Metadata-Version: 2.1
Name: easy-yolo
Version: 0.0.1
Summary: Library to easily test YOLOv3 models
Home-page: https://github.com/pandeyarjun242
Author: Arjun Pandey
Author-email: apandey@jpischool.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: opencv-python

# Easy_Yolo

Pre-Requisites = OpenCV-Python

Running on YOLO model on an image. Place .cfg, .weights and .names in same directory
Sample
from easy_yolo.yolo-img import YoloImg
x = YoloImg('example.jpg', 'example.weights', 'example.cfg',example.names)
x.run_model()

Running on YOLO model on a video. Place .cfg, .weights and .names in same directory
Sample
from easy_yolo.yolo-vid import Yolov3Video
x = Yolov3Video('example.mp4', 'example.weights', 'example.cfg',example.names)
x.run_model()

Running on YOLO model on a webcam. Place .cfg, .weights and .names in same directory
Sample
from easy_yolo.yolo-cam import Yolov3Camera
For webcam:
x = Yolov3Camera(0, 'example.weights', 'example.cfg',example.names)
x.run_model()

For Youtube Livestream:
x = Yolov3Camera('youtube_url', 'example.weights', 'example.cfg',example.names)
x.run_model()

