opencv场景识别

一、需求

1、现状

​ 现在有一个ppt插入软件，可以根据图片的名字，将对应的图片插入到ppt中去。

​ 但是，既然都简化了，拍完的照片还需要挑选然后重命名，实际上也是一个麻烦且琐碎的活，于是试图简化这一步骤。

2、设想

​ 将拍好的图片分类重命名

二、模型使用

1、opencv dnn支持的功能

​ Opencv Dnn可以对图像和视频完成基于深度学习的计算机视觉推理。opencv DNN支持的功能：

• 图像分类
• 目标检测
• 图像分割
• 文字检测和识别
• 姿态估计
• 深度估计
• 人脸验证和检测
• 人体重新识别

2、ANN_MLP相关知识

1. 介绍

   人工神经网络——多层感知器

   先创建一个model，所有的权重都设置为0。

   网络训练层使用一层输入和输出的向量，训练过程可以重复重复不止一次，权重可以基于新的训练数据调整权重

2. 一些函数

   • create()：创建一个model

   • setLayerSizes()：指定每层中神经元的数量，包括输入和输出层。第一个元素指定输入图层中的元素数。最后一个元素-输出图层中的元素数

     例子的数量要和标签的数量相同。第一项为图片的像素数，最后一项 为训练的种类数

      Mat layerSizes = (Mat_<int>(1, 4) << image_rows*image_cols, int(image_rows*image_cols / 2), int(image_rows*image_cols / 2), class_num);bp->setLayerSizes(layerSizes);
     

   • setActivationFunction()：设置激活函数

     setActivationFunction(int type, double param1 = 0, double param2 = 0)

     type	含义
     IDENTITY	$f(x)=x$
     SIGMOID_SYM	$f(x)=\beta \ast \frac{1-e^{\alpha x}}{1+e^{-\alpha x}}$
     GAUSSIAN	$f(x)=\beta e^{-\alpha x\ast x}$
     RELU	$f(x)=max(0,x)$
     LEAKYRELU	对于x>0，$f(x)=x$；对于x<0，$f(x)=\alpha x$

     bp->setActivationFunction(ANN_MLP::SIGMOID_SYM, 1, 1);
     

   • setTrainMethod()

     训练方式/传播方式

     cv::ml::ANN_MLP::setTrainMethod(int method,double param1 = 0,double param2 = 0 )

     method	函数
     BACKPROP	反向传播算法
     RPROP	弹性反向传播
     ANNEAL	模拟退火算法

   • setTermCriteria

     设置迭代算法终止的判断条件

     TermCriteria：迭代算法终止的判断条件

     class CV_EXPORTS TermCriteria {
     public:enum Type{COUNT = 1;MAX_ITER=COUNT;EPS=2;}TermCriteria(int type, int maxCount, double epsilon);int type;int maxCount;double epsilon;
     }
     

     类变量有三个参数：类型、迭代的 最大次数、特定的阈值

   • 加载分类器

     Ptr<ANN_MLP> bp = StatModel::load<ANN_MLP>("*.xml");
     Ptr<ANN_MLP> bp = ANN_MLP::load<ANN_MLP>("*.xml");
     Ptr<ANN_MLP> bp = Algorithm::load<ANN_MLP>("*.xml");
     

   • predict函数

     float predict( InputArray samples, OutputArray results=noArray(), int flags=0 )

     最终的预测结果 应该是第二个参数OutputArray results

3、图像分类模型训练学习

​ 没试过相关的模型训练，先尝试一下，参考：https://blog.csdn.net/qq_15985873/article/details/125087166

