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| import gradio as gr | |
| import torch | |
| from PIL import Image | |
| import subprocess | |
| import os | |
| import PIL | |
| from pathlib import Path | |
| import uuid | |
| # Images | |
| torch.hub.download_url_to_file('https://miro.medium.com/max/1400/1*EYFejGUjvjPcc4PZTwoufw.jpeg', '1*EYFejGUjvjPcc4PZTwoufw.jpeg') | |
| torch.hub.download_url_to_file('https://production-media.paperswithcode.com/tasks/ezgif-frame-001_OZzxdny.jpg', 'ezgif-frame-001_OZzxdny.jpg') | |
| torch.hub.download_url_to_file('https://favtutor.com/resources/images/uploads/Social_Distancing_Covid_19__1.jpg', 'Social_Distancing_Covid_19__1.jpg') | |
| torch.hub.download_url_to_file('https://nkcf.org/wp-content/uploads/2017/11/people.jpg', 'people.jpg') | |
| def yolo(im): | |
| file_name = str(uuid.uuid4()) | |
| im.save(f'{file_name}.jpg') | |
| os.system(f"python tools/infer.py --weights yolov6s.pt --source {str(file_name)}.jpg --project ''") | |
| img = PIL.Image.open(f"exp/{file_name}.jpg") | |
| os.remove(f"exp/{file_name}.jpg") | |
| os.remove(f'{file_name}.jpg') | |
| return img | |
| inputs = gr.inputs.Image(type='pil', label="Original Image") | |
| outputs = gr.outputs.Image(type="pil", label="Output Image") | |
| title = "YOLOv6 - Demo" | |
| description = "YOLOv6 is a single-stage object detection framework dedicated to industrial applications, with hardware-friendly efficient design and high performance. Here is a quick Gradio Demo for testing YOLOv6s model. More details from <a href='https://github.com/meituan/YOLOv6'>https://github.com/meituan/YOLOv6</a> " | |
| article = "<p>YOLOv6-nano achieves 35.0 mAP on COCO val2017 dataset with 1242 FPS on T4 using TensorRT FP16 for bs32 inference, and YOLOv6-s achieves 43.1 mAP on COCO val2017 dataset with 520 FPS on T4 using TensorRT FP16 for bs32 inference. More information at <a href='https://github.com/meituan/YOLOv6'>https://github.com/meituan/YOLOv6</a></p>" | |
| examples = [['1*EYFejGUjvjPcc4PZTwoufw.jpeg'], ['ezgif-frame-001_OZzxdny.jpg'], ['Social_Distancing_Covid_19__1.jpg'], ['people.jpg']] | |
| gr.Interface(yolo, inputs, outputs, title=title, description=description, article=article, examples=examples, analytics_enabled = True, enable_queue=True).launch(inline=False, share=False, debug=False) |