Spaces:
Runtime error
Runtime error
File size: 4,293 Bytes
ae8a7b7 43097e0 40280ea 57034fc 5cb1e06 3593298 43097e0 3593298 aef2e78 3593298 aef2e78 40280ea aef2e78 43097e0 40280ea be142ce c4e4c6d be142ce c4e4c6d be142ce 3593298 40280ea 3593298 ca29da8 3593298 ca29da8 3593298 edf8ac9 3593298 aef2e78 3593298 aef2e78 3593298 d0dce42 be142ce d0dce42 c4e4c6d be142ce c4e4c6d edf8ac9 aef2e78 43097e0 40280ea |
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 |
import streamlit as st
import cv2
import numpy as np
import datetime
import os
import time
import base64
import re
import glob
from camera_input_live import camera_input_live
# Set wide layout
st.set_page_config(layout="wide")
# Decorator for caching images
def get_image_count():
return {'count': 0}
# Function Definitions for Camera Feature
def save_image(image, image_count):
timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
filename = f"captured_image_{timestamp}_{image_count['count']}.png"
image_count['count'] += 1
bytes_data = image.getvalue()
cv2_img = cv2.imdecode(np.frombuffer(bytes_data, np.uint8), cv2.IMREAD_COLOR)
cv2.imwrite(filename, cv2_img)
return filename
def get_image_base64(image_path):
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode()
# Function Definitions for Chord Sheet Feature
def process_line(line):
if re.search(r'\b[A-G][#b]?m?\b', line):
line = re.sub(r'\b([A-G][#b]?m?)\b', r"<img src='\1.png' style='height:20px;'>", line)
return line
def process_sheet(sheet):
processed_lines = []
for line in sheet.split('\n'):
processed_line = process_line(line)
processed_lines.append(processed_line)
return '<br>'.join(processed_lines)
# Main Function
def main():
# Layout Configuration
col1, col2 = st.columns([2, 3])
# Camera Section
with col1:
#st.markdown("πΉ Real-Time Camera Stream π")
st.markdown("## ποΈβπ¨οΈ Eye on the World: Real-Time Camera Stream π")
#st.markdown("π΄ Live Feed: Real-Time Camera Stream π₯")
#st.markdown("π Instant Vision: Real-Time Camera Stream πΈ")
#st.markdown("π΅οΈββοΈ Spy Mode: Real-Time Camera Stream πΆοΈ")
#st.markdown("π Explore Now: Real-Time Camera Stream π")
#st.markdown("π‘ Illuminate: Real-Time Camera Stream π¦")
#st.markdown("π Views Unfold: Real-Time Camera Stream ποΈ")
#st.markdown("β¨ Magic Lens: Real-Time Camera Stream π")
snapshot_interval = st.slider("Snapshot Interval (seconds)", 1, 10, 5)
image_placeholder = st.empty()
if 'captured_images' not in st.session_state:
st.session_state['captured_images'] = []
if 'last_captured' not in st.session_state:
st.session_state['last_captured'] = time.time()
image = camera_input_live()
if image is not None:
image_placeholder.image(image)
if time.time() - st.session_state['last_captured'] > snapshot_interval:
image_count = get_image_count()
filename = save_image(image, image_count)
st.session_state['captured_images'].append(filename)
st.session_state['last_captured'] = time.time()
sidebar_html = "<div style='display:flex;flex-direction:column;'>"
for img_file in st.session_state['captured_images']:
image_base64 = get_image_base64(img_file)
sidebar_html += f"<img src='data:image/png;base64,{image_base64}' style='width:100px;'><br>"
sidebar_html += "</div>"
st.sidebar.markdown("## Captured Images")
st.sidebar.markdown(sidebar_html, unsafe_allow_html=True)
# JavaScript Timer
st.markdown(f"<script>setInterval(function() {{ document.getElementById('timer').innerHTML = new Date().toLocaleTimeString(); }}, 1000);</script><div>Current Time: <span id='timer'></span></div>", unsafe_allow_html=True)
# Chord Sheet Section
with col2:
st.markdown("## π¬ Action! Real-Time Camera Stream Highlights π½οΈ")
all_files = [f for f in glob.glob("*.png") if ' by ' in f]
selected_file = st.selectbox("Choose a Dataset:", all_files)
if selected_file:
with open(selected_file, 'r', encoding='utf-8') as file:
sheet = file.read()
st.markdown(process_sheet(sheet), unsafe_allow_html=True)
# Trigger a rerun only when the snapshot interval is reached
if 'last_captured' in st.session_state and time.time() - st.session_state['last_captured'] > snapshot_interval:
st.experimental_rerun()
if __name__ == "__main__":
main()
|