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下载yoloV5框架

GitHub - 超极电/约洛夫5：YOLOv5 🚀在pyTorch>ONNX>核心ML>TFLite

准备数据集

使用labelimg工具制作目标检测数据集

• 打开Anaconda Prompt

输入代码即可

• pip install labelimg

具体使用方法可参考

(1条消息) 使用labelimg工具制作目标检测数据集_知跃的博客-CSDN博客

将标注好的数据集转为txt

• labelimg工具生成的标签是xml格式的，之前的目标检测用的都是xml格式，但是yolov5框架使用的是txt格式，所以第一步需要将xml格式的标签转换为txt格式才可以

import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join
 
 
 
 
def convert(size, box):
 
    x_center = (box[0] + box[1]) / 2.0
    y_center = (box[2] + box[3]) / 2.0
    x = x_center / size[0]
    y = y_center / size[1]
 
    w = (box[1] - box[0]) / size[0]
    h = (box[3] - box[2]) / size[1]
 
 
    return (x, y, w, h)
 
 
def convert_annotation(xml_files_path, save_txt_files_path, classes):
    xml_files = os.listdir(xml_files_path)
    print(xml_files)
    for xml_name in xml_files:
        print(xml_name)
        xml_file = os.path.join(xml_files_path, xml_name)
        out_txt_path = os.path.join(save_txt_files_path, xml_name.split('.')[0] + '.txt')
        out_txt_f = open(out_txt_path, 'w')
        tree = ET.parse(xml_file)
        root = tree.getroot()
        size = root.find('size')
        w = int(size.find('width').text)
        h = int(size.find('height').text)
 
        for obj in root.iter('object'):
            difficult = obj.find('difficult').text
            cls = obj.find('name').text
            if cls not in classes or int(difficult) == 1:
                continue
            cls_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))
            # b=(xmin, xmax, ymin, ymax)
            print(w, h, b)
            bb = convert((w, h), b)
            out_txt_f.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
 
 
if __name__ == "__main__":
   
    classes = ['blackgan', 'mei']
    # 1、voc格式的xml标签文件路径
    xml_files1 = r'D:\2021file\Biye\yolov5-master\VOC2007\Annotations'
    # 2、转化为yolo格式的txt标签文件存储路径
    save_txt_files1 = r'D:\2021file\Biye\yolov5-master\VOC2007\labels'
 
    convert_annotation(xml_files1, save_txt_files1, classes)

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