Update app.py
Browse files
app.py
CHANGED
@@ -8,34 +8,36 @@ import numpy as np
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model = YOLO('best (1).pt')
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def response(image):
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def
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conf_threshold: gr.Slider = 0.4,
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iou_threshold: gr.Slider = 0.50):
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model = YOLO('best (1).pt')
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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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render = render_result(model=model, image=image, result=results[0], rect_th = 1, text_th = 1
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inputs = [
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@@ -71,5 +73,5 @@ title = "YOLOv8 Custom Object Detection by Uyen Nguyen"
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# Launch the Gradio interface in debug mode with queue enabled
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# yolo_app.launch()
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iface = gr.Interface(fn=
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iface.launch()
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model = YOLO('best (1).pt')
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# def response(image):
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# print(image)
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# results = model(image)
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# for i, r in enumerate(results):
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# # Plot results image
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# im_bgr = r.plot()
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# im_rgb = im_bgr[..., ::-1] # Convert BGR to RGB
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# # im_rgb = Image.fromarray(im_rgb)
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# return im_rgb
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def response2(image: gr.Image = None,image_size: gr.Slider = 640, conf_threshold: gr.Slider = 0.5, iou_threshold: gr.Slider = 0.6):
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model = YOLO('best (1).pt')
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results = model.predict(image, conf=conf_threshold, iou=iou_threshold, imgsz=image_size)
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box = results[0].boxes
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# render = render_result(model=model, image=image, result=results[0], rect_th = 1, text_th = 1
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for i, r in enumerate(results):
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# Plot results image
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im_bgr = r.plot()
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im_rgb = im_bgr[..., ::-1] # Convert BGR to RGB
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# im_rgb = Image.fromarray(im_rgb)
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return im_rgb
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inputs = [
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# Launch the Gradio interface in debug mode with queue enabled
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# yolo_app.launch()
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iface = gr.Interface(fn=response2, inputs=inputs, outputs=outputs)
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iface.launch()
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