mirror of
https://github.com/opencv/opencv.git
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new implementation of gpu::PyrLKOpticalFlow::dense (1.5 - 2x faster)
This commit is contained in:
Vladislav Vinogradov
parent
af6b2e4e96
commit
2e2bd55729
@ -1754,7 +1754,6 @@ public:
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winSize = Size(21, 21);
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maxLevel = 3;
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iters = 30;
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derivLambda = 0.5;
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useInitialFlow = false;
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minEigThreshold = 1e-4f;
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getMinEigenVals = false;
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@ -1769,7 +1768,6 @@ public:
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Size winSize;
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int maxLevel;
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int iters;
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double derivLambda;
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bool useInitialFlow;
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float minEigThreshold;
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bool getMinEigenVals;
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@ -208,11 +208,18 @@ INSTANTIATE_TEST_CASE_P(Video, PyrLKOpticalFlowSparse, testing::Combine(
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//////////////////////////////////////////////////////
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// PyrLKOpticalFlowDense
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GPU_PERF_TEST_1(PyrLKOpticalFlowDense, cv::gpu::DeviceInfo)
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IMPLEMENT_PARAM_CLASS(Levels, int)
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IMPLEMENT_PARAM_CLASS(Iters, int)
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GPU_PERF_TEST(PyrLKOpticalFlowDense, cv::gpu::DeviceInfo, WinSize, Levels, Iters)
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{
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cv::gpu::DeviceInfo devInfo = GetParam();
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cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
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cv::gpu::setDevice(devInfo.deviceID());
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int winSize = GET_PARAM(1);
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int levels = GET_PARAM(2);
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int iters = GET_PARAM(3);
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cv::Mat frame0_host = readImage("gpu/opticalflow/frame0.png", cv::IMREAD_GRAYSCALE);
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ASSERT_FALSE(frame0_host.empty());
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@ -226,9 +233,13 @@ GPU_PERF_TEST_1(PyrLKOpticalFlowDense, cv::gpu::DeviceInfo)
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cv::gpu::PyrLKOpticalFlow pyrLK;
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pyrLK.winSize = cv::Size(winSize, winSize);
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pyrLK.maxLevel = levels - 1;
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pyrLK.iters = iters;
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pyrLK.dense(frame0, frame1, u, v);
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declare.time(10);
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declare.time(30);
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TEST_CYCLE()
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{
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@ -236,7 +247,11 @@ GPU_PERF_TEST_1(PyrLKOpticalFlowDense, cv::gpu::DeviceInfo)
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}
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}
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INSTANTIATE_TEST_CASE_P(Video, PyrLKOpticalFlowDense, ALL_DEVICES);
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INSTANTIATE_TEST_CASE_P(Video, PyrLKOpticalFlowDense, testing::Combine(
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ALL_DEVICES,
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testing::Values(WinSize(3), WinSize(5), WinSize(7), WinSize(9), WinSize(13), WinSize(17), WinSize(21)),
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testing::Values(Levels(1), Levels(2), Levels(3)),
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testing::Values(Iters(1), Iters(10))));
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//////////////////////////////////////////////////////
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// FarnebackOpticalFlowTest
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@ -40,7 +40,7 @@
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//
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// Copyright (c) 2010, Paul Furgale, Chi Hay Tong
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//
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// The original code was written by Paul Furgale and Chi Hay Tong
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// The original code was written by Paul Furgale and Chi Hay Tong
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// and later optimized and prepared for integration into OpenCV by Itseez.
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//
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//M*/
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@ -50,9 +50,9 @@
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#include "opencv2/gpu/device/functional.hpp"
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#include "opencv2/gpu/device/limits.hpp"
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namespace cv { namespace gpu { namespace device
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namespace cv { namespace gpu { namespace device
