potok/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2026-07-30 15:53:03 +04:00
Code Packages Releases Wiki Activity

refactored generalized hough (both CPU and GPU):

Browse Source 
removed set/get methods from Algorithm (implement owns)
removed GHT_* enumeration
This commit is contained in:
Vladislav Vinogradov
2013-06-25 17:39:05 +04:00
parent 4b234fa0a5
commit 7b87d72d80
9 changed files with 1086 additions and 1993 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/gpuimgproc/doc/hough.rst
+7 -86
View File
@@ -213,98 +213,19 @@ Creates implementation for :ocv:class:`gpu::HoughCirclesDetector` .
gpu::GeneralizedHough
---------------------
.. ocv:class:: gpu::GeneralizedHough : public Algorithm
Base class for generalized hough transform. ::
class CV_EXPORTS GeneralizedHough : public Algorithm
{
public:
static Ptr<GeneralizedHough> create(int method);
virtual void setTemplate(InputArray templ, int cannyThreshold = 100, Point templCenter = Point(-1, -1)) = 0;
virtual void setTemplate(InputArray edges, InputArray dx, InputArray dy, Point templCenter = Point(-1, -1)) = 0;
virtual void detect(InputArray image, OutputArray positions, int cannyThreshold = 100) = 0;
virtual void detect(InputArray edges, InputArray dx, InputArray dy, OutputArray positions) = 0;
virtual void downloadResults(InputArray d_positions, OutputArray h_positions, OutputArray h_votes = noArray()) = 0;
};
Finds arbitrary template in the grayscale image using Generalized Hough Transform.
gpu::GeneralizedHough::create
-----------------------------
Creates implementation for :ocv:class:`gpu::GeneralizedHough` .
.. ocv:function:: Ptr<GeneralizedHough> gpu::GeneralizedHough::create(int method)
:param method: Combination of flags ( ``cv::GeneralizedHough::GHT_POSITION`` , ``cv::GeneralizedHough::GHT_SCALE`` , ``cv::GeneralizedHough::GHT_ROTATION`` ) specifying transformation to find.
For full affine transformations (move + scale + rotation) [Guil1999]_ algorithm is used, otherwise [Ballard1981]_ algorithm is used.
gpu::GeneralizedHough::setTemplate
gpu::createGeneralizedHoughBallard
----------------------------------
Set template to search.
Creates implementation for generalized hough transform from [Ballard1981]_ .
.. ocv:function:: void gpu::GeneralizedHough::setTemplate(InputArray templ, int cannyThreshold = 100, Point templCenter = Point(-1, -1))
.. ocv:function:: void gpu::GeneralizedHough::setTemplate(InputArray edges, InputArray dx, InputArray dy, Point templCenter = Point(-1, -1))
:param templ: Template image. Canny edge detector will be applied to extract template edges.
:param cannyThreshold: Threshold value for Canny edge detector.
:param templCenter: Center for rotation. By default image center will be used.
:param edges: Edge map for template image.
:param dx: First derivative of template image in the vertical direction. Support only ``CV_32S`` type.
:param dy: First derivative of template image in the horizontal direction. Support only ``CV_32S`` type.
.. ocv:function:: Ptr<GeneralizedHoughBallard> gpu::createGeneralizedHoughBallard()
gpu::GeneralizedHough::detect
-----------------------------
Finds template (set by :ocv:func:`gpu::GeneralizedHough::setTemplate` ) in the grayscale image.
gpu::createGeneralizedHoughGuil
-------------------------------
Creates implementation for generalized hough transform from [Guil1999]_ .
.. ocv:function:: void gpu::GeneralizedHough::detect(InputArray image, OutputArray positions, int cannyThreshold = 100)
.. ocv:function:: void gpu::GeneralizedHough::detect(InputArray edges, InputArray dx, InputArray dy, OutputArray positions)
:param templ: Input image. Canny edge detector will be applied to extract template edges.
