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| import gradio as gr | |
| import torch | |
| ############# | |
| def yolov7_inference( | |
| image: gr.Image = None, | |
| conf_threshold: gr.Slider = 0.20, | |
| ): | |
| device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | |
| path = 'y7-prdef.pt' | |
| model = torch.hub.load("WongKinYiu/yolov7","custom",f"{path}") | |
| model.conf = conf_threshold | |
| results = model([image], size=640) | |
| return results.render()[0] | |
| inputs = [ | |
| gr.Image(type="filepath", label="Input"), | |
| gr.Slider(minimum=0.0, maximum=1.0, value=0.2, step=0.05, label="Confidence Threshold", interactive=True), | |
| ] | |
| outputs = [ | |
| gr.Image(type="filepath"), | |
| ] | |
| css = ".output_image {height: 40rem !important; width: 100% !important;}" | |
| demo_app = gr.Interface( | |
| fn=yolov7_inference, | |
| inputs=inputs, | |
| outputs=outputs, | |
| title="Fast detection of jar lid defects using Yolov7", | |
| description = """ | |
| This application is detecting damaged jar lids. Type of damages include deformations, holes or scratches. The object detection notebook can be found at <a href="https://www.kaggle.com/rrighart">Kaggle</a> | |
| Contact: Ruthger Righart | |
| Email: rrighart@googlemail.com | |
| Web: <a href="https://www.rrighart.com">www.rrighart.com</a> | |
| """, | |
| article = "<p style='text-align: center'><a href='https://www.rrighart.com' target='_blank'>Webpage</a></p> <p style='text-align: center'><a href='https://www.kaggle.com/code/rrighart/detection-of-product-defects-using-yolov7' target='_blank'>Kaggle</a></p>", | |
| examples = [['example1.JPG'], ['example2.JPG'], ['example3.JPG']], | |
| #examples = [['example1.JPG', 0.50], ['example2.JPG', 0.50], ['example3.JPG', 0.50]], | |
| css=css, | |
| cache_examples=True, | |
| ) | |
| demo_app.queue().launch(debug=False) | |