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