1. removed trt h & cpp

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<projectDescription>

	<name>tensorflow-feed-from-gpu</name>

	<comment></comment>

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	<buildSpec>

		<buildCommand>

			<name>org.eclipse.cdt.core.cBuilder</name>

			<triggers>clean,full,incremental,</triggers>

			<arguments>

			</arguments>

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cmake_minimum_required(VERSION 3.8)

cmake_minimum_required(VERSION 3.16)

set(ENV{CUDACXX} /usr/local/cuda/bin/nvcc)

project(tf_detector_example LANGUAGES CXX CUDA)

cmake_policy(SET CMP0074 OLD)

    infer_with_trt.cpp

    inference_base.cpp 

    inference_tf.cpp

    inference_trt.cpp

    channel_first.cu

    )

#include "inference_trt.h"

using namespace cv;

using namespace std;

int InferenceTensorRT::ReadGraph()

{

    auto runtimeEngineContext = CreateTrtEngineAndContext(graphFile, isInt8);

    runtime = std::get<0>(runtimeEngineContext);

    engine = std::get<1>(runtimeEngineContext);

    context = std::get<2>(runtimeEngineContext);

    return 0;

}

int InferenceTensorRT::ReadClassLabels()

{

    populateClassLabels(labelsVector, labelsFile);

    return 0;

}

int InferenceTensorRT::doInference(cv::cuda::GpuMat &d_frame)

{

    auto inferenceTuple = doInferenceWithTrt(d_frame, context, labelsVector);

    detections = std::get<0>(inferenceTuple);

    numDetections = std::get<1>(inferenceTuple);

    return 0;

}

void InferenceTensorRT::visualize(cv::cuda::GpuMat &d_frame, double fps)

{

    Mat img;

    d_frame.download(img);

    for (int p = 0; p < N; ++p)

    {

        for (int i = 0; i < numDetections[p]; ++i)

        {

            float *det = &detections[0] + (p * detectionOutputParam.keepTopK + i) * 7;

            if (det[2] < visualizeThreshold)

                continue;

            // Output format for each detection is stored in the below order

            // [image_id, label, confidence, xmin, ymin, xmax, ymax]

            assert((int)det[1] < OUTPUT_CLS_SIZE);

            std::string storeName = outFileRoot + labelsVector[(int)det[1]] + "-" + std::to_string(det[2]) + ".jpg";

            if (debug & 0x2)

            {

                // det array idxs: (4, 3) = (y0, x0), (6, 5) = (y1, x1)

                // dets are in absolute coordinates: 0 <= pt <= 1

                drawBoundingBoxOnImage(img, det[4], det[3], det[6], det[5], det[2], labelsVector[(int)det[1]]);

            }

        }

    }

    if (debug & 0x2)

    {

        string framework("TensorRT");

        if (isInt8)

        {

            framework += " (INT8)";

        }

        auto color = Scalar(0, 255, 255);

        drawFrameworkSignature(img, fps, framework, color);

    }

}

#pragma once

#include "inference_base.h"

using namespace std;

class InferenceTensorRT : public InferenceBase

{

private:

  IRuntime *runtime;

  ICudaEngine *engine;

  IExecutionContext *context;

  bool isInt8;

  //batch size

  const int N = 1;

  const float visualizeThreshold = 0.5;

  vector<string> labelsVector;

  vector<int> numDetections;

  vector<float> detections;

  string outFileRoot;

protected:

  int ReadGraph() override;

  int ReadClassLabels() override;

  int doInference(cv::cuda::GpuMat &d_frame) override;

  void visualize(cv::cuda::GpuMat&, double) override;

public:

  InferenceTensorRT(const string &labelsFile, const string &graphFile, bool isInt8, double threshScore = 0.5, double threshIOU = 0.8, int dbg = 0, string outFile="")

      : InferenceBase(labelsFile, graphFile, threshScore, threshIOU, dbg)

      , labelsVector()

      , numDetections(N)

      , detections(N * detectionOutputParam.keepTopK * 7)

      , outFileRoot(outFile)

      , isInt8(isInt8)

  {

  }

  virtual ~InferenceTensorRT()

  {

    if(context != nullptr)

    {

      context->destroy();

    }

    if(engine != nullptr)

    {

      engine->destroy();

    }

    if(runtime != nullptr)

    {

      runtime->destroy();

    }

  }

};

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