| |
|
|
| import cv2 |
| import numpy as np |
| import imutils |
| from PIL import Image, ImageEnhance |
| from scipy.ndimage import interpolation as inter |
|
|
| def sharpen(img: np.ndarray, factor: float): |
| gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) |
| img_pil = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) |
| enhancer = ImageEnhance.Sharpness(img_pil).enhance(factor) |
| if gray.std() < 30: |
| enhancer = ImageEnhance.Contrast(enhancer).enhance(factor) |
| |
| |
| return cv2.cvtColor(np.array(enhancer), cv2.COLOR_RGB2BGR) |
|
|
| def correct_skew(image: np.ndarray, delta=1, limit=5): |
| def determine_score(arr, angle): |
| data = inter.rotate(arr, angle, reshape=False, order=0) |
| histogram = np.sum(data, axis=1, dtype=float) |
| score = np.sum((histogram[1:] - histogram[:-1]) ** 2, dtype=float) |
| return histogram, score |
|
|
| img_new = sharpen(image, 100) |
| img_new = imutils.resize(img_new, height=680) |
| gray = cv2.cvtColor(img_new, cv2.COLOR_BGR2GRAY) |
| thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1] |
|
|
| scores = [] |
| angles = np.arange(-limit, limit + delta, delta) |
| for angle in angles: |
| histogram, score = determine_score(thresh, angle) |
| scores.append(score) |
|
|
| best_angle = angles[scores.index(max(scores))] |
| |
| if best_angle == 0: |
| return image |
|
|
| (h, w) = image.shape[:2] |
| center = (w // 2, h // 2) |
| M = cv2.getRotationMatrix2D(center, best_angle, 1.0) |
| corrected = cv2.warpAffine(image, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE) |
|
|
| return corrected |
