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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+gesture_recognizer.task filter=lfs diff=lfs merge=lfs -text

gesture_recognizer.task

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+version https://git-lfs.github.com/spec/v1
+oid sha256:13aa0bd33974391c1b8d31e5cf77e29df346f3633781c49d7d307bdf8f2de85b
+size 8764485

main.py

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+# without hand landmarking
+# import streamlit as st
+# import mediapipe as mp
+# import cv2
+# import os
+# import time
+# from queue import Queue
+
+# # Import necessary components from MediaPipe
+# BaseOptions = mp.tasks.BaseOptions
+# GestureRecognizer = mp.tasks.vision.GestureRecognizer
+# GestureRecognizerOptions = mp.tasks.vision.GestureRecognizerOptions
+# GestureRecognizerResult = mp.tasks.vision.GestureRecognizerResult
+# VisionRunningMode = mp.tasks.vision.RunningMode
+
+# # Correct path to the Gesture Recognizer model file
+# model_path = './model/model/gesture_recognizer.task'
+
+# # Check if file exists
+# if not os.path.exists(model_path):
+#     raise FileNotFoundError(f"Model file not found at {model_path}")
+
+# # Queue to share results between the callback and main thread
+# gesture_queue = Queue()
+
+# # Callback function to process results and add them to the queue
+# def print_result(result: GestureRecognizerResult, output_image: mp.Image, timestamp_ms: int):
+#     results = []  # Collect gesture results
+#     if result.gestures:
+#         for hand_gestures in result.gestures:
+#             for gesture in hand_gestures:
+#                 results.append(f"Gesture: **{gesture.category_name}**, Confidence: **{gesture.score:.2f}**")
+#     else:
+#         results.append("No gestures detected.")
+#     gesture_queue.put(results)
+
+# # Configure the Gesture Recognizer
+# options = GestureRecognizerOptions(
+#     base_options=BaseOptions(model_asset_path=model_path),
+#     running_mode=VisionRunningMode.LIVE_STREAM,
+#     result_callback=print_result
+# )
+
+# # Custom App Header
+# st.markdown("<h1 style='text-align: center; color: #4CAF50;'>Gesture Recognition App 🚀</h1>", unsafe_allow_html=True)
+# st.markdown("<p style='text-align: center; color: grey;'>Recognize hand gestures in real time with MediaPipe and Streamlit</p>", unsafe_allow_html=True)
+
+# # Sidebar for User Controls
+# st.sidebar.title("Control Panel")
+# run_app = st.sidebar.button("Start Gesture Recognition")
+# st.sidebar.write("Toggle the button above to start the app.")
+
+# # Placeholder for video feed and results
+# video_placeholder = st.empty()  # Placeholder for the video feed
+# result_placeholder = st.empty()  # Placeholder for gesture results
+
+# # Footer with branding
+# st.sidebar.markdown(
+#     "<hr><p style='text-align: center;'>Made with ❤️ using Streamlit</p>", unsafe_allow_html=True
+# )
+
+# if run_app:
+#     st.markdown("<h2 style='text-align: center;'>Processing Video Feed...</h2>", unsafe_allow_html=True)
+#     cap = cv2.VideoCapture(0)
+
+#     # Initialize a monotonically increasing timestamp
+#     start_time = time.time()
+
+#     with GestureRecognizer.create_from_options(options) as recognizer:
+#         while cap.isOpened():
+#             success, frame = cap.read()
+#             if not success:
+#                 st.warning("No frames available from the video feed.")