:param positions: Output vector of found objects. Each vector is encoded as a 4-element floating-point vector :math:`(x, y, scale, angle)` .
:param cannyThreshold: Threshold value for Canny edge detector.
:param edges: Edge map for input image.
:param dx: First derivative of input image in the vertical direction. Support only ``CV_32S`` type.
:param dy: First derivative of input image in the horizontal direction. Support only ``CV_32S`` type.
gpu::GeneralizedHough::downloadResults
--------------------------------------
Downloads results from :ocv:func:`gpu::GeneralizedHough::detect` to host memory.
.. ocv:function:: void gpu::GeneralizedHough::downloadResult(InputArray d_positions, OutputArray h_positions, OutputArray h_votes = noArray())
:param d_lines: Result of :ocv:func:`gpu::GeneralizedHough::detect` .
:param h_lines: Output host array.
:param h_votes: Optional output array for votes. Each vector is encoded as a 3-element integer-point vector :math:`(position_votes, scale_votes, angle_votes)` .
.. ocv:function:: Ptr<GeneralizedHoughGuil> gpu::createGeneralizedHoughGuil()
modules/gpuimgproc/include/opencv2/gpuimgproc.hpp
+5 -16
View File
@@ -283,24 +283,13 @@ CV_EXPORTS Ptr<HoughCirclesDetector> createHoughCirclesDetector(float dp, float
//////////////////////////////////////
// GeneralizedHough
//! finds arbitrary template in the grayscale image using Generalized Hough Transform
//! Ballard, D.H. (1981). Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition 13 (2): 111-122.
//! Detects position only without traslation and rotation
CV_EXPORTS Ptr<GeneralizedHoughBallard> createGeneralizedHoughBallard();
//! Guil, N., González-Linares, J.M. and Zapata, E.L. (1999). Bidimensional shape detection using an invariant approach. Pattern Recognition 32 (6): 1025-1038.
class CV_EXPORTS GeneralizedHough : public Algorithm
{
public:
static Ptr<GeneralizedHough> create(int method);
//! set template to search
virtual void setTemplate(InputArray templ, int cannyThreshold = 100, Point templCenter = Point(-1, -1)) = 0;
virtual void setTemplate(InputArray edges, InputArray dx, InputArray dy, Point templCenter = Point(-1, -1)) = 0;
//! find template on image
virtual void detect(InputArray image, OutputArray positions, int cannyThreshold = 100) = 0;
virtual void detect(InputArray edges, InputArray dx, InputArray dy, OutputArray positions) = 0;
virtual void downloadResults(InputArray d_positions, OutputArray h_positions, OutputArray h_votes = noArray()) = 0;
};
//! Detects position, traslation and rotation
CV_EXPORTS Ptr<GeneralizedHoughGuil> createGeneralizedHoughGuil();
////////////////////////// Corners Detection ///////////////////////////
modules/gpuimgproc/perf/perf_hough.cpp
+63 -34
View File
@@ -227,23 +227,59 @@ PERF_TEST_P(Sz_Dp_MinDist, HoughCircles,
//////////////////////////////////////////////////////////////////////
// GeneralizedHough
enum { GHT_POSITION = cv::GeneralizedHough::GHT_POSITION,
GHT_SCALE = cv::GeneralizedHough::GHT_SCALE,
GHT_ROTATION = cv::GeneralizedHough::GHT_ROTATION