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{
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namespace pyrlk
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namespace pyrlk
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{
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__constant__ int c_cn;
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__constant__ float c_minEigThreshold;
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@ -65,7 +65,7 @@ namespace cv { namespace gpu { namespace device
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void loadConstants(int cn, float minEigThreshold, int2 winSize, int iters)
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{
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int2 halfWin = make_int2((winSize.x - 1) / 2, (winSize.y - 1) / 2);
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int2 halfWin = make_int2((winSize.x - 1) / 2, (winSize.y - 1) / 2);
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cudaSafeCall( cudaMemcpyToSymbol(c_cn, &cn, sizeof(int)) );
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cudaSafeCall( cudaMemcpyToSymbol(c_minEigThreshold, &minEigThreshold, sizeof(float)) );
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cudaSafeCall( cudaMemcpyToSymbol(c_winSize_x, &winSize.x, sizeof(int)) );
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@ -87,7 +87,7 @@ namespace cv { namespace gpu { namespace device
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const uchar src_val0 = src(y > 0 ? y - 1 : 1, x);
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const uchar src_val1 = src(y, x);
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const uchar src_val2 = src(y < rows - 1 ? y + 1 : rows - 2, x);
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dx_buf(y, x) = (src_val0 + src_val2) * 3 + src_val1 * 10;
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dy_buf(y, x) = src_val2 - src_val0;
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}
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@ -113,7 +113,7 @@ namespace cv { namespace gpu { namespace device
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}
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}
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void calcSharrDeriv_gpu(DevMem2Db src, DevMem2D_<short> dx_buf, DevMem2D_<short> dy_buf, DevMem2D_<short> dIdx, DevMem2D_<short> dIdy, int cn,
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void calcSharrDeriv_gpu(DevMem2Db src, DevMem2D_<short> dx_buf, DevMem2D_<short> dy_buf, DevMem2D_<short> dIdx, DevMem2D_<short> dIdy, int cn,
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cudaStream_t stream)
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{
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dim3 block(32, 8);
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@ -182,21 +182,21 @@ namespace cv { namespace gpu { namespace device
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__syncthreads();
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#if __CUDA_ARCH__ > 110
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if (tid < 128)
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{
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smem1[tid] = val1 += smem1[tid + 128];
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smem2[tid] = val2 += smem2[tid + 128];
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smem3[tid] = val3 += smem3[tid + 128];
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}
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if (tid < 128)
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{
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smem1[tid] = val1 += smem1[tid + 128];
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smem2[tid] = val2 += smem2[tid + 128];
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smem3[tid] = val3 += smem3[tid + 128];
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}
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__syncthreads();
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#endif
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if (tid < 64)
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{
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smem1[tid] = val1 += smem1[tid + 64];
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smem2[tid] = val2 += smem2[tid + 64];
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if (tid < 64)
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{
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smem1[tid] = val1 += smem1[tid + 64];
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smem2[tid] = val2 += smem2[tid + 64];
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smem3[tid] = val3 += smem3[tid + 64];
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}
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}
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__syncthreads();
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if (tid < 32)
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@ -205,28 +205,28 @@ namespace cv { namespace gpu { namespace device
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volatile float* vmem2 = smem2;
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volatile float* vmem3 = smem3;
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vmem1[tid] = val1 += vmem1[tid + 32];
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vmem2[tid] = val2 += vmem2[tid + 32];
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vmem1[tid] = val1 += vmem1[tid + 32];
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vmem2[tid] = val2 += vmem2[tid + 32];
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vmem3[tid] = val3 += vmem3[tid + 32];
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vmem1[tid] = val1 += vmem1[tid + 16];
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vmem2[tid] = val2 += vmem2[tid + 16];
