diff --git a/modules/cudaimgproc/src/cuda/hough_segments.cu b/modules/cudaimgproc/src/cuda/hough_segments.cu index ee50e00c6..ca433d30d 100644 --- a/modules/cudaimgproc/src/cuda/hough_segments.cu +++ b/modules/cudaimgproc/src/cuda/hough_segments.cu @@ -117,7 +117,7 @@ namespace cv { namespace cuda { namespace device if (dir.x < 0) dir = -dir; } - else if (pb[1].x == cols - 1 && (pb[0].y >= 0 && pb[0].y < rows)) + else if (pb[1].x == cols - 1 && (pb[1].y >= 0 && pb[1].y < rows)) { p0 = pb[1]; if (dir.x > 0) diff --git a/modules/cudaimgproc/src/generalized_hough.cpp b/modules/cudaimgproc/src/generalized_hough.cpp index d68b76e62..9810bed3a 100644 --- a/modules/cudaimgproc/src/generalized_hough.cpp +++ b/modules/cudaimgproc/src/generalized_hough.cpp @@ -239,8 +239,9 @@ namespace void GeneralizedHoughBase::detectImpl(InputArray image, OutputArray positions, OutputArray votes) { #ifndef HAVE_OPENCV_CUDAFILTERS - (void) templ; - (void) templCenter; + (void) image; + (void) positions; + (void) votes; throw_no_cuda(); #else calcEdges(image, imageEdges_, imageDx_, imageDy_); diff --git a/modules/imgproc/src/hough.cpp b/modules/imgproc/src/hough.cpp index 5d5dde27d..ead62d1d7 100644 --- a/modules/imgproc/src/hough.cpp +++ b/modules/imgproc/src/hough.cpp @@ -42,6 +42,7 @@ //M*/ #include "precomp.hpp" +#include "opencl_kernels_imgproc.hpp" namespace cv { @@ -220,7 +221,7 @@ HoughLinesSDiv( const Mat& img, std::vector lst; CV_Assert( img.type() == CV_8UC1 ); - CV_Assert( linesMax > 0 && rho > 0 && theta > 0 ); + CV_Assert( linesMax > 0 ); threshold = MIN( threshold, 255 ); @@ -652,13 +653,201 @@ HoughLinesProbabilistic( Mat& image, } } +#ifdef HAVE_OPENCL + +#define OCL_MAX_LINES 4096 + +static bool ocl_makePointsList(InputArray _src, OutputArray _pointsList, InputOutputArray _counters) +{ + UMat src = _src.getUMat(); + _pointsList.create(1, (int) src.total(), CV_32SC1); + UMat pointsList = _pointsList.getUMat(); + UMat counters = _counters.getUMat(); + ocl::Device dev = ocl::Device::getDefault(); + + const int pixPerWI = 16; + int workgroup_size = min((int) dev.maxWorkGroupSize(), (src.cols + pixPerWI - 1)/pixPerWI); + ocl::Kernel pointListKernel("make_point_list", ocl::imgproc::hough_lines_oclsrc, + format("-D MAKE_POINTS_LIST -D GROUP_SIZE=%d -D LOCAL_SIZE=%d", workgroup_size, src.cols)); + if (pointListKernel.empty()) + return false; + + pointListKernel.args(ocl::KernelArg::ReadOnly(src), ocl::KernelArg::WriteOnlyNoSize(pointsList), + ocl::KernelArg::PtrWriteOnly(counters)); + + size_t localThreads[2] = { workgroup_size, 1 }; + size_t globalThreads[2] = { workgroup_size, src.rows }; + + return pointListKernel.run(2, globalThreads, localThreads, false); } +static bool ocl_fillAccum(InputArray _pointsList, OutputArray _accum, int total_points, double rho, double theta, int numrho, int numangle) +{ + UMat pointsList = _pointsList.getUMat(); + _accum.create(numangle + 2, numrho + 2, CV_32SC1); + UMat accum = _accum.getUMat(); + ocl::Device dev = ocl::Device::getDefault(); + + float irho = (float) (1 / rho); + int workgroup_size = min((int) dev.maxWorkGroupSize(), total_points); + + ocl::Kernel fillAccumKernel; + size_t localThreads[2]; + size_t globalThreads[2]; + + size_t local_memory_needed = (numrho + 2)*sizeof(int); + if (local_memory_needed > dev.localMemSize()) + { + accum.setTo(Scalar::all(0)); + fillAccumKernel.create("fill_accum_global", ocl::imgproc::hough_lines_oclsrc, + format("-D FILL_ACCUM_GLOBAL")); + if (fillAccumKernel.empty()) + return