Update app.py

app.py

@@ -10,14 +10,11 @@ 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"
@@ -31,7 +28,6 @@ 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)
@@ -44,12 +40,9 @@ def process_sheet(sheet):
         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("✨ Magic Lens: Real-Time Camera Stream 🌈")
 
@@ -63,37 +56,29 @@ def main():
 
         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)
+            rgb_image = cv2.cvtColor(cv2.imdecode(np.frombuffer(image.getvalue(), np.uint8), cv2.IMREAD_COLOR), 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")
 
@@ -116,10 +101,8 @@ def main():
         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 📽️")
 
@@ -131,7 +114,6 @@ def main():
                 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()