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vmem1[tid] = val1 += vmem1[tid + 16];
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vmem2[tid] = val2 += vmem2[tid + 16];
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vmem3[tid] = val3 += vmem3[tid + 16];
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vmem1[tid] = val1 += vmem1[tid + 8];
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vmem2[tid] = val2 += vmem2[tid + 8];
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vmem1[tid] = val1 += vmem1[tid + 8];
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vmem2[tid] = val2 += vmem2[tid + 8];
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vmem3[tid] = val3 += vmem3[tid + 8];
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vmem1[tid] = val1 += vmem1[tid + 4];
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vmem2[tid] = val2 += vmem2[tid + 4];
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vmem1[tid] = val1 += vmem1[tid + 4];
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vmem2[tid] = val2 += vmem2[tid + 4];
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vmem3[tid] = val3 += vmem3[tid + 4];
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vmem1[tid] = val1 += vmem1[tid + 2];
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vmem2[tid] = val2 += vmem2[tid + 2];
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vmem1[tid] = val1 += vmem1[tid + 2];
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vmem2[tid] = val2 += vmem2[tid + 2];
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vmem3[tid] = val3 += vmem3[tid + 2];
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vmem1[tid] = val1 += vmem1[tid + 1];
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vmem2[tid] = val2 += vmem2[tid + 1];
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vmem1[tid] = val1 += vmem1[tid + 1];
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vmem2[tid] = val2 += vmem2[tid + 1];
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vmem3[tid] = val3 += vmem3[tid + 1];
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}
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}
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@ -238,19 +238,19 @@ namespace cv { namespace gpu { namespace device
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__syncthreads();
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#if __CUDA_ARCH__ > 110
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if (tid < 128)
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{
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smem1[tid] = val1 += smem1[tid + 128];
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smem2[tid] = val2 += smem2[tid + 128];
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}
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if (tid < 128)
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{
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smem1[tid] = val1 += smem1[tid + 128];
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smem2[tid] = val2 += smem2[tid + 128];
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}
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__syncthreads();
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#endif
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if (tid < 64)
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{
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smem1[tid] = val1 += smem1[tid + 64];
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smem2[tid] = val2 += smem2[tid + 64];
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}
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if (tid < 64)
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{
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smem1[tid] = val1 += smem1[tid + 64];
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smem2[tid] = val2 += smem2[tid + 64];
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}
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__syncthreads();
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if (tid < 32)
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@ -258,23 +258,23 @@ namespace cv { namespace gpu { namespace device
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volatile float* vmem1 = smem1;
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volatile float* vmem2 = smem2;
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vmem1[tid] = val1 += vmem1[tid + 32];
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vmem2[tid] = val2 += vmem2[tid + 32];
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vmem1[tid] = val1 += vmem1[tid + 32];
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vmem2[tid] = val2 += vmem2[tid + 32];
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vmem1[tid] = val1 += vmem1[tid + 16];
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vmem2[tid] = val2 += vmem2[tid + 16];
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vmem1[tid] = val1 += vmem1[tid + 16];
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vmem2[tid] = val2 += vmem2[tid + 16];
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vmem1[tid] = val1 += vmem1[tid + 8];
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vmem2[tid] = val2 += vmem2[tid + 8];
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vmem1[tid] = val1 += vmem1[tid + 8];
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vmem2[tid] = val2 += vmem2[tid + 8];
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vmem1[tid] = val1 += vmem1[tid + 4];
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vmem2[tid] = val2 += vmem2[tid + 4];
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vmem1[tid] = val1 += vmem1[tid + 4];
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vmem2[tid] = val2 += vmem2[tid + 4];
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vmem1[tid] = val1 += vmem1[tid + 2];
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vmem2[tid] = val2 += vmem2[tid + 2];
