9.7 visual studio 搭建yolov10的onnx的预测（c++）
1.环境配置
在进行onnx预测前，需要搭建的环境如下:
1.opencv环境的配置，可参考博客:9.2 c++搭建opencv环境-CSDN博客
2.libtorch环境的配置，可参考博客：9.4 visualStudio 2022 配置 cuda 和 torch (c++)-CSDN博客
3.cuda环境的配置，可参考博客：9.4 visualStudio 2022 配置 cuda 和 torch (c++)-CSDN博客
4.onnx环境的配置,可参考博客:VS2019配置ONNXRuntime c++环境_microsoft.ml.onnxruntime-CSDN博客
2.yolov10的c++代码
该代码做了部分的修改，最后调试成功。具体的代码如下:
main.cpp
#include <iostream>
//#include <getopt.h>
#include "yolov5v8_dnn.h"
#include "yolov5v8_ort.h"
using namespace std;
using namespace cv;
void main(int argc, char** argv)
{
string img_path = "E:\\vs\\daima\\1_8\\Project1\\x64\\Release\\street.jpg";
string model_path = "E:\\vs\\daima\\1_8\\Project1\\x64\\Release\\yolov8n-seg.onnx";
string test_cls = "dnn";
if (test_cls == "dnn") {
// Input the path of model ("yolov8s.onnx" or "yolov5s.onnx") to run Inference with yolov8/yolov5 (ONNX)
// Note that in this example the classes are hard-coded and 'classes.txt' is a place holder.
Inference inf(model_path, cv::Size(640, 640), "classes.txt", true);
cv::Mat frame = cv::imread(img_path);
std::vector<Detection> output = inf.runInference(frame);
if (output.size() != 0) inf.DrawPred(frame, output);
else cout << "Detect Nothing!" << endl;
}
if (test_cls == "ort") {
DCSP_CORE* yoloDetector = new DCSP_CORE;
#ifdef USE_CUDA
//DCSP_INIT_PARAM params{ model_path, YOLO_ORIGIN_V5, {640, 640}, 0.25, 0.45, 0.5, true }; // GPU FP32 inference
DCSP_INIT_PARAM params{ model_path, YOLO_ORIGIN_V5_HALF, {640, 640}, 0.25, 0.45, 0.5, true }; // GPU FP16 inference
#else
DCSP_INIT_PARAM params{ model_path, YOLO_ORIGIN_V5, {640, 640},0.25, 0.45, 0.5, false }; // CPU inference
#endif
yoloDetector->CreateSession(params);
cv::Mat img = cv::imread(img_path);
std::vector<DCSP_RESULT> res;
yoloDetector->RunSession(img, res);
if (res.size() != 0) yoloDetector->DrawPred(img, res);
else cout << "Detect Nothing!" << endl;
}
}
yolov5v8_dnn.cpp
#include "yolov5v8_dnn.h"
using namespace std;
Inference::Inference(const std::string& onnxModelPath, const cv::Size& modelInputShape, const std::string& classesTxtFile, const bool& runWithCuda)
{
modelPath = onnxModelPath;
modelShape = modelInputShape;
classesPath = classesTxtFile;
cudaEnabled = runWithCuda;
loadOnnxNetwork();
// loadClassesFromFile(); The classes are hard-coded for this example
}
std::vector<Detection> Inference::runInference(const cv::Mat& input)
{
cv::Mat SrcImg = input;
cv::Mat netInputImg;
cv::Vec4d params;
LetterBox(SrcImg, netInputImg, params, cv::Size(modelShape.width, modelShape.height));
cv::Mat blob;
cv::dnn::blobFromImage(netInputImg, blob, 1.0 / 255.0, modelShape, cv::Scalar(), true, false);
net.setInput(blob);
std::vector<cv::Mat> outputs;
net.forward(outputs, net.getUnconnectedOutLayersNames());
if (outputs.size() == 2) RunSegmentation = true;
int rows = outputs[0].size[1];
int dimensions = outputs[0].size[2];
bool yolov8 = false;
// yolov5 has an output of shape (batchSize, 25200, 85) (Num classes + box[x,y,w,h] + confidence[c])
// yolov8 has an output of shape (batchSize, 84, 8400) (Num classes + box[x,y,w,h])
if (dimensions > rows) // Check if the shape[2] is more than shape[1] (yolov8)
{
yolov8 = true;
rows = outputs[0].size[2];
dimensions = outputs[0].size[1];
outputs[0] = outputs[0].reshape(1, dimensions);
cv::transpose(outputs[0], outputs[0]);
}
float* data = (float*)outputs[0].data;
std::vector<int> class_ids;
std::vector<float> confidences;
std::vector<cv::Rect> boxes;
std::vector<vector<float>> picked_proposals;
for (int i = 0; i < rows; ++i)
{
int _segChannels;
if (yolov8)
{
float* classes_scores = data + 4;
cv::Mat scores(1, classes.size(), CV_32FC1, classes_scores);
cv::Point class_id;
double maxClassScore;
minMaxLoc(scores, 0, &maxClassScore, 0, &class_id);
if (maxClassScore > modelScoreThreshold)
{
if (RunSegmentation) {
_segChannels = outputs[1].size[1];
vector<float> temp_proto(data + classes.size() + 4, data + classes.size() + 4 + _segChannels);
picked_proposals.push_back(temp_proto);
}
confidences.push_back(maxClassScore);
class_ids.push_back(class_id.x);
float x = (data[0] - params[2]) / params[0];
float y = (data[1] - params[3]) / params[1];
float w = data[2] / params[0];
float h = data[3] / params[1];
int left = MAX(round(x - 0.5 * w + 0.5), 0);
int top = MAX(round(y - 0.5 * h + 0.5), 0);
if ((left + w) > SrcImg.cols) { w = SrcImg.cols - left; }
if ((top + h) > SrcImg.rows) { h = SrcImg.rows - top; }
boxes.push_back(cv::Rect(left, top, int(w), int(h)));
}
}
else // yolov5
{
float confidence = data[4];
if (confidence >= modelConfidenceThreshold)
{
float* classes_scores = data + 5;
cv::Mat scores(1, classes.size(), CV_32FC1, classes_scores);
cv::Point class_id;
double max_class_score;
minMaxLoc(scores, 0, &max_class_score, 0, &class_id);
if (max_class_score > modelScoreThreshold)
{
if (RunSegmentation) {
_segChannels = outputs[1].size[1];
vector<float> temp_proto(data + classes.size() + 5, data + classes.size() + 5 + _segChannels);
picked_proposals.push_back(temp_proto);
}
confidences.push_back(confidence);
class_ids.push_back(class_id.x);
float x = (data[0] - params[2]) / params[0];
float y = (data[1] - params[3]) / params[1];
float w = data[2] / params[0];
float h = data[3] / params[1];
int left = MAX(round(x - 0.5 * w + 0.5), 0);
int top = MAX(round(y - 0.5 * h + 0.5), 0);
if ((left + w) > SrcImg.cols) { w = SrcImg.cols - left; }
if ((top + h) > SrcImg.rows) { h = SrcImg.rows - top; }
boxes.push_back(cv::Rect(left, top, int(w), int(h)));
}
}
}
data += dimensions;
}
std::vector<int> nms_result;
cv::dnn::NMSBoxes(boxes, confidences, modelScoreThreshold, modelNMSThreshold, nms_result);
std::vector<Detection> detections{};
std::vector<vector<float>> temp_mask_proposals;
for (unsigned long i = 0; i < nms_result.size(); ++i)
{
int idx = nms_result[i];
Detection result;
