Lines Matching defs:kernel
593 // Computes an unnormalized half kernel (right side). Returns the summation of all the half
594 // kernel values.
599 // Compute half kernel values at half pixel steps out from the center.
610 // Create a Gaussian half-kernel (right side) and a summed area table given a sigma and number
611 // of discrete steps. The half kernel is normalized to sum to 0.5.
616 // The half kernel should sum to 0.5 not 1.0.
626 // Applies the 1D half kernel vertically at points along the x axis to a circle centered at the
659 // This relies on having a half kernel computed for the Gaussian and a table of applications of
660 // the half kernel in y to columns at (evalX - halfKernel, evalX - halfKernel + 1, ..., evalX +
684 // Since we applied a half kernel in y we multiply acc by 2 (the circle is symmetric about
689 // This function creates a profile of a blurred circle. It does this by computing a kernel for
691 // an array of vertical applications of the half kernel to the circle along the x axis. The
692 // table of y evaluations has 2 * k + n entries where k is the size of the half kernel and n is
695 // kernel entry and sum these values to compute the profile entry.
702 // The full kernel is 6 sigmas wide.
707 // Number of x steps at which to apply kernel in y to cover all the profile samples in x.
729 // The full kernel is 6 sigmas wide.
735 // The half kernel should sum to 0.5.
744 // Populate the profile from the middle to the left edge (by flipping the half kernel and
1011 // We define our coord system so that the point at which we're evaluating a kernel
1162 // Apply a gaussian 'kernel' horizontally at the specified 'x', 'y' location.
1166 const float* kernel,
1182 accum += kernel[i] * eval_V(topVec[xSampleLoc], y, integral, integralSize, sixSigma);
1208 std::unique_ptr<float[]> kernel(new float[kernelSize]);
1210 SkGpuBlurUtils::Compute1DGaussianKernel(kernel.get(), xformedSigma, radius);
1246 kernel.get(),