首先，我檢測到圖像右側(cè)的20個黑框，然后將x和寬度添加到列表中：image = cv2.imread(args["image"])

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

(_, thresh) = cv2.threshold(gray, 220, 255,cv2.THRESH_BINARY)

kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 1))

closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)

dilation = cv2.dilate(closed,None,iterations = 5)

(_,contours,_)=cv2.findContours(dilation.copy(),cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)

sorted_rows = sorted(contours, key = cv2.contourArea, reverse = True)[1:21]

for idx,row in enumerate(sorted_rows):

(_x,y,w,h) = cv2.boundingRect(row)

rows.append([(y),(y+h)])

然后我檢測到五列：(_, thresh) = cv2.threshold(gray, 127, 255,cv2.THRESH_BINARY)

closed = cv2.erode(thresh, None, iterations = 4)

(_,contours,_)=cv2.findContours(closed.copy(),cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)

sorted_cols = sorted(contours, key = cv2.contourArea, reverse = True)[1:6]

for col in sorted_cols:

# add to list

(x,y,w,h) = cv2.boundingRect(col)

cols.append([(x),(x+w)])

接下來，我分別按照x和y的位置對這兩個列表進行排序：cols = sorted(cols, key = lambda x: x[0])

rows = sorted(rows, key = lambda x: x[0])

然后我遍歷columns列表和rows列表來構(gòu)建交叉點的坐標，在那里我可以找到五個復(fù)選框count_iterations = 0

for col in cols:

for row in rows:

count_iterations +=1

crop = image[row[0]:row[1], col[0]: col[1]]

denoised = cv2.fastNlMeansDenoisingColored(crop,None,10,10,7,21)

edges = cv2.Canny(denoised,220,250)

closed = cv2.dilate(edges, None, iterations = 1)

(_,contours,_)= cv2.findContours(closed.copy(),cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)

checkboxes = sorted(contours, key = cv2.contourArea, reverse = True)[:5]

crdn_checkboxes = []

for check_box in checkboxes:

crdn_checkboxes.append(cv2.boundingRect(check_box))

最后，我按像素的位置對它們進行排序，最后選中它們的一個：crdn_checkboxes = sorted(crdn_checkboxes, key = lambda x: x[0])

for idx, crdn in enumerate(crdn_checkboxes):

x,y,w,h = crdn

check_box = crop[y:y+h,x:x+w]

check_box_img_gray = cv2.cvtColor(check_box, cv2.COLOR_BGR2GRAY)

(_, thresh) = cv2.threshold(check_box_img_gray,200,255,cv2.THRESH_BINARY)

height,width = thresh.shape[:2]

tot_px = height * width

checked_box = cv2.countNonZero(thresh) / float(tot_px)

if checked_box < 0.6:

print "Found"

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