result.class_id = class_ids[idx];
result.confidence = confidences[idx];
std::random_device rd;
std::mt19937 gen(rd());
std::uniform_int_distribution<int> dis(100, 255);
result.color = cv::Scalar(dis(gen),
dis(gen),
dis(gen));
result.className = classes[result.class_id];
result.box = boxes[idx];
if (RunSegmentation) temp_mask_proposals.push_back(picked_proposals[idx]);
if (result.box.width != 0 && result.box.height != 0) detections.push_back(result);
}
if (RunSegmentation) {
cv::Mat mask_proposals;
for (int i = 0; i < temp_mask_proposals.size(); ++i)
mask_proposals.push_back(cv::Mat(temp_mask_proposals[i]).t());
GetMask(mask_proposals, outputs[1], params, SrcImg.size(), detections);
}
return detections;
}
void Inference::loadClassesFromFile()
{
std::ifstream inputFile(classesPath);
if (inputFile.is_open())
{
std::string classLine;
while (std::getline(inputFile, classLine))
classes.push_back(classLine);
inputFile.close();
}
}
void Inference::loadOnnxNetwork()
{
net = cv::dnn::readNetFromONNX(modelPath);
if (cudaEnabled)
{
std::cout << "\nRunning on CUDA" << std::endl;
net.setPreferableBackend(cv::dnn::DNN_BACKEND_CUDA);
net.setPreferableTarget(cv::dnn::DNN_TARGET_CUDA);
}
else
{
std::cout << "\nRunning on CPU" << std::endl;
net.setPreferableBackend(cv::dnn::DNN_BACKEND_OPENCV);
net.setPreferableTarget(cv::dnn::DNN_TARGET_CPU);
}
}
void Inference::LetterBox(const cv::Mat& image, cv::Mat& outImage, cv::Vec4d& params, const cv::Size& newShape,
bool autoShape, bool scaleFill, bool scaleUp, int stride, const cv::Scalar& color)
{
if (false) {
int maxLen = MAX(image.rows, image.cols);
outImage = cv::Mat::zeros(cv::Size(maxLen, maxLen), CV_8UC3);
image.copyTo(outImage(cv::Rect(0, 0, image.cols, image.rows)));
params[0] = 1;
params[1] = 1;
params[3] = 0;
params[2] = 0;
}
cv::Size shape = image.size();
float r = std::min((float)newShape.height / (float)shape.height,
(float)newShape.width / (float)shape.width);
if (!scaleUp)
r = std::min(r, 1.0f);
float ratio[2]{ r, r };
int new_un_pad[2] = { (int)std::round((float)shape.width * r),(int)std::round((float)shape.height * r) };
auto dw = (float)(newShape.width - new_un_pad[0]);
auto dh = (float)(newShape.height - new_un_pad[1]);
if (autoShape)
{
dw = (float)((int)dw % stride);
dh = (float)((int)dh % stride);
}
else if (scaleFill)
{
dw = 0.0f;
dh = 0.0f;
new_un_pad[0] = newShape.width;
new_un_pad[1] = newShape.height;
ratio[0] = (float)newShape.width / (float)shape.width;
ratio[1] = (float)newShape.height / (float)shape.height;
}
dw /= 2.0f;
dh /= 2.0f;
if (shape.width != new_un_pad[0] && shape.height != new_un_pad[1])
{
cv::resize(image, outImage, cv::Size(new_un_pad[0], new_un_pad[1]));
}
else {
outImage = image.clone();
}
int top = int(std::round(dh - 0.1f));
int bottom = int(std::round(dh + 0.1f));
int left = int(std::round(dw - 0.1f));
int right = int(std::round(dw + 0.1f));
params[0] = ratio[0];
params[1] = ratio[1];
params[2] = left;
params[3] = top;
cv::copyMakeBorder(outImage, outImage, top, bottom, left, right, cv::BORDER_CONSTANT, color);
}
void Inference::GetMask(const cv::Mat& maskProposals, const cv::Mat& mask_protos, const cv::Vec4d& params, const cv::Size& srcImgShape, std::vector<Detection>& output) {
if (output.size() == 0) return;
int _segChannels = mask_protos.size[1];
int _segHeight = mask_protos.size[2];
int _segWidth = mask_protos.size[3];
cv::Mat protos = mask_protos.reshape(0, { _segChannels,_segWidth * _segHeight });
cv::Mat matmulRes = (maskProposals * protos).t();
cv::Mat masks = matmulRes.reshape(output.size(), { _segHeight,_segWidth });
vector<cv::Mat> maskChannels;
split(masks, maskChannels);
for (int i = 0; i < output.size(); ++i) {
cv::Mat dest, mask;
//sigmoid
cv::exp(-maskChannels[i], dest);
dest = 1.0 / (1.0 + dest);
cv::Rect roi(int(params[2] / modelShape.width * _segWidth), int(params[3] / modelShape.height * _segHeight), int(_segWidth - params[2] / 2), int(_segHeight - params[3] / 2));
dest = dest(roi);
cv::resize(dest, mask, srcImgShape, cv::INTER_NEAREST);
//crop
cv::Rect temp_rect = output[i].box;
mask = mask(temp_rect) > modelScoreThreshold;
output[i].boxMask = mask;
}
}
void Inference::DrawPred(cv::Mat& img, vector<Detection>& result) {
int detections = result.size();
std::cout << "Number of detections:" << detections << std::endl;
cv::Mat mask = img.clone();
for (int i = 0; i < detections; ++i)
{
Detection detection = result[i];
cv::Rect box = detection.box;
cv::Scalar color = detection.color;
// Detection box
cv::rectangle(img, box, color, 2);
mask(detection.box).setTo(color, detection.boxMask);
// Detection box text
std::string classString = detection.className + ' ' + std::to_string(detection.confidence).substr(0, 4);
cv::Size textSize = cv::getTextSize(classString, cv::FONT_HERSHEY_DUPLEX, 1, 2, 0);
cv::Rect textBox(box.x, box.y - 40, textSize.width + 10, textSize.height + 20);
cv::rectangle(img, textBox, color, cv::FILLED);
cv::putText(img, classString, cv::Point(box.x + 5, box.y - 10), cv::FONT_HERSHEY_DUPLEX, 1, cv::Scalar(0, 0, 0), 2, 0);
}
// Detection mask
if (RunSegmentation) cv::addWeighted(img, 0.5, mask, 0.5, 0, img); //将mask加在原图上面
cv::imshow("Inference", img);
cv::imwrite("out.bmp", img);
cv::waitKey();
cv::destroyWindow("Inference");
}yolov5v8_ort.cpp
#define _CRT_SECURE_NO_WARNINGS
#include "yolov5v8_ort.h"
#include <regex>
#include <random>
#define benchmark
using namespace std;
DCSP_CORE::DCSP_CORE() {
}
DCSP_CORE::~DCSP_CORE() {
delete session;
}
#ifdef USE_CUDA
namespace Ort
{
template<>
struct TypeToTensorType<half> { static constexpr ONNXTensorElementDataType type = ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT16; };
}
#endif
template<typename T>
char* BlobFromImage(cv::Mat& iImg, T& iBlob) {
int channels = iImg.channels();
int imgHeight = iImg.rows;
