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1f4e4ae | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 | import streamlit as st
import cv2
import numpy as np
from streamlit_webrtc import webrtc_streamer, VideoTransformerBase
from av import VideoFrame
import mediapipe as mp
import tensorflow as tf
# Page config
st.set_page_config(
page_title="Sign Language Digits Interpreter",
page_icon="🔢",
layout="wide"
)
# Load model and labels
@st.cache_resource
def load_model():
try:
interpreter = tf.lite.Interpreter(model_path="models/model.tflite")
interpreter.allocate_tensors()
with open("models/labels.txt", 'r') as f:
labels = [line.strip() for line in f.readlines()]
return interpreter, labels
except Exception as e:
st.error(f"Error loading model: {e}")
return None, []
interpreter, labels = load_model()
# MediaPipe setup
mp_hands = mp.solutions.hands
mp_drawing = mp.solutions.drawing_utils
hands = mp_hands.Hands(
static_image_mode=False,
max_num_hands=1,
min_detection_confidence=0.5,
min_tracking_confidence=0.5
)
def extract_hand_features(landmarks):
"""Extract 63 features from hand landmarks."""
if not landmarks:
return np.zeros(63)
# Convert landmarks to numpy array
landmarks_array = np.array([(lm.x, lm.y, lm.z) for lm in landmarks.landmark])
# Normalize relative to wrist (first landmark)
wrist = landmarks_array[0]
normalized = landmarks_array - wrist
# Scale by hand size
hand_size = np.linalg.norm(landmarks_array[12] - wrist)
if hand_size > 0:
normalized = normalized / hand_size
# Flatten to 63 features
features = normalized.flatten()
# Ensure exactly 63 features
if len(features) > 63:
features = features[:63]
elif len(features) < 63:
features = np.pad(features, (0, 63 - len(features)))
return features
def predict_digit(features):
"""Predict digit from features."""
if interpreter is None:
return "Model not loaded", 0.0
try:
# Prepare input
input_data = np.expand_dims(features, axis=0).astype(np.float32)
# Run inference
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()
# Get prediction
output = interpreter.get_tensor(output_details[0]['index'])
predicted_class = np.argmax(output[0])
confidence = float(np.max(output[0]))
return labels[predicted_class] if predicted_class < len(labels) else str(predicted_class), confidence
except Exception as e:
return f"Error: {str(e)}", 0.0
class DigitRecognizer(VideoTransformerBase):
def __init__(self):
self.prediction = "Show your hand"
self.confidence = 0.0
self.frame_count = 0
def recv(self, frame: VideoFrame) -> VideoFrame:
image = frame.to_ndarray(format="bgr24")
self.frame_count += 1
# Process every 5th frame to reduce computation
if self.frame_count % 5 == 0:
# Convert to RGB for MediaPipe
rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
results = hands.process(rgb_image)
if results.multi_hand_landmarks:
# Get first hand
hand_landmarks = results.multi_hand_landmarks[0]
# Draw landmarks
mp_drawing.draw_landmarks(
image, hand_landmarks, mp_hands.HAND_CONNECTIONS)
# Extract features and predict
features = extract_hand_features(hand_landmarks)
self.prediction, self.confidence = predict_digit(features)
else:
self.prediction = "No hand detected"
self.confidence = 0.0
# Add text overlay
color = (0, 255, 0) if self.confidence > 0.5 else (0, 0, 255)
cv2.putText(
image,
f"{self.prediction} ({self.confidence:.2f})",
(20, 40),
cv2.FONT_HERSHEY_SIMPLEX,
1.0,
color,
2
)
return VideoFrame.from_ndarray(image, format="bgr24")
# Main app
st.title("🔢 Sign Language Digits Interpreter")
st.markdown("Show hand signs for digits 0-9 to the camera!")
# Sidebar controls
st.sidebar.title("Controls")
confidence_threshold = st.sidebar.slider("Confidence Threshold", 0.0, 1.0, 0.5)
# Create columns
col1, col2 = st.columns([2, 1])
with col1:
# Webcam component
ctx = webrtc_streamer(
key="digit_recognition",
video_processor_factory=DigitRecognizer,
media_stream_constraints={"video": True, "audio": False},
async_processing=True,
)
with col2:
st.markdown("### Current Prediction")
if ctx.video_processor:
# Display current prediction
prediction_placeholder = st.empty()
confidence_placeholder = st.empty()
if hasattr(ctx.video_processor, 'prediction'):
if ctx.video_processor.confidence > confidence_threshold:
prediction_placeholder.markdown(f"# {ctx.video_processor.prediction}")
else:
prediction_placeholder.markdown("# -")
confidence_placeholder.progress(int(ctx.video_processor.confidence * 100))
st.text(f"Confidence: {ctx.video_processor.confidence:.2f}")
else:
st.warning("Turn on your webcam to start!")
# Instructions
st.markdown("---")
st.markdown("""
### Instructions:
1. Allow camera access when prompted
2. Show your hand clearly in the camera view
3. Make digit signs (0-9) with your hand
4. Hold the sign steady for best results
5. Adjust confidence threshold if needed
### Tips:
- Ensure good lighting
- Keep your hand in the center of the frame
- Make clear, distinct digit signs
- Wait a moment between different signs
""")
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