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import gradio as gr |
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import torch |
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from sahi.prediction import ObjectPrediction |
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from sahi.utils.cv import visualize_object_predictions, read_image |
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from ultralyticsplus import YOLO, render_result |
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image_path = [ |
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['test/web form.jpg', 'foduucom/web-form-ui-field-detection', 640, 0.25, 0.45], |
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['test/web form2.jpg', 'foduucom/web-form-ui-field-detection', 640, 0.25, 0.45] |
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] |
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def yolov8_inference( |
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image: gr.inputs.Image = None, |
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model_path: gr.inputs.Dropdown = None, |
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image_size: gr.inputs.Slider = 640, |
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conf_threshold: gr.inputs.Slider = 0.25, |
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iou_threshold: gr.inputs.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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Returns: |
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Rendered image |
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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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image = read_image(image) |
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results = model.predict(image) |
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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.Dropdown(["foduucom/web-form-ui-field-detection"], |
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default="foduucom/web-form-ui-field-detection", label="Model"), |
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gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"), |
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gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"), |
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gr.inputs.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.outputs.Image(type="filepath", label="Output Image") |
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title = "Ui form : web form ui field Detection in Images" |
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interface_image = 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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examples=image_path, |
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cache_examples=False, |
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theme='huggingface' |
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) |
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gr.TabbedInterface( |
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[interface_image], |
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tab_names=['Image inference'] |
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).queue().launch() |
