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Add C++ Classify inference example (#6868)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
DennisJ 2023-12-10 23:41:24 +08:00 • committed by GitHub
parent 1b37a13131
commit b62b20d517
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4 changed files with 380 additions and 223 deletions
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146
examples/YOLOv8-ONNXRuntime-CPP/main.cpp
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@ -3,18 +3,22 @@
#include "inference.h"
#include <filesystem>
#include <fstream>
#include <random>
void file_iterator(DCSP_CORE *&p) {
void Detector(YOLO_V8*& p) {
std::filesystem::path current_path = std::filesystem::current_path();
std::filesystem::path imgs_path = current_path / "images";
for (auto &i: std::filesystem::directory_iterator(imgs_path)) {
if (i.path().extension() == ".jpg" || i.path().extension() == ".png" || i.path().extension() == ".jpeg") {
for (auto& i : std::filesystem::directory_iterator(imgs_path))
{
if (i.path().extension() == ".jpg" || i.path().extension() == ".png" || i.path().extension() == ".jpeg")
{
std::string img_path = i.path().string();
cv::Mat img = cv::imread(img_path);
std::vector<DCSP_RESULT> res;
std::vector<DL_RESULT> res;
p->RunSession(img, res);
for (auto &re: res) {
for (auto& re : res)
{
cv::RNG rng(cv::getTickCount());
cv::Scalar color(rng.uniform(0, 256), rng.uniform(0, 256), rng.uniform(0, 256));
@ -23,24 +27,24 @@ void file_iterator(DCSP_CORE *&p) {
float confidence = floor(100 * re.confidence) / 100;
std::cout << std::fixed << std::setprecision(2);
std::string label = p->classes[re.classId] + " " +
std::to_string(confidence).substr(0, std::to_string(confidence).size() - 4);
std::to_string(confidence).substr(0, std::to_string(confidence).size() - 4);
cv::rectangle(
img,
cv::Point(re.box.x, re.box.y - 25),
cv::Point(re.box.x + label.length() * 15, re.box.y),
color,
cv::FILLED
img,
cv::Point(re.box.x, re.box.y - 25),
cv::Point(re.box.x + label.length() * 15, re.box.y),
color,
cv::FILLED
);
cv::putText(
img,
label,
cv::Point(re.box.x, re.box.y - 5),
cv::FONT_HERSHEY_SIMPLEX,
0.75,
cv::Scalar(0, 0, 0),
2
img,
label,
cv::Point(re.box.x, re.box.y - 5),
cv::FONT_HERSHEY_SIMPLEX,
0.75,
cv::Scalar(0, 0, 0),
2
);
@ -53,10 +57,51 @@ void file_iterator(DCSP_CORE *&p) {
}
}
int read_coco_yaml(DCSP_CORE *&p) {
void Classifier(YOLO_V8*& p)
{
std::filesystem::path current_path = std::filesystem::current_path();
std::filesystem::path imgs_path = current_path;// / "images"
std::random_device rd;
std::mt19937 gen(rd());
std::uniform_int_distribution<int> dis(0, 255);
for (auto& i : std::filesystem::directory_iterator(imgs_path))
{
if (i.path().extension() == ".jpg" || i.path().extension() == ".png")
{
std::string img_path = i.path().string();
//std::cout << img_path << std::endl;
cv::Mat img = cv::imread(img_path);
std::vector<DL_RESULT> res;
char* ret = p->RunSession(img, res);
float positionY = 50;
for (int i = 0; i < res.size(); i++)
{
int r = dis(gen);
int g = dis(gen);
int b = dis(gen);
cv::putText(img, std::to_string(i) + ":", cv::Point(10, positionY), cv::FONT_HERSHEY_SIMPLEX, 1, cv::Scalar(b, g, r), 2);
cv::putText(img, std::to_string(res.at(i).confidence), cv::Point(70, positionY), cv::FONT_HERSHEY_SIMPLEX, 1, cv::Scalar(b, g, r), 2);
positionY += 50;
}
cv::imshow("TEST_CLS", img);
cv::waitKey(0);
cv::destroyAllWindows();
//cv::imwrite("E:\\output\\" + std::to_string(k) + ".png", img);
}
}
}
int ReadCocoYaml(YOLO_V8*& p) {
// Open the YAML file
std::ifstream file("coco.yaml");
if (!file.is_open()) {
if (!file.is_open())
{
std::cerr << "Failed to open file" << std::endl;
return 1;
}
@ -64,17 +109,22 @@ int read_coco_yaml(DCSP_CORE *&p) {
// Read the file line by line
std::string line;
std::vector<std::string> lines;
while (std::getline(file, line)) {
while (std::getline(file, line))
{
lines.push_back(line);
}
// Find the start and end of the names section
std::size_t start = 0;
std::size_t end = 0;
for (std::size_t i = 0; i < lines.size(); i++) {
if (lines[i].find("names:") != std::string::npos) {
for (std::size_t i = 0; i < lines.size(); i++)
{
if (lines[i].find("names:") != std::string::npos)
{
start = i + 1;
} else if (start > 0 && lines[i].find(':') == std::string::npos) {
}
else if (start > 0 && lines[i].find(':') == std::string::npos)
{
end = i;
break;
}
@ -82,7 +132,8 @@ int read_coco_yaml(DCSP_CORE *&p) {
// Extract the names
std::vector<std::string> names;
for (std::size_t i = start; i < end; i++) {
for (std::size_t i = start; i < end; i++)
{
std::stringstream ss(lines[i]);
std::string name;
std::getline(ss, name, ':'); // Extract the number before the delimiter
@ -95,19 +146,48 @@ int read_coco_yaml(DCSP_CORE *&p) {
}
int main() {
DCSP_CORE *yoloDetector = new DCSP_CORE;
std::string model_path = "yolov8n.onnx";
read_coco_yaml(yoloDetector);
void DetectTest()
{
YOLO_V8* yoloDetector = new YOLO_V8;
ReadCocoYaml(yoloDetector);
DL_INIT_PARAM params;
params.rectConfidenceThreshold = 0.1;
params.iouThreshold = 0.5;
params.modelPath = "yolov8n.onnx";
params.imgSize = { 640, 640 };
#ifdef USE_CUDA
params.cudaEnable = true;
// GPU FP32 inference
DCSP_INIT_PARAM params{ model_path, YOLO_ORIGIN_V8, {640, 640}, 0.1, 0.5, true };
params.modelType = YOLO_DETECT_V8;
// GPU FP16 inference
// DCSP_INIT_PARAM params{ model_path, YOLO_ORIGIN_V8_HALF, {640, 640}, 0.1, 0.5, true };
//Note: change fp16 onnx model
//params.modelType = YOLO_DETECT_V8_HALF;
#else
// CPU inference
DCSP_INIT_PARAM params{model_path, YOLO_ORIGIN_V8, {640, 640}, 0.1, 0.5, false};
params.modelType = YOLO_DETECT_V8;
params.cudaEnable = false;
#endif
yoloDetector->CreateSession(params);
file_iterator(yoloDetector);
Detector(yoloDetector);
}
void ClsTest()
{
YOLO_V8* yoloDetector = new YOLO_V8;
std::string model_path = "cls.onnx";
ReadCocoYaml(yoloDetector);
DL_INIT_PARAM params{ model_path, YOLO_CLS, {224, 224} };
yoloDetector->CreateSession(params);
Classifier(yoloDetector);
}
int main()
{
//DetectTest();
ClsTest();
}