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import numpy as np
import gradio as gr
from detect import predict
from config import PASCAL_CLASSES
def inference(
org_img: np.ndarray,
iou_thresh: float, thresh: float,
show_cam: str,
transparency: float,
):
outputs = predict(org_img, iou_thresh, thresh, show_cam, transparency)
return outputs
title = "YoloV3 from Scratch on Pascal VOC Dataset with GradCAM"
description = f"Pytorch Implemetation of YoloV3 trained from scratch on Pascal VOC dataset with GradCAM \n Class in pascol voc: {', '.join(PASCAL_CLASSES)}"
examples = [
["images/000014.jpg", 0.5, 0.4, True, 0.5],
["images/000017.jpg", 0.6, 0.5, True, 0.5],
["images/000018.jpg", 0.55, 0.45, True, 0.5],
["images/000030.jpg", 0.5, 0.4, True, 0.5],
["images/Puppies.jpg", 0.6, 0.7, True, 0.5],
]
demo = gr.Interface(
inference,
inputs=[
gr.Image(label="Input Image"),
gr.Slider(0, 1, value=0.5, label="IOU Threshold"),
gr.Slider(0, 1, value=0.4, label="Threshold"),
gr.Checkbox(label="Show Grad Cam"),
gr.Slider(0, 1, value=0.5, label="Opacity of GradCAM"),
],
outputs=[
gr.Gallery(rows=2, columns=1),
],
title=title,
description=description,
examples=examples,
)
demo.launch()
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