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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
import face_recognition
# 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)
# Load a sample image and learn how to recognize it
known_image = face_recognition.load_image_file("known_face.jpg")
known_encoding = face_recognition.face_encodings(known_image)[0]
# Main Function
def main():
# Layout Configuration
col1, col2 = st.columns([2, 3])
# Camera Section
with col1:
st.markdown("✨ Magic Lens: Real-Time Camera Stream 🌈")
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:
# Convert the image to RGB format for face_recognition
rgb_image = cv2.cvtColor(np.array(image), cv2.COLOR_BGR2RGB)
# Detect faces in the image
face_locations = face_recognition.face_locations(rgb_image)
face_encodings = face_recognition.face_encodings(rgb_image, face_locations)
# Iterate over detected faces and compare with known face
for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
matches = face_recognition.compare_faces([known_encoding], face_encoding)
if True in matches:
# If a match is found, draw a green rectangle and label
cv2.rectangle(rgb_image, (left, top), (right, bottom), (0, 255, 0), 2)
cv2.putText(rgb_image, "Known Face", (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
else:
# If no match, draw a red rectangle
cv2.rectangle(rgb_image, (left, top), (right, bottom), (0, 0, 255), 2)
# Convert the RGB image back to BGR format for display
bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)
image_placeholder.image(bgr_image, channels="BGR")
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("## 🎬 Action! Real-Time Camera Stream Highlights 📽️")
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() |