File size: 4,503 Bytes
d1b31ce 031ec86 d1b31ce c0f6432 6e3fd26 c0f6432 031ec86 b0005f4 c0f6432 031ec86 c83f8fa b0005f4 031ec86 c0f6432 c83f8fa c0f6432 6be1573 c0f6432 c83f8fa c0f6432 c83f8fa c0f6432 6be1573 c83f8fa 6be1573 c83f8fa 6be1573 6e3fd26 031ec86 c0f6432 031ec86 c83f8fa 031ec86 c0f6432 031ec86 c83f8fa 031ec86 c0f6432 c83f8fa c0f6432 c83f8fa c0f6432 031ec86 b0005f4 |
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 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 |
"""
File: app.py
Author: Elena Ryumina and Dmitry Ryumin
Description: Description: Main application file for Facial_Expression_Recognition.
The file defines the Gradio interface, sets up the main blocks,
and includes event handlers for various components.
License: MIT License
"""
import gradio as gr
# Importing necessary components for the Gradio app
from app.description import DESCRIPTION_STATIC, DESCRIPTION_DYNAMIC
from app.authors import AUTHORS
from app.app_utils import preprocess_image_and_predict, preprocess_video_and_predict
def clear_static_info():
return (
gr.Image(value=None, type="pil"),
gr.Image(value=None, scale=1, elem_classes="dl5"),
gr.Image(value=None, scale=1, elem_classes="dl2"),
gr.Label(value=None, num_top_classes=3, scale=1, elem_classes="dl3"),
)
def clear_dynamic_info():
return (
gr.Video(value=None),
gr.Video(value=None),
gr.Video(value=None),
gr.Video(value=None),
gr.Plot(value=None),
)
with gr.Blocks(css="app.css") as demo:
with gr.Tab("Dynamic App"):
gr.Markdown(value=DESCRIPTION_DYNAMIC)
with gr.Row():
with gr.Column(scale=2):
input_video = gr.Video(elem_classes="video1")
with gr.Row():
clear_btn_dynamic = gr.Button(
value="Clear", interactive=True, scale=1
)
submit_dynamic = gr.Button(
value="Submit", interactive=True, scale=1, elem_classes="submit"
)
with gr.Column(scale=2, elem_classes="dl4"):
with gr.Row():
output_video = gr.Video(label="Original video", scale=1, elem_classes="video2")
output_face = gr.Video(label="Pre-processed video", scale=1, elem_classes="video3")
output_heatmaps = gr.Video(label="Heatmaps", scale=1, elem_classes="video4")
output_statistics = gr.Plot(label="Statistics of emotions", elem_classes="stat")
gr.Examples(
["videos/video1.mp4",
"videos/video2.mp4"],
[input_video],
)
with gr.Tab("Static App"):
gr.Markdown(value=DESCRIPTION_STATIC)
with gr.Row():
with gr.Column(scale=2, elem_classes="dl1"):
input_image = gr.Image(label="Original image", type="pil")
with gr.Row():
clear_btn = gr.Button(
value="Clear", interactive=True, scale=1, elem_classes="clear"
)
submit = gr.Button(
value="Submit", interactive=True, scale=1, elem_classes="submit"
)
with gr.Column(scale=1, elem_classes="dl4"):
with gr.Row():
output_image = gr.Image(label="Face", scale=1, elem_classes="dl5")
output_heatmap = gr.Image(label="Heatmap", scale=1, elem_classes="dl2")
output_label = gr.Label(num_top_classes=3, scale=1, elem_classes="dl3")
gr.Examples(
[
"images/fig7.jpg",
"images/fig1.jpg",
"images/fig2.jpg",
"images/fig3.jpg",
"images/fig4.jpg",
"images/fig5.jpg",
"images/fig6.jpg",
],
[input_image],
)
with gr.Tab("Authors"):
gr.Markdown(value=AUTHORS)
submit.click(
fn=preprocess_image_and_predict,
inputs=[input_image],
outputs=[output_image, output_heatmap, output_label],
queue=True,
)
clear_btn.click(
fn=clear_static_info,
inputs=[],
outputs=[input_image, output_image, output_heatmap, output_label],
queue=True,
)
submit_dynamic.click(
fn=preprocess_video_and_predict,
inputs=input_video,
outputs=[
output_video,
output_face,
output_heatmaps,
output_statistics
],
queue=True,
)
clear_btn_dynamic.click(
fn=clear_dynamic_info,
inputs=[],
outputs=[
input_video,
output_video,
output_face,
output_heatmaps,
output_statistics
],
queue=True,
)
if __name__ == "__main__":
demo.queue(api_open=False).launch(share=False)
|