Labelme标注流程
使用前注意：如果标注前需要对图片尺寸进行修改，务必先修改图片尺寸，再标注
"""
修改图片尺寸的代码
"""
from PIL import Image
import os
Files_path = r"C:\Users\dd\Desktop\标注远距离图片" # C:\Users\dd\Desktop\标注远距离图片\data 注意图片是放在data文件夹下的！！！
labels_num = len(os.listdir(Files_path))
print(labels_num)
for i in range(labels_num):
image_dir = os.path.join(Files_path, str(os.listdir(Files_path)[i]))
print(image_dir)
image_list = os.listdir(image_dir)
for image_name in image_list:
image_path = os.path.join(image_dir, image_name)
img = Image.open(image_path)
print(img)
x_s = 512
y_s = 512
Resize_image = img.resize((x_s, y_s), Image.ANTIALIAS)
Resize_out_path = r"C:\Users\dd\Desktop\标注远距离图片"
Resize_image.save(Resize_out_path + '/' + str(os.listdir(Files_path)[i]) + '/' + str(image_name))
1.按照下图中流程标注：
（下面的图片顶端的路径调整一下，C:\Users\dd\Desktop\标注远距离图片\left_0.jpg 变为 C:\Users\dd\Desktop\标注远距离图片\data\left_0.jpg）
2.处理标注后的json文件
2.1 修改json_to_dataset.py文件的内容：（如果已经替换过则忽略）
参考链接
在D:\Anaconda\envs\labelme\Lib\site-packages\labelme\cli 可以看到json_to_dataset.py文件
修改为：
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()
2.2 处理json文件
命令行输入：
F:\>cd F:\Anaconda\Scripts
F:\Anaconda\Scripts>labelme_json_to_dataset.exe C:\Users\dd\Desktop\标注远距离图片\标注后文件
3.使用Python批量提取不同json文件夹中的img.png 和 label.png图片：
3.1 如果json文件夹的命名各不相同，为了提取方便，先使用Python批量修改一个文件夹下所有文件夹的名字，这里统一改成了rgb_i_json，其中i从1到n，代码如下：
import os
dirs = os.listdir(r'C:\Users\dd\Desktop\标注远距离图片\json_to_data')
i = 1
for dir in dirs:
# 获取每个文件夹
print(dir)
# 获取文件夹的名字
oldName = os.path.join(r'C:\Users\dd\Desktop\标注远距离图片\json_to_data', dir)
newName = os.path.join(r'C:\Users\dd\Desktop\标注远距离图片\json_to_data', 'rgb_'+str(i)+'_json')
# 执行修改名称的方法.参数1告诉他以前的名字叫啥;参数2告诉他新名字叫啥
os.rename(oldName, newName)
i += 1
3.2 然后，分别提取rgb_i_json文件夹中的各个img.png和label.png到两个新的文件夹中，代码如下：
import os
import shutil
path = input('_json文件夹所在的路径:')
new_path = input('需保存的路径:')
count = os.listdir(path)
for j in range(1, len(count) + 1):
for root, dirs, files in os.walk(path):
if len(dirs) == 0:
for i in range(len(files)):
print("i=", i)
# 样例：
# if files[i].find('label.png') != -1:
# shutil.copy(os.path.join(path + '/rgb_' + str(j).zfill(4) + '_json/', files[i]),
# os.path.join(new_path, 'rgb_' + str(j).zfill(4) + '.png'))
# 将label.png提取出来并且按照顺序命名1.png....
if files[i].find('label.png') != -1:
shutil.copy(os.path.join(path + '/rgb_' + str(j).zfill(1) + '_json/', files[i]),
os.path.join(new_path, str(j).zfill(1) + '.png'))
# 将img.png提取出来并且按照顺序命名1.png.....
# if files[i].find('img.png') != -1:
# shutil.copy(os.path.join(path + '/rgb_' + str(j).zfill(1) + '_json/', files[i]),
# os.path.join(new_path, str(j).zfill(1) + '.png'))