Pushes the app file

app.py

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+"""Demo to show clips from AudioSet with Doppler effect."""
+import os
+from os.path import join, exists, dirname, abspath, basename
+import json
+from glob import glob
+
+from tqdm import tqdm
+import numpy as np
+import pandas as pd
+import streamlit as st
+import matplotlib.pyplot as plt
+# from moviepy.video.io.VideoFileClip import VideoFileClip
+
+import warnings
+warnings.simplefilter(action='ignore')
+
+curr_filepath = abspath(__file__)
+repo_path = dirname(dirname(curr_filepath))
+
+
+def make_grid(cols,rows):
+    grid = [0]*cols
+    for i in range(cols):
+        with st.container():
+            grid[i] = st.columns(rows)
+    return grid
+
+
+# Filter out samples with (possible) Doppler effect
+doppler_classes = [
+    'airplane',
+    'ambulance siren',
+    # 'race car, auto racing', # Typically captured from within the car
+    'subway, metro, underground',
+    'car passing by',
+    # 'motorboat, speedboat acceleration', # Typically captured from within the boat
+    'railroad car, train wagon',
+    # 'helicopter',
+    # 'driving snowmobile', # Typically captured from within the snowmobile
+    'airplane flyby',
+]
+
+if __name__ == "__main__":
+    
+    # Streamlit app code
+    st.set_page_config(layout="wide")
+    st.title("Clips from VGGSound (possibly with Doppler effect) 🎬")
+    
+    # load data
+    if "df" not in st.session_state:
+        csv_path = "./data/vggsound.csv"
+        df = pd.read_csv(csv_path)
+        df.columns = ["video_id", "start_seconds", "label", "split"]
+        df["end_seconds"] = df["start_seconds"] + 10.
+        df = df[df["label"].isin(doppler_classes)]
+        st.session_state.df = df
+    else:
+        df = st.session_state.df
+
+
+    st.markdown(f"**Total number of relevant clips**: {len(df)}", unsafe_allow_html=True)
+    # st.markdown("---")
+    
+    # # plot histogram
+    # arr = np.random.normal(1, 1, size=100)
+    # fig, ax = plt.subplots(1, 1, figsize=(1, 1))
+    # ax.hist(arr, bins=20)
+    # st.pyplot(fig)
+    
+    # plot st bar chart
+    st.markdown("**Distribution of classes**")
+    count_df = df["label"].value_counts().reset_index()
+    # sort by count
+    count_df = count_df.sort_values(by="label", ascending=False)
+    print(count_df)
+    st.bar_chart(count_df, width=300, height=0)
+    
+    reload_button = st.button("Reload")
+    NUM = 9
+    indices = np.random.randint(0, len(st.session_state.df), NUM)
+    if reload_button:
+        indices = np.random.randint(0, len(st.session_state.df), NUM)
+    
+    videoids = []
+    segments = []
+    labels = []
+    for index in indices:
+        sample = st.session_state.df.iloc[index].to_dict()
+        video_id = sample["video_id"]
+        videoids.append(video_id)
+        start_time = sample["start_seconds"]
+        end_time = sample["end_seconds"]
+        segments.append((start_time, end_time))
+        labels.append(sample["label"])
+    
+    # st.markdown(f"Showing Foley segments from a clip in movie: **{video_id}**")
+    # Create a grid of videos
+    grid = make_grid(3, 3)
+    per_video_width = 360
+    per_video_height = 240
+
+    # Add videos to the grid
+    for idx in range(0, min(len(segments), 9)):
+        i, j = idx // 3, idx % 3
+    
+        start, end = segments[idx]
+        duration = end - start
+        video_id = videoids[idx]
+
+        grid[i][j].caption(f"Segment duration: {duration}")
+        url = f"https://www.youtube.com/embed/{video_id}?start={int(start)}&end={int(end)}"
+        html_code = f"""
+        <iframe height="{per_video_height}" width="{per_video_width}" src="{url}" frameborder="0" allowfullscreen></iframe>
+        """
+        grid[i][j].markdown(html_code, unsafe_allow_html=True)
+        grid[i][j].caption(f"{labels[idx]}")
+    
+