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search for topic: Awesome xxx

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pnpm add -D gh-pages
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{
  "scripts": {
    "build": "vite build", 
    "deploy": "npm run build && gh-pages -d dist"
  }
}

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无意在网上看到了编程语言的鄙视链，我anyscript(ts)位列末端，Rust以其编码效率和执行效率、面向现代化，没有历史包袱的优点成为鄙视链上高于C/C++的存在

关于webassembly的开发需求 LLM建议我从原本薄弱的C++路径直接转到Rust上

C++基础薄弱，想要写出能在浏览器安全、高效运行的 Wasm 代码，需要补齐的短板太多. 挑战 Wasm 80% 的精力花在“如何不写崩内存”和“如何配通编译环境”上，而不是业务逻辑。Rust 的设计初衷就包含了内存安全和现代工具链，这与 Wasm 的需求完美契合

Rust学习曲线陡峭 所有权和生命周期概念确实比 C++ 难懂 好在 Rust 一旦通过编译，代码大概率就是正确的

《Rust and WebAssembly》

Rust 在图形学领域（尤其是涉及 WebAssembly、现代 API 如 Vulkan/Metal、以及引擎开发）已经展现出了独特的、甚至超越 C++ 的优势

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无间

刘邦被围

安史之乱潼关失守

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设置RichText以使文本中的样式生效

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Label {
    textFormat: Text.RichText;
    text: "<style>a:link { color: " + Theme.highlightColor + "; }</style>" +
          "<a href=https://together.jolla.com/'>Jolla2Gether</a>";
    }
}

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AP(Access Point)

通常说的是无线AP，即无线网络接入点，无线路由器的天线就是AP，其作用是收发网络数据信号，在网络中相当于交换机，家装中做全屋网络覆盖会安装AP设备 （因为常常是商用，其功能不如路由器售价却比路由器贵很多）

AC(Access Controller)

接入控制器，集中化控制局域网内可控的无线AP, 应用场景如商场的不同区域AP，统一连接到AC，使得顾客从一个区域到另一个区域，始终连接统一网络

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安装TensorFlow Object Detection API

参考PaddleX准备anaconda和cuda

参考安装Tensorflow2

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conda create -n tensorflow python=3.10
conda activate tensorflow
pip install tensorflow

pip自动安装最新release版本 2.0之后不再区分cpu和gpu版本

测试安装，在python中引用tensorflow， 对一个随机张量的求和

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python -c "import tensorflow as tf;print(tf.reduce_sum(tf.random.normal([1000, 1000])))"
# 结果形如tf.Tensor(-1082.7711, shape=(), dtype=float32)

install the TensorFlow Object Detection API：
下载模型库 新建一个Tensorflow目录并将模型库clone/解压到目录下
TensorFlow/
└─ models/
├─ community/
├─ official/
├─ orbit/
├─ research/
└── …
根据Readme的提示安装official library
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# 将model路径添加到PYTHONPATH环境变量
set PYTHONPATH=%PYTHONPATH%;C:\Users\qqqst\Documents\TensorFlow\models
# 安装requirements
pip3 install --user -r models/official/requirements.txt


下载protobuf添加到环境变量path,对于windows下载 protoc---win64.zip并解压，注意protobuf--.zip是source code 添加路径如D:\Software\protoc-25.1-win64\bin到path

使用protoc编译模型的python版本(与.proto一一对应的.py文件)

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# cmdline in TensorFlow/models/research
for /f %i in ('dir /b object_detection\protos\*.proto') do protoc object_detection\protos\%i --python_out=.

install pycocotools (依赖vc++ 2015)

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pip install cython
pip install git+https://github.com/philferriere/cocoapi.git#subdirectory=PythonAPI

看到Object Detection for TensorFlow 2.0的setup script

Install the Object Detection API

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# From within TensorFlow/models/research/
cp object_detection\packages\tf2\setup.py .
python -m pip install .

调用test脚本以测试安装
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# From within TensorFlow/models/research/
python object_detection/builders/model_builder_tf2_test.py

</del>

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关于版本号和构建编号

The standard build numbering convention makes use of a fourth numerical indicator which is appended
to the release number, where the fourth indicator is the build number.

  • For verification builds, the build number starts with a “1”, and increments with each successive
    build. For each successive release, the build indicator starts again with zero’s.
  • For development builds, the build number starts with “20001” and increments with each
    successive build. For each successive release, the build indicator starts again with zero’s.

Examples:
4.3.0.20134 – The 134th development build of release 4.3.0
4.3.0.4 – The 4th verification build of release 4.3.0

Release image naming convention -> Example: GxPicture_3.4.3.7.ISO