Deep-Illuminator:Deep Illuminator是设计用于图像重新照明的数据增强工具。 它可用于轻松高效地生成单个图像的多种照明方式

[{"title":"（ 53 个子文件 5.22MB ） Deep-Illuminator:Deep Illuminator是设计用于图像重新照明的数据增强工具。 它可用于轻松高效地生成单个图像的多种照明方式","children":[{"title":"Deep-Illuminator-master","children":[{"title":"Dockerfile <span style='color:#111;'> 195B </span>","children":null,"spread":false},{"title":"assets","children":[{"title":"combined.gif <span style='color:#111;'> 2.87MB </span>","children":null,"spread":false}],"spread":true},{"title":"README.md <span style='color:#111;'> 3.31KB </span>","children":null,"spread":false},{"title":"app","children":[{"title":"probe_relighting","children":[{"title":"generate_images.py <span style='color:#111;'> 904B </span>","children":null,"spread":false},{"title":"utils","children":[{"title":"options.py <span style='color:#111;'> 396B </span>","children":null,"spread":false},{"title":"preprocessing.py <span style='color:#111;'> 1009B </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 0B </span>","children":null,"spread":false},{"title":"base_model.py <span style='color:#111;'> 505B </span>","children":null,"spread":false},{"title":"demotools.py <span style='color:#111;'> 2.20KB </span>","children":null,"spread":false},{"title":"blocks.py <span style='color:#111;'> 12.43KB </span>","children":null,"spread":false}],"spread":true},{"title":"originals","children":[{"title":"example.jpg <span style='color:#111;'> 268.07KB </span>","children":null,"spread":false}],"spread":true},{"title":"output","children":[{"title":"eg.jpg <span style='color:#111;'> 0B </span>","children":null,"spread":false}],"spread":true},{"title":"streamlit","children":[{"title":"streamlit_app.py <span style='color:#111;'> 8.48KB </span>","children":null,"spread":false},{"title":"example_images","children":[{"title":"mannequin.jpg <span style='color:#111;'> 139.12KB </span>","children":null,"spread":false},{"title":"outdoor.jpg <span style='color:#111;'> 514.83KB </span>","children":null,"spread":false},{"title":"office.jpg <span style='color:#111;'> 268.07KB </span>","children":null,"spread":false}],"spread":true},{"title":"vae","children":[{"title":"models","children":[{"title":"base.py <span style='color:#111;'> 842B </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 90B </span>","children":null,"spread":false},{"title":"beta_vae.py <span style='color:#111;'> 6.24KB </span>","children":null,"spread":false}],"spread":false},{"title":"configs","children":[{"title":"gammavae.yaml <span style='color:#111;'> 453B </span>","children":null,"spread":false},{"title":"wae_mmd_imq.yaml <span style='color:#111;'> 449B </span>","children":null,"spread":false},{"title":"vampvae.yaml <span style='color:#111;'> 393B </span>","children":null,"spread":false},{"title":"swae.yaml <span style='color:#111;'> 498B </span>","children":null,"spread":false},{"title":"logcosh_vae.yaml <span style='color:#111;'> 430B </span>","children":null,"spread":false},{"title":"dip_vae.yaml <span style='color:#111;'> 439B </span>","children":null,"spread":false},{"title":"vae.yaml <span style='color:#111;'> 399B </span>","children":null,"spread":false},{"title":"factorvae.yaml <span style='color:#111;'> 510B </span>","children":null,"spread":false},{"title":"cat_vae.yaml <span style='color:#111;'> 509B </span>","children":null,"spread":false},{"title":"joint_vae.yaml <span style='color:#111;'> 718B </span>","children":null,"spread":false},{"title":"infovae.yaml <span style='color:#111;'> 550B </span>","children":null,"spread":false},{"title":"miwae.yaml <span style='color:#111;'> 430B </span>","children":null,"spread":false},{"title":"bhvae.yaml <span style='color:#111;'> 425B </span>","children":null,"spread":false},{"title":"iwae.yaml <span style='color:#111;'> 406B </span>","children":null,"spread":false},{"title":"wae_mmd_rbf.yaml <span style='color:#111;'> 450B </span>","children":null,"spread":false},{"title":"dfc_vae.yaml <span style='color:#111;'> 391B </span>","children":null,"spread":false},{"title":"vq_vae.yaml <span style='color:#111;'> 447B </span>","children":null,"spread":false},{"title":"cvae.yaml <span style='color:#111;'> 425B </span>","children":null,"spread":false},{"title":"mssim_vae.yaml <span style='color:#111;'> 395B </span>","children":null,"spread":false},{"title":"betatc_vae.yaml <span style='color:#111;'> 460B </span>","children":null,"spread":false},{"title":"lvae.yaml <span style='color:#111;'> 469B </span>","children":null,"spread":false},{"title":"hvae.yaml <span style='color:#111;'> 433B </span>","children":null,"spread":false},{"title":"bbvae.yaml <span style='color:#111;'> 473B </span>","children":null,"spread":false}],"spread":false},{"title":"utils.py <span style='color:#111;'> 620B </span>","children":null,"spread":false},{"title":"experiment.py <span style='color:#111;'> 9.42KB </span>","children":null,"spread":false},{"title":"streamlit_vae.py <span style='color:#111;'> 3.18KB </span>","children":null,"spread":false}],"spread":true},{"title":"temp","children":[{"title":"MID Averaged Probes_Office_1_None.gif <span style='color:#111;'> 2.22MB </span>","children":null,"spread":false}],"spread":false}],"spread":true},{"title":"requirements.txt <span style='color:#111;'> 68B </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 0B </span>","children":null,"spread":false},{"title":"network.py <span style='color:#111;'> 3.40KB </span>","children":null,"spread":false},{"title":"network_config.yaml <span style='color:#111;'> 1.48KB </span>","children":null,"spread":false},{"title":"data","children":[{"title":"__init__.py <span style='color:#111;'> 0B </span>","children":null,"spread":false}],"spread":false}],"spread":true},{"title":"setup.py <span style='color:#111;'> 120B </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 1.30KB </span>","children":null,"spread":false}],"spread":true}],"spread":true}],"spread":true}]