remove debug detect at scale method

This commit is contained in:
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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@ -248,7 +248,6 @@ Implementation of soft (stageless) cascaded detector. ::
virtual ~SCascade(); virtual ~SCascade();
virtual bool load(const FileNode& fn); 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, 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; 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. 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, 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. :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 stream: a high-level CUDA stream abstraction used for asynchronous execution.
:param level: used for execution cascade on specific scales pyramid level.
SCascade::genRoi SCascade::genRoi
-------------------------- --------------------------

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@ -1577,9 +1577,7 @@ public:
// Param objects is an output array of Detections represented as GpuMat of detections (SCascade::Detection) // 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. // 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 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, 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. // Convert ROI matrix into the suitable for detect method.
// Param roi is an input matrix of the same size as the image. // Param roi is an input matrix of the same size as the image.

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@ -45,15 +45,6 @@
#include <stdio.h> #include <stdio.h>
#include <float.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 cv { namespace gpu { namespace device {
namespace icf { namespace icf {
@ -254,12 +245,12 @@ __global__ void soft_cascade(const CascadeInvoker<Policy> invoker, Detection* ob
template<typename Policy> template<typename Policy>
void CascadeInvoker<Policy>::operator()(const PtrStepSzb& roi, const PtrStepSzi& hogluv, 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 fw = roi.rows;
int fh = roi.cols; 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)); uint* ctr = (uint*)(counter.ptr(0));
Detection* det = (Detection*)objects.ptr(); Detection* det = (Detection*)objects.ptr();
@ -268,26 +259,16 @@ void CascadeInvoker<Policy>::operator()(const PtrStepSzb& roi, const PtrStepSzi&
cudaChannelFormatDesc desc = cudaCreateChannelDesc<int>(); cudaChannelFormatDesc desc = cudaCreateChannelDesc<int>();
cudaSafeCall( cudaBindTexture2D(0, thogluv, hogluv.data, desc, hogluv.cols, hogluv.rows, hogluv.step)); cudaSafeCall( cudaBindTexture2D(0, thogluv, hogluv.data, desc, hogluv.cols, hogluv.rows, hogluv.step));
cudaChannelFormatDesc desc_roi = cudaCreateChannelDesc<float2>(); cudaChannelFormatDesc desc_roi = cudaCreateChannelDesc<typename Policy::roi_type>();
cudaSafeCall( cudaBindTexture2D(0, troi, roi.data, desc_roi, roi.cols / 8, roi.rows, roi.step)); cudaSafeCall( cudaBindTexture2D(0, troi, roi.data, desc_roi, roi.cols / Policy::STA_Y, roi.rows, roi.step));
const CascadeInvoker<Policy> inv = *this; const CascadeInvoker<Policy> inv = *this;
if (scale == -1)
{
soft_cascade<Policy, false><<<grid, Policy::block(), 0, stream>>>(inv, det, max_det, ctr, 0); soft_cascade<Policy, false><<<grid, Policy::block(), 0, stream>>>(inv, det, max_det, ctr, 0);
cudaSafeCall( cudaGetLastError()); cudaSafeCall( cudaGetLastError());
grid = dim3(fw, fh / Policy::STA_Y, scales - downscales); grid = dim3(fw, fh / Policy::STA_Y, scales - downscales);
soft_cascade<Policy, true><<<grid, Policy::block(), 0, stream>>>(inv, det, max_det, ctr, 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);
}
if (!stream) 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, 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;
} }
}}} }}}

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@ -118,9 +118,10 @@ struct __align__(16) Detection
struct GK107PolicyX4 struct GK107PolicyX4
{ {
enum {WARP = 32, STA_X = WARP, STA_Y = 8, SHRINKAGE = 4}; enum {WARP = 32, STA_X = WARP, STA_Y = 8, SHRINKAGE = 4};
typedef float2 roi_type;
static const dim3 block() 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; int scales;
void operator()(const PtrStepSzb& roi, const PtrStepSzi& hogluv, PtrStepSz<uchar4> objects, 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> template<bool isUp>
__device void detect(Detection* objects, const uint ndetections, uint* ctr, const int downscales) const; __device void detect(Detection* objects, const uint ndetections, uint* ctr, const int downscales) const;