+#                 break
+
+#             # Compute the current timestamp in milliseconds
+#             current_time_ms = int((time.time() - start_time) * 1000)
+
+#             # Convert frame to a MediaPipe Image
+#             mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=frame)
+
+#             # Perform gesture recognition asynchronously
+#             recognizer.recognize_async(mp_image, current_time_ms)
+
+#             # Display the frame in Streamlit
+#             frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+#             video_placeholder.image(frame_rgb, channels="RGB", caption="Gesture Recognition", use_column_width=True)
+
+#             # Retrieve and display gesture results from the queue
+#             while not gesture_queue.empty():
+#                 results = gesture_queue.get()
+#                 result_placeholder.markdown(
+#                     "<h3 style='text-align: center; color: #FF5722;'>Detected Gestures</h3>",
+#                     unsafe_allow_html=True,
+#                 )
+#                 result_placeholder.markdown(
+#                     "<ul>" + "".join([f"<li>{result}</li>" for result in results]) + "</ul>",
+#                     unsafe_allow_html=True,
+#                 )
+
+#     cap.release()
+
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+# with hand landmark
+import streamlit as st
+import mediapipe as mp
+import cv2
+import os
+import time
+from queue import Queue
+
+# Initialize MediaPipe Hands for hand landmark detection
+mp_hands = mp.solutions.hands
+mp_drawing = mp.solutions.drawing_utils
+mp_drawing_styles = mp.solutions.drawing_styles
+
+# Import necessary components from MediaPipe Gesture Recognizer
+BaseOptions = mp.tasks.BaseOptions
+GestureRecognizer = mp.tasks.vision.GestureRecognizer
+GestureRecognizerOptions = mp.tasks.vision.GestureRecognizerOptions
+GestureRecognizerResult = mp.tasks.vision.GestureRecognizerResult
+VisionRunningMode = mp.tasks.vision.RunningMode
+
+# Correct path to the Gesture Recognizer model file
+model_path = './model/model/gesture_recognizer.task'
+
+# Check if file exists
+if not os.path.exists(model_path):
+    raise FileNotFoundError(f"Model file not found at {model_path}")
+
+# Queue to share results between the callback and main thread
+gesture_queue = Queue()
+
+# Callback function to process gesture results and add them to the queue
+def print_result(result: GestureRecognizerResult, output_image: mp.Image, timestamp_ms: int):
+    results = []  # Collect gesture results
+    if result.gestures:
+        for hand_gestures in result.gestures:
+            for gesture in hand_gestures:
+                results.append(f"{gesture.category_name} (Confidence: {gesture.score:.2f})")  # Include confidence
+    else:
+        results.append("No gestures detected.")
+    gesture_queue.put(results)
+
+# Configure the Gesture Recognizer
+options = GestureRecognizerOptions(
+    base_options=BaseOptions(model_asset_path=model_path),
+    running_mode=VisionRunningMode.LIVE_STREAM,
+    result_callback=print_result
+)
+
+# Initialize session state for saving gestures
+if "recognized_gestures" not in st.session_state:
+    st.session_state.recognized_gestures = []
+
+# Custom App Header
+st.markdown(
+    """
+    <style>
+    .header {text-align: center; color: #4CAF50; margin-top: -50px;}
+    .description {text-align: center; color: grey; font-size: 16px;}
+    </style>
+    <h1 class="header">Gesture & Hand Landmark Detection 🚀</h1>
+    <p class="description">Recognize and save hand gestures in real time with MediaPipe.</p>
+    """,
+    unsafe_allow_html=True,
+)
+
+# Sidebar for User Controls
+st.sidebar.title("Control Panel")
+st.sidebar.markdown("<hr>", unsafe_allow_html=True)
+
+max_num_hands = st.sidebar.slider("Max Number of Hands", 1, 2, 1)
+skip_frames = st.sidebar.slider("Process Every Nth Frame", 1, 10, 5)
+resolution = st.sidebar.selectbox("Frame Resolution", ["320x240", "640x480"], index=0)
+
+st.sidebar.markdown("<hr>", unsafe_allow_html=True)
+
+# Start and Stop buttons
+if "run_app" not in st.session_state:
+    st.session_state.run_app = False
+
+col1, col2 = st.sidebar.columns(2)
+if col1.button("▶ Start"):
+    st.session_state.run_app = True
+
+if col2.button("⏹ Stop"):
+    st.session_state.run_app = False
+
+# Clear history button
+if st.sidebar.button("🗑️ Clear History"):
+    st.session_state.recognized_gestures = []
+
+# Layout with columns: Live camera feed on the left, gesture log box on the right
+col_feed, col_log = st.columns([3, 2])
+
+with col_feed:
+    st.markdown("### Live Camera Feed")
+    video_placeholder = st.empty()  # Placeholder for the video feed
+
+with col_log:
+    st.markdown("### Gesture Log")
+    current_gesture_box = st.empty()  # Box to display the most recent gesture dynamically
+    st.markdown("### Gesture History")
+    gesture_history_box = st.empty()  # Box to display all recognized gestures dynamically
+
+# Footer with branding
+st.sidebar.markdown(
+    """
+    <style>
+    .footer {text-align: center; font-size: 12px; color: grey; margin-top: 20px;}
+    </style>
+    <p class="footer">Made by Marco Chen, William Taka, Rigoberto Ponce using Streamlit, MediaPipe & OpenCV</p>
+    """,
+    unsafe_allow_html=True,
+)
+
+if st.session_state.run_app:
+    cap = cv2.VideoCapture(0)
+
+    # Parse resolution
+    res_width, res_height = map(int, resolution.split("x"))
+
+    # Initialize a monotonically increasing timestamp
+    start_time = time.time()
+
+    with GestureRecognizer.create_from_options(options) as recognizer, mp_hands.Hands(
+        max_num_hands=max_num_hands,
+        model_complexity=0,  # Simplified model for performance
+        min_detection_confidence=0.3,
+        min_tracking_confidence=0.3
+    ) as hands:
+        frame_count = 0
+        while st.session_state.run_app and cap.isOpened():
+            success, frame = cap.read()
+            if not success:
+                st.warning("No frames available from the video feed.")
+                break
+
+            frame_count += 1
+            if frame_count % skip_frames != 0:
+                continue
+
+            # Flip and resize frame
+            frame = cv2.flip(frame, 1)
+            frame = cv2.resize(frame, (res_width, res_height))
+            frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+
+            # Perform hand landmark detection
+            hand_results = hands.process(frame_rgb)
+
+            # Perform gesture recognition
+            mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=frame)
+            current_time_ms = int((time.time() - start_time) * 1000)
+            recognizer.recognize_async(mp_image, current_time_ms)
+
+            # Draw hand landmarks on the frame
+            if hand_results.multi_hand_landmarks:
+                for hand_landmarks in hand_results.multi_hand_landmarks:
+                    mp_drawing.draw_landmarks(
+                        frame,
+                        hand_landmarks,
+                        mp_hands.HAND_CONNECTIONS,
+                        mp_drawing_styles.get_default_hand_landmarks_style(),
+                        mp_drawing_styles.get_default_hand_connections_style(),
+                    )
+
+            # Retrieve and display gesture results from the queue
+            while not gesture_queue.empty():
+                results = gesture_queue.get()
+                if results:
+                    new_gesture = results[-1]
+
+                    # Extract label and confidence safely
+                    if " (Confidence: " in new_gesture:
+                        label, confidence = new_gesture.split(" (Confidence: ")
+                        confidence = confidence.rstrip(")")  # Remove the trailing parenthesis
+                    else:
+                        label = new_gesture
+                        confidence = "N/A"
+
+                    # Add new gesture to history only if it's not already logged
+                    if label.isalpha() and new_gesture not in st.session_state.recognized_gestures:
+                        st.session_state.recognized_gestures.append(new_gesture)
+
+                        # Update current gesture display
+                        current_gesture_box.markdown(
+                            f"<h4 style='text-align: center; color: #4CAF50;'>Gesture: {label}<br>Confidence: {confidence}</h4>",
+                            unsafe_allow_html=True,
+                        )
+
+                        # Update gesture history display
+                        gesture_history_box.text_area(
+                            "Gesture History:",
+                            value="\n".join(reversed(st.session_state.recognized_gestures)),
+                            height=300,
+                            disabled=True,
+                        )
+
+            # Display the frame with hand landmarks and gesture results
+            video_placeholder.image(frame, channels="BGR", caption="Gesture & Hand Landmark Detection", use_column_width=True)
+
+    cap.release()
+
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