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
网络异常，图片无法展示
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输入代码即可
pip install labelimg
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)
