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feat: 去除Onnxruntime的依赖，使用cv::DNN进行推理feature.onnx #48

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feat: 去除Onnxruntime的依赖，使用cv::DNN进行推理feature.onnx #48

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8 changes: 2 additions & 6 deletions CMakeLists.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -6,13 +6,9 @@ set(CMAKE_CXX_STANDARD 11)
set(CMAKE_CXX_STANDARD_REQUIRED ON)


find_package (Eigen3 3.3 REQUIRED NO_MODULE)
find_package (Eigen3 REQUIRED NO_MODULE)


set(ONNXRUNTIME_DIR "/home/a/lib/onnxruntime-linux-x64-1.12.1")

include_directories("${ONNXRUNTIME_DIR}/include"
)
find_package(OpenCV 4 REQUIRED )


Expand Down Expand Up @@ -45,7 +41,7 @@ add_executable(DeepSORT
tracker/bytetrack/src/utils.cpp

main.cpp)
target_link_libraries(DeepSORT PRIVATE "${ONNXRUNTIME_DIR}/lib/libonnxruntime.so" ${OpenCV_LIBS} Eigen3::Eigen)
target_link_libraries(DeepSORT PRIVATE ${OpenCV_LIBS} Eigen3::Eigen)



Expand Down
26 changes: 2 additions & 24 deletions tracker/deepsort/include/FeatureTensor.h
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Original file line number Diff line number Diff line change
Expand Up @@ -16,12 +16,7 @@
#include <string>
#include <vector>
#include <stdexcept>
#include <onnxruntime_cxx_api.h>
#include "opencv2/opencv.hpp"
#include "opencv2/core/core.hpp"
#include <opencv2/dnn/dnn.hpp>
#include <opencv2/imgcodecs.hpp>
#include <opencv2/imgproc.hpp>
typedef unsigned char uint8;

template <typename T>
Expand Down Expand Up @@ -50,7 +45,6 @@ class FeatureTensor
public:
static FeatureTensor *getInstance();
bool getRectsFeature(const cv::Mat &img, DETECTIONS &d);
void preprocess(cv::Mat &imageBGR, std::vector<float> &inputTensorValues, size_t &inputTensorSize);

private:
FeatureTensor();
Expand All @@ -60,26 +54,10 @@ class FeatureTensor
bool init();
~FeatureTensor();

void tobuffer(const std::vector<cv::Mat> &imgs, uint8 *buf);

void preprocess(cv::Mat &imageBGR);
public:
void test();

static constexpr const int width_ = 64;
static constexpr const int height_ = 128;

std::array<float, width_ * height_> input_image_{};

std::array<float, k_feature_dim> results_{};

Ort::Env env;
Ort::Session session_{env, k_feature_model_path.c_str(), Ort::SessionOptions{nullptr}};

Ort::Value input_tensor_{nullptr};
std::array<int64_t, 4> input_shape_{1, 3, width_, height_};

Ort::Value output_tensor_{nullptr};
std::array<int64_t, 2> output_shape_{1, k_feature_dim};

std::vector<int64_t> inputDims_;
cv::dnn::Net net;
};
92 changes: 11 additions & 81 deletions tracker/deepsort/src/FeatureTensor.cpp
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Expand Up @@ -41,22 +41,12 @@ FeatureTensor::~FeatureTensor()
bool FeatureTensor::init()
{

Ort::TypeInfo inputTypeInfo = session_.GetInputTypeInfo(0);
auto inputTensorInfo = inputTypeInfo.GetTensorTypeAndShapeInfo();

ONNXTensorElementDataType inputType = inputTensorInfo.GetElementType();
std::cout << "Input Type: " << inputType << std::endl;

inputDims_ = inputTensorInfo.GetShape();
std::cout << "Input Dimensions: " << inputDims_ << std::endl; // [-1, 3, 128, 64]
inputDims_[0] = 1;
this->net = cv::dnn::readNetFromONNX(k_feature_model_path);
std::cout << "FeatureTensor::init() " << std::endl;


return true;
}

void FeatureTensor::preprocess(cv::Mat &imageBGR, std::vector<float> &inputTensorValues, size_t &inputTensorSize)
void FeatureTensor::preprocess(cv::Mat &imageBGR)
{

