-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.
-
-.. 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))
+Creates implementation for generalized hough transform from [Ballard1981]_ .
- :param templ: Template image. Canny edge detector will be applied to extract template edges.
+.. ocv:function:: Ptr<GeneralizedHoughBallard> gpu::createGeneralizedHoughBallard()
- :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.
-
-
-
-gpu::GeneralizedHough::detect
------------------------------
-Finds template (set by :ocv:func:`gpu::GeneralizedHough::setTemplate` ) in the grayscale image.
-
-.. 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.
+gpu::createGeneralizedHoughGuil
+-------------------------------
+Creates implementation for generalized hough transform from [Guil1999]_ .
- :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()
//////////////////////////////////////
// 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.
-//! 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;
+//! Detects position only without traslation and rotation
+CV_EXPORTS Ptr<GeneralizedHoughBallard> createGeneralizedHoughBallard();
- //! 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;
-};
+//! Guil, N., González-Linares, J.M. and Zapata, E.L. (1999). Bidimensional shape detection using an invariant approach. Pattern Recognition 32 (6): 1025-1038.
+//! Detects position, traslation and rotation
+CV_EXPORTS Ptr<GeneralizedHoughGuil> createGeneralizedHoughGuil();
////////////////////////// Corners Detection ///////////////////////////
//////////////////////////////////////////////////////////////////////
// GeneralizedHough
-enum { GHT_POSITION = cv::GeneralizedHough::GHT_POSITION,
- GHT_SCALE = cv::GeneralizedHough::GHT_SCALE,
- GHT_ROTATION = cv::GeneralizedHough::GHT_ROTATION
- };
+PERF_TEST_P(Sz, GeneralizedHoughBallard, 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());
+
+ 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);
-CV_FLAGS(GHMethod, GHT_POSITION, GHT_SCALE, GHT_ROTATION);
+ TEST_CYCLE() alg->detect(edges, dx, dy, positions);
-DEF_PARAM_TEST(Method_Sz, GHMethod, cv::Size);
+ CPU_SANITY_CHECK(positions);
+ }
+}
-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, GeneralizedHoughGuil, 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());
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::Mat positions;
+ cv::Ptr<cv::GeneralizedHoughGuil> alg = cv::createGeneralizedHoughGuil();
+ alg->setMaxAngle(90.0);
+ alg->setAngleStep(2.0);
- cv::Ptr<cv::GeneralizedHough> hough = cv::GeneralizedHough::create(method);
- if (method & GHT_ROTATION)
- {
- hough->set("maxAngle", 90.0);
- hough->set("angleStep", 2.0);
- }
+ cv::Mat positions;
- hough->setTemplate(templ);
+ alg->setTemplate(templ);
- TEST_CYCLE() hough->detect(edges, dx, dy, positions);
+ TEST_CYCLE() alg->detect(edges, dx, dy, positions);
CPU_SANITY_CHECK(positions);
}
}
////////////////////////////////////////////////////////////////////////
- // 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
struct FeatureTable
#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER) || !defined(HAVE_OPENCV_GPUARITHM)
-Ptr<gpu::GeneralizedHough> cv::gpu::GeneralizedHough::create(int) { throw_no_cuda(); return Ptr<GeneralizedHough>(); }
+Ptr<GeneralizedHoughBallard> cv::gpu::createGeneralizedHoughBallard() { throw_no_cuda(); return Ptr<GeneralizedHoughBallard>(); }
+
+Ptr<GeneralizedHoughGuil> cv::gpu::createGeneralizedHoughGuil() { throw_no_cuda(); return Ptr<GeneralizedHoughGuil>(); }
#else /* !defined (HAVE_CUDA) */
float dp, int levels);
int Ballard_Pos_findPosInHist_gpu(PtrStepSzi hist, float4* out, int3* votes, int maxSize, float dp, int threshold);
- 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);
- 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 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);
- 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 Guil_Full_setTemplFeatures(PtrStepb p1_pos, PtrStepb p1_theta, PtrStepb p2_pos, PtrStepb d12, PtrStepb r1, PtrStepb r2);
void Guil_Full_setImageFeatures(PtrStepb p1_pos, PtrStepb p1_theta, PtrStepb p2_pos, PtrStepb d12, PtrStepb r1, PtrStepb r2);
void Guil_Full_buildTemplFeatureList_gpu(const unsigned int* coordList, const float* thetaList, int pointsCount,
}
}}}
+// common
+
namespace
{
- /////////////////////////////////////
- // GeneralizedHoughBase
-
- class GeneralizedHoughBase : public gpu::GeneralizedHough
+ class GeneralizedHoughBase
{
- public:
+ protected:
GeneralizedHoughBase();
+ virtual ~GeneralizedHoughBase() {}
- void setTemplate(InputArray templ, int cannyThreshold = 100, Point templCenter = Point(-1, -1));
- void setTemplate(InputArray edges, InputArray dx, InputArray dy, Point templCenter = Point(-1, -1));
+ void setTemplateImpl(InputArray templ, Point templCenter);
+ void setTemplateImpl(InputArray edges, InputArray dx, InputArray dy, Point templCenter);
- void detect(InputArray image, OutputArray positions, int cannyThreshold = 100);
- void detect(InputArray edges, InputArray dx, InputArray dy, OutputArray positions);
+ void detectImpl(InputArray image, OutputArray positions, OutputArray votes);
+ void detectImpl(InputArray edges, InputArray dx, InputArray dy, OutputArray positions, OutputArray votes);
- void downloadResults(InputArray d_positions, OutputArray h_positions, OutputArray h_votes = noArray());
+ void buildEdgePointList(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy);
- protected:
- virtual void setTemplateImpl(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy, Point templCenter) = 0;
- virtual void detectImpl(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy, OutputArray positions) = 0;
+ virtual void processTempl() = 0;
+ virtual void processImage() = 0;
+
+ int cannyLowThresh_;
+ int cannyHighThresh_;
+ double minDist_;
+ double dp_;
+ int maxBufferSize_;
+
+ Size templSize_;
+ Point templCenter_;
+ GpuMat templEdges_;
+ GpuMat templDx_;
+ GpuMat templDy_;
+
+ Size imageSize_;
+ GpuMat imageEdges_;
+ GpuMat imageDx_;
+ GpuMat imageDy_;
+
+ GpuMat edgePointList_;
+
+ GpuMat outBuf_;
+ int posCount_;
private:
#ifdef HAVE_OPENCV_GPUFILTERS
- GpuMat dx_, dy_;
- GpuMat edges_;
+ void calcEdges(InputArray src, GpuMat& edges, GpuMat& dx, GpuMat& dy);
+#endif
+
+ void filterMinDist();
+ void convertTo(OutputArray positions, OutputArray votes);
+
+#ifdef HAVE_OPENCV_GPUFILTERS
Ptr<gpu::CannyEdgeDetector> canny_;
Ptr<gpu::Filter> filterDx_;
Ptr<gpu::Filter> filterDy_;
#endif
+
+ std::vector<float4> oldPosBuf_;
+ std::vector<int3> oldVoteBuf_;
+ std::vector<float4> newPosBuf_;
+ std::vector<int3> newVoteBuf_;
+ std::vector<int> indexies_;
};
GeneralizedHoughBase::GeneralizedHoughBase()
{
+ cannyLowThresh_ = 50;
+ cannyHighThresh_ = 100;
+ minDist_ = 1.0;
+ dp_ = 1.0;
+
+ maxBufferSize_ = 10000;
+
#ifdef HAVE_OPENCV_GPUFILTERS
- canny_ = gpu::createCannyEdgeDetector(50, 100);
+ canny_ = gpu::createCannyEdgeDetector(cannyLowThresh_, cannyHighThresh_);
filterDx_ = gpu::createSobelFilter(CV_8UC1, CV_32S, 1, 0);
filterDy_ = gpu::createSobelFilter(CV_8UC1, CV_32S, 0, 1);
#endif
}
- void GeneralizedHoughBase::setTemplate(InputArray _templ, int cannyThreshold, Point templCenter)
+#ifdef HAVE_OPENCV_GPUFILTERS
+ void GeneralizedHoughBase::calcEdges(InputArray _src, GpuMat& edges, GpuMat& dx, GpuMat& dy)
{
-#ifndef HAVE_OPENCV_GPUFILTERS
- (void) _templ;
- (void) cannyThreshold;
- (void) templCenter;
- throw_no_cuda();
-#else
- GpuMat templ = _templ.getGpuMat();
-
- CV_Assert( templ.type() == CV_8UC1 );
- CV_Assert( cannyThreshold > 0 );
+ GpuMat src = _src.getGpuMat();
- ensureSizeIsEnough(templ.size(), CV_32SC1, dx_);
- ensureSizeIsEnough(templ.size(), CV_32SC1, dy_);
+ CV_Assert( src.type() == CV_8UC1 );
+ CV_Assert( cannyLowThresh_ > 0 && cannyLowThresh_ < cannyHighThresh_ );
- filterDx_->apply(templ, dx_);
- filterDy_->apply(templ, dy_);
+ ensureSizeIsEnough(src.size(), CV_32SC1, dx);
+ ensureSizeIsEnough(src.size(), CV_32SC1, dy);
- ensureSizeIsEnough(templ.size(), CV_8UC1, edges_);
+ filterDx_->apply(src, dx);
+ filterDy_->apply(src, dy);
- canny_->setLowThreshold(cannyThreshold / 2);
- canny_->setHighThreshold(cannyThreshold);
- canny_->detect(dx_, dy_, edges_);
+ ensureSizeIsEnough(src.size(), CV_8UC1, edges);
- if (templCenter == Point(-1, -1))
- templCenter = Point(templ.cols / 2, templ.rows / 2);
-
- setTemplateImpl(edges_, dx_, dy_, templCenter);
-#endif
- }
-
- void GeneralizedHoughBase::setTemplate(InputArray _edges, InputArray _dx, InputArray _dy, Point templCenter)
- {
- GpuMat edges = _edges.getGpuMat();
- GpuMat dx = _dx.getGpuMat();
- GpuMat dy = _dy.getGpuMat();
-
- if (templCenter == Point(-1, -1))
- templCenter = Point(edges.cols / 2, edges.rows / 2);
-
- setTemplateImpl(edges, dx, dy, templCenter);
+ canny_->setLowThreshold(cannyLowThresh_);
+ canny_->setHighThreshold(cannyHighThresh_);
+ canny_->detect(dx, dy, edges);
}
+#endif
- void GeneralizedHoughBase::detect(InputArray _image, OutputArray positions, int cannyThreshold)
+ void GeneralizedHoughBase::setTemplateImpl(InputArray templ, Point templCenter)
{
#ifndef HAVE_OPENCV_GPUFILTERS
- (void) _image;
- (void) positions;
- (void) cannyThreshold;
+ (void) templ;
+ (void) templCenter;
throw_no_cuda();
#else
- GpuMat image = _image.getGpuMat();
-
- CV_Assert( image.type() == CV_8UC1 );
- CV_Assert( cannyThreshold > 0 );
-
- ensureSizeIsEnough(image.size(), CV_32SC1, dx_);
- ensureSizeIsEnough(image.size(), CV_32SC1, dy_);
-
- filterDx_->apply(image, dx_);
- filterDy_->apply(image, dy_);
+ calcEdges(templ, templEdges_, templDx_, templDy_);
- ensureSizeIsEnough(image.size(), CV_8UC1, edges_);
+ if (templCenter == Point(-1, -1))
+ templCenter = Point(templEdges_.cols / 2, templEdges_.rows / 2);
- canny_->setLowThreshold(cannyThreshold / 2);
- canny_->setHighThreshold(cannyThreshold);
- canny_->detect(dx_, dy_, edges_);
+ templSize_ = templEdges_.size();
+ templCenter_ = templCenter;
- detectImpl(edges_, dx_, dy_, positions);
+ processTempl();
#endif
}
- void GeneralizedHoughBase::detect(InputArray _edges, InputArray _dx, InputArray _dy, OutputArray positions)
+ void GeneralizedHoughBase::setTemplateImpl(InputArray edges, InputArray dx, InputArray dy, Point templCenter)
{
- GpuMat edges = _edges.getGpuMat();
- GpuMat dx = _dx.getGpuMat();
- GpuMat dy = _dy.getGpuMat();
-
- detectImpl(edges, dx, dy, positions);
- }
-
- void GeneralizedHoughBase::downloadResults(InputArray _d_positions, OutputArray h_positions, OutputArray h_votes)
- {
- GpuMat d_positions = _d_positions.getGpuMat();
-
- if (d_positions.empty())
- {
- h_positions.release();
- if (h_votes.needed())
- h_votes.release();
- return;
- }
+ edges.getGpuMat().copyTo(templEdges_);
+ dx.getGpuMat().copyTo(templDx_);
+ dy.getGpuMat().copyTo(templDy_);
- CV_Assert( d_positions.rows == 2 && d_positions.type() == CV_32FC4 );
+ CV_Assert( templEdges_.type() == CV_8UC1 );
+ CV_Assert( templDx_.type() == CV_32FC1 && templDx_.size() == templEdges_.size() );
+ CV_Assert( templDy_.type() == templDx_.type() && templDy_.size() == templEdges_.size() );
- d_positions.row(0).download(h_positions);
-
- if (h_votes.needed())
- {
- GpuMat d_votes(1, d_positions.cols, CV_32SC3, d_positions.ptr<int3>(1));
- d_votes.download(h_votes);
- }
- }
+ if (templCenter == Point(-1, -1))
+ templCenter = Point(templEdges_.cols / 2, templEdges_.rows / 2);
- /////////////////////////////////////
- // GHT_Pos
+ templSize_ = templEdges_.size();
+ templCenter_ = templCenter;
- template <typename T, class A> void releaseVector(std::vector<T, A>& v)
- {
- std::vector<T, A> empty;
- empty.swap(v);
+ processTempl();
}
- class GHT_Pos : public GeneralizedHoughBase
+ void GeneralizedHoughBase::detectImpl(InputArray image, OutputArray positions, OutputArray votes)
{
- public:
- GHT_Pos();
-
- protected:
- void setTemplateImpl(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy, Point templCenter);
- void detectImpl(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy, OutputArray positions);
- void releaseImpl();
-
- virtual void processTempl() = 0;
- virtual void processImage() = 0;
-
- void buildEdgePointList(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy);
- void filterMinDist();
- void convertTo(OutputArray positions);
-
- int maxSize;
- double minDist;
-
- Size templSize;
- Point templCenter;
- GpuMat templEdges;
- GpuMat templDx;
- GpuMat templDy;
-
- Size imageSize;
- GpuMat imageEdges;
- GpuMat imageDx;
- GpuMat imageDy;
-
- GpuMat edgePointList;
-
- GpuMat outBuf;
- int posCount;
-
- std::vector<float4> oldPosBuf;
- std::vector<int3> oldVoteBuf;
- std::vector<float4> newPosBuf;
- std::vector<int3> newVoteBuf;
- std::vector<int> indexies;
- };
-
- GHT_Pos::GHT_Pos()
- {
- maxSize = 10000;
- minDist = 1.0;
- }
+#ifndef HAVE_OPENCV_GPUFILTERS
+ (void) templ;
+ (void) templCenter;
+ throw_no_cuda();
+#else
+ calcEdges(image, imageEdges_, imageDx_, imageDy_);
- void GHT_Pos::setTemplateImpl(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy, Point templCenter_)
- {
- templSize = edges.size();
- templCenter = templCenter_;
+ imageSize_ = imageEdges_.size();
- ensureSizeIsEnough(templSize, edges.type(), templEdges);
- ensureSizeIsEnough(templSize, dx.type(), templDx);
- ensureSizeIsEnough(templSize, dy.type(), templDy);
+ posCount_ = 0;
