pip install OpenCV-Python imutils scikit-image NumPy

import cv2
import imutils
from skimage.filters import threshold_local
from transform import perspective_transform

# Passing the image path
original_img = cv2.imread('sample.jpg')
copy = original_img.copy()

# The resized height in hundreds
ratio = original_img.shape[0] / 500.0
img_resize = imutils.resize(original_img, height=500)

# Displaying output
cv2.imshow('Resized image', img_resize)

# Waiting for the user to press any key
cv2.waitKey(0)

gray_image = cv2.cvtColor(img_resize, cv2.COLOR_BGR2GRAY)
cv2.imshow('Grayed Image', gray_image)
cv2.waitKey(0)

blurred_image = cv2.GaussianBlur(gray_image, (5, 5), 0)
edged_img = cv2.Canny(blurred_image, 75, 200)
cv2.imshow('Image edges', edged_img)
cv2.waitKey(0)

cnts, _ = cv2.findContours(edged_img, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:5]

for c in cnts:
    peri = cv2.arcLength(c, True)
    approx = cv2.approxPolyDP(c, 0.02 * peri, True)

    if len(approx) == 4:
        doc = approx
        break

p = []

for d in doc:
    tuple_point = tuple(d[0])
    cv2.circle(img_resize, tuple_point, 3, (0, 0, 255), 4)
    p.append(tuple_point)

cv2.imshow('Circled corner points', img_resize)
cv2.waitKey(0)

warped_image = perspective_transform(copy, doc.reshape(4, 2) * ratio)
warped_image = cv2.cvtColor(warped_image, cv2.COLOR_BGR2GRAY)
cv2.imshow("Warped Image", imutils.resize(warped_image, height=650))
cv2.waitKey(0)

import numpy as np
import cv2

def order_points(pts):
   # initializing the list of coordinates to be ordered
   rect = np.zeros((4, 2), dtype = "float32")

   s = pts.sum(axis = 1)

   # top-left point will have the smallest sum
   rect[0] = pts[np.argmin(s)]

   # bottom-right point will have the largest sum
   rect[2] = pts[np.argmax(s)]

   '''computing the difference between the points, the
   top-right point will have the smallest difference,
   whereas the bottom-left will have the largest difference'''
   diff = np.diff(pts, axis = 1)
   rect[1] = pts[np.argmin(diff)]
   rect[3] = pts[np.argmax(diff)]

   # returns ordered coordinates
   return rect

def perspective_transform(image, pts):
   # unpack the ordered coordinates individually
   rect = order_points(pts)
   (tl, tr, br, bl) = rect

   '''compute the width of the new image, which will be the
   maximum distance between bottom-right and bottom-left
   x-coordinates or the top-right and top-left x-coordinates'''
   widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
   widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
   maxWidth = max(int(widthA), int(widthB))

   '''compute the height of the new image, which will be the
   maximum distance between the top-left and bottom-left y-coordinates'''
   heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
   heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
   maxHeight = max(int(heightA), int(heightB))

   '''construct the set of destination points to obtain an overhead shot'''
   dst = np.array([
      [0, 0],
      [maxWidth - 1, 0],
      [maxWidth - 1, maxHeight - 1],
      [0, maxHeight - 1]], dtype = "float32")

   # compute the perspective transform matrix
   transform_matrix = cv2.getPerspectiveTransform(rect, dst)

   # Apply the transform matrix
   warped = cv2.warpPerspective(image, transform_matrix, (maxWidth, maxHeight))

   # return the warped image
   return warped

T = threshold_local(warped_image, 11, offset=10, method="gaussian")
warped = (warped_image > T).astype("uint8") * 255
cv2.imwrite('./'+'scan'+'.png',warped)

cv2.imshow("Final Scanned image", imutils.resize(warped, height=650))
cv2.waitKey(0)
cv2.destroyAllWindows()