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%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 <https://www.cpubenchmark.net/cpu.php?cpu=Intel+Core+i7-3612QM+%40+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 <https://www.cpubenchmark.net/cpu.php?cpu=Intel+Core+i7-3612QM+%40+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 <https://www.cpubenchmark.net/cpu.php?cpu=Intel+Core+i7-3612QM+%40+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 <Python_Bot@openeuler.org> - 0.1.1-1
- Package Spec generated
|
