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import gradio as gr |
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import torch |
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from ultralyticsplus import YOLO, render_result |
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torch.hub.download_url_to_file('https://external-content.duckduckgo.com/iu/?u=https%3A%2F%2Ftexashafts.com%2Fwp-content%2Fuploads%2F2016%2F04%2Fconstruction-worker.jpg', 'one.jpg') |
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torch.hub.download_url_to_file( |
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'https://www.pearsonkoutcherlaw.com/wp-content/uploads/2020/06/Construction-Workers.jpg', 'two.jpg') |
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torch.hub.download_url_to_file( |
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'https://nssgroup.com/wp-content/uploads/2019/02/Building-maintenance-blog.jpg', 'three.jpg') |
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def yoloV8_func(image: gr.inputs.Image = None, |
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image_size: gr.inputs.Slider = 640, |
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conf_threshold: gr.inputs.Slider = 0.4, |
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iou_threshold: gr.inputs.Slider = 0.50): |
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"""_summary_ |
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Args: |
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image (gr.inputs.Image, optional): _description_. Defaults to None. |
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image_size (gr.inputs.Slider, optional): _description_. Defaults to 640. |
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conf_threshold (gr.inputs.Slider, optional): _description_. Defaults to 0.4. |
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iou_threshold (gr.inputs.Slider, optional): _description_. Defaults to 0.50. |
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""" |
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model_path = "best.pt" |
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model = YOLO("foduucom/table-detection-and-extraction") |
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results = model.predict(image, |
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conf=conf_threshold, |
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iou=iou_threshold, |
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imgsz=image_size) |
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box = results[0].boxes |
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print("Object type:", box.cls) |
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print("Coordinates:", box.xyxy) |
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print("Probability:", box.conf) |
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render = render_result(model=model, image=image, result=results[0]) |
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return render |
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inputs = [ |
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gr.inputs.Image(type="filepath", label="Input Image"), |
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gr.inputs.Slider(minimum=320, maximum=1280, default=640, |
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step=32, label="Image Size"), |
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gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, |
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step=0.05, label="Confidence Threshold"), |
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gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, |
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step=0.05, label="IOU Threshold"), |
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] |
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outputs = gr.outputs.Image(type="filepath", label="Output Image") |
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title = "YOLOv8 101: Custome Object Detection on Construction Workers " |
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examples = [['one.jpg', 640, 0.5, 0.7], |
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['two.jpg', 800, 0.5, 0.6], |
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['three.jpg', 900, 0.5, 0.8]] |
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yolo_app = gr.Interface( |
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fn=yoloV8_func, |
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inputs=inputs, |
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outputs=outputs, |
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title=title, |
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examples=examples, |
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cache_examples=True, |
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) |
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yolo_app.launch(debug=True, enable_queue=True) |
