Styjun

enter image description hereI want to read a bubble sheet in python using cv2. But my code is not giving me exact contours or exact count of circles. I

def normalize(im):
return cv2.normalize(im, np.zeros(im.shape), 0, 255,norm_type=cv2.NORM_MINMAX)

# load the image and show it 
image = cv2.imread("/home/junaid/PycharmProjects/omr/img/scan_score.jpg")

cropped_1 = image[1278:2082, 517:1006].copy()
cv2.imshow("cropped", cropped_1)

edges = cv2.Canny(cropped_1, 100, 200)

blurred = cv2.GaussianBlur(cropped_1, (11, 11), 10)

im = normalize(cv2.cvtColor(blurred, cv2.COLOR_BGR2GRAY))

ret, im = cv2.threshold(im, 160, 255, cv2.THRESH_BINARY)

thresh = cv2.threshold(im, 0, 255,cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]

cnts = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)


cnts = imutils.grab_contours(cnts)

# print(cnts)

questionCnts = []

for c in cnts:
 ar = cv2.contourArea(c)
 cv2.drawContours(thresh, c, -1,(255,0,0),3)
 if ar>=2800 and ar<= 2900:
 questionCnts.append(c)
 # if h >= 42 or h <= 44 and w>=56 or w <= 59 and ar>=1.4 and ar<=1.5:
 # questionCnts.append(c)

 # print(len(cnts))
 print(len(questionCnts))

 questionCnts = contours.sort_contours(questionCnts, method="top-to-bottom") 
 [0]
 # print(questionCnts)

 correct = 0

 for (q,i) in enumerate(np.arange(0, len(questionCnts), 4)):
 cnts = contours.sort_contours(questionCnts[i:i + 4])[0]
 bubbled = None
 # print(cnts)

 for (j, c) in enumerate(cnts):
 mask = np.zeros(thresh.shape, dtype="uint8")
 print(mask)
 cv2.drawContours(mask, [c], -1, (255,0,0), 1)

 mask = cv2.bitwise_and(thresh, thresh, mask=mask)
 total = cv2.countNonZero(mask)

 if bubbled is None or total > bubbled[0]:
 bubbled = (total, j)

 color = (0, 0, 255)
 k = 0

 if k == bubbled[1]:
 color = (0, 255, 0)
 correct += 1
 print("correct", correct)

 cv2.drawContours(im, [cnts[k]], -1, color, 3)


 # grab the test taker
 score = (correct / 13.0) * 100
 print("[INFO] score: :.2f%".format(score))
 cv2.putText(im, ":.2f%".format(score), (10, 30), 
 cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)
 # cv2.imshow("Original", image)
 cv2.imshow("Exam", im)

 cv2.waitKey(0)