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Merge branch 'thrust_tutorial' of http://github.com/dtmoodie/opencv into thrust_tutorial
Conflicts: samples/cpp/tutorial_code/gpu/gpu-thrust-interop/Thrust_interop.hpp
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@ -23,13 +23,11 @@ The following code will produce an iterator for a GpuMat
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@snippet samples/cpp/tutorial_code/gpu/gpu-thrust-interop/Thrust_interop.hpp begin_itr
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@snippet samples/cpp/tutorial_code/gpu/gpu-thrust-interop/Thrust_interop.hpp end_itr
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Our goal is to have an iterator that will start at the beginning of the matrix, and increment correctly to access continuous matrix elements. This is trivial for a continuous row, but how about for a column
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of a pitched matrix? To do this we need the iterator to be aware of the matrix dimensions and step. This information is embedded in the step_functor.
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Our goal is to have an iterator that will start at the beginning of the matrix, and increment correctly to access continuous matrix elements. This is trivial for a continuous row, but how about for a column of a pitched matrix? To do this we need the iterator to be aware of the matrix dimensions and step. This information is embedded in the step_functor.
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@snippet samples/cpp/tutorial_code/gpu/gpu-thrust-interop/Thrust_interop.hpp step_functor
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The step functor takes in an index value and returns the appropriate
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offset from the beginning of the matrix. The counting iterator simply increments over the range of pixel elements. Combined into the transform_iterator we have an iterator that counts from 0 to M*N and correctly
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increments to account for the pitched memory of a GpuMat. Unfortunately this does not include any memory location information, for that we need a thrust::device_ptr. By combining a device pointer with the
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transform_iterator we can point thrust to the first element of our matrix and have it step accordingly.
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increments to account for the pitched memory of a GpuMat. Unfortunately this does not include any memory location information, for that we need a thrust::device_ptr. By combining a device pointer with the transform_iterator we can point thrust to the first element of our matrix and have it step accordingly.
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Fill a GpuMat with random numbers
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----
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@ -47,13 +45,12 @@ Now we will populate our matrix with values between 0 and 10 with a thrust trans
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Sort a column of a GpuMat in place
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----
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Lets fill matrix elements with random values and an index. Afterwards we will sort the random numbers and the indecies.
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Lets fill matrix elements with random values and an index. Afterwards we will sort the random numbers and the indecies.
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@snippet samples/cpp/tutorial_code/gpu/gpu-thrust-interop/main.cu sort
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Copy values greater than 0 to a new gpu matrix while using streams
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----
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In this example we're going to see how cv::cuda::Streams can be used with thrust. Unfortunately this specific example uses functions that must return
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results to the CPU so it isn't the optimal use of streams.
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In this example we're going to see how cv::cuda::Streams can be used with thrust. Unfortunately this specific example uses functions that must return results to the CPU so it isn't the optimal use of streams.
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@snippet samples/cpp/tutorial_code/gpu/gpu-thrust-interop/main.cu copy_greater
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@ -8,20 +8,19 @@
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//! [prg]
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struct prg
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{
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float a, b;
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float a, b;
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__host__ __device__
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prg(float _a = 0.f, float _b = 1.f) : a(_a), b(_b) {};
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__host__ __device__
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prg(float _a = 0.f, float _b = 1.f) : a(_a), b(_b) {};
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__host__ __device__
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float operator()(const unsigned int n) const
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{
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thrust::default_random_engine rng;
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thrust::uniform_real_distribution<float> dist(a, b);
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rng.discard(n);
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return dist(rng);
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}
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__host__ __device__
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float operator()(const unsigned int n) const
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{
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thrust::default_random_engine rng;
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thrust::uniform_real_distribution<float> dist(a, b);
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rng.discard(n);
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return dist(rng);
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}
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};
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//! [prg]
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@ -29,83 +28,83 @@ struct prg
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//! [pred_greater]
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template<typename T> struct pred_greater
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{
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T value;
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__host__ __device__ pred_greater(T value_) : value(value_){}
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__host__ __device__ bool operator()(const T& val) const
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{
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return val > value;
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}
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T value;
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__host__ __device__ pred_greater(T value_) : value(value_){}
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__host__ __device__ bool operator()(const T& val) const
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{
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return val > value;
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}
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};
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//! [pred_greater]
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int main(void)
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{
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// Generate a 2 channel row matrix with 100 elements. Set the first channel to be the element index, and the second to be a randomly
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// generated value. Sort by the randomly generated value while maintaining index association.