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vmem1[tid] = val1 += vmem1[tid + 2];
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vmem2[tid] = val2 += vmem2[tid + 2];
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vmem1[tid] = val1 += vmem1[tid + 1];
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vmem2[tid] = val2 += vmem2[tid + 1];
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vmem1[tid] = val1 += vmem1[tid + 1];
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vmem2[tid] = val2 += vmem2[tid + 1];
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}
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}
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@ -284,29 +284,29 @@ namespace cv { namespace gpu { namespace device
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__syncthreads();
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#if __CUDA_ARCH__ > 110
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if (tid < 128)
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{
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smem1[tid] = val1 += smem1[tid + 128];
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}
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if (tid < 128)
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{
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smem1[tid] = val1 += smem1[tid + 128];
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}
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__syncthreads();
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#endif
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if (tid < 64)
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{
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smem1[tid] = val1 += smem1[tid + 64];
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}
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if (tid < 64)
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{
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smem1[tid] = val1 += smem1[tid + 64];
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}
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__syncthreads();
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if (tid < 32)
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{
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volatile float* vmem1 = smem1;
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vmem1[tid] = val1 += vmem1[tid + 32];
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vmem1[tid] = val1 += vmem1[tid + 16];
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vmem1[tid] = val1 += vmem1[tid + 8];
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vmem1[tid] = val1 += vmem1[tid + 32];
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vmem1[tid] = val1 += vmem1[tid + 16];
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vmem1[tid] = val1 += vmem1[tid + 8];
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vmem1[tid] = val1 += vmem1[tid + 4];
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vmem1[tid] = val1 += vmem1[tid + 2];
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vmem1[tid] = val1 += vmem1[tid + 1];
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vmem1[tid] = val1 += vmem1[tid + 2];
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vmem1[tid] = val1 += vmem1[tid + 1];
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}
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}
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@ -341,7 +341,7 @@ namespace cv { namespace gpu { namespace device
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{
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status[blockIdx.x] = 0;
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if (calcErr)
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if (calcErr)
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err[blockIdx.x] = 0;
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}
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@ -349,7 +349,7 @@ namespace cv { namespace gpu { namespace device
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}
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// extract the patch from the first image, compute covariation matrix of derivatives
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float A11 = 0;
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float A12 = 0;
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float A22 = 0;
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@ -359,7 +359,7 @@ namespace cv { namespace gpu { namespace device
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int dIdy_patch[PATCH_Y][PATCH_X];
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for (int y = threadIdx.y, i = 0; y < c_winSize_y; y += blockDim.y, ++i)
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{
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{
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for (int x = threadIdx.x, j = 0; x < c_winSize_x_cn; x += blockDim.x, ++j)
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{
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I_patch[i][j] = linearFilter(I, prevPt, x, y);
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@ -369,7 +369,7 @@ namespace cv { namespace gpu { namespace device
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dIdx_patch[i][j] = ixval;
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dIdy_patch[i][j] = iyval;
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A11 += ixval * ixval;
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A12 += ixval * iyval;
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A22 += iyval * iyval;
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@ -382,7 +382,7 @@ namespace cv { namespace gpu { namespace device
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A11 = smem1[0];
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A12 = smem2[0];