};
CV_FLAGS(GHMethod, GHT_POSITION, GHT_SCALE, GHT_ROTATION);
DEF_PARAM_TEST(Method_Sz, GHMethod, cv::Size);
PERF_TEST_P(Method_Sz, GeneralizedHough,
Combine(Values(GHMethod(GHT_POSITION), GHMethod(GHT_POSITION | GHT_SCALE), GHMethod(GHT_POSITION | GHT_ROTATION), GHMethod(GHT_POSITION | GHT_SCALE | GHT_ROTATION)),
GPU_TYPICAL_MAT_SIZES))
PERF_TEST_P(Sz, GeneralizedHoughBallard, GPU_TYPICAL_MAT_SIZES)
{
declare.time(10);
const int method = GET_PARAM(0);
const cv::Size imageSize = GET_PARAM(1);
const cv::Size imageSize = GetParam();
const cv::Mat templ = readImage("cv/shared/templ.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(templ.empty());
cv::Mat image(imageSize, CV_8UC1, cv::Scalar::all(0));
templ.copyTo(image(cv::Rect(50, 50, templ.cols, templ.rows)));
cv::Mat edges;
cv::Canny(image, edges, 50, 100);
cv::Mat dx, dy;
cv::Sobel(image, dx, CV_32F, 1, 0);
cv::Sobel(image, dy, CV_32F, 0, 1);
if (PERF_RUN_GPU())
{
cv::Ptr<cv::GeneralizedHoughBallard> alg = cv::gpu::createGeneralizedHoughBallard();
const cv::gpu::GpuMat d_edges(edges);
const cv::gpu::GpuMat d_dx(dx);
const cv::gpu::GpuMat d_dy(dy);
cv::gpu::GpuMat positions;
alg->setTemplate(cv::gpu::GpuMat(templ));
TEST_CYCLE() alg->detect(d_edges, d_dx, d_dy, positions);
GPU_SANITY_CHECK(positions);
}
else
{
cv::Ptr<cv::GeneralizedHoughBallard> alg = cv::createGeneralizedHoughBallard();
cv::Mat positions;
alg->setTemplate(templ);
TEST_CYCLE() alg->detect(edges, dx, dy, positions);
CPU_SANITY_CHECK(positions);
}
}
PERF_TEST_P(Sz, GeneralizedHoughGuil, GPU_TYPICAL_MAT_SIZES)
{
declare.time(10);
const cv::Size imageSize = GetParam();
const cv::Mat templ = readImage("cv/shared/templ.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(templ.empty());
@@ -281,39 +317,32 @@ PERF_TEST_P(Method_Sz, GeneralizedHough,
if (PERF_RUN_GPU())
{
cv::Ptr<cv::GeneralizedHoughGuil> alg = cv::gpu::createGeneralizedHoughGuil();
alg->setMaxAngle(90.0);
alg->setAngleStep(2.0);
const cv::gpu::GpuMat d_edges(edges);
const cv::gpu::GpuMat d_dx(dx);
const cv::gpu::GpuMat d_dy(dy);
cv::gpu::GpuMat posAndVotes;
cv::gpu::GpuMat positions;
cv::Ptr<cv::gpu::GeneralizedHough> d_hough = cv::gpu::GeneralizedHough::create(method);
if (method & GHT_ROTATION)
{
d_hough->set("maxAngle", 90.0);
d_hough->set("angleStep", 2.0);
}
alg->setTemplate(cv::gpu::GpuMat(templ));
d_hough->setTemplate(cv::gpu::GpuMat(templ));
TEST_CYCLE() alg->detect(d_edges, d_dx, d_dy, positions);
TEST_CYCLE() d_hough->detect(d_edges, d_dx, d_dy, posAndVotes);
const cv::gpu::GpuMat positions(1, posAndVotes.cols, CV_32FC4, posAndVotes.data);
GPU_SANITY_CHECK(positions);
}
else
{
cv::Ptr<cv::GeneralizedHoughGuil> alg = cv::createGeneralizedHoughGuil();
alg->setMaxAngle(90.0);
alg->setAngleStep(2.0);
cv::Mat positions;
cv::Ptr<cv::GeneralizedHough> hough = cv::GeneralizedHough::create(method);
if (method & GHT_ROTATION)
{
hough->set("maxAngle", 90.0);
hough->set("angleStep", 2.0);
}
alg->setTemplate(templ);
hough->setTemplate(templ);
TEST_CYCLE() hough->detect(edges, dx, dy, positions);
