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import gradio as gr
from demo import automask_image_app, automask_video_app, sahi_autoseg_app
def image_app():
with gr.Blocks():
with gr.Row():
with gr.Column():
seg_automask_image_file = gr.Image(type="filepath").style(height=260)
with gr.Row():
with gr.Column():
seg_automask_image_model_type = gr.Dropdown(
choices=[
"vit_h",
"vit_l",
"vit_b",
],
value="vit_l",
label="Model Type",
)
seg_automask_image_min_area = gr.Number(
value=0,
label="Min Area",
)
with gr.Row():
with gr.Column():
seg_automask_image_points_per_side = gr.Slider(
minimum=0,
maximum=32,
step=2,
value=16,
label="Points per Side",
)
seg_automask_image_points_per_batch = gr.Slider(
minimum=0,
maximum=64,
step=2,
value=64,
label="Points per Batch",
)
seg_automask_image_predict = gr.Button(value="Generator")
with gr.Column():
output_image = gr.Image()
seg_automask_image_predict.click(
fn=automask_image_app,
inputs=[
seg_automask_image_file,
seg_automask_image_model_type,
seg_automask_image_points_per_side,
seg_automask_image_points_per_batch,
seg_automask_image_min_area,
],
outputs=[output_image],
)
def video_app():
with gr.Blocks():
with gr.Row():
with gr.Column():
seg_automask_video_file = gr.Video().style(height=260)
with gr.Row():
with gr.Column():
seg_automask_video_model_type = gr.Dropdown(
choices=[
"vit_h",
"vit_l",
"vit_b",
],
value="vit_l",
label="Model Type",
)
seg_automask_video_min_area = gr.Number(
value=1000,
label="Min Area",
)
with gr.Row():
with gr.Column():
seg_automask_video_points_per_side = gr.Slider(
minimum=0,
maximum=32,
step=2,
value=16,
label="Points per Side",
)
seg_automask_video_points_per_batch = gr.Slider(
minimum=0,
maximum=64,
step=2,
value=64,
label="Points per Batch",
)
seg_automask_video_predict = gr.Button(value="Generator")
with gr.Column():
output_video = gr.Video()
seg_automask_video_predict.click(
fn=automask_video_app,
inputs=[
seg_automask_video_file,
seg_automask_video_model_type,
seg_automask_video_points_per_side,
seg_automask_video_points_per_batch,
seg_automask_video_min_area,
],
outputs=[output_video],
)
def sahi_app():
with gr.Blocks():
with gr.Row():
with gr.Column():
sahi_image_file = gr.Image(type="filepath").style(height=260)
sahi_autoseg_model_type = gr.Dropdown(
choices=[
"vit_h",
"vit_l",
"vit_b",
],
value="vit_l",
label="Sam Model Type",
)
with gr.Row():
with gr.Column():
sahi_model_type = gr.Dropdown(
choices=[
"yolov5",
"yolov8",
],
value="yolov5",
label="Detector Model Type",
)
sahi_image_size = gr.Slider(
minimum=0,
maximum=1600,
step=32,
value=640,
label="Image Size",
)
sahi_overlap_width = gr.Slider(
minimum=0,
maximum=1,
step=0.1,
value=0.2,
label="Overlap Width",
)
sahi_slice_width = gr.Slider(
minimum=0,
maximum=640,
step=32,
value=256,
label="Slice Width",
)
with gr.Row():
with gr.Column():
sahi_model_path = gr.Dropdown(
choices=[
"yolov5l.pt",
"yolov5l6.pt",
"yolov8l.pt",
"yolov8x.pt"
],
value="yolov5l6.pt",
label="Detector Model Path",
)
sahi_conf_th = gr.Slider(
minimum=0,
maximum=1,
step=0.1,
value=0.2,
label="Confidence Threshold",
)
sahi_overlap_height = gr.Slider(
minimum=0,
maximum=1,
step=0.1,
value=0.2,
label="Overlap Height",
)
sahi_slice_height = gr.Slider(
minimum=0,
maximum=640,
step=32,
value=256,
label="Slice Height",
)
sahi_image_predict = gr.Button(value="Generator")
with gr.Column():
output_image = gr.Image()
sahi_image_predict.click(
fn=sahi_autoseg_app,
inputs=[
sahi_image_file,
sahi_autoseg_model_type,
sahi_model_type,
sahi_model_path,
sahi_conf_th,
sahi_image_size,
sahi_slice_height,
sahi_slice_width,
sahi_overlap_height,
sahi_overlap_width,
],
outputs=[output_image],
)
def metaseg_app():
app = gr.Blocks()
with app:
with gr.Row():
with gr.Column():
with gr.Tab("Image"):
image_app()
with gr.Tab("Video"):
video_app()
with gr.Tab("SAHI"):
sahi_app()
app.queue(concurrency_count=1)
app.launch(debug=True, enable_queue=True)
if __name__ == "__main__":
metaseg_app()
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