%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