dewapper.py

#!/usr/bin/python
# -*- coding: utf-8 -*-
# -----------------------------------------
# author      : Ahmet Ozlu
# mail        : ahmetozlu93@gmail.com
# date        : 05.05.2019
# -----------------------------------------

from utils.transform import four_point_transform
from skimage.filters import threshold_local
import numpy as np
import argparse
import cv2
import imutils

def dewarp_book(image):
	# get input image ration to keep best output resolution quality
	ratio = image.shape[0] / 500.0
	# copy source image for filter operations
	orig = image.copy()
	# resize the input image	
	image = imutils.resize(image, height = 500)

	# convert rgb input image to grayscale
	gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
	gray = cv2.GaussianBlur(gray, (5, 5), 0)

	# sigma parameter is used for automatic Canny edge detection
	sigma=0.33

	# compute the median of the single channel pixel intensities
	v = np.median(image)

	# apply automatic Canny edge detection using the computed median
	lower = int(max(0, (1.0 - sigma) * v))
	upper = int(min(255, (1.0 + sigma) * v))
	edged = cv2.Canny(image, lower, upper)

	# perform dilate morphological filter to connect teh image pixel points
	'''kernel = np.ones((5,5),np.uint8)
	edged = cv2.dilate(edged,kernel,iterations = 1)'''

	# find contours
	cnts = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
	cnts = imutils.grab_contours(cnts)
	cnts = sorted(cnts, key = cv2.contourArea, reverse = True)[:5]
	# loop over the contours
	for c in cnts:
		peri = cv2.arcLength(c, True)
		approx = cv2.approxPolyDP(c, 0.02 * peri, True)

		if len(approx) == 4:
			screenCnt = approx
			break
	# apply the four point transform for book dewarping
	warped = four_point_transform(orig, screenCnt.reshape(4, 2) * ratio)
	return warped