Metadata-Version: 2.1
Name: easy_yolo
Version: 0.0.2
Summary: Library to easily test YOLOv3 models
Home-page: https://github.com/pandeyarjun242
Author: Arjun Pandey
Author-email: apandey@jpischool.com
License: UNKNOWN
Description: # Easy_Yolo - One liner Yolov3 object detection
        
        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()
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
