/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions
 * are met:
 *  * Redistributions of source code must retain the above copyright
 *    notice, this list of conditions and the following disclaimer.
 *  * Redistributions in binary form must reproduce the above copyright
 *    notice, this list of conditions and the following disclaimer in the
 *    documentation and/or other materials provided with the distribution.
 *  * Neither the name of NVIDIA CORPORATION nor the names of its
 *    contributors may be used to endorse or promote products derived
 *    from this software without specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
 * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
 * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
 * PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
 * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
 * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
 * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
 * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
 * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
 * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 */

#include <assert.h>
#include "convolutionFFT2D_common.h"

////////////////////////////////////////////////////////////////////////////////
// Reference straightforward CPU convolution
////////////////////////////////////////////////////////////////////////////////
extern "C" void convolutionClampToBorderCPU(float *h_Result, float *h_Data,
                                            float *h_Kernel, int dataH,
                                            int dataW, int kernelH, int kernelW,
                                            int kernelY, int kernelX) {
  for (int y = 0; y < dataH; y++)
    for (int x = 0; x < dataW; x++) {
      double sum = 0;

      for (int ky = -(kernelH - kernelY - 1); ky <= kernelY; ky++)
        for (int kx = -(kernelW - kernelX - 1); kx <= kernelX; kx++) {
          int dy = y + ky;
          int dx = x + kx;

          if (dy < 0) dy = 0;

          if (dx < 0) dx = 0;

          if (dy >= dataH) dy = dataH - 1;

          if (dx >= dataW) dx = dataW - 1;

          sum += h_Data[dy * dataW + dx] *
                 h_Kernel[(kernelY - ky) * kernelW + (kernelX - kx)];
        }

      h_Result[y * dataW + x] = (float)sum;
    }
}