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| import streamlit as st | |
| import cv2 | |
| import numpy as np | |
| import mediapipe as mp | |
| import joblib | |
| import pandas as pd | |
| from numpy.linalg import norm | |
| # Function to load the Random Forest model | |
| def load_model(): | |
| try: | |
| return joblib.load('best_random_forest_model.pkl') | |
| except Exception as e: | |
| st.error(f"Error loading model: {e}") | |
| return None | |
| # Load the model using the cached function | |
| model = load_model() | |
| # Ensure the model is loaded before proceeding | |
| if model is None: | |
| st.stop() | |
| # Initialize MediaPipe Hands | |
| def load_mediapipe_model(): | |
| mp_hands = mp.solutions.hands | |
| return mp_hands.Hands(static_image_mode=True, max_num_hands=1, min_detection_confidence=0.5) | |
| hands = load_mediapipe_model() | |
| mp_drawing = mp.solutions.drawing_utils | |
| # Function to normalize landmarks | |
| def normalize_landmarks(landmarks): | |
| # Center the landmarks | |
| center = np.mean(landmarks, axis=0) | |
| landmarks_centered = landmarks - center | |
| # Scale the landmarks to unit variance | |
| std_dev = np.std(landmarks_centered, axis=0) | |
| landmarks_normalized = landmarks_centered / std_dev | |
| # Replace NaN values with 0 (in case of division by zero) | |
| landmarks_normalized = np.nan_to_num(landmarks_normalized) | |
| return landmarks_normalized | |
| # Function to calculate angles between landmarks | |
| def calculate_angles(landmarks): | |
| angles = [] | |
| for i in range(20): | |
| for j in range(i + 1, 21): | |
| vector = landmarks[j] - landmarks[i] | |
| angle_x = np.arccos(np.clip(vector[0] / norm(vector), -1.0, 1.0)) | |
| angle_y = np.arccos(np.clip(vector[1] / norm(vector), -1.0, 1.0)) | |
| angles.extend([angle_x, angle_y]) | |
| return angles | |
| # Streamlit app | |
| st.title("ASL Recognition App") | |
| # Upload image using Streamlit's file uploader | |
| uploaded_file = st.file_uploader("Upload an image of an ASL sign", type=["jpg", "jpeg", "png"]) | |
| if uploaded_file is not None: | |
| try: | |
| # Read the image | |
| image = cv2.imdecode(np.frombuffer(uploaded_file.read(), np.uint8), cv2.IMREAD_COLOR) | |
| image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) | |
| # Process the image and find hand landmarks | |
| results = hands.process(image_rgb) | |
| if results.multi_hand_landmarks: | |
| for hand_landmarks in results.multi_hand_landmarks: | |
| # Draw landmarks on the image | |
| mp_drawing.draw_landmarks(image, hand_landmarks, mp.solutions.hands.HAND_CONNECTIONS) | |
| # Extract and normalize landmarks | |
| landmarks = np.array([[lm.x, lm.y] for lm in hand_landmarks.landmark]) | |
| landmarks_normalized = normalize_landmarks(landmarks) | |
| # Calculate angles using normalized landmarks | |
| angles = calculate_angles(landmarks_normalized) | |
| # Prepare input with feature names | |
| angle_columns = [f'angle_{i}' for i in range(len(angles))] | |
| angles_df = pd.DataFrame([angles], columns=angle_columns) | |
| # Predict the alphabet | |
| probabilities = model.predict_proba(angles_df)[0] | |
| top_indices = np.argsort(probabilities)[::-1][:5] | |
| top_probabilities = probabilities[top_indices] | |
| top_classes = model.classes_[top_indices] | |
| # Display the top 5 predictions | |
| st.write("Top 5 Predicted Alphabets:") | |
| for i in range(5): | |
| st.write(f"{top_classes[i]}: {top_probabilities[i]:.2f}") | |
| # Display the image with landmarks | |
| st.image(image, caption="Processed Image with Landmarks", use_column_width=True) | |
| else: | |
| st.write("No hands detected. Please try another image.") | |
| except Exception as e: | |
| st.error(f"Error processing image: {e}") |