added gpu::HoughLinesP function
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@ -867,6 +867,11 @@ CV_EXPORTS void HoughLines(const GpuMat& src, GpuMat& lines, float rho, float th
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CV_EXPORTS void HoughLines(const GpuMat& src, GpuMat& lines, HoughLinesBuf& buf, float rho, float theta, int threshold, bool doSort = false, int maxLines = 4096);
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CV_EXPORTS void HoughLinesDownload(const GpuMat& d_lines, OutputArray h_lines, OutputArray h_votes = noArray());
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//! HoughLinesP
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//! finds line segments in the black-n-white image using probabalistic Hough transform
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CV_EXPORTS void HoughLinesP(const GpuMat& image, GpuMat& lines, HoughLinesBuf& buf, float rho, float theta, int minLineLength, int maxLineGap, int maxLines = 4096);
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//! HoughCircles
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struct HoughCirclesBuf
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@ -1706,6 +1706,16 @@ PERF_TEST_P(Sz_Depth_Cn, ImgProc_ImagePyramidGetLayer, Combine(GPU_TYPICAL_MAT_S
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}
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namespace {
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struct Vec4iComparator
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{
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bool operator()(const cv::Vec4i& a, const cv::Vec4i b) const
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{
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if (a[0] != b[0]) return a[0] < b[0];
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else if(a[1] != b[1]) return a[1] < b[1];
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else if(a[2] != b[2]) return a[2] < b[2];
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else return a[3] < b[3];
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}
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};
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struct Vec3fComparator
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{
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bool operator()(const cv::Vec3f& a, const cv::Vec3f b) const
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@ -1784,6 +1794,62 @@ PERF_TEST_P(Sz, ImgProc_HoughLines, GPU_TYPICAL_MAT_SIZES)
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}
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}
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//////////////////////////////////////////////////////////////////////
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// HoughLinesP
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DEF_PARAM_TEST_1(Image, std::string);
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PERF_TEST_P(Image, ImgProc_HoughLinesP, testing::Values("cv/shared/pic5.png", "stitching/a1.png"))
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{
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declare.time(30.0);
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std::string fileName = getDataPath(GetParam());
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const float rho = 1.0f;
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const float theta = static_cast<float>(CV_PI / 180.0);
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const int threshold = 100;
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const int minLineLenght = 50;
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const int maxLineGap = 5;
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cv::Mat image = cv::imread(fileName, cv::IMREAD_GRAYSCALE);
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cv::Mat mask;
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cv::Canny(image, mask, 50, 100);
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if (PERF_RUN_GPU())
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{
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cv::gpu::GpuMat d_mask(mask);
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cv::gpu::GpuMat d_lines;
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cv::gpu::HoughLinesBuf d_buf;
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cv::gpu::HoughLinesP(d_mask, d_lines, d_buf, rho, theta, minLineLenght, maxLineGap);
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TEST_CYCLE()
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{
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cv::gpu::HoughLinesP(d_mask, d_lines, d_buf, rho, theta, minLineLenght, maxLineGap);
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}
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cv::Mat h_lines(d_lines);
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cv::Vec4i* begin = h_lines.ptr<cv::Vec4i>();
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cv::Vec4i* end = h_lines.ptr<cv::Vec4i>() + h_lines.cols;
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std::sort(begin, end, Vec4iComparator());
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SANITY_CHECK(h_lines);
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}
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else
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{
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std::vector<cv::Vec4i> lines;
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cv::HoughLinesP(mask, lines, rho, theta, threshold, minLineLenght, maxLineGap);
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TEST_CYCLE()
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{
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cv::HoughLinesP(mask, lines, rho, theta, threshold, minLineLenght, maxLineGap);
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}
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std::sort(lines.begin(), lines.end(), Vec4iComparator());
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SANITY_CHECK(lines);
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}
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}
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//////////////////////////////////////////////////////////////////////
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// HoughCircles
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@ -293,6 +293,201 @@ namespace cv { namespace gpu { namespace device
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return totalCount;
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}
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////////////////////////////////////////////////////////////////////////
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// houghLinesProbabilistic
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texture<uchar, cudaTextureType2D, cudaReadModeElementType> tex_mask(false, cudaFilterModePoint, cudaAddressModeClamp);
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__global__ void houghLinesProbabilistic(const PtrStepSzi accum,
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int4* out, const int maxSize,
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const float rho, const float theta,
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const int lineGap, const int lineLength,
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const int rows, const int cols)
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{
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const int r = blockIdx.x * blockDim.x + threadIdx.x;
