OpenCVOMR表-在Python中檢測標記的答案-獲得正確的二值化

在解決 OMR 問題時，我不能完全正確地檢測到所有標記的答案。這是我的輸入表。

import cv2
import numpy as np

# read image
img = cv2.imread('omr_sheet.jpg')
h, w = img.shape[:2]

# trim 15 from bottom to remove partial answer
img = img[0:h-15, 0:w]

# threshold on color
lower=(120,60,80)
upper=(160,100,120)
thresh = cv2.inRange(img, lower, upper)

# apply morphology close
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (15,15))
morph = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
morph = cv2.morphologyEx(morph, cv2.MORPH_OPEN, kernel)

# get contours
result = img.copy() 
centers = []
contours = cv2.findContours(morph, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
contours = contours[0] if len(contours) == 2 else contours[1]
print("count:", len(contours))
print('')
i = 1
for cntr in contours:
    M = cv2.moments(cntr)
    cx = int(M["m10"] / M["m00"])
    cy = int(M["m01"] / M["m00"])
    centers.append((cx,cy))
    cv2.circle(result, (cx, cy), 20, (0, 255, 0), -1)
    pt = (cx,cy)
    print("circle #:",i, "center:",pt)
    i = i   1
    
# print list of centers
#print(centers)

# save results
cv2.imwrite('omr_sheet_thresh.png',thresh)
cv2.imwrite('omr_sheet_morph.png',morph)
cv2.imwrite('omr_sheet_result.png',result)
# show results
cv2.imshow("thresh", thresh)
cv2.imshow("morph", morph)
cv2.imshow("result", result)

cv2.waitKey(0)
cv2.destroyAllWindows()

閾值影像：

形態影像：

結果：

答案：

count: 48

circle #: 1 center: (77, 3151)
circle #: 2 center: (78, 3087)
circle #: 3 center: (77, 3021)
circle #: 4 center: (76, 2959)
circle #: 5 center: (79, 2892)
circle #: 6 center: (77, 2830)
circle #: 7 center: (77, 2762)
circle #: 8 center: (78, 2695)
circle #: 9 center: (77, 2631)
circle #: 10 center: (76, 2566)
circle #: 11 center: (78, 2500)
circle #: 12 center: (77, 2435)
circle #: 13 center: (77, 2373)
circle #: 14 center: (77, 2301)
circle #: 15 center: (79, 2238)
circle #: 16 center: (79, 2178)
circle #: 17 center: (77, 2108)
circle #: 18 center: (78, 2045)
circle #: 19 center: (80, 1980)
circle #: 20 center: (78, 1913)
circle #: 21 center: (78, 1848)
circle #: 22 center: (80, 1786)
circle #: 23 center: (77, 1722)
circle #: 24 center: (77, 1657)
circle #: 25 center: (79, 1593)
circle #: 26 center: (79, 1524)
circle #: 27 center: (80, 1461)
circle #: 28 center: (77, 1395)
circle #: 29 center: (79, 1332)
circle #: 30 center: (76, 1265)
circle #: 31 center: (80, 1203)
circle #: 32 center: (73, 1136)
circle #: 33 center: (77, 1072)
circle #: 34 center: (80, 1007)
circle #: 35 center: (77, 944)
circle #: 36 center: (78, 878)
circle #: 37 center: (75, 815)
circle #: 38 center: (77, 747)
circle #: 39 center: (77, 684)
circle #: 40 center: (79, 618)
circle #: 41 center: (77, 554)
circle #: 42 center: (78, 488)
circle #: 43 center: (80, 423)
circle #: 44 center: (78, 359)
circle #: 45 center: (77, 293)
circle #: 46 center: (78, 232)
circle #: 47 center: (77, 165)
circle #: 48 center: (78, 102)

添加：

這種解決方案可能對墨水的顏色不太敏感。只需使用 cv2.inRange() 在白色上設定閾值，然后反轉。

import cv2
import numpy as np

# read image
img = cv2.imread('omr_sheet.jpg')
h, w = img.shape[:2]

# trim 15 from bottom and 5 from right to remove partial answer and extraneous red
img = img[0:h-15, 0:w-5]

# threshold on white color
lower=(225,225,225)
upper=(255,255,255)
thresh = cv2.inRange(img, lower, upper)
thresh = 255 - thresh