int imgWidth = iImg.cols;
for (int c = 0; c < channels; c++) {
for (int h = 0; h < imgHeight; h++) {
for (int w = 0; w < imgWidth; w++) {
iBlob[c * imgWidth * imgHeight + h * imgWidth + w] = typename std::remove_pointer<T>::type(
(iImg.at<cv::Vec3b>(h, w)[c]) / 255.0f);
}
}
}
return RET_OK;
}
char* PreProcess(cv::Mat& iImg, std::vector<int> iImgSize, cv::Mat& oImg) {
cv::Mat img = iImg.clone();
cv::resize(iImg, oImg, cv::Size(iImgSize.at(0), iImgSize.at(1)));
if (img.channels() == 1) {
cv::cvtColor(oImg, oImg, cv::COLOR_GRAY2BGR);
}
cv::cvtColor(oImg, oImg, cv::COLOR_BGR2RGB);
return RET_OK;
}
void LetterBox(const cv::Mat& image, cv::Mat& outImage, cv::Vec4d& params, const cv::Size& newShape = cv::Size(640, 640),
bool autoShape = false, bool scaleFill = false, bool scaleUp = true, int stride = 32, const cv::Scalar& color = cv::Scalar(114, 114, 114))
{
if (false) {
int maxLen = MAX(image.rows, image.cols);
outImage = cv::Mat::zeros(cv::Size(maxLen, maxLen), CV_8UC3);
image.copyTo(outImage(cv::Rect(0, 0, image.cols, image.rows)));
params[0] = 1;
params[1] = 1;
params[3] = 0;
params[2] = 0;
}
cv::Size shape = image.size();
float r = std::min((float)newShape.height / (float)shape.height,
(float)newShape.width / (float)shape.width);
if (!scaleUp)
r = std::min(r, 1.0f);
float ratio[2]{ r, r };
int new_un_pad[2] = { (int)std::round((float)shape.width * r),(int)std::round((float)shape.height * r) };
auto dw = (float)(newShape.width - new_un_pad[0]);
auto dh = (float)(newShape.height - new_un_pad[1]);
if (autoShape)
{
dw = (float)((int)dw % stride);
dh = (float)((int)dh % stride);
}
else if (scaleFill)
{
dw = 0.0f;
dh = 0.0f;
new_un_pad[0] = newShape.width;
new_un_pad[1] = newShape.height;
ratio[0] = (float)newShape.width / (float)shape.width;
ratio[1] = (float)newShape.height / (float)shape.height;
}
dw /= 2.0f;
dh /= 2.0f;
if (shape.width != new_un_pad[0] && shape.height != new_un_pad[1])
{
cv::resize(image, outImage, cv::Size(new_un_pad[0], new_un_pad[1]));
}
else {
outImage = image.clone();
}
int top = int(std::round(dh - 0.1f));
int bottom = int(std::round(dh + 0.1f));
int left = int(std::round(dw - 0.1f));
int right = int(std::round(dw + 0.1f));
params[0] = ratio[0];
params[1] = ratio[1];
params[2] = left;
params[3] = top;
cv::copyMakeBorder(outImage, outImage, top, bottom, left, right, cv::BORDER_CONSTANT, color);
}
void GetMask(const int* const _seg_params, const float& rectConfidenceThreshold, const cv::Mat& maskProposals, const cv::Mat& mask_protos, const cv::Vec4d& params, const cv::Size& srcImgShape, std::vector<DCSP_RESULT>& output) {
int _segChannels = *_seg_params;
int _segHeight = *(_seg_params + 1);
int _segWidth = *(_seg_params + 2);
int _netHeight = *(_seg_params + 3);
int _netWidth = *(_seg_params + 4);
cv::Mat protos = mask_protos.reshape(0, { _segChannels,_segWidth * _segHeight });
cv::Mat matmulRes = (maskProposals * protos).t();
cv::Mat masks = matmulRes.reshape(output.size(), { _segHeight,_segWidth });
std::vector<cv::Mat> maskChannels;
split(masks, maskChannels);
for (int i = 0; i < output.size(); ++i) {
cv::Mat dest, mask;
//sigmoid
cv::exp(-maskChannels[i], dest);
dest = 1.0 / (1.0 + dest);
cv::Rect roi(int(params[2] / _netWidth * _segWidth), int(params[3] / _netHeight * _segHeight), int(_segWidth - params[2] / 2), int(_segHeight - params[3] / 2));
dest = dest(roi);
cv::resize(dest, mask, srcImgShape, cv::INTER_NEAREST);
//crop
cv::Rect temp_rect = output[i].box;
mask = mask(temp_rect) > rectConfidenceThreshold;
output[i].boxMask = mask;
}
}
void DCSP_CORE::DrawPred(cv::Mat& img, std::vector<DCSP_RESULT>& result) {
int detections = result.size();
std::cout << "Number of detections:" << detections << std::endl;
cv::Mat mask = img.clone();
for (int i = 0; i < detections; ++i)
{
DCSP_RESULT detection = result[i];
cv::Rect box = detection.box;
cv::Scalar color = detection.color;
// Detection box
cv::rectangle(img, box, color, 2);
mask(detection.box).setTo(color, detection.boxMask);
// Detection box text
std::string classString = detection.className + ' ' + std::to_string(detection.confidence).substr(0, 4);
cv::Size textSize = cv::getTextSize(classString, cv::FONT_HERSHEY_DUPLEX, 1, 2, 0);
cv::Rect textBox(box.x, box.y - 40, textSize.width + 10, textSize.height + 20);
cv::rectangle(img, textBox, color, cv::FILLED);
cv::putText(img, classString, cv::Point(box.x + 5, box.y - 10), cv::FONT_HERSHEY_DUPLEX, 1, cv::Scalar(0, 0, 0), 2, 0);
}
// Detection mask
if (RunSegmentation) cv::addWeighted(img, 0.5, mask, 0.5, 0, img); //将mask加在原图上面
cv::imshow("Inference", img);
cv::imwrite("out.bmp", img);
cv::waitKey();
cv::destroyWindow("Inference");
}
char* DCSP_CORE::CreateSession(DCSP_INIT_PARAM& iParams) {
char* Ret = RET_OK;
std::regex pattern("[\u4e00-\u9fa5]");
bool result = std::regex_search(iParams.ModelPath, pattern);
if (result) {
char str_tmp[] = "[DCSP_ONNX]:Model path error.Change your model path without chinese characters.";
Ret = str_tmp;
std::cout << Ret << std::endl;
return Ret;
}
try {
modelConfidenceThreshold = iParams.modelConfidenceThreshold;
rectConfidenceThreshold = iParams.RectConfidenceThreshold;
iouThreshold = iParams.iouThreshold;
imgSize = iParams.imgSize;
modelType = iParams.ModelType;
env = Ort::Env(ORT_LOGGING_LEVEL_WARNING, "Yolo");
Ort::SessionOptions sessionOption;
if (iParams.CudaEnable) {
cudaEnable = iParams.CudaEnable;
OrtCUDAProviderOptions cudaOption;
cudaOption.device_id = 0;
sessionOption.AppendExecutionProvider_CUDA(cudaOption);
}
sessionOption.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_ALL);
sessionOption.SetIntraOpNumThreads(iParams.IntraOpNumThreads);
sessionOption.SetLogSeverityLevel(iParams.LogSeverityLevel);
#ifdef _WIN32
int ModelPathSize = MultiByteToWideChar(CP_UTF8, 0, iParams.ModelPath.c_str(), static_cast<int>(iParams.ModelPath.length()), nullptr, 0);
