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Darknetpy is a simple binding for darknet’s yolo (v4) detector.

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

Project details


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Source Distribution

darknetpy-4.2.tar.gz (10.0 kB view hashes)

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