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Parent(s):
7ce11a0
Update app.py
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app.py
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@@ -40,7 +40,7 @@ def yolov8_inference(
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return annotated_image
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image_input = gr.inputs.Image() # Adjust the shape according to your requirements
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inputs = [
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),
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gr.Slider(minimum=0.0, maximum=1.0, value=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 = "Fire Smoke Demo"
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import os
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@@ -58,12 +58,84 @@ examples = [
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["f2.jpg", 0.25, 0.45],
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["f3.jpg", 0.25, 0.45],
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]
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return annotated_image
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'''
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image_input = gr.inputs.Image() # Adjust the shape according to your requirements
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inputs = [
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),
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gr.Slider(minimum=0.0, maximum=1.0, value=0.45, step=0.05, label="IOU Threshold"),
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]
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'''
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outputs = gr.Image(type="filepath", label="Output Image")
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title = "Fire Smoke Demo"
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import os
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["f2.jpg", 0.25, 0.45],
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["f3.jpg", 0.25, 0.45],
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]
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outputs_images = [
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["1.jpg"], # First example: an output image for the cat example
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["2.jpg"] # Second example: an output image for the dog example
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,["3.jpg"]
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]
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readme_html = """
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<html>
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<head>
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<style>
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.description {
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margin: 20px;
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padding: 10px;
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border: 1px solid #ccc;
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}
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</style>
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</head>
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<body>
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<div class="description">
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<p><strong>More details:</strong></p>
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<p>We present a demo for performing object segmentation with training a Yolov8-seg on Fire and Smoke dataset. The model was trained on 141 training images and validated on 40 images.</p>
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<p><strong>Usage:</strong></p>
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<p>You can upload Fire-smoke images, and the demo will provide you with your segmented image.</p>
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<p><strong>Dataset:</strong></p>
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<p>This dataset comprises a total of 201 images, which are divided into three distinct sets for various purposes:</p>
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<ul>
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<li><strong>Training Set:</strong> It includes 141 images and is intended for training the model.</li>
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<li><strong>Validation Set:</strong> There are 40 images in the validation set, which is used for optimizing model parameters during development.</li>
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<li><strong>Test Set:</strong> This set consists of 20 images and serves as a separate evaluation dataset to assess the performance of trained models.</li>
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</ul>
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<p><strong>License:</strong> This dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0).</p>
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<p>To access and download this dataset, please follow this link: <a href=" https://universe.roboflow.com/roboflow-universe-projects/fire-and-smoke-segmentation" target="_blank">Dataset Download</a></p>
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</body>
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</html>
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"""
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with gr.Blocks() as demo:
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gr.Markdown(
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"""
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<div style="text-align: center;">
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<h1> Fire Smoke Demo</h1>
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Powered by <a href="https://Tuba.ai">Tuba</a>
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</div>
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"""
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)
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# Define the input components and add them to the layout
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with gr.Row():
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image_input = gr.inputs.Image()
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outputs = gr.Image(type="filepath", label="Output Image")
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# Define the output component and add it to the layout
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with gr.Row():
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conf_slider=gr.Slider(minimum=0.0, maximum=1.0, value=0.25, step=0.05, label="Confidence Threshold" )
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with gr.Row():
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IOU_Slider=gr.Slider(minimum=0.0, maximum=1.0, value=0.45, step=0.05, label="IOU Threshold")
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button = gr.Button("Run")
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# Define the event listener that connects the input and output components and triggers the function
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button.click(fn=yolov8_inference, inputs=[image_input, conf_slider,IOU_Slider], outputs=outputs, api_name="yolov8_inference")
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gr.Examples(
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fn=yolov8_inference,
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examples=examples,
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inputs=[image_input, conf_slider,IOU_Slider],
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outputs=[outputs]
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)
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# gr.Examples(inputs=examples, outputs=outputs_images)
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# Add the description below the layout
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gr.Markdown(readme_html)
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# Launch the app
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demo.launch(share=False)
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