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//! [sort]
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{
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cv::cuda::GpuMat d_data(1, 100, CV_32SC2);
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// Thrust compatible begin and end iterators to channel 1 of this matrix
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auto keyBegin = GpuMatBeginItr<int>(d_data, 1);
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auto keyEnd = GpuMatEndItr<int>(d_data, 1);
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// Thrust compatible begin and end iterators to channel 0 of this matrix
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auto idxBegin = GpuMatBeginItr<int>(d_data, 0);
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auto idxEnd = GpuMatEndItr<int>(d_data, 0);
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// Fill the index channel with a sequence of numbers from 0 to 100
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thrust::sequence(idxBegin, idxEnd);
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// Fill the key channel with random numbers between 0 and 10. A counting iterator is used here to give an integer value for each location as an input to prg::operator()
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thrust::transform(thrust::make_counting_iterator(0), thrust::make_counting_iterator(d_data.cols), keyBegin, prg(0, 10));
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// Sort the key channel and index channel such that the keys and indecies stay together
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thrust::sort_by_key(keyBegin, keyEnd, idxBegin);
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// Generate a 2 channel row matrix with 100 elements. Set the first channel to be the element index, and the second to be a randomly
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// generated value. Sort by the randomly generated value while maintaining index association.
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//! [sort]
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{
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cv::cuda::GpuMat d_data(1, 100, CV_32SC2);
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// Thrust compatible begin and end iterators to channel 1 of this matrix
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auto keyBegin = GpuMatBeginItr<int>(d_data, 1);
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auto keyEnd = GpuMatEndItr<int>(d_data, 1);
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// Thrust compatible begin and end iterators to channel 0 of this matrix
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auto idxBegin = GpuMatBeginItr<int>(d_data, 0);
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auto idxEnd = GpuMatEndItr<int>(d_data, 0);
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// Fill the index channel with a sequence of numbers from 0 to 100
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thrust::sequence(idxBegin, idxEnd);
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// Fill the key channel with random numbers between 0 and 10. A counting iterator is used here to give an integer value for each location as an input to prg::operator()
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thrust::transform(thrust::make_counting_iterator(0), thrust::make_counting_iterator(d_data.cols), keyBegin, prg(0, 10));
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// Sort the key channel and index channel such that the keys and indecies stay together
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thrust::sort_by_key(keyBegin, keyEnd, idxBegin);
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cv::Mat h_idx(d_data);
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}
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//! [sort]
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cv::Mat h_idx(d_data);
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}
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//! [sort]
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// Randomly fill a row matrix with 100 elements between -1 and 1
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//! [random]
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{
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cv::cuda::GpuMat d_value(1, 100, CV_32F);
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auto valueBegin = GpuMatBeginItr<float>(d_value);
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auto valueEnd = GpuMatEndItr<float>(d_value);
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thrust::transform(thrust::make_counting_iterator(0), thrust::make_counting_iterator(d_value.cols), valueBegin, prg(-1, 1));
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// Randomly fill a row matrix with 100 elements between -1 and 1
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//! [random]
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{
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cv::cuda::GpuMat d_value(1, 100, CV_32F);
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auto valueBegin = GpuMatBeginItr<float>(d_value);
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auto valueEnd = GpuMatEndItr<float>(d_value);
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thrust::transform(thrust::make_counting_iterator(0), thrust::make_counting_iterator(d_value.cols), valueBegin, prg(-1, 1));
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cv::Mat h_value(d_value);
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}
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//! [random]
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cv::Mat h_value(d_value);
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}
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//! [random]
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// OpenCV has count non zero, but what if you want to count a specific value?
