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import gradio as gr
from inference import combined_inference_live
from model_loader import EXTENDED_MODELS
# Gradio Web Interface
with gr.Blocks() as app:
gr.Markdown("# Live Object Detection: Full vs. Slicing-Based Detection")
gr.Markdown("""
**How It Works**:
- **Full Inference**: Runs a single pass over the entire frame.
- **Slicing Detection**: Divides the frame into patches, detects objects, applies Non-Maximum Suppression (NMS).
- **Outputs retain the original BGR format for color accuracy**.
""")
with gr.Row():
webcam = gr.Image(sources="webcam", streaming=True, label="Webcam Feed")
model_dd = gr.Dropdown(label="Select Model", choices=list(EXTENDED_MODELS.keys()), value="YOLOv8")
with gr.Row():
conf_slider = gr.Slider(0, 1.0, value=0.25, step=0.05, label="Confidence Threshold")
slice_slider = gr.Slider(128, 1024, value=512, step=1, label="Slice Size")
overlap_slider = gr.Slider(0.0, 0.5, value=0.2, step=0.05, label="Overlap Rate")
fi_output = gr.Image(label="Full Inference")
slice_output = gr.Image(label="Slicing + NMS")
webcam.stream(
fn=combined_inference_live,
inputs=[webcam, model_dd, conf_slider, slice_slider, overlap_slider],
outputs=[fi_output, slice_output]
)
# Run the Gradio app
app.launch()
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