- edges.copyTo(templEdges);
- dx.copyTo(templDx);
- dy.copyTo(templDy);
+ processImage();
- processTempl();
+ if (posCount_ == 0)
+ {
+ positions.release();
+ if (votes.needed())
+ votes.release();
+ }
+ else
+ {
+ if (minDist_ > 1)
+ filterMinDist();
+ convertTo(positions, votes);
+ }
+#endif
}
- void GHT_Pos::detectImpl(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy, OutputArray positions)
+ void GeneralizedHoughBase::detectImpl(InputArray edges, InputArray dx, InputArray dy, OutputArray positions, OutputArray votes)
{
- imageSize = edges.size();
+ edges.getGpuMat().copyTo(imageEdges_);
+ dx.getGpuMat().copyTo(imageDx_);
+ dy.getGpuMat().copyTo(imageDy_);
- ensureSizeIsEnough(imageSize, edges.type(), imageEdges);
- ensureSizeIsEnough(imageSize, dx.type(), imageDx);
- ensureSizeIsEnough(imageSize, dy.type(), imageDy);
+ CV_Assert( imageEdges_.type() == CV_8UC1 );
+ CV_Assert( imageDx_.type() == CV_32FC1 && imageDx_.size() == imageEdges_.size() );
+ CV_Assert( imageDy_.type() == imageDx_.type() && imageDy_.size() == imageEdges_.size() );
- edges.copyTo(imageEdges);
- dx.copyTo(imageDx);
- dy.copyTo(imageDy);
+ imageSize_ = imageEdges_.size();
- posCount = 0;
+ posCount_ = 0;
processImage();
- if (posCount == 0)
+ if (posCount_ == 0)
+ {
positions.release();
+ if (votes.needed())
+ votes.release();
+ }
else
{
- if (minDist > 1)
+ if (minDist_ > 1)
filterMinDist();
- convertTo(positions);
+ convertTo(positions, votes);
}
}
- void GHT_Pos::releaseImpl()
- {
- templSize = Size();
- templCenter = Point(-1, -1);
- templEdges.release();
- templDx.release();
- templDy.release();
-
- imageSize = Size();
- imageEdges.release();
- imageDx.release();
- imageDy.release();
-
- edgePointList.release();
-
- outBuf.release();
- posCount = 0;
-
- releaseVector(oldPosBuf);
- releaseVector(oldVoteBuf);
- releaseVector(newPosBuf);
- releaseVector(newVoteBuf);
- releaseVector(indexies);
- }
-
- void GHT_Pos::buildEdgePointList(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy)
+ void GeneralizedHoughBase::buildEdgePointList(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy)
{
using namespace cv::gpu::cudev::ght;
0
};
- CV_Assert(edges.type() == CV_8UC1);
- CV_Assert(dx.size() == edges.size());
- CV_Assert(dy.type() == dx.type() && dy.size() == edges.size());
+ CV_Assert( edges.type() == CV_8UC1 );
+ CV_Assert( dx.size() == edges.size() );
+ CV_Assert( dy.type() == dx.type() && dy.size() == edges.size() );
const func_t func = funcs[dx.depth()];
- CV_Assert(func != 0);
+ CV_Assert( func != 0 );
- edgePointList.cols = (int) (edgePointList.step / sizeof(int));
- ensureSizeIsEnough(2, edges.size().area(), CV_32SC1, edgePointList);
+ edgePointList_.cols = (int) (edgePointList_.step / sizeof(int));
+ ensureSizeIsEnough(2, edges.size().area(), CV_32SC1, edgePointList_);
- edgePointList.cols = func(edges, dx, dy, edgePointList.ptr<unsigned int>(0), edgePointList.ptr<float>(1));
+ edgePointList_.cols = func(edges, dx, dy, edgePointList_.ptr<unsigned int>(0), edgePointList_.ptr<float>(1));
}
struct IndexCmp
}
};
- void GHT_Pos::filterMinDist()
+ void GeneralizedHoughBase::filterMinDist()
{
- oldPosBuf.resize(posCount);
- oldVoteBuf.resize(posCount);
+ oldPosBuf_.resize(posCount_);
+ oldVoteBuf_.resize(posCount_);
- cudaSafeCall( cudaMemcpy(&oldPosBuf[0], outBuf.ptr(0), posCount * sizeof(float4), cudaMemcpyDeviceToHost) );
- cudaSafeCall( cudaMemcpy(&oldVoteBuf[0], outBuf.ptr(1), posCount * sizeof(int3), cudaMemcpyDeviceToHost) );
+ cudaSafeCall( cudaMemcpy(&oldPosBuf_[0], outBuf_.ptr(0), posCount_ * sizeof(float4), cudaMemcpyDeviceToHost) );
+ cudaSafeCall( cudaMemcpy(&oldVoteBuf_[0], outBuf_.ptr(1), posCount_ * sizeof(int3), cudaMemcpyDeviceToHost) );
- indexies.resize(posCount);
- for (int i = 0; i < posCount; ++i)
- indexies[i] = i;
- std::sort(indexies.begin(), indexies.end(), IndexCmp(&oldVoteBuf[0]));
+ indexies_.resize(posCount_);
+ for (int i = 0; i < posCount_; ++i)
+ indexies_[i] = i;
+ std::sort(indexies_.begin(), indexies_.end(), IndexCmp(&oldVoteBuf_[0]));
- newPosBuf.clear();
- newVoteBuf.clear();
- newPosBuf.reserve(posCount);
- newVoteBuf.reserve(posCount);
+ newPosBuf_.clear();
+ newVoteBuf_.clear();
+ newPosBuf_.reserve(posCount_);
+ newVoteBuf_.reserve(posCount_);
- const int cellSize = cvRound(minDist);
- const int gridWidth = (imageSize.width + cellSize - 1) / cellSize;
- const int gridHeight = (imageSize.height + cellSize - 1) / cellSize;
+ const int cellSize = cvRound(minDist_);
+ const int gridWidth = (imageSize_.width + cellSize - 1) / cellSize;
+ const int gridHeight = (imageSize_.height + cellSize - 1) / cellSize;
std::vector< std::vector<Point2f> > grid(gridWidth * gridHeight);
- const double minDist2 = minDist * minDist;
+ const double minDist2 = minDist_ * minDist_;
- for (int i = 0; i < posCount; ++i)
+ for (int i = 0; i < posCount_; ++i)
{
- const int ind = indexies[i];
+ const int ind = indexies_[i];
- Point2f p(oldPosBuf[ind].x, oldPosBuf[ind].y);
+ Point2f p(oldPosBuf_[ind].x, oldPosBuf_[ind].y);
bool good = true;
{
grid[yCell * gridWidth + xCell].push_back(p);
- newPosBuf.push_back(oldPosBuf[ind]);
- newVoteBuf.push_back(oldVoteBuf[ind]);
+ newPosBuf_.push_back(oldPosBuf_[ind]);
+ newVoteBuf_.push_back(oldVoteBuf_[ind]);
}
}
- posCount = static_cast<int>(newPosBuf.size());
- cudaSafeCall( cudaMemcpy(outBuf.ptr(0), &newPosBuf[0], posCount * sizeof(float4), cudaMemcpyHostToDevice) );
- cudaSafeCall( cudaMemcpy(outBuf.ptr(1), &newVoteBuf[0], posCount * sizeof(int3), cudaMemcpyHostToDevice) );
+ posCount_ = static_cast<int>(newPosBuf_.size());
+ cudaSafeCall( cudaMemcpy(outBuf_.ptr(0), &newPosBuf_[0], posCount_ * sizeof(float4), cudaMemcpyHostToDevice) );
+ cudaSafeCall( cudaMemcpy(outBuf_.ptr(1), &newVoteBuf_[0], posCount_ * sizeof(int3), cudaMemcpyHostToDevice) );
}
- void GHT_Pos::convertTo(OutputArray positions)
+ void GeneralizedHoughBase::convertTo(OutputArray positions, OutputArray votes)
{
- ensureSizeIsEnough(2, posCount, CV_32FC4, positions);
- GpuMat(2, posCount, CV_32FC4, outBuf.data, outBuf.step).copyTo(positions);
+ ensureSizeIsEnough(1, posCount_, CV_32FC4, positions);
+ GpuMat(1, posCount_, CV_32FC4, outBuf_.ptr(0), outBuf_.step).copyTo(positions);
+
+ if (votes.needed())
+ {
+ ensureSizeIsEnough(1, posCount_, CV_32FC3, votes);
+ GpuMat(1, posCount_, CV_32FC4, outBuf_.ptr(1), outBuf_.step).copyTo(votes);
+ }
}
+}
- /////////////////////////////////////
- // POSITION Ballard
+// GeneralizedHoughBallard
- class GHT_Ballard_Pos : public GHT_Pos
+namespace
+{
+ class GeneralizedHoughBallardImpl : public GeneralizedHoughBallard, private GeneralizedHoughBase
{
public:
- AlgorithmInfo* info() const;
-
- GHT_Ballard_Pos();
-
- protected:
- void releaseImpl();
+ GeneralizedHoughBallardImpl();
- void processTempl();
- void processImage();
+ void setTemplate(InputArray templ, Point templCenter) { setTemplateImpl(templ, templCenter); }
+ void setTemplate(InputArray edges, InputArray dx, InputArray dy, Point templCenter) { setTemplateImpl(edges, dx, dy, templCenter); }
- virtual void calcHist();
- virtual void findPosInHist();
+ void detect(InputArray image, OutputArray positions, OutputArray votes) { detectImpl(image, positions, votes); }
+ void detect(InputArray edges, InputArray dx, InputArray dy, OutputArray positions, OutputArray votes) { detectImpl(edges, dx, dy, positions, votes); }
- int levels;
- int votesThreshold;
- double dp;
+ void setCannyLowThresh(int cannyLowThresh) { cannyLowThresh_ = cannyLowThresh; }
+ int getCannyLowThresh() const { return cannyLowThresh_; }
- GpuMat r_table;
- GpuMat r_sizes;
+ void setCannyHighThresh(int cannyHighThresh) { cannyHighThresh_ = cannyHighThresh; }
+ int getCannyHighThresh() const { return cannyHighThresh_; }
- GpuMat hist;
- };
+ void setMinDist(double minDist) { minDist_ = minDist; }
+ double getMinDist() const { return minDist_; }
- CV_INIT_ALGORITHM(GHT_Ballard_Pos, "GeneralizedHough_GPU.POSITION",
- obj.info()->addParam(obj, "maxSize", obj.maxSize, false, 0, 0,
- "Maximal size of inner buffers.");
- obj.info()->addParam(obj, "minDist", obj.minDist, false, 0, 0,
- "Minimum distance between the centers of the detected objects.");
- obj.info()->addParam(obj, "levels", obj.levels, false, 0, 0,
- "R-Table levels.");
- obj.info()->addParam(obj, "votesThreshold", obj.votesThreshold, false, 0, 0,
- "The accumulator threshold for the template centers at the detection stage. The smaller it is, the more false positions may be detected.");
- obj.info()->addParam(obj, "dp", obj.dp, false, 0, 0,
- "Inverse ratio of the accumulator resolution to the image resolution."));
-
- GHT_Ballard_Pos::GHT_Ballard_Pos()
- {
- levels = 360;
- votesThreshold = 100;
- dp = 1.0;
- }
+ void setDp(double dp) { dp_ = dp; }
+ double getDp() const { return dp_; }
- void GHT_Ballard_Pos::releaseImpl()
- {
- GHT_Pos::releaseImpl();
+ void setMaxBufferSize(int maxBufferSize) { maxBufferSize_ = maxBufferSize; }
+ int getMaxBufferSize() const { return maxBufferSize_; }
- r_table.release();
- r_sizes.release();
+ void setLevels(int levels) { levels_ = levels; }
+ int getLevels() const { return levels_; }
- hist.release();
- }
+ void setVotesThreshold(int votesThreshold) { votesThreshold_ = votesThreshold; }
+ int getVotesThreshold() const { return votesThreshold_; }
- void GHT_Ballard_Pos::processTempl()
- {
- using namespace cv::gpu::cudev::ght;
+ private:
+ void processTempl();
+ void processImage();
- CV_Assert(levels > 0);
+ void calcHist();
+ void findPosInHist();
- buildEdgePointList(templEdges, templDx, templDy);
+ int levels_;
+ int votesThreshold_;
- ensureSizeIsEnough(levels + 1, maxSize, CV_16SC2, r_table);
- ensureSizeIsEnough(1, levels + 1, CV_32SC1, r_sizes);
- r_sizes.setTo(Scalar::all(0));
+ GpuMat r_table_;
+ GpuMat r_sizes_;
- if (edgePointList.cols > 0)
- {
- buildRTable_gpu(edgePointList.ptr<unsigned int>(0), edgePointList.ptr<float>(1), edgePointList.cols,
- r_table, r_sizes.ptr<int>(), make_short2(templCenter.x, templCenter.y), levels);
- gpu::min(r_sizes, maxSize, r_sizes);
- }
- }
+ GpuMat hist_;
+ };
- void GHT_Ballard_Pos::processImage()
+ GeneralizedHoughBallardImpl::GeneralizedHoughBallardImpl()
{
- calcHist();
- findPosInHist();
+ levels_ = 360;
+ votesThreshold_ = 100;
}
- void GHT_Ballard_Pos::calcHist()
+ void GeneralizedHoughBallardImpl::processTempl()
{
using namespace cv::gpu::cudev::ght;
- CV_Assert(levels > 0 && r_table.rows == (levels + 1) && r_sizes.cols == (levels + 1));
- CV_Assert(dp > 0.0);
-
- const double idp = 1.0 / dp;
+ CV_Assert( levels_ > 0 );
- buildEdgePointList(imageEdges, imageDx, imageDy);
+ buildEdgePointList(templEdges_, templDx_, templDy_);
- ensureSizeIsEnough(cvCeil(imageSize.height * idp) + 2, cvCeil(imageSize.width * idp) + 2, CV_32SC1, hist);
- hist.setTo(Scalar::all(0));
+ ensureSizeIsEnough(levels_ + 1, maxBufferSize_, CV_16SC2, r_table_);
+ ensureSizeIsEnough(1, levels_ + 1, CV_32SC1, r_sizes_);
+ r_sizes_.setTo(Scalar::all(0));
- if (edgePointList.cols > 0)
+ if (edgePointList_.cols > 0)
{
- Ballard_Pos_calcHist_gpu(edgePointList.ptr<unsigned int>(0), edgePointList.ptr<float>(1), edgePointList.cols,
- r_table, r_sizes.ptr<int>(),
- hist,
- (float)dp, levels);
+ buildRTable_gpu(edgePointList_.ptr<unsigned int>(0), edgePointList_.ptr<float>(1), edgePointList_.cols,
+ r_table_, r_sizes_.ptr<int>(), make_short2(templCenter_.x, templCenter_.y), levels_);
+ gpu::min(r_sizes_, maxBufferSize_, r_sizes_);
}
}
- void GHT_Ballard_Pos::findPosInHist()
+ void GeneralizedHoughBallardImpl::processImage()
{
- using namespace cv::gpu::cudev::ght;
-
- CV_Assert(votesThreshold > 0);
-
- ensureSizeIsEnough(2, maxSize, CV_32FC4, outBuf);
-
- posCount = Ballard_Pos_findPosInHist_gpu(hist, outBuf.ptr<float4>(0), outBuf.ptr<int3>(1), maxSize, (float)dp, votesThreshold);
- }
-
- /////////////////////////////////////
- // POSITION & SCALE
-
- class GHT_Ballard_PosScale : public GHT_Ballard_Pos
- {
- public:
- AlgorithmInfo* info() const;
-
- GHT_Ballard_PosScale();
-
- protected:
- void calcHist();
- void findPosInHist();
-
- double minScale;
- double maxScale;
- double scaleStep;
- };
-
- CV_INIT_ALGORITHM(GHT_Ballard_PosScale, "GeneralizedHough_GPU.POSITION_SCALE",
- obj.info()->addParam(obj, "maxSize", obj.maxSize, false, 0, 0,
- "Maximal size of inner buffers.");
- obj.info()->addParam(obj, "minDist", obj.minDist, false, 0, 0,
- "Minimum distance between the centers of the detected objects.");
- obj.info()->addParam(obj, "levels", obj.levels, false, 0, 0,
- "R-Table levels.");
- obj.info()->addParam(obj, "votesThreshold", obj.votesThreshold, false, 0, 0,
- "The accumulator threshold for the template centers at the detection stage. The smaller it is, the more false positions may be detected.");
- obj.info()->addParam(obj, "dp", obj.dp, false, 0, 0,
- "Inverse ratio of the accumulator resolution to the image resolution.");
- obj.info()->addParam(obj, "minScale", obj.minScale, false, 0, 0,
- "Minimal scale to detect.");
- obj.info()->addParam(obj, "maxScale", obj.maxScale, false, 0, 0,
- "Maximal scale to detect.");
- obj.info()->addParam(obj, "scaleStep", obj.scaleStep, false, 0, 0,
- "Scale step."));
-
- GHT_Ballard_PosScale::GHT_Ballard_PosScale()
- {
- minScale = 0.5;
- maxScale = 2.0;
- scaleStep = 0.05;
+ calcHist();
+ findPosInHist();
}
