import mediapipe as mp import cv2 import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import math # Positional Encoding class PositionalEncoding(nn.Module): def __init__(self, d_model, max_len=100): super(PositionalEncoding, self).__init__() pe = torch.zeros(max_len, d_model) position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1) div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model)) pe[:, 0::2] = torch.sin(position * div_term) pe[:, 1::2] = torch.cos(position * div_term) pe = pe.unsqueeze(0) self.register_buffer('pe', pe) def forward(self, x): return x + self.pe[:, :x.size(1), :] # Model architecture class TransformerCNN_ASL(nn.Module): def __init__(self, input_dim=77, num_classes=250, d_model=512, nhead=8, num_layers=6, dim_feedforward=2048): super(TransformerCNN_ASL, self).__init__() self.input_dim = input_dim self.d_model = d_model # Input projection self.input_projection = nn.Linear(input_dim, d_model) self.input_norm = nn.LayerNorm(d_model) # Positional encoding self.pos_encoder = PositionalEncoding(d_model, max_len=100) # Transformer Encoder with Self-Attention encoder_layer = nn.TransformerEncoderLayer( d_model=d_model, nhead=nhead, dim_feedforward=dim_feedforward, dropout=0.1, activation='gelu', batch_first=True, norm_first=True ) self.transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_layers) # CNN Blocks for pattern detection self.conv1 = nn.Conv1d(d_model, 1024, kernel_size=3, padding=1) self.bn1 = nn.BatchNorm1d(1024) self.pool1 = nn.MaxPool1d(2) self.dropout1 = nn.Dropout(0.3) self.conv2 = nn.Conv1d(1024, 2048, kernel_size=3, padding=1) self.bn2 = nn.BatchNorm1d(2048) self.pool2 = nn.MaxPool1d(2) self.dropout2 = nn.Dropout(0.3) self.conv3 = nn.Conv1d(2048, 4096, kernel_size=3, padding=1) self.bn3 = nn.BatchNorm1d(4096) self.pool3 = nn.AdaptiveMaxPool1d(1) # Global pooling self.dropout3 = nn.Dropout(0.4) # Fully connected layers self.fc1 = nn.Linear(4096, 4096) self.bn_fc1 = nn.BatchNorm1d(4096) self.dropout_fc1 = nn.Dropout(0.5) self.fc2 = nn.Linear(4096, 2048) self.bn_fc2 = nn.BatchNorm1d(2048) self.dropout_fc2 = nn.Dropout(0.4) self.fc3 = nn.Linear(2048, 1024) self.bn_fc3 = nn.BatchNorm1d(1024) self.dropout_fc3 = nn.Dropout(0.3) self.fc4 = nn.Linear(1024, num_classes) def forward(self, x): batch_size = x.size(0) # Project to d_model x = self.input_projection(x) x = self.input_norm(x) x = x.unsqueeze(1) # Add positional encoding x = self.pos_encoder(x) # Transformer encoder with self-attention x = self.transformer_encoder(x) # Reshape for CNN x = x.permute(0, 2, 1) # CNN pattern detection x = F.gelu(self.bn1(self.conv1(x))) x = self.pool1(x) x = self.dropout1(x) x = F.gelu(self.bn2(self.conv2(x))) x = self.pool2(x) x = self.dropout2(x) x = F.gelu(self.bn3(self.conv3(x))) x = self.pool3(x) x = self.dropout3(x) # Flatten x = x.view(batch_size, -1) # Fully connected layers x = F.gelu(self.bn_fc1(self.fc1(x))) x = self.dropout_fc1(x) x = F.gelu(self.bn_fc2(self.fc2(x))) x = self.dropout_fc2(x) x = F.gelu(self.bn_fc3(self.fc3(x))) x = self.dropout_fc3(x) x = self.fc4(x) return x # Load the trained model print("Loading model...") checkpoint = torch.load('asl_kaggle_transformer.pth', map_location='cpu') label_encoder = checkpoint['label_encoder'] num_classes = checkpoint['num_classes'] input_dim = checkpoint['input_dim'] config = checkpoint['model_config'] model = TransformerCNN_ASL( input_dim=input_dim, num_classes=num_classes, d_model=config['d_model'], nhead=config['nhead'], num_layers=config['num_layers'], dim_feedforward=config['dim_feedforward'] ) model.load_state_dict(checkpoint['model_state_dict']) model.eval() total_params = sum(p.numel() for p in model.parameters()) print(f"Loaded Transformer+CNN model") print(f"Total parameters: {total_params:,}") print(f"Number of ASL signs: {num_classes}") print(f"Sample signs: {label_encoder.classes_[:10]}") # Setup MediaPipe BaseOptions = mp.tasks.BaseOptions HandLandmarker = mp.tasks.vision.HandLandmarker HandLandmarkerOptions = mp.tasks.vision.HandLandmarkerOptions VisionRunningMode = mp.tasks.vision.RunningMode options = HandLandmarkerOptions( base_options=BaseOptions(model_asset_path='hand_landmarker.task'), running_mode=VisionRunningMode.VIDEO, num_hands=1, min_hand_detection_confidence=0.5, min_hand_presence_confidence=0.5, min_tracking_confidence=0.5 ) landmarker = HandLandmarker.create_from_options(options) def get_optimized_features(hand_landmarks): """ Extract optimally normalized relative coordinates from MediaPipe landmarks Returns 77 features """ # Extract raw coordinates points = np.array([[lm.x, lm.y, lm.z] for lm in hand_landmarks], dtype=np.float32) # Step 1: Translation invariance - center on wrist wrist = points[0].copy() points_centered = points - wrist # Step 2: Scale invariance - normalize by palm size palm_size = np.linalg.norm(points[9] - points[0]) # wrist to middle finger base if palm_size < 1e-6: palm_size = 1.0 points_normalized = points_centered / palm_size # Step 3: Standardization mean = np.mean(points_normalized, axis=0) std = np.std(points_normalized, axis=0) + 1e-8 points_standardized = (points_normalized - mean) / std # Flatten base features (63 features) features = points_standardized.flatten() # Step 4: Derived features finger_tips = [4, 8, 12, 16, 20] # Thumb, Index, Middle, Ring, Pinky # Distances between consecutive fingertips (4 distances) tip_distances = [] for i in range(len(finger_tips) - 1): dist = np.linalg.norm(points_normalized[finger_tips[i]] - points_normalized[finger_tips[i + 1]]) tip_distances.append(dist) # Distance of each fingertip from palm center (5 distances) palm_center = np.mean(points_normalized[[0, 5, 9, 13, 17]], axis=0) tip_to_palm = [] for tip in finger_tips: dist = np.linalg.norm(points_normalized[tip] - palm_center) tip_to_palm.append(dist) # Finger curl indicators (5 curls) finger_curls = [] finger_bases = [1, 5, 9, 13, 17] for base, tip in zip(finger_bases, finger_tips): curl = np.linalg.norm(points_normalized[tip] - points_normalized[base]) finger_curls.append(curl) # Combine all features: 63 + 4 + 5 + 5 = 77 all_features = np.concatenate([ features, tip_distances, tip_to_palm, finger_curls ]) return all_features.astype(np.float32) # Initialize webcam cap = cv2.VideoCapture(0) if not cap.isOpened(): print("Error: Cannot open webcam") exit() # Set camera resolution for better performance cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280) cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 720) frame_count = 0 fps_counter = 0 fps_start_time = cv2.getTickCount() current_fps = 0 # Prediction smoothing buffer from collections import deque prediction_buffer = deque(maxlen=10) print("\n" + "=" * 60) print("ASL Recognition - Transformer+CNN Model") print("=" * 60) print("Controls:") print(" ESC - Exit") print(" SPACE - Clear prediction buffer") print(" 'h' - Toggle hand landmarks visibility") print("=" * 60 + "\n") show_landmarks = True with torch.no_grad(): while True: success, image = cap.read() if not success: print("Failed to read frame from webcam") break # Flip image horizontally for mirror view image = cv2.flip(image, 1) # Convert to MediaPipe format mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=image) # Detect hands results = landmarker.detect_for_video(mp_image, frame_count) frame_count += 1 # Calculate FPS fps_counter += 1 if fps_counter >= 30: fps_end_time = cv2.getTickCount() time_diff = (fps_end_time - fps_start_time) / cv2.getTickFrequency() current_fps = fps_counter / time_diff fps_counter = 0 fps_start_time = cv2.getTickCount() # Process hand landmarks if detected if results.hand_landmarks and len(results.hand_landmarks) > 0: hand_landmarks = results.hand_landmarks[0] # Draw hand landmarks if enabled if show_landmarks: # Draw connections connections = [ (0, 1), (1, 2), (2, 3), (3, 4), # Thumb (0, 5), (5, 6), (6, 7), (7, 8), # Index (0, 9), (9, 10), (10, 11), (11, 12), # Middle (0, 13), (13, 14), (14, 15), (15, 16), # Ring (0, 