Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import time
+import random
+from PIL import Image
+
+# Additional imports for CoTracker3 Demo
+import torch
+import imageio.v3 as iio
+import matplotlib.pyplot as plt
+import colorsys
+import tempfile
+import cv2
+import os
+
+# Set up the page configuration
+st.set_page_config(
+ page_title="Streamlit Super Fun Guide 🎈",
+ page_icon="🎈",
+ layout="wide",
+ initial_sidebar_state="expanded",
+)
+
+# Add a header with an emoji
+st.title("Welcome to the Streamlit Super Fun Guide 🎉")
+
+# Add a limerick
+st.write("""
+There once was a coder so bright,
+Who coded with all of their might.
+With Streamlit's delight,
+They coded all night,
+And apps popped up left and right!
+""")
+
+# Add a wise quote
+st.write("> “Any fool can write code that a computer can understand. Good programmers write code that humans can understand.” – Martin Fowler")
+
+# Sidebar navigation
+st.sidebar.title("Navigation 🧭")
+options = st.sidebar.radio("Go to", [
+ "Introduction 🚀",
+ "Upload Media 📁",
+ "Animated Charts 📊",
+ "Caching Demo 🗃️",
+ "Query Parameters 🔍",
+ "Character Gallery 🐾",
+ "CoTracker3 Demo 🕵️‍♂️"
+])
+
+# Introduction Page
+if options == "Introduction 🚀":
+ st.header("Introduction 🚀")
+ st.write("Hello there 👋")
+ st.write("Thanks for stopping by! Let's embark on a fun journey to learn Streamlit together!")
+ st.write("Here's a joke to start with:")
+ st.write("> Why did the programmer quit his job? Because he didn't get arrays! 😂")
+ st.image("https://media.giphy.com/media/3oEjI6SIIHBdRxXI40/giphy.gif")
+
+# Upload Media Page
+elif options == "Upload Media 📁":
+ st.header("Upload Media Files 📁")
+ st.write("You can upload images, videos, and audio files here, and we'll display them in galleries!")
+ uploaded_files = st.file_uploader("Choose media files", accept_multiple_files=True, type=["png", "jpg", "jpeg", "mp4", "mp3", "wav"])
+
+ if uploaded_files:
+ images = []
+ videos = []
+ audios = []
+ for uploaded_file in uploaded_files:
+ if uploaded_file.type.startswith("image/"):
+ images.append(uploaded_file)
+ elif uploaded_file.type.startswith("video/"):
+ videos.append(uploaded_file)
+ elif uploaded_file.type.startswith("audio/"):
+ audios.append(uploaded_file)
+ 
+ if images:
+ st.subheader("Image Gallery 🖼️")
+ cols = st.columns(3)
+ for idx, img in enumerate(images):
+ with cols[idx % 3]:
+ st.image(img, use_column_width=True)
+ 
+ if videos:
+ st.subheader("Video Gallery 🎥")
+ for vid in videos:
+ st.video(vid)
+ 
+ if audios:
+ st.subheader("Audio Gallery 🎵")
+ for aud in audios:
+ st.audio(aud)
+
+# Animated Charts Page
+elif options == "Animated Charts 📊":
+ st.header("Animated Charts 📊")
+ st.write("Watch the data come alive with live-updating charts!")
+
+ # Live-updating line chart
+ progress_bar = st.progress(0)
+ status_text = st.empty()
+ chart = st.line_chart(np.random.randn(10, 2))
+
+ for i in range(100):
+ new_rows = np.random.randn(1, 2)
+ status_text.text(f"Iteration {i+1}")
+ chart.add_rows(new_rows)
+ progress_bar.progress((i + 1) % 100)
+ time.sleep(0.1)
+ st.success("Animation Complete! 🎉")
+
+# Caching Demo Page
+elif options == "Caching Demo 🗃️":
+ st.header("Caching Demo 🗃️")
+ st.write("This demonstrates how caching can speed up your app.")
+ 
+ @st.cache_data
+ def expensive_computation(n):
+ time.sleep(2) # Simulate a long computation
+ return np.random.randn(n)
+ 
+ st.write("Click the button to compute some data.")
+ if st.button("Compute Data 🔄"):
+ with st.spinner("Computing..."):
+ data = expensive_computation(1000)
+ st.success("Computation Complete! 🎉")
+ st.line_chart(data)
+ else:
+ st.write("Awaiting your command...")
+
+# Query Parameters Page
+elif options == "Query Parameters 🔍":
+ st.header("Query Parameters 🔍")
+ st.write("You can control the app via URL query parameters!")
+ st.write("Try adding `?name=YourName` to the URL and see what happens.")
+ 
+ name = st.experimental_get_query_params().get("name", ["Stranger"])[0]
+ st.write(f"Hello, {name}! 👋")
+ st.write("Change the 'name' parameter in the URL to personalize this message.")
+
+# Character Gallery Page
+elif options == "Character Gallery 🐾":
+ st.header("Character Gallery 🐾")
+ st.write("Meet our delightful characters!")
+ 
+ characters = [
+ {"name": "Leo", "emoji": "🦁"},
+ {"name": "Mia", "emoji": "🐱"},
+ {"name": "Max", "emoji": "🐶"},
+ {"name": "Zoe", "emoji": "🦊"},
+ {"name": "Sam", "emoji": "🐵"},
+ {"name": "Lily", "emoji": "🐰"},
+ {"name": "Oscar", "emoji": "🐼"},
+ {"name": "Ella", "emoji": "🐨"},
+ {"name": "Jack", "emoji": "🐸"},
+ {"name": "Nina", "emoji": "🐙"},
+ {"name": "Charlie", "emoji": "🐵"},
+ {"name": "Daisy", "emoji": "🐷"},
+ {"name": "Felix", "emoji": "🐧"},
+ {"name": "Grace", "emoji": "🐮"},
+ {"name": "Henry", "emoji": "🐴"},
+ ]
+ 
+ random.shuffle(characters)
+ cols = st.columns(5)
+ for idx, character in enumerate(characters):
+ with cols[idx % 5]:
+ st.write(f"{character['emoji']} **{character['name']}**")
+
+# CoTracker3 Demo Page
+elif options == "CoTracker3 Demo 🕵️‍♂️":
+ st.header("CoTracker3 Demo 🕵️‍♂️")
+ st.write("This demo showcases point tracking using [CoTracker3](https://cotracker3.github.io/).")