- void GHT_Ballard_PosScale::calcHist()
+ void GeneralizedHoughBallardImpl::calcHist()
{
using namespace cv::gpu::cudev::ght;
- CV_Assert(levels > 0 && r_table.rows == (levels + 1) && r_sizes.cols == (levels + 1));
- CV_Assert(dp > 0.0);
- CV_Assert(minScale > 0.0 && minScale < maxScale);
- CV_Assert(scaleStep > 0.0);
+ CV_Assert( levels_ > 0 && r_table_.rows == (levels_ + 1) && r_sizes_.cols == (levels_ + 1) );
+ CV_Assert( dp_ > 0.0);
- const double idp = 1.0 / dp;
- const int scaleRange = cvCeil((maxScale - minScale) / scaleStep);
- const int rows = cvCeil(imageSize.height * idp);
- const int cols = cvCeil(imageSize.width * idp);
+ const double idp = 1.0 / dp_;
- buildEdgePointList(imageEdges, imageDx, imageDy);
+ buildEdgePointList(imageEdges_, imageDx_, imageDy_);
- ensureSizeIsEnough((scaleRange + 2) * (rows + 2), cols + 2, CV_32SC1, hist);
- hist.setTo(Scalar::all(0));
+ ensureSizeIsEnough(cvCeil(imageSize_.height * idp) + 2, cvCeil(imageSize_.width * idp) + 2, CV_32SC1, hist_);
+ hist_.setTo(Scalar::all(0));
- if (edgePointList.cols > 0)
+ if (edgePointList_.cols > 0)
{
- Ballard_PosScale_calcHist_gpu(edgePointList.ptr<unsigned int>(0), edgePointList.ptr<float>(1), edgePointList.cols,
- r_table, r_sizes.ptr<int>(),
- hist, rows, cols,
- (float)minScale, (float)scaleStep, scaleRange, (float)dp, levels);
+ Ballard_Pos_calcHist_gpu(edgePointList_.ptr<unsigned int>(0), edgePointList_.ptr<float>(1), edgePointList_.cols,
+ r_table_, r_sizes_.ptr<int>(),
+ hist_,
+ (float)dp_, levels_);
}
}
- void GHT_Ballard_PosScale::findPosInHist()
+ void GeneralizedHoughBallardImpl::findPosInHist()
{
using namespace cv::gpu::cudev::ght;
- CV_Assert(votesThreshold > 0);
-
- const double idp = 1.0 / dp;
- const int scaleRange = cvCeil((maxScale - minScale) / scaleStep);
- const int rows = cvCeil(imageSize.height * idp);
- const int cols = cvCeil(imageSize.width * idp);
+ CV_Assert( votesThreshold_ > 0 );
- ensureSizeIsEnough(2, maxSize, CV_32FC4, outBuf);
+ ensureSizeIsEnough(2, maxBufferSize_, CV_32FC4, outBuf_);
- posCount = Ballard_PosScale_findPosInHist_gpu(hist, rows, cols, scaleRange, outBuf.ptr<float4>(0), outBuf.ptr<int3>(1), maxSize, (float)minScale, (float)scaleStep, (float)dp, votesThreshold);
+ posCount_ = Ballard_Pos_findPosInHist_gpu(hist_, outBuf_.ptr<float4>(0), outBuf_.ptr<int3>(1), maxBufferSize_, (float)dp_, votesThreshold_);
}
+}
- /////////////////////////////////////
- // POSITION & Rotation
+Ptr<GeneralizedHoughBallard> cv::gpu::createGeneralizedHoughBallard()
+{
+ return new GeneralizedHoughBallardImpl;
+}
- class GHT_Ballard_PosRotation : public GHT_Ballard_Pos
+// GeneralizedHoughGuil
+
+namespace
+{
+ class GeneralizedHoughGuilImpl : public GeneralizedHoughGuil, private GeneralizedHoughBase
{
public:
- AlgorithmInfo* info() const;
+ GeneralizedHoughGuilImpl();
- GHT_Ballard_PosRotation();
+ void setTemplate(InputArray templ, Point templCenter) { setTemplateImpl(templ, templCenter); }
+ void setTemplate(InputArray edges, InputArray dx, InputArray dy, Point templCenter) { setTemplateImpl(edges, dx, dy, templCenter); }
- protected:
- void calcHist();
- void findPosInHist();
+ void detect(InputArray image, OutputArray positions, OutputArray votes) { detectImpl(image, positions, votes); }
+ void detect(InputArray edges, InputArray dx, InputArray dy, OutputArray positions, OutputArray votes) { detectImpl(edges, dx, dy, positions, votes); }
- double minAngle;
- double maxAngle;
- double angleStep;
- };
+ void setCannyLowThresh(int cannyLowThresh) { cannyLowThresh_ = cannyLowThresh; }
+ int getCannyLowThresh() const { return cannyLowThresh_; }
- CV_INIT_ALGORITHM(GHT_Ballard_PosRotation, "GeneralizedHough_GPU.POSITION_ROTATION",
- obj.info()->addParam(obj, "maxSize", obj.maxSize, false, 0, 0,
- "Maximal size of inner buffers.");
- obj.info()->addParam(obj, "minDist", obj.minDist, false, 0, 0,
- "Minimum distance between the centers of the detected objects.");
- obj.info()->addParam(obj, "levels", obj.levels, false, 0, 0,
- "R-Table levels.");
- obj.info()->addParam(obj, "votesThreshold", obj.votesThreshold, false, 0, 0,
- "The accumulator threshold for the template centers at the detection stage. The smaller it is, the more false positions may be detected.");
- obj.info()->addParam(obj, "dp", obj.dp, false, 0, 0,
- "Inverse ratio of the accumulator resolution to the image resolution.");
- obj.info()->addParam(obj, "minAngle", obj.minAngle, false, 0, 0,
- "Minimal rotation angle to detect in degrees.");
- obj.info()->addParam(obj, "maxAngle", obj.maxAngle, false, 0, 0,
- "Maximal rotation angle to detect in degrees.");
- obj.info()->addParam(obj, "angleStep", obj.angleStep, false, 0, 0,
- "Angle step in degrees."));
-
- GHT_Ballard_PosRotation::GHT_Ballard_PosRotation()
- {
- minAngle = 0.0;
- maxAngle = 360.0;
- angleStep = 1.0;
- }
+ void setCannyHighThresh(int cannyHighThresh) { cannyHighThresh_ = cannyHighThresh; }
+ int getCannyHighThresh() const { return cannyHighThresh_; }
- void GHT_Ballard_PosRotation::calcHist()
- {
- using namespace cv::gpu::cudev::ght;
+ void setMinDist(double minDist) { minDist_ = minDist; }
+ double getMinDist() const { return minDist_; }
- CV_Assert(levels > 0 && r_table.rows == (levels + 1) && r_sizes.cols == (levels + 1));
- CV_Assert(dp > 0.0);
- CV_Assert(minAngle >= 0.0 && minAngle < maxAngle && maxAngle <= 360.0);
- CV_Assert(angleStep > 0.0 && angleStep < 360.0);
+ void setDp(double dp) { dp_ = dp; }
+ double getDp() const { return dp_; }
- const double idp = 1.0 / dp;
- const int angleRange = cvCeil((maxAngle - minAngle) / angleStep);
- const int rows = cvCeil(imageSize.height * idp);
- const int cols = cvCeil(imageSize.width * idp);
+ void setMaxBufferSize(int maxBufferSize) { maxBufferSize_ = maxBufferSize; }
+ int getMaxBufferSize() const { return maxBufferSize_; }
- buildEdgePointList(imageEdges, imageDx, imageDy);
+ void setXi(double xi) { xi_ = xi; }
+ double getXi() const { return xi_; }
- ensureSizeIsEnough((angleRange + 2) * (rows + 2), cols + 2, CV_32SC1, hist);
- hist.setTo(Scalar::all(0));
+ void setLevels(int levels) { levels_ = levels; }
+ int getLevels() const { return levels_; }
- if (edgePointList.cols > 0)
- {
- Ballard_PosRotation_calcHist_gpu(edgePointList.ptr<unsigned int>(0), edgePointList.ptr<float>(1), edgePointList.cols,
- r_table, r_sizes.ptr<int>(),
- hist, rows, cols,
- (float)minAngle, (float)angleStep, angleRange, (float)dp, levels);
- }
- }
+ void setAngleEpsilon(double angleEpsilon) { angleEpsilon_ = angleEpsilon; }
+ double getAngleEpsilon() const { return angleEpsilon_; }
- void GHT_Ballard_PosRotation::findPosInHist()
- {
- using namespace cv::gpu::cudev::ght;
+ void setMinAngle(double minAngle) { minAngle_ = minAngle; }
+ double getMinAngle() const { return minAngle_; }
- CV_Assert(votesThreshold > 0);
+ void setMaxAngle(double maxAngle) { maxAngle_ = maxAngle; }
+ double getMaxAngle() const { return maxAngle_; }
- const double idp = 1.0 / dp;
- const int angleRange = cvCeil((maxAngle - minAngle) / angleStep);
- const int rows = cvCeil(imageSize.height * idp);
- const int cols = cvCeil(imageSize.width * idp);
+ void setAngleStep(double angleStep) { angleStep_ = angleStep; }
+ double getAngleStep() const { return angleStep_; }
- ensureSizeIsEnough(2, maxSize, CV_32FC4, outBuf);
+ void setAngleThresh(int angleThresh) { angleThresh_ = angleThresh; }
+ int getAngleThresh() const { return angleThresh_; }
- posCount = Ballard_PosRotation_findPosInHist_gpu(hist, rows, cols, angleRange, outBuf.ptr<float4>(0), outBuf.ptr<int3>(1), maxSize, (float)minAngle, (float)angleStep, (float)dp, votesThreshold);
- }
+ void setMinScale(double minScale) { minScale_ = minScale; }
+ double getMinScale() const { return minScale_; }
- /////////////////////////////////////////
- // POSITION & SCALE & ROTATION
+ void setMaxScale(double maxScale) { maxScale_ = maxScale; }
+ double getMaxScale() const { return maxScale_; }
- double toRad(double a)
- {
- return a * CV_PI / 180.0;
- }
+ void setScaleStep(double scaleStep) { scaleStep_ = scaleStep; }
+ double getScaleStep() const { return scaleStep_; }
- double clampAngle(double a)
- {
- double res = a;
-
- while (res > 360.0)
- res -= 360.0;
- while (res < 0)
- res += 360.0;
+ void setScaleThresh(int scaleThresh) { scaleThresh_ = scaleThresh; }
+ int getScaleThresh() const { return scaleThresh_; }
- return res;
- }
+ void setPosThresh(int posThresh) { posThresh_ = posThresh; }
+ int getPosThresh() const { return posThresh_; }
- bool angleEq(double a, double b, double eps = 1.0)
- {
- return (fabs(clampAngle(a - b)) <= eps);
- }
+ private:
+ void processTempl();
+ void processImage();
- class GHT_Guil_Full : public GHT_Pos
- {
- public:
- AlgorithmInfo* info() const;
+ double xi_;
+ int levels_;
+ double angleEpsilon_;
- GHT_Guil_Full();
+ double minAngle_;
+ double maxAngle_;
+ double angleStep_;
+ int angleThresh_;
- protected:
- void releaseImpl();
+ double minScale_;
+ double maxScale_;
+ double scaleStep_;
+ int scaleThresh_;
- void processTempl();
- void processImage();
+ int posThresh_;
struct Feature
{
int maxSize;
void create(int levels, int maxCapacity, bool isTempl);
- void release();
};
typedef void (*set_func_t)(PtrStepb p1_pos, PtrStepb p1_theta, PtrStepb p2_pos, PtrStepb d12, PtrStepb r1, PtrStepb r2);
void calcScale(double angle);
void calcPosition(double angle, int angleVotes, double scale, int scaleVotes);
- double xi;
- int levels;
- double angleEpsilon;
+ Feature templFeatures_;
+ Feature imageFeatures_;
- double minAngle;
- double maxAngle;
- double angleStep;
- int angleThresh;
+ std::vector< std::pair<double, int> > angles_;
+ std::vector< std::pair<double, int> > scales_;
- double minScale;
- double maxScale;
- double scaleStep;
- int scaleThresh;
+ GpuMat hist_;
+ std::vector<int> h_buf_;
+ };
- double dp;
- int posThresh;
+ double toRad(double a)
+ {
+ return a * CV_PI / 180.0;
+ }
- Feature templFeatures;
- Feature imageFeatures;
+ double clampAngle(double a)
+ {
+ double res = a;
- std::vector< std::pair<double, int> > angles;
- std::vector< std::pair<double, int> > scales;
+ while (res > 360.0)
+ res -= 360.0;
+ while (res < 0)
+ res += 360.0;
- GpuMat hist;
- std::vector<int> h_buf;
- };
+ return res;
+ }
- CV_INIT_ALGORITHM(GHT_Guil_Full, "GeneralizedHough_GPU.POSITION_SCALE_ROTATION",
- obj.info()->addParam(obj, "minDist", obj.minDist, false, 0, 0,
- "Minimum distance between the centers of the detected objects.");
- obj.info()->addParam(obj, "maxSize", obj.maxSize, false, 0, 0,
- "Maximal size of inner buffers.");
- obj.info()->addParam(obj, "xi", obj.xi, false, 0, 0,
- "Angle difference in degrees between two points in feature.");
- obj.info()->addParam(obj, "levels", obj.levels, false, 0, 0,
- "Feature table levels.");
- obj.info()->addParam(obj, "angleEpsilon", obj.angleEpsilon, false, 0, 0,
- "Maximal difference between angles that treated as equal.");
- obj.info()->addParam(obj, "minAngle", obj.minAngle, false, 0, 0,
- "Minimal rotation angle to detect in degrees.");
- obj.info()->addParam(obj, "maxAngle", obj.maxAngle, false, 0, 0,
- "Maximal rotation angle to detect in degrees.");
- obj.info()->addParam(obj, "angleStep", obj.angleStep, false, 0, 0,
- "Angle step in degrees.");
- obj.info()->addParam(obj, "angleThresh", obj.angleThresh, false, 0, 0,
- "Angle threshold.");
- obj.info()->addParam(obj, "minScale", obj.minScale, false, 0, 0,
- "Minimal scale to detect.");
- obj.info()->addParam(obj, "maxScale", obj.maxScale, false, 0, 0,
- "Maximal scale to detect.");
- obj.info()->addParam(obj, "scaleStep", obj.scaleStep, false, 0, 0,
- "Scale step.");
- obj.info()->addParam(obj, "scaleThresh", obj.scaleThresh, false, 0, 0,
- "Scale threshold.");
- obj.info()->addParam(obj, "dp", obj.dp, false, 0, 0,
- "Inverse ratio of the accumulator resolution to the image resolution.");
- obj.info()->addParam(obj, "posThresh", obj.posThresh, false, 0, 0,
- "Position threshold."));
-
- GHT_Guil_Full::GHT_Guil_Full()
+ bool angleEq(double a, double b, double eps = 1.0)
{
- maxSize = 1000;
- xi = 90.0;
- levels = 360;
- angleEpsilon = 1.0;
-
- minAngle = 0.0;
- maxAngle = 360.0;
- angleStep = 1.0;
- angleThresh = 15000;
-
- minScale = 0.5;
- maxScale = 2.0;
- scaleStep = 0.05;
- scaleThresh = 1000;
-
- dp = 1.0;
- posThresh = 100;
+ return (fabs(clampAngle(a - b)) <= eps);
}
- void GHT_Guil_Full::releaseImpl()
+ GeneralizedHoughGuilImpl::GeneralizedHoughGuilImpl()
{
- GHT_Pos::releaseImpl();
+ maxBufferSize_ = 1000;
+
+ xi_ = 90.0;
+ levels_ = 360;
+ angleEpsilon_ = 1.0;
- templFeatures.release();
- imageFeatures.release();
+ minAngle_ = 0.0;
+ maxAngle_ = 360.0;
+ angleStep_ = 1.0;
+ angleThresh_ = 15000;
- releaseVector(angles);
- releaseVector(scales);
+ minScale_ = 0.5;
+ maxScale_ = 2.0;
+ scaleStep_ = 0.05;
+ scaleThresh_ = 1000;
- hist.release();
- releaseVector(h_buf);
+ posThresh_ = 100;
}
- void GHT_Guil_Full::processTempl()
+ void GeneralizedHoughGuilImpl::processTempl()
{
using namespace cv::gpu::cudev::ght;
- buildFeatureList(templEdges, templDx, templDy, templFeatures,
+ buildFeatureList(templEdges_, templDx_, templDy_, templFeatures_,
Guil_Full_setTemplFeatures, Guil_Full_buildTemplFeatureList_gpu,
- true, templCenter);
+ true, templCenter_);
- h_buf.resize(templFeatures.sizes.cols);