#include <iostream>
#include <opencv2/opencv.hpp>
#include <vector>
using namespace std;
using namespace cv;
using namespace ml;#define ROW 45
#define COL 30
#define TYPE_NUM 5
#define SUM 100
// string fruits[5] = {"apple", "cabbage", "carrot", "cucumber", "pear"};
string fruits[5] = {"apple", "cabbage", "carrot", "banana", "pear"};
Mat Label = Mat::eye(5, 5, CV_32FC1);void fruitAnnPrePro(Mat &src, Mat &dst) {src.convertTo(src, CV_32FC1);resize(src, src, Size(ROW, COL));dst.push_back(src.reshape(0,1));
}void trainChar(string &root_path, string &model_path) {vector<string> dir_path;for(string s : fruits)dir_path.push_back(root_path + s +"\\");Mat trainDataMat,trainLabelMat;vector<vector<string>> files(TYPE_NUM, vector<string>());for(int i = 0; i < dir_path.size(); i++) {glob(dir_path[i], files[i], false);}for(int i = 0; i < dir_path.size(); i++) {printf("%d  size   %d\n", i, files[i].size() );}for(int i = 0; i < files.size(); i++) {for(int j = 0; j < files[i].size(); j++) {Mat src = imread(files[i][j], IMREAD_GRAYSCALE);if(src.empty()){break;}fruitAnnPrePro(src, trainDataMat);trainLabelMat.push_back(Label.row(i));}}imshow("trainData", trainDataMat);waitKey(0);cout << trainDataMat.size() << endl;cout << trainLabelMat.size() << endl;Ptr<ANN_MLP> model = ANN_MLP::create();Mat labelSizes = (Mat_<int>(1,5) << 1350, 1350, 1350, 10, TYPE_NUM);model->setLayerSizes(labelSizes);model->setActivationFunction(ANN_MLP::SIGMOID_SYM, 1, 1);model->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 300,0.95));model->setTrainMethod(ANN_MLP::BACKPROP, 0, 1);Ptr<TrainData> trainData = TrainData::create(trainDataMat, ROW_SAMPLE, trainLabelMat);printf("开始训练！\n");model->train(trainData);// //保存模型model->save(model_path);printf("+++++++++++++++++++++++++++训练完成+++++++++++++++++++++++++++\n\n");
}void useModel(string imgPath, string &model_path) {Mat src = imread(imgPath, IMREAD_GRAYSCALE);Mat testDataMat;fruitAnnPrePro(src, testDataMat);Ptr<ANN_MLP> model = ANN_MLP::create();model = Algorithm::load<ANN_MLP>(model_path);Mat res;double maxVal;Point maxLoc;for(int i = 0; i < testDataMat.rows; i++) {model->predict(testDataMat.row(i), res);cout << format(res, Formatter::FMT_NUMPY) << endl;minMaxLoc(res, NULL, &maxVal, NULL, &maxLoc);printf("测试结果：%s 置信度： %f% \n", fruits[maxLoc.x].c_str(), maxVal * 100);}
}int main() {// string rootpath = "cpp\\opencv\\fruitClassify\\img\\";string rootpath = "C:\\Users\\CaCu999\\Pictures\\img\\";string modelpath = "cpp\\opencv\\fruitClassify\\model\\fruitModel.xml";trainChar(rootpath, modelpath);// string imgpath = "cpp\\opencv\\fruitClassify\\img\\apple\\r1_304.jpg";string imgpath = "C:\\Users\\CaCu999\\Pictures\\img\\banana\\10.jpg";useModel(imgpath, modelpath);return 0;
}

4、目标检测模型

1. 知识了解

   仔细想想，我需要的不是图像的分类，而是内部的物体的识别。所以需要的是目标检测

   https://blog.csdn.net/virobotics/article/details/124008160

2. 物体识别的概念

   需要解决的问题是目标在哪里以及其状态

   秒检测用的比较多的主要是RCNN，spp-net，fast-rcnn；yolo系列（yolov3和yolov4）；还有SSD，ResNet等

3. YOLO算法概述

   对出入的图片，将图片分为mxm个方格。当某个物体的中心点落在了某个方格中，该方格负责预测该物体。每个方格会被预测物体产生n个候选框并生成每个框的置信度。最后选区置信度比较高的方框作为结果

5、opencv调用darknet物体识别模型

1. darknet模型获取

   • 文件

     • cfg文件：模型描述文件
     • weights文件：模型权重文件

   • yolov3

     https://github.com/pjreddie/darknet/blob/master/cfg/yolov3.cfg
     https://pjreddie.com/media/files/yolov3.weights

     https://github.com/pjreddie/darknet/blob/master/data/coco.names

   • yolov4

     https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.cfg
     https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights

2. python代码

   直接粘贴的

   import cv2
   cv=cv2
   import numpy as np
   import time#读取模型
   net = cv2.dnn.readNetFromDarknet("cpp\\opencv\\ObjectDetection\\model\\yolov3.cfg", "cpp\\opencv\\ObjectDetection\\model\\yolov3.weights")
   #gpu计算之类的……
   # net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)
   # net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)
   #阈值
   confThreshold = 0.5  #Confidence threshold
   nmsThreshold = 0.4   #Non-maximum suppression threshold
   #读取图片
   frame=cv2.imread("cpp\\opencv\\ObjectDetection\\img\\20221122_110416.jpg")
   #等比例缩小
   frame = cv2.resize(frame,((int)(frame.shape[1]/10),(int)(frame.shape[0]/10)))
   print(frame.shape[0] * frame.shape[1] / 100)
   #标签文件并读取所有的标签
   classesFile = "cpp\\opencv\\ObjectDetection\\model\\coco.names";
   classes = None
   with open(classesFile, 'rt') as f:classes = f.read().rstrip('\n').split('\n')#读取网络名字
   def getOutputsNames(net):# Get the names of all the layers in the networklayersNames = net.getLayerNames()# Get the names of the output layers, i.e. the layers with unconnected outputsreturn [layersNames[i - 1] for i in net.getUnconnectedOutLayers()]
   print(getOutputsNames(net))
   # Remove the bounding boxes with low confidence using non-maxima suppressiondef postprocess(frame, outs):frameHeight = frame.shape[0]frameWidth = frame.shape[1]# Scan through all the bounding boxes output from the network and keep only the# ones with high confidence scores. Assign the box's class label as the class with the highest score.classIds = []confidences = []boxes = []# 解析结果for out in outs:print(out.shape)for detection in out:scores = detection[5:]classId = np.argmax(scores)confidence = scores[classId]if confidence > confThreshold:center_x = int(detection[0] * frameWidth)center_y = int(detection[1] * frameHeight)width = int(detection[2] * frameWidth)height = int(detection[3] * frameHeight)left = int(center_x - width / 2)top = int(center_y - height / 2)classIds.append(classId)confidences.append(float(confidence))boxes.append([left, top, width, height])# Perform non maximum suppression to eliminate redundant overlapping boxes with# lower confidences.print(boxes)print(confidences)  indices = cv2.dnn.NMSBoxes(boxes, confidences, confThreshold, nmsThreshold) for i in indices:#print(i)#i = i[0]box = boxes[i]left = box[0]top = box[1]width = box[2]height = box[3]drawPred(classIds[i], confidences[i], left, top, left + width, top + height)# Draw the predicted bounding box
   def drawPred(classId, conf, left, top, right, bottom):# Draw a bounding box.cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255))label = '%.2f' % conf    # Get the label for the class name and its confidenceif classes:assert(classId < len(classes))label = '%s:%s' % (classes[classId], label)#Display the label at the top of the bounding boxlabelSize, baseLine = cv.getTextSize(label, cv.FONT_HERSHEY_SIMPLEX, 0.5, 1)top = max(top, labelSize[1])cv2.putText(frame, label, (left, top), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,255))
   #读取图片，改为416*416的大小
   blob = cv2.dnn.blobFromImage(frame, 1/255, (416, 416), [0,0,0], 1, crop=False)
   t1=time.time()
   #设置输入的图片获取结果
   net.setInput(blob)
   #使用模型
   outs = net.forward(getOutputsNames(net))
   print(time.time()-t1)
   postprocess(frame, outs)
   t, _ = net.getPerfProfile()
   label = 'Inference time: %.2f ms' % (t * 1000.0 / cv.getTickFrequency())
   cv2.putText(frame, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255))
   print(frame.shape)
   cv2.imshow("result",frame)
   cv2.waitKey(0)
   

3. 遇到的一些问题

   • blobFromImage(frame, (double)1/255, Size(416, 416),Scalar(0,0,0), true);需要注意这里的收缩比例以及需要转为double

   • net.forward的输出结果

     一共80个label，输出结果为n*85。第1和2个是方框中心点(x,y)的位置的比例。第3和4个是宽和高的比例。第五个不知道。后面的80个是这个区域的每个标签的可能性

