车轮识别检测数据集3016张含(voc/yolo/json)三种格式标签

#xml批量转化成labelMe中的json格式，一一对应 import xml.etree.ElementTree as ET # 读取xml。 import os from PIL import Image,ImageDraw,ImageFont import os import json def parse_rec(rootPath,file): pathFile = os.path.join(rootPath, file) root = ET.parse(pathFile) # 解析读取xml函数 floder = root.find('folder').text filename= root.find('filename').text #path= root.find('path').text #print(floder,filename,path) sz = root.find('size') width = int(sz[0].text) height = int(sz[1].text) print(width,height) data = {} data['imagePath'] = filename data['flags'] = {} data['imageWidth'] = width data['imageHeight'] = height data['imageData'] = None data['version'] = "4.5.6" data["shapes"] =[] for child in root.findall('object'): # 找到图片中的所有框 sub = child.find('bndbox') # 找到框的标注值并进行读取 xmin = float(sub[0].text) ymin = float(sub[1].text) xmax = float(sub[2].text) ymax = float(sub[3].text) points= [[xmin,ymin],[xmax,ymax]] itemData = {'points': []} itemData['points'].extend(points) name = child.find("name").text itemData["flag"]={} itemData["group_id"]=None itemData["shape_type"]="rectangle" itemData["label"]=name data["shapes"].append(itemData) (filename, extension) = os.path.splitext(file) jsonName=".".join([filename,"json"]) print(rootPath,jsonName) jsonPath=os.path.join(rootPath,jsonName) with open(jsonPath, "w") as f: json.dump(data, f) print("加载入文件完成...") if __name__ == '__main__': path=r"G:\360downloads\car\car_xm" #xml文件存放位置 for root,dirs,files in os.walk(path): for file in files: if file.endswith(".xml"): parse_rec(root,file)