- cudaSafeCall( cudaMemcpy(&h_buf[0], templFeatures.sizes.data, h_buf.size() * sizeof(int), cudaMemcpyDeviceToHost) );
- templFeatures.maxSize = *max_element(h_buf.begin(), h_buf.end());
+ h_buf_.resize(templFeatures_.sizes.cols);
+ cudaSafeCall( cudaMemcpy(&h_buf_[0], templFeatures_.sizes.data, h_buf_.size() * sizeof(int), cudaMemcpyDeviceToHost) );
+ templFeatures_.maxSize = *std::max_element(h_buf_.begin(), h_buf_.end());
}
- void GHT_Guil_Full::processImage()
+ void GeneralizedHoughGuilImpl::processImage()
{
using namespace cv::gpu::cudev::ght;
- CV_Assert(levels > 0);
- CV_Assert(templFeatures.sizes.cols == levels + 1);
- CV_Assert(minAngle >= 0.0 && minAngle < maxAngle && maxAngle <= 360.0);
- CV_Assert(angleStep > 0.0 && angleStep < 360.0);
- CV_Assert(angleThresh > 0);
- CV_Assert(minScale > 0.0 && minScale < maxScale);
- CV_Assert(scaleStep > 0.0);
- CV_Assert(scaleThresh > 0);
- CV_Assert(dp > 0.0);
- CV_Assert(posThresh > 0);
+ CV_Assert( levels_ > 0 );
+ CV_Assert( templFeatures_.sizes.cols == levels_ + 1 );
+ CV_Assert( minAngle_ >= 0.0 && minAngle_ < maxAngle_ && maxAngle_ <= 360.0 );
+ CV_Assert( angleStep_ > 0.0 && angleStep_ < 360.0 );
+ CV_Assert( angleThresh_ > 0 );
+ CV_Assert( minScale_ > 0.0 && minScale_ < maxScale_ );
+ CV_Assert( scaleStep_ > 0.0 );
+ CV_Assert( scaleThresh_ > 0 );
+ CV_Assert( dp_ > 0.0 );
+ CV_Assert( posThresh_ > 0 );
- const double iAngleStep = 1.0 / angleStep;
- const int angleRange = cvCeil((maxAngle - minAngle) * iAngleStep);
+ const double iAngleStep = 1.0 / angleStep_;
+ const int angleRange = cvCeil((maxAngle_ - minAngle_) * iAngleStep);
- const double iScaleStep = 1.0 / scaleStep;
- const int scaleRange = cvCeil((maxScale - minScale) * iScaleStep);
+ const double iScaleStep = 1.0 / scaleStep_;
+ const int scaleRange = cvCeil((maxScale_ - minScale_) * iScaleStep);
- const double idp = 1.0 / dp;
- const int histRows = cvCeil(imageSize.height * idp);
- const int histCols = cvCeil(imageSize.width * idp);
+ const double idp = 1.0 / dp_;
+ const int histRows = cvCeil(imageSize_.height * idp);
+ const int histCols = cvCeil(imageSize_.width * idp);
- ensureSizeIsEnough(histRows + 2, std::max(angleRange + 1, std::max(scaleRange + 1, histCols + 2)), CV_32SC1, hist);
- h_buf.resize(std::max(angleRange + 1, scaleRange + 1));
+ ensureSizeIsEnough(histRows + 2, std::max(angleRange + 1, std::max(scaleRange + 1, histCols + 2)), CV_32SC1, hist_);
+ h_buf_.resize(std::max(angleRange + 1, scaleRange + 1));
- ensureSizeIsEnough(2, maxSize, CV_32FC4, outBuf);
+ ensureSizeIsEnough(2, maxBufferSize_, CV_32FC4, outBuf_);
- buildFeatureList(imageEdges, imageDx, imageDy, imageFeatures,
+ buildFeatureList(imageEdges_, imageDx_, imageDy_, imageFeatures_,
Guil_Full_setImageFeatures, Guil_Full_buildImageFeatureList_gpu,
false);
calcOrientation();
- for (size_t i = 0; i < angles.size(); ++i)
+ for (size_t i = 0; i < angles_.size(); ++i)
{
- const double angle = angles[i].first;
- const int angleVotes = angles[i].second;
+ const double angle = angles_[i].first;
+ const int angleVotes = angles_[i].second;
calcScale(angle);
- for (size_t j = 0; j < scales.size(); ++j)
+ for (size_t j = 0; j < scales_.size(); ++j)
{
- const double scale = scales[j].first;
- const int scaleVotes = scales[j].second;
+ const double scale = scales_[j].first;
+ const int scaleVotes = scales_[j].second;
calcPosition(angle, angleVotes, scale, scaleVotes);
}
}
}
- void GHT_Guil_Full::Feature::create(int levels, int maxCapacity, bool isTempl)
+ void GeneralizedHoughGuilImpl::Feature::create(int levels, int maxCapacity, bool isTempl)
{
if (!isTempl)
{
maxSize = 0;
}
- void GHT_Guil_Full::Feature::release()
+ void GeneralizedHoughGuilImpl::buildFeatureList(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy, Feature& features,
+ set_func_t set_func, build_func_t build_func, bool isTempl, Point2d center)
{
- p1_pos.release();
- p1_theta.release();
- p2_pos.release();
+ CV_Assert( levels_ > 0 );
- d12.release();
+ const double maxDist = sqrt((double) templSize_.width * templSize_.width + templSize_.height * templSize_.height) * maxScale_;
- r1.release();
- r2.release();
-
- sizes.release();
-
- maxSize = 0;
- }
-
- void GHT_Guil_Full::buildFeatureList(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy, Feature& features,
- set_func_t set_func, build_func_t build_func, bool isTempl, Point2d center)
- {
- CV_Assert(levels > 0);
-
- const double maxDist = sqrt((double) templSize.width * templSize.width + templSize.height * templSize.height) * maxScale;
-
- features.create(levels, maxSize, isTempl);
+ features.create(levels_, maxBufferSize_, isTempl);
set_func(features.p1_pos, features.p1_theta, features.p2_pos, features.d12, features.r1, features.r2);
buildEdgePointList(edges, dx, dy);
- if (edgePointList.cols > 0)
+ if (edgePointList_.cols > 0)
{
- build_func(edgePointList.ptr<unsigned int>(0), edgePointList.ptr<float>(1), edgePointList.cols,
- features.sizes.ptr<int>(), maxSize, (float)xi, (float)angleEpsilon, levels, make_float2((float)center.x, (float)center.y), (float)maxDist);
+ build_func(edgePointList_.ptr<unsigned int>(0), edgePointList_.ptr<float>(1), edgePointList_.cols,
+ features.sizes.ptr<int>(), maxBufferSize_, (float)xi_, (float)angleEpsilon_, levels_, make_float2((float)center.x, (float)center.y), (float)maxDist);
}
}
- void GHT_Guil_Full::calcOrientation()
+ void GeneralizedHoughGuilImpl::calcOrientation()
{
using namespace cv::gpu::cudev::ght;
- const double iAngleStep = 1.0 / angleStep;
- const int angleRange = cvCeil((maxAngle - minAngle) * iAngleStep);
+ const double iAngleStep = 1.0 / angleStep_;
+ const int angleRange = cvCeil((maxAngle_ - minAngle_) * iAngleStep);
- hist.setTo(Scalar::all(0));
- Guil_Full_calcOHist_gpu(templFeatures.sizes.ptr<int>(), imageFeatures.sizes.ptr<int>(0), hist.ptr<int>(),
- (float)minAngle, (float)maxAngle, (float)angleStep, angleRange, levels, templFeatures.maxSize);
- cudaSafeCall( cudaMemcpy(&h_buf[0], hist.data, h_buf.size() * sizeof(int), cudaMemcpyDeviceToHost) );
+ hist_.setTo(Scalar::all(0));
+ Guil_Full_calcOHist_gpu(templFeatures_.sizes.ptr<int>(), imageFeatures_.sizes.ptr<int>(0), hist_.ptr<int>(),
+ (float)minAngle_, (float)maxAngle_, (float)angleStep_, angleRange, levels_, templFeatures_.maxSize);
+ cudaSafeCall( cudaMemcpy(&h_buf_[0], hist_.data, h_buf_.size() * sizeof(int), cudaMemcpyDeviceToHost) );
- angles.clear();
+ angles_.clear();
for (int n = 0; n < angleRange; ++n)
{
- if (h_buf[n] >= angleThresh)
+ if (h_buf_[n] >= angleThresh_)
{
- const double angle = minAngle + n * angleStep;
- angles.push_back(std::make_pair(angle, h_buf[n]));
+ const double angle = minAngle_ + n * angleStep_;
+ angles_.push_back(std::make_pair(angle, h_buf_[n]));
}
}
}
- void GHT_Guil_Full::calcScale(double angle)
+ void GeneralizedHoughGuilImpl::calcScale(double angle)
{
using namespace cv::gpu::cudev::ght;
- const double iScaleStep = 1.0 / scaleStep;
- const int scaleRange = cvCeil((maxScale - minScale) * iScaleStep);
+ const double iScaleStep = 1.0 / scaleStep_;
+ const int scaleRange = cvCeil((maxScale_ - minScale_) * iScaleStep);
- hist.setTo(Scalar::all(0));
- Guil_Full_calcSHist_gpu(templFeatures.sizes.ptr<int>(), imageFeatures.sizes.ptr<int>(0), hist.ptr<int>(),
- (float)angle, (float)angleEpsilon, (float)minScale, (float)maxScale,
- (float)iScaleStep, scaleRange, levels, templFeatures.maxSize);
- cudaSafeCall( cudaMemcpy(&h_buf[0], hist.data, h_buf.size() * sizeof(int), cudaMemcpyDeviceToHost) );
+ hist_.setTo(Scalar::all(0));
+ Guil_Full_calcSHist_gpu(templFeatures_.sizes.ptr<int>(), imageFeatures_.sizes.ptr<int>(0), hist_.ptr<int>(),
+ (float)angle, (float)angleEpsilon_, (float)minScale_, (float)maxScale_,
+ (float)iScaleStep, scaleRange, levels_, templFeatures_.maxSize);
+ cudaSafeCall( cudaMemcpy(&h_buf_[0], hist_.data, h_buf_.size() * sizeof(int), cudaMemcpyDeviceToHost) );
- scales.clear();
+ scales_.clear();
for (int s = 0; s < scaleRange; ++s)
{
- if (h_buf[s] >= scaleThresh)
+ if (h_buf_[s] >= scaleThresh_)
{
- const double scale = minScale + s * scaleStep;
- scales.push_back(std::make_pair(scale, h_buf[s]));
+ const double scale = minScale_ + s * scaleStep_;
+ scales_.push_back(std::make_pair(scale, h_buf_[s]));
}
}
}
- void GHT_Guil_Full::calcPosition(double angle, int angleVotes, double scale, int scaleVotes)
+ void GeneralizedHoughGuilImpl::calcPosition(double angle, int angleVotes, double scale, int scaleVotes)
{
using namespace cv::gpu::cudev::ght;
- hist.setTo(Scalar::all(0));
- Guil_Full_calcPHist_gpu(templFeatures.sizes.ptr<int>(), imageFeatures.sizes.ptr<int>(0), hist,
- (float)angle, (float)angleEpsilon, (float)scale, (float)dp, levels, templFeatures.maxSize);
+ hist_.setTo(Scalar::all(0));
+ Guil_Full_calcPHist_gpu(templFeatures_.sizes.ptr<int>(), imageFeatures_.sizes.ptr<int>(0), hist_,
+ (float)angle, (float)angleEpsilon_, (float)scale, (float)dp_, levels_, templFeatures_.maxSize);
- posCount = Guil_Full_findPosInHist_gpu(hist, outBuf.ptr<float4>(0), outBuf.ptr<int3>(1),
- posCount, maxSize, (float)angle, angleVotes,
- (float)scale, scaleVotes, (float)dp, posThresh);
+ posCount_ = Guil_Full_findPosInHist_gpu(hist_, outBuf_.ptr<float4>(0), outBuf_.ptr<int3>(1),
+ posCount_, maxBufferSize_, (float)angle, angleVotes,
+ (float)scale, scaleVotes, (float)dp_, posThresh_);
}
}
-Ptr<gpu::GeneralizedHough> cv::gpu::GeneralizedHough::create(int method)
+Ptr<GeneralizedHoughGuil> cv::gpu::createGeneralizedHoughGuil()
{
- switch (method)
- {
- case cv::GeneralizedHough::GHT_POSITION:
- CV_Assert( !GHT_Ballard_Pos_info_auto.name().empty() );
- return new GHT_Ballard_Pos();
-
- case (cv::GeneralizedHough::GHT_POSITION | cv::GeneralizedHough::GHT_SCALE):
- CV_Assert( !GHT_Ballard_PosScale_info_auto.name().empty() );
- return new GHT_Ballard_PosScale();
-
- case (cv::GeneralizedHough::GHT_POSITION | cv::GeneralizedHough::GHT_ROTATION):
- CV_Assert( !GHT_Ballard_PosRotation_info_auto.name().empty() );
- return new GHT_Ballard_PosRotation();
-
- case (cv::GeneralizedHough::GHT_POSITION | cv::GeneralizedHough::GHT_SCALE | cv::GeneralizedHough::GHT_ROTATION):
- CV_Assert( !GHT_Guil_Full_info_auto.name().empty() );
- return new GHT_Guil_Full();
-
- default:
- CV_Error(Error::StsBadArg, "Unsupported method");
- return Ptr<GeneralizedHough>();
- }
+ return new GeneralizedHoughGuilImpl;
}
#endif /* !defined (HAVE_CUDA) */
{
};
-GPU_TEST_P(GeneralizedHough, POSITION)
+GPU_TEST_P(GeneralizedHough, Ballard)
{
const cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
cv::gpu::setDevice(devInfo.deviceID());
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());
//! finds arbitrary template in the grayscale image using Generalized Hough Transform
+class CV_EXPORTS GeneralizedHough : public Algorithm
+{
+public:
+ //! set template to search
+ virtual void setTemplate(InputArray templ, 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, OutputArray votes = noArray()) = 0;
+ virtual void detect(InputArray edges, InputArray dx, InputArray dy, OutputArray positions, OutputArray votes = noArray()) = 0;
+
+ //! Canny low threshold.
+ virtual void setCannyLowThresh(int cannyLowThresh) = 0;
+ virtual int getCannyLowThresh() const = 0;
+
+ //! Canny high threshold.
+ virtual void setCannyHighThresh(int cannyHighThresh) = 0;
+ virtual int getCannyHighThresh() const = 0;
+
+ //! Minimum distance between the centers of the detected objects.
+ virtual void setMinDist(double minDist) = 0;
+ virtual double getMinDist() const = 0;
+
+ //! Inverse ratio of the accumulator resolution to the image resolution.
+ virtual void setDp(double dp) = 0;
+ virtual double getDp() const = 0;
+
+ //! Maximal size of inner buffers.
+ virtual void setMaxBufferSize(int maxBufferSize) = 0;
+ virtual int getMaxBufferSize() const = 0;
+};
+
//! 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
+class CV_EXPORTS GeneralizedHoughBallard : public GeneralizedHough
+{
+public:
+ //! R-Table levels.
+ virtual void setLevels(int levels) = 0;
+ virtual int getLevels() const = 0;
+
+ //! The accumulator threshold for the template centers at the detection stage. The smaller it is, the more false positions may be detected.
+ virtual void setVotesThreshold(int votesThreshold) = 0;
+ virtual int getVotesThreshold() const = 0;
+};
+
//! 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
+//! Detects position, traslation and rotation
+class CV_EXPORTS GeneralizedHoughGuil : public GeneralizedHough
{
public:
- enum { GHT_POSITION = 0,
- GHT_SCALE = 1,
- GHT_ROTATION = 2
- };
+ //! Angle difference in degrees between two points in feature.
+ virtual void setXi(double xi) = 0;
+ virtual double getXi() const = 0;
- static Ptr<GeneralizedHough> create(int method);
+ //! Feature table levels.
+ virtual void setLevels(int levels) = 0;
+ virtual int getLevels() const = 0;
- virtual ~GeneralizedHough();
+ //! Maximal difference between angles that treated as equal.
+ virtual void setAngleEpsilon(double angleEpsilon) = 0;
+ virtual double getAngleEpsilon() const = 0;
- //! set template to search
- void setTemplate(InputArray templ, int cannyThreshold = 100, Point templCenter = Point(-1, -1));
- void setTemplate(InputArray edges, InputArray dx, InputArray dy, Point templCenter = Point(-1, -1));
+ //! Minimal rotation angle to detect in degrees.
