儀表識別環境安裝

１．cuda

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

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（標注工具）:

(1) install

#PaddlePaddle 3.0的GPU版本安裝
# 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# 簡單驗證ocr功能
from paddleocr import PaddleOCRocr = PaddleOCR(use_doc_orientation_classify=False, use_doc_unwarping=False, use_textline_orientation=False) # 文本檢測+文本識別
# ocr = PaddleOCR(use_doc_orientation_classify=True, use_doc_unwarping=True) # 文本圖像預處理+文本檢測+方向分類+文本識別
# ocr = PaddleOCR(use_doc_orientation_classify=False, use_doc_unwarping=False) # 文本檢測+文本行方向分類+文本識別
# 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) # 更換 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安裝

# 安裝python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/＃通過whl包安裝與運行
pip3 install PPOCRLabel
pip3 install trash-cli
# pyqt5和opencv版本不匹配，修改內容
pip uninstall opencv-contrib-python-headless
pip3 install opencv-contrib-python-headless==4.11.0.86
## 選擇標簽模式來啟動
PPOCRLabel --lang ch  # 啟動【普通模式】，用于打【檢測+識別】場景的標簽
PPOCRLabel --lang ch --kie True  # 啟動 【KIE 模式】，用于打【檢測+識別+關鍵字提取】場景的標簽

3.YOLO

ref:

• 使用：https://docs.ultralytics.com/zh/quickstart/#__tabbed_1_3
• 環境依賴和限制：https://github.com/ultralytics/ultralytics/blob/main/pyproject.toml

install

#　pip 安裝
pip install ultralytics＃　注意:
# 如果在CUDA 環境中安裝，最佳做法是安裝 ultralytics, pytorch和 pytorch-cuda 在同一命令中。這允許 conda 軟件包管理器解決任何沖突。或者，安裝 pytorch-cuda 最后覆蓋CPU pytorch 如有必要，請將該程序包添加到"... "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