RECT];
cv::FileNodeIterator r_it = ftn.begin();
uchar4 rect;
rect.x = saturate_cast<uchar>((int)*(r_it++));
rect.y = saturate_cast<uchar>((int)*(r_it++));
rect.z = saturate_cast<uchar>((int)*(r_it++));
rect.w = saturate_cast<uchar>((int)*(r_it++));
inIt +=2;
int featureIdx = (int)(*(inIt++));
if (isUPOctave)
{
rect.z -= rect.x;
rect.w -= rect.y;
}
float orig_threshold = (float)(*(inIt++));
unsigned int th = saturate_cast<unsigned int>((int)orig_threshold);
cv::Rect& r = feature_rects[featureIdx];
uchar4 rect;
rect.x = saturate_cast<uchar>(r.x);
rect.y = saturate_cast<uchar>(r.y);
rect.z = saturate_cast<uchar>(r.width);
rect.w = saturate_cast<uchar>(r.height);
unsigned int channel = saturate_cast<unsigned int>((int)(*ftrs)[SC_F_CHANNEL]);
vnodes.push_back(Node(rect, channel, th));
++ftrs;
}
unsigned int channel = saturate_cast<unsigned int>(feature_channels[featureIdx]);
vnodes.push_back(Node(rect, channel, th));
}
fns = (*ftr)[SC_LEAF];
inIt = fns.begin(), inIt_end = fns.end();
for (; inIt != inIt_end; ++inIt)
vleaves.push_back((float)(*inIt));
intfns = octfn[SC_LEAF];
inIt = intfns.begin(), inIt_end = intfns.end();
for (; inIt != inIt_end; ++inIt)
{
vleaves.push_back((float)(*inIt));
}
}
feature_offset += octave.stages * 3;
++octIndex;
}
cv::Mat hoctaves(1, (int) (voctaves.size() * sizeof(Octave)), CV_8UC1, (uchar*)&(voctaves[0]));
@ -400,7 +412,7 @@ public:
// 160x120x10
GpuMat shrunk;
// temporial mat for integrall
// temporal mat for integral
GpuMat integralBuffer;
// 161x121x10
@ -567,7 +579,7 @@ private:
cv::gpu::cartToPolar(dfdx, dfdy, mag, ang, true, s);
// normolize magnitude to uchar interval and angles to 6 bins
// normalize magnitude to uchar interval and angles to 6 bins
GpuMat nmag(fplane, cv::Rect(0, 4 * fh, fw, fh));
GpuMat nang(fplane, cv::Rect(0, 5 * fh, fw, fh));

