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import spaces
import gradio as gr
import cv2
import tempfile
from ultralytics import YOLOv10
model = YOLOv10.from_pretrained(f'jameslahm/yolov10s')
@spaces.GPU
def yolov10_inference(image, conf_threshold):
width, _ = image.size
results = model.predict(source=image, imgsz=width, conf=conf_threshold)
annotated_image = results[0].plot()
return annotated_image[:, :, ::-1]
def app():
with gr.Blocks():
with gr.Row():
with gr.Column():
image = gr.Image(type="pil", label="Image", visible=True)
conf_threshold = gr.Slider(
label="Confidence Threshold",
minimum=0.0,
maximum=1.0,
step=0.05,
value=0.25,
)
with gr.Column():
output_image = gr.Image(type="numpy", label="Annotated Image", visible=True)
image.stream(
fn=yolov10_inference,
inputs=[image, conf_threshold],
outputs=[image],
stream_every=0.1,
time_limit=30
)
gradio_app = gr.Blocks()
with gradio_app:
gr.HTML(
"""
<h1 style='text-align: center'>
YOLOv10: Real-Time End-to-End Object Detection
</h1>
""")
gr.HTML(
"""
<h3 style='text-align: center'>
<a href='https://arxiv.org/abs/2405.14458' target='_blank'>arXiv</a> | <a href='https://github.com/THU-MIG/yolov10' target='_blank'>github</a>
</h3>
""")
with gr.Row():
with gr.Column():
app()
if __name__ == '__main__':
gradio_app.launch()
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