// pre-processing the Image
Expand All @@ -65,9 +55,9 @@ void FeatureTensor::preprocess(cv::Mat &imageBGR, std::vector<float> &inputTenso

// step 2: Resize the image.
cv::Mat resizedImageBGR, resizedImageRGB, resizedImage, preprocessedImage;
cv::resize(imageBGR, resizedImageBGR,
cv::Size(inputDims_.at(3), inputDims_.at(2)),
cv::InterpolationFlags::INTER_CUBIC);
cv::resize(imageBGR, resizedImageBGR,
cv::Size(width_, height_),
cv::InterpolationFlags::INTER_CUBIC);

// cv::resize(imageBGR, resizedImageBGR,
// cv::Size(64, 128));
Expand Down Expand Up @@ -96,11 +86,7 @@ void FeatureTensor::preprocess(cv::Mat &imageBGR, std::vector<float> &inputTenso
// step 8: Convert the image to CHW RGB float format.
// HWC to CHW
cv::dnn::blobFromImage(resizedImage, preprocessedImage);
inputTensorSize = vectorProduct(inputDims_);
inputTensorValues.assign(preprocessedImage.begin<float>(),
preprocessedImage.end<float>());

std::cout << "inputTensorSize:" << inputTensorValues.size() << std::endl;
this->net.setInput(preprocessedImage);
}

bool FeatureTensor::getRectsFeature(const cv::Mat &img, DETECTIONS &d)
Expand All @@ -118,72 +104,16 @@ bool FeatureTensor::getRectsFeature(const cv::Mat &img, DETECTIONS &d)
rc.height = (rc.y + rc.height <= img.rows ? rc.height : (img.rows - rc.y));

cv::Mat mattmp = img(rc).clone();
preprocess(mattmp);

std::vector<float> inputTensorValues;
size_t inputTensorSize;
preprocess(mattmp, inputTensorValues, inputTensorSize);

const char *input_names[] = {"input"}; //输入节点名
const char *output_names[] = {"output"}; //输出节点名

auto memory_info = Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeDefault);
output_tensor_ = Ort::Value::CreateTensor<float>(memory_info, results_.data(), results_.size(), output_shape_.data(), output_shape_.size());

std::vector<Ort::Value> inputTensors;
inputTensors.push_back(Ort::Value::CreateTensor<float>(
memory_info, inputTensorValues.data(), inputTensorSize, inputDims_.data(),
inputDims_.size()));


session_.Run(Ort::RunOptions{nullptr}, input_names, inputTensors.data(), 1, output_names, &output_tensor_, 1);

Ort::TensorTypeAndShapeInfo shape_info = output_tensor_.GetTensorTypeAndShapeInfo();


size_t dim_count = shape_info.GetDimensionsCount();
std::cout << "dim_count:" << dim_count << std::endl;
cv::Mat preds = this->net.forward("output");
auto *ptr = preds.ptr<float>(0);


int64_t dims[2];
shape_info.GetDimensions(dims, sizeof(dims) / sizeof(dims[0]));
std::cout << "output shape:" << dims[0] << "," << dims[1] << std::endl;


float *f = output_tensor_.GetTensorMutableData<float>();
for (int i = 0; i < dims[1]; i++) //sisyphus
for (int i = 0; i < preds.cols; i++)
{
dbox.feature[i] = f[i];
dbox.feature[i] = ptr[i];
}
}

return true;
}

void FeatureTensor::tobuffer(const std::vector<cv::Mat> &imgs, uint8 *buf)
{
int pos = 0;
for (const cv::Mat &img : imgs)
{
int Lenth = img.rows * img.cols * 3;
int nr = img.rows;
int nc = img.cols;
if (img.isContinuous())
{
nr = 1;
nc = Lenth;
}
for (int i = 0; i < nr; i++)
{
const uchar *inData = img.ptr<uchar>(i);
for (int j = 0; j < nc; j++)
{
buf[pos] = *inData++;
pos++;
}
} // end for
} // end imgs;
}
void FeatureTensor::test()
{
return;
}