工业仪表识别（一）环境安装

仪表识别环境安装

１．cuda

cuda 11.8 intall（cuda11.8、cuda12.6按照需求安装）

ref:

• https://developer.nvidia.com/cuda-11-8-0-download-archive?target_os=Linux&target_arch=x86_64&Distribution=Ubuntu&target_version=22.04&target_type=deb_network

wget https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_520.61.05_linux.run
sudo sh cuda_11.8.0_520.61.05_linux.run

env setting

vim ~/.bashrc
export CUDA_HOME=/usr/local/cuda-11.8
export LD_LIBRARY_PATH=${CUDA_HOME}/lib64
export PATH=${CUDA_HOME}/bin:${PATH}

anaconda

conda create -n cv_meter python=3.10 -y

2.PaddleOCR

ref:

• paddlepaddle: https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/develop/install/pip/linux-pip.html
• paddleocr:
• ppocrlabel（标注工具）:

(1) install

#PaddlePaddle 3.0的GPU版本安装
# CUDA 11.8
python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/# paddleocr
python -m pip install paddleocr==3.0.0# 简单验证ocr功能
from paddleocr import PaddleOCRocr = PaddleOCR(use_doc_orientation_classify=False, use_doc_unwarping=False, use_textline_orientation=False) # 文本检测+文本识别
# ocr = PaddleOCR(use_doc_orientation_classify=True, use_doc_unwarping=True) # 文本图像预处理+文本检测+方向分类+文本识别
# ocr = PaddleOCR(use_doc_orientation_classify=False, use_doc_unwarping=False) # 文本检测+文本行方向分类+文本识别
# ocr = PaddleOCR(
#     text_detection_model_name="PP-OCRv5_mobile_det",
#     text_recognition_model_name="PP-OCRv5_mobile_rec",
#     use_doc_orientation_classify=False,
#     use_doc_unwarping=False,
#     use_textline_orientation=False) # 更换 PP-OCRv5_mobile 模型
result = ocr.predict("./general_ocr_002.png")
for res in result:res.print()res.save_to_img("output")res.save_to_json("output")

(2) ppocrlabel安装

# 安装python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/＃通过whl包安装与运行
pip3 install PPOCRLabel
pip3 install trash-cli
# pyqt5和opencv版本不匹配，修改内容
pip uninstall opencv-contrib-python-headless
pip3 install opencv-contrib-python-headless==4.11.0.86
## 选择标签模式来启动
PPOCRLabel --lang ch  # 启动【普通模式】，用于打【检测+识别】场景的标签
PPOCRLabel --lang ch --kie True  # 启动 【KIE 模式】，用于打【检测+识别+关键字提取】场景的标签

3.YOLO

ref:

• 使用：https://docs.ultralytics.com/zh/quickstart/#__tabbed_1_3
• 环境依赖和限制：https://github.com/ultralytics/ultralytics/blob/main/pyproject.toml

install

#　pip 安装
pip install ultralytics＃　注意:
# 如果在CUDA 环境中安装，最佳做法是安装 ultralytics, pytorch和 pytorch-cuda 在同一命令中。这允许 conda 软件包管理器解决任何冲突。或者，安装 pytorch-cuda 最后覆盖CPU pytorch 如有必要，请将该程序包添加到"... "中。
# Install all packages together using conda
#conda install -c pytorch -c nvidia -c conda-forge pytorch torchvision pytorch-cuda=11.8 ultralytics# or 
# Install the ultralytics package using conda
#conda install -c conda-forge ultralytics

eval

from ultralytics import YOLO# Load a model
model = YOLO("yolo11n.pt")  # pretrained YOLO11n model# Run batched inference on a list of images
results = model(["image1.jpg", "image2.jpg"])  # return a list of Results objects# Process results list
for result in results:boxes = result.boxes  # Boxes object for bounding box outputsmasks = result.masks  # Masks object for segmentation masks outputskeypoints = result.keypoints  # Keypoints object for pose outputsprobs = result.probs  # Probs object for classification outputsobb = result.obb  # Oriented boxes object for OBB outputsresult.show()  # display to screenresult.save(filename="result.jpg")  # save to disk