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编写labels.txt，内容包括背景(background)和类别名称，多类别就写上你的类别名称。buildings的labels.txt文档内容如下：
background
buildings
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将路径切换到要打标签的图像文件夹所在的目录下，执行命令：labelme buildings --labels labels.txt
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将labelme设置成'Saved Automatically'（点击labelme软件左上角的file就有此选项），然后开始标记工作。
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标记完成后保存的是json数据，路径在标记图像的目录下，接着需要将json文件转换成VOC格式。假设将转换的图像保存在buildings_VOC下。执行命令：python label2VOC.py buildings buildings_VOC --labels labels.txt。labels.txt就是你一开始编写的txt文件。
label2VOC.py代码如下：
#!/usr/bin/env python # labelme data_annotated --labels labels.txt --nodata --validatelabel exact # ./labelme2voc.py data_annotated data_dataset_voc --labels labels.txt from __future__ import print_function import argparse import glob import os import os.path as osp import sys import imgviz import numpy as np import labelme def main(): parser = argparse.ArgumentParser( formatter_class=argparse.ArgumentDefaultsHelpFormatter ) parser.add_argument("input_dir", help="input annotated directory") parser.add_argument("output_dir", help="output dataset directory") parser.add_argument("--labels", help="labels file", required=True) parser.add_argument( "--noviz", help="no visualization", action="store_true" ) args = parser.parse_args() if osp.exists(args.output_dir): print("Output directory already exists:", args.output_dir) sys.exit(1) os.makedirs(args.output_dir) os.makedirs(osp.join(args.output_dir, "JPEGImages")) os.makedirs(osp.join(args.output_dir, "SegmentationClass")) os.makedirs(osp.join(args.output_dir, "SegmentationClassPNG")) if not args.noviz: os.makedirs( osp.join(args.output_dir, "SegmentationClassVisualization") ) print("Creating dataset:", args.output_dir) class_names = [] class_name_to_id = {} for i, line in enumerate(open(args.labels).readlines()): class_id = i - 1 # starts with -1 class_name = line.strip() class_name_to_id[class_name] = class_id if class_id == -1: assert class_name == "__ignore__" continue elif class_id == 0: assert class_name == "_background_" class_names.append(class_name) class_names = tuple(class_names) print("class_names:", class_names) out_class_names_file = osp.join(args.output_dir, "class_names.txt") with open(out_class_names_file, "w") as f: f.writelines("\n".join(class_names)) print("Saved class_names:", out_class_names_file) for filename in glob.glob(osp.join(args.input_dir, "*.json")): print("Generating dataset from:", filename) label_file = labelme.LabelFile(filename=filename) base = osp.splitext(osp.basename(filename))[0] out_img_file = osp.join(args.output_dir, "JPEGImages", base + ".jpg") out_lbl_file = osp.join( args.output_dir, "SegmentationClass", base + ".npy" ) out_png_file = osp.join( args.output_dir, "SegmentationClassPNG", base + ".png" ) if not args.noviz: out_viz_file = osp.join( args.output_dir, "SegmentationClassVisualization", base + ".jpg", ) with open(out_img_file, "wb") as f: f.write(label_file.imageData) img = labelme.utils.img_data_to_arr(label_file.imageData) lbl, _ = labelme.utils.shapes_to_label( img_shape=img.shape, shapes=label_file.shapes, label_name_to_value=class_name_to_id, ) labelme.utils.lblsave(out_png_file, lbl) np.save(out_lbl_file, lbl) if not args.noviz: viz = imgviz.label2rgb( label=lbl, img=imgviz.rgb2gray(img), font_size=15, label_names=class_names, loc="rb", ) imgviz.io.imsave(out_viz_file, viz) if __name__ == "__main__": main()
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生成完成后的文件架构如下：JPEGImages保存的是原图。SegmentationClass保存的是npy的文件，是原图的标签，可以用于训练，如何训练.npy文件后面再研究。SegmentationClassPNG保存的是标签，是单通道的图像，以调色板格式保存的。SegmentationClassVisualization保存的是原图+标签，用于可是化打标签的结果。class_names.txt用于保存标签的类别。
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到此，可以说数据标签已经完毕了。可以使用JPEGImages下的图像作为原图，SegmentationClassPNG作为标签送入网络进行训练。但是我们一般用单通道，0,1,2,3表示类别，因此我们也可以再做一次转换，将SegmentationClassPNG转换成那种全黑的图。（这里我不太会表达。）修改一下ConvertVOC2Gray.py里面的图像的路径，然后执行文件即可。ConvertVOC2Gray.py主要修改以下代码（路径以及保存路径）：
tf.flags.DEFINE_string('original_gt_folder',#读取voc格式的png图片路径 r'.\buildings_VOC\SegmentationClassPNG',#default 'Original ground truth annotations.')#help tf.flags.DEFINE_string('segmentation_format', 'png', 'Segmentation format.') tf.flags.DEFINE_string('output_dir',#保存路径 r'.\buildings_VOC\labels', 'folder to save modified ground truth annotations.')
"""Removes the color map from segmentation annotations. Removes the color map from the ground truth segmentation annotations and save the results to output_dir. """ #源程序链接：https://github.com/tensorflow/models/tree/master/research/deeplab/datasets #该程序用于将voc格式的索引图片转换为可用于语义分割的灰度图片，因为TensorFlow版本的缘故，在原程序上做了少许改动 #tf.__version__==1.12 import glob import os.path import numpy as np from PIL import Image import tensorflow as tf #FLAGS = tf.compat.v1.flags.FLAGS FLAGS = tf.flags.FLAGS tf.flags.DEFINE_string('original_gt_folder',#读取voc格式的png图片路径 r'.\cityBuilding_VOC\SegmentationClassPNG',#default 'Original ground truth annotations.')#help tf.flags.DEFINE_string('segmentation_format', 'png', 'Segmentation format.') tf.flags.DEFINE_string('output_dir',#保存路径 r'.\cityBuilding_VOC\labels', 'folder to save modified ground truth annotations.') def _remove_colormap(filename): """Removes the color map from the annotation. Args: filename: Ground truth annotation filename. Returns: Annotation without color map. """ return np.array(Image.open(filename)) def _save_annotation(annotation, filename): """Saves the annotation as png file. Args: annotation: Segmentation annotation. filename: Output filename. """ pil_image = Image.fromarray(annotation.astype(dtype=np.uint8)) ''' with tf.io.gfile.GFile(filename, mode='w') as f: #with open(filename, mode='w') as f: print(f) pil_image.save(f, 'PNG') ''' pil_image.save(filename) def main(unused_argv): # Create the output directory if not exists. if not os.path.exists(FLAGS.output_dir): os.makedirs(FLAGS.output_dir) #if not tf.io.gfile.isdir(FLAGS.output_dir): #tf.io.gfile.makedirs(FLAGS.output_dir) annotations = glob.glob(os.path.join(FLAGS.original_gt_folder, '*.' + FLAGS.segmentation_format)) for annotation in annotations: raw_annotation = _remove_colormap(annotation) filename = os.path.basename(annotation)[:-4] _save_annotation(raw_annotation, os.path.join( FLAGS.output_dir, filename + '.' + FLAGS.segmentation_format)) if __name__ == '__main__': #tf.compat.v1.app.run() tf.app.run()
然后可以看到building_VOC下的labels文件夹有的标签图。虽然是全黑，但是通过np.unique可以看到有0,1两种类别的像素。到此我们的数据集已经制作完成了。
- 参考
https://github.com/wkentaro/labelme/tree/master/examples/semantic_segmentation
https://blog.csdn.net/Winters____/article/details/105659632
- 类似语义分割数据集一样，对图像进行标注，生成json文件。要注意一点的是：不同的实例之间的标签尽可能不一样，如person的实例分为person.1，person.2，当然你也可以按照你的风格用不同的分割符，如“，”，“。”等。这样方便后续生成yaml文件。为什么要这样做？看一下第二步json_to_dataset就知道了。
"shapes": [
{
"label": "sign",
"points": [
[
1065.8518518518517,
535.5925925925926
],
[
954.7407407407406,
744.8518518518518
],
[
791.7777777777776,
1133.7407407407406
],
[
791.7777777777776,
1174.4814814814813
],
[
1049.185185185185,
1170.7777777777776
],
[
1389.9259259259259,
1135.5925925925926
],
[
1389.9259259259259,
1102.2592592592591
],
[
1119.5555555555554,
561.5185185185185
],
[
1099.185185185185,
531.8888888888888
]
],
"group_id": 1,
"shape_type": "polygon",
"flags": {}
},
{
"label": "sign.1",
"points": [
[
443.62962962962956,
1259.6666666666665
],
[
778.8148148148148,
1567.074074074074
],
[
754.7407407407406,
1352.2592592592591
],
[
423.25925925925924,
1074.4814814814815
]
],
"group_id": null,
"shape_type": "polygon",
"flags": {}
},
{
"label": "sign",
"points": [
[
2136.222222222222,
559.6666666666666
],
[
2264.0,
807.8148148148148
],
[
2591.7777777777774,
793.0
],
[
2539.9259259259256,
596.7037037037037
],
[
2338.074074074074,
441.1481481481481
],
[
2260.296296296296,
437.4444444444444
]
],
"group_id": 3,
"shape_type": "polygon",
"flags": {}
}
]- 生成训练集，运行json_to_dataset文件，注意自己的路径以及实例标签的分隔符。
import json
import os
import os.path as osp
import warnings
import PIL.Image
import yaml
from labelme import utils
import base64
from glob import glob
import shutil
'''
labelme:4.5.7
args:
dataset: 使用labelme标注完成的数据集，其中包括标注图以及json文件存放的位置
train_dir：保存的待训练的数据集，imgs--原图 mask--掩膜 yaml--yaml存放
注意：标注的时候后面以.区分实例，如person.1， person.2，以此类推
'''
def main(dataset = "./dataset/JPEGImages", train_dir = './dataset', env_command = 'conda activate labelme', split_flag = '.'):
# 批量处理json文件
if env_command is not None:
os.system(env_command)
json_file = os.path.join(dataset, '*.json')
for item in glob(json_file):
os.system(f"labelme_json_to_dataset.exe {item}")
save_path = os.path.join(dataset, '*.json')
for json_path in glob(save_path):
# 保存yaml文件
json_basename = os.path.basename(json_path).split('.')[0]
data = json.load(open(json_path))
if data['imageData']:
imageData = data['imageData']
else:
imagePath = os.path.join(os.path.dirname(json_path), data['imagePath'])
with open(imagePath, 'rb') as f:
imageData = f.read()
imageData = base64.b64encode(imageData).decode('utf-8')
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.split(split_flag)[0])
assert label_values == list(range(len(label_values)))
yaml_path = os.path.join(train_dir, "yaml")
if not os.path.exists(yaml_path):
os.mkdir(yaml_path)
warnings.warn('info.yaml is being replaced by label_names.txt')
info = dict(label_names=label_names)
with open(osp.join(yaml_path, str(json_basename)+'.yaml'), 'w') as f:
yaml.safe_dump(info, f, default_flow_style=False)
print('Saved : %s' % str(json_basename+'.yaml'))
# copy ori image to imgs and rename
src_path = os.path.join(dataset, json_basename+'_json', 'img.png')
dst_path = os.path.join(train_dir, 'images')
if not os.path.exists(dst_path):
os.mkdir(dst_path)
shutil.copy(src_path, os.path.join(dst_path, json_basename+'.png'))
# copy mask img to mask
src_path = os.path.join(dataset, json_basename+'_json', 'label.png')
dst_path = os.path.join(train_dir, 'mask')
if not os.path.exists(dst_path):
os.mkdir(dst_path)
shutil.copy(src_path, os.path.join(dst_path, json_basename+'.png'))
if __name__ == '__main__':
main()- 最终设定的目录下生成images、mask、yaml文件夹，分别存放原图，掩膜图，yaml文件。具体的Demo可以参照Instance_dataset_demo