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const int n = blockIdx.y * blockDim.y + threadIdx.y;
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if (r >= accum.cols - 2 || n >= accum.rows - 2)
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return;
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const int curVotes = accum(n + 1, r + 1);
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if (curVotes >= lineLength &&
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curVotes > accum(n, r) &&
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curVotes > accum(n, r + 1) &&
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curVotes > accum(n, r + 2) &&
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curVotes > accum(n + 1, r) &&
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curVotes > accum(n + 1, r + 2) &&
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curVotes > accum(n + 2, r) &&
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curVotes > accum(n + 2, r + 1) &&
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curVotes > accum(n + 2, r + 2))
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{
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const float radius = (r - (accum.cols - 2 - 1) * 0.5f) * rho;
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const float angle = n * theta;
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float cosa;
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float sina;
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sincosf(angle, &sina, &cosa);
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float2 p0 = make_float2(cosa * radius, sina * radius);
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float2 dir = make_float2(-sina, cosa);
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float2 pb[4] = {make_float2(-1, -1), make_float2(-1, -1), make_float2(-1, -1), make_float2(-1, -1)};
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float a;
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if (dir.x != 0)
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{
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a = -p0.x / dir.x;
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pb[0].x = 0;
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pb[0].y = p0.y + a * dir.y;
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a = (cols - 1 - p0.x) / dir.x;
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pb[1].x = cols - 1;
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pb[1].y = p0.y + a * dir.y;
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}
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if (dir.y != 0)
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{
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a = -p0.y / dir.y;
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pb[2].x = p0.x + a * dir.x;
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pb[2].y = 0;
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a = (rows - 1 - p0.y) / dir.y;
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pb[3].x = p0.x + a * dir.x;
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pb[3].y = rows - 1;
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}
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if (pb[0].x == 0 && (pb[0].y >= 0 && pb[0].y < rows))
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{
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p0 = pb[0];
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if (dir.x < 0)
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dir = -dir;
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}
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else if (pb[1].x == cols - 1 && (pb[0].y >= 0 && pb[0].y < rows))
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{
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p0 = pb[1];
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if (dir.x > 0)
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dir = -dir;
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}
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else if (pb[2].y == 0 && (pb[2].x >= 0 && pb[2].x < cols))
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{
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p0 = pb[2];
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if (dir.y < 0)
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dir = -dir;
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}
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else if (pb[3].y == rows - 1 && (pb[3].x >= 0 && pb[3].x < cols))
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{
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p0 = pb[3];
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if (dir.y > 0)
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dir = -dir;
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}
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float2 d;
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if (::fabsf(dir.x) > ::fabsf(dir.y))
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{
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d.x = dir.x > 0 ? 1 : -1;
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d.y = dir.y / ::fabsf(dir.x);
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}
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else
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{
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d.x = dir.x / ::fabsf(dir.y);
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d.y = dir.y > 0 ? 1 : -1;
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}
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float2 line_end[2];
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int gap;
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bool inLine = false;
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float2 p1 = p0;
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if (p1.x < 0 || p1.x >= cols || p1.y < 0 || p1.y >= rows)
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return;
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for (;;)
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{
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if (tex2D(tex_mask, p1.x, p1.y))
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{
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gap = 0;
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if (!inLine)
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{
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line_end[0] = p1;
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line_end[1] = p1;
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inLine = true;
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}
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else
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{
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line_end[1] = p1;
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}
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}
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else if (inLine)
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{
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if (++gap > lineGap)
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{
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bool good_line = ::abs(line_end[1].x - line_end[0].x) >= lineLength ||
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::abs(line_end[1].y - line_end[0].y) >= lineLength;