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A22 = smem3[0];
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A11 *= SCALE;
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A12 *= SCALE;
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A22 *= SCALE;
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@ -390,8 +390,8 @@ namespace cv { namespace gpu { namespace device
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{
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float D = A11 * A22 - A12 * A12;
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float minEig = (A22 + A11 - ::sqrtf((A11 - A22) * (A11 - A22) + 4.f * A12 * A12)) / (2 * c_winSize_x * c_winSize_y);
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if (calcErr && GET_MIN_EIGENVALS && tid == 0)
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if (calcErr && GET_MIN_EIGENVALS && tid == 0)
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err[blockIdx.x] = minEig;
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if (minEig < c_minEigThreshold || D < numeric_limits<float>::epsilon())
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@ -403,7 +403,7 @@ namespace cv { namespace gpu { namespace device
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}
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D = 1.f / D;
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A11 *= D;
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A12 *= D;
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A22 *= D;
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@ -411,8 +411,8 @@ namespace cv { namespace gpu { namespace device
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float2 nextPt = nextPts[blockIdx.x];
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nextPt.x *= 2.f;
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nextPt.y *= 2.f;
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nextPt.y *= 2.f;
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nextPt.x -= c_halfWin_x;
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nextPt.y -= c_halfWin_y;
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@ -428,7 +428,7 @@ namespace cv { namespace gpu { namespace device
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float b1 = 0;
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float b2 = 0;
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for (int y = threadIdx.y, i = 0; y < c_winSize_y; y += blockDim.y, ++i)
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{
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for (int x = threadIdx.x, j = 0; x < c_winSize_x_cn; x += blockDim.x, ++j)
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@ -439,7 +439,7 @@ namespace cv { namespace gpu { namespace device
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b2 += diff * dIdy_patch[i][j];
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}
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}
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reduce(b1, b2, smem1, smem2, tid);
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__syncthreads();
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@ -448,11 +448,11 @@ namespace cv { namespace gpu { namespace device
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b1 *= SCALE;
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b2 *= SCALE;
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float2 delta;
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delta.x = A12 * b2 - A22 * b1;
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delta.y = A12 * b1 - A11 * b2;
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nextPt.x += delta.x;
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nextPt.y += delta.y;
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@ -495,7 +495,7 @@ namespace cv { namespace gpu { namespace device
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template <int PATCH_X, int PATCH_Y>
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void lkSparse_caller(DevMem2Db I, DevMem2Db J, DevMem2D_<short> dIdx, DevMem2D_<short> dIdy,
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const float2* prevPts, float2* nextPts, uchar* status, float* err, bool GET_MIN_EIGENVALS, int ptcount,
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const float2* prevPts, float2* nextPts, uchar* status, float* err, bool GET_MIN_EIGENVALS, int ptcount,
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int level, dim3 block, cudaStream_t stream)
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{
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dim3 grid(ptcount);
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@ -532,109 +532,147 @@ namespace cv { namespace gpu { namespace device
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}
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void lkSparse_gpu(DevMem2Db I, DevMem2Db J, DevMem2D_<short> dIdx, DevMem2D_<short> dIdy,
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const float2* prevPts, float2* nextPts, uchar* status, float* err, bool GET_MIN_EIGENVALS, int ptcount,
|
||||
const float2* prevPts, float2* nextPts, uchar* status, float* err, bool GET_MIN_EIGENVALS, int ptcount,
|
||||
int level, dim3 block, dim3 patch, cudaStream_t stream)
|
||||
{
|
||||
typedef void (*func_t)(DevMem2Db I, DevMem2Db J, DevMem2D_<short> dIdx, DevMem2D_<short> dIdy,
|
||||
const float2* prevPts, float2* nextPts, uchar* status, float* err, bool GET_MIN_EIGENVALS, int ptcount,
|
||||
const float2* prevPts, float2* nextPts, uchar* status, float* err, bool GET_MIN_EIGENVALS, int ptcount,
|
||||
int level, dim3 block, cudaStream_t stream);
|
||||
|
||||
static const func_t funcs[5][5] =
|
||||
static const func_t funcs[5][5] =
|
||||
{
|
||||
{lkSparse_caller<1, 1>, lkSparse_caller<2, 1>, lkSparse_caller<3, 1>, lkSparse_caller<4, 1>, lkSparse_caller<5, 1>},
|
||||
{lkSparse_caller<1, 2>, lkSparse_caller<2, 2>, lkSparse_caller<3, 2>, lkSparse_caller<4, 2>, lkSparse_caller<5, 2>},
|
||||
