如何使用labelme中的AI多边形（AI-polygon）标注

文章目录

• 1.创建labelme虚拟环境
• 2.下载AI标注模型
• 3.修改配置文件
• 4.愉快地使用labelme的AI标注工具

1.创建labelme虚拟环境

（1）创建基础环境并激活

conda create -n labelme python=3.8conda activate labelme

（2）安装labelme

pip install labelme -i https://pypi.tuna.tsinghua.edu.cn/simple/ numpy

（3）使用labelme启动

如果是第一次装labelme，打开图像路径，右键图像后选择Create AI-Polygon，软件会自动下载并安装AI标注模型，我的下载速度太慢，导致第一次下载失败，最后选择了手动安装。

2.下载AI标注模型

可以选择在官网上下载AI自动标注模型下载地址

如果连不到外网，可以通过迅雷网盘或者百度网盘提取模型

迅雷网盘链接：https://pan.xunlei.com/s/VNkyiDkG9ORZRr7Mhx4ru3I8A1#
提取码：2dbf

百度网盘链接：https://pan.baidu.com/s/11xrWH4p_auHl-cKYjZ899Q” />

3.修改配置文件

（1）找到"E:\programFiles\anaconda3\envs\labelme\Lib\site-packages\labelme\ai\__init__.py"文件，并修改里面的模型路径。

# flake8: noqaimport loggingimport sysfrom qtpy import QT_VERSION__appname__ = "labelme"# Semantic Versioning 2.0.0: https://semver.org/# 1. MAJOR version when you make incompatible API changes;# 2. MINOR version when you add functionality in a backwards-compatible manner;# 3. PATCH version when you make backwards-compatible bug fixes.# e.g., 1.0.0a0, 1.0.0a1, 1.0.0b0, 1.0.0rc0, 1.0.0, 1.0.0.post0__version__ = "5.4.0a0"QT4 = QT_VERSION[0] == "4"QT5 = QT_VERSION[0] == "5"del QT_VERSIONPY2 = sys.version[0] == "2"PY3 = sys.version[0] == "3"del sysfrom labelme.label_file import LabelFilefrom labelme import testingfrom labelme import utilsimport collectionsfrom .models.segment_anything import SegmentAnythingModel# NOQAModel = collections.namedtuple("Model", ["name", "encoder_weight", "decoder_weight"])Weight = collections.namedtuple("Weight", ["url", "md5"])# MODELS = [# Model(# name="Segment-Anything (speed)",# encoder_weight=Weight(# url="https://github.com/wkentaro/labelme/releases/download/sam-20230416/sam_vit_b_01ec64.quantized.encoder.onnx",# NOQA# md5="80fd8d0ab6c6ae8cb7b3bd5f368a752c",# ),# decoder_weight=Weight(# url="https://github.com/wkentaro/labelme/releases/download/sam-20230416/sam_vit_b_01ec64.quantized.decoder.onnx",# NOQA# md5="4253558be238c15fc265a7a876aaec82",# ),# ),# Model(# name="Segment-Anything (balanced)",# encoder_weight=Weight(# url="https://github.com/wkentaro/labelme/releases/download/sam-20230416/sam_vit_l_0b3195.quantized.encoder.onnx",# NOQA# md5="080004dc9992724d360a49399d1ee24b",# ),# decoder_weight=Weight(# url="https://github.com/wkentaro/labelme/releases/download/sam-20230416/sam_vit_l_0b3195.quantized.decoder.onnx",# NOQA# md5="851b7faac91e8e23940ee1294231d5c7",# ),# ),# Model(# name="Segment-Anything (accuracy)",# encoder_weight=Weight(# url="https://github.com/wkentaro/labelme/releases/download/sam-20230416/sam_vit_h_4b8939.quantized.encoder.onnx",# NOQA# md5="958b5710d25b198d765fb6b94798f49e",# ),# decoder_weight=Weight(# url="https://github.com/wkentaro/labelme/releases/download/sam-20230416/sam_vit_h_4b8939.quantized.decoder.onnx",# NOQA# md5="a997a408347aa081b17a3ffff9f42a80",# ),# ),# ]MODELS = [Model(name="Segment-Anything (speed)",encoder_weight=Weight(url="E:\programFiles\\anaconda3\envs\labelme\Lib\site-packages\labelme\model_file\sam_vit_b_01ec64.quantized.encoder.onnx",# NOQAmd5="80fd8d0ab6c6ae8cb7b3bd5f368a752c",),decoder_weight=Weight(url="E:\programFiles\\anaconda3\envs\labelme\Lib\site-packages\labelme\model_file\sam_vit_b_01ec64.quantized.decoder.onnx",# NOQAmd5="4253558be238c15fc265a7a876aaec82",),),Model(name="Segment-Anything (balanced)",encoder_weight=Weight(url="E:\\programFiles\\anaconda3\\envs\\labelme\\Lib\\site-packages\\labelme\\model_file\\sam_vit_l_0b3195.quantized.encoder.onnx",# NOQAmd5="080004dc9992724d360a49399d1ee24b",),decoder_weight=Weight(url="E:\\programFiles\\anaconda3\\envs\\labelme\\Lib\\site-packages\\labelme\\model_file\\sam_vit_l_0b3195.quantized.decoder.onnx",# NOQAmd5="851b7faac91e8e23940ee1294231d5c7",),),Model(name="Segment-Anything (accuracy)",encoder_weight=Weight(url="E:\\programFiles\\anaconda3\\envs\\labelme\\Lib\\site-packages\\labelme\\model_file\\sam_vit_h_4b8939.quantized.decoder.onnx",# NOQAmd5="958b5710d25b198d765fb6b94798f49e",),decoder_weight=Weight(url="E:\\programFiles\\anaconda3\\envs\\labelme\\Lib\\site-packages\\labelme\\model_file\\sam_vit_h_4b8939.quantized.encoder.onnx",# NOQAmd5="a997a408347aa081b17a3ffff9f42a80",),),]

（2）找到E:\programFiles\anaconda3\envs\labelme\Lib\site-packages\labelme\widgets\canvas.py文件夹并修改initializeAiModel方法

def initializeAiModel(self, name):if name not in [model.name for model in labelme.ai.MODELS]:raise ValueError("Unsupported ai model: %s" % name)model = [model for model in labelme.ai.MODELS if model.name == name][0]if self._ai_model is not None and self._ai_model.name == model.name:logger.debug("AI model is already initialized: %r" % model.name)else:logger.debug("Initializing AI model: %r" % model.name)self._ai_model = labelme.ai.SegmentAnythingModel(name=model.name,# encoder_path=gdown.cached_download(# url=model.encoder_weight.url,# md5=model.encoder_weight.md5,# ),# decoder_path=gdown.cached_download(# url=model.decoder_weight.url,# md5=model.decoder_weight.md5,# ),encoder_path=model.encoder_weight.url,decoder_path=model.decoder_weight.url,)self._ai_model.set_image(image=labelme.utils.img_qt_to_arr(self.pixmap.toImage()))

4.愉快地使用labelme的AI标注工具

这样再激活虚拟环境，使用labelme命令打开标注工具，右键选择AI标注，双击标注完成。

参考链接：labelme加载AI模型