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"""
Signature preprocessing module for image normalization and preparation.
"""
import cv2
import numpy as np
import torch
from PIL import Image
from typing import Tuple, Union, Optional
import albumentations as A
from albumentations.pytorch import ToTensorV2
class SignaturePreprocessor:
"""
Handles preprocessing of signature images for the verification model.
"""
def __init__(self, target_size: Tuple[int, int] = (224, 224)):
"""
Initialize the preprocessor.
Args:
target_size: Target size for signature images (height, width)
"""
self.target_size = target_size
self.transform = A.Compose([
A.Resize(target_size[0], target_size[1]),
A.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
ToTensorV2()
])
def load_image(self, image_path: str) -> np.ndarray:
"""
Load image from file path.
Args:
image_path: Path to the image file
Returns:
Loaded image as numpy array
"""
try:
image = cv2.imread(image_path)
if image is None:
raise ValueError(f"Could not load image from {image_path}")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
return image
except Exception as e:
raise ValueError(f"Error loading image {image_path}: {str(e)}")
def preprocess_image(self, image: Union[str, np.ndarray, Image.Image]) -> torch.Tensor:
"""
Preprocess a signature image for model input.
Args:
image: Image as file path, numpy array, or PIL Image
Returns:
Preprocessed image as torch tensor
"""
# Convert to numpy array if needed
if isinstance(image, str):
image = self.load_image(image)
elif isinstance(image, Image.Image):
image = np.array(image)
elif isinstance(image, torch.Tensor):
image = image.numpy()
# Ensure image is in RGB format
if len(image.shape) == 3 and image.shape[2] == 3:
pass # Already RGB
elif len(image.shape) == 2:
image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
else:
raise ValueError(f"Unsupported image format with shape: {image.shape}")
# Apply transformations
transformed = self.transform(image=image)
return transformed['image']
def enhance_signature(self, image: np.ndarray) -> np.ndarray:
"""
Enhance signature image quality.
Args:
image: Input signature image
Returns:
Enhanced signature image
"""
# Convert to grayscale for processing
if len(image.shape) == 3:
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
else:
gray = image.copy()
# Apply adaptive thresholding to get binary image
binary = cv2.adaptiveThreshold(
gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2
)
# Morphological operations to clean up the signature
kernel = np.ones((2, 2), np.uint8)
cleaned = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel)
cleaned = cv2.morphologyEx(cleaned, cv2.MORPH_OPEN, kernel)
# Convert back to RGB
if len(image.shape) == 3:
enhanced = cv2.cvtColor(cleaned, cv2.COLOR_GRAY2RGB)
else:
enhanced = cleaned
return enhanced
def normalize_signature(self, image: np.ndarray) -> np.ndarray:
"""
Normalize signature image for consistent processing.
Args:
image: Input signature image
Returns:
Normalized signature image
"""
# Convert to grayscale
if len(image.shape) == 3:
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
else:
gray = image.copy()
# Find signature contours
contours, _ = cv2.findContours(gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if not contours:
return image
# Get bounding box of the signature
x, y, w, h = cv2.boundingRect(max(contours, key=cv2.contourArea))
# Crop to signature area with some padding
padding = 10
x1 = max(0, x - padding)
y1 = max(0, y - padding)
x2 = min(image.shape[1], x + w + padding)
y2 = min(image.shape[0], y + h + padding)
cropped = image[y1:y2, x1:x2]
# Resize to target size while maintaining aspect ratio
h_orig, w_orig = cropped.shape[:2]
aspect_ratio = w_orig / h_orig
if aspect_ratio > 1:
new_w = self.target_size[1]
new_h = int(new_w / aspect_ratio)
else:
new_h = self.target_size[0]
new_w = int(new_h * aspect_ratio)
resized = cv2.resize(cropped, (new_w, new_h))
# Create canvas with target size
canvas = np.ones((self.target_size[0], self.target_size[1], 3), dtype=np.uint8) * 255
# Center the signature on the canvas
y_offset = (self.target_size[0] - new_h) // 2
x_offset = (self.target_size[1] - new_w) // 2
canvas[y_offset:y_offset+new_h, x_offset:x_offset+new_w] = resized
return canvas
def preprocess_batch(self, images: list) -> torch.Tensor:
"""
Preprocess a batch of signature images.
Args:
images: List of images to preprocess
Returns:
Batch of preprocessed images as torch tensor
"""
processed_images = []
for image in images:
processed = self.preprocess_image(image)
processed_images.append(processed)
return torch.stack(processed_images)
class SignatureAugmentation:
"""
Data augmentation for signature images during training.
"""
def __init__(self, target_size: Tuple[int, int] = (224, 224)):
"""
Initialize augmentation pipeline.
Args:
target_size: Target size for signature images
"""
self.target_size = target_size
# Training augmentations
self.train_transform = A.Compose([
A.Resize(target_size[0], target_size[1]),
A.HorizontalFlip(p=0.3),
A.Rotate(limit=15, p=0.5),
A.RandomBrightnessContrast(
brightness_limit=0.2,
contrast_limit=0.2,
p=0.5
),
A.GaussNoise(var_limit=(10.0, 50.0), p=0.3),
A.ElasticTransform(
alpha=1,
sigma=50,
alpha_affine=50,
p=0.3
),
A.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
ToTensorV2()
])
# Validation augmentations (minimal)
self.val_transform = A.Compose([
A.Resize(target_size[0], target_size[1]),
A.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
ToTensorV2()
])
def augment(self, image: np.ndarray, is_training: bool = True) -> torch.Tensor:
"""
Apply augmentation to signature image.
Args:
image: Input signature image
is_training: Whether to apply training augmentations
Returns:
Augmented image as torch tensor
"""
transform = self.train_transform if is_training else self.val_transform
transformed = transform(image=image)
return transformed['image']
def augment_batch(self, images: list, is_training: bool = True) -> torch.Tensor:
"""
Apply augmentation to a batch of signature images.
Args:
images: List of images to augment
is_training: Whether to apply training augmentations
Returns:
Batch of augmented images as torch tensor
"""
augmented_images = []
for image in images:
augmented = self.augment(image, is_training)
augmented_images.append(augmented)
return torch.stack(augmented_images)
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