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if (good_line)
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{
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const int ind = ::atomicAdd(&g_counter, 1);
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if (ind < maxSize)
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out[ind] = make_int4(line_end[0].x, line_end[0].y, line_end[1].x, line_end[1].y);
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}
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gap = 0;
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inLine = false;
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}
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}
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p1 = p1 + d;
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if (p1.x < 0 || p1.x >= cols || p1.y < 0 || p1.y >= rows)
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{
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if (inLine)
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{
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bool good_line = ::abs(line_end[1].x - line_end[0].x) >= lineLength ||
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::abs(line_end[1].y - line_end[0].y) >= lineLength;
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if (good_line)
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{
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const int ind = ::atomicAdd(&g_counter, 1);
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if (ind < maxSize)
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out[ind] = make_int4(line_end[0].x, line_end[0].y, line_end[1].x, line_end[1].y);
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}
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}
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break;
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}
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}
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}
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}
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int houghLinesProbabilistic_gpu(PtrStepSzb mask, PtrStepSzi accum, int4* out, int maxSize, float rho, float theta, int lineGap, int lineLength)
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{
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void* counterPtr;
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cudaSafeCall( cudaGetSymbolAddress(&counterPtr, g_counter) );
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cudaSafeCall( cudaMemset(counterPtr, 0, sizeof(int)) );
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const dim3 block(32, 8);
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const dim3 grid(divUp(accum.cols - 2, block.x), divUp(accum.rows - 2, block.y));
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bindTexture(&tex_mask, mask);
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houghLinesProbabilistic<<<grid, block>>>(accum,
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out, maxSize,
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rho, theta,
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lineGap, lineLength,
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mask.rows, mask.cols);
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cudaSafeCall( cudaGetLastError() );
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cudaSafeCall( cudaDeviceSynchronize() );
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int totalCount;
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cudaSafeCall( cudaMemcpy(&totalCount, counterPtr, sizeof(int), cudaMemcpyDeviceToHost) );
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totalCount = ::min(totalCount, maxSize);
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return totalCount;
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}
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////////////////////////////////////////////////////////////////////////
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// circlesAccumCenters
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@ -52,6 +52,8 @@ void cv::gpu::HoughLines(const GpuMat&, GpuMat&, float, float, int, bool, int) {
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void cv::gpu::HoughLines(const GpuMat&, GpuMat&, HoughLinesBuf&, float, float, int, bool, int) { throw_nogpu(); }
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void cv::gpu::HoughLinesDownload(const GpuMat&, OutputArray, OutputArray) { throw_nogpu(); }
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void cv::gpu::HoughLinesP(const GpuMat&, GpuMat&, HoughLinesBuf&, float, float, int, int, int) { throw_nogpu(); }
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void cv::gpu::HoughCircles(const GpuMat&, GpuMat&, int, float, float, int, int, int, int, int) { throw_nogpu(); }
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void cv::gpu::HoughCircles(const GpuMat&, GpuMat&, HoughCirclesBuf&, int, float, float, int, int, int, int, int) { throw_nogpu(); }
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void cv::gpu::HoughCirclesDownload(const GpuMat&, OutputArray) { throw_nogpu(); }
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@ -157,6 +159,57 @@ void cv::gpu::HoughLinesDownload(const GpuMat& d_lines, OutputArray h_lines_, Ou
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}
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}
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//////////////////////////////////////////////////////////
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// HoughLinesP
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namespace cv { namespace gpu { namespace device
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{
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namespace hough
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{
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int houghLinesProbabilistic_gpu(PtrStepSzb mask, PtrStepSzi accum, int4* out, int maxSize, float rho, float theta, int lineGap, int lineLength);
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}
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}}}
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void cv::gpu::HoughLinesP(const GpuMat& src, GpuMat& lines, HoughLinesBuf& buf, float rho, float theta, int minLineLength, int maxLineGap, int maxLines)
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{
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using namespace cv::gpu::device::hough;
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CV_Assert( src.type() == CV_8UC1 );
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CV_Assert( src.cols < std::numeric_limits<unsigned short>::max() );
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CV_Assert( src.rows < std::numeric_limits<unsigned short>::max() );
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ensureSizeIsEnough(1, src.size().area(), CV_32SC1, buf.list);
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unsigned int* srcPoints = buf.list.ptr<unsigned int>();
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const int pointsCount = buildPointList_gpu(src, srcPoints);
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if (pointsCount == 0)
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{
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lines.release();
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return;
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}
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const int numangle = cvRound(CV_PI / theta);
