自己有服务器怎么搭建网站,电子商务网站建设与维护教案,延安市建设厅网站,wordpress手机瀑布流Opencv_CUDA实现推理图像前处理与后处理
通过trt 或者 openvino部署深度学习算法时#xff0c;往往会通过opencv的Mat及算法将图像转换为固定的格式作为输入openvino图像的前后处理后边将在单独的文章中写出今晚空闲搜了一些opencv_cuda的使用方法#xff0c;在此总结一下前…Opencv_CUDA实现推理图像前处理与后处理
通过trt 或者 openvino部署深度学习算法时往往会通过opencv的Mat及算法将图像转换为固定的格式作为输入openvino图像的前后处理后边将在单独的文章中写出今晚空闲搜了一些opencv_cuda的使用方法在此总结一下前提是已经通过CMake将cuda和opencv重新编译好了C库
1.前处理
参考【基于opencv-cuda的常见图像预处理】 // -------------- opencv ----------------------- #
#include opencv2/opencv.hpp
#include opencv2/core/core.hpp
#include opencv2/highgui/highgui.hpp
// ---------------- opencv-cuda ---------------- #
#include opencv2/cudawarping.hpp
#include opencv2/cudaarithm.hpp
#include opencv2/cudaimgproc.hpp// ------------ cuda ------------------------- #
#include cuda_runtime_api.h
// ------------------- nvinfer1 ------------------ #
#include NvInfer.h// ------------ standard libraries --------------- #
#include iostream
#include assert.h
#include string
#include vector// ---------------------------------------------- #void preprocessImage(const std::string image_path, float* gpu_input,nvinfer1::Dims3 dims)
{// read imagecv::Mat frame cv::imread(image_path);if(frame.empty()){std::cerr failed to load image: image_path ! std::endl;return;}// uploadcv::cuda::GpuMat gpu_frame;gpu_frame.upload(frame);// resize// CHW orderauto input_width dims.d[2];auto input_height dims.d[1];auto channels dims.d[0];auto input_size cv::Size(input_width, input_height);cv::cuda::GpuMat resized;cv::cuda::resize(gpu_frame, resized, input_size, 0, 0, cv::INTER_LINEAR);//* ------------------------ Pytorch ToTensor and Normalize ------------------- */cv::cuda::GpuMat flt_image;resized.convertTo(flt_image, CV_32FC3, 1.f/255.f);cv::cuda::subtract(flt_image, cv::Scalar(0.485f, 0.346f, 0.406f), flt_image,cv::noArray(), -1);cv::cuda::divide(flt_image, cv::Scalar(0.229f, 0.224f, 0.225f), flt_image, 1, -1);//* ----------------------------------------------------------------------------------- /// BGR To RGBcv::cuda::GpuMat rgb;cv::cuda::cvtColor(flt_image, rgb, cv::COLOR_BGR2RGB);// toTensor(copy data to input float pointer channel by channel)std::vectorcv::cuda::GpuMat rgb_out;for(size_t i0; ichannels; i){rgb_out.emplace_back(cv::cuda::GpuMat(cv::Size(input_width, input_height), CV_32FC1, gpu_input i * input_width * input_height));}cv::cuda::split(flt_image, rgb_out); // opencv HWC order - CHW order
}// calculate size of tensor
size_t getSizeByDim(const nvinfer1::Dims dims)
{size_t size 1;for (size_t i 0; i dims.nbDims; i){size * dims.d[i];}return size;
}int main()
{std::string image_path ./turkish_coffee.jpg;// CHW ordernvinfer1::Dims3 input_dim(3, 640, 640);auto input_size getSizeByDim(input_dim) * sizeof(float);// allocate gpu memory for network inference// 此处的buffer可以认为是TensorRT engine推理时在GPU上分配的输入显存std::vectorvoid* buffers(1);cudaMalloc(buffers[0], input_size);// preprocesspreprocessImage(image_path, (float*)buffers[0], input_dim);// downloadcv::cuda::GpuMat gpu_output;std::vectorcv::cuda::GpuMat resized;for (size_t i 0; i 3; i){resized.emplace_back(cv::cuda::GpuMat(cv::Size(input_dim.d[2], input_dim.d[1]), CV_32FC1, (float*)buffers[0] i * input_dim.d[2] * input_dim.d[1]));}cv::cuda::merge(resized, gpu_output);cv::cuda::GpuMat image_out;// normalizegpu_output.convertTo(image_out, CV_32FC3, 1.f * 255.f);// downloadcv::Mat dst;image_out.download(dst);cv::imwrite(../01_test_demo.jpg, dst);for(void* buf:buffers){cudaFree(buf);}return 0;
}原图与结果图
2. 输出后处理
下边通过一个trt demo展示一下后处理操作源码实现如下
#include iostream
#include fstream
#include NvInfer.h
#include memory
#include NvOnnxParser.h
#include vector
#include cuda_runtime_api.h
#include opencv2/imgcodecs.hpp
#include opencv2/core/cuda.hpp
#include opencv2/cudawarping.hpp
#include opencv2/core.hpp
#include opencv2/cudaarithm.hpp
#include algorithm
#include numeric// destroy TensorRT objects if something goes wrong
struct TRTDestroy
{template class Tvoid operator()(T* obj) const{if (obj){obj-destroy();}}
};template class T
using TRTUniquePtr std::unique_ptrT, TRTDestroy;// calculate size of tensor
size_t getSizeByDim(const nvinfer1::Dims dims)
{size_t size 1;for (size_t i 0; i dims.nbDims; i){size * dims.d[i];}return size;
