File size: 13,112 Bytes
8eab354 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 |
"""
Siamese network implementation for signature verification.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Tuple, Optional, Union
import numpy as np
from .feature_extractor import SignatureFeatureExtractor, CustomCNNFeatureExtractor
class SiameseNetwork(nn.Module):
"""
Siamese network for signature verification using twin feature extractors.
"""
def __init__(self,
feature_extractor: str = 'resnet18',
feature_dim: int = 512,
distance_metric: str = 'cosine',
pretrained: bool = True):
"""
Initialize the Siamese network.
Args:
feature_extractor: Type of feature extractor ('resnet18', 'resnet34', 'resnet50', 'custom')
feature_dim: Dimension of feature vectors
distance_metric: Distance metric ('cosine', 'euclidean', 'learned')
pretrained: Whether to use pretrained weights
"""
super(SiameseNetwork, self).__init__()
self.feature_dim = feature_dim
self.distance_metric = distance_metric
# Create feature extractor
if feature_extractor == 'custom':
self.feature_extractor = CustomCNNFeatureExtractor(feature_dim=feature_dim)
else:
self.feature_extractor = SignatureFeatureExtractor(
backbone=feature_extractor,
feature_dim=feature_dim,
pretrained=pretrained
)
# Distance metric layer
if distance_metric == 'learned':
self.distance_layer = nn.Sequential(
nn.Linear(feature_dim * 2, feature_dim),
nn.ReLU(inplace=True),
nn.Dropout(0.3),
nn.Linear(feature_dim, 1),
nn.Sigmoid()
)
else:
self.distance_layer = None
def forward(self,
signature1: torch.Tensor,
signature2: torch.Tensor) -> torch.Tensor:
"""
Forward pass through the Siamese network.
Args:
signature1: First signature batch (B, C, H, W)
signature2: Second signature batch (B, C, H, W)
Returns:
Similarity scores (B, 1) or distances (B, 1)
"""
# Extract features from both signatures
features1 = self.feature_extractor(signature1)
features2 = self.feature_extractor(signature2)
# Compute similarity/distance
if self.distance_metric == 'cosine':
similarity = F.cosine_similarity(features1, features2, dim=1)
return similarity.unsqueeze(1)
elif self.distance_metric == 'euclidean':
distance = F.pairwise_distance(features1, features2)
# Convert distance to similarity (inverse relationship)
similarity = 1 / (1 + distance)
return similarity.unsqueeze(1)
elif self.distance_metric == 'learned':
# Concatenate features and pass through learned distance layer
combined_features = torch.cat([features1, features2], dim=1)
similarity = self.distance_layer(combined_features)
return similarity
else:
raise ValueError(f"Unsupported distance metric: {self.distance_metric}")
def extract_features(self, signature: torch.Tensor) -> torch.Tensor:
"""
Extract features from a single signature.
Args:
signature: Signature batch (B, C, H, W)
Returns:
Feature vectors (B, feature_dim)
"""
return self.feature_extractor(signature)
def compute_similarity(self,
features1: torch.Tensor,
features2: torch.Tensor) -> torch.Tensor:
"""
Compute similarity between two feature vectors.
Args:
features1: First feature batch (B, feature_dim)
features2: Second feature batch (B, feature_dim)
Returns:
Similarity scores (B, 1)
"""
if self.distance_metric == 'cosine':
return F.cosine_similarity(features1, features2, dim=1).unsqueeze(1)
elif self.distance_metric == 'euclidean':
distance = F.pairwise_distance(features1, features2)
return (1 / (1 + distance)).unsqueeze(1)
elif self.distance_metric == 'learned':
combined_features = torch.cat([features1, features2], dim=1)
return self.distance_layer(combined_features)
else:
raise ValueError(f"Unsupported distance metric: {self.distance_metric}")
class TripletSiameseNetwork(nn.Module):
"""
Siamese network with triplet loss for signature verification.
"""
def __init__(self,
feature_extractor: str = 'resnet18',
feature_dim: int = 512,
margin: float = 1.0,
pretrained: bool = True):
"""
Initialize the triplet Siamese network.
Args:
feature_extractor: Type of feature extractor
feature_dim: Dimension of feature vectors
margin: Margin for triplet loss
pretrained: Whether to use pretrained weights
"""
super(TripletSiameseNetwork, self).__init__()
self.feature_dim = feature_dim
self.margin = margin
# Create feature extractor
if feature_extractor == 'custom':
self.feature_extractor = CustomCNNFeatureExtractor(feature_dim=feature_dim)
else:
self.feature_extractor = SignatureFeatureExtractor(
backbone=feature_extractor,
feature_dim=feature_dim,
pretrained=pretrained
)
def forward(self,
anchor: torch.Tensor,
positive: torch.Tensor,
negative: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Forward pass for triplet training.
