include/openpose_private/gpu/cuda.hu

#ifndef OPENPOSE_PRIVATE_GPU_CUDA_HU
#define OPENPOSE_PRIVATE_GPU_CUDA_HU

// Note: This class should only be included if CUDA is enabled

#include <cuda.h>
#include <cuda_runtime.h>

namespace op
{
    // VERY IMPORTANT: These fast functions does NOT work for negative integer numbers.
    // E.g., positiveIntRound(-180.f) = -179.

    // Round functions
    // Signed
    template<typename T>
    inline __device__ char positiveCharRoundCuda(const T a)
    {
        return char(a+0.5f);
    }

    template<typename T>
    inline __device__ signed char positiveSCharRoundCuda(const T a)
    {
        return (signed char)(a+0.5f);
    }

    template<typename T>
    inline __device__ int positiveIntRoundCuda(const T a)
    {
        return int(a+0.5f);
    }

    template<typename T>
    inline __device__ long positiveLongRoundCuda(const T a)
    {
        return long(a+0.5f);
    }

    template<typename T>
    inline __device__ long long positiveLongLongRoundCuda(const T a)
    {
        return (long long)(a+0.5f);
    }

    // Unsigned
    template<typename T>
    inline __device__ unsigned char uCharRoundCuda(const T a)
    {
        return (unsigned char)(a+0.5f);
    }

    template<typename T>
    inline __device__ unsigned int uIntRoundCuda(const T a)
    {
        return (unsigned int)(a+0.5f);
    }

    template<typename T>
    inline __device__ unsigned long ulongRoundCuda(const T a)
    {
        return (unsigned long)(a+0.5f);
    }

    template<typename T>
    inline __device__ unsigned long long uLongLongRoundCuda(const T a)
    {
        return (unsigned long long)(a+0.5f);
    }

    // Max/min functions
    template<class T>
    inline __device__ T fastMaxCuda(const T a, const T b)
    {
        return (a > b ? a : b);
    }

    template<class T>
    inline __device__ T fastMinCuda(const T a, const T b)
    {
        return (a < b ? a : b);
    }

    template<class T>
    inline __device__ T fastTruncateCuda(const T value, const T min = 0, const T max = 1)
    {
        return fastMinCuda(max, fastMaxCuda(min, value));
    }

    // Cubic interpolation
    template <typename T>
    inline __device__ void cubicSequentialData(
        int* xIntArray, int* yIntArray, T& dx, T& dy, const T xSource, const T ySource, const int widthSource,
        const int heightSource)
    {
        xIntArray[1] = fastTruncateCuda(int(floor(xSource)), 0, widthSource - 1);
        xIntArray[0] = fastMaxCuda(0, xIntArray[1] - 1);
        xIntArray[2] = fastMinCuda(widthSource - 1, xIntArray[1] + 1);
        xIntArray[3] = fastMinCuda(widthSource - 1, xIntArray[2] + 1);
        dx = xSource - xIntArray[1];

        yIntArray[1] = fastTruncateCuda(int(floor(ySource)), 0, heightSource - 1);
        yIntArray[0] = fastMaxCuda(0, yIntArray[1] - 1);
        yIntArray[2] = fastMinCuda(heightSource - 1, yIntArray[1] + 1);
        yIntArray[3] = fastMinCuda(heightSource - 1, yIntArray[2] + 1);
        dy = ySource - yIntArray[1];
    }

    template <typename T>
    inline __device__ T cubicInterpolate(const T v0, const T v1, const T v2, const T v3, const T dx)
    {
        // http://www.paulinternet.nl/?page=bicubic
        // const auto a = (-0.5f * v0 + 1.5f * v1 - 1.5f * v2 + 0.5f * v3);
        // const auto b = (v0 - 2.5f * v1 + 2.0 * v2 - 0.5 * v3);
        // const auto c = (-0.5f * v0 + 0.5f * v2);
        // out = ((a * dx + b) * dx + c) * dx + v1;
        return (-0.5f * v0 + 1.5f * v1 - 1.5f * v2 + 0.5f * v3) * dx * dx * dx
                + (v0 - 2.5f * v1 + 2.f * v2 - 0.5f * v3) * dx * dx
                - 0.5f * (v0 - v2) * dx // + (-0.5f * v0 + 0.5f * v2) * dx
                + v1;
        // return v1 + 0.5f * dx * (v2 - v0 + dx * (2.f * v0 - 5.f * v1 + 4.f * v2 - v3 + dx * (3.f * (v1 - v2) + v3 - v0)));
    }