{lkSparse_caller<1, 3>, lkSparse_caller<2, 3>, lkSparse_caller<3, 3>, lkSparse_caller<4, 3>, lkSparse_caller<5, 3>},
|
||||
{lkSparse_caller<1, 4>, lkSparse_caller<2, 4>, lkSparse_caller<3, 4>, lkSparse_caller<4, 4>, lkSparse_caller<5, 4>},
|
||||
{lkSparse_caller<1, 5>, lkSparse_caller<2, 5>, lkSparse_caller<3, 5>, lkSparse_caller<4, 5>, lkSparse_caller<5, 5>}
|
||||
};
|
||||
};
|
||||
|
||||
funcs[patch.y - 1][patch.x - 1](I, J, dIdx, dIdy,
|
||||
prevPts, nextPts, status, err, GET_MIN_EIGENVALS, ptcount,
|
||||
prevPts, nextPts, status, err, GET_MIN_EIGENVALS, ptcount,
|
||||
level, block, stream);
|
||||
}
|
||||
|
||||
template <bool calcErr, bool GET_MIN_EIGENVALS>
|
||||
__global__ void lkDense(const PtrStepb I, const PtrStepb J, const PtrStep<short> dIdx, const PtrStep<short> dIdy,
|
||||
PtrStepf u, PtrStepf v, PtrStepf err, const int rows, const int cols)
|
||||
texture<uchar, cudaTextureType2D, cudaReadModeElementType> tex_I(false, cudaFilterModePoint, cudaAddressModeClamp);
|
||||
texture<float, cudaTextureType2D, cudaReadModeElementType> tex_J(false, cudaFilterModeLinear, cudaAddressModeClamp);
|
||||
|
||||
template <bool calcErr>
|
||||
__global__ void lkDense(PtrStepf u, PtrStepf v, const PtrStepf prevU, const PtrStepf prevV, PtrStepf err, const int rows, const int cols)
|
||||
{
|
||||
const int x = blockIdx.x * blockDim.x + threadIdx.x;
|
||||
const int y = blockIdx.y * blockDim.y + threadIdx.y;
|
||||
extern __shared__ int smem[];
|
||||
|
||||
const int patchWidth = blockDim.x + 2 * c_halfWin_x;
|
||||
const int patchHeight = blockDim.y + 2 * c_halfWin_y;
|
||||
|
||||
int* I_patch = smem;
|
||||
int* dIdx_patch = I_patch + patchWidth * patchHeight;
|
||||
int* dIdy_patch = dIdx_patch + patchWidth * patchHeight;
|
||||
|
||||
const int xBase = blockIdx.x * blockDim.x;
|
||||
const int yBase = blockIdx.y * blockDim.y;
|
||||
|
||||
for (int i = threadIdx.y; i < patchHeight; i += blockDim.y)
|
||||
{
|
||||
for (int j = threadIdx.x; j < patchWidth; j += blockDim.x)
|
||||
{
|
||||
float x = xBase - c_halfWin_x + j + 0.5f;
|
||||
float y = yBase - c_halfWin_y + i + 0.5f;
|
||||
|
||||
I_patch[i * patchWidth + j] = tex2D(tex_I, x, y);
|
||||
|
||||
// Sharr Deriv
|
||||
|
||||
dIdx_patch[i * patchWidth + j] = 3 * tex2D(tex_I, x+1, y-1) + 10 * tex2D(tex_I, x+1, y) + 3 * tex2D(tex_I, x+1, y+1) -
|
||||
(3 * tex2D(tex_I, x-1, y-1) + 10 * tex2D(tex_I, x-1, y) + 3 * tex2D(tex_I, x-1, y+1));
|
||||
|
||||
dIdy_patch[i * patchWidth + j] = 3 * tex2D(tex_I, x-1, y+1) + 10 * tex2D(tex_I, x, y+1) + 3 * tex2D(tex_I, x+1, y+1) -
|
||||
(3 * tex2D(tex_I, x-1, y-1) + 10 * tex2D(tex_I, x, y-1) + 3 * tex2D(tex_I, x+1, y-1));
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
const int x = xBase + threadIdx.x;
|
||||
const int y = yBase + threadIdx.y;
|
||||
|
||||
if (x >= cols || y >= rows)
|
||||
return;
|
||||
|
||||
// extract the patch from the first image, compute covariation matrix of derivatives
|
||||
|
||||
float A11 = 0;
|
||||
float A12 = 0;
|
||||
float A22 = 0;
|
||||
int A11i = 0;
|
||||
int A12i = 0;
|
||||
int A22i = 0;
|
||||
|
||||
for (int i = 0; i < c_winSize_y; ++i)
|
||||
{
|
||||
{
|
||||
for (int j = 0; j < c_winSize_x; ++j)
|
||||
{
|
||||
int ixval = dIdx(y - c_halfWin_y + i, x - c_halfWin_x + j);
|
||||
int iyval = dIdy(y - c_halfWin_y + i, x - c_halfWin_x + j);
|
||||
int dIdx = dIdx_patch[(threadIdx.y + i) * patchWidth + (threadIdx.x + j)];
|
||||
int dIdy = dIdy_patch[(threadIdx.y + i) * patchWidth + (threadIdx.x + j)];
|
||||
|
||||
A11 += ixval * ixval;
|
||||
A12 += ixval * iyval;
|
||||
A22 += iyval * iyval;
|
||||
A11i += dIdx * dIdx;
|
||||
A12i += dIdx * dIdy;
|
||||
A22i += dIdy * dIdy;
|
||||
}
|
||||
}
|
||||
|
||||
A11 *= SCALE;
|
||||
A12 *= SCALE;
|
||||
A22 *= SCALE;
|
||||
|
||||
float A11 = A11i;
|
||||
float A12 = A12i;
|
||||
float A22 = A22i;
|
||||
|
||||
float D = A11 * A22 - A12 * A12;
|
||||
|
||||
if (D < numeric_limits<float>::epsilon())
|
||||
{
|
||||
float D = A11 * A22 - A12 * A12;
|
||||
float minEig = (A22 + A11 - ::sqrtf((A11 - A22) * (A11 - A22) + 4.f * A12 * A12)) / (2 * c_winSize_x * c_winSize_y);
|
||||
if (calcErr)
|
||||
err(y, x) = numeric_limits<float>::max();
|
||||
|
||||
if (calcErr && GET_MIN_EIGENVALS)
|
||||
err(y, x) = minEig;
|
||||
|
||||
if (minEig < c_minEigThreshold || D < numeric_limits<float>::epsilon())
|
||||
return;
|
||||
|
||||
D = 1.f / D;
|
||||
|
||||
A11 *= D;
|
||||
A12 *= D;
|
||||
A22 *= D;
|
||||
return;
|
||||
}
|
||||
|
||||
D = 1.f / D;
|
||||
|
||||
A11 *= D;
|
||||
A12 *= D;
|
||||
A22 *= D;
|
||||
|
||||
float2 nextPt;
|
||||
nextPt.x = x - c_halfWin_x + u(y, x);
|
||||
nextPt.y = y - c_halfWin_y + v(y, x);
|
||||
nextPt.x = x + prevU(y/2, x/2) * 2.0f;
|
||||
nextPt.y = y + prevV(y/2, x/2) * 2.0f;
|
||||
|
||||
for (int k = 0; k < c_iters; ++k)
|
||||
{
|
||||
if (nextPt.x < -c_winSize_x || nextPt.x >= cols || nextPt.y < -c_winSize_y || nextPt.y >= rows)
|
||||
return;
|
||||
if (nextPt.x < 0 || nextPt.x >= cols || nextPt.y < 0 || nextPt.y >= rows)
|
||||
{
|
||||
if (calcErr)
|
||||
err(y, x) = numeric_limits<float>::max();
|
||||
|
||||
return;
|
||||
}
|
||||
|
||||
int b1 = 0;
|
||||
int b2 = 0;
|
||||
|
||||
float b1 = 0;
|
||||
float b2 = 0;
|
||||
|
||||
for (int i = 0; i < c_winSize_y; ++i)
|
||||
{
|
||||
for (int j = 0; j < c_winSize_x; ++j)
|
||||
{
|
||||
int I_val = I(y - c_halfWin_y + i, x - c_halfWin_x + j);
|
||||
int I = I_patch[(threadIdx.y + i) * patchWidth + threadIdx.x + j];
|
||||
int J = tex2D(tex_J, nextPt.x - c_halfWin_x + j + 0.5f, nextPt.y - c_halfWin_y + i + 0.5f);
|
||||
|
||||
int diff = linearFilter(J, nextPt, j, i) - CV_DESCALE(I_val * (1 << W_BITS), W_BITS1 - 5);
|
||||
|
||||
b1 += diff * dIdx(y - c_halfWin_y + i, x - c_halfWin_x + j);
|
||||
b2 += diff * dIdy(y - c_halfWin_y + i, x - c_halfWin_x + j);
|
||||
int diff = (J - I) * 32;
|
||||
|
||||
int dIdx = dIdx_patch[(threadIdx.y + i) * patchWidth + (threadIdx.x + j)];
|
||||
int dIdy = dIdy_patch[(threadIdx.y + i) * patchWidth + (threadIdx.x + j)];
|
||||
|
||||