false; + globalThreads[0] = workgroup_size; globalThreads[1] = numangle; + fillAccumKernel.args(ocl::KernelArg::ReadOnlyNoSize(pointsList), ocl::KernelArg::WriteOnlyNoSize(accum), + total_points, irho, (float) theta, numrho, numangle); + return fillAccumKernel.run(2, globalThreads, NULL, false); + } + else + { + fillAccumKernel.create("fill_accum_local", ocl::imgproc::hough_lines_oclsrc, + format("-D FILL_ACCUM_LOCAL -D LOCAL_SIZE=%d -D BUFFER_SIZE=%d", workgroup_size, numrho + 2)); + if (fillAccumKernel.empty()) + return false; + localThreads[0] = workgroup_size; localThreads[1] = 1; + globalThreads[0] = workgroup_size; globalThreads[1] = numangle+2; + fillAccumKernel.args(ocl::KernelArg::ReadOnlyNoSize(pointsList), ocl::KernelArg::WriteOnlyNoSize(accum), + total_points, irho, (float) theta, numrho, numangle); + return fillAccumKernel.run(2, globalThreads, localThreads, false); + } +} + +static bool ocl_HoughLines(InputArray _src, OutputArray _lines, double rho, double theta, int threshold, + double min_theta, double max_theta) +{ + CV_Assert(_src.type() == CV_8UC1); + + if (max_theta < 0 || max_theta > CV_PI ) { + CV_Error( CV_StsBadArg, "max_theta must fall between 0 and pi" ); + } + if (min_theta < 0 || min_theta > max_theta ) { + CV_Error( CV_StsBadArg, "min_theta must fall between 0 and max_theta" ); + } + if (!(rho > 0 && theta > 0)) { + CV_Error( CV_StsBadArg, "rho and theta must be greater 0" ); + } + + UMat src = _src.getUMat(); + int numangle = cvRound((max_theta - min_theta) / theta); + int numrho = cvRound(((src.cols + src.rows) * 2 + 1) / rho); + + UMat pointsList; + UMat counters(1, 2, CV_32SC1, Scalar::all(0)); + + if (!ocl_makePointsList(src, pointsList, counters)) + return false; + + int total_points = counters.getMat(ACCESS_READ).at(0, 0); + if (total_points <= 0) + { + _lines.assign(UMat(0,0,CV_32FC2)); + return true; + } + + UMat accum; + if (!ocl_fillAccum(pointsList, accum, total_points, rho, theta, numrho, numangle)) + return false; + + const int pixPerWI = 8; + ocl::Kernel getLinesKernel("get_lines", ocl::imgproc::hough_lines_oclsrc, + format("-D GET_LINES")); + if (getLinesKernel.empty()) + return false; + + int linesMax = threshold > 0 ? min(total_points*numangle/threshold, OCL_MAX_LINES) : OCL_MAX_LINES; + UMat lines(linesMax, 1, CV_32FC2); + + getLinesKernel.args(ocl::KernelArg::ReadOnly(accum), ocl::KernelArg::WriteOnlyNoSize(lines), + ocl::KernelArg::PtrWriteOnly(counters), linesMax, threshold, (float) rho, (float) theta); + + size_t globalThreads[2] = { (numrho + pixPerWI - 1)/pixPerWI, numangle }; + if (!getLinesKernel.run(2, globalThreads, NULL, false)) + return false; + + int total_lines = min(counters.getMat(ACCESS_READ).at(0, 1), linesMax); + if (total_lines > 0) + _lines.assign(lines.rowRange(Range(0, total_lines))); + else + _lines.assign(UMat(0,0,CV_32FC2)); + return true; +} + +static bool ocl_HoughLinesP(InputArray _src, OutputArray _lines, double rho, double theta, int threshold, + double minLineLength, double maxGap) +{ + CV_Assert(_src.type() == CV_8UC1); + + if (!