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remove debug detect at scale method

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marina.kolpakova 2012-11-14 14:21:22 +04:00
parent 72e2b8b370
commit 8acfbde68e
6 changed files with 53 additions and 106 deletions
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4
modules/gpu/doc/object_detection.rst
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@ -248,7 +248,6 @@ Implementation of soft (stageless) cascaded detector. ::
virtual ~SCascade();
virtual bool load(const FileNode& fn);
virtual void detect(InputArray image, InputArray rois, OutputArray objects, Stream& stream = Stream::Null()) const;
virtual void detect(InputArray image, InputArray rois, OutputArray objects, const int level, Stream& stream = Stream::Null()) const;
void genRoi(InputArray roi, OutputArray mask, Stream& stream = Stream::Null()) const;
};
@ -292,7 +291,6 @@ SCascade::detect
Apply cascade to an input frame and return the vector of Decection objcts.
.. ocv:function:: void detect(InputArray image, InputArray rois, OutputArray objects, Stream& stream = Stream::Null()) const
.. ocv:function:: void detect(InputArray image, InputArray rois, OutputArray objects, const int level, Stream& stream = Stream::Null()) const
:param image: a frame on which detector will be applied.
@ -302,8 +300,6 @@ Apply cascade to an input frame and return the vector of Decection objcts.
:param stream: a high-level CUDA stream abstraction used for asynchronous execution.
:param level: used for execution cascade on specific scales pyramid level.
SCascade::genRoi
--------------------------

2
modules/gpu/include/opencv2/gpu/gpu.hpp
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@ -1577,9 +1577,7 @@ public:
// Param objects is an output array of Detections represented as GpuMat of detections (SCascade::Detection)
// The first element of the matrix is actually a count of detections.
// Param stream is stream is a high-level CUDA stream abstraction used for asynchronous execution
// Param level used for execution cascade on specific scales pyramid level.
virtual void detect(InputArray image, InputArray rois, OutputArray objects, Stream& stream = Stream::Null()) const;
virtual void detect(InputArray image, InputArray rois, OutputArray objects, const int level, Stream& stream = Stream::Null()) const;
// Convert ROI matrix into the suitable for detect method.
// Param roi is an input matrix of the same size as the image.

37
modules/gpu/src/cuda/isf-sc.cu
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@ -45,15 +45,6 @@
#include <stdio.h>
#include <float.h>
// #define LOG_CUDA_CASCADE
#if defined LOG_CUDA_CASCADE
# define dprintf(format, ...) \
do { printf(format, __VA_ARGS__); } while (0)
#else
# define dprintf(format, ...)
#endif
namespace cv { namespace gpu { namespace device {
namespace icf {
@ -254,12 +245,12 @@ __global__ void soft_cascade(const CascadeInvoker<Policy> invoker, Detection* ob
template<typename Policy>
void CascadeInvoker<Policy>::operator()(const PtrStepSzb& roi, const PtrStepSzi& hogluv,
PtrStepSz<uchar4> objects, PtrStepSzi counter, const int downscales, const int scale, const cudaStream_t& stream) const
PtrStepSz<uchar4> objects, PtrStepSzi counter, const int downscales, const cudaStream_t& stream) const
{
int fw = roi.rows;
int fh = roi.cols;
dim3 grid(fw, fh / Policy::STA_Y, (scale == -1) ? downscales : 1);
dim3 grid(fw, fh / Policy::STA_Y, downscales);
uint* ctr = (uint*)(counter.ptr(0));
Detection* det = (Detection*)objects.ptr();
@ -268,26 +259,16 @@ void CascadeInvoker<Policy>::operator()(const PtrStepSzb& roi, const PtrStepSzi&
cudaChannelFormatDesc desc = cudaCreateChannelDesc<int>();
cudaSafeCall( cudaBindTexture2D(0, thogluv, hogluv.data, desc, hogluv.cols, hogluv.rows, hogluv.step));
cudaChannelFormatDesc desc_roi = cudaCreateChannelDesc<float2>();
cudaSafeCall( cudaBindTexture2D(0, troi, roi.data, desc_roi, roi.cols / 8, roi.rows, roi.step));
cudaChannelFormatDesc desc_roi = cudaCreateChannelDesc<typename Policy::roi_type>();
cudaSafeCall( cudaBindTexture2D(0, troi, roi.data, desc_roi, roi.cols / Policy::STA_Y, roi.rows, roi.step));
const CascadeInvoker<Policy> inv = *this;
if (scale == -1)
{
soft_cascade<Policy, false><<<grid, Policy::block(), 0, stream>>>(inv, det, max_det, ctr, 0);
cudaSafeCall( cudaGetLastError());
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);
soft_cascade<Policy, true><<<grid, Policy::block(), 0, stream>>>(inv, det, max_det, ctr, downscales);
}
else
{
if (scale >= downscales)
soft_cascade<Policy, true><<<grid, Policy::block(), 0, stream>>>(inv, det, max_det, ctr, scale);
else
soft_cascade<Policy, false><<<grid, Policy::block(), 0, stream>>>(inv, det, max_det, ctr, scale);
}
grid = dim3(fw, fh / Policy::STA_Y, scales - downscales);
soft_cascade<Policy, true><<<grid, Policy::block(), 0, stream>>>(inv, det, max_det, ctr, downscales);
if (!stream)
{
@ -297,7 +278,7 @@ void CascadeInvoker<Policy>::operator()(const PtrStepSzb& roi, const PtrStepSzi&
}
template void CascadeInvoker<GK107PolicyX4>::operator()(const PtrStepSzb& roi, const PtrStepSzi& hogluv,
PtrStepSz<uchar4> objects, PtrStepSzi counter, const int downscales, const int scale, const cudaStream_t& stream) const;
PtrStepSz<uchar4> objects, PtrStepSzi counter, const int downscales, const cudaStream_t& stream) const;
}
}}}