wchar_t* wide_cstr = new wchar_t[ModelPathSize + 1];
MultiByteToWideChar(CP_UTF8, 0, iParams.ModelPath.c_str(), static_cast<int>(iParams.ModelPath.length()), wide_cstr, ModelPathSize);
wide_cstr[ModelPathSize] = L'\0';
const wchar_t* modelPath = wide_cstr;
#else
const char* modelPath = iParams.ModelPath.c_str();
#endif // _WIN32
session = new Ort::Session(env, modelPath, sessionOption);
Ort::AllocatorWithDefaultOptions allocator;
size_t inputNodesNum = session->GetInputCount();
for (size_t i = 0; i < inputNodesNum; i++) {
Ort::AllocatedStringPtr input_node_name = session->GetInputNameAllocated(i, allocator);
char* temp_buf = new char[50];
strcpy(temp_buf, input_node_name.get());
inputNodeNames.push_back(temp_buf);
}
size_t OutputNodesNum = session->GetOutputCount();
for (size_t i = 0; i < OutputNodesNum; i++) {
Ort::AllocatedStringPtr output_node_name = session->GetOutputNameAllocated(i, allocator);
char* temp_buf = new char[10];
strcpy(temp_buf, output_node_name.get());
outputNodeNames.push_back(temp_buf);
}
if (outputNodeNames.size() == 2) RunSegmentation = true;
options = Ort::RunOptions{ nullptr };
WarmUpSession();
return RET_OK;
}
catch (const std::exception& e) {
const char* str1 = "[DCSP_ONNX]:";
const char* str2 = e.what();
std::string result = std::string(str1) + std::string(str2);
char* merged = new char[result.length() + 1];
std::strcpy(merged, result.c_str());
std::cout << merged << std::endl;
delete[] merged;
char str_tmps[] = "[DCSP_ONNX]:Create session failed.";
char* strs = str_tmps;
return strs;
}
}
char* DCSP_CORE::RunSession(cv::Mat& iImg, std::vector<DCSP_RESULT>& oResult) {
#ifdef benchmark
clock_t starttime_1 = clock();
#endif // benchmark
char* Ret = RET_OK;
cv::Mat processedImg;
cv::Vec4d params;
//resize图片尺寸，PreProcess是直接resize，LetterBox有padding操作
//PreProcess(iImg, imgSize, processedImg);
LetterBox(iImg, processedImg, params, cv::Size(imgSize.at(1), imgSize.at(0)));
if (modelType < 4) {
float* blob = new float[processedImg.total() * 3];
BlobFromImage(processedImg, blob);
std::vector<int64_t> inputNodeDims = { 1, 3, imgSize.at(0), imgSize.at(1) };
TensorProcess(starttime_1, params, iImg, blob, inputNodeDims, oResult);
}
else {
#ifdef USE_CUDA
half* blob = new half[processedImg.total() * 3];
BlobFromImage(processedImg, blob);
std::vector<int64_t> inputNodeDims = { 1,3,imgSize.at(0),imgSize.at(1) };
TensorProcess(starttime_1, params, iImg, blob, inputNodeDims, oResult);
#endif
}
return Ret;
}
template<typename N>
char* DCSP_CORE::TensorProcess(clock_t& starttime_1, cv::Vec4d& params, cv::Mat& iImg, N* blob, std::vector<int64_t>& inputNodeDims, std::vector<DCSP_RESULT>& oResult) {
Ort::Value inputTensor = Ort::Value::CreateTensor<typename std::remove_pointer<N>::type>(
Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeCPU), blob, 3 * imgSize.at(0) * imgSize.at(1), inputNodeDims.data(), inputNodeDims.size());
#ifdef benchmark
clock_t starttime_2 = clock();
#endif // benchmark
auto outputTensor = session->Run(options, inputNodeNames.data(), &inputTensor, 1, outputNodeNames.data(), outputNodeNames.size());
#ifdef benchmark
clock_t starttime_3 = clock();
#endif // benchmark
std::vector<int64_t> _outputTensorShape;
_outputTensorShape = outputTensor[0].GetTensorTypeAndShapeInfo().GetShape();
//auto output = outputTensor[0].GetTensorMutableData<typename std::remove_pointer<N>::type>();
N* output = outputTensor[0].GetTensorMutableData<N>();
delete blob;
// yolov5 has an output of shape (batchSize, 25200, 85) (Num classes + box[x,y,w,h] + confidence[c])
// yolov8 has an output of shape (batchSize, 84, 8400) (Num classes + box[x,y,w,h])
// yolov5
int dimensions = _outputTensorShape[1];
int rows = _outputTensorShape[2];
cv::Mat rowData;
if (modelType < 3)
rowData = cv::Mat(dimensions, rows, CV_32F, output);
else
rowData = cv::Mat(dimensions, rows, CV_16S, output);
// yolov8
if (rows > dimensions) {
dimensions = _outputTensorShape[2];
rows = _outputTensorShape[1];
rowData = rowData.t();
}
std::vector<int> class_ids;
std::vector<float> confidences;
std::vector<cv::Rect> boxes;
std::vector<std::vector<float>> picked_proposals;
N* data = (N*)rowData.data;
for (int i = 0; i < dimensions; ++i) {
switch (modelType) {
case 0://V5_ORIGIN_FP32
case 7://V5_ORIGIN_FP16
{
N confidence = data[4];
if (confidence >= modelConfidenceThreshold)
{
cv::Mat scores;
if (modelType < 3) scores = cv::Mat(1, classes.size(), CV_32FC1, data + 5);
else scores = cv::Mat(1, classes.size(), CV_16SC1, data + 5);
cv::Point class_id;
double max_class_score;
minMaxLoc(scores, 0, &max_class_score, 0, &class_id);
max_class_score = *(data + 5 + class_id.x) * confidence;
if (max_class_score > rectConfidenceThreshold)
{
if (RunSegmentation) {
int _segChannels = outputTensor[1].GetTensorTypeAndShapeInfo().GetShape()[1];
std::vector<float> temp_proto(data + classes.size() + 5, data + classes.size() + 5 + _segChannels);
picked_proposals.push_back(temp_proto);
}
confidences.push_back(confidence);
class_ids.push_back(class_id.x);
float x = (data[0] - params[2]) / params[0];
float y = (data[1] - params[3]) / params[1];
float w = data[2] / params[0];
float h = data[3] / params[1];
int left = MAX(round(x - 0.5 * w + 0.5), 0);
int top = MAX(round(y - 0.5 * h + 0.5), 0);
if ((left + w) > iImg.cols) { w = iImg.cols - left; }
if ((top + h) > iImg.rows) { h = iImg.rows - top; }
boxes.emplace_back(cv::Rect(left, top, int(w), int(h)));
}
}
break;
}
case 1://V8_ORIGIN_FP32
case 4://V8_ORIGIN_FP16
{
cv::Mat scores;
if (modelType < 3) scores = cv::Mat(1, classes.size(), CV_32FC1, data + 4);
else scores = cv::Mat(1, classes.size(), CV_16SC1, data + 4);
cv::Point class_id;
double maxClassScore;
cv::minMaxLoc(scores, 0, &maxClassScore, 0, &class_id);
maxClassScore = *(data + 4 + class_id.x);
