cnstream/source/common/postprocess/postprocess_yolov5.cpp

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 *  Licensed under the Apache License, Version 2.0 (the "License");
 *  you may not use this file except in compliance with the License.
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#include <algorithm>
#include <cmath>
#include <memory>
#include <mutex>
#include <vector>

#include "cnstream_frame_va.hpp"
#include "postproc.hpp"
#include "cnstream_logging.hpp"

static auto range_0_1 = [](float num) { return std::max(.0f, std::min(1.0f, num)); };

/**
 * @brief Postprocessing for YOLOv5 neural network
 * The input frame of the model should keep aspect ratio.
 */
class PostprocYolov5 : public cnstream::Postproc {
 public:
  int Execute(const std::vector<float*>& net_outputs, const std::shared_ptr<edk::ModelLoader>& model,
              const std::shared_ptr<cnstream::CNFrameInfo>& package) {
    LOGF_IF(DEMO, model->InputNum() != 1);
    LOGF_IF(DEMO, model->OutputNum() != 1);
    LOGF_IF(DEMO, net_outputs.size() != 1);
    auto input_shape = model->InputShape(0);
    cnstream::CNDataFramePtr frame = package->collection.Get<cnstream::CNDataFramePtr>(cnstream::kCNDataFrameTag);
    const int img_w = frame->width;
    const int img_h = frame->height;
    const int model_input_w = static_cast<int>(input_shape.W());
    const int model_input_h = static_cast<int>(input_shape.H());
    const float* net_output = net_outputs[0];

    // scaling factors
    const float scaling_factors = std::min(1.0 * model_input_w / img_w, 1.0 * model_input_h / img_h);

    // The input frame of the model should keep aspect ratio.
    // If mlu resize and convert operator is used as preproc, parameter keep_aspect_ratio of Inferencer module
    // should be set to true in config json file.
    // If cpu preproc is used as preproc, please make sure keep aspect ratio in custom preproc.
    // Scaler does not support keep aspect ratio.
    // If the input frame does not keep aspect ratio, set scaled_w = model_input_w and scaled_h = model_input_h

    // scaled size
    const int scaled_w = scaling_factors * img_w;
    const int scaled_h = scaling_factors * img_h;

    // bboxes
    const int box_num = static_cast<int>(net_output[0]);
    int box_step = 7;
    auto range_0_1 = [](float num) { return std::max(.0f, std::min(1.0f, num)); };
    cnstream::CNInferObjsPtr objs_holder =
      package->collection.Get<cnstream::CNInferObjsPtr>(cnstream::kCNInferObjsTag);
    cnstream::CNObjsVec &objs = objs_holder->objs_;
    for (int box_idx = 0; box_idx < box_num; ++box_idx) {
      float left = net_output[64 + box_idx * box_step + 3];
      float right = net_output[64 + box_idx * box_step + 5];
      float top = net_output[64 + box_idx * box_step + 4];
      float bottom = net_output[64 + box_idx * box_step + 6];

      // rectify
      left = (left - (model_input_w - scaled_w) / 2) / scaled_w;
      right = (right - (model_input_w - scaled_w) / 2) / scaled_w;
      top = (top - (model_input_h - scaled_h) / 2) / scaled_h;
      bottom = (bottom - (model_input_h - scaled_h) / 2) / scaled_h;
      left = range_0_1(left);
      right = range_0_1(right);
      top = range_0_1(top);
      bottom = range_0_1(bottom);

      auto obj = std::make_shared<cnstream::CNInferObject>();
      obj->id = std::to_string(static_cast<int>(net_output[64 + box_idx * box_step + 1]));
      obj->score = net_output[64 + box_idx * box_step + 2];

      obj->bbox.x = left;
      obj->bbox.y = top;
      obj->bbox.w = std::min(1.0f - obj->bbox.x, right - left);
      obj->bbox.h = std::min(1.0f - obj->bbox.y, bottom - top);

      if (obj->bbox.h <= 0 || obj->bbox.w <= 0 || (obj->score < threshold_ && threshold_ > 0)) continue;
      std::lock_guard<std::mutex> objs_mutex(objs_holder->mutex_);
      objs.push_back(obj);
    }

    return 0;
  }

 private:
  DECLARE_REFLEX_OBJECT_EX(PostprocYolov5, cnstream::Postproc);
};  // class PostprocessYolov5

IMPLEMENT_REFLEX_OBJECT_EX(PostprocYolov5, cnstream::Postproc);