From 3b21abf22345a609bcfbc7632066b02c6203e5e8 Mon Sep 17 00:00:00 2001 From: CoprDistGit Date: Tue, 11 Apr 2023 07:46:41 +0000 Subject: automatic import of python-mtcnn --- .gitignore | 1 + python-mtcnn.spec | 176 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ sources | 1 + 3 files changed, 178 insertions(+) create mode 100644 python-mtcnn.spec create mode 100644 sources diff --git a/.gitignore b/.gitignore index e69de29..ae46250 100644 --- a/.gitignore +++ b/.gitignore @@ -0,0 +1 @@ +/mtcnn-0.1.1.tar.gz diff --git a/python-mtcnn.spec b/python-mtcnn.spec new file mode 100644 index 0000000..27e1669 --- /dev/null +++ b/python-mtcnn.spec @@ -0,0 +1,176 @@ +%global _empty_manifest_terminate_build 0 +Name: python-mtcnn +Version: 0.1.1 +Release: 1 +Summary: Multi-task Cascaded Convolutional Neural Networks for Face Detection, based on TensorFlow +License: MIT +URL: http://github.com/ipazc/mtcnn +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/ef/11/d549caa104ac2a03b6ef32bfca841ebb04c99ddf704b197c272bcdec054d/mtcnn-0.1.1.tar.gz +BuildArch: noarch + +Requires: python3-keras +Requires: python3-opencv-python + +%description +The following tables shows the benchmark of this mtcnn implementation running on an `Intel i7-3612QM CPU @ 2.10GHz `_, with a **CPU-based** Tensorflow 1.4.1. + - Pictures containing a single frontal face: ++------------+--------------+---------------+-----+ +| Image size | Total pixels | Process time | FPS | ++============+==============+===============+=====+ +| 460x259 | 119,140 | 0.118 seconds | 8.5 | ++------------+--------------+---------------+-----+ +| 561x561 | 314,721 | 0.227 seconds | 4.5 | ++------------+--------------+---------------+-----+ +| 667x1000 | 667,000 | 0.456 seconds | 2.2 | ++------------+--------------+---------------+-----+ +| 1920x1200 | 2,304,000 | 1.093 seconds | 0.9 | ++------------+--------------+---------------+-----+ +| 4799x3599 | 17,271,601 | 8.798 seconds | 0.1 | ++------------+--------------+---------------+-----+ + - Pictures containing 10 frontal faces: ++------------+--------------+---------------+-----+ +| Image size | Total pixels | Process time | FPS | ++============+==============+===============+=====+ +| 474x224 | 106,176 | 0.185 seconds | 5.4 | ++------------+--------------+---------------+-----+ +| 736x348 | 256,128 | 0.290 seconds | 3.4 | ++------------+--------------+---------------+-----+ +| 2100x994 | 2,087,400 | 1.286 seconds | 0.7 | ++------------+--------------+---------------+-----+ +MODEL +##### +By default the MTCNN bundles a face detection weights model. +The model is adapted from the Facenet's MTCNN implementation, merged in a single file located inside the folder 'data' relative +to the module's path. It can be overriden by injecting it into the MTCNN() constructor during instantiation. +The model must be numpy-based containing the 3 main keys "pnet", "rnet" and "onet", having each of them the weights of each of the layers of the network. +For more reference about the network definition, take a close look at the paper from *Zhang et al. (2016)* [ZHANG2016]_. +LICENSE +####### +`MIT License`_. + +%package -n python3-mtcnn +Summary: Multi-task Cascaded Convolutional Neural Networks for Face Detection, based on TensorFlow +Provides: python-mtcnn +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-mtcnn +The following tables shows the benchmark of this mtcnn implementation running on an `Intel i7-3612QM CPU @ 2.10GHz `_, with a **CPU-based** Tensorflow 1.4.1. + - Pictures containing a single frontal face: ++------------+--------------+---------------+-----+ +| Image size | Total pixels | Process time | FPS | ++============+==============+===============+=====+ +| 460x259 | 119,140 | 0.118 seconds | 8.5 | ++------------+--------------+---------------+-----+ +| 561x561 | 314,721 | 0.227 seconds | 4.5 | ++------------+--------------+---------------+-----+ +| 667x1000 | 