我认为这是一个很棒的帖子,也是ARK的一个很好的解决方案。很好地布置和解释。
我正在研究类似的问题,并完成了整个工作。进行了一些更改(例如,xrange到range,cv2.findContours中的参数),但是应该可以立即使用(Python 3.5,Anaconda)。
这是上述元素的汇编,并添加了一些缺少的代码(即,标记点)。
'''
/programming/10196198/how-to-remove-convexity-defects-in-a-sudoku-square
'''
import cv2
import numpy as np
img = cv2.imread('test.png')
winname="raw image"
cv2.namedWindow(winname)
cv2.imshow(winname, img)
cv2.moveWindow(winname, 100,100)
img = cv2.GaussianBlur(img,(5,5),0)
winname="blurred"
cv2.namedWindow(winname)
cv2.imshow(winname, img)
cv2.moveWindow(winname, 100,150)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
mask = np.zeros((gray.shape),np.uint8)
kernel1 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(11,11))
winname="gray"
cv2.namedWindow(winname)
cv2.imshow(winname, gray)
cv2.moveWindow(winname, 100,200)
close = cv2.morphologyEx(gray,cv2.MORPH_CLOSE,kernel1)
div = np.float32(gray)/(close)
res = np.uint8(cv2.normalize(div,div,0,255,cv2.NORM_MINMAX))
res2 = cv2.cvtColor(res,cv2.COLOR_GRAY2BGR)
winname="res2"
cv2.namedWindow(winname)
cv2.imshow(winname, res2)
cv2.moveWindow(winname, 100,250)
#find elements
thresh = cv2.adaptiveThreshold(res,255,0,1,19,2)
img_c, contour,hier = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
max_area = 0
best_cnt = None
for cnt in contour:
area = cv2.contourArea(cnt)
if area > 1000:
if area > max_area:
max_area = area
best_cnt = cnt
cv2.drawContours(mask,[best_cnt],0,255,-1)
cv2.drawContours(mask,[best_cnt],0,0,2)
res = cv2.bitwise_and(res,mask)
winname="puzzle only"
cv2.namedWindow(winname)
cv2.imshow(winname, res)
cv2.moveWindow(winname, 100,300)
# vertical lines
kernelx = cv2.getStructuringElement(cv2.MORPH_RECT,(2,10))
dx = cv2.Sobel(res,cv2.CV_16S,1,0)
dx = cv2.convertScaleAbs(dx)
cv2.normalize(dx,dx,0,255,cv2.NORM_MINMAX)
ret,close = cv2.threshold(dx,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
close = cv2.morphologyEx(close,cv2.MORPH_DILATE,kernelx,iterations = 1)
img_d, contour, hier = cv2.findContours(close,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
for cnt in contour:
x,y,w,h = cv2.boundingRect(cnt)
if h/w > 5:
cv2.drawContours(close,[cnt],0,255,-1)
else:
cv2.drawContours(close,[cnt],0,0,-1)
close = cv2.morphologyEx(close,cv2.MORPH_CLOSE,None,iterations = 2)
closex = close.copy()
winname="vertical lines"
cv2.namedWindow(winname)
cv2.imshow(winname, img_d)
cv2.moveWindow(winname, 100,350)
# find horizontal lines
kernely = cv2.getStructuringElement(cv2.MORPH_RECT,(10,2))
dy = cv2.Sobel(res,cv2.CV_16S,0,2)
dy = cv2.convertScaleAbs(dy)
cv2.normalize(dy,dy,0,255,cv2.NORM_MINMAX)
ret,close = cv2.threshold(dy,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
close = cv2.morphologyEx(close,cv2.MORPH_DILATE,kernely)
img_e, contour, hier = cv2.findContours(close,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
for cnt in contour:
x,y,w,h = cv2.boundingRect(cnt)
if w/h > 5:
cv2.drawContours(close,[cnt],0,255,-1)
else:
cv2.drawContours(close,[cnt],0,0,-1)
close = cv2.morphologyEx(close,cv2.MORPH_DILATE,None,iterations = 2)
closey = close.copy()
winname="horizontal lines"
cv2.namedWindow(winname)
cv2.imshow(winname, img_e)
cv2.moveWindow(winname, 100,400)
# intersection of these two gives dots
res = cv2.bitwise_and(closex,closey)
winname="intersections"
cv2.namedWindow(winname)
cv2.imshow(winname, res)
cv2.moveWindow(winname, 100,450)
# text blue
textcolor=(0,255,0)
# points green
pointcolor=(255,0,0)
# find centroids and sort
img_f, contour, hier = cv2.findContours(res,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
centroids = []
for cnt in contour:
mom = cv2.moments(cnt)
(x,y) = int(mom['m10']/mom['m00']), int(mom['m01']/mom['m00'])
cv2.circle(img,(x,y),4,(0,255,0),-1)
centroids.append((x,y))
# sorting
centroids = np.array(centroids,dtype = np.float32)
c = centroids.reshape((100,2))
c2 = c[np.argsort(c[:,1])]
b = np.vstack([c2[i*10:(i+1)*10][np.argsort(c2[i*10:(i+1)*10,0])] for i in range(10)])
bm = b.reshape((10,10,2))
# make copy
labeled_in_order=res2.copy()
for index, pt in enumerate(b):
cv2.putText(labeled_in_order,str(index),tuple(pt),cv2.FONT_HERSHEY_DUPLEX, 0.75, textcolor)
cv2.circle(labeled_in_order, tuple(pt), 5, pointcolor)
winname="labeled in order"
cv2.namedWindow(winname)
cv2.imshow(winname, labeled_in_order)
cv2.moveWindow(winname, 100,500)
# create final
output = np.zeros((450,450,3),np.uint8)
for i,j in enumerate(b):
ri = int(i/10) # row index
ci = i%10 # column index
if ci != 9 and ri!=9:
src = bm[ri:ri+2, ci:ci+2 , :].reshape((4,2))
dst = np.array( [ [ci*50,ri*50],[(ci+1)*50-1,ri*50],[ci*50,(ri+1)*50-1],[(ci+1)*50-1,(ri+1)*50-1] ], np.float32)
retval = cv2.getPerspectiveTransform(src,dst)
warp = cv2.warpPerspective(res2,retval,(450,450))
output[ri*50:(ri+1)*50-1 , ci*50:(ci+1)*50-1] = warp[ri*50:(ri+1)*50-1 , ci*50:(ci+1)*50-1].copy()
winname="final"
cv2.namedWindow(winname)
cv2.imshow(winname, output)
cv2.moveWindow(winname, 600,100)
cv2.waitKey(0)
cv2.destroyAllWindows()