app.py

from imutils.perspective import four_point_transform
from imutils import contours
import numpy as np
import argparse
import imutils
import cv2
import pandas as pd

# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True,
	help="path to the input image")
args = vars(ap.parse_args())
# define the answer key which maps the question number
# to the correct answer
df = pd.read_csv('answer.csv')
ANSWER_KEY = {}
for i in df.values:
    print(i[0], i[1])
    ANSWER_KEY[i[0]] = i[1]

print(ANSWER_KEY)
# load the image, convert it to grayscale, blur it
# slightly, then find edges
image = cv2.imread(args["image"])
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
edged = cv2.Canny(blurred, 75, 200)

# find contours in the edge map, then initialize
# the contour that corresponds to the document
cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,
	cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
docCnt = None

# ensure that at least one contour was found
if len(cnts) > 0:
    # sort the contours according to their size in
    # descending order
    cnts = sorted(cnts, key=cv2.contourArea, reverse=True)
    # loop over the sorted contours
    for c in cnts:
        # 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 we have found the paper
        if len(approx) == 4:
            docCnt = approx
            break

# apply a four point perspective transform to both the
# original image and grayscale image to obtain a top-down
# birds eye view of the paper
paper = four_point_transform(image, docCnt.reshape(4, 2))
warped = four_point_transform(gray, docCnt.reshape(4, 2))

# apply Otsu's thresholding method to binarize the warped
# piece of paper
thresh = cv2.threshold(warped, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]

# find contours in the thresholded image, then initialize
# the list of contours that correspond to questions
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
questionCnts = []
# loop over the contours
for c in cnts:
	# compute the bounding box of the contour, then use the
	# bounding box to derive the aspect ratio
	(x, y, w, h) = cv2.boundingRect(c)
	ar = w / float(h)
	# in order to label the contour as a question, region
	# should be sufficiently wide, sufficiently tall, and
	# have an aspect ratio approximately equal to 1
	if w >= 20 and h >= 20 and ar >= 0.9 and ar <= 1.1:
		questionCnts.append(c)

#print(len(questionCnts))

# sort the question contours top-to-bottom, then initialize
# the total number of correct answers
questionCnts = contours.sort_contours(questionCnts,
	method="top-to-bottom")[0]
correct = 0
# each question has 5 possible answers, to loop over the
# question in batches of 5
for (q, i) in enumerate(np.arange(0, len(questionCnts), 5)):
    # sort the contours for the current question from
    # left to right, then initialize the index of the
    # bubbled answer
    #print(q)
    #print(i)
    cnts = contours.sort_contours(questionCnts[i:i + 5])[0]
    bubbled = None

    # loop over the sorted contours
    for (j, c) in enumerate(cnts):
        # construct a mask that reveals only the current
        # "bubble" for the question
        mask = np.zeros(thresh.shape, dtype="uint8")
        cv2.drawContours(mask, [c], -1, 255, -1)
        # apply the mask to the thresholded image, then
        # count the number of non-zero pixels in the
        # bubble area
        mask = cv2.bitwise_and(thresh, thresh, mask=mask)
        total = cv2.countNonZero(mask)
        # if the current total has a larger number of total
        # non-zero pixels, then we are examining the currently
        # bubbled-in answer
        if bubbled is None or total > bubbled[0]:
            bubbled = (total, j)
        
    # initialize the contour color and the index of the
    # *correct* answer
    color = (0, 0, 255)
    k = ANSWER_KEY[q+1]
    # check to see if the bubbled answer is correct
    if k == bubbled[1]+1:
        color = (0, 255, 0)
        correct += 1
    # draw the outline of the correct answer on the test
    cv2.drawContours(paper, [cnts[k-1]], -1, color, 3)

# grab the test taker
score = (correct / 5.0) * 100
print("[INFO] score: {:.2f}%".format(score))
cv2.putText(paper, "{:.2f}%".format(score), (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)
cv2.imshow("Original", image)
cv2.imshow("Exam", paper)
cv2.waitKey(0)