【Yolov8 Opencv C++系列保姆教程】Yolov8 opencv c++ 版本保姆教程，Yolov8训练自己的数据集，实现红绿灯识别及红绿灯故障检测，红绿灯故障识别。-CSDN博客

Excerpt
文章浏览阅读5.5k次，点赞29次，收藏76次。Yolov8 Opencv C++系列保姆教程，通过一个红绿灯识别的案例，实现了Yolov8 的应用全流程的过程，整个算法最终只依赖了opencv，部署会更加方便、易用。基于Yolov8训练自己的数据集，实现红绿灯识别及红绿灯故障检测，红绿灯故障识别。

一、Yolov8简介

1、yolov8 源码地址：

工程链接：https://github.com/ultralytics/ultralytics

2、官方文档：

CLI - Ultralytics YOLOv8 Docs

3、预训练模型百度网盘地址：

训练时需要用到，下载的网址较慢：

如果模型下载不了，加QQ：187100248.

链接: https://pan.baidu.com/s/1YfMxRPGk8LF75a4cbgYxGg 提取码: rd7b

二、模型训练

1、标定红绿灯数据：

类别为23类，分别为：

红绿灯类别
red_light	green_light	yellow_light	off_light	part_ry_light	part_rg_light
part_yg_light	ryg_light	countdown_off_light	countdown_on_light	shade_light	zero
one	two	three	four	five	six
seven	eight	nine	brokeNumber	brokenLight

标注工具地址：AI标注工具Labelme和LabelImage Labelme和LabelImage集成工具_labelimage与labelme-CSDN博客

标注后图像格式

2、训练环境：

1）、Ubuntu18.04；

2）、Cuda11.7 + CUDNN8.0.6；

3）、opencv4.5.5；

4）、PyTorch1.8.1-GPU；

5）、python3.9

3、数据转化：

1）、需要把上面标定的数据集中的.xml文件转换为.txt，转换代码为：

import os

import shutil

import xml.etree.ElementTree as ET

from xml.etree.ElementTree import Element, SubElement

from PIL import Image

import cv2

classes = ['red_light', 'green_light', 'yellow_light', 'off_light', 'part_ry_light', 'part_rg_light', 'part_yg_light', 'ryg_light',

'countdown_off_light', 'countdown_on_light','shade_light','zero','one','two','three','four','five','six','seven',

'eight','nine','brokeNumber','brokenLight']

class Xml_make(object):

def __init__(self):

super().__init__()

def __indent(self, elem, level=0):

i = "\n" + level * "\t"

if len(elem):

if not elem.text or not elem.text.strip():

elem.text = i + "\t"

if not elem.tail or not elem.tail.strip():

elem.tail = i

for elem in elem:

self.__indent(elem, level + 1)

if not elem.tail or not elem.tail.strip():

elem.tail = i

else:

if level and (not elem.tail or not elem.tail.strip()):

elem.tail = i

def _imageinfo(self, list_top):

annotation_root = ET.Element('annotation')

annotation_root.set('verified', 'no')

tree = ET.ElementTree(annotation_root)

'''

0:xml_savepath 1:folder,2:filename,3:path

4:checked,5:width,6:height,7:depth

'''

folder_element = ET.Element('folder')

folder_element.text = list_top[1]

annotation_root.append(folder_element)

filename_element = ET.Element('filename')

filename_element.text = list_top[2]

annotation_root.append(filename_element)

path_element = ET.Element('path')

path_element.text = list_top[3]

annotation_root.append(path_element)

# checked_element = ET.Element('checked')

# checked_element.text = list_top[4]

# annotation_root.append(checked_element)

source_element = ET.Element('source')

database_element = SubElement(source_element, 'database')

database_element.text = 'Unknown'

annotation_root.append(source_element)

size_element = ET.Element('size')

width_element = SubElement(size_element, 'width')

width_element.text = str(list_top[5])

height_element = SubElement(size_element, 'height')

height_element.text = str(list_top[6])

depth_element = SubElement(size_element, 'depth')

depth_element.text = str(list_top[7])

annotation_root.append(size_element)

segmented_person_element = ET.Element('segmented')

segmented_person_element.text = '0'

annotation_root.append(segmented_person_element)

return tree, annotation_root

def _bndbox(self, annotation_root, list_bndbox):

for i in range(0, len(list_bndbox), 9):

object_element = ET.Element('object')

name_element = SubElement(object_element, 'name')

name_element.text = list_bndbox[i]