667,000 | 0.456 seconds | 2.2 | ++------------+--------------+---------------+-----+ +| 1920x1200 | 2,304,000 | 1.093 seconds | 0.9 | ++------------+--------------+---------------+-----+ +| 4799x3599 | 17,271,601 | 8.798 seconds | 0.1 | ++------------+--------------+---------------+-----+ + - Pictures containing 10 frontal faces: ++------------+--------------+---------------+-----+ +| Image size | Total pixels | Process time | FPS | ++============+==============+===============+=====+ +| 474x224 | 106,176 | 0.185 seconds | 5.4 | ++------------+--------------+---------------+-----+ +| 736x348 | 256,128 | 0.290 seconds | 3.4 | ++------------+--------------+---------------+-----+ +| 2100x994 | 2,087,400 | 1.286 seconds | 0.7 | ++------------+--------------+---------------+-----+ +MODEL +##### +By default the MTCNN bundles a face detection weights model. +The model is adapted from the Facenet's MTCNN implementation, merged in a single file located inside the folder 'data' relative +to the module's path. It can be overriden by injecting it into the MTCNN() constructor during instantiation. +The model must be numpy-based containing the 3 main keys "pnet", "rnet" and "onet", having each of them the weights of each of the layers of the network. +For more reference about the network definition, take a close look at the paper from *Zhang et al. (2016)* [ZHANG2016]_. +LICENSE +####### +`MIT License`_. + +%package help +Summary: Development documents and examples for mtcnn +Provides: python3-mtcnn-doc +%description help +The following tables shows the benchmark of this mtcnn implementation running on an `Intel i7-3612QM CPU @ 2.10GHz `_, with a **CPU-based** Tensorflow 1.4.1. + - Pictures containing a single frontal face: ++------------+--------------+---------------+-----+ +| Image size | Total pixels | Process time | FPS | ++============+==============+===============+=====+ +| 460x259 | 119,140 | 0.118 seconds | 8.5 | ++------------+--------------+---------------+-----+ +| 561x561 | 314,721 | 0.227 seconds | 4.5 | ++------------+--------------+---------------+-----+ +| 667x1000 | 667,000 | 0.456 seconds | 2.2 | ++------------+--------------+---------------+-----+ +| 1920x1200 | 2,304,000 | 1.093 seconds | 0.9 | ++------------+--------------+---------------+-----+ +| 4799x3599 | 17,271,601 | 8.798 seconds | 0.1 | ++------------+--------------+---------------+-----+ + - Pictures containing 10 frontal faces: ++------------+--------------+---------------+-----+ +| Image size | Total pixels | Process time | FPS | ++============+==============+===============+=====+ +| 474x224 | 106,176 | 0.185 seconds | 5.4 | ++------------+--------------+---------------+-----+ +| 736x348 | 256,128 | 0.290 seconds | 3.4 | ++------------+--------------+---------------+-----+ +| 2100x994 | 2,087,400 | 1.286 seconds | 0.7 | ++------------+--------------+---------------+-----+ +MODEL +##### +By default the MTCNN bundles a face detection weights model. +The model is adapted from the Facenet's MTCNN implementation, merged in a single file located inside the folder 'data' relative +to the module's path. It can be overriden by injecting it into the MTCNN() constructor during instantiation. +The model must be numpy-based containing the 3 main keys "pnet", "rnet" and "onet", having each of them the weights of each of the layers of the network. +For more reference about the network definition, take a close look at the paper from *Zhang et al. (2016)* [ZHANG2016]_. +LICENSE +####### +`MIT License`_. + +%prep +%autosetup -n mtcnn-0.1.1 + +%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-mtcnn -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Tue Apr 11 2023 Python_Bot - 0.1.1-1 +- Package Spec generated diff --git a/sources b/sources new file mode 100644 index 0000000..513ba61 --- /dev/null +++ b/sources @@ -0,0 +1 @@ +bea6a6cd819651d77ea0793575efb7f9 mtcnn-0.1.1.tar.gz -- cgit v1.2.3