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

ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required = True,
	help = "Path to the image to be scanned")
args = vars(ap.parse_args())

image = cv2.imread(args["image"])
ratio = image.shape[0] / 500.0
orig = image.copy()
image = imutils.resize(image, height = 500)

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5,5),0)
edged = cv2.Canny(gray, 75, 200)

# print("STEP 1: Edge Detection")
# cv2.imshow("Image",image)
# cv2.imshow("Edged",edged)
# cv2.waitKey(0)
# cv2.destroyAllWindows()

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]

for c in cnts:
	# approximate the contour
	peri = cv2.arcLength(c, True)
	approx = cv2.approxPolyDP(c, 0.02 * peri, True)
	# if our approximated contour has four points, then we
	# can assume that we have found our screen
	if len(approx) == 4:
		screenCnt = approx
		break
# show the contour (outline) of the piece of paper
# print("STEP 2: Find contours of paper")
# cv2.drawContours(image, [screenCnt], -1, (0, 255, 0), 2)
# cv2.imshow("Outline", image)
# cv2.waitKey(0)
# cv2.destroyAllWindows()

warped = four_point_transform(orig, screenCnt.reshape(4, 2) * ratio)
# convert the warped image to grayscale, then threshold it
# to give it that 'black and white' paper effect
warped = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)
T = threshold_local(warped, 11, offset = 10, method = "gaussian")
warped = (warped > T).astype("uint8") * 255
# show the original and scanned images
print("STEP 3: Apply perspective transform")
cv2.imshow("Original", imutils.resize(orig, height = 650))
cv2.imshow("Scanned", imutils.resize(warped, height = 650))
cv2.waitKey(0)