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import os
import sys
sys.path.append("../")
import gradio as gr
import torch
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams["font.family"] = "serif"
import decord
import PIL, PIL.Image
import librosa
from IPython.display import Markdown, display
import pandas as pd
from util import *
css = """
<style>
body {
font-family: 'Arial', serif;
margin: 0;
padding: 0;
color: black;
}
.header {
display: flex;
align-items: center;
justify-content: center;
margin-top: 5px;
color: black;
}
.footer {
display: flex;
align-items: center;
justify-content: center;
margin-top: 5px;
}
.image {
margin-right: 20px;
}
.content {
text-align: center;
color: black;
}
.title {
font-size: 2.5em;
font-weight: bold;
margin-bottom: 10px;
}
.authors {
color: #4a90e2;
font-size: 1.05em;
margin: 10px 0;
}
.affiliations {
font-size: 1.em;
margin-bottom: 20px;
}
.buttons {
display: flex;
justify-content: center;
gap: 10px;
}
.button {
background-color: #545758;
text-decoration: none;
padding: 8px 16px;
border-radius: 5px;
font-size: 1.05em;
}
.button:hover {
background-color: #333;
}
</style>
"""
header = css + """
<div class="header">
<!-- <div class="image">
<img src="./media_assets/pouring-water-logo5.png" alt="logo" width="100">
</div> -->
<div class="content">
<img src="https://bpiyush.github.io/pouring-water-website/assets/pouring-water-logo5.png" alt="logo" width="80" style="margin-bottom: -50px; margin-right: 30px;">
<div class="title" style="font-size: 44px; margin-left: -30px;">The Sound of Water</div>
<div style="font-size: 30px; margin-left: -30px;"><b>Inferring Physical Properties from Pouring Liquids</b></div>
<div class="authors">
<a style="color: #92eaff; href="https://bpiyush.github.io/">Piyush Bagad</a><sup>1</sup>,
<a style="color: #92eaff; href="https://makarandtapaswi.github.io/">Makarand Tapaswi</a><sup>2</sup>,
<a style="color: #92eaff; href="https://www.ceessnoek.info/">Cees G. M. Snoek</a><sup>3</sup>,
<a style="color: #92eaff; href="https://www.robots.ox.ac.uk/~az/">Andrew Zisserman</a><sup>1</sup>,
</div>
<div class="affiliations">
<sup>1</sup>University of Oxford, <sup>2</sup>IIIT Hyderabad, <sup>3</sup>University of Amsterdam
</div>
<div class="buttons">
<a href="#" style="color: #92eaff;" class="button">arXiv</a>
<a href="https://bpiyush.github.io/pouring-water-website/" style="color: #92eaff;" class="button">π Project</a>
<a href="https://github.com/bpiyush/SoundOfWater" style="color: #92eaff;" class="button"> <img src="https://bpiyush.github.io/pouring-water-website/assets/github-logo.png" alt="logo" style="height:16px; float: left;"> Code</a>
<a href="https://huggingface.co/datasets/bpiyush/sound-of-water" style="color: #92eaff;" class="button">π€ Data</a>
<a href="https://huggingface.co/bpiyush/sound-of-water-models" style="color: #92eaff;" class="button">π€ Models</a>
<a href="#" style="color: #92eaff;" class="button">π― Demo</a>
</div>
</div>
</div>
"""
footer = css + """
<div class="header" style="justify-content: left;">
<div class="content" style="font-size: 16px;">
Please give us a π on <a href='https://github.com/bpiyush/SoundOfWater'>Github</a> if you like our work!
Tips to get better results:
<br><br>
<ol style="text-align: left; font-size: 14px; margin-left: 30px">
<li>Make sure there is not too much noise such that the pouring is audible.</li>
<li>Note that the video is not used during the inference. Only the audio must be clear enough.</li>
</ol>
</div>
</div>
"""
# def process_input(video=None, youtube_link=None, start_time=None, end_time=None):
# if video:
# return f"Video file uploaded: {video.name}"
# elif youtube_link and start_time and end_time:
# return f"YouTube link: {youtube_link} (Start: {start_time}, End: {end_time})"
# else:
# return "Please upload a video or provide a YouTube link with start and end times."
def configure_input():
gr.Markdown(
"#### Either upload a video file or provide a YouTube link with start and end times."
)
video_input = gr.Video(label="Upload Video", height=480)
youtube_link_start = gr.Textbox(label="YouTube Link (Start time)")
youtube_link_end = gr.Textbox(label="YouTube Link (End time)")
return [video_input, youtube_link_start, youtube_link_end]
# Example usage in a Gradio interface
def process_input(video, youtube_link_start, youtube_link_end):
if video is not None:
print(video)
# Load model globally
model = load_model()
# The input is a video file path
video_path = video
# Load first frame
frame = load_frame(video_path)
# Load spectrogram
S = load_spectrogram(video_path)
# Load audio tensor
audio = load_audio_tensor(video_path)
# Get output
z_audio, y_audio = get_model_output(audio, model)
# Show image output
image, df_show, tsne_image = show_output(frame, S, y_audio, z_audio)
return image, df_show, gr.Markdown(note), tsne_image
elif (youtube_link_start is not None) and (youtube_link_end is not None):
# Using the provided YouTube link
# Example: https://youtu.be/6-HVn8Jzzuk?t=10
start_link = f"Processing YouTube link: {youtube_link_start}"
end_link = f"Processing YouTube link: {youtube_link_end}"
# Get video ID
video_id = youtube_link_start.split("/")[-1].split("?")[0]
assert video_id == youtube_link_end.split("/")[-1].split("?")[0], "Video IDs do not match"
start_time = float(youtube_link_start.split("t=")[-1])
end_time = float(youtube_link_end.split("t=")[-1])
raise NotImplementedError("YouTube link processing is not implemented yet")
else:
return "No input provided"
def greet(name, is_morning, temperature):
salutation = "Good morning" if is_morning else "Good evening"
greeting = f"{salutation} {name}. It is {temperature} degrees today"
celsius = (temperature - 32) * 5 / 9
return greeting, round(celsius, 2)
note = """
**Note**: Radius (as well as height) estimation depends on accurate wavelength estimation towards the end.
Thus, it may not be accurate if the wavelength is not estimated correctly at the end.
$$
H = l(0) = \\frac{\lambda(0) - \lambda(T)}{4} \ \ \\text{and} \ \ R = \\frac{\lambda(T)}{4\\beta}
$$
"""
def configure_outputs():
image_wide = gr.Image(label="Estimated pitch")
dataframe = gr.DataFrame(label="Estimated physical properties")
image_tsne = gr.Image(label="TSNE of features", width=300)
markdown = gr.Markdown(label="Note")
# ["image", "dataframe", "image", "markdown"]
return [image_wide, dataframe, markdown, image_tsne]
# Configure pre-defined examples
examples = [
["./media_assets/example_video.mp4", None, None],
["./media_assets/ayNzH0uygFw_9.0_21.0.mp4", None, None],
["./media_assets/biDn0Gi6V8U_7.0_15.0.mp4", None, None],
["./media_assets/goWgiQQMugA_2.5_9.0.mp4", None, None],
["./media_assets/K87g4RvO-9k_254.0_259.0.mp4", None, None],
]
# Define Gradio interface
with gr.Blocks(
css=custom_css,
theme=gr.themes.Default(),
) as demo:
# Add the header
gr.HTML(header)
gr.Interface(
fn=process_input,
inputs=configure_input(),
outputs=configure_outputs(),
examples=examples,
)
# Add the footer
gr.HTML(footer)
# Launch the interface
demo.launch(allowed_paths=["."], share=True) |