# apply morphology close
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (15,15))
morph = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (7,7))
morph = cv2.morphologyEx(morph, cv2.MORPH_CLOSE, kernel)

# get contours
result = img.copy() 
contours = cv2.findContours(morph, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
contours = contours[0] if len(contours) == 2 else contours[1]
print("count:", len(contours))
print('')
i = 1
for cntr in contours:
    M = cv2.moments(cntr)
    cx = int(M["m10"] / M["m00"])
    cy = int(M["m01"] / M["m00"])
    cv2.circle(result, (cx, cy), 20, (0, 255, 0), -1)
    pt = (cx,cy)
    print("circle #:",i, "center:",pt)
    i = i   1
    
# save results
cv2.imwrite('omr_sheet_thresh2.png',thresh)
cv2.imwrite('omr_sheet_morph2.png',morph)
cv2.imwrite('omr_sheet_result2.png',result)

# show results
cv2.imshow("thresh", thresh)
cv2.imshow("morph", morph)
cv2.imshow("result", result)

cv2.waitKey(0)
cv2.destroyAllWindows()

閾值影像：

形態影像：

結果：

中心：

count: 48

circle #: 1 center: (78, 3150)
circle #: 2 center: (77, 3087)
circle #: 3 center: (78, 3020)
circle #: 4 center: (77, 2959)
circle #: 5 center: (78, 2892)
circle #: 6 center: (77, 2826)
circle #: 7 center: (77, 2760)
circle #: 8 center: (78, 2694)
circle #: 9 center: (79, 2630)
circle #: 10 center: (78, 2567)
circle #: 11 center: (78, 2501)
circle #: 12 center: (77, 2435)
circle #: 13 center: (75, 2373)
circle #: 14 center: (78, 2310)
circle #: 15 center: (78, 2240)
circle #: 16 center: (77, 2176)
circle #: 17 center: (78, 2106)
circle #: 18 center: (77, 2042)
circle #: 19 center: (79, 1979)
circle #: 20 center: (78, 1912)
circle #: 21 center: (78, 1848)
circle #: 22 center: (79, 1785)
circle #: 23 center: (78, 1722)
circle #: 24 center: (78, 1656)
circle #: 25 center: (78, 1589)
circle #: 26 center: (78, 1525)
circle #: 27 center: (79, 1458)
circle #: 28 center: (78, 1396)
circle #: 29 center: (78, 1328)
circle #: 30 center: (77, 1264)
circle #: 31 center: (78, 1201)
circle #: 32 center: (76, 1135)
circle #: 33 center: (78, 1071)
circle #: 34 center: (78, 1006)
circle #: 35 center: (77, 944)
circle #: 36 center: (77, 878)
circle #: 37 center: (77, 814)
circle #: 38 center: (77, 746)
circle #: 39 center: (78, 683)
circle #: 40 center: (77, 618)
circle #: 41 center: (77, 553)
circle #: 42 center: (78, 488)
circle #: 43 center: (78, 423)
circle #: 44 center: (77, 359)
circle #: 45 center: (76, 293)
circle #: 46 center: (79, 232)
circle #: 47 center: (75, 165)
circle #: 48 center: (79, 101)

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分析：

標記的氣泡是藍色墨水，是相應 LAB 顏色空間的 B 通道中的主色。

• B 通道捕捉影像中的藍色和黃色
• 而 A 通道捕獲紅色和綠色
• L通道突出影像中的亮度內容

要了解有關 LAB 空間的更多資訊，

# Otsu threshold on B-channel
th = cv2.threshold(b_channel,0,255,cv2.THRESH_BINARY_INV cv2.THRESH_OTSU)[1]

# morphological operations to close up the gaps within
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5))
closing = cv2.morphologyEx(th, cv2.MORPH_CLOSE, kernel, iterations = 2)

# finding contours and drawing those above certain area onto black screen
contours, h = cv2.findContours(closing, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

black = np.zeros((img.shape[0], img.shape[1], 3), np.uint8)
for c in contours:
    area = cv2.contourArea(c)
    if area > 100:
        black = cv2.drawContours(black, [c], -1, (0, 255, 0), -1)

標籤：opencv 图像处理 欧姆尔

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