OpenCV OMR Sheet - 检测 Python 中的标记答案 - 获得正确的二值化
OpenCV OMR Sheet - Detect Marked Answers in Python - Getting Proper Binarization
在解决 OMR 问题时，我无法完全正确地检测到所有标记的答案。
这是我的输入 sheet.
Input Image
我的图像二值化代码。
import cv2
image = cv2.imread('input.png')
img = cv2.GaussianBlur(image,(5,5),0)
res, img = cv2.threshold(img, 60, 255, cv2.THRESH_BINARY)
img = 255 - img
cv2.imwrite('output.png',img)
我得到的结果。
Output generated
我更改了高斯模糊和阈值参数如下。
img = cv2.GaussianBlur(image,(7,7),0)
res, img = cv2.threshold(img, 90, 255, cv2.THRESH_BINARY)
此更改后我得到的结果。
Result after changing Parameters
我想要的结果应该是这样的。
Desired Result
这是我检测答案的完整代码。
def solve(img,n_row = 50):
height, width, channels = img.shape
n_col = 4
xShift = int(width/n_col)
yShift = int(height/n_row)
img = cv2.resize(img, (n_col * xShift, n_row*yShift))
img = cv2.GaussianBlur(img,(5,5),0)
res, img = cv2.threshold(img, 60, 255, cv2.THRESH_BINARY)
img = 255 - img
for row in range(0, n_row):
tmp_img = img [row*yShift + 5:(row+1)*yShift - 5,]
area_sum = []
for col in range(n_col):
area_sum.append(np.sum(tmp_img[1:,col*xShift :(col+1)*xShift]))
y = str(area_sum > np.median(area_sum) * 1)
result.append(area_sum > np.median(area_sum) * 5)
如果有人能帮我解决这个问题，我将不胜感激。
回答建议：
如何检查每个边界矩形内的白色像素数，并仅保留面积大于最小值的边界 rectangles/contours。
inputImg= cv2.imread('input.jpg')
img = cv2.cvtColor(inputImg, cv2.COLOR_BGR2GRAY)
mask = np.zeros(img.shape[:2], dtype=img.dtype)
ret, otsu_threshold = cv2.threshold(img, 120, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
contours, hierarchy = cv2.findContours(otsu_threshold, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
for c in contours:
x,y,w,h = cv2.boundingRect(c)
if cv2.contourArea(c) > 1500:
cv2.rectangle(otsu_threshold, (x, y), (x+w, y+h), (0,255,0), 2)
cv2.imshow('Otsu', otsu_threshold)
cv2.waitKey(0)
分析：
标记的气泡是蓝色墨水，它是相应 LAB 颜色 space 的 B-channel 中的主色。
- B-channel 捕捉图像中的蓝色和黄色
- 虽然 A-channel 捕获了红色和绿色
- L-channel高亮图像中的亮度内容
了解更多关于实验室的信息spacego through this page
代码：
以下代码片段展示了它在这里的用途：
img = cv2.imread('omr.jpg')
# convert to LAB space
lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
# store B-channel
b_channel = lab[:,:,2]
请注意下面的阴影气泡有多么明显：
# 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)
这是 Python/OpenCV 中的一种方法。
- 读取输入
- 颜色阈值
- 应用形态学来填充圆圈
- 获取外部轮廓
- 对于每个轮廓，获取质心并在质心位置的输入上绘制半径 = 20 的实心圆
- 保存结果
输入：
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()
阈值图像：
形态图像：
结果：
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：
这个解决方案可能对墨水的颜色不太敏感。只需使用 cv2.inRange() 在白色上设置阈值，然后反转。
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()
阈值图像：
形态图像：
结果：
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)
在解决 OMR 问题时，我无法完全正确地检测到所有标记的答案。
这是我的输入 sheet.
Input Image
我的图像二值化代码。
import cv2
image = cv2.imread('input.png')
img = cv2.GaussianBlur(image,(5,5),0)
res, img = cv2.threshold(img, 60, 255, cv2.THRESH_BINARY)
img = 255 - img
cv2.imwrite('output.png',img)
我得到的结果。
Output generated
我更改了高斯模糊和阈值参数如下。
img = cv2.GaussianBlur(image,(7,7),0)
res, img = cv2.threshold(img, 90, 255, cv2.THRESH_BINARY)
此更改后我得到的结果。
Result after changing Parameters
我想要的结果应该是这样的。 Desired Result
这是我检测答案的完整代码。
def solve(img,n_row = 50):
height, width, channels = img.shape
n_col = 4
xShift = int(width/n_col)
yShift = int(height/n_row)
img = cv2.resize(img, (n_col * xShift, n_row*yShift))
img = cv2.GaussianBlur(img,(5,5),0)
res, img = cv2.threshold(img, 60, 255, cv2.THRESH_BINARY)
img = 255 - img
for row in range(0, n_row):
tmp_img = img [row*yShift + 5:(row+1)*yShift - 5,]
area_sum = []
for col in range(n_col):
area_sum.append(np.sum(tmp_img[1:,col*xShift :(col+1)*xShift]))
y = str(area_sum > np.median(area_sum) * 1)
result.append(area_sum > np.median(area_sum) * 5)
如果有人能帮我解决这个问题，我将不胜感激。
回答建议：
如何检查每个边界矩形内的白色像素数，并仅保留面积大于最小值的边界 rectangles/contours。
inputImg= cv2.imread('input.jpg')
img = cv2.cvtColor(inputImg, cv2.COLOR_BGR2GRAY)
mask = np.zeros(img.shape[:2], dtype=img.dtype)
ret, otsu_threshold = cv2.threshold(img, 120, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
contours, hierarchy = cv2.findContours(otsu_threshold, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
for c in contours:
x,y,w,h = cv2.boundingRect(c)
if cv2.contourArea(c) > 1500:
cv2.rectangle(otsu_threshold, (x, y), (x+w, y+h), (0,255,0), 2)
cv2.imshow('Otsu', otsu_threshold)
cv2.waitKey(0)
分析：
标记的气泡是蓝色墨水，它是相应 LAB 颜色 space 的 B-channel 中的主色。
- B-channel 捕捉图像中的蓝色和黄色
- 虽然 A-channel 捕获了红色和绿色
- L-channel高亮图像中的亮度内容
了解更多关于实验室的信息spacego through this page
代码：
以下代码片段展示了它在这里的用途：
img = cv2.imread('omr.jpg')
# convert to LAB space
lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
# store B-channel
b_channel = lab[:,:,2]
请注意下面的阴影气泡有多么明显：
# 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)
这是 Python/OpenCV 中的一种方法。
- 读取输入
- 颜色阈值
- 应用形态学来填充圆圈
- 获取外部轮廓
- 对于每个轮廓，获取质心并在质心位置的输入上绘制半径 = 20 的实心圆
- 保存结果
输入：
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()
阈值图像：
形态图像：
结果：
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：
这个解决方案可能对墨水的颜色不太敏感。只需使用 cv2.inRange() 在白色上设置阈值，然后反转。
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()
阈值图像：
形态图像：
结果：
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)
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