scan.py

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())


def order_points(pts):
        
        rect = np.zeros((4, 2), dtype = "float32")

        s = pts.sum(axis = 1)
        rect[0] = pts[np.argmin(s)]
        rect[2] = pts[np.argmax(s)]

     
        diff = np.diff(pts, axis = 1)
        rect[1] = pts[np.argmin(diff)]
        rect[3] = pts[np.argmax(diff)]

        return rect


def four_point_transform(image, pts):

        rect = order_points(pts)
        (tl, tr, br, bl) = rect


        widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
        widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
        maxWidth = max(int(widthA), int(widthB))


        heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
        heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
        maxHeight = max(int(heightA), int(heightB))


        dst = np.array([
                [0, 0],
                [maxWidth - 1, 0],
                [maxWidth - 1, maxHeight - 1],
                [0, maxHeight - 1]], dtype = "float32")


        M = cv2.getPerspectiveTransform(rect, dst)
        warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))

        return warped

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)




cnts = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
cnts = sorted(cnts, key = cv2.contourArea, reverse = True)[:5]


for c in cnts:
       
        peri = cv2.arcLength(c, True)
        approx = cv2.approxPolyDP(c, 0.01 * peri, True)

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



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
cv2.imwrite("warped.jpg",warped)