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# src/preprocessing.py
import cv2
import numpy as np
from pathlib import Path
def load_image(image_path: str) -> np.ndarray:
if not Path(image_path).exists():
raise FileNotFoundError(f"Image not found : {image_path}")
image = cv2.imread(image_path)
if image is None:
raise ValueError(f"Could not load image: {image_path}")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
return image
def convert_to_grayscale(image: np.ndarray) -> np.ndarray:
if image is None:
raise ValueError(f"Image is None, cannot convert to grayscale")
if len(image.shape) ==2:
return image
return cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
def remove_noise(image: np.ndarray, kernel_size: int = 3) -> np.ndarray:
if image is None:
raise ValueError(f"Image is None, cannot remove noise")
if kernel_size <= 0:
raise ValueError("Kernel size must be positive")
if kernel_size % 2 == 0:
raise ValueError("Kernel size must be odd")
denoised_image = cv2.GaussianBlur(image, (kernel_size, kernel_size), 0)
return denoised_image
def binarize(image: np.ndarray, method: str = 'adaptive', block_size: int=11, C: int=2) -> np.ndarray:
if image is None:
raise ValueError(f"Image is None, cannot binarize")
if image.ndim != 2:
raise ValueError("Input image must be grayscale for binarization")
if method == 'simple':
_, binary_image = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY)
elif method == 'adaptive':
binary_image = cv2.adaptiveThreshold(image, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY, block_size, C)
else:
raise ValueError(f"Unknown binarization method: {method}")
return binary_image
def deskew(image):
pass
def preprocess_pipeline(image: np.ndarray,
steps: list = ['grayscale', 'denoise', 'binarize'],
denoise_kernel: int = 3,
binarize_method: str = 'adaptive',
binarize_block_size: int = 11,
binarize_C: int = 2) -> np.ndarray:
if image is None:
raise ValueError("Input image is None")
processed = image
for step in steps:
if step == 'grayscale':
processed = convert_to_grayscale(processed)
elif step == 'denoise':
processed = remove_noise(processed, kernel_size=denoise_kernel)
elif step == 'binarize':
processed = binarize(processed,
method=binarize_method,
block_size=binarize_block_size,
C=binarize_C)
else:
raise ValueError(f"Unknown preprocessing step: {step}")
return processed
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