+ st.write("Upload a video or select one of the example videos, then click **Track Points** to see CoTracker3 in action!")
+
+ # Example videos
+ example_videos = {
+ "Apple": "https://github.com/facebookresearch/co-tracker/raw/refs/heads/main/assets/apple.mp4",
+ "Bear": "https://github.com/facebookresearch/co-tracker/raw/refs/heads/main/assets/bear.mp4",
+ "Paragliding Launch": "https://github.com/facebookresearch/co-tracker/raw/refs/heads/main/assets/paragliding-launch.mp4",
+ "Paragliding": "https://github.com/facebookresearch/co-tracker/raw/refs/heads/main/assets/paragliding.mp4",
+ "Cat": "https://github.com/facebookresearch/co-tracker/raw/refs/heads/main/assets/cat.mp4",
+ "Pillow": "https://github.com/facebookresearch/co-tracker/raw/refs/heads/main/assets/pillow.mp4",
+ "Teddy": "https://github.com/facebookresearch/co-tracker/raw/refs/heads/main/assets/teddy.mp4",
+ "Backpack": "https://github.com/facebookresearch/co-tracker/raw/refs/heads/main/assets/backpack.mp4",
+ }
+
+ # Video uploader
+ uploaded_video = st.file_uploader("Upload a video (mp4 format)", type=["mp4"])
+
+ # Option to select example videos
+ selected_example = st.selectbox("Or select an example video", ["None"] + list(example_videos.keys()))
+
+ # Tracking button
+ if st.button("Track Points"):
+ with st.spinner("Processing..."):
+ # Load the video
+ if uploaded_video is not None:
+ # Process the uploaded video
+ video_bytes = uploaded_video.read()
+ with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as tmp_file:
+ tmp_file.write(video_bytes)
+ tmp_video_path = tmp_file.name
+ frames = iio.imread(tmp_video_path, plugin="pyav")
+ elif selected_example != "None":
+ # Download and read the example video
+ video_url = example_videos[selected_example]
+ frames = iio.imread(video_url, plugin="pyav")
+ else:
+ st.warning("Please upload a video or select an example video.")
+ st.stop()
+ 
+ # Check if video is too long
+ FRAME_LIMIT = 300
+ if frames.shape[0] > FRAME_LIMIT:
+ frames = frames[:FRAME_LIMIT]
+ st.warning(f"Video is too long. Only the first {FRAME_LIMIT} frames will be processed.")
+ 
+ # Process with CoTracker3
+ device = 'cuda' if torch.cuda.is_available() else 'cpu'
+ grid_size = 10
+ video = torch.tensor(frames).permute(0, 3, 1, 2)[None].float().to(device) # B T C H W
+
+ # Run Offline CoTracker
+ cotracker = torch.hub.load("facebookresearch/co-tracker", "cotracker3_offline").to(device)
+ pred_tracks, pred_visibility = cotracker(video, grid_size=grid_size) # B T N 2, B T N 1
+
+ # Visualize the results
+ def get_colors(num_colors):
+ colors = []
+ for i in np.arange(0.0, 360.0, 360.0 / num_colors):
+ hue = i / 360.0
+ lightness = (50 + np.random.rand() * 10) / 100.0
+ saturation = (90 + np.random.rand() * 10) / 100.0
+ color = colorsys.hls_to_rgb(hue, lightness, saturation)
+ colors.append(
+ (int(color[0] * 255), int(color[1] * 255), int(color[2] * 255))
+ )
+ random.shuffle(colors)
+ return colors
+
+ def paint_point_track(frames, point_tracks, visibles, colormap=None):
+ num_points, num_frames = point_tracks.shape[0:2]
+ if colormap is None:
+ colormap = get_colors(num_colors=num_points)
+ height, width = frames.shape[1:3]
+ radius = int(round(min(height, width) * 0.015))
+ video = frames.copy()
+ for t in range(num_frames):
+ image = video[t]
+ for i in range(num_points):
+ x, y = point_tracks[i, t, :]
+ x = int(np.clip(x, 0, width - 1))
+ y = int(np.clip(y, 0, height - 1))
+ if visibles[i, t]:
+ cv2.circle(image, (x, y), radius, colormap[i], -1)
+ video[t] = image
+ return video
+
+ # Prepare data for visualization
+ pred_tracks = pred_tracks[0].cpu().numpy().transpose(1, 0, 2) # N x T x 2
+ pred_visibility = pred_visibility[0].cpu().numpy().transpose(1, 0) # N x T
+
+ output_frames = paint_point_track(frames, pred_tracks, pred_visibility)
+
+ # Save the output video
+ output_video_path = os.path.join(tempfile.gettempdir(), "output_video.mp4")
+ writer = imageio.get_writer(output_video_path, fps=25)
+ for frame in output_frames:
+ writer.append_data(frame)
+ writer.close()
+
+ # Display the output video
+ st.video(output_video_path)
+
+ st.success("Tracking Complete! 🎉")
+
+# Footer with self-deprecating humor
+st.write("If you find any bugs, remember they're just features in disguise! 🐞")