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"", 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 '
'.join(processed_lines) # 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) # Check if the known face image exists if os.path.isfile("known_face.jpg"): known_image = face_recognition.load_image_file("known_face.jpg") known_encoding = face_recognition.face_encodings(known_image)[0] else: known_encoding = None # Iterate over detected faces and compare with known face for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings): if known_encoding is not None: 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) else: # If no known face is registered, draw a blue rectangle cv2.rectangle(rgb_image, (left, top), (right, bottom), (255, 0, 0), 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() if st.button("Register Known Face"): if image is not None: cv2.imwrite("known_face.jpg", np.array(image)) st.success("Known face registered successfully!") sidebar_html = "
" for img_file in st.session_state['captured_images']: image_base64 = get_image_base64(img_file) sidebar_html += f"
" sidebar_html += "
" st.sidebar.markdown("## Captured Images") st.sidebar.markdown(sidebar_html, unsafe_allow_html=True) # JavaScript Timer st.markdown(f"
Current Time:
", 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()