}// get classes names
std::vectorstd::string getClassNames(const std::string imagenet_classes)
{std::ifstream classes_file(imagenet_classes);std::vectorstd::string classes;if (!classes_file.good()){std::cerr ERROR: cant read file with classes names.\n;return classes;}std::string class_name;while (std::getline(classes_file, class_name)){classes.push_back(class_name);}return classes;
}// preprocessing stage ------------------------------------------------------------------------------------------------
void preprocessImage(const std::string image_path, float* gpu_input, const nvinfer1::Dims dims)
{// read input imagecv::Mat frame cv::imread(image_path);if (frame.empty()){std::cerr Input image image_path load failed\n;return;}cv::cuda::GpuMat gpu_frame;// upload image to GPUgpu_frame.upload(frame);auto input_width dims.d[2];auto input_height dims.d[1];auto channels dims.d[0];auto input_size cv::Size(input_width, input_height);// resizecv::cuda::GpuMat resized;cv::cuda::resize(gpu_frame, resized, input_size, 0, 0, cv::INTER_NEAREST);// normalizecv::cuda::GpuMat flt_image;resized.convertTo(flt_image, CV_32FC3, 1.f / 255.f);cv::cuda::subtract(flt_image, cv::Scalar(0.485f, 0.456f, 0.406f), flt_image, cv::noArray(), -1);cv::cuda::divide(flt_image, cv::Scalar(0.229f, 0.224f, 0.225f), flt_image, 1, -1);// to tensorstd::vectorcv::cuda::GpuMat chw;for (size_t i 0; i channels; i){chw.emplace_back(cv::cuda::GpuMat(input_size, CV_32FC1, gpu_input i * input_width * input_height));}cv::cuda::split(flt_image, chw);
}// post-processing stage ----------------------------------------------------------------------------------------------
void postprocessResults(float *gpu_output, const nvinfer1::Dims dims, int batch_size)
{// get class namesauto classes getClassNames(imagenet_classes.txt);// copy results from GPU to CPUstd::vectorfloat cpu_output(getSizeByDim(dims) * batch_size);cudaMemcpy(cpu_output.data(), gpu_output, cpu_output.size() * sizeof(float), cudaMemcpyDeviceToHost);// calculate softmaxstd::transform(cpu_output.begin(), cpu_output.end(), cpu_output.begin(), [](float val) {return std::exp(val);});auto sum std::accumulate(cpu_output.begin(), cpu_output.end(), 0.0);// find top classes predicted by the modelstd::vectorint indices(getSizeByDim(dims) * batch_size);std::iota(indices.begin(), indices.end(), 0); // generate sequence 0, 1, 2, 3, ..., 999std::sort(indices.begin(), indices.end(), [cpu_output](int i1, int i2) {return cpu_output[i1] cpu_output[i2];});// print resultsint i 0;while (cpu_output[indices[i]] / sum 0.005){if (classes.size() indices[i]){std::cout class: classes[indices[i]] | ;}std::cout confidence: 100 * cpu_output[indices[i]] / sum % | index: indices[i] \n;i;}
}// main pipeline ------------------------------------------------------------------------------------------------------
int main(int argc, char* argv[])
{if (argc 3){std::cerr usage: argv[0] model.onnx image.jpg\n;return -1;}std::string model_path(argv[1]);std::string image_path(argv[2]);int batch_size 1;// initialize TensorRT engine and parse ONNX modelTRTUniquePtrnvinfer1::ICudaEngine engine{nullptr};//初始化engine.........省略// get sizes of input and output and allocate memory required for input data and for output datastd::vectornvinfer1::Dims input_dims; // we expect only one inputstd::vectornvinfer1::Dims output_dims; // and one outputstd::vectorvoid* buffers(engine-getNbBindings()); // buffers for input and output datafor (size_t i 0; i engine-getNbBindings(); i){auto binding_size getSizeByDim(engine-getBindingDimensions(i)) * batch_size * sizeof(float);cudaMalloc(buffers[i], binding_size);if (engine-bindingIsInput(i)){input_dims.emplace_back(engine-getBindingDimensions(i));}else{output_dims.emplace_back(engine-getBindingDimensions(i));}}if (input_dims.empty() || output_dims.empty()){std::cerr Expect at least one input and one output for network\n;return -1;}// preprocess input datapreprocessImage(image_path, (float *) buffers[0], input_dims[0]);// inferencecontext-enqueue(batch_size, buffers.data(), 0, nullptr);// postprocess resultspostprocessResults((float *) buffers[1], output_dims[0], batch_size);for (void* buf : buffers){cudaFree(buf);}return 0;
}
