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import gradio as gr
import spaces
from huggingface_hub import hf_hub_download
import os
# Function to download models from Hugging Face
def download_models(model_id):
# Check if the model file exists locally
local_model_path = "./gelan-c-seg.pt"
if not os.path.exists(local_model_path):
# Download the model from Hugging Face if it doesn't exist locally
hf_hub_download("merve/yolov9", filename="gelan-c-seg.pt", local_dir="./")
return local_model_path
@spaces.GPU
def yolov9_inference(img_path, model_id, image_size, conf_threshold, iou_threshold):
"""
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.
"""
# Import YOLOv9
import yolov9
# Load the model
model_path = download_models(model_id)
model = yolov9.load(model_path, device="cpu")
# 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-seg.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],
)
gradio_app = gr.Blocks()
with gradio_app:
gr.HTML(
"""
<h1 style='text-align: center'>
YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information
</h1>
""")
gr.HTML(
"""
<h3 style='text-align: center'>
Follow me for more!
<a href='https://twitter.com/kadirnar_ai' target='_blank'>Twitter</a> | <a href='https://github.com/kadirnar' target='_blank'>Github</a> | <a href='https://www.linkedin.com/in/kadir-nar/' target='_blank'>Linkedin</a> | <a href='https://www.huggingface.co/kadirnar/' target='_blank'>HuggingFace</a>
</h3>
""")
with gr.Row():
with gr.Column():
app()
gradio_app.launch(debug=True)
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