deepstream_yolo/nvdsinfer_custom_impl_Yolo/nvdsparsebbox_Yolo.cpp

/*
 * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
 *
 * Permission is hereby granted, free of charge, to any person obtaining a
 * copy of this software and associated documentation files (the "Software"),
 * to deal in the Software without restriction, including without limitation
 * the rights to use, copy, modify, merge, publish, distribute, sublicense,
 * and/or sell copies of the Software, and to permit persons to whom the
 * Software is furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in
 * all copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL
 * THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
 * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
 * DEALINGS IN THE SOFTWARE.
 *
 * Edited by Marcos Luciano
 * https://www.github.com/marcoslucianops
 */

#include "nvdsinfer_custom_impl.h"

#include "utils.h"

extern "C" bool
NvDsInferParseYolo(std::vector<NvDsInferLayerInfo> const& outputLayersInfo, NvDsInferNetworkInfo const& networkInfo,
    NvDsInferParseDetectionParams const& detectionParams, std::vector<NvDsInferParseObjectInfo>& objectList);

extern "C" bool
NvDsInferParseYoloE(std::vector<NvDsInferLayerInfo> const& outputLayersInfo, NvDsInferNetworkInfo const& networkInfo,
    NvDsInferParseDetectionParams const& detectionParams, std::vector<NvDsInferParseObjectInfo>& objectList);

static NvDsInferParseObjectInfo
convertBBox(const float& bx1, const float& by1, const float& bx2, const float& by2, const uint& netW, const uint& netH)
{
  NvDsInferParseObjectInfo b;

  float x1 = bx1;
  float y1 = by1;
  float x2 = bx2;
  float y2 = by2;

  x1 = clamp(x1, 0, netW);
  y1 = clamp(y1, 0, netH);
  x2 = clamp(x2, 0, netW);
  y2 = clamp(y2, 0, netH);

  b.left = x1;
  b.width = clamp(x2 - x1, 0, netW);
  b.top = y1;
  b.height = clamp(y2 - y1, 0, netH);

  return b;
}

static void
addBBoxProposal(const float bx1, const float by1, const float bx2, const float by2, const uint& netW, const uint& netH,
    const int maxIndex, const float maxProb, std::vector<NvDsInferParseObjectInfo>& binfo)
{
  NvDsInferParseObjectInfo bbi = convertBBox(bx1, by1, bx2, by2, netW, netH);

  if (bbi.width < 1 || bbi.height < 1)
      return;

  bbi.detectionConfidence = maxProb;
  bbi.classId = maxIndex;
  binfo.push_back(bbi);
}

static std::vector<NvDsInferParseObjectInfo>
decodeTensorYolo(const float* detection, const uint& outputSize, const uint& count, const uint& netW, const uint& netH,
    const std::vector<float>& preclusterThreshold)
{
  std::vector<NvDsInferParseObjectInfo> binfo;

  for (uint b = 0; b < outputSize; ++b) {
    float maxProb = count == 6 ? detection[b * count + 4] : detection[b * count + 4] * detection[b * count + 6];
    int maxIndex = (int) detection[b * count + 5];

    if (maxProb < preclusterThreshold[maxIndex])
      continue;

    float bxc = detection[b * count + 0];
    float byc = detection[b * count + 1];
    float bw = detection[b * count + 2];
    float bh = detection[b * count + 3];

    float bx1 = bxc - bw / 2;
    float by1 = byc - bh / 2;
    float bx2 = bx1 + bw;
    float by2 = by1 + bh;

    addBBoxProposal(bx1, by1, bx2, by2, netW, netH, maxIndex, maxProb, binfo);
  }

  return binfo;
}

static std::vector<NvDsInferParseObjectInfo>
decodeTensorYoloE(const float* detection, const uint& outputSize, const uint& count, const uint& netW, const uint& netH,
    const std::vector<float>& preclusterThreshold)
{
  std::vector<NvDsInferParseObjectInfo> binfo;

  for (uint b = 0; b < outputSize; ++b) {
    float maxProb = count == 6 ? detection[b * count + 4] : detection[b * count + 4] * detection[b * count + 6];
    int maxIndex = (int) detection[b * count + 5];

    if (maxProb < preclusterThreshold[maxIndex])
      continue;

    float bx1 = detection[b * count + 0];
    float by1 = detection[b * count + 1];
    float bx2 = detection[b * count + 2];
    float by2 = detection[b * count + 3];

    addBBoxProposal(bx1, by1, bx2, by2, netW, netH, maxIndex, maxProb, binfo);
  }

  return binfo;
}

static bool
NvDsInferParseCustomYolo(std::vector<NvDsInferLayerInfo> const& outputLayersInfo, NvDsInferNetworkInfo const& networkInfo,
    NvDsInferParseDetectionParams const& detectionParams, std::vector<NvDsInferParseObjectInfo>& objectList)
{
  if (outputLayersInfo.empty()) {
    std::cerr << "ERROR: Could not find output layer in bbox parsing" << std::endl;
    return false;
  }

  std::vector<NvDsInferParseObjectInfo> objects;

  const NvDsInferLayerInfo& layer = outputLayersInfo[0];

  const uint outputSize = layer.inferDims.d[0];
  const uint count = layer.inferDims.d[1];

  std::vector<NvDsInferParseObjectInfo> outObjs = decodeTensorYolo((const float*) (layer.buffer), outputSize, count,
      networkInfo.width, networkInfo.height, detectionParams.perClassPreclusterThreshold);

  objects.insert(objects.end(), outObjs.begin(), outObjs.end());

  objectList = objects;

  return true;
}

static bool
NvDsInferParseCustomYoloE(std::vector<NvDsInferLayerInfo> const& outputLayersInfo, NvDsInferNetworkInfo const& networkInfo,
    NvDsInferParseDetectionParams const& detectionParams, std::vector<NvDsInferParseObjectInfo>& objectList)
{
  if (outputLayersInfo.empty()) {
    std::cerr << "ERROR: Could not find output layer in bbox parsing" << std::endl;
    return false;
  }

  std::vector<NvDsInferParseObjectInfo> objects;

  const NvDsInferLayerInfo& layer = outputLayersInfo[0];

  const uint outputSize = layer.inferDims.d[0];
  const uint count = layer.inferDims.d[1];

  std::vector<NvDsInferParseObjectInfo> outObjs = decodeTensorYoloE((const float*) (layer.buffer), outputSize, count,
      networkInfo.width, networkInfo.height, detectionParams.perClassPreclusterThreshold);

  objects.insert(objects.end(), outObjs.begin(), outObjs.end());

  objectList = objects;

  return true;
}

extern "C" bool
NvDsInferParseYolo(std::vector<NvDsInferLayerInfo> const& outputLayersInfo, NvDsInferNetworkInfo const& networkInfo,
    NvDsInferParseDetectionParams const& detectionParams, std::vector<NvDsInferParseObjectInfo>& objectList)
{
  return NvDsInferParseCustomYolo(outputLayersInfo, networkInfo, detectionParams, objectList);
}

extern "C" bool
NvDsInferParseYoloE(std::vector<NvDsInferLayerInfo> const& outputLayersInfo, NvDsInferNetworkInfo const& networkInfo,
    NvDsInferParseDetectionParams const& detectionParams, std::vector<NvDsInferParseObjectInfo>& objectList)
{
  return NvDsInferParseCustomYoloE(outputLayersInfo, networkInfo, detectionParams, objectList);
}

CHECK_CUSTOM_PARSE_FUNC_PROTOTYPE(NvDsInferParseYolo);