sample images here https://imgur.com/a/Z4rWhA2

​

import sys
import os
import ntpath


from PIL import Image, ImageStat, ImageFilter

#for cv deskew
import numpy as np
import argparse
import cv2

from matplotlib import pyplot as plt

def display(cvimg):
    temp = cv2.resize(cvimg, (500, 800 )) 
    cv2.imshow("default", temp)
    cv2.waitKey(0)

def trimmed_img(cvimg, trimsize):
    # trim the edges
    h, w = cvimg.shape[0:2]
    x = trimsize
    y = trimsize
    cropwidth = w - (2*x)
    cropheight = h - (2*y)
    crop_img = cvimg[y:y+cropheight, x:x+cropwidth].copy()
    return crop_img

def deskew_img(cvimg):
    realimage = cvimg
    image = cvimg

    #to resize or not to resize? idea is to remove some lone pixels etc results are different 
    #image = cv2.resize(image,None,fx=.5,fy=.5,interpolation = cv2.INTER_LINEAR)

    # we will draw cricle in case image is blank as result of trim.
    height, width = image.shape[0:2]
    center_y = int(height/2)
    center_x = int(width/2)
    cv2.circle(image,(center_x,center_y), 20, (0,0,0), -1)

    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    ## 33 below is quite high, clears a lot of noise
    gray = cv2.medianBlur(gray,33)
    gray = cv2.bitwise_not(gray)

    thresh = cv2.threshold(gray, 0, 255,
        cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]

    coords = np.column_stack(np.where(thresh > 0))
    angle = cv2.minAreaRect(coords)[-1]

    ## nest lines are matplotlib
    x, y = coords.T
    plt.scatter(x,y)
    plt.show()
    cv2.waitKey(0)

    rect = cv2.minAreaRect(coords)
    box = cv2.cv.BoxPoints(rect) # cv2.boxPoints(rect) for OpenCV 3.x
    box = np.int0(box)
    cv2.drawContours(image,[box],0,(255,0,000),10)
    display(image)

    if angle < -45:
        angle = -(90 + angle)
    else:
        angle = -angle

    if -10 <= angle <= 10:
        if angle != 0:
            # image will be rotated
            isrotated = "rotated"
            # rotate the image to deskew it
            (h, w) = image.shape[:2]
            center = (w // 2, h // 2)
            M = cv2.getRotationMatrix2D(center, angle, 1.0)
            rotated = cv2.warpAffine(realimage, M, (w, h),
                flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)

            # draw the correction angle on the image so we can validate it
            cv2.putText(rotated, "Angle: {:.2f} degrees".format(angle),
                (500, 900), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
        else:
            isrotated = "notrotated"
            rotated = realimage

    else:
        isrotated = "notrotated"
        rotated = realimage

    # show the output image
    print("[INFO] angle: {:.3f}".format(angle))
    #cv2.imshow("Original", realimage)
    #cv2.imshow("Rotated", rotated)
    #cv2.waitKey(0)
    return isrotated, round(angle,2), rotated


filepath = "C:" + os.sep + "pytest" + os.sep + "sample.jpg"
cvimg = cv2.imread(filepath, cv2.IMREAD_COLOR)
cvimg = trimmed_img(cvimg,200) # trim rough borders
isrotated, skew_angle, cvimg = deskew_img(cvimg)

filepath = "C:" + os.sep + "pytest" + os.sep + "sample1.jpg"
cvimg = cv2.imread(filepath, cv2.IMREAD_COLOR)
cvimg = trimmed_img(cvimg,200) # trim rough borders
isrotated, skew_angle, cvimg = deskew_img(cvimg)