Created
December 14, 2018 12:18
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Simple version of deskewing
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| import sys | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| from PIL import Image as im | |
| from scipy.ndimage import interpolation as inter | |
| input_file = sys.argv[1] | |
| img = im.open(input_file) | |
| # convert to binary | |
| wd, ht = img.size | |
| pix = np.array(img.convert('1').getdata(), np.uint8) | |
| bin_img = 1 - (pix.reshape((ht, wd)) / 255.0) | |
| plt.imshow(bin_img, cmap='gray') | |
| plt.savefig('binary.png') | |
| def find_score(arr, angle): | |
| data = inter.rotate(arr, angle, reshape=False, order=0) | |
| hist = np.sum(data, axis=1) | |
| score = np.sum((hist[1:] - hist[:-1]) ** 2) | |
| return hist, score | |
| delta = 1 | |
| limit = 5 | |
| angles = np.arange(-limit, limit+delta, delta) | |
| scores = [] | |
| for angle in angles: | |
| hist, score = find_score(bin_img, angle) | |
| scores.append(score) | |
| best_score = max(scores) | |
| best_angle = angles[scores.index(best_score)] | |
| print('Best angle: {}'.formate(best_angle)) | |
| # correct skew | |
| data = inter.rotate(bin_img, best_angle, reshape=False, order=0) | |
| img = im.fromarray((255 * data).astype("uint8")).convert("RGB") | |
| img.save('skew_corrected.png') |
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