b1 += diff * dIdx;
|
||||
b2 += diff * dIdy;
|
||||
}
|
||||
}
|
||||
|
||||
b1 *= SCALE;
|
||||
b2 *= SCALE;
|
||||
|
||||
float2 delta;
|
||||
delta.x = A12 * b2 - A22 * b1;
|
||||
delta.y = A12 * b1 - A11 * b2;
|
||||
|
||||
|
||||
nextPt.x += delta.x;
|
||||
nextPt.y += delta.y;
|
||||
|
||||
@ -642,57 +680,50 @@ namespace cv { namespace gpu { namespace device
|
||||
break;
|
||||
}
|
||||
|
||||
u(y, x) = nextPt.x - x + c_halfWin_x;
|
||||
v(y, x) = nextPt.y - y + c_halfWin_y;
|
||||
u(y, x) = nextPt.x - x;
|
||||
v(y, x) = nextPt.y - y;
|
||||
|
||||
if (calcErr && !GET_MIN_EIGENVALS)
|
||||
if (calcErr)
|
||||
{
|
||||
float errval = 0.0f;
|
||||
int errval = 0;
|
||||
|
||||
for (int i = 0; i < c_winSize_y; ++i)
|
||||
{
|
||||
for (int j = 0; j < c_winSize_x; ++j)
|
||||
{
|
||||
int I_val = I(y - c_halfWin_y + i, x - c_halfWin_x + j);
|
||||
int diff = linearFilter(J, nextPt, j, i) - CV_DESCALE(I_val * (1 << W_BITS), W_BITS1 - 5);
|
||||
errval += ::fabsf((float)diff);
|
||||
int I = I_patch[(threadIdx.y + i) * patchWidth + threadIdx.x + j];
|
||||
int J = tex2D(tex_J, nextPt.x - c_halfWin_x + j + 0.5f, nextPt.y - c_halfWin_y + i + 0.5f);
|
||||
|
||||
errval += ::abs(J - I);
|
||||
}
|
||||
}
|
||||
|
||||
errval /= 32 * c_winSize_x_cn * c_winSize_y;
|
||||
|
||||
err(y, x) = errval;
|
||||
err(y, x) = static_cast<float>(errval) / (c_winSize_x * c_winSize_y);
|
||||
}
|
||||
}
|
||||
|
||||
void lkDense_gpu(DevMem2Db I, DevMem2Db J, DevMem2D_<short> dIdx, DevMem2D_<short> dIdy,
|
||||
DevMem2Df u, DevMem2Df v, DevMem2Df* err, bool GET_MIN_EIGENVALS, cudaStream_t stream)
|
||||
void lkDense_gpu(DevMem2Db I, DevMem2Df J, DevMem2Df u, DevMem2Df v, DevMem2Df prevU, DevMem2Df prevV,
|
||||
DevMem2Df err, int2 winSize, cudaStream_t stream)
|
||||
{
|
||||
dim3 block(32, 8);
|
||||
dim3 block(16, 16);
|
||||
dim3 grid(divUp(I.cols, block.x), divUp(I.rows, block.y));
|
||||
|
||||
if (err)
|
||||
bindTexture(&tex_I, I);
|
||||
bindTexture(&tex_J, J);
|
||||
|
||||
int2 halfWin = make_int2((winSize.x - 1) / 2, (winSize.y - 1) / 2);
|
||||
const int patchWidth = block.x + 2 * halfWin.x;
|
||||
const int patchHeight = block.y + 2 * halfWin.y;
|
||||
size_t smem_size = 3 * patchWidth * patchHeight * sizeof(int);
|
||||
|
||||
if (err.data)
|
||||
{
|
||||
if (GET_MIN_EIGENVALS)
|
||||
{
|
||||
cudaSafeCall( cudaFuncSetCacheConfig(lkDense<true, true>, cudaFuncCachePreferL1) );
|
||||
|
||||
lkDense<true, true><<<grid, block, 0, stream>>>(I, J, dIdx, dIdy, u, v, *err, I.rows, I.cols);
|
||||
cudaSafeCall( cudaGetLastError() );
|
||||
}
|
||||
else
|
||||
{
|
||||
cudaSafeCall( cudaFuncSetCacheConfig(lkDense<true, false>, cudaFuncCachePreferL1) );
|
||||
|
||||
lkDense<true, false><<<grid, block, 0, stream>>>(I, J, dIdx, dIdy, u, v, *err, I.rows, I.cols);
|
||||
cudaSafeCall( cudaGetLastError() );
|
||||
}
|
||||
lkDense<true><<<grid, block, smem_size, stream>>>(u, v, prevU, prevV, err, I.rows, I.cols);
|
||||
cudaSafeCall( cudaGetLastError() );
|
||||
}
|
||||
else
|
||||
{
|
||||
cudaSafeCall( cudaFuncSetCacheConfig(lkDense<false, false>, cudaFuncCachePreferL1) );
|
||||
|
||||
lkDense<false, false><<<grid, block, 0, stream>>>(I, J, dIdx, dIdy, u, v, PtrStepf(), I.rows, I.cols);
|
||||
lkDense<false><<<grid, block, smem_size, stream>>>(u, v, prevU, prevV, PtrStepf(), I.rows, I.cols);
|
||||
cudaSafeCall( cudaGetLastError() );
|
||||
}
|
||||
|
||||
|
||||
@ -66,8 +66,8 @@ namespace cv { namespace gpu { namespace device
|
||||
const float2* prevPts, float2* nextPts, uchar* status, float* err, bool GET_MIN_EIGENVALS, int ptcount,
|
||||
int level, dim3 block, dim3 patch, cudaStream_t stream = 0);
|
||||
|
||||
void lkDense_gpu(DevMem2Db I, DevMem2Db J, DevMem2D_<short> dIdx, DevMem2D_<short> dIdy,
|
||||
DevMem2Df u, DevMem2Df v, DevMem2Df* err, bool GET_MIN_EIGENVALS, cudaStream_t stream = 0);
|
||||
void lkDense_gpu(DevMem2Db I, DevMem2Df J, DevMem2Df u, DevMem2Df v, DevMem2Df prevU, DevMem2Df prevV,
|
||||
DevMem2Df err, int2 winSize, cudaStream_t stream = 0);
|
||||
}
|
||||
}}}
|
||||
|
||||
@ -160,16 +160,11 @@ void cv::gpu::PyrLKOpticalFlow::sparse(const GpuMat& prevImg, const GpuMat& next
|
||||
return;
|
||||
}
|
||||
|
||||
derivLambda = std::min(std::max(derivLambda, 0.0), 1.0);
|
||||
|
||||
iters = std::min(std::max(iters, 0), 100);
|
||||
|
||||
const int cn = prevImg.channels();
|
||||
|
||||
dim3 block, patch;
|
||||
calcPatchSize(winSize, cn, block, patch, isDeviceArch11_);
|
||||
calcPatchSize(winSize, cn, block, patch, isDeviceArch11_);
|
||||
|
||||
CV_Assert(derivLambda >= 0);
|
||||
CV_Assert(maxLevel >= 0 && winSize.width > 2 && winSize.height > 2);
|
||||
CV_Assert(prevImg.size() == nextImg.size() && prevImg.type() == nextImg.type());
|
||||
CV_Assert(patch.x > 0 && patch.x < 6 && patch.y > 0 && patch.y < 6);
|
||||
@ -227,80 +222,53 @@ void cv::gpu::PyrLKOpticalFlow::dense(const GpuMat& prevImg, const GpuMat& nextI
|
||||
{
|
||||
using namespace cv::gpu::device::pyrlk;
|
||||
|
||||
derivLambda = std::min(std::max(derivLambda, 0.0), 1.0);
|
||||
|
||||
iters = std::min(std::max(iters, 0), 100);
|
||||
|
||||
CV_Assert(prevImg.type() == CV_8UC1);
|
||||
CV_Assert(prevImg.size() == nextImg.size() && prevImg.type() == nextImg.type());
|
||||
CV_Assert(derivLambda >= 0);
|
||||
CV_Assert(maxLevel >= 0 && winSize.width > 2 && winSize.height > 2);
|
||||
|
||||
if (useInitialFlow)
|
||||
{
|
||||
CV_Assert(u.size() == prevImg.size() && u.type() == CV_32FC1);
|
||||
CV_Assert(v.size() == prevImg.size() && v.type() == CV_32FC1);
|
||||
}
|
||||
else
|
||||
{
|
||||
u.create(prevImg.size(), CV_32FC1);
|
||||
v.create(prevImg.size(), CV_32FC1);
|
||||
|
||||
u.setTo(Scalar::all(0));
|
||||
v.setTo(Scalar::all(0));
|
||||
}
|
||||
CV_Assert(maxLevel >= 0);
|
||||
CV_Assert(winSize.width > 2 && winSize.height > 2);
|
||||
|
||||
if (err)
|
||||
err->create(prevImg.size(), CV_32FC1);
|
||||
|
||||
// build the image pyramids.
|
||||
// we pad each level with +/-winSize.{width|height}
|
||||
// pixels to simplify the further patch extraction.
