skewing detection and correction using python with opencv

skewing.py

import numpy as np
import math
import cv2

def compute_skew(file_name):
    
    #load in grayscale:
    src = cv2.imread(file_name,0)
    height, width = src.shape[0:2]
    
    #invert the colors of our image:
    cv2.bitwise_not(src, src)
    
    #Hough transform:
    minLineLength = width/2.0
    maxLineGap = 20
    lines = cv2.HoughLinesP(src,1,np.pi/180,100,minLineLength,maxLineGap)
    
    #calculate the angle between each line and the horizontal line:
    angle = 0.0
    nb_lines = len(lines)
    
    
    for line in lines:
        angle += math.atan2(line[0][3]*1.0 - line[0][1]*1.0,line[0][2]*1.0 - line[0][0]*1.0);
    
    angle /= nb_lines*1.0
    
    return angle* 180.0 / np.pi


def deskew(file_name,angle):
    
    #load in grayscale:
    img = cv2.imread(file_name,0)
    
    #invert the colors of our image:
    cv2.bitwise_not(img, img)
    
    #compute the minimum bounding box:
    non_zero_pixels = cv2.findNonZero(img)
    center, wh, theta = cv2.minAreaRect(non_zero_pixels)
    
    root_mat = cv2.getRotationMatrix2D(center, angle, 1)
    rows, cols = img.shape
    rotated = cv2.warpAffine(img, root_mat, (cols, rows), flags=cv2.INTER_CUBIC)


    #Border removing:
    sizex = np.int0(wh[0])
    sizey = np.int0(wh[1])
    print theta
    if theta > -45 :
        temp = sizex
        sizex= sizey
        sizey= temp
    return cv2.getRectSubPix(rotated, (sizey,sizex), center)
  

file_path = 'put img path here'  
angel = compute_skew(file_path)
dst = deskew(file_path,angel)
cv2.imshow("Result",dst)
cv2.waitKey(0)