技能备份 - 2026-04-15 (40个技能)
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# Preprocessing module
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#!/usr/bin/env python3
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"""
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Preprocessor - Image preprocessing utilities for OCR
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This module provides various image preprocessing techniques to improve OCR accuracy:
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- Denoising
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- Contrast enhancement
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- Binarization
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- Deskew
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- Resolution enhancement
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"""
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import cv2
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import numpy as np
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from pathlib import Path
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from typing import Optional
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def denoise_image(image: np.ndarray, h: int = 10) -> np.ndarray:
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"""
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Apply denoising to image.
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Args:
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image: Input image
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h: Denoising strength (higher = more denoising)
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Returns:
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Denoised image
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"""
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return cv2.fastNlMeansDenoisingColored(image, None, h, h, 7, 21)
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def enhance_contrast(image: np.ndarray) -> np.ndarray:
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"""
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Enhance image contrast using CLAHE.
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Args:
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image: Input image (grayscale or BGR)
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Returns:
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Contrast-enhanced image
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"""
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if len(image.shape) == 3:
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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else:
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gray = image.copy()
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clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
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return clahe.apply(gray)
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def binarize_image(image: np.ndarray, method: str = 'adaptive') -> np.ndarray:
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"""
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Convert image to binary (black & white).
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Args:
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image: Input image (grayscale)
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method: 'adaptive', 'otsu', or 'fixed'
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Returns:
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Binary image
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"""
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if method == 'adaptive':
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return cv2.adaptiveThreshold(
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image, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
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cv2.THRESH_BINARY, 11, 2
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)
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elif method == 'otsu':
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_, thresh = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
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return thresh
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else: # fixed
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_, thresh = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY)
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return thresh
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def deskew_image(image: np.ndarray) -> np.ndarray:
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"""
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Correct image skew.
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Args:
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image: Input image
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Returns:
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Deskewed image
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"""
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coords = np.column_stack(np.where(image > 0))
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angle = cv2.minAreaRect(coords)[-1]
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if angle < -45:
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angle = -(90 + angle)
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else:
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angle = -angle
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(h, w) = image.shape[:2]
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center = (w // 2, h // 2)
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M = cv2.getRotationMatrix2D(center, angle, 1.0)
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return cv2.warpAffine(image, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)
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def resize_image(image: np.ndarray, scale: float = 2.0) -> np.ndarray:
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"""
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Resize image for better OCR.
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Args:
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image: Input image
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scale: Scale factor (e.g., 2.0 = 2x larger)
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Returns:
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Resized image
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"""
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new_size = tuple(int(dim * scale) for dim in image.shape[:2][::-1])
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return cv2.resize(image, new_size, interpolation=cv2.INTER_CUBIC)
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def preprocess_pipeline(
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image: np.ndarray,
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denoise: bool = True,
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enhance: bool = True,
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binarize: bool = True,
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deskew: bool = False,
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resize_scale: Optional[float] = None
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) -> np.ndarray:
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"""
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Apply preprocessing pipeline.
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Args:
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image: Input image
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denoise: Apply denoising
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enhance: Enhance contrast
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binarize: Binarize image
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deskew: Correct skew
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resize_scale: Optional scale factor for resizing
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Returns:
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Preprocessed image
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"""
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output = image.copy()
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steps = []
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if denoise:
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output = denoise_image(output)
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steps.append('denoise')
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if enhance:
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output = enhance_contrast(output)
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steps.append('enhance')
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if deskew:
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output = deskew_image(output)
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steps.append('deskew')
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if resize_scale and resize_scale > 1.0:
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output = resize_image(output, resize_scale)
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steps.append(f'resize_{resize_scale}x')
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if binarize and len(output.shape) == 2:
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output = binarize_image(output)
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steps.append('binarize')
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return output
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def preprocess_file(
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input_path: str,
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output_path: Optional[str] = None,
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**kwargs
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) -> str:
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"""
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Preprocess an image file.
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Args:
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input_path: Input image file path
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output_path: Output file path (optional)
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**kwargs: Preprocessing parameters
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Returns:
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Output file path
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"""
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# Read image
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image = cv2.imread(input_path)
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if image is None:
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raise ValueError(f"Could not load image: {input_path}")
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# Preprocess
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processed = preprocess_pipeline(image, **kwargs)
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# Save
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if output_path is None:
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input_path = Path(input_path)
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output_path = str(input_path.parent / f"{input_path.stem}_processed{input_path.suffix}")
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cv2.imwrite(output_path, processed)
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return output_path
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def quick_preview(image: np.ndarray) -> None:
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"""
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Display image preview using OpenCV.
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Args:
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image: Image to display
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"""
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cv2.imshow('Preprocessed Image', image)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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if __name__ == '__main__':
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import argparse
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parser = argparse.ArgumentParser(description='Image preprocessing for OCR')
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parser.add_argument('input', help='Input image file')
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parser.add_argument('--output', '-o', help='Output file path')
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parser.add_argument('--denoise', action='store_true', default=True, help='Apply denoising')
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parser.add_argument('--no-denoise', action='store_false', dest='denoise')
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parser.add_argument('--enhance', action='store_true', default=True, help='Enhance contrast')
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parser.add_argument('--no-enhance', action='store_false', dest='enhance')
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parser.add_argument('--binarize', action='store_true', default=True, help='Binarize image')
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parser.add_argument('--no-binarize', action='store_false', dest='binarize')
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parser.add_argument('--resize', type=float, help='Resize scale factor (e.g., 2.0)')
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args = parser.parse_args()
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output = preprocess_file(
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args.input,
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args.output,
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denoise=args.denoise,
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enhance=args.enhance,
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binarize=args.binarize,
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resize_scale=args.resize
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)
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print(f"Preprocessed image saved to: {output}")
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