Files
Track-Anything/app.py
2023-04-13 13:40:10 +00:00

92 lines
2.8 KiB
Python

import gradio as gr
from demo import automask_image_app, automask_video_app, sahi_autoseg_app
import argparse
import cv2
import time
def pause_video():
print(time.time())
def play_video():
print("play video")
print(time.time)
with gr.Blocks() as iface:
with gr.Row():
with gr.Column(scale=1.0):
seg_automask_video_file = gr.Video().style(height=720)
gr.Video.g
seg_automask_video_file.play(fn=play_video)
seg_automask_video_file.pause(fn=pause_video)
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")
# Display the first frame
# with gr.Column():
# first_frame = gr.Image(type="pil", interactive=True, elem_id="first_frame")
# seg_automask_firstframe = gr.Button(value="Find target")
# video_input = gr.inputs.Video(type="mp4")
# output = gr.outputs.Image(type="pil")
# gr.Interface(fn=capture_frame, inputs=seg_automask_video_file, outputs=first_frame)
# 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],
# )
iface.queue(concurrency_count=1)
iface.launch(debug=True, enable_queue=True, server_port=12212, server_name="0.0.0.0")