TEST_CYCLE() alg->detect(edges, dx, dy, positions);
CPU_SANITY_CHECK(positions);
}
modules/gpuimgproc/src/cuda/generalized_hough.cu
-262
View File
@@ -307,268 +307,6 @@ namespace cv { namespace gpu { namespace cudev
return totalCount;
}
////////////////////////////////////////////////////////////////////////
// Ballard_PosScale
__global__ void Ballard_PosScale_calcHist(const unsigned int* coordList, const float* thetaList,
PtrStep<short2> r_table, const int* r_sizes,
PtrStepi hist, const int rows, const int cols,
const float minScale, const float scaleStep, const int scaleRange,
const float idp, const float thetaScale)
{
const unsigned int coord = coordList[blockIdx.x];
float2 p;
p.x = (coord & 0xFFFF);
p.y = (coord >> 16) & 0xFFFF;
const float theta = thetaList[blockIdx.x];
const int n = __float2int_rn(theta * thetaScale);
const short2* r_row = r_table.ptr(n);
const int r_row_size = r_sizes[n];
for (int j = 0; j < r_row_size; ++j)
{
const float2 d = saturate_cast<float2>(r_row[j]);
for (int s = threadIdx.x; s < scaleRange; s += blockDim.x)
{
const float scale = minScale + s * scaleStep;
float2 c = p - scale * d;
c.x *= idp;
c.y *= idp;
if (c.x >= 0 && c.x < cols && c.y >= 0 && c.y < rows)
::atomicAdd(hist.ptr((s + 1) * (rows + 2) + __float2int_rn(c.y + 1)) + __float2int_rn(c.x + 1), 1);
}
}
}
void Ballard_PosScale_calcHist_gpu(const unsigned int* coordList, const float* thetaList, int pointsCount,
PtrStepSz<short2> r_table, const int* r_sizes,
PtrStepi hist, int rows, int cols,
float minScale, float scaleStep, int scaleRange,
float dp, int levels)
{
const dim3 block(256);
const dim3 grid(pointsCount);
const float idp = 1.0f / dp;
const float thetaScale = levels / (2.0f * CV_PI_F);
Ballard_PosScale_calcHist<<<grid, block>>>(coordList, thetaList,
r_table, r_sizes,
hist, rows, cols,
minScale, scaleStep, scaleRange,
idp, thetaScale);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );
}
__global__ void Ballard_PosScale_findPosInHist(const PtrStepi hist, const int rows, const int cols, const int scaleRange,
float4* out, int3* votes, const int maxSize,
const float minScale, const float scaleStep, const float dp, const int threshold)
{
const int x = blockIdx.x * blockDim.x + threadIdx.x;
const int y = blockIdx.y * blockDim.y + threadIdx.y;
if (x >= cols || y >= rows)
return;
for (int s = 0; s < scaleRange; ++s)
{
const float scale = minScale + s * scaleStep;
const int prevScaleIdx = (s) * (rows + 2);
const int curScaleIdx = (s + 1) * (rows + 2);
const int nextScaleIdx = (s + 2) * (rows + 2);
const int curVotes = hist(curScaleIdx + y + 1, x + 1);
if (curVotes > threshold &&
curVotes > hist(curScaleIdx + y + 1, x) &&
curVotes >= hist(curScaleIdx + y + 1, x + 2) &&
curVotes > hist(curScaleIdx + y, x + 1) &&
curVotes >= hist(curScaleIdx + y + 2, x + 1) &&
curVotes > hist(prevScaleIdx + y + 1, x + 1) &&
curVotes >= hist(nextScaleIdx + y + 1, x + 1))
{
const int ind = ::atomicAdd(&g_counter, 1);
if (ind < maxSize)
{
out[ind] = make_float4(x * dp, y * dp, scale, 0.0f);