+ virtual void setMinAngle(double minAngle) = 0;
+ virtual double getMinAngle() const = 0;
- //! find template on image
- void detect(InputArray image, OutputArray positions, OutputArray votes = cv::noArray(), int cannyThreshold = 100);
- void detect(InputArray edges, InputArray dx, InputArray dy, OutputArray positions, OutputArray votes = cv::noArray());
+ //! Maximal rotation angle to detect in degrees.
+ virtual void setMaxAngle(double maxAngle) = 0;
+ virtual double getMaxAngle() const = 0;
- void release();
+ //! Angle step in degrees.
+ virtual void setAngleStep(double angleStep) = 0;
+ virtual double getAngleStep() const = 0;
-protected:
- virtual void setTemplateImpl(const Mat& edges, const Mat& dx, const Mat& dy, Point templCenter) = 0;
- virtual void detectImpl(const Mat& edges, const Mat& dx, const Mat& dy, OutputArray positions, OutputArray votes) = 0;
- virtual void releaseImpl() = 0;
-
-private:
- Mat edges_;
- Mat dx_;
- Mat dy_;
+ //! Angle votes threshold.
+ virtual void setAngleThresh(int angleThresh) = 0;
+ virtual int getAngleThresh() const = 0;
+
+ //! Minimal scale to detect.
+ virtual void setMinScale(double minScale) = 0;
+ virtual double getMinScale() const = 0;
+
+ //! Maximal scale to detect.
+ virtual void setMaxScale(double maxScale) = 0;
+ virtual double getMaxScale() const = 0;
+
+ //! Scale step.
+ virtual void setScaleStep(double scaleStep) = 0;
+ virtual double getScaleStep() const = 0;
+
+ //! Scale votes threshold.
+ virtual void setScaleThresh(int scaleThresh) = 0;
+ virtual int getScaleThresh() const = 0;
+
+ //! Position votes threshold.
+ virtual void setPosThresh(int posThresh) = 0;
+ virtual int getPosThresh() const = 0;
};
CV_EXPORTS Ptr<CLAHE> createCLAHE(double clipLimit = 40.0, Size tileGridSize = Size(8, 8));
+//! 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.
+//! Detects position, traslation and rotation
+CV_EXPORTS Ptr<GeneralizedHoughGuil> createGeneralizedHoughGuil();
+
} // cv
#endif
using namespace cv;
+// common
+
namespace
{
- /////////////////////////////////////
- // Common
-
- template <typename T, class A> void releaseVector(std::vector<T, A>& v)
- {
- std::vector<T, A> empty;
- empty.swap(v);
- }
-
double toRad(double a)
{
return a * CV_PI / 180.0;
return fabs(v) > std::numeric_limits<float>::epsilon();
}
- class GHT_Pos : public GeneralizedHough
+ class GeneralizedHoughBase
{
- public:
- GHT_Pos();
-
protected:
- void setTemplateImpl(const Mat& edges, const Mat& dx, const Mat& dy, Point templCenter);
- void detectImpl(const Mat& edges, const Mat& dx, const Mat& dy, OutputArray positions, OutputArray votes);
- void releaseImpl();
+ GeneralizedHoughBase();
+ virtual ~GeneralizedHoughBase() {}
+
+ void setTemplateImpl(InputArray templ, Point templCenter);
+ void setTemplateImpl(InputArray edges, InputArray dx, InputArray dy, Point templCenter);
+
+ void detectImpl(InputArray image, OutputArray positions, OutputArray votes);
+ void detectImpl(InputArray edges, InputArray dx, InputArray dy, OutputArray positions, OutputArray votes);
virtual void processTempl() = 0;
virtual void processImage() = 0;
+ int cannyLowThresh_;
+ int cannyHighThresh_;
+ double minDist_;
+ double dp_;
+
+ Size templSize_;
+ Point templCenter_;
+ Mat templEdges_;
+ Mat templDx_;
+ Mat templDy_;
+
+ Size imageSize_;
+ Mat imageEdges_;
+ Mat imageDx_;
+ Mat imageDy_;
+
+ std::vector<Vec4f> posOutBuf_;
+ std::vector<Vec3i> voteOutBuf_;
+
+ private:
+ void calcEdges(InputArray src, Mat& edges, Mat& dx, Mat& dy);
void filterMinDist();
void convertTo(OutputArray positions, OutputArray votes);
+ };
- double minDist;
+ GeneralizedHoughBase::GeneralizedHoughBase()
+ {
+ cannyLowThresh_ = 50;
+ cannyHighThresh_ = 100;
+ minDist_ = 1.0;
+ dp_ = 1.0;
+ }
- Size templSize;
- Point templCenter;
- Mat templEdges;
- Mat templDx;
- Mat templDy;
+ void GeneralizedHoughBase::calcEdges(InputArray _src, Mat& edges, Mat& dx, Mat& dy)
+ {
+ Mat src = _src.getMat();
- Size imageSize;
- Mat imageEdges;
- Mat imageDx;
- Mat imageDy;
+ CV_Assert( src.type() == CV_8UC1 );
+ CV_Assert( cannyLowThresh_ > 0 && cannyLowThresh_ < cannyHighThresh_ );
- std::vector<Vec4f> posOutBuf;
- std::vector<Vec3i> voteOutBuf;
- };
+ Canny(src, edges, cannyLowThresh_, cannyHighThresh_);
+ Sobel(src, dx, CV_32F, 1, 0);
+ Sobel(src, dy, CV_32F, 0, 1);
+ }
- GHT_Pos::GHT_Pos()
+ void GeneralizedHoughBase::setTemplateImpl(InputArray templ, Point templCenter)
{
- minDist = 1.0;
+ calcEdges(templ, templEdges_, templDx_, templDy_);
+
+ if (templCenter == Point(-1, -1))
+ templCenter = Point(templEdges_.cols / 2, templEdges_.rows / 2);
+
+ templSize_ = templEdges_.size();
+ templCenter_ = templCenter;
+
+ processTempl();
}
- void GHT_Pos::setTemplateImpl(const Mat& edges, const Mat& dx, const Mat& dy, Point templCenter_)
+ void GeneralizedHoughBase::setTemplateImpl(InputArray edges, InputArray dx, InputArray dy, Point templCenter)
{
- templSize = edges.size();
- templCenter = templCenter_;
- edges.copyTo(templEdges);
- dx.copyTo(templDx);
- dy.copyTo(templDy);
+ edges.getMat().copyTo(templEdges_);
+ dx.getMat().copyTo(templDx_);
+ dy.getMat().copyTo(templDy_);
+
+ CV_Assert( templEdges_.type() == CV_8UC1 );
+ CV_Assert( templDx_.type() == CV_32FC1 && templDx_.size() == templEdges_.size() );
+ CV_Assert( templDy_.type() == templDx_.type() && templDy_.size() == templEdges_.size() );
+
+ if (templCenter == Point(-1, -1))
+ templCenter = Point(templEdges_.cols / 2, templEdges_.rows / 2);
+
+ templSize_ = templEdges_.size();
+ templCenter_ = templCenter;
processTempl();
}
- void GHT_Pos::detectImpl(const Mat& edges, const Mat& dx, const Mat& dy, OutputArray positions, OutputArray votes)
+ void GeneralizedHoughBase::detectImpl(InputArray image, OutputArray positions, OutputArray votes)
{
- imageSize = edges.size();
- edges.copyTo(imageEdges);
- dx.copyTo(imageDx);
- dy.copyTo(imageDy);
+ calcEdges(image, imageEdges_, imageDx_, imageDy_);
+
+ imageSize_ = imageEdges_.size();
- posOutBuf.clear();
- voteOutBuf.clear();
+ posOutBuf_.clear();
+ voteOutBuf_.clear();
processImage();
- if (!posOutBuf.empty())
+ if (!posOutBuf_.empty())
{
- if (minDist > 1)
+ if (minDist_ > 1)
filterMinDist();
convertTo(positions, votes);
}
}
}
- void GHT_Pos::releaseImpl()
+ void GeneralizedHoughBase::detectImpl(InputArray edges, InputArray dx, InputArray dy, OutputArray positions, OutputArray votes)
{
- templSize = Size();
- templCenter = Point(-1, -1);
- templEdges.release();
- templDx.release();
- templDy.release();
-
- imageSize = Size();
- imageEdges.release();
- imageDx.release();
- imageDy.release();
-
- releaseVector(posOutBuf);
- releaseVector(voteOutBuf);
+ edges.getMat().copyTo(imageEdges_);
+ dx.getMat().copyTo(imageDx_);
+ dy.getMat().copyTo(imageDy_);
+
+ CV_Assert( imageEdges_.type() == CV_8UC1 );
+ CV_Assert( imageDx_.type() == CV_32FC1 && imageDx_.size() == imageEdges_.size() );
+ CV_Assert( imageDy_.type() == imageDx_.type() && imageDy_.size() == imageEdges_.size() );
+
+ imageSize_ = imageEdges_.size();
+
+ posOutBuf_.clear();
+ voteOutBuf_.clear();
+
+ processImage();
+
+ if (!posOutBuf_.empty())
+ {
+ if (minDist_ > 1)
+ filterMinDist();
+ convertTo(positions, votes);
+ }
+ else
+ {
+ positions.release();
+ if (votes.needed())
+ votes.release();
+ }
}
class Vec3iGreaterThanIdx
const Vec3i* arr;
};
- void GHT_Pos::filterMinDist()
+ void GeneralizedHoughBase::filterMinDist()
{
- size_t oldSize = posOutBuf.size();
- const bool hasVotes = !voteOutBuf.empty();
+ size_t oldSize = posOutBuf_.size();
+ const bool hasVotes = !voteOutBuf_.empty();
- CV_Assert(!hasVotes || voteOutBuf.size() == oldSize);
+ CV_Assert( !hasVotes || voteOutBuf_.size() == oldSize );
- std::vector<Vec4f> oldPosBuf(posOutBuf);
- std::vector<Vec3i> oldVoteBuf(voteOutBuf);
+ std::vector<Vec4f> oldPosBuf(posOutBuf_);
+ std::vector<Vec3i> oldVoteBuf(voteOutBuf_);
std::vector<size_t> indexies(oldSize);
for (size_t i = 0; i < oldSize; ++i)
indexies[i] = i;
std::sort(indexies.begin(), indexies.end(), Vec3iGreaterThanIdx(&oldVoteBuf[0]));
- posOutBuf.clear();
- voteOutBuf.clear();
+ posOutBuf_.clear();
+ voteOutBuf_.clear();
- const int cellSize = cvRound(minDist);
- const int gridWidth = (imageSize.width + cellSize - 1) / cellSize;
- const int gridHeight = (imageSize.height + cellSize - 1) / cellSize;
+ const int cellSize = cvRound(minDist_);
+ const int gridWidth = (imageSize_.width + cellSize - 1) / cellSize;
+ const int gridHeight = (imageSize_.height + cellSize - 1) / cellSize;
std::vector< std::vector<Point2f> > grid(gridWidth * gridHeight);
- const double minDist2 = minDist * minDist;
+ const double minDist2 = minDist_ * minDist_;
for (size_t i = 0; i < oldSize; ++i)
{
{
grid[yCell * gridWidth + xCell].push_back(p);
- posOutBuf.push_back(oldPosBuf[ind]);
+ posOutBuf_.push_back(oldPosBuf[ind]);
if (hasVotes)
- voteOutBuf.push_back(oldVoteBuf[ind]);
+ voteOutBuf_.push_back(oldVoteBuf[ind]);
}
}
}
- void GHT_Pos::convertTo(OutputArray _positions, OutputArray _votes)
+ void GeneralizedHoughBase::convertTo(OutputArray _positions, OutputArray _votes)
{
- const int total = static_cast<int>(posOutBuf.size());
- const bool hasVotes = !voteOutBuf.empty();
+ const int total = static_cast<int>(posOutBuf_.size());
+ const bool hasVotes = !voteOutBuf_.empty();
- CV_Assert(!hasVotes || voteOutBuf.size() == posOutBuf.size());
+ CV_Assert( !hasVotes || voteOutBuf_.size() == posOutBuf_.size() );
_positions.create(1, total, CV_32FC4);
Mat positions = _positions.getMat();
- Mat(1, total, CV_32FC4, &posOutBuf[0]).copyTo(positions);
+ Mat(1, total, CV_32FC4, &posOutBuf_[0]).copyTo(positions);
if (_votes.needed())
{
if (!hasVotes)
+ {
_votes.release();
+ }
else
{
_votes.create(1, total, CV_32SC3);
Mat votes = _votes.getMat();
- Mat(1, total, CV_32SC3, &voteOutBuf[0]).copyTo(votes);
+ Mat(1, total, CV_32SC3, &voteOutBuf_[0]).copyTo(votes);
}
}
}
+}
- /////////////////////////////////////
- // POSITION Ballard
+// GeneralizedHoughBallard
- class GHT_Ballard_Pos : public GHT_Pos
+namespace
+{
+ class GeneralizedHoughBallardImpl : public GeneralizedHoughBallard, private GeneralizedHoughBase
{
public:
- AlgorithmInfo* info() const;
+ GeneralizedHoughBallardImpl();
- GHT_Ballard_Pos();
+ void setTemplate(InputArray templ, Point templCenter) { setTemplateImpl(templ, templCenter); }
+ void setTemplate(InputArray edges, InputArray dx, InputArray dy, Point templCenter) { setTemplateImpl(edges, dx, dy, templCenter); }
- protected:
- void releaseImpl();
+ void detect(InputArray image, OutputArray positions, OutputArray votes) { detectImpl(image, positions, votes); }
+ void detect(InputArray edges, InputArray dx, InputArray dy, OutputArray positions, OutputArray votes) { detectImpl(edges, dx, dy, positions, votes); }
+
+ void setCannyLowThresh(int cannyLowThresh) { cannyLowThresh_ = cannyLowThresh; }
+ int getCannyLowThresh() const { return cannyLowThresh_; }
+
+ void setCannyHighThresh(int cannyHighThresh) { cannyHighThresh_ = cannyHighThresh; }
+ int getCannyHighThresh() const { return cannyHighThresh_; }
+
+ void setMinDist(double minDist) { minDist_ = minDist; }
+ double getMinDist() const { return minDist_; }
+
+ void setDp(double dp) { dp_ = dp; }
+ double getDp() const { return dp_; }
+
+ void setMaxBufferSize(int) { }
+ int getMaxBufferSize() const { return 0; }
+ void setLevels(int levels) { levels_ = levels; }
+ int getLevels() const { return levels_; }
+
+ void setVotesThreshold(int votesThreshold) { votesThreshold_ = votesThreshold; }
+ int getVotesThreshold() const { return votesThreshold_; }
+
+ private:
void processTempl();
void processImage();
- virtual void calcHist();
- virtual void findPosInHist();
+ void calcHist();
+ void findPosInHist();
- int levels;
- int votesThreshold;
- double dp;
+ int levels_;
+ int votesThreshold_;
- std::vector< std::vector<Point> > r_table;
- Mat hist;
+ std::vector< std::vector<Point> > r_table_;
+ Mat hist_;
};
- CV_INIT_ALGORITHM(GHT_Ballard_Pos, "GeneralizedHough.POSITION",
- obj.info()->addParam(obj, "minDist", obj.minDist, false, 0, 0,
- "Minimum distance between the centers of the detected objects.");
- obj.info()->addParam(obj, "levels", obj.levels, false, 0, 0,
- "R-Table levels.");
- obj.info()->addParam(obj, "votesThreshold", obj.votesThreshold, false, 0, 0,
- "The accumulator threshold for the template centers at the detection stage. The smaller it is, the more false positions may be detected.");
- obj.info()->addParam(obj, "dp", obj.dp, false, 0, 0,
- "Inverse ratio of the accumulator resolution to the image resolution."));
-
- GHT_Ballard_Pos::GHT_Ballard_Pos()
+ GeneralizedHoughBallardImpl::GeneralizedHoughBallardImpl()
{
- levels = 360;
- votesThreshold = 100;
- dp = 1.0;
+ levels_ = 360;
+ votesThreshold_ = 100;
}
- void GHT_Ballard_Pos::releaseImpl()
+ void GeneralizedHoughBallardImpl::processTempl()
{
- GHT_Pos::releaseImpl();
+ CV_Assert( levels_ > 0 );
- releaseVector(r_table);
- hist.release();
- }
+ const double thetaScale = levels_ / 360.0;
- void GHT_Ballard_Pos::processTempl()
- {
- CV_Assert(templEdges.type() == CV_8UC1);
