{ "info": { "author": "Madhawa Vidanapathirana", "author_email": "madhawavidanapathirana@gmail.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Intended Audience :: Science/Research", "License :: OSI Approved :: Apache Software License", "Operating System :: POSIX :: Linux", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Text Processing :: Linguistic" ], "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).\nAlso compatible with other Darknet Object Detection models.\n\n![OutputImage](https://raw.githubusercontent.com/madhawav/YOLO3-4-Py/master/doc/output.jpg)\nImage source: http://absfreepic.com/free-photos/download/crowded-cars-on-street-4032x2272_48736.html\n\n# Prerequisites\n* Python 3.5+\n* Linux x86-64 Operating System\n* nVidia CUDA SDK (for GPU version only. Make sure nvcc is available in PATH variable.)\n\n# Sample Usage\nNote: This sample code requires OpenCV with python bindings installed. (`pip3 install opencv-python==3.4.0`)\n\n1) Create a directory to host sample code and navigate to it.\n2) Download and execute [this script](https://github.com/madhawav/YOLO3-4-Py/blob/master/download_models.sh) to download model files.\n3) Create sampleApp.py with following code. Specify SAMPLE_INPUT_IMAGE.\n ```python\n from pydarknet import Detector, Image\n import cv2\n \n net = Detector(bytes(\"cfg/yolov3.cfg\", encoding=\"utf-8\"), bytes(\"weights/yolov3.weights\", encoding=\"utf-8\"), 0, bytes(\"cfg/coco.data\",encoding=\"utf-8\"))\n \n img = cv2.imread('SAMPLE_INPUT_IMAGE')\n img_darknet = Image(img)\n \n results = net.detect(img_darknet)\n \n for cat, score, bounds in results:\n x, y, w, h = bounds\n cv2.rectangle(img, (int(x - w / 2), int(y - h / 2)), (int(x + w / 2), int(y + h / 2)), (255, 0, 0), thickness=2)\n cv2.putText(img,str(cat.decode(\"utf-8\")),(int(x),int(y)),cv2.FONT_HERSHEY_COMPLEX,1,(255,255,0))\n \n cv2.imshow(\"output\", img)\n cv2.waitKey(0)\n ```\n4) Execute sampleApp.py `python sampleApp.py`.\n\n# Installation\nyolo34py comes in 2 variants, _CPU Only Version_ and _GPU Version_. Installation may take a while since it involves downloading and compiling of darknet.\n\n## __CPU Only Version__\nThis version is configured on darknet compiled with flag GPU = 0.\n```bash\npip3 install numpy\npip3 install yolo34py\n```\n\n## GPU Version:\nThis version is configured on darknet compiled with flag GPU = 1.\n```bash\npip3 install numpy\npip3 install yolo34py-gpu\n```\n\n\n# More Information\n* For more details on yolo34py (This python wrapper):\n - GitHub Repo: https://github.com/madhawav/YOLO3-4-Py\n - This is the place to discuss about issues of yolo34py. \n - Your contributions are greatly appreciated. \n* For more details on YOLO V3:\n - Website from Authors: https://pjreddie.com/yolo\n* For more details on Darknet, the base API wrapped by this library\n - Website: https://pjreddie.com/darknet/\n - GitHub: https://github.com/pjreddie/darknet\n \n\n# License\n* yolo34py (this wrapper) is under [Apache License 2.0](https://github.com/madhawav/YOLO3-4-Py/blob/master/LICENSE).\n* The version of darknet wrapped by yolo34py is [public domain](https://github.com/madhawav/darknet/blob/master/LICENSE).", "description_content_type": "text/markdown", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/madhawav/YOLO3-4-Py", "keywords": "yolo darknet object detection vision", "license": "YOLO34Py wrapper is under Apache 2.0. 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