scan.py

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

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")

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

    if len(approx) == 4:
        screenCnt = approx

        print("STEP 2: Find contours of paper")

        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: Done")
        cv2.imwrite("scanned.jpg", imutils.resize(warped, height=650))