    template <typename T>
    inline __device__ T bicubicInterpolate(
        const T* const sourcePtr, const T xSource, const T ySource, const int widthSource, const int heightSource,
        const int widthSourcePtr)
    {
        int xIntArray[4];
        int yIntArray[4];
        T dx;
        T dy;
        cubicSequentialData(xIntArray, yIntArray, dx, dy, xSource, ySource, widthSource, heightSource);

        T temp[4];
        for (unsigned char i = 0; i < 4; i++)
        {
            const auto offset = yIntArray[i]*widthSourcePtr;
            temp[i] = cubicInterpolate(
                sourcePtr[offset + xIntArray[0]], sourcePtr[offset + xIntArray[1]], sourcePtr[offset + xIntArray[2]],
                sourcePtr[offset + xIntArray[3]], dx);
        }
        return cubicInterpolate(temp[0], temp[1], temp[2], temp[3], dy);
    }

    template <typename T>
    inline __device__ T bicubicInterpolate(
        const unsigned char* const sourcePtr, const T xSource, const T ySource, const int widthSource,
        const int heightSource, const int widthSourcePtr)
    {
        int xIntArray[4];
        int yIntArray[4];
        T dx;
        T dy;
        cubicSequentialData(xIntArray, yIntArray, dx, dy, xSource, ySource, widthSource, heightSource);

        T temp[4];
        for (unsigned char i = 0; i < 4; i++)
        {
            const auto offset = yIntArray[i]*widthSourcePtr;
            temp[i] = cubicInterpolate(
                T(sourcePtr[offset + xIntArray[0]]), T(sourcePtr[offset + xIntArray[1]]),
                T(sourcePtr[offset + xIntArray[2]]), T(sourcePtr[offset + xIntArray[3]]), dx);
        }
        return cubicInterpolate(temp[0], temp[1], temp[2], temp[3], dy);
    }

    template <typename T>
    inline __device__ T bicubicInterpolate8Times(
        const T* const sourcePtr, const T xSource, const T ySource, const int widthSource, const int heightSource,
        const int threadIdxX, const int threadIdxY)
    {
        // Now we only need dx and dy
        const T dx = xSource - fastTruncateCuda(int(floor(xSource)), 0, widthSource - 1);
        const T dy = ySource - fastTruncateCuda(int(floor(ySource)), 0, heightSource - 1);

        T temp[4];
        for (unsigned char i = 0; i < 4; i++)
        {
            const auto offset = 5 * (i + (threadIdxY > 3 ? 1 : 0)) + (threadIdxX > 3 ? 1 : 0);
            temp[i] = cubicInterpolate(
                sourcePtr[offset], sourcePtr[offset+1], sourcePtr[offset+2],
                sourcePtr[offset+3], dx);
        }
        return cubicInterpolate(temp[0], temp[1], temp[2], temp[3], dy);
    }

    template <typename T>
    inline __device__ T addWeighted(const T value1, const T value2, const T alphaValue2)
    {
        return (1.f - alphaValue2) * value1 + alphaValue2 * value2;
    }

    template <typename T>
    inline __device__ void addColorWeighted(
        T& colorR, T& colorG, T& colorB, const T* const colorToAdd, const T alphaColorToAdd)
    {
        colorR = addWeighted(colorR, colorToAdd[0], alphaColorToAdd);
        colorG = addWeighted(colorG, colorToAdd[1], alphaColorToAdd);
        colorB = addWeighted(colorB, colorToAdd[2], alphaColorToAdd);
    }
}

#endif // OPENPOSE_PRIVATE_GPU_CUDA_HU