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Merge pull request #661 from cuda-geek:merge-cpu-gpu-detections

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cuda-geek 2013-03-18 15:34:02 +04:00 committed by OpenCV Buildbot
commit 3f8d87d82c
8 changed files with 46 additions and 54 deletions
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26
modules/softcascade/include/opencv2/softcascade.hpp
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@ -49,18 +49,21 @@
namespace cv { namespace softcascade { namespace cv { namespace softcascade {
// Representation of detectors result. // Representation of detectors result.
// We assume that image is less then 2^16x2^16.
struct CV_EXPORTS Detection struct CV_EXPORTS Detection
{ {
// Default object type.
enum {PEDESTRIAN = 1};
// Creates Detection from an object bounding box and confidence. // Creates Detection from an object bounding box and confidence.
// Param b is a bounding box // Param b is a bounding box
// Param c is a confidence that object belongs to class k // Param c is a confidence that object belongs to class k
// Param k is an object class // Param k is an object class
Detection(const cv::Rect& b, const float c, int k = PEDESTRIAN) : bb(b), confidence(c), kind(k) {} Detection(const cv::Rect& b, const float c, int k = PEDESTRIAN);
cv::Rect bb() const;
enum {PEDESTRIAN = 1};
cv::Rect bb; ushort x;
ushort y;
ushort w;
ushort h;
float confidence; float confidence;
int kind; int kind;
}; };
@ -247,19 +250,6 @@ class CV_EXPORTS SCascade : public cv::Algorithm
{ {
public: public:
// Representation of detectors result.
struct CV_EXPORTS Detection
{
ushort x;
ushort y;
ushort w;
ushort h;
float confidence;
int kind;
enum {PEDESTRIAN = 0};
};
enum { NO_REJECT = 1, DOLLAR = 2, /*PASCAL = 4,*/ DEFAULT = NO_REJECT, NMS_MASK = 0xF}; enum { NO_REJECT = 1, DOLLAR = 2, /*PASCAL = 4,*/ DEFAULT = NO_REJECT, NMS_MASK = 0xF};
// An empty cascade will be created. // An empty cascade will be created.

10
modules/softcascade/perf/perf_cuda_softcascade.cpp
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@ -27,8 +27,8 @@ void fixture##_##name::__cpu() { FAIL() << "No such CPU implementation analogy";
namespace { namespace {
struct DetectionLess struct DetectionLess
{ {
bool operator()(const cv::softcascade::SCascade::Detection& a, bool operator()(const cv::softcascade::Detection& a,
const cv::softcascade::SCascade::Detection& b) const const cv::softcascade::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.y != b.y) return a.y < b.y;
@ -41,7 +41,7 @@ namespace {
{ {
cv::Mat detections(objects); cv::Mat detections(objects);
typedef cv::softcascade::SCascade::Detection Detection; typedef cv::softcascade::Detection Detection;
Detection* begin = (Detection*)(detections.ptr<char>(0)); Detection* begin = (Detection*)(detections.ptr<char>(0));
Detection* end = (Detection*)(detections.ptr<char>(0) + detections.cols); Detection* end = (Detection*)(detections.ptr<char>(0) + detections.cols);
std::sort(begin, end, DetectionLess()); std::sort(begin, end, DetectionLess());
@ -73,7 +73,7 @@ RUN_GPU(SCascadeTest, detect)
ASSERT_TRUE(cascade.load(fs.getFirstTopLevelNode())); ASSERT_TRUE(cascade.load(fs.getFirstTopLevelNode()));
cv::gpu::GpuMat objectBoxes(1, 10000 * sizeof(cv::softcascade::SCascade::Detection), CV_8UC1), rois(colored.size(), CV_8UC1); cv::gpu::GpuMat objectBoxes(1, 10000 * sizeof(cv::softcascade::Detection), CV_8UC1), rois(colored.size(), CV_8UC1);
rois.setTo(1); rois.setTo(1);
cascade.detect(colored, rois, objectBoxes); cascade.detect(colored, rois, objectBoxes);
@ -215,7 +215,7 @@ RUN_GPU(SCascadeTest, detectStream)
ASSERT_TRUE(cascade.load(fs.getFirstTopLevelNode())); ASSERT_TRUE(cascade.load(fs.getFirstTopLevelNode()));
cv::gpu::GpuMat objectBoxes(1, 10000 * sizeof(cv::softcascade::SCascade::Detection), CV_8UC1), rois(colored.size(), CV_8UC1); cv::gpu::GpuMat objectBoxes(1, 10000 * sizeof(cv::softcascade::Detection), CV_8UC1), rois(colored.size(), CV_8UC1);
rois.setTo(1); rois.setTo(1);
cv::gpu::Stream s; cv::gpu::Stream s;

