Created
March 1, 2020 06:40
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compute skew angle and deskew in python (opencv)
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import numpy as np | |
import math | |
import cv2 | |
def rotate_image(image, angle): | |
image_center = tuple(np.array(image.shape[1::-1]) / 2) | |
rot_mat = cv2.getRotationMatrix2D(image_center, angle, 1.0) | |
result = cv2.warpAffine(image, rot_mat, image.shape[1::-1], flags=cv2.INTER_LINEAR) | |
return result | |
def compute_skew(src_img): | |
if len(src_img.shape) == 3: | |
h, w, _ = src_img.shape | |
elif len(src_img.shape) == 2: | |
h, w = src_img.shape | |
else: | |
print('upsupported image type') | |
img = cv2.medianBlur(src_img, 3) | |
edges = cv2.Canny(img, threshold1 = 30, threshold2 = 100, apertureSize = 3, L2gradient = True) | |
lines = cv2.HoughLinesP(edges, 1, math.pi/180, 30, minLineLength=w / 4.0, maxLineGap=h/4.0) | |
angle = 0.0 | |
nlines = lines.size | |
#print(nlines) | |
cnt = 0 | |
for x1, y1, x2, y2 in lines[0]: | |
ang = np.arctan2(y2 - y1, x2 - x1) | |
#print(ang) | |
if math.fabs(ang) <= 30: # excluding extreme rotations | |
angle += ang | |
cnt += 1 | |
if cnt == 0: | |
return 0.0 | |
return (angle / cnt)*180/math.pi | |
def deskew(src_img): | |
return rotate_image(src_img, compute_skew(src_img)) | |
if __name__ == '__main__': | |
import cv2 | |
img = cv2.imread('test.png') | |
corrected_img = deskew(img) |
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