2023年7月3日发(作者：)

深度学习之遥感图像标注（⼆）import argparseimport jsonimport osimport as ospimport warningsimport copyimport numpy as npimport rom skimage import ioimport yamlfrom labelme import utilsdef main(): parser = ntParser() _argument('json_file') # 标注⽂件json所在的⽂件夹 _argument('-o', '--out', default=None) args = _args() json_file = _file list = r(json_file) # 获取json⽂件列表 for i in range(0, len(list)): path = (json_file, list[i]) # 获取每个json⽂件的绝对路径 filename = list[i][:-5] # 提取出.json前的字符作为⽂件名，以便后续保存Label图⽚的时候使⽤ extension = list[i][-4:] if extension == 'json': if (path): data = (open(path)) img = _b64_to_arr(data['imageData']) # 根据'imageData'字段的字符可以得到原图像 # lbl为label图⽚（标注的地⽅⽤类别名对应的数字来标，其他为0）lbl_names为label名和数字的对应关系字典 lbl, lbl_names = e_shapes_to_label(, data['shapes']) # data['shapes']是json⽂件中记录着标注的位置及label等信息的字段 #captions = ['%d: %s' % (l, name) for l, name in enumerate(lbl_names)] #lbl_viz = _label(lbl, img, captions) out_dir = me(list[i])[:-5]+'_json' out_dir = (e(list[i]), out_dir) if not (out_dir): (out_dir) ray(img).save((out_dir, '{}_'.format(filename))) ray(lbl).save((out_dir, '{}_'.format(filename))) #ray(lbl_viz).save((out_dir, '{}_'.format(filename))) with open((out_dir, 'label_'), 'w') as f: for lbl_name in lbl_names: (lbl_name + 'n') (' is being replaced by label_') info = dict(label_names=lbl_names) with open((out_dir, ''), 'w') as f: _dump(info, f, default_flow_style=False) print('Saved to: %s' % out_dir)if __name__ == '__main__': main()修改好以后cmd到labelme_json_to_位置输⼊labelme_json_to_ e:(json⽂件夹）结果⼆、批量将PNG可视化import osfrom PIL import Imagefilename = r("E:pytorch001")base_dir = "E:pytorch001"new_dir = "E:pytorch"for img in filename: image = (base_dir + img).convert('L') ette([0, 0, 0,

255, 0, 0, 255, 255, 0, 255, 153, 0]) (new_dir + img)