if (maxClassScore > rectConfidenceThreshold) {
if (RunSegmentation) {
int _segChannels = outputTensor[1].GetTensorTypeAndShapeInfo().GetShape()[1];
std::vector<float> temp_proto(data + classes.size() + 4, data + classes.size() + 4 + _segChannels);
picked_proposals.push_back(temp_proto);
}
confidences.push_back(maxClassScore);
class_ids.push_back(class_id.x);
float x = (data[0] - params[2]) / params[0];
float y = (data[1] - params[3]) / params[1];
float w = data[2] / params[0];
float h = data[3] / params[1];
int left = MAX(round(x - 0.5 * w + 0.5), 0);
int top = MAX(round(y - 0.5 * h + 0.5), 0);
if ((left + w) > iImg.cols) { w = iImg.cols - left; }
if ((top + h) > iImg.rows) { h = iImg.rows - top; }
boxes.emplace_back(cv::Rect(left, top, int(w), int(h)));
}
break;
}
}
data += rows;
}
std::vector<int> nmsResult;
cv::dnn::NMSBoxes(boxes, confidences, rectConfidenceThreshold, iouThreshold, nmsResult);
std::vector<std::vector<float>> temp_mask_proposals;
for (int i = 0; i < nmsResult.size(); ++i) {
int idx = nmsResult[i];
DCSP_RESULT result;
result.classId = class_ids[idx];
result.confidence = confidences[idx];
result.box = boxes[idx];
result.className = classes[result.classId];
std::random_device rd;
std::mt19937 gen(rd());
std::uniform_int_distribution<int> dis(100, 255);
result.color = cv::Scalar(dis(gen), dis(gen), dis(gen));
if (result.box.width != 0 && result.box.height != 0) oResult.push_back(result);
if (RunSegmentation) temp_mask_proposals.push_back(picked_proposals[idx]);
}
if (RunSegmentation) {
cv::Mat mask_proposals;
for (int i = 0; i < temp_mask_proposals.size(); ++i)
mask_proposals.push_back(cv::Mat(temp_mask_proposals[i]).t());
std::vector<int64_t> _outputMaskTensorShape;
_outputMaskTensorShape = outputTensor[1].GetTensorTypeAndShapeInfo().GetShape();
int _segChannels = _outputMaskTensorShape[1];
int _segWidth = _outputMaskTensorShape[2];
int _segHeight = _outputMaskTensorShape[3];
N* pdata = outputTensor[1].GetTensorMutableData<N>();
std::vector<float> mask(pdata, pdata + _segChannels * _segWidth * _segHeight);
int _seg_params[5] = { _segChannels, _segWidth, _segHeight, inputNodeDims[2], inputNodeDims[3] };
cv::Mat mask_protos = cv::Mat(mask);
GetMask(_seg_params, rectConfidenceThreshold, mask_proposals, mask_protos, params, iImg.size(), oResult);
}
#ifdef benchmark
clock_t starttime_4 = clock();
double pre_process_time = (double)(starttime_2 - starttime_1) / CLOCKS_PER_SEC * 1000;
double process_time = (double)(starttime_3 - starttime_2) / CLOCKS_PER_SEC * 1000;
double post_process_time = (double)(starttime_4 - starttime_3) / CLOCKS_PER_SEC * 1000;
if (cudaEnable) {
std::cout << "[DCSP_ONNX(CUDA)]: " << pre_process_time << "ms pre-process, " << process_time
<< "ms inference, " << post_process_time << "ms post-process." << std::endl;
}
else {
std::cout << "[DCSP_ONNX(CPU)]: " << pre_process_time << "ms pre-process, " << process_time
<< "ms inference, " << post_process_time << "ms post-process." << std::endl;
}
#endif // benchmark
return RET_OK;
}
char* DCSP_CORE::WarmUpSession() {
clock_t starttime_1 = clock();
cv::Mat iImg = cv::Mat(cv::Size(imgSize.at(0), imgSize.at(1)), CV_8UC3);
cv::Mat processedImg;
cv::Vec4d params;
//resize图片尺寸，PreProcess是直接resize，LetterBox有padding操作
//PreProcess(iImg, imgSize, processedImg);
LetterBox(iImg, processedImg, params, cv::Size(imgSize.at(1), imgSize.at(0)));
if (modelType < 4) {
float* blob = new float[iImg.total() * 3];
BlobFromImage(processedImg, blob);
std::vector<int64_t> YOLO_input_node_dims = { 1, 3, imgSize.at(0), imgSize.at(1) };
Ort::Value input_tensor = Ort::Value::CreateTensor<float>(
Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeCPU), blob, 3 * imgSize.at(0) * imgSize.at(1),
YOLO_input_node_dims.data(), YOLO_input_node_dims.size());
auto output_tensors = session->Run(options, inputNodeNames.data(), &input_tensor, 1, outputNodeNames.data(), outputNodeNames.size());
delete[] blob;
clock_t starttime_4 = clock();
double post_process_time = (double)(starttime_4 - starttime_1) / CLOCKS_PER_SEC * 1000;
if (cudaEnable) {
std::cout << "[DCSP_ONNX(CUDA)]: " << "Cuda warm-up cost " << post_process_time << " ms. " << std::endl;
}
}
else {
#ifdef USE_CUDA
half* blob = new half[iImg.total() * 3];
BlobFromImage(processedImg, blob);
std::vector<int64_t> YOLO_input_node_dims = { 1,3,imgSize.at(0),imgSize.at(1) };
Ort::Value input_tensor = Ort::Value::CreateTensor<half>(Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeCPU), blob, 3 * imgSize.at(0) * imgSize.at(1), YOLO_input_node_dims.data(), YOLO_input_node_dims.size());
auto output_tensors = session->Run(options, inputNodeNames.data(), &input_tensor, 1, outputNodeNames.data(), outputNodeNames.size());
delete[] blob;
clock_t starttime_4 = clock();
double post_process_time = (double)(starttime_4 - starttime_1) / CLOCKS_PER_SEC * 1000;
if (cudaEnable)
{
std::cout << "[DCSP_ONNX(CUDA)]: " << "Cuda warm-up cost " << post_process_time << " ms. " << std::endl;
}
#endif
}
return RET_OK;
}
yolov5v8_dnn.h
#ifndef YOLOV5V8_DNN_H
#define YOLOV5V8_DNN_H
// Cpp native
#include <fstream>
#include <vector>
#include <string>
#include <random>
// OpenCV / DNN / Inference
#include <opencv2/imgproc.hpp>
#include <opencv2/opencv.hpp>
#include <opencv2/dnn.hpp>
struct Detection
{
int class_id{ 0 };
std::string className{};
float confidence{ 0.0 };
cv::Scalar color{};
cv::Rect box{};
cv::Mat boxMask;
};
class Inference
{
public:
Inference(const std::string& onnxModelPath, const cv::Size& modelInputShape = { 640, 640 }, const std::string& classesTxtFile = "", const bool& runWithCuda = true);
std::vector<Detection> runInference(const cv::Mat& input);
void DrawPred(cv::Mat& img, std::vector<Detection>& result);
private:
void loadClassesFromFile();
void loadOnnxNetwork();
void LetterBox(const cv::Mat& image, cv::Mat& outImage,