(rho > 0 && theta > 0)) { + CV_Error( CV_StsBadArg, "rho and theta must be greater 0" ); + } + + UMat src = _src.getUMat(); + int numangle = cvRound(CV_PI / theta); + int numrho = cvRound(((src.cols + src.rows) * 2 + 1) / rho); + + UMat pointsList; + UMat counters(1, 2, CV_32SC1, Scalar::all(0)); + + if (!ocl_makePointsList(src, pointsList, counters)) + return false; + + int total_points = counters.getMat(ACCESS_READ).at(0, 0); + if (total_points <= 0) + { + _lines.assign(UMat(0,0,CV_32SC4)); + return true; + } + + UMat accum; + if (!ocl_fillAccum(pointsList, accum, total_points, rho, theta, numrho, numangle)) + return false; + + ocl::Kernel getLinesKernel("get_lines", ocl::imgproc::hough_lines_oclsrc, + format("-D GET_LINES_PROBABOLISTIC")); + if (getLinesKernel.empty()) + return false; + + int linesMax = threshold > 0 ? min(total_points*numangle/threshold, OCL_MAX_LINES) : OCL_MAX_LINES; + UMat lines(linesMax, 1, CV_32SC4); + + getLinesKernel.args(ocl::KernelArg::ReadOnly(accum), ocl::KernelArg::ReadOnly(src), + ocl::KernelArg::WriteOnlyNoSize(lines), ocl::KernelArg::PtrWriteOnly(counters), + linesMax, threshold, (int) minLineLength, (int) maxGap, (float) rho, (float) theta); + + size_t globalThreads[2] = { numrho, numangle }; + if (!getLinesKernel.run(2, globalThreads, NULL, false)) + return false; + + int total_lines = min(counters.getMat(ACCESS_READ).at(0, 1), linesMax); + if (total_lines > 0) + _lines.assign(lines.rowRange(Range(0, total_lines))); + else + _lines.assign(UMat(0,0,CV_32SC4)); + + return true; +} + +#endif /* HAVE_OPENCL */ + +} void cv::HoughLines( InputArray _image, OutputArray _lines, double rho, double theta, int threshold, double srn, double stn, double min_theta, double max_theta ) { + CV_OCL_RUN(srn == 0 && stn == 0 && _image.isUMat() && _lines.isUMat(), + ocl_HoughLines(_image, _lines, rho, theta, threshold, min_theta, max_theta)); + Mat image = _image.getMat(); std::vector lines; @@ -675,6 +864,9 @@ void cv::HoughLinesP(InputArray _image, OutputArray _lines, double rho, double theta, int threshold, double minLineLength, double maxGap ) { + CV_OCL_RUN(_image.isUMat() && _lines.isUMat(), + ocl_HoughLinesP(_image, _lines, rho, theta, threshold, minLineLength, maxGap)); + Mat image = _image.getMat(); std::vector lines; HoughLinesProbabilistic(image, (float)rho, (float)theta, threshold, cvRound(minLineLength), cvRound(maxGap), lines, INT_MAX); diff --git a/modules/imgproc/src/opencl/hough_lines.cl b/modules/imgproc/src/opencl/hough_lines.cl new file mode 100644 index 000000000..f318133ec --- /dev/null +++ b/modules/imgproc/src/opencl/hough_lines.cl @@ -0,0 +1,330 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +// Copyright (C) 2014, Itseez, Inc., all rights reserved. +// Third party copyrights are property of their respective owners. + +#define ACCUM(ptr) *((__global int*)(ptr)) + +#ifdef MAKE_POINTS_LIST + +__kernel void make_point_list(__global const uchar * src_ptr, int src_step, int src_offset, int src_rows, int src_cols, + __global uchar * list_ptr, int list_step, int list_offset, __global int* global_offset) +{ + int x = get_local_id(0); + int y = get_group_id(1); + + __local int l_index, l_offset; + __local int l_points[LOCAL_SIZE]; + __global const uchar * src = src_ptr + mad24(y, src_step, src_offset); + __global int * list = (__global int*)(list_ptr + list_offset); + + if (x == 0) + l_index = 0; + + barrier(CLK_LOCAL_MEM_FENCE); + + if (y < src_rows) + { + y <<= 16; + + for (int i=x; i < src_cols; i+=GROUP_SIZE) + { + if (src[i]) + { + int val = y | i; + int index = atomic_inc(&l_index); + l_points[index] = val; + } + } + } + + barrier(CLK_LOCAL_MEM_FENCE); + + if (x == 0) + l_offset = atomic_add(global_offset, l_index); + + barrier(CLK_LOCAL_MEM_FENCE); + + list += l_offset; + for (int i=x; i < l_index; i+=GROUP_SIZE) + { + list[i] = l_points[i]; + } +} + +#elif defined FILL_ACCUM_GLOBAL + +__kernel void