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import gradio as gr |
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import torch |
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from sahi.classification import ImageClassification |
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from sahi.utils.cv import visualize_object_predictions, read_image |
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from ultralyticsplus import YOLO |
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def yolov8_inference( |
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image: gr.Image = None, |
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model_path: gr.Dropdown = None, |
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image_size: gr.Slider = 640, |
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conf_threshold: gr.Slider = 0.25, |
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iou_threshold: gr.Slider = 0.45, |
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): |
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""" |
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YOLOv8 inference function |
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Args: |
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image: Input image |
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model_path: Path to the model |
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image_size: Image size |
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conf_threshold: Confidence threshold |
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iou_threshold: IOU threshold |
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""" |
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model = YOLO(model_path) |
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model.overrides['conf'] = conf_threshold |
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model.overrides['iou']= iou_threshold |
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model.overrides['agnostic_nms'] = False |
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model.overrides['max_det'] = 1000 |
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top_class_index = torch.argmax(results[0].probs).item() |
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Class = model.names[top_class_index] |
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print(Class) |
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inputs = [ |
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gr.Image(type="filepath", label="Input Image"), |
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gr.Dropdown(["foduucom/Tyre-Quality-Classification-AI"], |
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default="foduucom/Tyre-Quality-Classification-AI", label="Model"), |
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gr.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"), |
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gr.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"), |
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gr.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"), |
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] |
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outputs = gr.Image(type="filepath", label="Output Image") |
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title = "AI-Powered Tire Quality Inspection: YOLOv8s Enhanced Classification" |
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Space Description:""" |
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Welcome to our 🤖 AI-Powered Tire Quality Inspection Space – a cutting-edge solution harnessing the capabilities of YOLOv8s to revolutionize 🚗 tire quality control processes. |
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""" |
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About This Space: """ |
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This interactive platform empowers you to classify tires with unparalleled precision, utilizing a fine-tuned YOLOv8s model 🎯 specifically developed for identifying defects in tire manufacturing. By submitting an image of a tire, you can instantly determine whether it meets the rigorous quality standards required in the industry, helping to ensure safety and reliability in automotive products. |
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""" |
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examples = [['samples/1.jpeg', 'foduucom/thermal-image-object-detection', 640, 0.25, 0.45], ['samples/2.jpg', 'foduucom/thermal-image-object-detection', 640, 0.25, 0.45]] |
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demo_app = gr.Interface( |
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fn=yolov8_inference, |
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inputs=inputs, |
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outputs=outputs, |
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title=title, |
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description=description, |
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examples=examples, |
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cache_examples=True, |
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theme='huggingface', |
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) |
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demo_app.queue().launch(debug=True) |