     所以针对这个结果，需要做的是：从后面80个结果里面获取最大值和索引。这个索引也就是label的索引。如果这个最大值大于置信度的阈值，就获取前面的选框区域

4. c++代码

   #include <iostream>
   #include <opencv2/opencv.hpp>
   #include <fstream>using namespace std;
   using namespace cv;
   using namespace dnn;void readClasses(vector<string> &classes, string classFile) {ifstream readFile;readFile.open(classFile);if(readFile.is_open()) {// printf("文件打开成功！\n开始读取内容……\n");string label;while(getline(readFile, label)) {// printf("%s\n", label.c_str());classes.push_back(label);}// printf("读取结束，一共%d个标签\n", classes.size());} else {cerr << "文件打开失败" << endl;}readFile.close();
   }vector<string> getOutputsNames(Net net) {vector<string> layers = net.getLayerNames();cout << layers.size()<< endl;//获得末端连接神经网络名字，用于指定forward输出层的名字auto idx = net.getUnconnectedOutLayers();vector<string> res;for(int i = 0; i < idx.size(); i++) {res.push_back(layers[idx[i] - 1]);}for(int i = 0; i < res.size(); i++) {cout << res[i] << endl;}return res;
   }void readAndGetRes(string imgPath,Mat &frame, vector<Mat> &outs) {Net net = readNetFromDarknet("cpp\\opencv\\ObjectDetection\\model\\yolov3.cfg", "cpp\\opencv\\ObjectDetection\\model\\yolov3.weights");frame = imread(imgPath);resize(frame, frame, Size(frame.cols / 10, frame.rows / 10));string classFile = "cpp\\opencv\\ObjectDetection\\model\\coco.names";Mat blob = blobFromImage(frame, (double)1/255, Size(416, 416),Scalar(0,0,0), true);time_t startTime;time(&startTime);net.setInput(blob);net.forward(outs, getOutputsNames(net));
   }void postProcess(Mat &frame,vector<Mat> probs) {vector<string> classes;readClasses(classes, "cpp\\opencv\\ObjectDetection\\model\\coco.names");int w = frame.cols;int h = frame.rows;float confThreshold = 0.5;float nmxThreshold = 0.4;vector<int> classIds;vector<float> confidences;vector<Rect> boxes;for(auto prob: probs) {cout << prob.size() << endl;for(int i = 0; i < prob.rows; i++) {//获取该选框的所有label的可能性Mat scores = prob.row(i).colRange(5, prob.cols);//获取选框的位置vector<float> detect = prob.row(i).colRange(0, 4);Point maxLoc;double confidence;//获取最大可能的可能性以及它的索引minMaxLoc(scores, 0, &confidence, 0, &maxLoc);if(confidence > confThreshold) {printf("confidence   %0.2f, loc %s,  %0.2f  %0.2f  %0.2f  %0.2f %0.2f\n",confidence, classes[maxLoc.x].c_str(), detect[0], detect[1], detect[2], detect[3], detect[4]);int center_x = detect[0] * w;int center_y = detect[1] * h;int width = detect[2] * w;int height = detect[3] * h;Rect box = Rect(center_x - width / 2, center_y - height / 2, width, height);boxes.push_back(box);confidences.push_back(confidence);classIds.push_back(maxLoc.x);}}}vector<int> indices;NMSBoxes(boxes, confidences, confThreshold, nmxThreshold, indices);cout << "the res " <<  indices.size() << endl;for(int i:indices) {cout << i << endl;rectangle(frame, boxes[i], Scalar(0, 0, 255));string label = format("%s:%0.2f%", classes[classIds[i]].c_str(), confidences[i]*100);cout << label << endl;putText(frame, label, Point(boxes[i].x,boxes[i].y), FONT_HERSHEY_SIMPLEX,0.5,Scalar(255,255,255));cout << label << endl;}cout << "this is end" << endl;imshow("label frame",frame);waitKey(0);
   }int main() {vector<Mat> res;Mat img;readAndGetRes("cpp\\opencv\\ObjectDetection\\img\\20221122_110416.jpg", img, res);postProcess(img, res);return 0;
   }
   