5
modules/gpu/src/icf.hpp
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@ -118,9 +118,10 @@ struct __align__(16) Detection
struct GK107PolicyX4
{
enum {WARP = 32, STA_X = WARP, STA_Y = 8, SHRINKAGE = 4};
typedef float2 roi_type;
static const dim3 block()
{
return dim3(GK107PolicyX4::STA_X, GK107PolicyX4::STA_Y);
return dim3(STA_X, STA_Y);
}
};
@ -146,7 +147,7 @@ struct CascadeInvoker
int scales;
void operator()(const PtrStepSzb& roi, const PtrStepSzi& hogluv, PtrStepSz<uchar4> objects,
PtrStepSzi counter, const int downscales, const int csale = -1, const cudaStream_t& stream = 0) const;
PtrStepSzi counter, const int downscales, const cudaStream_t& stream = 0) const;
template<bool isUp>
__device void detect(Detection* objects, const uint ndetections, uint* ctr, const int downscales) const;

35
modules/gpu/src/softcascade.cpp
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@ -311,13 +311,13 @@ struct cv::gpu::SCascade::Fields
leaves.upload(hleaves);
}
void detect(int scale, const cv::gpu::GpuMat& roi, const cv::gpu::GpuMat& count, cv::gpu::GpuMat& objects, const cudaStream_t& stream) const
void detect(const cv::gpu::GpuMat& roi, const cv::gpu::GpuMat& count, cv::gpu::GpuMat& objects, const cudaStream_t& stream) const
{
cudaMemset(count.data, 0, sizeof(Detection));
cudaSafeCall( cudaGetLastError());
device::icf::CascadeInvoker<device::icf::GK107PolicyX4> invoker
= device::icf::CascadeInvoker<device::icf::GK107PolicyX4>(levels, octaves, stages, nodes, leaves);
invoker(roi, hogluv, objects, count, downscales, scale, stream);
invoker(roi, hogluv, objects, count, downscales, stream);
}
void preprocess(const cv::gpu::GpuMat& colored, Stream& s)
@ -521,36 +521,7 @@ void cv::gpu::SCascade::detect(InputArray image, InputArray _rois, OutputArray _
objects = GpuMat(objects, cv::Rect( sizeof(Detection), 0, objects.cols - sizeof(Detection), 1));
cudaStream_t stream = StreamAccessor::getStream(s);
flds.detect(-1, rois, tmp, objects, stream);
}
void cv::gpu::SCascade::detect(InputArray image, InputArray _rois, OutputArray _objects, const int level, Stream& s) const
{
CV_Assert(fields);
const GpuMat colored = image.getGpuMat();
// only color images are supperted
CV_Assert(colored.type() == CV_8UC3 || colored.type() == CV_32SC1);
Fields& flds = *fields;
if (colored.type() == CV_8UC3)
{
// only this window size allowed
// CV_Assert(colored.cols == Fields::FRAME_WIDTH && colored.rows == Fields::FRAME_HEIGHT);
flds.preprocess(colored, s);
}
else
{
colored.copyTo(flds.hogluv);
}
GpuMat rois = _rois.getGpuMat(), objects = _objects.getGpuMat();
GpuMat tmp = GpuMat(objects, cv::Rect(0, 0, sizeof(Detection), 1));
objects = GpuMat(objects, cv::Rect( sizeof(Detection), 0, objects.cols - sizeof(Detection), 1));
cudaStream_t stream = StreamAccessor::getStream(s);
flds.detect(level, rois, tmp, objects, stream);
flds.detect(rois, tmp, objects, stream);
}
void cv::gpu::SCascade::genRoi(InputArray _roi, OutputArray _mask, Stream& stream) const