cv::Vec4d& params, //[ratio_x,ratio_y,dw,dh]
const cv::Size& newShape = cv::Size(640, 640),
bool autoShape = false,
bool scaleFill = false,
bool scaleUp = true,
int stride = 32,
const cv::Scalar& color = cv::Scalar(114, 114, 114));
void GetMask(const cv::Mat& maskProposals, const cv::Mat& mask_protos, const cv::Vec4d& params, const cv::Size& srcImgShape, std::vector<Detection>& output);
private:
std::string modelPath{};
bool cudaEnabled{};
cv::Size2f modelShape{};
bool RunSegmentation = false;
float modelConfidenceThreshold{ 0.25 };
float modelScoreThreshold{ 0.45 };
float modelNMSThreshold{ 0.50 };
bool letterBoxForSquare = true;
cv::dnn::Net net;
std::string classesPath{};
std::vector<std::string> classes{ "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant",
"stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella",
"handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
"tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot",
"hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard",
"cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush" };
};
#endif // YOLOV5V8_DNN_H
yolov5v8_ort.h
#pragma once
#define RET_OK nullptr
#define USE_CUDA
#ifdef _WIN32
#include <Windows.h>
#include <direct.h>
#include <io.h>
#endif
#include <string>
#include <vector>
#include <cstdio>
#include <opencv2/opencv.hpp>
#include "onnxruntime_cxx_api.h"
#ifdef USE_CUDA
#include <cuda_fp16.h>
#endif
enum MODEL_TYPE {
//FLOAT32 MODEL
YOLO_ORIGIN_V5 = 0,//support
YOLO_ORIGIN_V8 = 1,//support
YOLO_POSE_V8 = 2,
YOLO_CLS_V8 = 3,
YOLO_ORIGIN_V8_HALF = 4,//support
YOLO_POSE_V8_HALF = 5,
YOLO_CLS_V8_HALF = 6,
YOLO_ORIGIN_V5_HALF = 7 //support
};
typedef struct _DCSP_INIT_PARAM {
std::string ModelPath;
MODEL_TYPE ModelType = YOLO_ORIGIN_V8;
std::vector<int> imgSize = { 640, 640 };
float modelConfidenceThreshold = 0.25;
float RectConfidenceThreshold = 0.6;
float iouThreshold = 0.5;
bool CudaEnable = false;
int LogSeverityLevel = 3;
int IntraOpNumThreads = 1;
} DCSP_INIT_PARAM;
typedef struct _DCSP_RESULT {
int classId;
std::string className;
float confidence;
cv::Rect box;
cv::Mat boxMask; //矩形框内mask
cv::Scalar color;
} DCSP_RESULT;
class DCSP_CORE {
public:
DCSP_CORE();
~DCSP_CORE();
public:
void DrawPred(cv::Mat& img, std::vector<DCSP_RESULT>& result);
char* CreateSession(DCSP_INIT_PARAM& iParams);
char* RunSession(cv::Mat& iImg, std::vector<DCSP_RESULT>& oResult);
char* WarmUpSession();
template<typename N>
char* TensorProcess(clock_t& starttime_1, cv::Vec4d& params, cv::Mat& iImg, N* blob, std::vector<int64_t>& inputNodeDims, std::vector<DCSP_RESULT>& oResult);
std::vector<std::string> classes{ "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant",
"stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella",
"handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
"tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot",
"hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard",
"cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush" };
private:
Ort::Env env;
Ort::Session* session;
bool cudaEnable;
Ort::RunOptions options;
bool RunSegmentation = false;
std::vector<const char*> inputNodeNames;
std::vector<const char*> outputNodeNames;
MODEL_TYPE modelType;
std::vector<int> imgSize;
float modelConfidenceThreshold;
float rectConfidenceThreshold;
float iouThreshold;
};
3.geotpt文件的配置
geotpt文件的配置比较简单，我们只需要写两个文件放入我们的工程就行，代码如下：
getopt.h
# ifndef __GETOPT_H_
# define __GETOPT_H_
# ifdef _GETOPT_API
# undef _GETOPT_API
# endif
//------------------------------------------------------------------------------
# if defined(EXPORTS_GETOPT) && defined(STATIC_GETOPT)
# error "The preprocessor definitions of EXPORTS_GETOPT and STATIC_GETOPT \
can only be used individually"
# elif defined(STATIC_GETOPT)
# pragma message("Warning static builds of getopt violate the Lesser GNU \
Public License")
# define _GETOPT_API
# elif defined(EXPORTS_GETOPT)
# pragma message("Exporting getopt library")
# define _GETOPT_API __declspec(dllexport)
# else
# pragma message("Importing getopt library")
# define _GETOPT_API __declspec(dllimport)
# endif
# include <tchar.h>
// Standard GNU options
# define null_argument 0 /*Argument Null*/
# define no_argument 0 /*Argument Switch Only*/
# define required_argument 1 /*Argument Required*/
# define optional_argument 2 /*Argument Optional*/
// Shorter Versions of options
# define ARG_NULL 0 /*Argument Null*/
# define ARG_NONE 0 /*Argument Switch Only*/
# define ARG_REQ 1 /*Argument Required*/
# define ARG_OPT 2 /*Argument Optional*/
// Change behavior for C\C++
# ifdef __cplusplus
# define _BEGIN_EXTERN_C extern "C" {
# define _END_EXTERN_C }
# define _GETOPT_THROW throw()
# else
# define _BEGIN_EXTERN_C
# define _END_EXTERN_C
# define _GETOPT_THROW
# endif
_BEGIN_EXTERN_C
extern _GETOPT_API TCHAR* optarg;
extern _GETOPT_API int optind;
extern _GETOPT_API int opterr;
extern _GETOPT_API int optopt;
struct option
{
/* The predefined macro variable __STDC__ is defined for C++, and it has the in-
teger value 0 when it is used in an #if statement, indicating that the C++ l-
anguage is not a proper superset of C, and that the compiler does not confor-
m to C. In C, __STDC__ has the integer value 1. */
# if defined (__STDC__) && __STDC__
const TCHAR* name;
# else
TCHAR* name;
# endif
int has_arg;
int* flag;
TCHAR val;
};
extern _GETOPT_API int getopt(int argc, TCHAR* const* argv
, const TCHAR* optstring) _GETOPT_THROW;
extern _GETOPT_API int getopt_long
(int ___argc, TCHAR* const* ___argv
, const TCHAR* __shortopts
, const struct option* __longopts
, int* __longind) _GETOPT_THROW;
extern _GETOPT_API int getopt_long_only
(int ___argc, TCHAR* const* ___argv
, const TCHAR* __shortopts
, const struct option* __longopts
, int* __longind) _GETOPT_THROW;
