libEMF Version 1.0 libEMF is a library for generating Enhanced Metafiles on systems which don't natively support the ECMA-234 Graphics Device Interface (GDI). The library is intended to be used as a driver for other graphics programs such as Grace or gnuplot. Therefore, it implements a very limited subset of the GDI. The only additional configuration option beyond the usual GNU autotools options is --enable-editing, which activates the function EditEnhMetaFile(). This will print the contents of a metafile in memory to stdout. The documentation starts at doc/html/index.html. The software is available from SourceForge. See https://sourceforge.net/projects/libemf/ Copyright (C) 2002 lignum Computing, Inc. <dallenbarnett@users.sourceforge.net>
时间: 2024-04-28 13:20:31 浏览: 8
"libEMF" 是一个用于在不支持 ECMA-234 图形设备接口(GDI)的系统上生成增强型元文件的库。该库旨在用作其他图形程序(如 Grace 或 gnuplot)的驱动程序。因此,它实现了非常有限的 GDI 子集。除了通常的 GNU autotools 选项之外,唯一的其他配置选项是 --enable-editing,它会激活 EditEnhMetaFile() 函数。这将在 stdout 中打印内存中元文件的内容。文档从 doc/html/index.html 开始。该软件可从 SourceForge 获得。版权所有 (C) 2002 lignum Computing,Inc. <dallenbarnett@users.sourceforge.net>。
相关问题
Error - ./simv.daidir is not generated with the -kdb option. Specify the -kdb option when generating the database by VCS tools.
这个错误提示是由于使用VCS工具生成数据库时,没有使用-kdb选项。在生成数据库时,应该使用以下命令:
```
vcs -kdb <database_name> <source_files>
```
其中`<database_name>`是你想要使用的数据库名称,`<source_files>`是你要编译的Verilog源文件的列表。请确保在生成数据库时使用了`-kdb`选项。
dcgan,cgan,pggan,sagan,biggan,Which model is more suitable for generating workpiece surface defects
Out of the given models, it is difficult to determine which one is more suitable for generating workpiece surface defects without further information about the specific context and requirements. However, in general, GAN models such as DCGAN and PGGAN have been used successfully for generating realistic images, including surface defects on workpieces. SAGAN and BigGAN are more advanced models that incorporate self-attention mechanisms and large-scale architectures, respectively, which may improve the quality and diversity of generated images. CGAN is a conditional GAN that takes additional information as input, such as the type of defect or material properties, which may be useful in generating more specific and targeted defects. Ultimately, the choice of model depends on the specific requirements and goals of the application.