awacke1 commited on
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fbc3a6c
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Create app.py

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  1. app.py +127 -0
app.py ADDED
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+ import streamlit as st
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+ import cv2
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+ import numpy as np
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+ import datetime
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+ import os
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+ import time
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+ import base64
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+ import re
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+ import glob
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+ from camera_input_live import camera_input_live
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+ import face_recognition
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+
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+ # Set wide layout
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+ st.set_page_config(layout="wide")
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+
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+ # Decorator for caching images
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+ def get_image_count():
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+ return {'count': 0}
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+
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+ # Function Definitions for Camera Feature
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+ def save_image(image, image_count):
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+ timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
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+ filename = f"captured_image_{timestamp}_{image_count['count']}.png"
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+ image_count['count'] += 1
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+ bytes_data = image.getvalue()
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+ cv2_img = cv2.imdecode(np.frombuffer(bytes_data, np.uint8), cv2.IMREAD_COLOR)
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+ cv2.imwrite(filename, cv2_img)
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+ return filename
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+
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+ def get_image_base64(image_path):
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+ with open(image_path, "rb") as image_file:
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+ return base64.b64encode(image_file.read()).decode()
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+
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+ # Function Definitions for Chord Sheet Feature
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+ def process_line(line):
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+ if re.search(r'\b[A-G][#b]?m?\b', line):
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+ line = re.sub(r'\b([A-G][#b]?m?)\b', r"<img src='\1.png' style='height:20px;'>", line)
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+ return line
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+
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+ def process_sheet(sheet):
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+ processed_lines = []
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+ for line in sheet.split('\n'):
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+ processed_line = process_line(line)
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+ processed_lines.append(processed_line)
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+ return '<br>'.join(processed_lines)
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+
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+ # Load a sample image and learn how to recognize it
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+ known_image = face_recognition.load_image_file("known_face.jpg")
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+ known_encoding = face_recognition.face_encodings(known_image)[0]
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+
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+ # Main Function
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+ def main():
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+ # Layout Configuration
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+ col1, col2 = st.columns([2, 3])
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+
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+ # Camera Section
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+ with col1:
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+ st.markdown("✨ Magic Lens: Real-Time Camera Stream 🌈")
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+
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+ snapshot_interval = st.slider("Snapshot Interval (seconds)", 1, 10, 5)
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+ image_placeholder = st.empty()
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+
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+ if 'captured_images' not in st.session_state:
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+ st.session_state['captured_images'] = []
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+ if 'last_captured' not in st.session_state:
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+ st.session_state['last_captured'] = time.time()
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+
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+ image = camera_input_live()
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+ if image is not None:
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+ # Convert the image to RGB format for face_recognition
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+ rgb_image = cv2.cvtColor(np.array(image), cv2.COLOR_BGR2RGB)
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+
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+ # Detect faces in the image
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+ face_locations = face_recognition.face_locations(rgb_image)
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+ face_encodings = face_recognition.face_encodings(rgb_image, face_locations)
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+
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+ # Iterate over detected faces and compare with known face
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+ for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
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+ matches = face_recognition.compare_faces([known_encoding], face_encoding)
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+
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+ if True in matches:
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+ # If a match is found, draw a green rectangle and label
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+ cv2.rectangle(rgb_image, (left, top), (right, bottom), (0, 255, 0), 2)
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+ cv2.putText(rgb_image, "Known Face", (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
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+ else:
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+ # If no match, draw a red rectangle
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+ cv2.rectangle(rgb_image, (left, top), (right, bottom), (0, 0, 255), 2)
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+
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+ # Convert the RGB image back to BGR format for display
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+ bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)
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+ image_placeholder.image(bgr_image, channels="BGR")
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+
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+ if time.time() - st.session_state['last_captured'] > snapshot_interval:
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+ image_count = get_image_count()
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+ filename = save_image(image, image_count)
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+ st.session_state['captured_images'].append(filename)
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+ st.session_state['last_captured'] = time.time()
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+
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+ sidebar_html = "<div style='display:flex;flex-direction:column;'>"
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+ for img_file in st.session_state['captured_images']:
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+ image_base64 = get_image_base64(img_file)
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+ sidebar_html += f"<img src='data:image/png;base64,{image_base64}' style='width:100px;'><br>"
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+ sidebar_html += "</div>"
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+ st.sidebar.markdown("## Captured Images")
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+ st.sidebar.markdown(sidebar_html, unsafe_allow_html=True)
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+
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+ # JavaScript Timer
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+ 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)
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+
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+ # Chord Sheet Section
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+ with col2:
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+ st.markdown("## 🎬 Action! Real-Time Camera Stream Highlights 📽️")
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+
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+ all_files = [f for f in glob.glob("*.png") if ' by ' in f]
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+ selected_file = st.selectbox("Choose a Dataset:", all_files)
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+
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+ if selected_file:
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+ with open(selected_file, 'r', encoding='utf-8') as file:
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+ sheet = file.read()
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+ st.markdown(process_sheet(sheet), unsafe_allow_html=True)
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+
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+ # Trigger a rerun only when the snapshot interval is reached
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+ if 'last_captured' in st.session_state and time.time() - st.session_state['last_captured'] > snapshot_interval:
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+ st.experimental_rerun()
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+
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+ if __name__ == "__main__":
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+ main()