   上述部分参考自：https://blog.csdn.net/virobotics/article/details/124008160

三、模型训练

1、现状

​ 之前尝试了使用yolo3进行了图像的识别，但是里面的label跟我说需要的不匹配，尝试针对自己的需求进行模型训练

2、步骤-模型编译

1. 下载代码

   git clone https://github.com/pjreddie/darknet.git

2. 源码编译-环境准备

   https://blog.csdn.net/m0_52571715/article/details/109713440

   • 环境准备

     1. GitBash运行make命令，提示bash: make: command not found

        说明我现有的环境变量里面不包含make.exe

        我现有的环境已经有c++的套件，去mingw/bin目录下看了一下，确实没有make.exe

        于是去网上下了一个make.exe复制进去了http://www.equation.com/servlet/equation.cmd?fa=make

     2. 尝试编译的时候发现，没有cuda库

        https://blog.csdn.net/weixin_43848614/article/details/117221384

        • 下载：https://developer.nvidia.com/cuda-toolkit-archive

        • 安装

          

          

        • 环境变量

          添加刚刚安装的位置NVIDIA GPU Computing Toolkit的那个

        • 测试

          打开cmd

          > nvcc --version
          nvcc: NVIDIA (R) Cuda compiler driver
          Copyright (c) 2005-2020 NVIDIA Corporation
          Built on Tue_Sep_15_19:12:04_Pacific_Daylight_Time_2020
          Cuda compilation tools, release 11.1, V11.1.74
          Build cuda_11.1.relgpu_drvr455TC455_06.29069683_0
          

     3. windows编译和解编译问题-失败了所以重来

        • Makefile：修改GPU=1

          GPU=1
          CUDNN=0
          OPENCV=0
          OPENMP=0
          DEBUG=0
          

        • Makefile：修改/usr/local/cuda/include/改成自己的路径

        • include/darknet.h:780:11: error: unknown type name ‘clock_t’;

          1. 改成clockid_t

             include/darknet.h:780:11: error: unknown type name 'clock_t'; did you mean 'clockid_t'?float sec(clock_t clocks);^~~~~~~clockid_t
             compilation terminated due to -Wfatal-errors.
             

          2. 直接注释了

             修改了类型以后冲突了，直接把这个头文件里面的注释了试试看

             ./src/utils.h:48:7: error: conflicting types for 'sec'float sec(clock_t clocks);^~~
             compilation terminated due to -Wfatal-errors.
             make: *** [makefile:89: obj/gemm.o] Error 1
             

        • nvcc fatal : Unsupported gpu architecture ‘compute_30’

          compute_30已经被cuda11放弃使用了，修改Makefile

          ARCH= -gencode arch=compute_30,code=sm_30 \-gencode arch=compute_35,code=sm_35 \-gencode arch=compute_50,code=[sm_50,compute_50] \-gencode arch=compute_52,code=[sm_52,compute_52]ARCH= -gencode arch=compute_35,code=sm_35 \-gencode arch=compute_50,code=[sm_50,compute_50] \-gencode arch=compute_52,code=[sm_52,compute_52]
          

        • nvcc fatal : Cannot find compiler ‘cl.exe’ in PATH

          懒得再装了，印象里好像在别的地方安装过。去搜了一下，在./Microsoft Visual Studio/2022/Community/VC/Tools/MSVC/14.35.32215/bin/Hostx86/x86/cl.exe找到了

          先试试看直接把文件复制到cuda目录下面，似乎可以了

        • nvcc fatal : Failed to preprocess host compiler properties

          配置cudnn

          

     4. 卸载并重新安装visual studio

        后面在尝试的过程中，出现了visual studio版本不对的通知，建议为2015-2019。本来直接跳过的，但是后面报错编译不过了QAQ

        • 卸载vs

          

        • 下载2019

          https://visualstudio.microsoft.com/zh-hans/vs/older-downloads/

          

        • 安装

          
          

          

     5. 重新下载代码编译

        • 下载：git clone https://github.com/AlexeyAB/darknet

        • 打开PowerShell 管理员，输入Set-ExecutionPolicy RemoteSigned

          

        • 进入darknet目录执行：.\build.ps1

          

