Document Scanner using OpenCV

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Utility functions

import numpy as npimport cv2from skimage.filters import threshold_localimport cv2import matplotlib.pyplot as plt%matplotlib inline

1. Euclidean distance: straight line distance between two points in a Euclidean plane.

def euclidean_distance(a,b):    return np.sqrt((a[0] - b[0]) ** 2 + (a[1] - b[1]) **2)

1. Function to sort vertices in a clockwise order

def sort_vertices(pts):    # Initialize a list of ordered co-ordinates (clockwise)    vertices = np.zeros((4, 2), dtype="float32")    s = pts.sum(axis=1)    # Top Left- Smallest Sum    vertices[0] = pts[np.argmin(s)]    # Bottom Right- Largest Sum    vertices[2] = pts[np.argmax(s)]    diff = np.diff(pts, axis=1)    # Top Right- Smallest Difference    vertices[1] = pts[np.argmin(diff)]    # Bottom Left- Largest Difference    vertices[3] = pts[np.argmax(diff)]    # Return the clockwise ordered coordinates    return vertices

1. Function for rectangular perspectice transformation

def four_point_perspective_transform(image, pts):    """    Takes four points and generates a rectangular perspective    transformation to get a top-down view of the image    """    # Order the points    rect = sort_vertices(pts)    (tl, tr, br, bl) = rect    # Estimate the width of the new image    # Max of width of top and bottom co-ordinates    widthA = euclidean_distance(br, bl)    widthB = euclidean_distance(tr, tl)    maxWidth = max(int(widthA), int(widthB))    # Estimate the height of the new image    # Max of the height of left and right coordinates    heightA = euclidean_distance(tr, br)    heightB = euclidean_distance(tl, bl)    maxHeight = max(int(heightA), int(heightB))    # Estimate the destination points using the calculated dimensions    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

1. Function to display images side-by-side

def display_sbs(imgLr, imgRr):    f = plt.figure()    f.add_subplot(1,2, 1)    plt.imshow(np.rot90(imgLr,0))    f.add_subplot(1,2, 2)    plt.imshow(np.rot90(imgRr,0))    plt.show(block=True)

Main program

Step 1: Reading the image and converting it into grayscale

image = cv2.imread("images/receipt.jpg")

image_copy = image.copy()image = cv2.resize(image, (1500, 800))image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)image_blurred = cv2.GaussianBlur(image_gray, (5, 5), 0)image_edge = cv2.Canny(image_gray, 75, 200)

display_sbs(image, image_edge)

Step 2: find contours on the image

cnts, hiers = cv2.findContours(image_edge.copy(), cv2.RETR_LIST,                        cv2.CHAIN_APPROX_SIMPLE)[-2:]# cnts = cnts[1]cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:5]for c in cnts:    peri = cv2.arcLength(c, True)    approx = cv2.approxPolyDP(c, 0.02 * peri, True)    if len(approx) == 4:        screenCnt = approx        breakcnts = cv2.findContours(image_edge.copy(), cv2.RETR_LIST,                                     cv2.CHAIN_APPROX_SIMPLE)cnts = cnts[1]cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:5]for c in cnts:    peri = cv2.arcLength(c, True)    approx = cv2.approxPolyDP(c, 0.02 * peri, True)    if len(approx) == 4:        screenCnt = approx        breakcnts

Step 3: draw contours on the image

cv2.drawContours(image, [screenCnt], -1, (0, 255, 0), 2)plt.imshow(np.rot90(image,0))plt.show(block=True)

Step 4: warp this image to display only the receipt

warped_image = four_point_perspective_transform(image, screenCnt.reshape(4,2))

warped_image = cv2.cvtColor(warped_image, cv2.COLOR_BGR2GRAY)T = threshold_local(warped_image, 11, offset=10, method="gaussian")warped_image = (warped_image > T).astype("uint8") * 255

display_sbs(image_copy, warped_image)

cv2.imwrite('./images/scanned.png', warped_image)print("Image scanned and saved")

Image scanned and saved