17), (17, 18), (18, 19), (19, 20), # Pinky (5, 9), (9, 13), (13, 17) # Palm ] # Get image dimensions h, w = image.shape[:2] # Draw connections for connection in connections: start_idx, end_idx = connection start = hand_landmarks[start_idx] end = hand_landmarks[end_idx] start_point = (int(start.x * w), int(start.y * h)) end_point = (int(end.x * w), int(end.y * h)) cv2.line(image, start_point, end_point, (0, 255, 0), 2) # Draw landmarks for i, landmark in enumerate(hand_landmarks): x = int(landmark.x * w) y = int(landmark.y * h) # Different colors for different parts if i == 0: # Wrist color = (255, 0, 0) radius = 8 elif i in [4, 8, 12, 16, 20]: # Fingertips color = (0, 0, 255) radius = 6 else: color = (0, 255, 0) radius = 4 cv2.circle(image, (x, y), radius, color, -1) cv2.circle(image, (x, y), radius + 2, (255, 255, 255), 1) # Extract features features = get_optimized_features(hand_landmarks) # Make prediction input_tensor = torch.FloatTensor(features).unsqueeze(0) output = model(input_tensor) probabilities = torch.softmax(output, dim=1)[0] # Get top prediction predicted_idx = torch.argmax(probabilities).item() confidence = probabilities[predicted_idx].item() predicted_sign = label_encoder.inverse_transform([predicted_idx])[0] # Add to buffer for smoothing if confidence > 0.3: # Only add if confident enough prediction_buffer.append(predicted_sign) # Get smoothed prediction (most common in buffer) if len(prediction_buffer) >= 5: from collections import Counter smoothed_sign = Counter(prediction_buffer).most_common(1)[0][0] else: smoothed_sign = predicted_sign # Get top 5 predictions top5_prob, top5_idx = torch.topk(probabilities, min(5, num_classes)) # Display prediction area (dark semi-transparent overlay) overlay = image.copy() cv2.rectangle(overlay, (10, 10), (500, 280), (0, 0, 0), -1) cv2.addWeighted(overlay, 0.7, image, 0.3, 0, image) # Display main prediction cv2.putText(image, f"Sign: {smoothed_sign}", (20, 50), cv2.FONT_HERSHEY_SIMPLEX, 1.5, (0, 255, 0), 3) cv2.putText(image, f"Confidence: {confidence:.1%}", (20, 90), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255), 2) # Display top 5 predictions cv2.putText(image, "Top 5:", (20, 130), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2) y_offset = 160 for i, (prob, idx) in enumerate(zip(top5_prob, top5_idx)): sign = label_encoder.inverse_transform([idx.item()])[0] prob_val = prob.item() # Color code by confidence if i == 0: color = (0, 255, 0) # Green for top elif prob_val > 0.1: color = (0, 255, 255) # Yellow for decent confidence else: color = (128, 128, 128) # Gray for low confidence cv2.putText(image, f"{i + 1}. {sign}: {prob_val:.1%}", (30, y_offset), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2) y_offset += 30 else: # No hand detected cv2.putText(image, "No hand detected", (20, 50), cv2.FONT_HERSHEY_SIMPLEX, 1.2, (0, 0, 255), 2) prediction_buffer.clear() # Display FPS and info info_y = image.shape[0] - 60 cv2.putText(image, f"FPS: {current_fps:.1f}", (20, info_y), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2) cv2.putText(image, f"Frame: {frame_count}", (20, info_y + 25), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2) # Display controls at bottom right controls_text = "ESC: Exit | SPACE: Clear | H: Landmarks" text_size = cv2.getTextSize(controls_text, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)[0] cv2.putText(image, controls_text, (image.shape[1] - text_size[0] - 10, image.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (200, 200, 200), 1) # Show the image cv2.imshow('ASL Recognition - Transformer+CNN', image) # Handle key presses key = cv2.waitKey(1) & 0xFF if key == 27: # ESC print("Exiting...") break elif key == 32: # SPACE prediction_buffer.clear() print("Prediction buffer cleared") elif key == ord('h') or key == ord('H'): show_landmarks = not show_landmarks print(f"Hand landmarks: {'ON' if show_landmarks else 'OFF'}") # Cleanup cap.release() cv2.destroyAllWindows() print("Recognition stopped.")