Metadata-Version: 2.1
Name: yolo34py
Version: 0.2
Summary: Python wrapper on YOLO 3.0 implementation by 'pjreddie': (https://pjreddie.com/yolo)
Home-page: https://github.com/madhawav/YOLO3-4-Py
Author: Madhawa Vidanapathirana
Author-email: madhawavidanapathirana@gmail.com
License: YOLO34Py wrapper is under Apache 2.0. Darknet is Public Domain.
Description: A Python wrapper on [pjreddie's](https://pjreddie.com/) implementation (authors' implementation) of [YOLO V3 Object Detector](https://pjreddie.com/darknet/yolo) on [Darknet](https://github.com/pjreddie/darknet).
        This wrapper is also compatible with other Darknet object detection models.
        
        ![OutputImage](https://raw.githubusercontent.com/madhawav/YOLO3-4-Py/master/doc/output.jpg)
        Image source: http://absfreepic.com/free-photos/download/crowded-cars-on-street-4032x2272_48736.html
        
        # Prerequisites
        * Python 3.6+
        * Linux x86-64 Operating System
        * NVIDIA CUDA SDK (for GPU version only. Make sure nvcc is available in PATH variable.)
        
        # Sample Usage
        Note: This sample code requires OpenCV with python bindings installed. (`pip3 install opencv-python==3.4.0`)
        
        1) Create a directory to host sample code and navigate to it.
        2) Download and execute [this script](https://github.com/madhawav/YOLO3-4-Py/blob/master/tools/download_models.sh) to download model files.
        3) Create sampleApp.py with following code. Specify SAMPLE_INPUT_IMAGE.
            ```python
            from pydarknet import Detector, Image
            import cv2
            
            net = Detector(bytes("cfg/yolov3.cfg", encoding="utf-8"), bytes("weights/yolov3.weights", encoding="utf-8"), 0, bytes("cfg/coco.data",encoding="utf-8"))
            
            img = cv2.imread('SAMPLE_INPUT_IMAGE')
            img_darknet = Image(img)
            
            results = net.detect(img_darknet)
                
            for category, score, bounds in results:
                x, y, w, h = bounds
                cv2.rectangle(img, (int(x - w / 2), int(y - h / 2)), (int(x + w / 2), int(y + h / 2)), (255, 0, 0), thickness=2)
                cv2.putText(img, category ,(int(x),int(y)),cv2.FONT_HERSHEY_COMPLEX,1,(255,255,0))
            
            cv2.imshow("output", img)
            cv2.waitKey(0)
            ```
        4) Execute sampleApp.py `python sampleApp.py`.
        
        # Installation
        yolo34py comes in 2 variants, _CPU Only Version_ and _GPU Version_. 
        Installation may take a while since it involves downloading and compiling darknet.
        
        ## __CPU Only Version__
        This version is configured on darknet compiled with flag GPU = 0.
        ```bash
        pip3 install requests # Used to download darknet
        pip3 install cython
        pip3 install numpy
        pip3 install yolo34py
        ```
        
        ## GPU Version:
        This version is configured on darknet compiled with flag GPU = 1.
        ```bash
        pip3 install requests # Used to download darknet
        pip3 install cython
        pip3 install numpy
        pip3 install yolo34py-gpu
        ```
        
        
        # More Information
        * For more details on yolo34py (This python wrapper):
           - GitHub: https://github.com/madhawav/YOLO3-4-Py
           - This is the place to discuss issues of yolo34py. 
           - Your contributions are greatly appreciated. 
        * For more details on YOLO V3:
           - Website from Authors: https://pjreddie.com/yolo
        * For more details on Darknet, the base API wrapped by this library
           - Website: https://pjreddie.com/darknet/
           - GitHub: https://github.com/pjreddie/darknet
           
        
        # License
        * yolo34py (this wrapper) is under [Apache License 2.0](https://github.com/madhawav/YOLO3-4-Py/blob/master/LICENSE).
        * The version of darknet wrapped by yolo34py is in [public domain](https://github.com/madhawav/darknet/blob/master/LICENSE). 
Keywords: yolo darknet object detection vision
Platform: linux-x86_64
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Text Processing :: Linguistic
Classifier: Operating System :: POSIX :: Linux
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.5
Description-Content-Type: text/markdown
