https://github.com/wkentaro/labelme
安装过程按照github教程就好，上面说的很详细，没遇到啥问题。
大家用到最多的就是 json文件的转化了，但是labelme自带转化工具，只能支持一次转化一个.json文件，所以需要修改代码，使得可以批量执行。
在lableme安装目录下有D:\Anaconda3\envs\labelme\Lib\site-packages\labelme\cli目录，可以看到json_to_dataset.py文件,将下面代码复制替换其中内容即可。
运行：
python D:\Anaconda3\envs\labelme\Scripts\labelme_json_to_dataset.exe I:\xj\Mask_RCNN_dataset\json
路径只需要输入到文件夹即可，不需要具体指定json文件。看你的根目录在哪里，就去哪里找输出文件，会生成一些跟.json同名的文件夹。
这里一共提供三个版本：
转自：https://blog.csdn.net/yql_617540298/article/details/81110685
import argparse
import json
import os
import os.path as osp
import warnings
import PIL.Image
import yaml
from labelme import utils
import base64
def main():
warnings.warn("This script is aimed to demonstrate how to convert the\n"
"JSON file to a single image dataset, and not to handle\n"
"multiple JSON files to generate a real-use dataset.")
parser = argparse.ArgumentParser()
parser.add_argument('json_file')
parser.add_argument('-o', '--out', default=None)
args = parser.parse_args()
json_file = args.json_file
if args.out is None:
out_dir = osp.basename(json_file).replace('.', '_')
out_dir = osp.join(osp.dirname(json_file), out_dir)
else:
out_dir = args.out
if not osp.exists(out_dir):
os.mkdir(out_dir)
count = os.listdir(json_file)
for i in range(0, len(count)):
path = os.path.join(json_file, count[i])
if os.path.isfile(path):
data = json.load(open(path))
if data['imageData']:
imageData = data['imageData']
else:
imagePath = os.path.join(os.path.dirname(path), data['imagePath'])
with open(imagePath, 'rb') as f:
imageData = f.read()
imageData = base64.b64encode(imageData).decode('utf-8')
img = utils.img_b64_to_arr(imageData)
label_name_to_value = {'_background_': 0}
for shape in data['shapes']:
label_name = shape['label']
if label_name in label_name_to_value:
label_value = label_name_to_value[label_name]
else:
label_value = len(label_name_to_value)
label_name_to_value[label_name] = label_value
# label_values must be dense
label_values, label_names = [], []
for ln, lv in sorted(label_name_to_value.items(), key=lambda x: x[1]):
label_values.append(lv)
label_names.append(ln)
assert label_values == list(range(len(label_values)))
lbl = utils.shapes_to_label(img.shape, data['shapes'], label_name_to_value)
captions = ['{}: {}'.format(lv, ln)
for ln, lv in label_name_to_value.items()]
lbl_viz = utils.draw_label(lbl, img, captions)
out_dir = osp.basename(count[i]).replace('.', '_')
out_dir = osp.join(osp.dirname(count[i]), out_dir)
if not osp.exists(out_dir):
os.mkdir(out_dir)
PIL.Image.fromarray(img).save(osp.join(out_dir, 'img.png'))
#PIL.Image.fromarray(lbl).save(osp.join(out_dir, 'label.png'))
utils.lblsave(osp.join(out_dir, 'label.png'), lbl)
PIL.Image.fromarray(lbl_viz).save(osp.join(out_dir, 'label_viz.png'))
with open(osp.join(out_dir, 'label_names.txt'), 'w') as f:
for lbl_name in label_names:
f.write(lbl_name + '\n')
warnings.warn('info.yaml is being replaced by label_names.txt')
info = dict(label_names=label_names)
with open(osp.join(out_dir, 'info.yaml'), 'w') as f:
yaml.safe_dump(info, f, default_flow_style=False)
print('Saved to: %s' % out_dir)
if __name__ == '__main__':
main()
但是存在两个问题
对于多类分割任务，任意一张图可能不包含所有分类。因此整个文件夹下生成的所有label图像中，不同图像中的相同类别的目标在label.png中可能对应不同的灰度值，使标注的label不具备统一性，因而出错。如下图:
从以上三个图可以看出，有的猫和狗被标注成了同一种像素值，造成了标注错乱现象，所以大家使用时候这里注意。
生成的 label.png，为 rgb三通道彩色图，并不是灰度图，想要灰度图，请看代码2
转自：https://blog.csdn.net/u010103202/article/details/81635436#commentsedit
import argparse
import json
import os
import os.path as osp
import warnings
import copy
import numpy as np
import PIL.Image
from skimage import io
import yaml
from labelme import utils
def main():
parser = argparse.ArgumentParser()
parser.add_argument('json_file')
parser.add_argument('-o', '--out', default=None)
args = parser.parse_args()
json_file = args.json_file
list = os.listdir(json_file)
for i in range(0, len(list)):
path = os.path.join(json_file, list[i])
filename = list[i][:-5] # .json
if os.path.isfile(path):
data = json.load(open(path))
img = utils.image.img_b64_to_arr(data['imageData'])
lbl, lbl_names = utils.shape.labelme_shapes_to_label(img.shape, data['shapes']) # labelme_shapes_to_label
captions = ['%d: %s' % (l, name) for l, name in enumerate(lbl_names)]
lbl_viz = utils.draw.draw_label(lbl, img, captions)
out_dir = osp.basename(list[i]).replace('.', '_')
out_dir = osp.join(osp.dirname(list[i]), out_dir)
if not osp.exists(out_dir):
os.mkdir(out_dir)
PIL.Image.fromarray(img).save(osp.join(out_dir, '{}.png'.format(filename)))
PIL.Image.fromarray(lbl).save(osp.join(out_dir, '{}_gt.png'.format(filename)))
PIL.Image.fromarray(lbl_viz).save(osp.join(out_dir, '{}_viz.png'.format(filename)))
with open(osp.join(out_dir, 'label_names.txt'), 'w') as f:
for lbl_name in lbl_names:
f.write(lbl_name + '\n')
warnings.warn('info.yaml is being replaced by label_names.txt')
info = dict(label_names=lbl_names)
with open(osp.join(out_dir, 'info.yaml'), 'w') as f:
yaml.safe_dump(info, f, default_flow_style=False)
print('Saved to: %s' % out_dir)
if __name__ == '__main__':
main()
这个能生成灰度图，但是还是存在 标注的label不具备统一性 的问题，请看代码3。
为了解决以上问题，修改代码如下，
import argparse
import json
import os
import os.path as osp
import warnings
import copy
import numpy as np
import PIL.Image
from skimage import io
import yaml
from labelme import utils
NAME_LABEL_MAP = {
'_background_': 0,
"baseball_diamond": 1,
"tennis_court": 2,
"basketball_court": 3,
"ground_track_field": 4,
}
LABEL_NAME_MAP = {
0: '_background_',
1: "airplane",
2: "ship",
3: "storage_tank",
4: "baseball_diamond",
5: "tennis_court",
6: "basketball_court",
7: "ground_track_field",
8: "harbor",
9: "bridge",
10: "vehicle",
}
def main():
parser = argparse.ArgumentParser()
parser.add_argument('json_file')
parser.add_argument('-o', '--out', default=None)
args = parser.parse_args()
json_file = args.json_file
list = os.listdir(json_file)
for i in range(0, len(list)):
path = os.path.join(json_file, list[i])
filename = list[i][:-5] # .json
if os.path.isfile(path):
data = json.load(open(path))
img = utils.image.img_b64_to_arr(data['imageData'])
lbl, lbl_names = utils.shape.labelme_shapes_to_label(img.shape, data['shapes']) # labelme_shapes_to_label
# modify labels according to NAME_LABEL_MAP
lbl_tmp = copy.copy(lbl)
for key_name in lbl_names:
old_lbl_val = lbl_names[key_name]
new_lbl_val = NAME_LABEL_MAP[key_name]
lbl_tmp[lbl == old_lbl_val] = new_lbl_val
lbl_names_tmp = {}
for key_name in lbl_names:
lbl_names_tmp[key_name] = NAME_LABEL_MAP[key_name]
# Assign the new label to lbl and lbl_names dict
lbl = np.array(lbl_tmp, dtype=np.int8)
lbl_names = lbl_names_tmp
captions = ['%d: %s' % (l, name) for l, name in enumerate(lbl_names)]
lbl_viz = utils.draw.draw_label(lbl, img, captions)
out_dir = osp.basename(list[i]).replace('.', '_')
out_dir = osp.join(osp.dirname(list[i]), out_dir)
if not osp.exists(out_dir):
os.mkdir(out_dir)
PIL.Image.fromarray(img).save(osp.join(out_dir, '{}.png'.format(filename)))
PIL.Image.fromarray(lbl).save(osp.join(out_dir, '{}_gt.png'.format(filename)))
PIL.Image.fromarray(lbl_viz).save(osp.join(out_dir, '{}_viz.png'.format(filename)))
with open(osp.join(out_dir, 'label_names.txt'), 'w') as f:
for lbl_name in lbl_names:
f.write(lbl_name + '\n')
warnings.warn('info.yaml is being replaced by label_names.txt')
info = dict(label_names=lbl_names)
with open(osp.join(out_dir, 'info.yaml'), 'w') as f:
yaml.safe_dump(info, f, default_flow_style=False)
print('Saved to: %s' % out_dir)
if __name__ == '__main__':
main()
但是此代码可能会报类似错：
IndexError: index 6 is out of bounds for axis 0 with size 4
我已经修改了代码中bug（大神可自行修改）,修改后的代码连接：json_to_dataset.py， （请原谅需要挣点积分的我，真的穷）
执行后的图像，如下图所示：
其中，生成的图片名称跟原标注图片一致，那种黑色的背景图，其实是有图像的，只不过猫咪的像素值为1，狗狗像素值为2。从三个图中可以对比看出，猫咪都被标记为了1，狗狗都被标记为了2，而且生成的为灰度图，解决了代码1出现的两个问题。