2
modules/softcascade/perf/perf_softcascade.cpp
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@ -17,7 +17,7 @@ void extractRacts(std::vector<Detection> objectBoxes, std::vector<Rect>& rects)
{ {
rects.clear(); rects.clear();
for (int i = 0; i < (int)objectBoxes.size(); ++i) for (int i = 0; i < (int)objectBoxes.size(); ++i)
rects.push_back(objectBoxes[i].bb); rects.push_back(objectBoxes[i].bb());
} }
} }

42
modules/softcascade/src/softcascade.cpp → modules/softcascade/src/detector.cpp
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@ -42,11 +42,14 @@
#include "precomp.hpp" #include "precomp.hpp"
using cv::softcascade::Detection; cv::softcascade::Detection::Detection(const cv::Rect& b, const float c, int k)
using cv::softcascade::Detector; : x(static_cast<ushort>(b.x)), y(static_cast<ushort>(b.y)),
using cv::softcascade::ChannelFeatureBuilder; w(static_cast<ushort>(b.width)), h(static_cast<ushort>(b.height)), confidence(c), kind(k) {}
using namespace cv; cv::Rect cv::softcascade::Detection::bb() const
{
return cv::Rect(x, y, w, h);
}
namespace { namespace {
@ -151,13 +154,13 @@ struct Level
scaleshift = static_cast<int>(relScale * (1 << 16)); scaleshift = static_cast<int>(relScale * (1 << 16));
} }
void addDetection(const int x, const int y, float confidence, std::vector<Detection>& detections) const void addDetection(const int x, const int y, float confidence, std::vector<cv::softcascade::Detection>& detections) const
{ {
// fix me // fix me
int shrinkage = 4;//(*octave).shrinkage; int shrinkage = 4;//(*octave).shrinkage;
cv::Rect rect(cvRound(x * shrinkage), cvRound(y * shrinkage), objSize.width, objSize.height); cv::Rect rect(cvRound(x * shrinkage), cvRound(y * shrinkage), objSize.width, objSize.height);
detections.push_back(Detection(rect, confidence)); detections.push_back(cv::softcascade::Detection(rect, confidence));
} }
float rescale(cv::Rect& scaledRect, const float threshold, int idx) const float rescale(cv::Rect& scaledRect, const float threshold, int idx) const
@ -183,7 +186,7 @@ struct ChannelStorage
size_t step; size_t step;
int model_height; int model_height;
cv::Ptr<ChannelFeatureBuilder> builder; cv::Ptr<cv::softcascade::ChannelFeatureBuilder> builder;
enum {HOG_BINS = 6, HOG_LUV_BINS = 10}; enum {HOG_BINS = 6, HOG_LUV_BINS = 10};
@ -192,7 +195,7 @@ struct ChannelStorage
model_height = cvRound(colored.rows / (float)shrinkage); model_height = cvRound(colored.rows / (float)shrinkage);
if (featureTypeStr == "ICF") featureTypeStr = "HOG6MagLuv"; if (featureTypeStr == "ICF") featureTypeStr = "HOG6MagLuv";
builder = ChannelFeatureBuilder::create(featureTypeStr); builder = cv::softcascade::ChannelFeatureBuilder::create(featureTypeStr);
(*builder)(colored, hog, cv::Size(cvRound(colored.cols / (float)shrinkage), model_height)); (*builder)(colored, hog, cv::Size(cvRound(colored.cols / (float)shrinkage), model_height));
step = hog.step1(); step = hog.step1();
@ -213,8 +216,7 @@ struct ChannelStorage
} }
struct cv::softcascade::Detector::Fields
struct Detector::Fields
{ {
float minScale; float minScale;
float maxScale; float maxScale;
@ -421,17 +423,17 @@ struct Detector::Fields
} }
}; };
