Code to implemet a basic document scanner

docScanner.py

import cv2
from scipy.spatial import distance as dist
import numpy as np

def resize(image, width=None, height=None, inter=cv2.INTER_AREA):
    dim = None
    (h, w) = image.shape[:2]

    # check to see if the width is None
    if width is None:
        r = height / float(h) #ratio
        dim = (int(w * r), height)

    # otherwise, the height is None
    else:
        r = width / float(w) #ratio
        dim = (width, int(h * r))

    # resize the image
    resized = cv2.resize(image, dim, interpolation=inter)

    # return the resized image
    return resized

def order_points(pts):
    # sort the points based on their x-coordinates
    xSorted = pts[np.argsort(pts[:, 0]), :]

    # grab the left-most and right-most points from the sorted
    # x-roodinate points
    leftMost = xSorted[:2, :]
    rightMost = xSorted[2:, :]

    # now, sort the left-most coordinates according to their
    # y-coordinates so we can grab the top-left and bottom-left
    # points, respectively
    leftMost = leftMost[np.argsort(leftMost[:, 1]), :]
    (tl, bl) = leftMost

    # now that we have the top-left coordinate, use it as an
    # anchor to calculate the Euclidean distance between the
    # top-left and right-most points; by the Pythagorean
    # theorem, the point with the largest distance will be
    # our bottom-right point
    D = dist.cdist(tl[np.newaxis], rightMost, "euclidean")[0]
    (br, tr) = rightMost[np.argsort(D)[::-1], :]

    # return the coordinates in top-left, top-right,
    # bottom-right, and bottom-left order
    return np.array([tl, tr, br, bl], dtype="float32")

def four_point_transform(image, pts):
    # obtain a consistent order of the points and unpack them
    # individually
    rect = order_points(pts)
    (tl, tr, br, bl) = rect

    # compute the width of the new image, which will be the
    # maximum distance between bottom-right and bottom-left
    # x-coordiates or the top-right and top-left x-coordinates
    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))

    # compute the height of the new image, which will be the
    # maximum distance between the top-right and bottom-right
    # y-coordinates or the top-left and bottom-left y-coordinates
    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))

    # now that we have the dimensions of the new image, construct
    # the set of destination points to obtain a "birds eye view",
    # (i.e. top-down view) of the image, again specifying points
    # in the top-left, top-right, bottom-right, and bottom-left
    # order
    dst = np.array([
        [0, 0],
        [maxWidth - 1, 0],
        [maxWidth - 1, maxHeight - 1],
        [0, maxHeight - 1]], dtype="float32")

    # compute the perspective transform matrix and then apply it
    M = cv2.getPerspectiveTransform(rect, dst)
    warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))

    # return the warped image
    return warped

def auto_canny(image, 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)

    # return the edged image
    return edged

# load image
image = cv2.imread('emp2.jpg')
orig = image.copy()
ratio = image.shape[0]/500.0
# resize image
resized = resize(image, height = 500)
# grayscale
gray = cv2.cvtColor(resized, cv2.COLOR_BGR2GRAY)
# Filter noise
blur = cv2.GaussianBlur(gray, (5,5), 0)
# Edge detect
edges = cv2.Canny(blur, 100, 250)
# find contours
contoured, contours, hierarchy = cv2.findContours(edges.copy(),
                                            cv2.RETR_TREE,
                                            cv2.CHAIN_APPROX_SIMPLE)
contours = sorted(contours, key = cv2.contourArea, reverse = True)[:5]
 
# loop over the contours
for c in contours:
	# 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
draw = cv2.drawContours(resized, contours, -1, (0,255,0), 3)

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)
##th = cv2.adaptiveThreshold(warped,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
##          cv2.THRESH_BINARY,11,2)
##warped = (warped < th).astype("uint8") * 255


#display images
##cv2.imshow('image(resized)', resized)
##cv2.imshow('grayed', gray)
##cv2.imshow('blurred', blur)
#cv2.imshow('edged', edges)
cv2.imshow('contours', contoured)
cv2.imshow('draw', draw)
cv2.imshow("Original", resize(orig, height = 450))
cv2.imshow("Scanned", resize(warped, height = 450))

# save image
cv2.imwrite('original.jpg', resize(orig, height=500))
cv2.imwrite('scan.jpg', resize(warped, height=500))