fill_accum_global(__global const uchar * list_ptr, int list_step, int list_offset, + __global uchar * accum_ptr, int accum_step, int accum_offset, + int total_points, float irho, float theta, int numrho, int numangle) +{ + int theta_idx = get_global_id(1); + int count_idx = get_global_id(0); + int glob_size = get_global_size(0); + float cosVal; + float sinVal = sincos(theta * ((float)theta_idx), &cosVal); + sinVal *= irho; + cosVal *= irho; + + __global const int * list = (__global const int*)(list_ptr + list_offset); + __global int* accum = (__global int*)(accum_ptr + mad24(theta_idx + 1, accum_step, accum_offset)); + const int shift = (numrho - 1) / 2; + + if (theta_idx < numangle) + { + for (int i = count_idx; i < total_points; i += glob_size) + { + const int val = list[i]; + const int x = (val & 0xFFFF); + const int y = (val >> 16) & 0xFFFF; + + int r = convert_int_rte(mad(x, cosVal, y * sinVal)) + shift; + atomic_inc(accum + r + 1); + } + } +} + +#elif defined FILL_ACCUM_LOCAL + +__kernel void fill_accum_local(__global const uchar * list_ptr, int list_step, int list_offset, + __global uchar * accum_ptr, int accum_step, int accum_offset, + int total_points, float irho, float theta, int numrho, int numangle) +{ + int theta_idx = get_group_id(1); + int count_idx = get_local_id(0); + + if (theta_idx > 0 && theta_idx < numangle + 1) + { + float cosVal; + float sinVal = sincos(theta * (float) (theta_idx-1), &cosVal); + sinVal *= irho; + cosVal *= irho; + + __local int l_accum[BUFFER_SIZE]; + for (int i=count_idx; i> 16; + + int r = convert_int_rte(mad(x, cosVal, y * sinVal)) + shift; + atomic_inc(l_accum + r + 1); + } + + barrier(CLK_LOCAL_MEM_FENCE); + + __global int* accum = (__global int*)(accum_ptr + mad24(theta_idx, accum_step, accum_offset)); + for (int i=count_idx; i threshold && curVote > ACCUM(accum - sizeof(int)) && curVote >= ACCUM(accum + sizeof(int)) && + curVote > ACCUM(accum - accum_step) && curVote >= ACCUM(accum + accum_step)) + { + int index = atomic_inc(lines_index); + + if (index < linesMax) + { + float radius = (x - (accum_cols - 3) * 0.5f) * rho; + float angle = y * theta; + + lines[index] = (float2)(radius, angle); + } + } + + accum += glob_size * (int) sizeof(int); + } + } +} + +#elif GET_LINES_PROBABOLISTIC + +__kernel void get_lines(__global const uchar * accum_ptr, int accum_step, int accum_offset, int accum_rows, int accum_cols, + __global const uchar * src_ptr, int src_step, int src_offset, int src_rows, int src_cols, + __global uchar * lines_ptr, int lines_step, int lines_offset, __global int* lines_index_ptr, + int linesMax, int threshold, int lineLength, int lineGap, float rho, float theta) +{ + int x = get_global_id(0); + int y = get_global_id(1); + + __global uchar* accum = accum_ptr + mad24(y+1, accum_step, mad24(x+1, (int) sizeof(int), accum_offset)); + __global int4* lines = (__global int4*)(lines_ptr + lines_offset); + __global int* lines_index = lines_index_ptr + 1; + + int curVote = ACCUM(accum); + + if (curVote >= threshold && + curVote > ACCUM(accum - accum_step - sizeof(int)) && + curVote > ACCUM(accum - accum_step) && + curVote > ACCUM(accum - accum_step + sizeof(int)) && + curVote > ACCUM(accum - sizeof(int)) && + curVote > ACCUM(accum + sizeof(int)) && + curVote > ACCUM(accum + accum_step - sizeof(int)) && + curVote > ACCUM(accum + accum_step) && + curVote > ACCUM(accum + accum_step + sizeof(int))) + { + const float radius = (x - (accum_cols - 2 - 1) * 0.5f) * rho; + const float angle = y * theta; + + float