76
modules/gpu/test/test_softcascade.cpp
View File

@ -195,55 +195,55 @@ GPU_TEST_P(SCascadeTestRoi, detect,
}
typedef ::testing::TestWithParam<std::tr1::tuple<cv::gpu::DeviceInfo, std::string, std::string, int> > SCascadeTestLevel;
GPU_TEST_P(SCascadeTestLevel, detect,
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::Range(0, 47)
))
{
cv::gpu::setDevice(GET_PARAM(0).deviceID());
// typedef ::testing::TestWithParam<std::tr1::tuple<cv::gpu::DeviceInfo, std::string, std::string, int> > SCascadeTestLevel;
// GPU_TEST_P(SCascadeTestLevel, detect,
// 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::Range(0, 47)
// ))
// {
// cv::gpu::setDevice(GET_PARAM(0).deviceID());
cv::gpu::SCascade cascade;
// cv::gpu::SCascade cascade;
cv::FileStorage fs(perf::TestBase::getDataPath(GET_PARAM(1)), cv::FileStorage::READ);
ASSERT_TRUE(fs.isOpened());
// cv::FileStorage fs(perf::TestBase::getDataPath(GET_PARAM(1)), cv::FileStorage::READ);
// ASSERT_TRUE(fs.isOpened());
ASSERT_TRUE(cascade.load(fs.getFirstTopLevelNode()));
// ASSERT_TRUE(cascade.load(fs.getFirstTopLevelNode()));
cv::Mat coloredCpu = cv::imread(cvtest::TS::ptr()->get_data_path() + GET_PARAM(2));
ASSERT_FALSE(coloredCpu.empty());
// cv::Mat coloredCpu = cv::imread(cvtest::TS::ptr()->get_data_path() + GET_PARAM(2));
// ASSERT_FALSE(coloredCpu.empty());
typedef cv::gpu::SCascade::Detection Detection;
GpuMat colored(coloredCpu), objectBoxes(1, 100 * sizeof(Detection), CV_8UC1), rois(colored.size(), CV_8UC1);
rois.setTo(1);
// typedef cv::gpu::SCascade::Detection Detection;
// GpuMat colored(coloredCpu), objectBoxes(1, 100 * sizeof(Detection), CV_8UC1), rois(colored.size(), CV_8UC1);
// rois.setTo(1);
cv::gpu::GpuMat trois;
cascade.genRoi(rois, trois);
objectBoxes.setTo(0);
int level = GET_PARAM(3);
cascade.detect(colored, trois, objectBoxes, level);
// cv::gpu::GpuMat trois;
// cascade.genRoi(rois, trois);
// objectBoxes.setTo(0);
// int level = GET_PARAM(3);
// cascade.detect(colored, trois, objectBoxes, level);
cv::Mat dt(objectBoxes);
// cv::Mat dt(objectBoxes);
Detection* dts = ((Detection*)dt.data) + 1;
int* count = dt.ptr<int>(0);
// Detection* dts = ((Detection*)dt.data) + 1;
// int* count = dt.ptr<int>(0);
cv::Mat result(coloredCpu);
// cv::Mat result(coloredCpu);
printTotal(std::cout, *count);
for (int i = 0; i < *count; ++i)
{
Detection d = dts[i];
print(std::cout, d);
cv::rectangle(result, cv::Rect(d.x, d.y, d.w, d.h), cv::Scalar(255, 0, 0, 255), 1);
}
// printTotal(std::cout, *count);
// for (int i = 0; i < *count; ++i)
// {
// Detection d = dts[i];
// print(std::cout, d);
// cv::rectangle(result, cv::Rect(d.x, d.y, d.w, d.h), cv::Scalar(255, 0, 0, 255), 1);
// }
writeResult(result, level);
SHOW(result);
}
// writeResult(result, level);
// SHOW(result);
// }
TEST(SCascadeTest, readCascade)
{