Args:
anchor: Anchor signature batch (B, C, H, W)
positive: Positive signature batch (B, C, H, W)
negative: Negative signature batch (B, C, H, W)
Returns:
Tuple of (anchor_features, positive_features, negative_features)
"""
anchor_features = self.feature_extractor(anchor)
positive_features = self.feature_extractor(positive)
negative_features = self.feature_extractor(negative)
return anchor_features, positive_features, negative_features
def extract_features(self, signature: torch.Tensor) -> torch.Tensor:
"""
Extract features from a single signature.
Args:
signature: Signature batch (B, C, H, W)
Returns:
Feature vectors (B, feature_dim)
"""
return self.feature_extractor(signature)
class SignatureVerifier:
"""
High-level interface for signature verification.
"""
def __init__(self,
model_path: Optional[str] = None,
feature_extractor: str = 'resnet18',
feature_dim: int = 512,
distance_metric: str = 'cosine',
device: str = 'auto'):
"""
Initialize the signature verifier.
Args:
model_path: Path to saved model weights
feature_extractor: Type of feature extractor
feature_dim: Dimension of feature vectors
distance_metric: Distance metric for comparison
device: Device to run inference on ('auto', 'cpu', 'cuda')
"""
self.device = self._get_device(device)
self.feature_dim = feature_dim
# Initialize model
self.model = SiameseNetwork(
feature_extractor=feature_extractor,
feature_dim=feature_dim,
distance_metric=distance_metric
).to(self.device)
# Load weights if provided
if model_path:
self.load_model(model_path)
if hasattr(self.model, 'eval'):
self.model.eval()
def _get_device(self, device: str) -> torch.device:
"""Get the appropriate device for inference."""
if device == 'auto':
return torch.device('cuda' if torch.cuda.is_available() else 'cpu')
else:
return torch.device(device)
def load_model(self, model_path: str):
"""Load model weights from file."""
checkpoint = torch.load(model_path, map_location=self.device)
if 'model_state_dict' in checkpoint:
self.model.load_state_dict(checkpoint['model_state_dict'])
else:
self.model.load_state_dict(checkpoint)
def save_model(self, model_path: str):
"""Save model weights to file."""
torch.save({
'model_state_dict': self.model.state_dict(),
'feature_dim': self.feature_dim
}, model_path)
def verify_signatures(self,
signature1: Union[str, torch.Tensor, np.ndarray],
signature2: Union[str, torch.Tensor, np.ndarray],
threshold: float = 0.5) -> Tuple[float, bool]:
"""
Verify if two signatures belong to the same person.
Args:
signature1: First signature (file path, tensor or numpy array)
signature2: Second signature (file path, tensor or numpy array)
threshold: Similarity threshold for verification
Returns:
Tuple of (similarity_score, is_genuine)
"""
# Handle file paths
if isinstance(signature1, str):
from ..data.preprocessing import SignaturePreprocessor
preprocessor = SignaturePreprocessor()
signature1 = preprocessor.preprocess_image(signature1)
if isinstance(signature2, str):
from ..data.preprocessing import SignaturePreprocessor
preprocessor = SignaturePreprocessor()
signature2 = preprocessor.preprocess_image(signature2)
# Convert to tensors if needed
if isinstance(signature1, np.ndarray):
signature1 = torch.from_numpy(signature1).float()
if isinstance(signature2, np.ndarray):
signature2 = torch.from_numpy(signature2).float()
# Add batch dimension if needed
if signature1.dim() == 3:
signature1 = signature1.unsqueeze(0)
if signature2.dim() == 3:
signature2 = signature2.unsqueeze(0)
# Move to device
signature1 = signature1.to(self.device)
signature2 = signature2.to(self.device)
# Compute similarity
with torch.no_grad():
similarity = self.model(signature1, signature2)
similarity_score = similarity.item()
is_genuine = similarity_score >= threshold
return similarity_score, is_genuine
def extract_signature_features(self, signature: Union[str, torch.Tensor, np.ndarray]) -> np.ndarray:
"""
Extract features from a signature.
Args:
signature: Signature (file path, tensor or numpy array)
Returns:
Feature vector as numpy array
"""
# Handle file paths
if isinstance(signature, str):
from ..data.preprocessing import SignaturePreprocessor
preprocessor = SignaturePreprocessor()
signature = preprocessor.preprocess_image(signature)
# Convert to tensor if needed
if isinstance(signature, np.ndarray):
signature = torch.from_numpy(signature).float()
# Add batch dimension if needed
if signature.dim() == 3:
signature = signature.unsqueeze(0)
# Move to device
signature = signature.to(self.device)
# Extract features
with torch.no_grad():
features = self.model.extract_features(signature)
features = features.cpu().numpy()
return features
def batch_verify(self,
signature_pairs: list,
threshold: float = 0.5) -> list:
"""
Verify multiple signature pairs in batch.
Args:
signature_pairs: List of (signature1, signature2) tuples
threshold: Similarity threshold for verification
Returns:
List of (similarity_score, is_genuine) tuples
"""
results = []
for sig1, sig2 in signature_pairs:
similarity, is_genuine = self.verify_signatures(sig1, sig2, threshold)
results.append((similarity, is_genuine))
return results
|