cosa; + float sina = sincos(angle, &cosa); + + float2 p0 = (float2)(cosa * radius, sina * radius); + float2 dir = (float2)(-sina, cosa); + + float2 pb[4] = { (float2)(-1, -1), (float2)(-1, -1), (float2)(-1, -1), (float2)(-1, -1) }; + float a; + + if (dir.x != 0) + { + a = -p0.x / dir.x; + pb[0].x = 0; + pb[0].y = p0.y + a * dir.y; + + a = (src_cols - 1 - p0.x) / dir.x; + pb[1].x = src_cols - 1; + pb[1].y = p0.y + a * dir.y; + } + + if (dir.y != 0) + { + a = -p0.y / dir.y; + pb[2].x = p0.x + a * dir.x; + pb[2].y = 0; + + a = (src_rows - 1 - p0.y) / dir.y; + pb[3].x = p0.x + a * dir.x; + pb[3].y = src_rows - 1; + } + + if (pb[0].x == 0 && (pb[0].y >= 0 && pb[0].y < src_rows)) + { + p0 = pb[0]; + if (dir.x < 0) + dir = -dir; + } + else if (pb[1].x == src_cols - 1 && (pb[1].y >= 0 && pb[1].y < src_rows)) + { + p0 = pb[1]; + if (dir.x > 0) + dir = -dir; + } + else if (pb[2].y == 0 && (pb[2].x >= 0 && pb[2].x < src_cols)) + { + p0 = pb[2]; + if (dir.y < 0) + dir = -dir; + } + else if (pb[3].y == src_rows - 1 && (pb[3].x >= 0 && pb[3].x < src_cols)) + { + p0 = pb[3]; + if (dir.y > 0) + dir = -dir; + } + + dir /= max(fabs(dir.x), fabs(dir.y)); + + float2 line_end[2]; + int gap; + bool inLine = false; + + if (p0.x < 0 || p0.x >= src_cols || p0.y < 0 || p0.y >= src_rows) + return; + + for (;;) + { + if (*(src_ptr + mad24(p0.y, src_step, p0.x + src_offset))) + { + gap = 0; + + if (!inLine) + { + line_end[0] = p0; + line_end[1] = p0; + inLine = true; + } + else + { + line_end[1] = p0; + } + } + else if (inLine) + { + if (++gap > lineGap) + { + bool good_line = fabs(line_end[1].x - line_end[0].x) >= lineLength || + fabs(line_end[1].y - line_end[0].y) >= lineLength; + + if (good_line) + { + int index = atomic_inc(lines_index); + if (index < linesMax) + lines[index] = (int4)(line_end[0].x, line_end[0].y, line_end[1].x, line_end[1].y); + } + + gap = 0; + inLine = false; + } + } + + p0 = p0 + dir; + if (p0.x < 0 || p0.x >= src_cols || p0.y < 0 || p0.y >= src_rows) + { + if (inLine) + { + bool good_line = fabs(line_end[1].x - line_end[0].x) >= lineLength || + fabs(line_end[1].y - line_end[0].y) >= lineLength; + + if (good_line) + { + int index = atomic_inc(lines_index); + if (index < linesMax) + lines[index] = (int4)(line_end[0].x, line_end[0].y, line_end[1].x, line_end[1].y); + } + + } + break; + } + } + + } +} + +#endif \ No newline at end of file diff --git a/modules/imgproc/test/ocl/test_houghlines.cpp b/modules/imgproc/test/ocl/test_houghlines.cpp new file mode 100644 index 000000000..1f9d802b9 --- /dev/null +++ b/modules/imgproc/test/ocl/test_houghlines.cpp @@ -0,0 +1,184 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +// Copyright (C) 2014, Itseez, Inc., all rights reserved. +// Third party copyrights are property of their respective owners. + +#include "../test_precomp.hpp" +#include "opencv2/ts/ocl_test.hpp" + +#ifdef HAVE_OPENCL + +namespace cvtest { +namespace ocl { + +struct Vec2fComparator +{ + bool operator()(const Vec2f& a, const Vec2f b) const + { + if(a[0] != b[0]) return a[0] < b[0]; + else return a[1] < b[1]; + } +}; + +/////////////////////////////// HoughLines //////////////////////////////////// + +PARAM_TEST_CASE(HoughLines, double, double, int) +{ + double rhoStep, thetaStep; + int threshold; + + Size src_size; + Mat src, dst; + UMat usrc, udst; + + virtual void SetUp() + { + rhoStep = GET_PARAM(0); + thetaStep = GET_PARAM(1); + threshold = GET_PARAM(2); + } + + virtual void generateTestData() + { + src_size = randomSize(500, 