# flag_element = SubElement(object_element, 'flag')

# flag_element.text = list_bndbox[i + 1]

pose_element = SubElement(object_element, 'pose')

pose_element.text = list_bndbox[i + 2]

truncated_element = SubElement(object_element, 'truncated')

truncated_element.text = list_bndbox[i + 3]

difficult_element = SubElement(object_element, 'difficult')

difficult_element.text = list_bndbox[i + 4]

bndbox_element = SubElement(object_element, 'bndbox')

xmin_element = SubElement(bndbox_element, 'xmin')

xmin_element.text = str(list_bndbox[i + 5])

ymin_element = SubElement(bndbox_element, 'ymin')

ymin_element.text = str(list_bndbox[i + 6])

xmax_element = SubElement(bndbox_element, 'xmax')

xmax_element.text = str(list_bndbox[i + 7])

ymax_element = SubElement(bndbox_element, 'ymax')

ymax_element.text = str(list_bndbox[i + 8])

annotation_root.append(object_element)

return annotation_root

def txt_to_xml(self, list_top, list_bndbox):

tree, annotation_root = self._imageinfo(list_top)

annotation_root = self._bndbox(annotation_root, list_bndbox)

self.__indent(annotation_root)

tree.write(list_top[0], encoding='utf-8', xml_declaration=True)

def txt_2_xml(source_path, xml_save_dir, jpg_save_dir,txt_dir):

COUNT = 0

for folder_path_tuple, folder_name_list, file_name_list in os.walk(source_path):

for file_name in file_name_list:

file_suffix = os.path.splitext(file_name)[-1]

if file_suffix != '.jpg':

continue

list_top = []

list_bndbox = []

path = os.path.join(folder_path_tuple, file_name)

xml_save_path = os.path.join(xml_save_dir, file_name.replace(file_suffix, '.xml'))

txt_path = os.path.join(txt_dir, file_name.replace(file_suffix, '.txt'))

filename = file_name#os.path.splitext(file_name)[0]

checked = 'NO'

#print(file_name)

im = Image.open(path)

im_w = im.size[0]

im_h = im.size[1]

shutil.copy(path, jpg_save_dir)

if im_w*im_h > 34434015:

print(file_name)

if im_w < 100:

print(file_name)

width = str(im_w)

height = str(im_h)

depth = '3'

flag = 'rectangle'

pose = 'Unspecified'

truncated = '0'

difficult = '0'

list_top.extend([xml_save_path, folder_path_tuple, filename, path, checked, width, height, depth])

for line in open(txt_path, 'r'):

line = line.strip()

info = line.split(' ')

name = classes[int(info[0])]

x_cen = float(info[1]) * im_w

y_cen = float(info[2]) * im_h

w = float(info[3]) * im_w

h = float(info[4]) * im_h

xmin = int(x_cen - w / 2) - 1

ymin = int(y_cen - h / 2) - 1

xmax = int(x_cen + w / 2) + 3

ymax = int(y_cen + h / 2) + 3

if xmin < 0:

xmin = 0

if ymin < 0:

ymin = 0

if xmax > im_w - 1:

xmax = im_w - 1

if ymax > im_h - 1:

ymax = im_h - 1

if w > 5 and h > 5:

list_bndbox.extend([name, flag, pose, truncated, difficult,str(xmin), str(ymin), str(xmax), str(ymax)])

if xmin < 0 or xmax > im_w - 1 or ymin < 0 or ymax > im_h - 1:

print(xml_save_path)

Xml_make().txt_to_xml(list_top, list_bndbox)

COUNT += 1

#print(COUNT, xml_save_path)

if __name__ == "__main__":

out_xml_path = "/home/TL_TrainData/" # .xml输出文件存放地址

out_jpg_path = "/home/TL_TrainData/" # .jpg输出文件存放地址

txt_path = "/home/Data/TrafficLight/trainData" # yolov3标注.txt和图片文件夹

images_path = "/home/TrafficLight/trainData" # image文件存放地址

txt_2_xml(images_path, out_xml_path, out_jpg_path, txt_path)

4、构造训练数据：

2）、训练样本数据构造，需要把分成images和labels，images下面放入图片，labels下面放入.txt文件:

分成images和labels

images

labels

5、训练样本：

1）、首先安装训练包：

1	pip install ultralytics

2）、修改训练数据参数coco128_light.yaml文件，这个是自己修改的。

# Ultralytics YOLO 🚀, AGPL-3.0 license

# COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics

# Example usage: yolo train data=coco128.yaml

# parent

# ├── ultralytics

# └── datasets

# └── coco128 ← downloads here (7 MB)

# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]

path: /home/Data/TrafficLight/datasets # dataset root dir

train: images # train images (relative to 'path') 128 images

val: images # val images (relative to 'path') 128 images

test: # test images (optional)

# Parameters

nc: 23 # number of classes

# Classes

names:

0: red_light

1: green_light

2: yellow_light

3: off_light

4: part_ry_light

5: part_rg_light

6: part_yg_light

7: ryg_light

8: countdown_off_light

9: countdown_on_light

10: shade_light

11: zero

12: one

13: two

14: three

15: four

16: five

17: six

18: seven

19: eight

20: nine

21: brokeNumber

22: brokenLight

# Download script/URL (optional)

#download: https://ultralytics.com/assets/coco128.zip

3）、执行 train_yolov8x_light.sh，内容为：

1	yolo detect train data=coco128_light.yaml model=./runs/last.pt epochs=100 imgsz=640 workers=16 batch=32

开始启动训练：

模型训练启动

三、验证模型：

1、图像测试：

from ultralytics import YOLO

model = YOLO('best.pt')

results = model('bus.jpg')

for r in results:

print(r.boxes)

2、视频测试：

import cv2

from ultralytics import YOLO

# Load the YOLOv8 model

model = YOLO('best.pt')

# Open the video file

video_path = "test_car_person_1080P.mp4"

cap = cv2.VideoCapture(video_path)

# Loop through the video frames

while cap.isOpened():

# Read a frame from the video

success, frame = cap.read()

if success:

# Run YOLOv8 inference on the frame

results = model(frame)

# Visualize the results on the frame

annotated_frame = results[0].plot()

# Display the annotated frame

cv2.imshow("YOLOv8 Inference", annotated_frame)

cv2.waitKey(10)

四、导出ONNX

1、训练输出，经过上面的训练后，得到训练生成文件，weights下生成了best.pt和last.pt：

训练数据生成文件

2、等训练完毕后，利用best.pt生成best.onnx，执行命令如下：

1	yolo export model=best.pt imgsz=640 format=onnx opset=12

五、Opencv实现Yolov8 C++ 识别

1、开发环境：

1）、win7/win10；

2）、vs2019；

3）、opencv4.7.0；

2、main函数代码：

#include <iostream>

#include <vector>

#include "opencv2/opencv.hpp"

#include "inference.h"

#include <io.h>

#include <thread>

#define socklen_t int

#pragma comment (lib, "ws2_32.lib")

using namespace std;

using namespace cv;

int getFiles(std::string path, std::vector<std::string>& files, std::vector<std::string>& names)

{

int i = 0;

intptr_t hFile = 0;

struct _finddata_t c_file;

std::string imageFile = path + "*.*";

if ((hFile = _findfirst(imageFile.c_str(), &c_file)) == -1L)

{

_findclose(hFile);

return -1;

}

else

{

while (true)

{

std::string strname(c_file.name);

if (std::string::npos != strname.find(".jpg") || std::string::npos != strname.find(".png") || std::string::npos != strname.find(".bmp"))

{

std::string fullName = path + c_file.name;

files.push_back(fullName);

std::string cutname = strname.substr(0, strname.rfind("."));

names.push_back(cutname);

}

if (_findnext(hFile, &c_file) != 0)

{

_findclose(hFile);

break;

}

}

}

return 0;

}

int main()

{

std::string projectBasePath = "./"; // Set your ultralytics base path

bool runOnGPU = true;

//

// Pass in either:

//

// "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(projectBasePath + "/best.onnx", cv::Size(640, 640), "classes.txt", runOnGPU);

std::vector<std::string> files;

std::vector<std::string> names;

getFiles("./test/", files, names);

//std::vector<std::string> imageNames;

//imageNames.push_back(projectBasePath + "/test/20221104_8336.jpg");

//imageNames.push_back(projectBasePath + "/test/20221104_8339.jpg");

for (int i = 0; i < files.size(); ++i)

{

cv::Mat frame = cv::imread(files[i]);

// Inference starts here...