127
modules/gpu/test/test_softcascade.cpp
View File

@ -51,20 +51,25 @@ using cv::gpu::GpuMat;
#if defined SHOW_DETECTIONS
# define SHOW(res) \
cv::imshow(#res, result);\
cv::imshow(#res, res); \
cv::waitKey(0);
#else
# define SHOW(res)
#endif
static std::string path(std::string relative)
{
return cvtest::TS::ptr()->get_data_path() + "../cv/cascadeandhog/" + relative;
}
TEST(SCascadeTest, readCascade)
{
std::string xml = cvtest::TS::ptr()->get_data_path() + "../cv/cascadeandhog/icf-template.xml";
std::string xml = path("cascades/inria_caltech-17.01.2013.xml");
cv::FileStorage fs(xml, cv::FileStorage::READ);
cv::gpu::SCascade cascade;
cv::FileStorage fs(xml, cv::FileStorage::READ);
ASSERT_TRUE(fs.isOpened());
ASSERT_TRUE(cascade.load(fs.getFirstTopLevelNode()));
}
@ -92,12 +97,6 @@ namespace
return rois[idx];
}
std::string itoa(long i)
{
static char s[65];
sprintf(s, "%ld", i);
return std::string(s);
}
void print(std::ostream &out, const Detection& d)
{
@ -127,6 +126,13 @@ namespace
#endif
}
std::string itoa(long i)
{
static char s[65];
sprintf(s, "%ld", i);
return std::string(s);
}
#if defined SHOW_DETECTIONS
std::string getImageName(int level)
{
@ -152,17 +158,20 @@ namespace
PARAM_TEST_CASE(SCascadeTestRoi, cv::gpu::DeviceInfo, std::string, std::string, int)
{
virtual void SetUp()
{
cv::gpu::setDevice(GET_PARAM(0).deviceID());
}
};
GPU_TEST_P(SCascadeTestRoi, Detect)
{
cv::gpu::setDevice(GET_PARAM(0).deviceID());
cv::Mat coloredCpu = cv::imread(cvtest::TS::ptr()->get_data_path() + GET_PARAM(2));
cv::Mat coloredCpu = cv::imread(path(GET_PARAM(2)));
ASSERT_FALSE(coloredCpu.empty());
cv::gpu::SCascade cascade;
cv::FileStorage fs(perf::TestBase::getDataPath(GET_PARAM(1)), cv::FileStorage::READ);
cv::FileStorage fs(path(GET_PARAM(1)), cv::FileStorage::READ);
ASSERT_TRUE(fs.isOpened());
ASSERT_TRUE(cascade.load(fs.getFirstTopLevelNode()));
@ -204,21 +213,41 @@ GPU_TEST_P(SCascadeTestRoi, Detect)
INSTANTIATE_TEST_CASE_P(GPU_SoftCascade, SCascadeTestRoi, testing::Combine(
ALL_DEVICES,
testing::Values(std::string("cv/cascadeandhog/sc_cvpr_2012_to_opencv.xml")),
testing::Values(std::string("../cv/cascadeandhog/bahnhof/image_00000000_0.png")),
testing::Values(std::string("cascades/inria_caltech-17.01.2013.xml"),
std::string("cascades/sc_cvpr_2012_to_opencv_new_format.xml")),
testing::Values(std::string("images/image_00000000_0.png")),
testing::Range(0, 5)));
struct SCascadeTestAll : testing::TestWithParam<cv::gpu::DeviceInfo>
////////////////////////////////////////
namespace {
struct Fixture
{
std::string path;
int expected;
Fixture(){}
Fixture(std::string p, int e): path(p), expected(e) {}
};
}
PARAM_TEST_CASE(SCascadeTestAll, cv::gpu::DeviceInfo, Fixture)
{
std::string xml;
int expected;
virtual void SetUp()
{
cv::gpu::setDevice(GetParam().deviceID());
cv::gpu::setDevice(GET_PARAM(0).deviceID());
xml = path(GET_PARAM(1).path);
expected = GET_PARAM(1).expected;
}
};
GPU_TEST_P(SCascadeTestAll, detect)
{
std::string xml = cvtest::TS::ptr()->get_data_path() + "../cv/cascadeandhog/sc_cvpr_2012_to_opencv.xml";
cv::gpu::SCascade cascade;
cv::FileStorage fs(xml, cv::FileStorage::READ);
@ -226,61 +255,36 @@ GPU_TEST_P(SCascadeTestAll, detect)
ASSERT_TRUE(cascade.load(fs.getFirstTopLevelNode()));
cv::Mat coloredCpu = cv::imread(cvtest::TS::ptr()->get_data_path()
+ "../cv/cascadeandhog/bahnhof/image_00000000_0.png");
cv::Mat coloredCpu = cv::imread(path("images/image_00000000_0.png"));
ASSERT_FALSE(coloredCpu.empty());
GpuMat colored(coloredCpu), objectBoxes, rois(colored.size(), CV_8UC1);
rois.setTo(0);
GpuMat sub(rois, cv::Rect(rois.cols / 4, rois.rows / 4,rois.cols / 2, rois.rows / 2));