- CV_Assert(templDx.type() == CV_32FC1 && templDx.size() == templSize);
- CV_Assert(templDy.type() == templDx.type() && templDy.size() == templSize);
- CV_Assert(levels > 0);
+ r_table_.resize(levels_ + 1);
+ std::for_each(r_table_.begin(), r_table_.end(), std::mem_fun_ref(&std::vector<Point>::clear));
- const double thetaScale = levels / 360.0;
-
- r_table.resize(levels + 1);
- for_each(r_table.begin(), r_table.end(), mem_fun_ref(&std::vector<Point>::clear));
-
- for (int y = 0; y < templSize.height; ++y)
+ for (int y = 0; y < templSize_.height; ++y)
{
- const uchar* edgesRow = templEdges.ptr(y);
- const float* dxRow = templDx.ptr<float>(y);
- const float* dyRow = templDy.ptr<float>(y);
+ const uchar* edgesRow = templEdges_.ptr(y);
+ const float* dxRow = templDx_.ptr<float>(y);
+ const float* dyRow = templDy_.ptr<float>(y);
- for (int x = 0; x < templSize.width; ++x)
+ for (int x = 0; x < templSize_.width; ++x)
{
const Point p(x, y);
{
const float theta = fastAtan2(dyRow[x], dxRow[x]);
const int n = cvRound(theta * thetaScale);
- r_table[n].push_back(p - templCenter);
+ r_table_[n].push_back(p - templCenter_);
}
}
}
}
- void GHT_Ballard_Pos::processImage()
+ void GeneralizedHoughBallardImpl::processImage()
{
calcHist();
findPosInHist();
}
- void GHT_Ballard_Pos::calcHist()
+ void GeneralizedHoughBallardImpl::calcHist()
{
- CV_Assert(imageEdges.type() == CV_8UC1);
- CV_Assert(imageDx.type() == CV_32FC1 && imageDx.size() == imageSize);
- CV_Assert(imageDy.type() == imageDx.type() && imageDy.size() == imageSize);
- CV_Assert(levels > 0 && r_table.size() == static_cast<size_t>(levels + 1));
- CV_Assert(dp > 0.0);
+ CV_Assert( imageEdges_.type() == CV_8UC1 );
+ CV_Assert( imageDx_.type() == CV_32FC1 && imageDx_.size() == imageSize_);
+ CV_Assert( imageDy_.type() == imageDx_.type() && imageDy_.size() == imageSize_);
+ CV_Assert( levels_ > 0 && r_table_.size() == static_cast<size_t>(levels_ + 1) );
+ CV_Assert( dp_ > 0.0 );
- const double thetaScale = levels / 360.0;
- const double idp = 1.0 / dp;
+ const double thetaScale = levels_ / 360.0;
+ const double idp = 1.0 / dp_;
- hist.create(cvCeil(imageSize.height * idp) + 2, cvCeil(imageSize.width * idp) + 2, CV_32SC1);
- hist.setTo(0);
+ hist_.create(cvCeil(imageSize_.height * idp) + 2, cvCeil(imageSize_.width * idp) + 2, CV_32SC1);
+ hist_.setTo(0);
- const int rows = hist.rows - 2;
- const int cols = hist.cols - 2;
+ const int rows = hist_.rows - 2;
+ const int cols = hist_.cols - 2;
- for (int y = 0; y < imageSize.height; ++y)
+ for (int y = 0; y < imageSize_.height; ++y)
{
- const uchar* edgesRow = imageEdges.ptr(y);
- const float* dxRow = imageDx.ptr<float>(y);
- const float* dyRow = imageDy.ptr<float>(y);
+ const uchar* edgesRow = imageEdges_.ptr(y);
+ const float* dxRow = imageDx_.ptr<float>(y);
+ const float* dyRow = imageDy_.ptr<float>(y);
- for (int x = 0; x < imageSize.width; ++x)
+ for (int x = 0; x < imageSize_.width; ++x)
{
const Point p(x, y);
const float theta = fastAtan2(dyRow[x], dxRow[x]);
const int n = cvRound(theta * thetaScale);
- const std::vector<Point>& r_row = r_table[n];
+ const std::vector<Point>& r_row = r_table_[n];
for (size_t j = 0; j < r_row.size(); ++j)
{
c.y = cvRound(c.y * idp);
if (c.x >= 0 && c.x < cols && c.y >= 0 && c.y < rows)
- ++hist.at<int>(c.y + 1, c.x + 1);
+ ++hist_.at<int>(c.y + 1, c.x + 1);
}
}
}
}
}
- void GHT_Ballard_Pos::findPosInHist()
+ void GeneralizedHoughBallardImpl::findPosInHist()
{
- CV_Assert(votesThreshold > 0);
+ CV_Assert( votesThreshold_ > 0 );
- const int histRows = hist.rows - 2;
- const int histCols = hist.cols - 2;
+ const int histRows = hist_.rows - 2;
+ const int histCols = hist_.cols - 2;
for(int y = 0; y < histRows; ++y)
{
- const int* prevRow = hist.ptr<int>(y);
- const int* curRow = hist.ptr<int>(y + 1);
- const int* nextRow = hist.ptr<int>(y + 2);
+ const int* prevRow = hist_.ptr<int>(y);
+ const int* curRow = hist_.ptr<int>(y + 1);
+ const int* nextRow = hist_.ptr<int>(y + 2);
for(int x = 0; x < histCols; ++x)
{
const int votes = curRow[x + 1];
- if (votes > votesThreshold && votes > curRow[x] && votes >= curRow[x + 2] && votes > prevRow[x + 1] && votes >= nextRow[x + 1])
+ if (votes > votesThreshold_ && votes > curRow[x] && votes >= curRow[x + 2] && votes > prevRow[x + 1] && votes >= nextRow[x + 1])
{
- posOutBuf.push_back(Vec4f(static_cast<float>(x * dp), static_cast<float>(y * dp), 1.0f, 0.0f));
- voteOutBuf.push_back(Vec3i(votes, 0, 0));
+ posOutBuf_.push_back(Vec4f(static_cast<float>(x * dp_), static_cast<float>(y * dp_), 1.0f, 0.0f));
+ voteOutBuf_.push_back(Vec3i(votes, 0, 0));
}
}
}
}
+}
- /////////////////////////////////////
- // POSITION & SCALE
-
- class GHT_Ballard_PosScale : public GHT_Ballard_Pos
- {
- public:
- AlgorithmInfo* info() const;
-
- GHT_Ballard_PosScale();
-
- protected:
- void calcHist();
- void findPosInHist();
-
- double minScale;
- double maxScale;
- double scaleStep;
+Ptr<GeneralizedHoughBallard> cv::createGeneralizedHoughBallard()
+{
+ return new GeneralizedHoughBallardImpl;
+}
- class Worker;
- friend class Worker;
- };
+// GeneralizedHoughGuil
- CV_INIT_ALGORITHM(GHT_Ballard_PosScale, "GeneralizedHough.POSITION_SCALE",
- obj.info()->addParam(obj, "minDist", obj.minDist, false, 0, 0,
- "Minimum distance between the centers of the detected objects.");
- obj.info()->addParam(obj, "levels", obj.levels, false, 0, 0,
- "R-Table levels.");
- obj.info()->addParam(obj, "votesThreshold", obj.votesThreshold, false, 0, 0,
- "The accumulator threshold for the template centers at the detection stage. The smaller it is, the more false positions may be detected.");
- obj.info()->addParam(obj, "dp", obj.dp, false, 0, 0,
- "Inverse ratio of the accumulator resolution to the image resolution.");
- obj.info()->addParam(obj, "minScale", obj.minScale, false, 0, 0,
- "Minimal scale to detect.");
- obj.info()->addParam(obj, "maxScale", obj.maxScale, false, 0, 0,
- "Maximal scale to detect.");
- obj.info()->addParam(obj, "scaleStep", obj.scaleStep, false, 0, 0,
- "Scale step."));
-
- GHT_Ballard_PosScale::GHT_Ballard_PosScale()
- {
- minScale = 0.5;
- maxScale = 2.0;
- scaleStep = 0.05;
- }
-
- class GHT_Ballard_PosScale::Worker : public ParallelLoopBody
+namespace
+{
+ class GeneralizedHoughGuilImpl : public GeneralizedHoughGuil, private GeneralizedHoughBase
{
public:
- explicit Worker(GHT_Ballard_PosScale* base_) : base(base_) {}
+ GeneralizedHoughGuilImpl();
- void operator ()(const Range& range) const;
+ void setTemplate(InputArray templ, Point templCenter) { setTemplateImpl(templ, templCenter); }
+ void setTemplate(InputArray edges, InputArray dx, InputArray dy, Point templCenter) { setTemplateImpl(edges, dx, dy, templCenter); }
- private:
- GHT_Ballard_PosScale* base;
- };
+ void detect(InputArray image, OutputArray positions, OutputArray votes) { detectImpl(image, positions, votes); }
+ void detect(InputArray edges, InputArray dx, InputArray dy, OutputArray positions, OutputArray votes) { detectImpl(edges, dx, dy, positions, votes); }
- void GHT_Ballard_PosScale::Worker::operator ()(const Range& range) const
- {
- const double thetaScale = base->levels / 360.0;
- const double idp = 1.0 / base->dp;
+ void setCannyLowThresh(int cannyLowThresh) { cannyLowThresh_ = cannyLowThresh; }
+ int getCannyLowThresh() const { return cannyLowThresh_; }
- for (int s = range.start; s < range.end; ++s)
- {
- const double scale = base->minScale + s * base->scaleStep;
+ void setCannyHighThresh(int cannyHighThresh) { cannyHighThresh_ = cannyHighThresh; }
+ int getCannyHighThresh() const { return cannyHighThresh_; }
- Mat curHist(base->hist.size[1], base->hist.size[2], CV_32SC1, base->hist.ptr(s + 1), base->hist.step[1]);
+ void setMinDist(double minDist) { minDist_ = minDist; }
+ double getMinDist() const { return minDist_; }
- for (int y = 0; y < base->imageSize.height; ++y)
- {
- const uchar* edgesRow = base->imageEdges.ptr(y);
- const float* dxRow = base->imageDx.ptr<float>(y);
- const float* dyRow = base->imageDy.ptr<float>(y);
+ void setDp(double dp) { dp_ = dp; }
+ double getDp() const { return dp_; }
- for (int x = 0; x < base->imageSize.width; ++x)
- {
- const Point2d p(x, y);
+ void setMaxBufferSize(int maxBufferSize) { maxBufferSize_ = maxBufferSize; }
+ int getMaxBufferSize() const { return maxBufferSize_; }
- if (edgesRow[x] && (notNull(dyRow[x]) || notNull(dxRow[x])))
- {
- const float theta = fastAtan2(dyRow[x], dxRow[x]);
- const int n = cvRound(theta * thetaScale);
+ void setXi(double xi) { xi_ = xi; }
+ double getXi() const { return xi_; }
- const std::vector<Point>& r_row = base->r_table[n];
+ void setLevels(int levels) { levels_ = levels; }
+ int getLevels() const { return levels_; }
- for (size_t j = 0; j < r_row.size(); ++j)
- {
- Point2d d = r_row[j];
- Point2d c = p - d * scale;
+ void setAngleEpsilon(double angleEpsilon) { angleEpsilon_ = angleEpsilon; }
+ double getAngleEpsilon() const { return angleEpsilon_; }
- c.x *= idp;
- c.y *= idp;
+ void setMinAngle(double minAngle) { minAngle_ = minAngle; }
+ double getMinAngle() const { return minAngle_; }
- if (c.x >= 0 && c.x < base->hist.size[2] - 2 && c.y >= 0 && c.y < base->hist.size[1] - 2)
- ++curHist.at<int>(cvRound(c.y + 1), cvRound(c.x + 1));
- }
- }
- }
- }
- }
- }
-
- void GHT_Ballard_PosScale::calcHist()
- {
- CV_Assert(imageEdges.type() == CV_8UC1);
- CV_Assert(imageDx.type() == CV_32FC1 && imageDx.size() == imageSize);
- CV_Assert(imageDy.type() == imageDx.type() && imageDy.size() == imageSize);
- CV_Assert(levels > 0 && r_table.size() == static_cast<size_t>(levels + 1));
- CV_Assert(dp > 0.0);
- CV_Assert(minScale > 0.0 && minScale < maxScale);
- CV_Assert(scaleStep > 0.0);
-
- const double idp = 1.0 / dp;
- const int scaleRange = cvCeil((maxScale - minScale) / scaleStep);
-
- const int sizes[] = {scaleRange + 2, cvCeil(imageSize.height * idp) + 2, cvCeil(imageSize.width * idp) + 2};
- hist.create(3, sizes, CV_32SC1);
- hist.setTo(0);
-
- parallel_for_(Range(0, scaleRange), Worker(this));
- }
-
- void GHT_Ballard_PosScale::findPosInHist()
- {
- CV_Assert(votesThreshold > 0);
-
- const int scaleRange = hist.size[0] - 2;
- const int histRows = hist.size[1] - 2;
- const int histCols = hist.size[2] - 2;
-
- for (int s = 0; s < scaleRange; ++s)
- {
- const float scale = static_cast<float>(minScale + s * scaleStep);
-
- const Mat prevHist(histRows + 2, histCols + 2, CV_32SC1, hist.ptr(s), hist.step[1]);
- const Mat curHist(histRows + 2, histCols + 2, CV_32SC1, hist.ptr(s + 1), hist.step[1]);
- const Mat nextHist(histRows + 2, histCols + 2, CV_32SC1, hist.ptr(s + 2), hist.step[1]);
-
- for(int y = 0; y < histRows; ++y)
- {
- const int* prevHistRow = prevHist.ptr<int>(y + 1);
- const int* prevRow = curHist.ptr<int>(y);
- const int* curRow = curHist.ptr<int>(y + 1);
- const int* nextRow = curHist.ptr<int>(y + 2);
- const int* nextHistRow = nextHist.ptr<int>(y + 1);
+ void setMaxAngle(double maxAngle) { maxAngle_ = maxAngle; }
+ double getMaxAngle() const { return maxAngle_; }
- for(int x = 0; x < histCols; ++x)
- {
- const int votes = curRow[x + 1];
-
- if (votes > votesThreshold &&
- votes > curRow[x] &&
- votes >= curRow[x + 2] &&
- votes > prevRow[x + 1] &&
- votes >= nextRow[x + 1] &&
- votes > prevHistRow[x + 1] &&
- votes >= nextHistRow[x + 1])
- {
- posOutBuf.push_back(Vec4f(static_cast<float>(x * dp), static_cast<float>(y * dp), scale, 0.0f));
- voteOutBuf.push_back(Vec3i(votes, votes, 0));
- }
- }
- }
- }
- }
+ void setAngleStep(double angleStep) { angleStep_ = angleStep; }
+ double getAngleStep() const { return angleStep_; }
- /////////////////////////////////////
- // POSITION & ROTATION
+ void setAngleThresh(int angleThresh) { angleThresh_ = angleThresh; }
+ int getAngleThresh() const { return angleThresh_; }
- class GHT_Ballard_PosRotation : public GHT_Ballard_Pos
- {
- public:
- AlgorithmInfo* info() const;
+ void setMinScale(double minScale) { minScale_ = minScale; }
+ double getMinScale() const { return minScale_; }
- GHT_Ballard_PosRotation();
+ void setMaxScale(double maxScale) { maxScale_ = maxScale; }
+ double getMaxScale() const { return maxScale_; }
- protected:
- void calcHist();
- void findPosInHist();
+ void setScaleStep(double scaleStep) { scaleStep_ = scaleStep; }
+ double getScaleStep() const { return scaleStep_; }
- double minAngle;
- double maxAngle;
- double angleStep;
+ void setScaleThresh(int scaleThresh) { scaleThresh_ = scaleThresh; }
+ int getScaleThresh() const { return scaleThresh_; }
- class Worker;
- friend class Worker;
- };
-
- CV_INIT_ALGORITHM(GHT_Ballard_PosRotation, "GeneralizedHough.POSITION_ROTATION",
- obj.info()->addParam(obj, "minDist", obj.minDist, false, 0, 0,
- "Minimum distance between the centers of the detected objects.");
- obj.info()->addParam(obj, "levels", obj.levels, false, 0, 0,
- "R-Table levels.");
- obj.info()->addParam(obj, "votesThreshold", obj.votesThreshold, false, 0, 0,
- "The accumulator threshold for the template centers at the detection stage. The smaller it is, the more false positions may be detected.");
- obj.info()->addParam(obj, "dp", obj.dp, false, 0, 0,
