EmanDiab

pts = screenCntList[0].reshape(4, 2)

# Define our rectangle 
rect = order_points(pts)

warped = four_point_transform(orig, screenCntList[0].reshape(4, 2) * ratio)
warped = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)
T = threshold_local(warped, 11, offset = 10, method = "gaussian")
warped = (warped > T).astype("uint8") * 255
cv2.imshow("Original", image)

EmanDiab

if not len(screenCntList) >= 2:  # there is no rectangle found
    print("No rectangle found")
elif scrWidths[0] != scrWidths[1]:  # mismatch in rect
    print("Mismatch in rectangle")

EmanDiab

# find contours in the edged image, keep only the largest ones, and initialize our screen contour
countours, hierarcy = cv2.findContours(edged, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
imageCopy = image.copy()
# approximate the contour

cnts = sorted(countours, key=cv2.contourArea, reverse=True)

screenCntList = []
scrWidths = []
for cnt in cnts:

EmanDiab

from skimage.filters import threshold_local
import numpy as np
import cv2
import imutils

image = cv2.imread('your photo name')
ratio = image.shape[0] / 500.0
orig = image.copy()
image = imutils.resize(image, height = 500)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)