【计算机视觉】使用opencv中dnn模块及openvino2022进行模型推理（C++接口）

使用openvino：（推理使用由onnx生成的xml及bin文件）

0.查看支持的推理设备：

ov::Core ie;
vector<string> availableDevices = ie.get_available_devices();
for (int i = 0; i < availableDevices.size(); i++) {
    qDebug()<<"supported device name : "<<availableDevices[i].c_str();
}
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1.构造模型：

ov::Core ie;
auto network = ie.read_model(xml,bin);
if(gpu)
{
auto compiled_model = ie.compile_model(network, "GPU");
infer_request = compiled_model.create_infer_request();
}
else
{
auto compiled_model = ie.compile_model(network, "CPU");
infer_request = compiled_model.create_infer_request();
}
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2.进行预处理工作，cv::mat转ov::tensor

计算机内存float*是一个一维数组线性存储，故相互赋值需将mat及tensor的索引统一为线性索引：从头逐一连续赋值；
一般而言有：

void cvImageToTensor(const cv::Mat & image, float *tensor, nvinfer1::Dims dimensions)
{
  const size_t channels = dimensions.d[1];
  const size_t height = dimensions.d[2];
  const size_t width = dimensions.d[3];
  // TODO: validate dimensions match
  const size_t stridesCv[3] = { width * channels, channels, 1 };
  const size_t strides[3] = { height * width, width, 1 };

  for (int i = 0; i < height; i++) 
  {
    for (int j = 0; j < width; j++) 
    {
      for (int k = 0; k < channels; k++) 
      {
        const size_t offsetCv = i * stridesCv[0] + j * stridesCv[1] + k * stridesCv[2];
        const size_t offset = k * strides[0] + i * strides[1] + j * strides[2];
        tensor[offset] = (float) image.data[offsetCv];
      }
    }
  }
}
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或使用opencv自己的取值函数at<>：

cv::Mat img = this->resize_image(frame, &newh, &neww, &padh, &padw);
    ov::Tensor input_tensor1 = infer_request.get_input_tensor(0);
    auto data1 = input_tensor1.data<float>();
    cv::cvtColor(img,img,cv::COLOR_BGR2RGB);
     for (int h = 0; h < 640; h++)
     {
         for (int w = 0; w < 640; w++)
         {
             for (int c = 0; c < 3; c++)
             {
                 //tensor:chw排列，这里待转tensor为（1，3，640，640）
                 int out_index = c * 640 * 640 + h * 640 + w;
                 //mat是hwc排列，原始mat为（640，640，3）
                 data1[out_index] = (float(img.at<cv::Vec3b>(h, w)[c])-127.5)/128.0;
             }
         }
     }
    infer_request.infer();
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3.推理后结果提取：

 float* pdata_score = infer_request.get_output_tensor(n).data<float>();  
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使用opencv自带的dnn模块进行推理

0.模型构造

这里使用的是onnx

cv::dnn::Net net;
 this->net = cv::dnn::readNet(config.modelfile);
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1.数据准备：原始mat变形为cv::dnn::Net的输入mat

cv::Mat img = this->resize_image(frame, &newh, &neww, &padh, &padw);
    cv::Mat blob;
    //先减127.5再调换RB通道，再除以128.0，得出的blob为（1,3,640,640)的形状
    cv::dnn::blobFromImage(img, blob, 1 / 128.0, cv::Size(this->inpWidth, this->inpHeight), cv::Scalar(127.5, 127.5, 127.5), true, false);
    
    this->net.setInput(blob);
    std::vector<cv::Mat> outs;
    this->net.forward(outs, this->net.getUnconnectedOutLayersNames());
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2.推理后结果提取：

float* pdata_score = (float*)outs[n * 3].data;
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