1920); + src.create(src_size, CV_8UC1); + src.setTo(Scalar::all(0)); + line(src, Point(0, 100), Point(100, 100), Scalar::all(255), 1); + line(src, Point(0, 200), Point(100, 200), Scalar::all(255), 1); + line(src, Point(0, 400), Point(100, 400), Scalar::all(255), 1); + line(src, Point(100, 0), Point(100, 200), Scalar::all(255), 1); + line(src, Point(200, 0), Point(200, 200), Scalar::all(255), 1); + line(src, Point(400, 0), Point(400, 200), Scalar::all(255), 1); + + src.copyTo(usrc); + } + + virtual void readRealTestData() + { + Mat img = readImage("shared/pic5.png", IMREAD_GRAYSCALE); + Canny(img, src, 100, 150, 3); + + src.copyTo(usrc); + } + + virtual void Near(double eps = 0.) + { + EXPECT_EQ(dst.size(), udst.size()); + + if (dst.total() > 0) + { + Mat lines_cpu, lines_gpu; + dst.copyTo(lines_cpu); + udst.copyTo(lines_gpu); + + std::sort(lines_cpu.begin(), lines_cpu.end(), Vec2fComparator()); + std::sort(lines_gpu.begin(), lines_gpu.end(), Vec2fComparator()); + + EXPECT_LE(TestUtils::checkNorm2(lines_cpu, lines_gpu), eps); + } + } +}; + +OCL_TEST_P(HoughLines, RealImage) +{ + readRealTestData(); + + OCL_OFF(cv::HoughLines(src, dst, rhoStep, thetaStep, threshold)); + OCL_ON(cv::HoughLines(usrc, udst, rhoStep, thetaStep, threshold)); + + Near(1e-5); +} + +OCL_TEST_P(HoughLines, GeneratedImage) +{ + for (int j = 0; j < test_loop_times; j++) + { + generateTestData(); + + OCL_OFF(cv::HoughLines(src, dst, rhoStep, thetaStep, threshold)); + OCL_ON(cv::HoughLines(usrc, udst, rhoStep, thetaStep, threshold)); + + Near(1e-5); + } +} + +/////////////////////////////// HoughLinesP /////////////////////////////////// + +PARAM_TEST_CASE(HoughLinesP, int, double, double) +{ + double rhoStep, thetaStep, minLineLength, maxGap; + int threshold; + + Size src_size; + Mat src, dst; + UMat usrc, udst; + + virtual void SetUp() + { + rhoStep = 1.0; + thetaStep = CV_PI / 180; + threshold = GET_PARAM(0); + minLineLength = GET_PARAM(1); + maxGap = GET_PARAM(2); + } + + virtual void readRealTestData() + { + Mat img = readImage("shared/pic5.png", IMREAD_GRAYSCALE); + Canny(img, src, 50, 200, 3); + + src.copyTo(usrc); + } + + virtual void Near(double eps = 0.) + { + Mat lines_gpu = udst.getMat(ACCESS_READ); + + if (dst.total() > 0 && lines_gpu.total() > 0) + { + Mat result_cpu(src.size(), CV_8UC1, Scalar::all(0)); + Mat result_gpu(src.size(), CV_8UC1, Scalar::all(0)); + + MatConstIterator_ it = dst.begin(), end = dst.end(); + for ( ; it != end; it++) + { + Vec4i p = *it; + line(result_cpu, Point(p[0], p[1]), Point(p[2], p[3]), Scalar(255)); + } + + it = lines_gpu.begin(), end = lines_gpu.end(); + for ( ; it != end; it++) + { + Vec4i p = *it; + line(result_gpu, Point(p[0], p[1]), Point(p[2], p[3]), Scalar(255)); + } + + EXPECT_MAT_SIMILAR(result_cpu, result_gpu, eps); + } + } +}; + + +OCL_TEST_P(HoughLinesP, RealImage) +{ + readRealTestData(); + + OCL_OFF(cv::HoughLinesP(src, dst, rhoStep, thetaStep, threshold, minLineLength, maxGap)); + OCL_ON(cv::HoughLinesP(usrc, udst, rhoStep, thetaStep, threshold, minLineLength, maxGap)); + + Near(0.25); +} + +OCL_INSTANTIATE_TEST_CASE_P(Imgproc, HoughLines, Combine(Values(1, 0.5), // rhoStep + Values(CV_PI / 180.0, CV_PI / 360.0), // thetaStep + Values(80, 150))); // threshold + +OCL_INSTANTIATE_TEST_CASE_P(Imgproc, HoughLinesP, Combine(Values(100, 150), // threshold + Values(50, 100), // minLineLength + Values(5, 10))); // maxLineGap + +} } // namespace cvtest::ocl + +#endif // HAVE_OPENCL \ No newline at end of file