Detector::Detector(const double mins, const double maxs, const int nsc, const int rej) cv::softcascade::Detector::Detector(const double mins, const double maxs, const int nsc, const int rej)
: fields(0), minScale(mins), maxScale(maxs), scales(nsc), rejCriteria(rej) {} : fields(0), minScale(mins), maxScale(maxs), scales(nsc), rejCriteria(rej) {}
Detector::~Detector() { delete fields;} cv::softcascade::Detector::~Detector() { delete fields;}
void Detector::read(const cv::FileNode& fn) void cv::softcascade::Detector::read(const cv::FileNode& fn)
{ {
Algorithm::read(fn); Algorithm::read(fn);
} }
bool Detector::load(const cv::FileNode& fn) bool cv::softcascade::Detector::load(const cv::FileNode& fn)
{ {
if (fields) delete fields; if (fields) delete fields;
@ -473,7 +475,7 @@ void DollarNMS(dvector& objects)
{ {
const Detection &b = *next; const Detection &b = *next;
const float ovl = overlap(a.bb, b.bb) / std::min(a.bb.area(), b.bb.area()); const float ovl = overlap(a.bb(), b.bb()) / std::min(a.bb().area(), b.bb().area());
if (ovl > DollarThreshold) if (ovl > DollarThreshold)
next = objects.erase(next); next = objects.erase(next);
@ -485,13 +487,13 @@ void DollarNMS(dvector& objects)
static void suppress(int type, std::vector<Detection>& objects) static void suppress(int type, std::vector<Detection>& objects)
{ {
CV_Assert(type == Detector::DOLLAR); CV_Assert(type == cv::softcascade::Detector::DOLLAR);
DollarNMS(objects); DollarNMS(objects);
} }
} }
void Detector::detectNoRoi(const cv::Mat& image, std::vector<Detection>& objects) const void cv::softcascade::Detector::detectNoRoi(const cv::Mat& image, std::vector<Detection>& objects) const
{ {
Fields& fld = *fields; Fields& fld = *fields;
// create integrals // create integrals
@ -518,7 +520,7 @@ void Detector::detectNoRoi(const cv::Mat& image, std::vector<Detection>& objects
if (rejCriteria != NO_REJECT) suppress(rejCriteria, objects); if (rejCriteria != NO_REJECT) suppress(rejCriteria, objects);
} }
void Detector::detect(cv::InputArray _image, cv::InputArray _rois, std::vector<Detection>& objects) const void cv::softcascade::Detector::detect(cv::InputArray _image, cv::InputArray _rois, std::vector<Detection>& objects) const
{ {
// only color images are suppered // only color images are suppered
cv::Mat image = _image.getMat(); cv::Mat image = _image.getMat();
@ -570,7 +572,7 @@ void Detector::detect(cv::InputArray _image, cv::InputArray _rois, std::vector<D
if (rejCriteria != NO_REJECT) suppress(rejCriteria, objects); if (rejCriteria != NO_REJECT) suppress(rejCriteria, objects);
} }
void Detector::detect(InputArray _image, InputArray _rois, OutputArray _rects, OutputArray _confs) const void cv::softcascade::Detector::detect(InputArray _image, InputArray _rois, OutputArray _rects, OutputArray _confs) const
{ {
std::vector<Detection> objects; std::vector<Detection> objects;
detect( _image, _rois, objects); detect( _image, _rois, objects);
@ -588,7 +590,7 @@ void Detector::detect(InputArray _image, InputArray _rois, OutputArray _rects,
int i = 0; int i = 0;
for (IDet it = objects.begin(); it != objects.end(); ++it, ++i) for (IDet it = objects.begin(); it != objects.end(); ++it, ++i)
{ {
rectPtr[i] = (*it).bb; rectPtr[i] = (*it).bb();
confPtr[i] = (*it).confidence; confPtr[i] = (*it).confidence;
} }
} }

