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| import gradio as gr | |
| import torch | |
| from PIL import Image | |
| import os | |
| cwd = os.getcwd() | |
| # !pip install -qr https://raw.githubusercontent.com/ultralytics/yolov5/master/requirements.txt gradio | |
| # Images | |
| torch.hub.download_url_to_file('https://i.imgur.com/Qi6I5Yf.png', 's1000_50_metre.jpg') | |
| torch.hub.download_url_to_file('https://i.imgur.com/kATwqiq.png', 's1000_100_metre.jpg') | |
| torch.hub.download_url_to_file('https://i.imgur.com/UhtsKNx.png', 's1000_150_metre.jpg') | |
| torch.hub.download_url_to_file('https://i.imgur.com/xXnXoEX.png', 's1000_200_metre.jpg') | |
| torch.hub.download_url_to_file('https://i.imgur.com/SU5yPAS.png', 's1000_250_metre.jpg') | |
| # Model | |
| #model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # force_reload=True to update | |
| model = torch.hub.load(cwd+'/yolov5', 'custom', path=cwd+'/saved_model/s1000_best.pt', source='local') # local model | |
| def yolo(im, size=640): | |
| g = (size / max(im.size)) # gain | |
| im = im.resize((int(x * g) for x in im.size), Image.ANTIALIAS) # resize | |
| results = model(im) # inference | |
| results.render() # updates results.imgs with boxes and labels | |
| return Image.fromarray(results.imgs[0]) | |
| inputs = gr.inputs.Image(type='pil', label="Original Image") | |
| outputs = gr.outputs.Image(type="pil", label="Output Image") | |
| title = "S1000 Detection" | |
| description = "YOLOv5 Gradio demo for object detection. Upload an image or click an example image to use." | |
| article = "<p style='text-align: center'>YOLOv5 is a family of compound-scaled object detection models trained on the COCO dataset, and includes " \ | |
| "simple functionality for Test Time Augmentation (TTA), model ensembling, hyperparameter evolution, " \ | |
| "and export to ONNX, CoreML and TFLite. <a href='https://github.com/ultralytics/yolov5'>Source code</a> |" \ | |
| "<a href='https://apps.apple.com/app/id1452689527'>iOS App</a> | <a href='https://pytorch.org/hub/ultralytics_yolov5'>PyTorch Hub</a></p>" | |
| '''path_folder = cwd+'/datasets/s1000/' | |
| examples = [[path_folder+'s1000_50_metre.jpg'], [path_folder+'s1000_100_metre.jpg'],[path_folder+'s1000_150_metre.jpg'],[path_folder+'s1000_200_metre.jpg'],[path_folder+'s1000_250_metre.jpg']]''' | |
| examples = [['s1000_50_metre.jpg'], ['s1000_100_metre.jpg'],['s1000_150_metre.jpg'],['s1000_200_metre.jpg'],['s1000_250_metre.jpg']] | |
| gr.Interface(yolo, inputs, outputs, title=title, description=description, article=article, examples=examples, analytics_enabled=False).launch( | |
| debug=True) | |
| ''' | |
| git init | |
| git config user.name bulentsofttech | |
| git config user.email bulent.softtech@gmail.com | |
| git add * | |
| git commit -m "WriteCommit" | |
| git push origin master | |
| ''' |