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

（1）创建基础环境并激活

conda create -n labelme python=3.8
conda 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?pwd=lg1j
提取码：lg1j

在anaconda虚拟环境中找到

E:\programFiles\anaconda3\envs\labelme\Lib\site-packages\labelme

此路径，将下载好的文件放入此文件夹下。

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 logging
import sys

from 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_VERSION

PY2 = sys.version[0]=="2"
PY3 = sys.version[0]=="3"del sys

from labelme.label_file import LabelFile
from labelme import testing
from labelme import utils
import collections

from.models.segment_anything import SegmentAnythingModel  # NOQA

Model = 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",# NOQA
            md5="80fd8d0ab6c6ae8cb7b3bd5f368a752c",),
        decoder_weight=Weight(
            url="E:\programFiles\\anaconda3\envs\labelme\Lib\site-packages\labelme\model_file\sam_vit_b_01ec64.quantized.decoder.onnx",# NOQA
            md5="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",# NOQA
            md5="080004dc9992724d360a49399d1ee24b",),
        decoder_weight=Weight(
            url="E:\\programFiles\\anaconda3\\envs\\labelme\\Lib\\site-packages\\labelme\\model_file\\sam_vit_l_0b3195.quantized.decoder.onnx",# NOQA
            md5="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",# NOQA
            md5="958b5710d25b198d765fb6b94798f49e",),
        decoder_weight=Weight(
            url="E:\\programFiles\\anaconda3\\envs\\labelme\\Lib\\site-packages\\labelme\\model_file\\sam_vit_h_4b8939.quantized.encoder.onnx",# NOQA
            md5="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

definitializeAiModel(self, name):if name notin[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 isnotNoneand 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