Create app.py

app.py

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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()