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import gradio as gr
import spaces
from huggingface_hub import hf_hub_download
def download_models(model_id):
hf_hub_download("merve/yolov9", filename=f"{model_id}", local_dir=f"./")
return f"./{model_id}"
@spaces.GPU
def yolov9_inference(img_path, model_id, image_size, conf_threshold, iou_threshold):
# Import YOLOv9
import yolov9
# Load the model
model_path = download_models(model_id)
model = yolov9.load(model_path, device="cuda:0")
# Set model parameters
model.conf = conf_threshold
model.iou = iou_threshold
# Perform inference
results = model(img_path, size=image_size)
# Optionally, show detection bounding boxes on image
output = results.render()
return output[0]
def app():
with gr.Blocks():
with gr.Row():
with gr.Column():
img_path = gr.Image(type="filepath", label="Image")
model_path = gr.Dropdown(
label="Model",
choices=[
"gelan-c.pt",
"gelan-e.pt",
"yolov9-c.pt",
"yolov9-e.pt",
],
value="gelan-e.pt",
)
image_size = gr.Slider(
label="Image Size",
minimum=320,
maximum=1280,
step=32,
value=640,
)
conf_threshold = gr.Slider(
label="Confidence Threshold",
minimum=0.1,
maximum=1.0,
step=0.1,
value=0.4,
)
iou_threshold = gr.Slider(
label="IoU Threshold",
minimum=0.1,
maximum=1.0,
step=0.1,
value=0.5,
)
yolov9_infer = gr.Button(value="Inference")
with gr.Column():
output_numpy = gr.Image(type="numpy",label="Output")
yolov9_infer.click(
fn=yolov9_inference,
inputs=[
img_path,
model_path,
image_size,
conf_threshold,
iou_threshold,
],
outputs=[output_numpy],
)
gr.Examples(
examples=[
[
"data/zidane.jpg",
"gelan-e.pt",
640,
0.4,
0.5,
],
[
"data/huggingface.jpg",
"yolov9-c.pt",
640,
0.4,
0.5,
],
],
fn=yolov9_inference,
inputs=[
img_path,
model_path,
image_size,
conf_threshold,
iou_threshold,
],
outputs=[output_numpy],
cache_examples=True,
)
gradio_app = gr.Blocks()
with gradio_app:
gr.HTML()
gr.HTML()
with gr.Row():
with gr.Column():
app()
gradio_app.launch(debug=True) |