1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
|
%global _empty_manifest_terminate_build 0
Name: python-omnidata-tools
Version: 0.0.23
Release: 1
Summary: Tooling for Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scans
License: omnidata # Add licenses and see current list in `setup.py`
URL: https://github.com/alexsax/omnidata_tools/tree/main/
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/f5/31/2748e589f1ff82f8fa42e340dd9033e04a80190562a2915f4d6b2bdc8315/omnidata_tools-0.0.23.tar.gz
BuildArch: noarch
Requires: python3-tqdm
Requires: python3-fastcore
Requires: python3-multiprocess
Requires: python3-aria2p[tui]
%description
# Omnidata Docs
> <strong>Quick links to docs</strong>: [ <a href='/omnidata-tools/pretrained.html'>Pretrained Models</a> ] [ <a href='/omnidata-tools/starter_dataset.html'>Starter Dataset ] [ <a href='//omnidata-tools/annotator_usage.html'>Annotator Demo</a> ]
**This site is intended to be a wiki/documentation site for everything that we open-sourced from the paper.** There are three main folders: the annotator, utilities (dataloaders, download tools, pretrained models, etc), and a code dump of stuff from the paper that is just for reference.
(Check out the main site for an overview of 'steerable datastes' and the 3D → 2D rendering pipeline).
<br>
#### Download the code
If you want to see and edit the code, then you can clone the github and install with:
```bash
git clone https://github.com/EPFL-VILAB/omnidata-tools
cd omnidata-tools
pip install -e . # this will install the python requirements (and also install the CLI)
```
This is probably the best option for you if you want to use the pretrained models, dataloaders, etc in other work.
<br>
#### Install just CLI tools (`omnitools`)
If you are only interested in using the [CLI tools](/omnidata-tools/omnitools.html), you can install them with: `pip install omnidata-tools`. This might be preferable if you only want to quickly download the starter data, or if you just want a simple way to manipulate the vision datasets output by the annotator.
_Note:_ The annotator can also be used with a [docker-based](/omnidata-tools/annotator_usage.html) CLI, but you don't need to use the annotator to use the starter dataset, pretrained models, or training code.
<br>
> ...were you looking for the [research paper](//omnidata.vision/#paper) or [project website](//omnidata.vision)?
<!-- <img src="https://raw.githubusercontent.com/alexsax/omnidata-tools/main/docs/images/omnidata_front_page.jpg?token=ABHLE3LC3U64F2QRVSOBSS3BPED24" alt="Website main page" style='max-width: 100%;'/> -->
%package -n python3-omnidata-tools
Summary: Tooling for Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scans
Provides: python-omnidata-tools
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-omnidata-tools
# Omnidata Docs
> <strong>Quick links to docs</strong>: [ <a href='/omnidata-tools/pretrained.html'>Pretrained Models</a> ] [ <a href='/omnidata-tools/starter_dataset.html'>Starter Dataset ] [ <a href='//omnidata-tools/annotator_usage.html'>Annotator Demo</a> ]
**This site is intended to be a wiki/documentation site for everything that we open-sourced from the paper.** There are three main folders: the annotator, utilities (dataloaders, download tools, pretrained models, etc), and a code dump of stuff from the paper that is just for reference.
(Check out the main site for an overview of 'steerable datastes' and the 3D → 2D rendering pipeline).
<br>
#### Download the code
If you want to see and edit the code, then you can clone the github and install with:
```bash
git clone https://github.com/EPFL-VILAB/omnidata-tools
cd omnidata-tools
pip install -e . # this will install the python requirements (and also install the CLI)
```
This is probably the best option for you if you want to use the pretrained models, dataloaders, etc in other work.
<br>
#### Install just CLI tools (`omnitools`)
If you are only interested in using the [CLI tools](/omnidata-tools/omnitools.html), you can install them with: `pip install omnidata-tools`. This might be preferable if you only want to quickly download the starter data, or if you just want a simple way to manipulate the vision datasets output by the annotator.
_Note:_ The annotator can also be used with a [docker-based](/omnidata-tools/annotator_usage.html) CLI, but you don't need to use the annotator to use the starter dataset, pretrained models, or training code.
<br>
> ...were you looking for the [research paper](//omnidata.vision/#paper) or [project website](//omnidata.vision)?
<!-- <img src="https://raw.githubusercontent.com/alexsax/omnidata-tools/main/docs/images/omnidata_front_page.jpg?token=ABHLE3LC3U64F2QRVSOBSS3BPED24" alt="Website main page" style='max-width: 100%;'/> -->
%package help
Summary: Development documents and examples for omnidata-tools
Provides: python3-omnidata-tools-doc
%description help
# Omnidata Docs
> <strong>Quick links to docs</strong>: [ <a href='/omnidata-tools/pretrained.html'>Pretrained Models</a> ] [ <a href='/omnidata-tools/starter_dataset.html'>Starter Dataset ] [ <a href='//omnidata-tools/annotator_usage.html'>Annotator Demo</a> ]
**This site is intended to be a wiki/documentation site for everything that we open-sourced from the paper.** There are three main folders: the annotator, utilities (dataloaders, download tools, pretrained models, etc), and a code dump of stuff from the paper that is just for reference.
(Check out the main site for an overview of 'steerable datastes' and the 3D → 2D rendering pipeline).
<br>
#### Download the code
If you want to see and edit the code, then you can clone the github and install with:
```bash
git clone https://github.com/EPFL-VILAB/omnidata-tools
cd omnidata-tools
pip install -e . # this will install the python requirements (and also install the CLI)
```
This is probably the best option for you if you want to use the pretrained models, dataloaders, etc in other work.
<br>
#### Install just CLI tools (`omnitools`)
If you are only interested in using the [CLI tools](/omnidata-tools/omnitools.html), you can install them with: `pip install omnidata-tools`. This might be preferable if you only want to quickly download the starter data, or if you just want a simple way to manipulate the vision datasets output by the annotator.
_Note:_ The annotator can also be used with a [docker-based](/omnidata-tools/annotator_usage.html) CLI, but you don't need to use the annotator to use the starter dataset, pretrained models, or training code.
<br>
> ...were you looking for the [research paper](//omnidata.vision/#paper) or [project website](//omnidata.vision)?
<!-- <img src="https://raw.githubusercontent.com/alexsax/omnidata-tools/main/docs/images/omnidata_front_page.jpg?token=ABHLE3LC3U64F2QRVSOBSS3BPED24" alt="Website main page" style='max-width: 100%;'/> -->
%prep
%autosetup -n omnidata-tools-0.0.23
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-omnidata-tools -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Thu May 18 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.23-1
- Package Spec generated
|