- "Inverse ratio of the accumulator resolution to the image resolution.");
- obj.info()->addParam(obj, "minAngle", obj.minAngle, false, 0, 0,
- "Minimal rotation angle to detect in degrees.");
- obj.info()->addParam(obj, "maxAngle", obj.maxAngle, false, 0, 0,
- "Maximal rotation angle to detect in degrees.");
- obj.info()->addParam(obj, "angleStep", obj.angleStep, false, 0, 0,
- "Angle step in degrees."));
-
- GHT_Ballard_PosRotation::GHT_Ballard_PosRotation()
- {
- minAngle = 0.0;
- maxAngle = 360.0;
- angleStep = 1.0;
- }
-
- class GHT_Ballard_PosRotation::Worker : public ParallelLoopBody
- {
- public:
- explicit Worker(GHT_Ballard_PosRotation* base_) : base(base_) {}
-
- void operator ()(const Range& range) const;
+ void setPosThresh(int posThresh) { posThresh_ = posThresh; }
+ int getPosThresh() const { return posThresh_; }
private:
- GHT_Ballard_PosRotation* base;
- };
-
- void GHT_Ballard_PosRotation::Worker::operator ()(const Range& range) const
- {
- const double thetaScale = base->levels / 360.0;
- const double idp = 1.0 / base->dp;
-
- for (int a = range.start; a < range.end; ++a)
- {
- const double angle = base->minAngle + a * base->angleStep;
-
- const double sinA = ::sin(toRad(angle));
- const double cosA = ::cos(toRad(angle));
-
- Mat curHist(base->hist.size[1], base->hist.size[2], CV_32SC1, base->hist.ptr(a + 1), base->hist.step[1]);
-
- for (int y = 0; y < base->imageSize.height; ++y)
- {
- const uchar* edgesRow = base->imageEdges.ptr(y);
- const float* dxRow = base->imageDx.ptr<float>(y);
- const float* dyRow = base->imageDy.ptr<float>(y);
-
- for (int x = 0; x < base->imageSize.width; ++x)
- {
- const Point2d p(x, y);
-
- if (edgesRow[x] && (notNull(dyRow[x]) || notNull(dxRow[x])))
- {
- double theta = fastAtan2(dyRow[x], dxRow[x]) - angle;
- if (theta < 0)
- theta += 360.0;
- const int n = cvRound(theta * thetaScale);
-
- const std::vector<Point>& r_row = base->r_table[n];
-
- for (size_t j = 0; j < r_row.size(); ++j)
- {
- Point2d d = r_row[j];
- Point2d c = p - Point2d(d.x * cosA - d.y * sinA, d.x * sinA + d.y * cosA);
-
- c.x *= idp;
- c.y *= idp;
-
- if (c.x >= 0 && c.x < base->hist.size[2] - 2 && c.y >= 0 && c.y < base->hist.size[1] - 2)
- ++curHist.at<int>(cvRound(c.y + 1), cvRound(c.x + 1));
- }
- }
- }
- }
- }
- }
-
- void GHT_Ballard_PosRotation::calcHist()
- {
- CV_Assert(imageEdges.type() == CV_8UC1);
- CV_Assert(imageDx.type() == CV_32FC1 && imageDx.size() == imageSize);
- CV_Assert(imageDy.type() == imageDx.type() && imageDy.size() == imageSize);
- CV_Assert(levels > 0 && r_table.size() == static_cast<size_t>(levels + 1));
- CV_Assert(dp > 0.0);
- CV_Assert(minAngle >= 0.0 && minAngle < maxAngle && maxAngle <= 360.0);
- CV_Assert(angleStep > 0.0 && angleStep < 360.0);
-
- const double idp = 1.0 / dp;
- const int angleRange = cvCeil((maxAngle - minAngle) / angleStep);
-
- const int sizes[] = {angleRange + 2, cvCeil(imageSize.height * idp) + 2, cvCeil(imageSize.width * idp) + 2};
- hist.create(3, sizes, CV_32SC1);
- hist.setTo(0);
-
- parallel_for_(Range(0, angleRange), Worker(this));
- }
-
- void GHT_Ballard_PosRotation::findPosInHist()
- {
- CV_Assert(votesThreshold > 0);
-
- const int angleRange = hist.size[0] - 2;
- const int histRows = hist.size[1] - 2;
- const int histCols = hist.size[2] - 2;
-
- for (int a = 0; a < angleRange; ++a)
- {
- const float angle = static_cast<float>(minAngle + a * angleStep);
-
- const Mat prevHist(histRows + 2, histCols + 2, CV_32SC1, hist.ptr(a), hist.step[1]);
- const Mat curHist(histRows + 2, histCols + 2, CV_32SC1, hist.ptr(a + 1), hist.step[1]);
- const Mat nextHist(histRows + 2, histCols + 2, CV_32SC1, hist.ptr(a + 2), hist.step[1]);
-
- for(int y = 0; y < histRows; ++y)
- {
- const int* prevHistRow = prevHist.ptr<int>(y + 1);
- const int* prevRow = curHist.ptr<int>(y);
- const int* curRow = curHist.ptr<int>(y + 1);
- const int* nextRow = curHist.ptr<int>(y + 2);
- const int* nextHistRow = nextHist.ptr<int>(y + 1);
-
- for(int x = 0; x < histCols; ++x)
- {
- const int votes = curRow[x + 1];
-
- if (votes > votesThreshold &&
- votes > curRow[x] &&
- votes >= curRow[x + 2] &&
- votes > prevRow[x + 1] &&
- votes >= nextRow[x + 1] &&
- votes > prevHistRow[x + 1] &&
- votes >= nextHistRow[x + 1])
- {
- posOutBuf.push_back(Vec4f(static_cast<float>(x * dp), static_cast<float>(y * dp), 1.0f, angle));
- voteOutBuf.push_back(Vec3i(votes, 0, votes));
- }
- }
- }
- }
- }
-
- /////////////////////////////////////////
- // POSITION & SCALE & ROTATION
-
- double clampAngle(double a)
- {
- double res = a;
-
- while (res > 360.0)
- res -= 360.0;
- while (res < 0)
- res += 360.0;
-
- return res;
- }
-
- bool angleEq(double a, double b, double eps = 1.0)
- {
- return (fabs(clampAngle(a - b)) <= eps);
- }
+ void processTempl();
+ void processImage();
- class GHT_Guil_Full : public GHT_Pos
- {
- public:
- AlgorithmInfo* info() const;
+ int maxBufferSize_;
+ double xi_;
+ int levels_;
+ double angleEpsilon_;
- GHT_Guil_Full();
+ double minAngle_;
+ double maxAngle_;
+ double angleStep_;
+ int angleThresh_;
- protected:
- void releaseImpl();
+ double minScale_;
+ double maxScale_;
+ double scaleStep_;
+ int scaleThresh_;
- void processTempl();
- void processImage();
+ int posThresh_;
struct ContourPoint
{
void calcScale(double angle);
void calcPosition(double angle, int angleVotes, double scale, int scaleVotes);
- int maxSize;
- double xi;
- int levels;
- double angleEpsilon;
+ std::vector< std::vector<Feature> > templFeatures_;
+ std::vector< std::vector<Feature> > imageFeatures_;
- double minAngle;
- double maxAngle;
- double angleStep;
- int angleThresh;
-
- double minScale;
- double maxScale;
- double scaleStep;
- int scaleThresh;
+ std::vector< std::pair<double, int> > angles_;
+ std::vector< std::pair<double, int> > scales_;
+ };
- double dp;
- int posThresh;
+ double clampAngle(double a)
+ {
+ double res = a;
- std::vector< std::vector<Feature> > templFeatures;
- std::vector< std::vector<Feature> > imageFeatures;
+ while (res > 360.0)
+ res -= 360.0;
+ while (res < 0)
+ res += 360.0;
- std::vector< std::pair<double, int> > angles;
- std::vector< std::pair<double, int> > scales;
- };
+ return res;
+ }
- CV_INIT_ALGORITHM(GHT_Guil_Full, "GeneralizedHough.POSITION_SCALE_ROTATION",
- obj.info()->addParam(obj, "minDist", obj.minDist, false, 0, 0,
- "Minimum distance between the centers of the detected objects.");
- obj.info()->addParam(obj, "maxSize", obj.maxSize, false, 0, 0,
- "Maximal size of inner buffers.");
- obj.info()->addParam(obj, "xi", obj.xi, false, 0, 0,
- "Angle difference in degrees between two points in feature.");
- obj.info()->addParam(obj, "levels", obj.levels, false, 0, 0,
- "Feature table levels.");
- obj.info()->addParam(obj, "angleEpsilon", obj.angleEpsilon, false, 0, 0,
- "Maximal difference between angles that treated as equal.");
- obj.info()->addParam(obj, "minAngle", obj.minAngle, false, 0, 0,
- "Minimal rotation angle to detect in degrees.");
- obj.info()->addParam(obj, "maxAngle", obj.maxAngle, false, 0, 0,
- "Maximal rotation angle to detect in degrees.");
- obj.info()->addParam(obj, "angleStep", obj.angleStep, false, 0, 0,
- "Angle step in degrees.");
- obj.info()->addParam(obj, "angleThresh", obj.angleThresh, false, 0, 0,
- "Angle threshold.");
- obj.info()->addParam(obj, "minScale", obj.minScale, false, 0, 0,
- "Minimal scale to detect.");
- obj.info()->addParam(obj, "maxScale", obj.maxScale, false, 0, 0,
- "Maximal scale to detect.");
- obj.info()->addParam(obj, "scaleStep", obj.scaleStep, false, 0, 0,
- "Scale step.");
- obj.info()->addParam(obj, "scaleThresh", obj.scaleThresh, false, 0, 0,
- "Scale threshold.");
- obj.info()->addParam(obj, "dp", obj.dp, false, 0, 0,
- "Inverse ratio of the accumulator resolution to the image resolution.");
- obj.info()->addParam(obj, "posThresh", obj.posThresh, false, 0, 0,
- "Position threshold."));
-
- GHT_Guil_Full::GHT_Guil_Full()
+ bool angleEq(double a, double b, double eps = 1.0)
{
- maxSize = 1000;
- xi = 90.0;
- levels = 360;
- angleEpsilon = 1.0;
-
- minAngle = 0.0;
- maxAngle = 360.0;
- angleStep = 1.0;
- angleThresh = 15000;
-
- minScale = 0.5;
- maxScale = 2.0;
- scaleStep = 0.05;
- scaleThresh = 1000;
-
- dp = 1.0;
- posThresh = 100;
+ return (fabs(clampAngle(a - b)) <= eps);
}
- void GHT_Guil_Full::releaseImpl()
+ GeneralizedHoughGuilImpl::GeneralizedHoughGuilImpl()
{
- GHT_Pos::releaseImpl();
+ maxBufferSize_ = 1000;
+ xi_ = 90.0;
+ levels_ = 360;
+ angleEpsilon_ = 1.0;
- releaseVector(templFeatures);
- releaseVector(imageFeatures);
+ minAngle_ = 0.0;
+ maxAngle_ = 360.0;
+ angleStep_ = 1.0;
+ angleThresh_ = 15000;
- releaseVector(angles);
- releaseVector(scales);
+ minScale_ = 0.5;
+ maxScale_ = 2.0;
+ scaleStep_ = 0.05;
+ scaleThresh_ = 1000;
+
+ posThresh_ = 100;
}
- void GHT_Guil_Full::processTempl()
+ void GeneralizedHoughGuilImpl::processTempl()
{
- buildFeatureList(templEdges, templDx, templDy, templFeatures, templCenter);
+ buildFeatureList(templEdges_, templDx_, templDy_, templFeatures_, templCenter_);
}
- void GHT_Guil_Full::processImage()
+ void GeneralizedHoughGuilImpl::processImage()
{
- buildFeatureList(imageEdges, imageDx, imageDy, imageFeatures);
+ buildFeatureList(imageEdges_, imageDx_, imageDy_, imageFeatures_);
calcOrientation();
- for (size_t i = 0; i < angles.size(); ++i)
+ for (size_t i = 0; i < angles_.size(); ++i)
{
- const double angle = angles[i].first;
- const int angleVotes = angles[i].second;
+ const double angle = angles_[i].first;
+ const int angleVotes = angles_[i].second;
calcScale(angle);
- for (size_t j = 0; j < scales.size(); ++j)
+ for (size_t j = 0; j < scales_.size(); ++j)
{
- const double scale = scales[j].first;
- const int scaleVotes = scales[j].second;
+ const double scale = scales_[j].first;
+ const int scaleVotes = scales_[j].second;
calcPosition(angle, angleVotes, scale, scaleVotes);
}
}
}
- void GHT_Guil_Full::buildFeatureList(const Mat& edges, const Mat& dx, const Mat& dy, std::vector< std::vector<Feature> >& features, Point2d center)
+ void GeneralizedHoughGuilImpl::buildFeatureList(const Mat& edges, const Mat& dx, const Mat& dy, std::vector< std::vector<Feature> >& features, Point2d center)
{
- CV_Assert(levels > 0);
+ CV_Assert( levels_ > 0 );
- const double maxDist = sqrt((double) templSize.width * templSize.width + templSize.height * templSize.height) * maxScale;
+ const double maxDist = sqrt((double) templSize_.width * templSize_.width + templSize_.height * templSize_.height) * maxScale_;
- const double alphaScale = levels / 360.0;
+ const double alphaScale = levels_ / 360.0;
std::vector<ContourPoint> points;
getContourPoints(edges, dx, dy, points);
- features.resize(levels + 1);
- for_each(features.begin(), features.end(), mem_fun_ref(&std::vector<Feature>::clear));
- for_each(features.begin(), features.end(), bind2nd(mem_fun_ref(&std::vector<Feature>::reserve), maxSize));
+ features.resize(levels_ + 1);
+ std::for_each(features.begin(), features.end(), std::mem_fun_ref(&std::vector<Feature>::clear));
+ std::for_each(features.begin(), features.end(), std::bind2nd(std::mem_fun_ref(&std::vector<Feature>::reserve), maxBufferSize_));
for (size_t i = 0; i < points.size(); ++i)
{
{
ContourPoint p2 = points[j];
- if (angleEq(p1.theta - p2.theta, xi, angleEpsilon))
+ if (angleEq(p1.theta - p2.theta, xi_, angleEpsilon_))
{
const Point2d d = p1.pos - p2.pos;
const int n = cvRound(f.alpha12 * alphaScale);
- if (features[n].size() < static_cast<size_t>(maxSize))
+ if (features[n].size() < static_cast<size_t>(maxBufferSize_))
features[n].push_back(f);
}
}
}
}
- void GHT_Guil_Full::getContourPoints(const Mat& edges, const Mat& dx, const Mat& dy, std::vector<ContourPoint>& points)
+ void GeneralizedHoughGuilImpl::getContourPoints(const Mat& edges, const Mat& dx, const Mat& dy, std::vector<ContourPoint>& points)
{
- CV_Assert(edges.type() == CV_8UC1);
- CV_Assert(dx.type() == CV_32FC1 && dx.size == edges.size);
- CV_Assert(dy.type() == dx.type() && dy.size == edges.size);
+ CV_Assert( edges.type() == CV_8UC1 );
+ CV_Assert( dx.type() == CV_32FC1 && dx.size == edges.size );
+ CV_Assert( dy.type() == dx.type() && dy.size == edges.size );
points.clear();
points.reserve(edges.size().area());
}
}
- void GHT_Guil_Full::calcOrientation()
+ void GeneralizedHoughGuilImpl::calcOrientation()
{
- CV_Assert(levels > 0);
- CV_Assert(templFeatures.size() == static_cast<size_t>(levels + 1));
- CV_Assert(imageFeatures.size() == templFeatures.size());
- CV_Assert(minAngle >= 0.0 && minAngle < maxAngle && maxAngle <= 360.0);
- CV_Assert(angleStep > 0.0 && angleStep < 360.0);
- CV_Assert(angleThresh > 0);
+ CV_Assert( levels_ > 0 );
+ CV_Assert( templFeatures_.size() == static_cast<size_t>(levels_ + 1) );
+ CV_Assert( imageFeatures_.size() == templFeatures_.size() );
+ CV_Assert( minAngle_ >= 0.0 && minAngle_ < maxAngle_ && maxAngle_ <= 360.0 );
+ CV_Assert( angleStep_ > 0.0 && angleStep_ < 360.0 );
+ CV_Assert( angleThresh_ > 0 );
- const double iAngleStep = 1.0 / angleStep;
- const int angleRange = cvCeil((maxAngle - minAngle) * iAngleStep);
+ const double iAngleStep = 1.0 / angleStep_;