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//! [count_value]
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{
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cv::cuda::GpuMat d_value(1, 100, CV_32S);
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d_value.setTo(cv::Scalar(0));
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d_value.colRange(10, 50).setTo(cv::Scalar(15));
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auto count = thrust::count(GpuMatBeginItr<int>(d_value), GpuMatEndItr<int>(d_value), 15);
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std::cout << count << std::endl;
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}
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//! [count_value]
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// OpenCV has count non zero, but what if you want to count a specific value?
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//! [count_value]
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{
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cv::cuda::GpuMat d_value(1, 100, CV_32S);
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d_value.setTo(cv::Scalar(0));
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d_value.colRange(10, 50).setTo(cv::Scalar(15));
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auto count = thrust::count(GpuMatBeginItr<int>(d_value), GpuMatEndItr<int>(d_value), 15);
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std::cout << count << std::endl;
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}
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//! [count_value]
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// Randomly fill an array then copy only values greater than 0. Perform these tasks on a stream.
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//! [copy_greater]
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{
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cv::cuda::GpuMat d_value(1, 100, CV_32F);
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auto valueBegin = GpuMatBeginItr<float>(d_value);
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auto valueEnd = GpuMatEndItr<float>(d_value);
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cv::cuda::Stream stream;
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//! [random_gen_stream]
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// Same as the random generation code from before except now the transformation is being performed on a stream
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thrust::transform(thrust::system::cuda::par.on(cv::cuda::StreamAccessor::getStream(stream)), thrust::make_counting_iterator(0), thrust::make_counting_iterator(d_value.cols), valueBegin, prg(-1, 1));
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//! [random_gen_stream]
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// Count the number of values we are going to copy
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int count = thrust::count_if(thrust::system::cuda::par.on(cv::cuda::StreamAccessor::getStream(stream)), valueBegin, valueEnd, pred_greater<float>(0.0));
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// Allocate a destination for copied values
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cv::cuda::GpuMat d_valueGreater(1, count, CV_32F);
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// Copy values that satisfy the predicate.
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thrust::copy_if(thrust::system::cuda::par.on(cv::cuda::StreamAccessor::getStream(stream)), valueBegin, valueEnd, GpuMatBeginItr<float>(d_valueGreater), pred_greater<float>(0.0));
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cv::Mat h_greater(d_valueGreater);
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}
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//! [copy_greater]
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return 0;
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// Randomly fill an array then copy only values greater than 0. Perform these tasks on a stream.
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//! [copy_greater]
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{
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cv::cuda::GpuMat d_value(1, 100, CV_32F);
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auto valueBegin = GpuMatBeginItr<float>(d_value);
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auto valueEnd = GpuMatEndItr<float>(d_value);
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cv::cuda::Stream stream;
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//! [random_gen_stream]
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// Same as the random generation code from before except now the transformation is being performed on a stream
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thrust::transform(thrust::system::cuda::par.on(cv::cuda::StreamAccessor::getStream(stream)), thrust::make_counting_iterator(0), thrust::make_counting_iterator(d_value.cols), valueBegin, prg(-1, 1));
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//! [random_gen_stream]
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// Count the number of values we are going to copy
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int count = thrust::count_if(thrust::system::cuda::par.on(cv::cuda::StreamAccessor::getStream(stream)), valueBegin, valueEnd, pred_greater<float>(0.0));
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// Allocate a destination for copied values
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cv::cuda::GpuMat d_valueGreater(1, count, CV_32F);
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// Copy values that satisfy the predicate.
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thrust::copy_if(thrust::system::cuda::par.on(cv::cuda::StreamAccessor::getStream(stream)), valueBegin, valueEnd, GpuMatBeginItr<float>(d_valueGreater), pred_greater<float>(0.0));
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cv::Mat h_greater(d_valueGreater);
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}
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//! [copy_greater]
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return 0;
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}
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