clock_t start, end;

float time;

start = clock();

std::vector<Detection> output = inf.runInference(frame);

end = clock();

time = (float)(end - start);//CLOCKS_PER_SEC;

printf("timeCount = %f\n", time);

int detections = output.size();

std::cout << "Number of detections:" << detections << std::endl;

for (int i = 0; i < detections; ++i)

{

Detection detection = output[i];

cv::Rect box = detection.box;

cv::Scalar color = detection.color;

// Detection box

cv::rectangle(frame, box, color, 2);

// 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(frame, textBox, color, cv::FILLED);

cv::putText(frame, classString, cv::Point(box.x + 5, box.y - 10), cv::FONT_HERSHEY_DUPLEX, 1, cv::Scalar(0, 0, 0), 2, 0);

}

// Inference ends here...

// This is only for preview purposes

float scale = 0.8;

cv::resize(frame, frame, cv::Size(frame.cols * scale, frame.rows * scale));

cv::imshow("Inference", frame);

cv::waitKey(10);

}

}

3、yolov8 头文件inference.h代码：

#ifndef INFERENCE_H

#define INFERENCE_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{};

};

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);

private:

void loadClassesFromFile();

void loadOnnxNetwork();

cv::Mat formatToSquare(const cv::Mat &source);

std::string modelPath{};

std::string classesPath{};

bool cudaEnabled{};

std::vector<std::string> classes{ "red_light", "green_light", "yellow_light", "off_light", "part_ry_light", "part_rg_light", "part_yg_light", "ryg_light","countdown_off_light", "countdown_on_light","shade_light","zero","one","two","three","four","five","six","seven","eight","nine","brokeNumber","brokenLight" };

cv::Size2f modelShape{};

float modelConfidenceThreshold {0.25};

float modelScoreThreshold {0.45};

float modelNMSThreshold {0.50};

bool letterBoxForSquare = true;

cv::dnn::Net net;

};

#endif // INFERENCE_H

4、yolov8 cpp文件inference.cpp代码：

#include "inference.h"

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 modelInput = input;

if (letterBoxForSquare && modelShape.width == modelShape.height)

modelInput = formatToSquare(modelInput);

cv::Mat blob;

cv::dnn::blobFromImage(modelInput, blob, 1.0/255.0, modelShape, cv::Scalar(), true, false);

net.setInput(blob);

std::vector<cv::Mat> outputs;

net.forward(outputs, net.getUnconnectedOutLayersNames());

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;

float x_factor = modelInput.cols / modelShape.width;

float y_factor = modelInput.rows / modelShape.height;

std::vector<int> class_ids;

std::vector<float> confidences;

std::vector<cv::Rect> boxes;

for (int i = 0; i < rows; ++i)

{

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)

{

confidences.push_back(maxClassScore);

class_ids.push_back(class_id.x);

float x = data[0];

float y = data[1];

float w = data[2];

float h = data[3];

int left = int((x - 0.5 * w) * x_factor);

int top = int((y - 0.5 * h) * y_factor);

int width = int(w * x_factor);

int height = int(h * y_factor);

boxes.push_back(cv::Rect(left, top, width, height));

}

}

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)

{

confidences.push_back(confidence);

class_ids.push_back(class_id.x);

float x = data[0];

float y = data[1];

float w = data[2];

float h = data[3];

int left = int((x - 0.5 * w) * x_factor);

int top = int((y - 0.5 * h) * y_factor);

int width = int(w * x_factor);

int height = int(h * y_factor);

boxes.push_back(cv::Rect(left, top, width, height));

}

}

}

data += dimensions;

}

std::vector<int> nms_result;

cv::dnn::NMSBoxes(boxes, confidences, modelScoreThreshold, modelNMSThreshold, nms_result);

std::vector<Detection> detections{};

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];

detections.push_back(result);

}

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);

}

}

cv::Mat Inference::formatToSquare(const cv::Mat &source)

{

int col = source.cols;

int row = source.rows;

int _max = MAX(col, row);

cv::Mat result = cv::Mat::zeros(_max, _max, CV_8UC3);

source.copyTo(result(cv::Rect(0, 0, col, row)));

return result;

}

4、效果图：

vs2019工程运行结果

红绿灯识别结果

【Yolov8 Opencv C++系列保姆教程】Yolov8 opencv c++ 版本保姆教程，Yolov8训练自己的数据集，实现红绿灯识别及红绿灯故障检测 ，红绿灯故障识别。-CSDN博客

Excerpt