+ const int angleRange = cvCeil((maxAngle_ - minAngle_) * iAngleStep);
std::vector<int> OHist(angleRange + 1, 0);
- for (int i = 0; i <= levels; ++i)
+ for (int i = 0; i <= levels_; ++i)
{
- const std::vector<Feature>& templRow = templFeatures[i];
- const std::vector<Feature>& imageRow = imageFeatures[i];
+ const std::vector<Feature>& templRow = templFeatures_[i];
+ const std::vector<Feature>& imageRow = imageFeatures_[i];
for (size_t j = 0; j < templRow.size(); ++j)
{
Feature imF = imageRow[k];
const double angle = clampAngle(imF.p1.theta - templF.p1.theta);
- if (angle >= minAngle && angle <= maxAngle)
+ if (angle >= minAngle_ && angle <= maxAngle_)
{
- const int n = cvRound((angle - minAngle) * iAngleStep);
+ const int n = cvRound((angle - minAngle_) * iAngleStep);
++OHist[n];
}
}
}
}
- angles.clear();
+ angles_.clear();
for (int n = 0; n < angleRange; ++n)
{
- if (OHist[n] >= angleThresh)
+ if (OHist[n] >= angleThresh_)
{
- const double angle = minAngle + n * angleStep;
- angles.push_back(std::make_pair(angle, OHist[n]));
+ const double angle = minAngle_ + n * angleStep_;
+ angles_.push_back(std::make_pair(angle, OHist[n]));
}
}
}
- void GHT_Guil_Full::calcScale(double angle)
+ void GeneralizedHoughGuilImpl::calcScale(double angle)
{
- CV_Assert(levels > 0);
- CV_Assert(templFeatures.size() == static_cast<size_t>(levels + 1));
- CV_Assert(imageFeatures.size() == templFeatures.size());
- CV_Assert(minScale > 0.0 && minScale < maxScale);
- CV_Assert(scaleStep > 0.0);
- CV_Assert(scaleThresh > 0);
+ CV_Assert( levels_ > 0 );
+ CV_Assert( templFeatures_.size() == static_cast<size_t>(levels_ + 1) );
+ CV_Assert( imageFeatures_.size() == templFeatures_.size() );
+ CV_Assert( minScale_ > 0.0 && minScale_ < maxScale_ );
+ CV_Assert( scaleStep_ > 0.0 );
+ CV_Assert( scaleThresh_ > 0 );
- const double iScaleStep = 1.0 / scaleStep;
- const int scaleRange = cvCeil((maxScale - minScale) * iScaleStep);
+ const double iScaleStep = 1.0 / scaleStep_;
+ const int scaleRange = cvCeil((maxScale_ - minScale_) * iScaleStep);
std::vector<int> SHist(scaleRange + 1, 0);
- for (int i = 0; i <= levels; ++i)
+ for (int i = 0; i <= levels_; ++i)
{
- const std::vector<Feature>& templRow = templFeatures[i];
- const std::vector<Feature>& imageRow = imageFeatures[i];
+ const std::vector<Feature>& templRow = templFeatures_[i];
+ const std::vector<Feature>& imageRow = imageFeatures_[i];
for (size_t j = 0; j < templRow.size(); ++j)
{
{
Feature imF = imageRow[k];
- if (angleEq(imF.p1.theta, templF.p1.theta, angleEpsilon))
+ if (angleEq(imF.p1.theta, templF.p1.theta, angleEpsilon_))
{
const double scale = imF.d12 / templF.d12;
- if (scale >= minScale && scale <= maxScale)
+ if (scale >= minScale_ && scale <= maxScale_)
{
- const int s = cvRound((scale - minScale) * iScaleStep);
+ const int s = cvRound((scale - minScale_) * iScaleStep);
++SHist[s];
}
}
}
}
- scales.clear();
+ scales_.clear();
for (int s = 0; s < scaleRange; ++s)
{
- if (SHist[s] >= scaleThresh)
+ if (SHist[s] >= scaleThresh_)
{
- const double scale = minScale + s * scaleStep;
- scales.push_back(std::make_pair(scale, SHist[s]));
+ const double scale = minScale_ + s * scaleStep_;
+ scales_.push_back(std::make_pair(scale, SHist[s]));
}
}
}
- void GHT_Guil_Full::calcPosition(double angle, int angleVotes, double scale, int scaleVotes)
+ void GeneralizedHoughGuilImpl::calcPosition(double angle, int angleVotes, double scale, int scaleVotes)
{
- CV_Assert(levels > 0);
- CV_Assert(templFeatures.size() == static_cast<size_t>(levels + 1));
- CV_Assert(imageFeatures.size() == templFeatures.size());
- CV_Assert(dp > 0.0);
- CV_Assert(posThresh > 0);
+ CV_Assert( levels_ > 0 );
+ CV_Assert( templFeatures_.size() == static_cast<size_t>(levels_ + 1) );
+ CV_Assert( imageFeatures_.size() == templFeatures_.size() );
+ CV_Assert( dp_ > 0.0 );
+ CV_Assert( posThresh_ > 0 );
const double sinVal = sin(toRad(angle));
const double cosVal = cos(toRad(angle));
- const double idp = 1.0 / dp;
+ const double idp = 1.0 / dp_;
- const int histRows = cvCeil(imageSize.height * idp);
- const int histCols = cvCeil(imageSize.width * idp);
+ const int histRows = cvCeil(imageSize_.height * idp);
+ const int histCols = cvCeil(imageSize_.width * idp);
Mat DHist(histRows + 2, histCols + 2, CV_32SC1, Scalar::all(0));
- for (int i = 0; i <= levels; ++i)
+ for (int i = 0; i <= levels_; ++i)
{
- const std::vector<Feature>& templRow = templFeatures[i];
- const std::vector<Feature>& imageRow = imageFeatures[i];
+ const std::vector<Feature>& templRow = templFeatures_[i];
+ const std::vector<Feature>& imageRow = imageFeatures_[i];
for (size_t j = 0; j < templRow.size(); ++j)
{
{
Feature imF = imageRow[k];
- if (angleEq(imF.p1.theta, templF.p1.theta, angleEpsilon))
+ if (angleEq(imF.p1.theta, templF.p1.theta, angleEpsilon_))
{
Point2d c1, c2;
{
const int votes = curRow[x + 1];
- if (votes > posThresh && votes > curRow[x] && votes >= curRow[x + 2] && votes > prevRow[x + 1] && votes >= nextRow[x + 1])
+ if (votes > posThresh_ && votes > curRow[x] && votes >= curRow[x + 2] && votes > prevRow[x + 1] && votes >= nextRow[x + 1])
{
- posOutBuf.push_back(Vec4f(static_cast<float>(x * dp), static_cast<float>(y * dp), static_cast<float>(scale), static_cast<float>(angle)));
- voteOutBuf.push_back(Vec3i(votes, scaleVotes, angleVotes));
+ posOutBuf_.push_back(Vec4f(static_cast<float>(x * dp_), static_cast<float>(y * dp_), static_cast<float>(scale), static_cast<float>(angle)));
+ voteOutBuf_.push_back(Vec3i(votes, scaleVotes, angleVotes));
}
}
}
}
}
-Ptr<GeneralizedHough> cv::GeneralizedHough::create(int method)
-{
- switch (method)
- {
- case GHT_POSITION:
- CV_Assert( !GHT_Ballard_Pos_info_auto.name().empty() );
- return new GHT_Ballard_Pos();
-
- case (GHT_POSITION | GHT_SCALE):
- CV_Assert( !GHT_Ballard_PosScale_info_auto.name().empty() );
- return new GHT_Ballard_PosScale();
-
- case (GHT_POSITION | GHT_ROTATION):
- CV_Assert( !GHT_Ballard_PosRotation_info_auto.name().empty() );
- return new GHT_Ballard_PosRotation();
-
- case (GHT_POSITION | GHT_SCALE | GHT_ROTATION):
- CV_Assert( !GHT_Guil_Full_info_auto.name().empty() );
- return new GHT_Guil_Full();
- }
-
- CV_Error(CV_StsBadArg, "Unsupported method");
- return Ptr<GeneralizedHough>();
-}
-
-cv::GeneralizedHough::~GeneralizedHough()
-{
-}
-
-void cv::GeneralizedHough::setTemplate(InputArray _templ, int cannyThreshold, Point templCenter)
-{
- Mat templ = _templ.getMat();
-
- CV_Assert(templ.type() == CV_8UC1);
- CV_Assert(cannyThreshold > 0);
-
- Canny(templ, edges_, cannyThreshold / 2, cannyThreshold);
- Sobel(templ, dx_, CV_32F, 1, 0);
- Sobel(templ, dy_, CV_32F, 0, 1);
-
- if (templCenter == Point(-1, -1))
- templCenter = Point(templ.cols / 2, templ.rows / 2);
-
- setTemplateImpl(edges_, dx_, dy_, templCenter);
-}
-
-void cv::GeneralizedHough::setTemplate(InputArray _edges, InputArray _dx, InputArray _dy, Point templCenter)
-{
- Mat edges = _edges.getMat();
- Mat dx = _dx.getMat();
- Mat dy = _dy.getMat();
-
- if (templCenter == Point(-1, -1))
- templCenter = Point(edges.cols / 2, edges.rows / 2);
-
- setTemplateImpl(edges, dx, dy, templCenter);
-}
-
-void cv::GeneralizedHough::detect(InputArray _image, OutputArray positions, OutputArray votes, int cannyThreshold)
-{
- Mat image = _image.getMat();
-
- CV_Assert(image.type() == CV_8UC1);
- CV_Assert(cannyThreshold > 0);
-
- Canny(image, edges_, cannyThreshold / 2, cannyThreshold);
- Sobel(image, dx_, CV_32F, 1, 0);
- Sobel(image, dy_, CV_32F, 0, 1);
-
- detectImpl(edges_, dx_, dy_, positions, votes);
-}
-
-void cv::GeneralizedHough::detect(InputArray _edges, InputArray _dx, InputArray _dy, OutputArray positions, OutputArray votes)
-{
- cv::Mat edges = _edges.getMat();
- cv::Mat dx = _dx.getMat();
- cv::Mat dy = _dy.getMat();
-
- detectImpl(edges, dx, dy, positions, votes);
-}
-
-void cv::GeneralizedHough::release()
+Ptr<GeneralizedHoughGuil> cv::createGeneralizedHoughGuil()
{
- edges_.release();
- dx_.release();
- dy_.release();
- releaseImpl();
+ return new GeneralizedHoughGuilImpl;
}
#include "opencv2/core.hpp"
#include "opencv2/core/utility.hpp"
#include "opencv2/imgproc.hpp"
-#include "opencv2/gpu.hpp"
+#include "opencv2/gpuimgproc.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/contrib.hpp"
using namespace std;
using namespace cv;
-using cv::gpu::GpuMat;
static Mat loadImage(const string& name)
{
CommandLineParser cmd(argc, argv,
"{ image i | pic1.png | input image }"
"{ template t | templ.png | template image }"
- "{ scale s | | estimate scale }"
- "{ rotation r | | estimate rotation }"
+ "{ full | | estimate scale and rotation }"
"{ gpu | | use gpu version }"
"{ minDist | 100 | minimum distance between the centers of the detected objects }"
"{ levels | 360 | R-Table levels }"
"{ minAngle | 0 | minimal rotation angle to detect in degrees }"
"{ maxAngle | 360 | maximal rotation angle to detect in degrees }"
"{ angleStep | 1 | angle step in degrees }"
- "{ maxSize | 1000 | maximal size of inner buffers }"
+ "{ maxBufSize | 1000 | maximal size of inner buffers }"
"{ help h ? | | print help message }"
);
const string templName = cmd.get<string>("template");
const string imageName = cmd.get<string>("image");
- const bool estimateScale = cmd.has("scale");
- const bool estimateRotation = cmd.has("rotation");
+ const bool full = cmd.has("full");
const bool useGpu = cmd.has("gpu");
const double minDist = cmd.get<double>("minDist");
const int levels = cmd.get<int>("levels");
const double minAngle = cmd.get<double>("minAngle");
const double maxAngle = cmd.get<double>("maxAngle");
const double angleStep = cmd.get<double>("angleStep");
- const int maxSize = cmd.get<int>("maxSize");
+ const int maxBufSize = cmd.get<int>("maxBufSize");
if (!cmd.check())
{
Mat templ = loadImage(templName);
Mat image = loadImage(imageName);
- int method = cv::GeneralizedHough::GHT_POSITION;
- if (estimateScale)
- method += cv::GeneralizedHough::GHT_SCALE;
- if (estimateRotation)
- method += cv::GeneralizedHough::GHT_ROTATION;
+ Ptr<GeneralizedHough> alg;
+
+ if (!full)
+ {
+ Ptr<GeneralizedHoughBallard> ballard = useGpu ? gpu::createGeneralizedHoughBallard() : createGeneralizedHoughBallard();
+
+ ballard->setMinDist(minDist);
+ ballard->setLevels(levels);
+ ballard->setDp(dp);
+ ballard->setMaxBufferSize(maxBufSize);
+ ballard->setVotesThreshold(votesThreshold);
+
+ alg = ballard;
+ }
+ else
+ {
+ Ptr<GeneralizedHoughGuil> guil = useGpu ? gpu::createGeneralizedHoughGuil() : createGeneralizedHoughGuil();
+
+ guil->setMinDist(minDist);
+ guil->setLevels(levels);
+ guil->setDp(dp);
+ guil->setMaxBufferSize(maxBufSize);
+
+ guil->setMinAngle(minAngle);
+ guil->setMaxAngle(maxAngle);
+ guil->setAngleStep(angleStep);
+ guil->setAngleThresh(angleThresh);
+
+ guil->setMinScale(minScale);
+ guil->setMaxScale(maxScale);
+ guil->setScaleStep(scaleStep);
+ guil->setScaleThresh(scaleThresh);
+
+ guil->setPosThresh(posThresh);
+
+ alg = guil;
+ }
vector<Vec4f> position;
- cv::TickMeter tm;
+ TickMeter tm;
if (useGpu)
{
- GpuMat d_templ(templ);
- GpuMat d_image(image);
- GpuMat d_position;
-
- Ptr<gpu::GeneralizedHough> d_hough = gpu::GeneralizedHough::create(method);
- d_hough->set("minDist", minDist);
- d_hough->set("levels", levels);
- d_hough->set("dp", dp);
- d_hough->set("maxSize", maxSize);
- if (estimateScale && estimateRotation)
- {
- d_hough->set("angleThresh", angleThresh);
- d_hough->set("scaleThresh", scaleThresh);
- d_hough->set("posThresh", posThresh);
- }
- else
- {
- d_hough->set("votesThreshold", votesThreshold);
- }
- if (estimateScale)
- {
- d_hough->set("minScale", minScale);
- d_hough->set("maxScale", maxScale);
- d_hough->set("scaleStep", scaleStep);
- }
- if (estimateRotation)
- {
- d_hough->set("minAngle", minAngle);
- d_hough->set("maxAngle", maxAngle);
- d_hough->set("angleStep", angleStep);
- }
-
- d_hough->setTemplate(d_templ);
+ gpu::GpuMat d_templ(templ);
+ gpu::GpuMat d_image(image);
+ gpu::GpuMat d_position;
+
+ alg->setTemplate(d_templ);
tm.start();
- d_hough->detect(d_image, d_position);
- d_hough->downloadResults(d_position, position);
+ alg->detect(d_image, d_position);
+ d_position.download(position);
tm.stop();
}
else
{
- Ptr<GeneralizedHough> hough = GeneralizedHough::create(method);
- hough->set("minDist", minDist);
- hough->set("levels", levels);
- hough->set("dp", dp);
- if (estimateScale && estimateRotation)
- {
- hough->set("angleThresh", angleThresh);
- hough->set("scaleThresh", scaleThresh);
- hough->set("posThresh", posThresh);
- hough->set("maxSize", maxSize);
- }
- else
- {
- hough->set("votesThreshold", votesThreshold);
- }
- if (estimateScale)
- {
- hough->set("minScale", minScale);
- hough->set("maxScale", maxScale);
- hough->set("scaleStep", scaleStep);
- }
- if (estimateRotation)
- {
- hough->set("minAngle", minAngle);
- hough->set("maxAngle", maxAngle);
- hough->set("angleStep", angleStep);
- }
-
- hough->setTemplate(templ);
+ alg->setTemplate(templ);
tm.start();
- hough->detect(image, position);
+ alg->detect(image, position);
tm.stop();
}
projects / platform / upstream / opencv.git / commitdiff