          从过程里面看

          • 下载了vcpkg

            

          • 确认VS

            

          • 确认CUDA

            

          • 编译命令及版本确认

            

          • cmake版本不对于是重新下

            

          • powershell版本不对又下

            

            ……后面太多了，懒得一一看了

3、步骤-模型训练

https://blog.csdn.net/qq_38915710/article/details/97112788

1. 准备数据集

   根目录新建myData文件夹，在myData文件夹下再新建三个文件夹，分别为annotations、ImageSets、JPEGImages，其中annotations文件下存放xml文件，ImageSets下新建main文件夹，其中建一个train.txt，其中放图片名称（不要带.jpg，只是单独的图片名称），JPEGImages下放要训练的图片。

   • 读取所有文件名

     #include <io.h>
     #include <vector>void readImageFile(string filePath) {struct _finddata_t fileinfo;intptr_t file_ptr;file_ptr = _findfirst(filePath.c_str(), &fileinfo);vector<string> files;if(file_ptr != -1) {do {cout << fileinfo.name << "   ";string name = fileinfo.name;int idx = name.find_last_of('.');if (idx == -1) continue;            string type = name.substr(idx);if(type == ".jpg" || type == ".jpeg" || type == ".png") {cout << name.substr(0, idx) << endl;files.push_back(name.substr(0, idx));}} while ((_findnext(file_ptr, &fileinfo)) == 0);_findclose(file_ptr);}ofstream outfile;outfile.open("D:\\Project\\OpencvProject\\darknet\\myData\\ImageSets\\main\\train.txt");if (!outfile.is_open()) {cerr << "open file failed" << endl;}for (string name : files) {outfile << name << endl;}outfile.close();
     }

     [外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-334444qg-1681956764395)(opencv场景识别.assets/image-20230418144352893.png)]

   • 准备标签

     • 下载labelimg：pip install labelimg

       后面下载了labelimg的代码，然后压成exe文件pyinstaller.exe --hidden-import=xml --hidden-import=xml.etree --hidden-import=xml.etree.ElementTree --hidden-import=lxml.etree -D -F -n labelImg -c "../labelImg.py" -p ../libs -p ../

       

     • 启动

       

       

     • 加标签

       

     • 转换标签

       python的（复制来的）

       import xml.etree.ElementTree as ET
       import pickle
       import os
       from os import listdir, getcwd
       from os.path import joinsets=[('myData', 'train')]  #元组数组classes = ['screen'] # each category's namedef convert(size, box):dw = 1./(size[0])dh = 1./(size[1])x = (box[0] + box[1])/2.0 - 1y = (box[2] + box[3])/2.0 - 1w = box[1] - box[0]h = box[3] - box[2]x = x*dww = w*dwy = y*dhh = h*dhreturn (x,y,w,h)def convert_annotation(wd, year, image_id):#读取xml，输出为txt文件in_file = open(wd + './Annotations/%s.xml'%(image_id))out_file = open(wd + './labels/%s.txt'%(image_id), 'w')#读取xml内容tree=ET.parse(in_file)root = tree.getroot()   #这两行表示从xml文件中读取内容size = root.find('size')    #从读取到的xml内容中发现sizew = int(size.find('width').text)    #取宽度h = int(size.find('height').text)   #取高度for obj in root.iter('object'):difficult = obj.find('difficult').textcls = obj.find('name').textcls_id = classes.index(cls)xmlbox = obj.find('bndbox')b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text))try:bb = convert((w,h), b)except:print(image_id)out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')if __name__ == '__main__':wd = getcwd()   #返回当前工作目录wd += '/darknet/myData/'   #定位到数据目录classes = open(wd + './Annotations/classes.txt').read().strip().split()print(classes)for year, image_set in sets:#判断并创建labels目录if not os.path.exists(wd + './labels/'):    #os.path.exists("")表示如果路径 path 存在，返回 True；如果路径 path 不存在，返回 False。os.makedirs(wd + './labels/')   #创建目录#读取train.txt下所有的图片名字image_ids = open(wd + './ImageSets/main/%s.txt'%(image_set)).read().strip().split() #打开该文件，并一次性读取该文件所有内容，再去掉文本中句子开头与结尾的符号的print(wd + './%s_%s.txt'%(year, image_set))# 在myData目录下创建myData_train文件list_file = open(wd + './%s_%s.txt'%(year, image_set), 'w') #打开myData_train该文件for image_id in image_ids:#在myData_train目录下写入图片的绝对路径-（有些问题，可能后缀不是jpg）list_file.write('%s/JPEGImages/%s.jpg\n'%(wd, image_id)) #在myData_train.txt里面写入图片地址+名称convert_annotation(wd, year, image_id)list_file.close()
       