// harly.he add for reentrant 12.09/2013
extern _GETOPT_API void getopt_reset() _GETOPT_THROW;
_END_EXTERN_C
// Undefine so the macros are not included
# undef _BEGIN_EXTERN_C
# undef _END_EXTERN_C
# undef _GETOPT_THROW
# undef _GETOPT_API
# endif // __GETOPT_H_getopt.c
# ifndef _CRT_SECURE_NO_WARNINGS
# define _CRT_SECURE_NO_WARNINGS
# endif
# include <stdlib.h>
# include <stdio.h>
# include <tchar.h>
# include "getopt.h"
# ifdef __cplusplus
# define _GETOPT_THROW throw()
# else
# define _GETOPT_THROW
# endif
enum ENUM_ORDERING
{
REQUIRE_ORDER, PERMUTE, RETURN_IN_ORDER
};
struct _getopt_data
{
int optind;
int opterr;
int optopt;
TCHAR* optarg;
int __initialized;
TCHAR* __nextchar;
int __ordering;
int __posixly_correct;
int __first_nonopt;
int __last_nonopt;
};
static struct _getopt_data getopt_data = { 0, 0, 0, NULL, 0, NULL, 0, 0, 0, 0 };
TCHAR* optarg = NULL;
int optind = 1;
int opterr = 1;
int optopt = _T('?');
static void exchange(TCHAR** argv, struct _getopt_data* d)
{
int bottom = d->__first_nonopt;
int middle = d->__last_nonopt;
int top = d->optind;
TCHAR* tem;
while (top > middle && middle > bottom)
{
if (top - middle > middle - bottom)
{
int len = middle - bottom;
register int i;
for (i = 0; i < len; i++)
{
tem = argv[bottom + i];
argv[bottom + i] = argv[top - (middle - bottom) + i];
argv[top - (middle - bottom) + i] = tem;
}
top -= len;
}
else
{
int len = top - middle;
register int i;
for (i = 0; i < len; i++)
{
tem = argv[bottom + i];
argv[bottom + i] = argv[middle + i];
argv[middle + i] = tem;
}
bottom += len;
}
}
d->__first_nonopt += (d->optind - d->__last_nonopt);
d->__last_nonopt = d->optind;
}
static const TCHAR* _getopt_initialize(const TCHAR* optstring
, struct _getopt_data* d
, int posixly_correct)
{
d->__first_nonopt = d->__last_nonopt = d->optind;
d->__nextchar = NULL;
d->__posixly_correct = posixly_correct
| !!_tgetenv(_T("POSIXLY_CORRECT"));
if (optstring[0] == _T('-'))
{
d->__ordering = RETURN_IN_ORDER;
++optstring;
}
else if (optstring[0] == _T('+'))
{
d->__ordering = REQUIRE_ORDER;
++optstring;
}
else if (d->__posixly_correct)
{
d->__ordering = REQUIRE_ORDER;
}
else
{
d->__ordering = PERMUTE;
}
return optstring;
}
int _getopt_internal_r(int argc
, TCHAR* const* argv
, const TCHAR* optstring
, const struct option* longopts
, int* longind
, int long_only
, struct _getopt_data* d
, int posixly_correct)
{
int print_errors = d->opterr;
if (argc < 1)
{
return -1;
}
d->optarg = NULL;
if (d->optind == 0 || !d->__initialized)
{
if (d->optind == 0)
{
d->optind = 1;
}
optstring = _getopt_initialize(optstring, d, posixly_correct);
d->__initialized = 1;
}
else if (optstring[0] == _T('-') || optstring[0] == _T('+'))
{
optstring++;
}
if (optstring[0] == _T(':'))
{
print_errors = 0;
}
if (d->__nextchar == NULL || *d->__nextchar == _T('\0'))
{
if (d->__last_nonopt > d->optind)
{
d->__last_nonopt = d->optind;
}
if (d->__first_nonopt > d->optind)
{
d->__first_nonopt = d->optind;
}
if (d->__ordering == PERMUTE)
{
if (d->__first_nonopt != d->__last_nonopt
&& d->__last_nonopt != d->optind)
{
exchange((TCHAR**)argv, d);
}
else if (d->__last_nonopt != d->optind)
{
d->__first_nonopt = d->optind;
}
while (d->optind
< argc
&& (argv[d->optind][0] != _T('-')
|| argv[d->optind][1] == _T('\0')))
{
d->optind++;
}
d->__last_nonopt = d->optind;
}
if (d->optind != argc && !_tcscmp(argv[d->optind], _T("--")))
{
d->optind++;
if (d->__first_nonopt != d->__last_nonopt
&& d->__last_nonopt != d->optind)
{
exchange((TCHAR**)argv, d);
}
else if (d->__first_nonopt == d->__last_nonopt)
{
d->__first_nonopt = d->optind;
}
d->__last_nonopt = argc;
d->optind = argc;
}
if (d->optind == argc)
{
if (d->__first_nonopt != d->__last_nonopt)
{
d->optind = d->__first_nonopt;
}
return -1;
}
if ((argv[d->optind][0] != _T('-')
|| argv[d->optind][1] == _T('\0')))
{
if (d->__ordering == REQUIRE_ORDER)
{
return -1;
}
d->optarg = argv[d->optind++];
return 1;
}
d->__nextchar = (argv[d->optind]
+ 1 + (longopts != NULL
&& argv[d->optind][1] == _T('-')));
}
if (longopts != NULL
&& (argv[d->optind][1] == _T('-')
|| (long_only && (argv[d->optind][2]
|| !_tcschr(optstring, argv[d->optind][1]))
)
)
)
{
TCHAR* nameend;
const struct option* p;
const struct option* pfound = NULL;
int exact = 0;
int ambig = 0;
int indfound = -1;
int option_index;
for (nameend = d->__nextchar;
*nameend && *nameend != _T('=');
nameend++)
;
for (p = longopts, option_index = 0; p->name; p++, option_index++)
{
if (!_tcsncmp(p->name, d->__nextchar, nameend - d->__nextchar))
{
if ((unsigned int)(nameend - d->__nextchar)
== (unsigned int)_tcslen(p->name))
{
pfound = p;
indfound = option_index;
exact = 1;
break;
}
else if (pfound == NULL)
{
pfound = p;
indfound = option_index;
}
else if (long_only
|| pfound->has_arg != p->has_arg
|| pfound->flag != p->flag
|| pfound->val != p->val)
{
ambig = 1;
}
}
}
if (ambig && !exact)
{
if (print_errors)
{
_ftprintf(stderr
, _T("%s: option '%s' is ambiguous\n")
, argv[0]
, argv[d->optind]);
}
d->__nextchar += _tcslen(d->__nextchar);
d->optind++;
d->optopt = 0;
return _T('?');
}
if (pfound != NULL)
{
option_index = indfound;
d->optind++;
if (*nameend)
{
if (pfound->has_arg)
{
d->optarg = nameend + 1;
}
else
{
if (print_errors)
{
if (argv[d->optind - 1][1] == _T('-'))
{
_ftprintf(stderr
, _T("%s: option '--%s' doesn't allow ")
_T("an argument\n")
, argv[0]
, pfound->name);
}
else
{
_ftprintf(stderr
, _T("%s: option '%c%s' doesn't allow ")
_T("an argument\n")
, argv[0]
, argv[d->optind - 1][0]
, pfound->name);
}
}
d->__nextchar += _tcslen(d->__nextchar);
d->optopt = pfound->val;
return _T('?');
}
}
else if (pfound->has_arg == 1)
{
if (d->optind < argc)
{
d->optarg = argv[d->optind++];
}
else
{
if (print_errors)
{
_ftprintf(stderr
, _T("%s: option '--%s' requires an ")
_T("argument\n")
, argv[0]
, pfound->name);
}
