from 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()

# We’ll assume that the largest contour in the image with exactly four 
# points is our piece of paper to be scanned.

cnts = cv2.findContours(edged.copy(),cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
print("using grab_contours",cnts)
cnts = sorted(cnts, key = cv2.contourArea, reverse=True)

for c in cnts:
    peri = cv2.arcLength(c, True)
    approx = cv2.approxPolyDP(c,0.02*peri,True)
    if len(approx)==4:
        screenCnt=approx
        print(approx)
        break
print("screenCnt",screenCnt)
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)
warped=cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)
T=threshold_local(warped,11,offset=10,method="gaussian")
warped = (warped>T).astype("uint8")*255

print("Step 3:Apply perspective transform")
cv2.imshow("Original", imutils.resize(orig,height=650))
cv2.imshow("Scanned", imutils.resize(warped,height=650))
cv2.imwrite("output1.jpeg",warped)
cv2.waitKey(0)