|
||||
|
||||
buildImagePyramid(prevImg, prevPyr_, true);
|
||||
buildImagePyramid(nextImg, nextPyr_, true);
|
||||
buildImagePyramid(u, uPyr_, false);
|
||||
buildImagePyramid(v, vPyr_, false);
|
||||
buildImagePyramid(prevImg, prevPyr_, false);
|
||||
|
||||
// dI/dx ~ Ix, dI/dy ~ Iy
|
||||
nextPyr_.resize(maxLevel + 1);
|
||||
nextImg.convertTo(nextPyr_[0], CV_32F);
|
||||
for (int level = 1; level <= maxLevel; ++level)
|
||||
pyrDown(nextPyr_[level - 1], nextPyr_[level]);
|
||||
|
||||
ensureSizeIsEnough(prevImg.rows + winSize.height * 2, prevImg.cols + winSize.width * 2, CV_16SC1, dx_buf_);
|
||||
ensureSizeIsEnough(prevImg.rows + winSize.height * 2, prevImg.cols + winSize.width * 2, CV_16SC1, dy_buf_);
|
||||
uPyr_.resize(2);
|
||||
vPyr_.resize(2);
|
||||
|
||||
loadConstants(1, minEigThreshold, make_int2(winSize.width, winSize.height), iters);
|
||||
ensureSizeIsEnough(prevImg.size(), CV_32FC1, uPyr_[0]);
|
||||
ensureSizeIsEnough(prevImg.size(), CV_32FC1, vPyr_[0]);
|
||||
ensureSizeIsEnough(prevImg.size(), CV_32FC1, uPyr_[1]);
|
||||
ensureSizeIsEnough(prevImg.size(), CV_32FC1, vPyr_[1]);
|
||||
uPyr_[1].setTo(Scalar::all(0));
|
||||
vPyr_[1].setTo(Scalar::all(0));
|
||||
|
||||
int2 winSize2i = make_int2(winSize.width, winSize.height);
|
||||
loadConstants(1, minEigThreshold, winSize2i, iters);
|
||||
|
||||
DevMem2Df derr = err ? *err : DevMem2Df();
|
||||
|
||||
int idx = 0;
|
||||
|
||||
for (int level = maxLevel; level >= 0; level--)
|
||||
{
|
||||
Size imgSize = prevPyr_[level].size();
|
||||
int idx2 = (idx + 1) & 1;
|
||||
|
||||
GpuMat dxWhole(imgSize.height + winSize.height * 2, imgSize.width + winSize.width * 2, dx_buf_.type(), dx_buf_.data, dx_buf_.step);
|
||||
GpuMat dyWhole(imgSize.height + winSize.height * 2, imgSize.width + winSize.width * 2, dy_buf_.type(), dy_buf_.data, dy_buf_.step);
|
||||
dxWhole.setTo(Scalar::all(0));
|
||||
dyWhole.setTo(Scalar::all(0));
|
||||
GpuMat dIdx = dxWhole(Rect(winSize.width, winSize.height, imgSize.width, imgSize.height));
|
||||
GpuMat dIdy = dyWhole(Rect(winSize.width, winSize.height, imgSize.width, imgSize.height));
|
||||
lkDense_gpu(prevPyr_[level], nextPyr_[level], uPyr_[idx], vPyr_[idx], uPyr_[idx2], vPyr_[idx2],
|
||||
level == 0 ? derr : DevMem2Df(), winSize2i);
|
||||
|
||||
calcSharrDeriv(prevPyr_[level], dIdx, dIdy);
|
||||
|
||||
lkDense_gpu(prevPyr_[level], nextPyr_[level], dIdx, dIdy, uPyr_[level], vPyr_[level],
|
||||
level == 0 && err ? &derr : 0, getMinEigenVals);
|
||||
|
||||
if (level == 0)
|
||||
{
|
||||
uPyr_[0].copyTo(u);
|
||||
vPyr_[0].copyTo(v);
|
||||
}
|
||||
else
|
||||
{
|
||||
resize(uPyr_[level], uPyr_[level - 1], uPyr_[level - 1].size());
|
||||
resize(vPyr_[level], vPyr_[level - 1], vPyr_[level - 1].size());
|
||||
|
||||
multiply(uPyr_[level - 1], Scalar::all(2), uPyr_[level - 1]);
|
||||
multiply(vPyr_[level - 1], Scalar::all(2), vPyr_[level - 1]);
|
||||
}
|
||||
if (level > 0)
|
||||
idx = idx2;
|
||||
}
|
||||
|
||||
uPyr_[idx].copyTo(u);
|
||||
vPyr_[idx].copyTo(v);
|
||||
}
|
||||
|
||||
#endif /* !defined (HAVE_CUDA) */
|
||||
|
||||
@ -159,7 +159,6 @@ int main(int argc, const char* argv[])
|
||||
"{ win_size | win_size | 21 | specify windows size [PyrLK] }"
|
||||
"{ max_level | max_level | 3 | specify max level [PyrLK] }"
|
||||
"{ iters | iters | 30 | specify iterations count [PyrLK] }"
|
||||
"{ deriv_lambda | deriv_lambda | 0.5 | specify deriv lambda [PyrLK] }"
|
||||
"{ points | points | 4000 | specify points count [GoodFeatureToTrack] }"
|
||||
"{ min_dist | min_dist | 0 | specify minimal distance between points [GoodFeatureToTrack] }";
|
||||
|
||||
@ -186,7 +185,6 @@ int main(int argc, const char* argv[])
|
||||
int winSize = cmd.get<int>("win_size");
|
||||
int maxLevel = cmd.get<int>("max_level");
|
||||
int iters = cmd.get<int>("iters");
|
||||
double derivLambda = cmd.get<double>("deriv_lambda");
|
||||
int points = cmd.get<int>("points");
|
||||
double minDist = cmd.get<double>("min_dist");
|
||||
|
||||
@ -235,7 +233,6 @@ int main(int argc, const char* argv[])
|
||||
d_pyrLK.winSize.height = winSize;
|
||||
d_pyrLK.maxLevel = maxLevel;
|
||||
d_pyrLK.iters = iters;
|
||||
d_pyrLK.derivLambda = derivLambda;
|
||||
|
||||
GpuMat d_frame0(frame0);
|
||||
GpuMat d_frame1(frame1);
|
||||
|
||||
Loading…
Reference in New Issue
Block a user