votes[ind] = make_int3(curVotes, curVotes, 0);
}
}
}
}
int Ballard_PosScale_findPosInHist_gpu(PtrStepi hist, int rows, int cols, int scaleRange, float4* out, int3* votes, int maxSize,
float minScale, float scaleStep, float dp, int threshold)
{
void* counterPtr;
cudaSafeCall( cudaGetSymbolAddress(&counterPtr, g_counter) );
cudaSafeCall( cudaMemset(counterPtr, 0, sizeof(int)) );
const dim3 block(32, 8);
const dim3 grid(divUp(cols, block.x), divUp(rows, block.y));
cudaSafeCall( cudaFuncSetCacheConfig(Ballard_PosScale_findPosInHist, cudaFuncCachePreferL1) );
Ballard_PosScale_findPosInHist<<<grid, block>>>(hist, rows, cols, scaleRange, out, votes,
maxSize, minScale, scaleStep, dp, threshold);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );
int totalCount;
cudaSafeCall( cudaMemcpy(&totalCount, counterPtr, sizeof(int), cudaMemcpyDeviceToHost) );
totalCount = ::min(totalCount, maxSize);
return totalCount;
}
////////////////////////////////////////////////////////////////////////
// Ballard_PosRotation
__global__ void Ballard_PosRotation_calcHist(const unsigned int* coordList, const float* thetaList,
PtrStep<short2> r_table, const int* r_sizes,
PtrStepi hist, const int rows, const int cols,
const float minAngle, const float angleStep, const int angleRange,
const float idp, const float thetaScale)
{
const unsigned int coord = coordList[blockIdx.x];
float2 p;
p.x = (coord & 0xFFFF);
p.y = (coord >> 16) & 0xFFFF;
const float thetaVal = thetaList[blockIdx.x];
for (int a = threadIdx.x; a < angleRange; a += blockDim.x)
{
const float angle = (minAngle + a * angleStep) * (CV_PI_F / 180.0f);
float sinA, cosA;
sincosf(angle, &sinA, &cosA);
float theta = thetaVal - angle;
if (theta < 0)
theta += 2.0f * CV_PI_F;
const int n = __float2int_rn(theta * thetaScale);
const short2* r_row = r_table.ptr(n);
const int r_row_size = r_sizes[n];
for (int j = 0; j < r_row_size; ++j)
{
const float2 d = saturate_cast<float2>(r_row[j]);
const float2 dr = make_float2(d.x * cosA - d.y * sinA, d.x * sinA + d.y * cosA);
float2 c = make_float2(p.x - dr.x, p.y - dr.y);
c.x *= idp;
c.y *= idp;
if (c.x >= 0 && c.x < cols && c.y >= 0 && c.y < rows)
::atomicAdd(hist.ptr((a + 1) * (rows + 2) + __float2int_rn(c.y + 1)) + __float2int_rn(c.x + 1), 1);
}
}
}
void Ballard_PosRotation_calcHist_gpu(const unsigned int* coordList, const float* thetaList, int pointsCount,
PtrStepSz<short2> r_table, const int* r_sizes,
PtrStepi hist, int rows, int cols,
float minAngle, float angleStep, int angleRange,
float dp, int levels)
{
const dim3 block(256);
const dim3 grid(pointsCount);
const float idp = 1.0f / dp;
const float thetaScale = levels / (2.0f * CV_PI_F);
Ballard_PosRotation_calcHist<<<grid, block>>>(coordList, thetaList,
r_table, r_sizes,
hist, rows, cols,
minAngle, angleStep, angleRange,
idp, thetaScale);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );
}
__global__ void Ballard_PosRotation_findPosInHist(const PtrStepi hist, const int rows, const int cols, const int angleRange,
float4* out, int3* votes, const int maxSize,
