import gradio as gr
import os
import spaces
import yolov9
from huggingface_hub import hf_hub_download
@spaces.GPU
def yolov9_inference(img_path, model_id='YOLOv9-S_X_LOCO-converted.pt', img_size=640, conf_thres=0.3, iou_thres=0.7):
"""
Load a YOLOv9 model, configure it, perform inference on an image, and optionally adjust
the input size and apply test time augmentation.
:param model_path: Path to the YOLOv9 model file.
:param conf_threshold: Confidence threshold for NMS.
:param iou_threshold: IoU threshold for NMS.
:param img_path: Path to the image file.
:param size: Optional, input size for inference.
:return: A tuple containing the detections (boxes, scores, categories) and the results object for further actions like displaying.
"""
model_path = download_models(model_id)
model = yolov9.load(model_path)
# Set model parameters
model.conf = conf_thres
model.iou = iou_thres
# Perform inference
results = model(img_path, size=img_size)
# Optionally, show detection bounding boxes on image
output = results.render()
return output[0]
def download_models(model_id):
hf_hub_download("KoniHD/LOCO-Detection", filename=f"{model_id}", local_dir=f"./",
token=os.getenv("HF_TOKEN"))
return f"./{model_id}"
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=[
"YOLOv9-S_X_LOCO-converted.pt",
"YOLOv9-S_X_LOCO.pt",
"YOLOv9-E_X_LOCO-converted.pt",
"YOLOv9-E_X_LOCO.pt",
],
value="YOLOv9-E_X_LOCO-converted.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/forklift.jpg",
"YOLOv9-S_X_LOCO-converted.pt",
640,
0.3,
0.7,
],
[
"data/hall.jpg",
"YOLOv9-E_X_LOCO-converted.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(
"""
YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information
""")
gr.HTML(
"""
""")
with gr.Row():
with gr.Column():
app()
gradio_app.launch(debug=True)