GAPI_GPU_KERNEL(GGPUGaussBlur, cv::gapi::imgproc::GGaussBlur)
{
static void run(const cv::UMat& in, const cv::Size& ksize, double sigmaX, double sigmaY, int borderType, const cv::Scalar& bordVal, cv::UMat &out)
{
if( borderType == cv::BORDER_CONSTANT )
{
cv::UMat temp_in;
int width_add = (ksize.width - 1) / 2;
int height_add = (ksize.height - 1) / 2;
cv::copyMakeBorder(in, temp_in, height_add, height_add, width_add, width_add, borderType, bordVal );
cv::Rect rect = cv::Rect(height_add, width_add, in.cols, in.rows);
cv::GaussianBlur(temp_in(rect), out, ksize, sigmaX, sigmaY, borderType);
}
else
cv::GaussianBlur(in, out, ksize, sigmaX, sigmaY, borderType);
}
};
GAPI_GPU_KERNEL(GGPUBoxFilter, cv::gapi::imgproc::GBoxFilter)
{
static void run(const cv::UMat& in, int ddepth, const cv::Size& ksize, const cv::Point& anchor, bool normalize, int borderType, const cv::Scalar& bordVal, cv::UMat &out)
{
if( borderType == cv::BORDER_CONSTANT )
{
cv::UMat temp_in;
int width_add = (ksize.width - 1) / 2;
int height_add = (ksize.height - 1) / 2;
cv::copyMakeBorder(in, temp_in, height_add, height_add, width_add, width_add, borderType, bordVal);
cv::Rect rect = cv::Rect(height_add, width_add, in.cols, in.rows);
cv::boxFilter(temp_in(rect), out, ddepth, ksize, anchor, normalize, borderType);
}
else
cv::boxFilter(in, out, ddepth, ksize, anchor, normalize, borderType);
}
};
GAPI_OCV_KERNEL(GCPUBlur, cv::gapi::imgproc::GBlur)
{
static void run(const cv::Mat& in, const cv::Size& ksize, const cv::Point& anchor, int borderType, const cv::Scalar& bordVal, cv::Mat &out)
{
if( borderType == cv::BORDER_CONSTANT )
{
cv::Mat temp_in;
int width_add = (ksize.width - 1) / 2;
int height_add = (ksize.height - 1) / 2;
cv::copyMakeBorder(in, temp_in, height_add, height_add, width_add, width_add, borderType, bordVal);
cv::Rect rect = cv::Rect(height_add, width_add, in.cols, in.rows);
cv::blur(temp_in(rect), out, ksize, anchor, borderType);
}
else
cv::blur(in, out, ksize, anchor, borderType);
}
};
GAPI_GPU_KERNEL(GGPUFilter2D, cv::gapi::imgproc::GFilter2D)
{
static void run(const cv::UMat& in, int ddepth, const cv::Mat& k, const cv::Point& anchor, const cv::Scalar& delta, int border,
const cv::Scalar& bordVal, cv::UMat &out)
{
if( border == cv::BORDER_CONSTANT )
{
cv::UMat temp_in;
int width_add = (k.cols - 1) / 2;
int height_add = (k.rows - 1) / 2;
cv::copyMakeBorder(in, temp_in, height_add, height_add, width_add, width_add, border, bordVal );
cv::Rect rect = cv::Rect(height_add, width_add, in.cols, in.rows);
cv::filter2D(temp_in(rect), out, ddepth, k, anchor, delta.val[0], border);
}
else
cv::filter2D(in, out, ddepth, k, anchor, delta.val[0], border);
}
};
void MLP::GetOutput(const std::vector<double> &input,
std::vector<double> * output,
std::vector<std::vector<double>> * all_layers_activations) const {
assert(input.size() == m_num_inputs);
int temp_size;
if (m_num_hidden_layers == 0)
temp_size = m_num_outputs;
else
temp_size = m_layers_nodes[1];
std::vector<double> temp_in(m_num_inputs, 0.0);
std::vector<double> temp_out(temp_size, 0.0);
temp_in = input;
for (int i = 0; i < m_layers.size(); ++i) {
if (i > 0) {
//Store this layer activation
if (all_layers_activations != nullptr)
all_layers_activations->emplace_back(std::move(temp_in));
temp_in.clear();
temp_in = temp_out;
temp_out.clear();
temp_out.resize(m_layers[i].GetOutputSize());
}
m_layers[i].GetOutputAfterActivationFunction(temp_in, &temp_out);
}
if (temp_out.size() > 1)
utils::Softmax(&temp_out);
*output = temp_out;
//Add last layer activation
if (all_layers_activations != nullptr)
all_layers_activations->emplace_back(std::move(temp_in));
}
