Document Scanner
Implementation of a document scanner using Python an OpenCV
Utility functions
Let's implement a few functions that we will use later.
import numpy as np
import cv2
from skimage.filters import threshold_local
import cv2
import matplotlib.pyplot as plt
%matplotlib inline
- 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)
- 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
- 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
- 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
Reading image
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)
Finally, save this image.
cv2.imwrite('./images/scanned.png', warped_image)
print("Image scanned and saved")
Image scanned and saved