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const int numrho = cvRound(((src.cols + src.rows) * 2 + 1) / rho);
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CV_Assert( numangle > 0 && numrho > 0 );
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ensureSizeIsEnough(numangle + 2, numrho + 2, CV_32SC1, buf.accum);
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buf.accum.setTo(Scalar::all(0));
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DeviceInfo devInfo;
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cudaDeviceProp prop;
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cudaSafeCall(cudaGetDeviceProperties(&prop, devInfo.deviceID()));
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linesAccum_gpu(srcPoints, pointsCount, buf.accum, rho, theta, prop.sharedMemPerBlock, devInfo.supports(FEATURE_SET_COMPUTE_20));
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ensureSizeIsEnough(1, maxLines, CV_32SC4, lines);
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int linesCount = houghLinesProbabilistic_gpu(src, buf.accum, lines.ptr<int4>(), maxLines, rho, theta, maxLineGap, minLineLength);
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if (linesCount > 0)
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lines.cols = linesCount;
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else
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lines.release();
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}
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//////////////////////////////////////////////////////////
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// HoughCircles
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89
samples/gpu/houghlines.cpp
Normal file
89
samples/gpu/houghlines.cpp
Normal file
@ -0,0 +1,89 @@
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#include <cmath>
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#include <iostream>
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#include "opencv2/core/core.hpp"
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#include "opencv2/highgui/highgui.hpp"
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#include "opencv2/imgproc/imgproc.hpp"
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#include "opencv2/gpu/gpu.hpp"
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using namespace std;
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using namespace cv;
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using namespace cv::gpu;
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static void help()
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{
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cout << "This program demonstrates line finding with the Hough transform." << endl;
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cout << "Usage:" << endl;
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cout << "./gpu-example-houghlines <image_name>, Default is pic1.png\n" << endl;
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}
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int main(int argc, const char* argv[])
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{
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const string filename = argc >= 2 ? argv[1] : "pic1.png";
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Mat src = imread(filename, IMREAD_GRAYSCALE);
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if (src.empty())
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{
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help();
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cout << "can not open " << filename << endl;
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return -1;
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}
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Mat mask;
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Canny(src, mask, 100, 200, 3);
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Mat dst_cpu;
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cvtColor(mask, dst_cpu, CV_GRAY2BGR);
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Mat dst_gpu = dst_cpu.clone();
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vector<Vec4i> lines_cpu;
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{
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const int64 start = getTickCount();
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HoughLinesP(mask, lines_cpu, 1, CV_PI / 180, 50, 60, 5);
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const double timeSec = (getTickCount() - start) / getTickFrequency();
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cout << "CPU Time : " << timeSec * 1000 << " ms" << endl;
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cout << "CPU Found : " << lines_cpu.size() << endl;
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}
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for (size_t i = 0; i < lines_cpu.size(); ++i)
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{
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Vec4i l = lines_cpu[i];
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line(dst_cpu, Point(l[0], l[1]), Point(l[2], l[3]), Scalar(0, 0, 255), 3, CV_AA);
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}
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GpuMat d_src(mask);
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GpuMat d_lines;
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HoughLinesBuf d_buf;
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{
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const int64 start = getTickCount();
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gpu::HoughLinesP(d_src, d_lines, d_buf, 1.0f, (float) (CV_PI / 180.0f), 50, 5);
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const double timeSec = (getTickCount() - start) / getTickFrequency();
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cout << "GPU Time : " << timeSec * 1000 << " ms" << endl;
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cout << "GPU Found : " << d_lines.cols << endl;
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}
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vector<Vec4i> lines_gpu;
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if (!d_lines.empty())
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{
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lines_gpu.resize(d_lines.cols);
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Mat h_lines(1, d_lines.cols, CV_32SC4, &lines_gpu[0]);
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d_lines.download(h_lines);
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}
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for (size_t i = 0; i < lines_gpu.size(); ++i)
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{
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Vec4i l = lines_gpu[i];
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line(dst_gpu, Point(l[0], l[1]), Point(l[2], l[3]), Scalar(0, 0, 255), 3, CV_AA);
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}
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imshow("source", src);
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imshow("detected lines [CPU]", dst_cpu);
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imshow("detected lines [GPU]", dst_gpu);
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waitKey();
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return 0;
|
||||
}
|
||||
|
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Reference in New Issue
Block a user