sub.setTo(cv::Scalar::all(1));
rois.setTo(1);
cascade.detect(colored, rois, objectBoxes);
typedef cv::gpu::SCascade::Detection Detection;
cv::Mat detections(objectBoxes);
int a = *(detections.ptr<int>(0));
ASSERT_EQ(a, 2448);
}
cv::Mat dt(objectBoxes);
GPU_TEST_P(SCascadeTestAll, detectOnIntegral)
{
std::string xml = cvtest::TS::ptr()->get_data_path() + "../cv/cascadeandhog/sc_cvpr_2012_to_opencv.xml";
cv::gpu::SCascade cascade;
cv::FileStorage fs(xml, cv::FileStorage::READ);
ASSERT_TRUE(fs.isOpened());
Detection* dts = ((Detection*)dt.data) + 1;
int* count = dt.ptr<int>(0);
ASSERT_TRUE(cascade.load(fs.getFirstTopLevelNode()));
printTotal(std::cout, *count);
std::string intPath = cvtest::TS::ptr()->get_data_path() + "../cv/cascadeandhog/integrals.xml";
cv::FileStorage fsi(intPath, cv::FileStorage::READ);
ASSERT_TRUE(fsi.isOpened());
GpuMat hogluv(121 * 10, 161, CV_32SC1);
for (int i = 0; i < 10; ++i)
for (int i = 0; i < *count; ++i)
{
cv::Mat channel;
fsi[std::string("channel") + itoa(i)] >> channel;
GpuMat gchannel(hogluv, cv::Rect(0, 121 * i, 161, 121));
gchannel.upload(channel);
Detection d = dts[i];
print(std::cout, d);
cv::rectangle(coloredCpu, cv::Rect(d.x, d.y, d.w, d.h), cv::Scalar(255, 0, 0, 255), 1);
}
GpuMat objectBoxes(1, 100000, CV_8UC1), rois(cv::Size(640, 480), CV_8UC1);
rois.setTo(1);
objectBoxes.setTo(0);
cascade.detect(hogluv, rois, objectBoxes);
typedef cv::gpu::SCascade::Detection Detection;
cv::Mat detections(objectBoxes);
int a = *(detections.ptr<int>(0));
ASSERT_EQ(a, 1024);
SHOW(coloredCpu);
ASSERT_EQ(*count, expected);
}
GPU_TEST_P(SCascadeTestAll, detectStream)
{
std::string xml = cvtest::TS::ptr()->get_data_path() + "../cv/cascadeandhog/sc_cvpr_2012_to_opencv.xml";
cv::gpu::SCascade cascade;
cv::FileStorage fs(xml, cv::FileStorage::READ);
@ -288,14 +292,11 @@ GPU_TEST_P(SCascadeTestAll, detectStream)
ASSERT_TRUE(cascade.load(fs.getFirstTopLevelNode()));
cv::Mat coloredCpu = cv::imread(cvtest::TS::ptr()->get_data_path()
+ "../cv/cascadeandhog/bahnhof/image_00000000_0.png");
cv::Mat coloredCpu = cv::imread(path("images/image_00000000_0.png"));
ASSERT_FALSE(coloredCpu.empty());
GpuMat colored(coloredCpu), objectBoxes(1, 100000, CV_8UC1), rois(colored.size(), CV_8UC1);
rois.setTo(0);
GpuMat sub(rois, cv::Rect(rois.cols / 4, rois.rows / 4,rois.cols / 2, rois.rows / 2));
sub.setTo(cv::Scalar::all(1));
rois.setTo(cv::Scalar::all(1));
cv::gpu::Stream s;
@ -306,9 +307,11 @@ GPU_TEST_P(SCascadeTestAll, detectStream)
typedef cv::gpu::SCascade::Detection Detection;
cv::Mat detections(objectBoxes);
int a = *(detections.ptr<int>(0));
ASSERT_EQ(a, 2448);
ASSERT_EQ(a, expected);
}
INSTANTIATE_TEST_CASE_P(GPU_SoftCascade, SCascadeTestAll, ALL_DEVICES);
INSTANTIATE_TEST_CASE_P(GPU_SoftCascade, SCascadeTestAll, testing::Combine( ALL_DEVICES,
testing::Values(Fixture("cascades/inria_caltech-17.01.2013.xml", 7),
Fixture("cascades/sc_cvpr_2012_to_opencv_new_format.xml", 1291))));
#endif

1
modules/objdetect/include/opencv2/objdetect/objdetect.hpp
View File

@ -488,7 +488,6 @@ protected:
Ptr<MaskGenerator> maskGenerator;
};
// Implementation of soft (stageless) cascaded detector.
class CV_EXPORTS_W SCascade : public Algorithm
{

3
modules/objdetect/src/softcascade.cpp
View File

@ -41,7 +41,6 @@
//M*/
#include "precomp.hpp"
#include <iostream>
namespace {
@ -365,7 +364,7 @@ struct cv::SCascade::Fields
std::string fformat = (string)root[FEATURE_FORMAT];
bool useBoxes = (fformat == "BOX");
// only HOG-like integral channel features cupported
// only HOG-like integral channel features supported
string featureTypeStr = (string)root[SC_FEATURE_TYPE];
CV_Assert(featureTypeStr == SC_ICF);