Spaces:
Sleeping
Sleeping
File size: 2,462 Bytes
8cc50cf 6cd9e92 934e326 8cc50cf 0152b73 758f1bb 934e326 758f1bb 0152b73 758f1bb 0152b73 8cc50cf 758f1bb 322d628 758f1bb ccfae83 758f1bb ccfae83 758f1bb 0358eb0 758f1bb 0152b73 758f1bb 0152b73 758f1bb 8cc50cf |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 |
import gradio as gr
from ultralytics import YOLO
# Import YOLOv9
import yolov9
# Define function to perform prediction with YOLO model
def predict_image(image, model_path, image_size, conf_threshold, iou_threshold):
# Load YOLO model
model = YOLO(model_path)
# Perform inference with YOLO model
results = model.predict(image, size=image_size, conf=conf_threshold, iou=iou_threshold)
# Render the output
output_image = results.render()
return output_image
# Define Gradio interface
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=[
"yolov9c-seg.pt",
],
value="yolov9c-seg.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("Submit")
with gr.Column():
output_image = gr.Image(type="numpy", label="Output")
yolov9_infer.click(
fn=predict_image,
inputs=[
img_path,
model_path,
image_size,
conf_threshold,
iou_threshold,
],
outputs=[output_image],
)
gradio_app = gr.Blocks()
with gradio_app:
gr.HTML(
"""
<h1 style='text-align: center'>
YOLOv9 Base Model
</h1>
""")
gr.HTML(
"""
<h3 style='text-align: center'>
</h3>
""")
with gr.Row():
with gr.Column():
app()
gradio_app.launch(debug=True)
|