- 授权协议: BSD
- 开发语言: Python
- 操作系统: Linux
- 软件首页: https://github.com/affinelayer/pix2pix-tensorflow
- 软件文档: https://coyee.com/article/11836-pix2pix-tensorflow-tensorflow-port-of-image-to-image-translation-with-conditional-adversarial-nets
软件介绍
pix2pix-tensorflow 实现了利用 Tensorflow 从一种图像转译成另一种图像的生成技术。基于 pix2pix 实现。pix2pix-tensorflow 将 pix2pix 中的 Torch 移植为 Tensorflow,同时包含来自 Torch 移植过来的色彩空间转换代码。
要求:Tensorflow 0.12.1
推荐:Linux with Tensorflow GPU edition + cuDNN
效果:
使用方法:
# clone this repo git clone https://github.com/affinelayer/pix2pix-tensorflow.git cd pix2pix-tensorflow # download the CMP Facades dataset http://cmp.felk.cvut.cz/~tylecr1/facade/ python tools/download-dataset.py facades # train the model (this may take 1-8 hours depending on GPU, on CPU you will be waiting for a bit) python pix2pix.py --mode train --output_dir facades_train --max_epochs 200 --input_dir facades/train --which_direction BtoA # test the model python pix2pix.py --mode test --output_dir facades_test --input_dir facades/val --checkpoint facades_train # Resize source images python tools/process.py --input_dir photos/original --operation resize --output_dir photos/resized # Create images with blank centers python tools/process.py --input_dir photos/resized --operation blank --output_dir photos/blank # Combine resized images with blanked images python tools/process.py --input_dir photos/resized --b_dir photos/blank --operation combine --output_dir photos/combined # Split into train/val set python tools/split.py --dir photos/combined
Perl高效编程
霍尔 / 胜春、王晖、张东亮、蒋永清 / 人民邮电出版社 / 2011-5 / 65.00元
《Perl高效编程(第2版)》,本书是Perl编程领域的“圣经级”著作。它提供了一百多个详实的应用案例,足以涵盖编程过程中经常遇到的方方面面,由此详细阐释出各种高效且简洁的写法。一起来看看 《Perl高效编程》 这本书的介绍吧!