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@ -311,13 +311,13 @@ struct cv::gpu::SCascade::Fields
leaves.upload(hleaves); 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)); cudaMemset(count.data, 0, sizeof(Detection));
cudaSafeCall( cudaGetLastError()); cudaSafeCall( cudaGetLastError());
device::icf::CascadeInvoker<device::icf::GK107PolicyX4> invoker device::icf::CascadeInvoker<device::icf::GK107PolicyX4> invoker
= device::icf::CascadeInvoker<device::icf::GK107PolicyX4>(levels, octaves, stages, nodes, leaves); = 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) 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)); objects = GpuMat(objects, cv::Rect( sizeof(Detection), 0, objects.cols - sizeof(Detection), 1));
cudaStream_t stream = StreamAccessor::getStream(s); cudaStream_t stream = StreamAccessor::getStream(s);
flds.detect(-1, rois, tmp, objects, stream); flds.detect(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);
} }
void cv::gpu::SCascade::genRoi(InputArray _roi, OutputArray _mask, Stream& stream) const void cv::gpu::SCascade::genRoi(InputArray _roi, OutputArray _mask, Stream& stream) const

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@ -195,55 +195,55 @@ GPU_TEST_P(SCascadeTestRoi, detect,
} }
typedef ::testing::TestWithParam<std::tr1::tuple<cv::gpu::DeviceInfo, std::string, std::string, int> > SCascadeTestLevel; // typedef ::testing::TestWithParam<std::tr1::tuple<cv::gpu::DeviceInfo, std::string, std::string, int> > SCascadeTestLevel;
GPU_TEST_P(SCascadeTestLevel, detect, // GPU_TEST_P(SCascadeTestLevel, detect,
testing::Combine( // testing::Combine(
ALL_DEVICES, // ALL_DEVICES,
testing::Values(std::string("cv/cascadeandhog/sc_cvpr_2012_to_opencv.xml")), // 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/bahnhof/image_00000000_0.png")),
testing::Range(0, 47) // testing::Range(0, 47)
)) // ))
{ // {
cv::gpu::setDevice(GET_PARAM(0).deviceID()); // 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); // cv::FileStorage fs(perf::TestBase::getDataPath(GET_PARAM(1)), cv::FileStorage::READ);
ASSERT_TRUE(fs.isOpened()); // 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)); // cv::Mat coloredCpu = cv::imread(cvtest::TS::ptr()->get_data_path() + GET_PARAM(2));
ASSERT_FALSE(coloredCpu.empty()); // ASSERT_FALSE(coloredCpu.empty());
typedef cv::gpu::SCascade::Detection Detection; // typedef cv::gpu::SCascade::Detection Detection;
GpuMat colored(coloredCpu), objectBoxes(1, 100 * sizeof(Detection), CV_8UC1), rois(colored.size(), CV_8UC1); // GpuMat colored(coloredCpu), objectBoxes(1, 100 * sizeof(Detection), CV_8UC1), rois(colored.size(), CV_8UC1);
rois.setTo(1); // rois.setTo(1);
cv::gpu::GpuMat trois; // cv::gpu::GpuMat trois;
cascade.genRoi(rois, trois); // cascade.genRoi(rois, trois);
objectBoxes.setTo(0); // objectBoxes.setTo(0);
int level = GET_PARAM(3); // int level = GET_PARAM(3);
cascade.detect(colored, trois, objectBoxes, level); // cascade.detect(colored, trois, objectBoxes, level);
cv::Mat dt(objectBoxes); // cv::Mat dt(objectBoxes);
Detection* dts = ((Detection*)dt.data) + 1; // Detection* dts = ((Detection*)dt.data) + 1;
int* count = dt.ptr<int>(0); // int* count = dt.ptr<int>(0);
cv::Mat result(coloredCpu); // cv::Mat result(coloredCpu);
printTotal(std::cout, *count); // printTotal(std::cout, *count);
for (int i = 0; i < *count; ++i) // for (int i = 0; i < *count; ++i)
{ // {
Detection d = dts[i]; // Detection d = dts[i];
print(std::cout, d); // print(std::cout, d);
cv::rectangle(result, cv::Rect(d.x, d.y, d.w, d.h), cv::Scalar(255, 0, 0, 255), 1); // cv::rectangle(result, cv::Rect(d.x, d.y, d.w, d.h), cv::Scalar(255, 0, 0, 255), 1);
} // }
writeResult(result, level); // writeResult(result, level);
SHOW(result); // SHOW(result);
} // }
TEST(SCascadeTest, readCascade) TEST(SCascadeTest, readCascade)
{ {