0
modules/softcascade/src/soft_cascade_octave.cpp → modules/softcascade/src/octave.cpp
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8
modules/softcascade/test/test_cuda_softcascade.cpp
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@ -76,7 +76,7 @@ TEST(SCascadeTest, readCascade)
namespace namespace
{ {
typedef cv::softcascade::SCascade::Detection Detection; typedef cv::softcascade::Detection Detection;
cv::Rect getFromTable(int idx) cv::Rect getFromTable(int idx)
{ {
@ -194,7 +194,7 @@ TEST_P(SCascadeTestRoi, Detect)
cascade.detect(colored, rois, objectBoxes); cascade.detect(colored, rois, objectBoxes);
cv::Mat dt(objectBoxes); cv::Mat dt(objectBoxes);
typedef cv::softcascade::SCascade::Detection Detection; typedef cv::softcascade::Detection Detection;
Detection* dts = ((Detection*)dt.data) + 1; Detection* dts = ((Detection*)dt.data) + 1;
int* count = dt.ptr<int>(0); int* count = dt.ptr<int>(0);
@ -262,7 +262,7 @@ TEST_P(SCascadeTestAll, detect)
cascade.detect(colored, rois, objectBoxes); cascade.detect(colored, rois, objectBoxes);
typedef cv::softcascade::SCascade::Detection Detection; typedef cv::softcascade::Detection Detection;
cv::Mat dt(objectBoxes); cv::Mat dt(objectBoxes);
@ -303,7 +303,7 @@ TEST_P(SCascadeTestAll, detectStream)
cascade.detect(colored, rois, objectBoxes, s); cascade.detect(colored, rois, objectBoxes, s);
s.waitForCompletion(); s.waitForCompletion();
typedef cv::softcascade::SCascade::Detection Detection; typedef cv::softcascade::Detection Detection;
cv::Mat detections(objectBoxes); cv::Mat detections(objectBoxes);
int a = *(detections.ptr<int>(0)); int a = *(detections.ptr<int>(0));
ASSERT_EQ(a, expected); ASSERT_EQ(a, expected);

7
samples/cpp/peopledetect.cpp
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@ -139,12 +139,11 @@ int main(int argc, char** argv)
std::stringstream conf(std::stringstream::in | std::stringstream::out); std::stringstream conf(std::stringstream::in | std::stringstream::out);
conf << d.confidence; conf << d.confidence;
cv::rectangle(frame, cv::Rect(d.bb.x, d.bb.y, d.bb.width, d.bb.height), cv::Scalar(b, 0, 255 - b, 255), 2); cv::rectangle(frame, cv::Rect((int)d.x, (int)d.y, (int)d.w, (int)d.h), cv::Scalar(b, 0, 255 - b, 255), 2);
cv::putText(frame, conf.str() , cv::Point(d.bb.x + 10, d.bb.y - 5),1, 1.1, cv::Scalar(25, 133, 255, 0), 1, CV_AA); cv::putText(frame, conf.str() , cv::Point((int)d.x + 10, (int)d.y - 5),1, 1.1, cv::Scalar(25, 133, 255, 0), 1, CV_AA);
if (wf) if (wf)
myfile << d.bb.x << "," << d.bb.y << "," myfile << d.x << "," << d.y << "," << d.w << "," << d.h << "," << d.confidence << "\n";
<< d.bb.width << "," << d.bb.height << "," << d.confidence << "\n";
} }
} }
} }

5
samples/gpu/softcascade.cpp
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@ -3,6 +3,8 @@
#include <opencv2/highgui.hpp> #include <opencv2/highgui.hpp>
#include <iostream> #include <iostream>
typedef cv::softcascade::Detection Detection;
int main(int argc, char** argv) int main(int argc, char** argv)
{ {
const std::string keys = const std::string keys =
@ -64,7 +66,7 @@ int main(int argc, char** argv)
return 1; return 1;
} }
cv::gpu::GpuMat objects(1, sizeof(SCascade::Detection) * 10000, CV_8UC1); cv::gpu::GpuMat objects(1, sizeof(Detection) * 10000, CV_8UC1);
cv::gpu::printShortCudaDeviceInfo(parser.get<int>("device")); cv::gpu::printShortCudaDeviceInfo(parser.get<int>("device"));
for (;;) for (;;)
{ {
@ -80,7 +82,6 @@ int main(int argc, char** argv)
cascade.detect(dframe, roi, objects); cascade.detect(dframe, roi, objects);
cv::Mat dt(objects); cv::Mat dt(objects);
typedef cv::softcascade::SCascade::Detection Detection;
Detection* dts = ((Detection*)dt.data) + 1; Detection* dts = ((Detection*)dt.data) + 1;
int* count = dt.ptr<int>(0); int* count = dt.ptr<int>(0);