NEU-DET钢材表面缺陷检测数据集

import xml.etree.ElementTree as ET import pickle import os from os import listdir, getcwd from os.path import join import glob classes = ["crazing", "inclusion", "patches", "pitted_surface", "rolled-in_scale", "scratches"] def convert(size, box): dw = 1./size[0] dh = 1./size[1] x = (box[0] + box[1])/2.0 y = (box[2] + box[3])/2.0 w = box[1] - box[0] h = box[3] - box[2] x = x*dw w = w*dw y = y*dh h = h*dh return (x,y,w,h) def convert_annotation(image_name): in_file = open('./ANNOTATIONS/'+image_name[:-3]+'xml') out_file = open('./LABELS/'+image_name[:-3]+'txt','w') tree=ET.parse(in_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'): cls = obj.find('name').text if cls not in classes: print(cls) 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)) bb = convert((w,h), b) out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n') wd = getcwd() if __name__ == '__main__': for image_path in glob.glob("./IMAGES/*.jpg"): image_name = image_path.split('\\')[-1] #print(image_path) convert_annotation(image_name)