const float minAngle, const float angleStep, const float dp, const int threshold)
{
const int x = blockIdx.x * blockDim.x + threadIdx.x;
const int y = blockIdx.y * blockDim.y + threadIdx.y;
if (x >= cols || y >= rows)
return;
for (int a = 0; a < angleRange; ++a)
{
const float angle = minAngle + a * angleStep;
const int prevAngleIdx = (a) * (rows + 2);
const int curAngleIdx = (a + 1) * (rows + 2);
const int nextAngleIdx = (a + 2) * (rows + 2);
const int curVotes = hist(curAngleIdx + y + 1, x + 1);
if (curVotes > threshold &&
curVotes > hist(curAngleIdx + y + 1, x) &&
curVotes >= hist(curAngleIdx + y + 1, x + 2) &&
curVotes > hist(curAngleIdx + y, x + 1) &&
curVotes >= hist(curAngleIdx + y + 2, x + 1) &&
curVotes > hist(prevAngleIdx + y + 1, x + 1) &&
curVotes >= hist(nextAngleIdx + y + 1, x + 1))
{
const int ind = ::atomicAdd(&g_counter, 1);
if (ind < maxSize)
{
out[ind] = make_float4(x * dp, y * dp, 1.0f, angle);
votes[ind] = make_int3(curVotes, 0, curVotes);
}
}
}
}
int Ballard_PosRotation_findPosInHist_gpu(PtrStepi hist, int rows, int cols, int angleRange, float4* out, int3* votes, int maxSize,
float minAngle, float angleStep, float dp, int threshold)
{
void* counterPtr;
cudaSafeCall( cudaGetSymbolAddress(&counterPtr, g_counter) );
cudaSafeCall( cudaMemset(counterPtr, 0, sizeof(int)) );
const dim3 block(32, 8);
const dim3 grid(divUp(cols, block.x), divUp(rows, block.y));
cudaSafeCall( cudaFuncSetCacheConfig(Ballard_PosRotation_findPosInHist, cudaFuncCachePreferL1) );
Ballard_PosRotation_findPosInHist<<<grid, block>>>(hist, rows, cols, angleRange, out, votes,
maxSize, minAngle, angleStep, dp, threshold);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );
int totalCount;
cudaSafeCall( cudaMemcpy(&totalCount, counterPtr, sizeof(int), cudaMemcpyDeviceToHost) );
totalCount = ::min(totalCount, maxSize);
return totalCount;
}
////////////////////////////////////////////////////////////////////////
// Guil_Full
modules/gpuimgproc/src/generalized_hough.cpp
+455 -734
View File
File diff suppressed because it is too large Load Diff
modules/gpuimgproc/test/test_hough.cpp
+6 -6
View File
@@ -193,7 +193,7 @@ PARAM_TEST_CASE(GeneralizedHough, cv::gpu::DeviceInfo, UseRoi)
{
};
GPU_TEST_P(GeneralizedHough, POSITION)
GPU_TEST_P(GeneralizedHough, Ballard)
{
const cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
cv::gpu::setDevice(devInfo.deviceID());
@@ -218,16 +218,16 @@ GPU_TEST_P(GeneralizedHough, POSITION)
templ.copyTo(imageROI);
}
cv::Ptr<cv::gpu::GeneralizedHough> hough = cv::gpu::GeneralizedHough::create(cv::GeneralizedHough::GHT_POSITION);
hough->set("votesThreshold", 200);
cv::Ptr<cv::GeneralizedHoughBallard> alg = cv::gpu::createGeneralizedHoughBallard();
alg->setVotesThreshold(200);
hough->setTemplate(loadMat(templ, useRoi));
alg->setTemplate(loadMat(templ, useRoi));
cv::gpu::GpuMat d_pos;
hough->detect(loadMat(image, useRoi), d_pos);
alg->detect(loadMat(image, useRoi), d_pos);
std::vector<cv::Vec4f> pos;
hough->downloadResults(d_pos, pos);
d_pos.download(pos);
ASSERT_EQ(gold_count, pos.size());