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import PIL.Image as Image
import gradio as gr
import spaces
from ultralytics import YOLOv10
@spaces.GPU
def predict_image(img, model_id, image_size, conf_threshold):
model = YOLOv10.from_pretrained(f'jameslahm/{model_id}')
results = model.predict(
source=img,
conf=conf_threshold,
show_labels=True,
show_conf=True,
imgsz=image_size,
)
for r in results:
im_array = r.plot()
im = Image.fromarray(im_array[..., ::-1])
return im
def app():
with gr.Blocks():
with gr.Row():
with gr.Column():
image = gr.Image(type="pil", label="Image")
model_id = gr.Dropdown(
label="Model",
choices=[
"yolov10n",
"yolov10s",
"yolov10m",
"yolov10b",
"yolov10l",
"yolov10x",
],
value="yolov10m",
)
image_size = gr.Slider(
label="Image Size",
minimum=320,
maximum=1280,
step=32,
value=640,
)
conf_threshold = gr.Slider(
label="Confidence Threshold",
minimum=0.0,
maximum=1.0,
step=0.05,
value=0.25,
)
yolov10_infer = gr.Button(value="Detect Objects")
with gr.Column():
output_image = gr.Image(type="pil", label="Annotated Image")
yolov10_infer.click(
fn=predict_image,
inputs=[
image,
model_id,
image_size,
conf_threshold,
],
outputs=[output_image],
)
gr.Examples(
examples=[
[
"bus.jpg",
"yolov10s",
640,
0.25,
],
[
"zidane.jpg",
"yolov10s",
640,
0.25,
],
],
fn=predict_image,
inputs=[
image,
model_id,
image_size,
conf_threshold,
],
outputs=[output_image],
cache_examples='lazy',
)
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() |