Opencv learning notes 8 -- answer sheet recognition

Import toolkit

# Import toolkit 
import numpy as np
import argparse
import imutils
import cv2
import matplotlib.pyplot as plt#Matplotlib yes RGB

#  right key 
ANSWER_KEY = {
    0: 1, 1: 4, 2: 0, 3: 3, 4: 1}

Defined function

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

	#  Find the corresponding coordinates in order 0123 Namely   Top left , The upper right , The lower right , The lower left 
	#  Calculate top left , The lower right 
	s = pts.sum(axis = 1)
	rect[0] = pts[np.argmin(s)]
	rect[2] = pts[np.argmax(s)]

	#  Count right up and left down 
	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):
    	#  Get the input coordinate point 
	rect = order_points(pts)
	(tl, tr, br, bl) = rect

	#  Calculate the input w and h value 
	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))

	#  Corresponding coordinate position after transformation 
	dst = np.array([
		[0, 0],
		[maxWidth - 1, 0],
		[maxWidth - 1, maxHeight - 1],
		[0, maxHeight - 1]], dtype = "float32")

	#  Calculate the transformation matrix 
	M = cv2.getPerspectiveTransform(rect, dst)
	warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))

	#  Return the result after transformation 
	return warped

def sort_contours(cnts, method="left-to-right"):
    reverse = False
    i = 0
    if method == "right-to-left" or method == "bottom-to-top":
        reverse = True
    if method == "top-to-bottom" or method == "bottom-to-top":
        i = 1
    boundingBoxes = [cv2.boundingRect(c) for c in cnts]
    (cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes),
                                        key=lambda b: b[1][i], reverse=reverse))
    return cnts, boundingBoxes

 # Show function 
def cv_show(name,img):
    b,g,r = cv2.split(img)
    img_rgb = cv2.merge((r,g,b))
    plt.imshow(img_rgb)
    plt.show()
def cv_show1(name,img):
    plt.imshow(img)
    plt.show()
    cv2.imshow(name,img)
    cv2.waitKey()
    cv2.destroyAllWindows()

scanning

#  Preprocessing 
image = cv2.imread("./images/test_01.png")
contours_img = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (5, 5), 0) # Gauss filtering 
cv_show1('blurred',blurred)
edged = cv2.Canny(blurred, 75, 200) # edge detection 
cv_show1('edged',edged)

#  Contour detection 
cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,
	cv2.CHAIN_APPROX_SIMPLE)[1]
cv2.drawContours(contours_img,cnts,-1,(0,0,255),3) 
cv_show('contours_img',contours_img)
docCnt = None

#  Make sure that... Is detected 
if len(cnts) > 0:
	#  Sort by outline size 
	cnts = sorted(cnts, key=cv2.contourArea, reverse=True)

	#  Traverse every contour 
	for c in cnts:
		#  The approximate 
		peri = cv2.arcLength(c, True)
		approx = cv2.approxPolyDP(c, 0.02 * peri, True)
        
		#  Prepare for perspective change 
		if len(approx) == 4:
			docCnt = approx
			break

#  Perform perspective transformation 
warped = four_point_transform(gray, docCnt.reshape(4, 2))
cv_show1('warped',warped)

Adaptive threshold processing

# Otsu's  Threshold processing 
thresh = cv2.threshold(warped, 0, 255,
	cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1] 
thresh_Contours = thresh.copy()
cv_show1('thresh_Contours',thresh_Contours)

Detect the outline of each option

#  Find the outline of each circle 
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
	cv2.CHAIN_APPROX_SIMPLE)[1]
cv2.drawContours(thresh_Contours,cnts,-1,(0,0,255),3) # Because it is a binary image , So as long as it's not 255,255,255 Will turn black 
print(len(cnts))
cv_show1('thresh_Contours',thresh_Contours)
questionCnts = []

82

#  Traverse 
for c in cnts:
	#  Calculate scale and size 
	(x, y, w, h) = cv2.boundingRect(c)
	ar = w / float(h)

	#  Specify the standard according to the actual situation 
	if w >= 20 and h >= 20 and ar >= 0.9 and ar <= 1.1:
		questionCnts.append(c)

Sort the outline to get the sequence number

#  Sort from top to bottom 
questionCnts = sort_contours(questionCnts,
	method="top-to-bottom")[0]
correct = 0

#  Each row has 5 An option 
for (q, i) in enumerate(np.arange(0, len(questionCnts), 5)):
	#  Sort 
	cnts = sort_contours(questionCnts[i:i + 5])[0]
	bubbled = None

	#  Traverse every result 
	for (j, c) in enumerate(cnts):
		#  Use mask To judge the result 
		mask = np.zeros(thresh.shape, dtype="uint8")
		cv2.drawContours(mask, [c], -1, 255, -1) #-1 Indicates filling 
		#  Choose this answer by calculating the number of nonzero points 
		mask = cv2.bitwise_and(thresh, thresh, mask=mask)
		total = cv2.countNonZero(mask)
		cv_show1('mask',mask)

		#  Judging by threshold 
		if bubbled is None or total > bubbled[0]:
			bubbled = (total, j)
	#  Compare the right answers 
	color = (0, 0, 255)
	k = ANSWER_KEY[q]

	#  To judge correctly 
	if k == bubbled[1]:
		color = (0, 255, 0)
		correct += 1

	#  mapping 
	cv2.drawContours(warped, [cnts[k]], -1, color, 3)

Print the results

score = (correct / 5.0) * 100
print("[INFO] score: {:.2f}%".format(score))
cv2.putText(warped, "{:.2f}%".format(score), (10, 30),
	cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)
cv_show("Original", image)
cv_show1("Exam", warped)

[INFO] score: 80.00%

Reference resources

1.【2021B Stand at the best OpenCV Course recommended 】OpenCV From introduction to practice The whole course