d->__nextchar += _tcslen(d->__nextchar);
d->optopt = pfound->val;
return optstring[0] == _T(':') ? _T(':') : _T('?');
}
}
d->__nextchar += _tcslen(d->__nextchar);
if (longind != NULL)
{
*longind = option_index;
}
if (pfound->flag)
{
*(pfound->flag) = pfound->val;
return 0;
}
return pfound->val;
}
if (!long_only
|| argv[d->optind][1]
== _T('-')
|| _tcschr(optstring
, *d->__nextchar)
== NULL)
{
if (print_errors)
{
if (argv[d->optind][1] == _T('-'))
{
/* --option */
_ftprintf(stderr
, _T("%s: unrecognized option '--%s'\n")
, argv[0]
, d->__nextchar);
}
else
{
/* +option or -option */
_ftprintf(stderr
, _T("%s: unrecognized option '%c%s'\n")
, argv[0]
, argv[d->optind][0]
, d->__nextchar);
}
}
d->__nextchar = (TCHAR*)_T("");
d->optind++;
d->optopt = 0;
return _T('?');
}
}
{
TCHAR c = *d->__nextchar++;
TCHAR* temp = (TCHAR*)_tcschr(optstring, c);
if (*d->__nextchar == _T('\0'))
{
++d->optind;
}
if (temp == NULL || c == _T(':') || c == _T(';'))
{
if (print_errors)
{
_ftprintf(stderr
, _T("%s: invalid option -- '%c'\n")
, argv[0]
, c);
}
d->optopt = c;
return _T('?');
}
if (temp[0] == _T('W') && temp[1] == _T(';'))
{
TCHAR* nameend;
const struct option* p;
const struct option* pfound = NULL;
int exact = 0;
int ambig = 0;
int indfound = 0;
int option_index;
if (*d->__nextchar != _T('\0'))
{
d->optarg = d->__nextchar;
d->optind++;
}
else if (d->optind == argc)
{
if (print_errors)
{
_ftprintf(stderr
, _T("%s: option requires an argument -- '%c'\n")
, argv[0]
, c);
}
d->optopt = c;
if (optstring[0] == _T(':'))
{
c = _T(':');
}
else
{
c = _T('?');
}
return c;
}
else
{
d->optarg = argv[d->optind++];
}
for (d->__nextchar = nameend = d->optarg;
*nameend && *nameend != _T('=');
nameend++)
;
for (p = longopts, option_index = 0;
p->name;
p++, option_index++)
{
if (!_tcsncmp(p->name
, d->__nextchar
, nameend - d->__nextchar))
{
if ((unsigned int)(nameend - d->__nextchar)
== _tcslen(p->name))
{
pfound = p;
indfound = option_index;
exact = 1;
break;
}
else if (pfound == NULL)
{
pfound = p;
indfound = option_index;
}
else if (long_only
|| pfound->has_arg != p->has_arg
|| pfound->flag != p->flag
|| pfound->val != p->val)
{
ambig = 1;
}
}
}
if (ambig && !exact)
{
if (print_errors)
{
_ftprintf(stderr
, _T("%s: option '-W %s' is ambiguous\n")
, argv[0]
, d->optarg);
}
d->__nextchar += _tcslen(d->__nextchar);
d->optind++;
return _T('?');
}
if (pfound != NULL)
{
option_index = indfound;
if (*nameend)
{
if (pfound->has_arg)
{
d->optarg = nameend + 1;
}
else
{
if (print_errors)
{
_ftprintf(stderr
, _T("%s: option '-W %s' doesn't allow ")
_T("an argument\n")
, argv[0]
, pfound->name);
}
d->__nextchar += _tcslen(d->__nextchar);
return _T('?');
}
}
else if (pfound->has_arg == 1)
{
if (d->optind < argc)
{
d->optarg = argv[d->optind++];
}
else
{
if (print_errors)
{
_ftprintf(stderr
, _T("%s: option '-W %s' requires an ")
_T("argument\n")
, argv[0]
, pfound->name);
}
d->__nextchar += _tcslen(d->__nextchar);
return optstring[0] == _T(':') ? _T(':') : _T('?');
}
}
else
{
d->optarg = NULL;
}
d->__nextchar += _tcslen(d->__nextchar);
if (longind != NULL)
{
*longind = option_index;
}
if (pfound->flag)
{
*(pfound->flag) = pfound->val;
return 0;
}
return pfound->val;
}
d->__nextchar = NULL;
return _T('W');
}
if (temp[1] == _T(':'))
{
if (temp[2] == _T(':'))
{
if (*d->__nextchar != _T('\0'))
{
d->optarg = d->__nextchar;
d->optind++;
}
else
{
d->optarg = NULL;
}
d->__nextchar = NULL;
}
else
{
if (*d->__nextchar != _T('\0'))
{
d->optarg = d->__nextchar;
d->optind++;
}
else if (d->optind == argc)
{
if (print_errors)
{
_ftprintf(stderr
, _T("%s: option requires an ")
_T("argument -- '%c'\n")
, argv[0]
, c);
}
d->optopt = c;
if (optstring[0] == _T(':'))
{
c = _T(':');
}
else
{
c = _T('?');
}
}
else
{
d->optarg = argv[d->optind++];
}
d->__nextchar = NULL;
}
}
return c;
}
}
int _getopt_internal(int argc
, TCHAR* const* argv
, const TCHAR* optstring
, const struct option* longopts
, int* longind
, int long_only
, int posixly_correct)
{
int result;
getopt_data.optind = optind;
getopt_data.opterr = opterr;
result = _getopt_internal_r(argc
, argv
, optstring
, longopts
, longind
, long_only
, &getopt_data
, posixly_correct);
optind = getopt_data.optind;
optarg = getopt_data.optarg;
optopt = getopt_data.optopt;
return result;
}
int getopt(int argc, TCHAR* const* argv, const TCHAR* optstring) _GETOPT_THROW
{
return _getopt_internal(argc
, argv
, optstring
, (const struct option*)0
, (int*)0
, 0
, 0);
}
int getopt_long(int argc
, TCHAR* const* argv
, const TCHAR* options
, const struct option* long_options
, int* opt_index) _GETOPT_THROW
{
return _getopt_internal(argc
, argv
, options
, long_options
, opt_index
, 0
, 0);
}
int _getopt_long_r(int argc
, TCHAR* const* argv
, const TCHAR* options
, const struct option* long_options
, int* opt_index
, struct _getopt_data* d)
{
return _getopt_internal_r(argc
, argv
, options
, long_options
, opt_index
, 0
, d
, 0);
}
int getopt_long_only(int argc
, TCHAR* const* argv
, const TCHAR* options
, const struct option* long_options
, int* opt_index) _GETOPT_THROW
{
return _getopt_internal(argc
, argv
, options
, long_options
, opt_index
, 1
, 0);
}
int _getopt_long_only_r(int argc
, TCHAR* const* argv
, const TCHAR* options
, const struct option* long_options
, int* opt_index
, struct _getopt_data* d)
{
return _getopt_internal_r(argc
, argv
, options
, long_options
, opt_index
, 1
, d
, 0);
}
void getopt_reset()
{
optarg = NULL;
optind = 1;
opterr = 1;
optopt = _T('?');
//
getopt_data.optind = 0;
getopt_data.opterr = 0;
getopt_data.optopt = 0;
getopt_data.optarg = NULL;
getopt_data.__initialized = 0;
getopt_data.__nextchar = NULL;
getopt_data.__ordering = 0;
getopt_data.__posixly_correct = 0;
getopt_data.__first_nonopt = 0;
getopt_data.__last_nonopt = 0;
}做完以上步骤后，就可以进行预测了。
注意：最好配置opencv4.8.1，其他版本的opencv可能会报如下错误:
