大家好,我是极智视界。本文详细介绍了 labelme 标注与处理分割数据的方法。
图像分割是计算机视觉任务中常见任务,有实例分割、语义分割、全景分割等之分,在送入分割任务前,需要先对数据做标注处理。大家知道,深度学习中数据的质量对最后的检测效果影响很大,所以数据标注的好坏重要性不言而喻。
下面开始。
1、安装 labelme
不管你是 windows 还是 linux 都可以这么安装:
# 首先安装anaconda,这个这里不多说 # 安装pyqt5 pip install -i https://pypi.douban.com/simple pyqt5 # 安装labelme pip install -i https://pypi.douban.com/simple labelme # 打开labelme ./labelme
然后会生成对应图片的json文件,里面会有label和标注的分割掩膜信息,差不多像这样:
2、内置 json to datset
2.1 单图json to dataset
直接执行:
labelme_json_dataset xxx.json
然后会生成:
- img.png:原图;
- label.png:掩膜图;
- label_viz.png:加背景的掩膜图;
- info.yaml、label_names.txt:标签信息;
2.2 批量json to dataset
找到 cli/json_to_dataset.py
目录,然后:
cd cli touch json_to_datasetP.py vim json_to_datasetP.py
加入如下内容:
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()
然后批量进行转换:
python path/cli/json_to_datasetP.py path/JPEGImages
若报错:
lbl_viz = utils.draw_label(lbl, img, captions)
AttributeError: module 'labelme.utils' has no attribute 'draw_label'
解决办法:需要更换labelme版本,需要降低labelme 版本到3.16.2 ,方法进入labelme环境中,键入 pip install labelme==3.16.2 就可以自动下载这个版本了,就可以成功了。
3、另一种分割标签制作
若你想生成类似下面这种标签:
原图:
对应标签 (背景为0,圆为1):
此标签为 8 位单通道图像,该方法支持最多 256 种类型。
可以通过以下脚本进行数据集的制作:
import cv2 import numpy as np import json import os # 0 1 2 3 # backg Dog Cat Fish category_types = ["Background", "Dog", "Cat", "Fish"] # 获取原始图像尺寸 img = cv2.imread("image.bmp") h, w = img.shape[:2] for root,dirs,files in os.walk("data/Annotations"): for file in files: mask = np.zeros([h, w, 1], np.uint8) # 创建一个大小和原图相同的空白图像 print(file[:-5]) jsonPath = "data/Annotations/" with open(jsonPath + file, "r") as f: label = json.load(f) shapes = label["shapes"] for shape in shapes: category = shape["label"] points = shape["points"] # 填充 points_array = np.array(points, dtype=np.int32) mask = cv2.fillPoly(mask, [points_array], category_types.index(category)) imgPath = "data/masks/" cv2.imwrite(imgPath + file[:-5] + ".png", mask)
以上是 4 个分类的情况。
到这里就大功告成了。以上分享了 labelme 标注与处理分割数据的方法,希望我的分享能对你的学习有一点帮助。