       c++

       本来想转为c++的实现的，但是后面发现，转化出来的结果与yonglabelImage的时候直接选择yolo格式一致=。=，只有精度有点区别。用c++先来一波复制

       #include <iostream>
       #include <io.h>
       #include <vector>
       #include <fstream>
       using namespace std;void readImageFile(string root) {string filePath = root + ".\\JPEGImages\\*";struct _finddata_t fileinfo;intptr_t file_ptr;file_ptr = _findfirst(filePath.c_str(), &fileinfo);filePath = filePath.substr(0, filePath.size()-1);vector<string> files;vector<string> absfiles;if(file_ptr != -1) {do {// cout << filePath + fileinfo.name << "   ";string name = fileinfo.name;int idx = name.find_last_of('.');if (idx == -1) continue;            string type = name.substr(idx);if(type == ".jpg" || type == ".jpeg" || type == ".png") {// cout << name.substr(0, idx) << endl;//保存文件名和绝对路径files.push_back(name.substr(0, idx));absfiles.push_back(filePath + name);}} while ((_findnext(file_ptr, &fileinfo)) == 0);_findclose(file_ptr);}//写入文件名和绝对路径ofstream outfile;ofstream trainfile;outfile.open(root + ".\\ImageSets\\main\\train.txt");trainfile.open(root + ".\\myData_train.txt");if (!outfile.is_open() || !trainfile.is_open()) {cerr << "open file failed" << endl;}for (int i = 0; i < files.size(); i++) {outfile << files[i] << endl;trainfile << absfiles[i] << endl;}outfile.close();trainfile.close();//调用cmd命令创建label文件夹string labelspath = root + "labels\\";string cmd = "mkdir " + labelspath;system(cmd.c_str());cmd = "copy " + root + ".\\Annotations\\*.txt " + labelspath;system(cmd.c_str());
       }int main() {string root = "D:\\Project\\OpencvProject\\darknet\\myData\\";readImageFile(root);return 0;
       }
       

       

2. 训练数据

   • 准备文件

     • ImgDetect.data

       #类数
       classes= 23
       #训练数据集
       train  = myData/myData_train.txt
       #测试数据集
       valid  = myData/myData_train.txt
       #类名
       names = myData/labels/classes.txt
       #结果
       backup = myData/
       

     • yolo.cfg

       [net]

       batch为一次可训练的图片数量
       subdivision是分割一次训练数量的值（使用该参数可以让显卡较差的电脑也能够使用高batch进行训练）。其两者的关系就是batch÷subdivision=实际上一次训练图片数量
       width和height是输入图片的尺寸，这个无需改变，只要是16的倍数即可
       max_batches是总共训练的轮数（官方推荐是：种类量2000，但最少6000。即如果你训练一个种类。则填6000，若训练4个种类则填8000）
       steps的值设置为0.6max_batches和0.8*max_batches即可

       https://blog.csdn.net/weixin_52939176/article/details/122554179

       

       [convolutional]和[yolo]

       主要修改，如上图所示的【convolutional】和【yolo】组（在yolo-tiny算法中共两组，yolo算法中三组）
       修改filters为：（种类数+5）*3，即种类数8，（8+5）*3=39
       修改classes为种类数

       

     • yolov3.weights

       需要和.cfg文件对应，直接去网上找

   • 开始训练

     darknet.exe detector train cfg_myData/ImgDetect.data cfg_myData/yolov3.cfg cfg_myData/yolov3.weights

     

     !可能是数据太少了，结果很差。重新来一下QAQ
     csdn的图片也太难传了，居然要一张张图片往里贴