Metadata-Version: 1.1
Name: darknetpy
Version: 4.1
Summary: UNKNOWN
Home-page: https://github.com/danielgatis/darknetpy
Author: Daniel Gatis Carrazzoni
Author-email: danielgatis@gmail.com
License: BSD License
Description: =========
        Darknetpy
        =========
        
        Darknetpy is a simple binding for darknet's yolo (v4) detector.
        
        .. image:: https://raw.githubusercontent.com/danielgatis/darknetpy/master/example/example.png
        
        Installation
        ============
        
        Install it from pypi
        
        ::
        
            curl https://sh.rustup.rs -sSf | sh
        
        ::
        
            rustup default nightly
        
        ::
        
            pip install darknetpy
        
        Install a pre-built binary
        
        ::
        
            pip install https://github.com/danielgatis/darknetpy/raw/master/dist/darknetpy-4.1-cp36-cp36m-linux_x86_64.whl
        
        Advanced options (only for pypi installation)
        ---------------------------------------------
        ::
        
            GPU=1 pip install darknetpy
        
        to build with CUDA to accelerate by using GPU (CUDA should be in /use/local/cuda).
        
        ::
        
            CUDNN=1 pip install darknetpy
        
        to build with cuDNN to accelerate training by using GPU (cuDNN should be in /usr/local/cudnn).
        
        ::
        
            OPENCV=1 pip install darknetpy
        
        to build with OpenCV.
        
        ::
        
            OPENMP=1 pip install darknetpy
        
        to build with OpenMP support to accelerate Yolo by using multi-core CPU.
        
        Usage
        =====
        
        In example.py::
        
            from darknetpy.detector import Detector
        
            detector = Detector('<absolute-path-to>/darknet/cfg/coco.data',
                                '<absolute-path-to>/darknet/cfg/yolo.cfg',
                                '<absolute-path-to>/darknet/yolo.weights')
        
            results = detector.detect('<absolute-path-to>/darknet/data/dog.jpg')
        
            print(results)
        
        Runing::
        
            python example.py
        
        
        Result::
        
            [{'right': 194, 'bottom': 353, 'top': 264, 'class': 'dog', 'prob': 0.8198755383491516, 'left': 71}]
        
        Build
        =====
        
        On the project root directory
        
        ::
        
            docker pull hoshizora/manylinux1-clang_x86_64
        
        ::
        
            docker run --rm -v `pwd`:/io hoshizora/manylinux1-clang_x86_64 /io/build-wheels.sh
        
Platform: Linux
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: POSIX :: Linux
