def xrdTyp(img,typ,db,trb):
'''rdTyp finds the screen area of a digit based on the x y co-ordinates
in the tables below. It passes this area to tMatch which returns
a number '''
#img = stdSize(imgx,typ)
img = erode(img,3)
img = dilate(img,5)
d = 150
#cvs(1,imgx,'rdTyp input')
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
# define range of blue color in HSV
h = 46; s = 20; v = 212
lower_blue = np.array([h,s,v]) #np.array([110,50,50])
upper_blue = np.array( [h+d,s+d,v+d]) #np.array([130,255,255])
# Threshold the HSV image to get only blue colors
thresh = cv2.inRange(hsv, lower_blue, upper_blue)
img = thresh.copy()
Y= {
# y1 y2 j limit
'wt' : [ 20, 320 , 4 ],
'fat': [ 35, 220 , 3 ],
'h2o': [ 30, 220 , 3 ]
}
XX = {
# 100 10 one tenth
'wt' : [(95, 145) , (165,265) , (280,385 ), (400,510) ],
'fat': [(15, 50 ) , (65,125) , (140,200), (0,0) ],
'h2o': [(5, 55) , (65,120 ) , (135,185), (0,0) ]
}
n = []
h,w = img.shape
#print 'id input w {} h {}'.format(w,h)
# look at each digit in the image by xx position
for j in range(0,Y[typ][2]): # loops across XX table above
y1 = Y[typ][0]; x1 = XX[typ][j][0]
y2 = Y[typ][1]; x2 = XX[typ][j][1]
digit = img[y1:y2, x1:x2].copy()
cv2.imwrite('digTest.png',digit) # save for future debug
h,w = digit.shape
#print 'xid input w {} h {}'.format(w,h)
#cvs(db,digit,'input digit')
# n.append( tMatch(digit,typ,db)) # interpret as a number
if db: print 'n is ', n
n.append(identifyN(digit,trb,0,db,typ) ) # train and test
if db: print ' rdTyp n ', n # exit here
nn = 0; j = -1
n.reverse()
#nn = 100 * n[1] + 10 * n[2] + n[3] + n[4]/10.0
for j, xin in enumerate(n):
nn = nn + xin * 10**(j-1)
cvs(db,img,typ,50)
return(nn)
def rdTyp(imgx,typ,db):
'''rdTyp finds the screen area of a digit based on the x y co-ordinates
in the tables below. It passes this area to tMatch which returns
a number '''
img = stdSize(imgx,typ)
d = 150
#cvs(1,imgx,'rdTyp input')
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
# define range of blue color in HSV
h = 46; s = 20; v = 212
lower_blue = np.array([h,s,v]) #np.array([110,50,50])
upper_blue = np.array( [h+d,s+d,v+d]) #np.array([130,255,255])
# Threshold the HSV image to get only blue colors
thresh = cv2.inRange(hsv, lower_blue, upper_blue)
img = thresh.copy()
Y= {
# y1 y2 j limit
'wt' : [ 45, 367 , 4 ],
'fat': [ 84, 344 , 3 ],
'h2o': [ 35, 230 , 3 ]
}
XX = {
# 100 10 one tenth
'wt' : [(152, 200) , (240,360) , (415,530), (580,720) ],
'fat': [(20, 80) , (100,220) , (250,350), (0,0) ],
'h2o': [(10, 90) , (110,210) , (235,320), (0,0) ]
}
n = []
# look at each digit in the image by xx position
for j in range(0,Y[typ][2]): # loops across XX table above
y1 =Y[typ][0]; y2 = Y[typ][1];
x1 =XX[typ][j][0]; x2=XX[typ][j][1]
digit = img[y1:y2, x1:x2].copy()
cv2.imwrite('digTest.png',digit) # save for future debug
n.append( tMatch(digit,typ,db)) # interpret as a number
if db: print ' rdTyp n ', n # exit here
nn = 0; j = -1
n.reverse()
#nn = 100 * n[1] + 10 * n[2] + n[3] + n[4]/10.0
for j, xin in enumerate(n):
nn = nn + xin * 10**(j-1)
cvs(db,img,typ,5000)
return(nn) # the decoded number
def evalGame(ROI,fd,rn,db):
''' we obtain the ROI region of interest from Part or as input from the
last screen processed by Maintest. We look for blob in the ROI and
evaluate them by matching to specific areas in the img.
'''
h,w = ROI.shape[:2]
# sROI = cv2.resize(ROI,(3*w,3*h)) # this may not be a good idea
cxcopy = ROI.copy() # copy to mark up for display # but we did it in CaptureDigits so .. .
cmask,cnt,hier = findNumbers(ROI,db) # incorporate hier check
if db: print hier.shape, len(cnt)
Scon = sorted(cnt, key = lambda cnt: tuple(cnt[cnt[:,:,0].argmin()][0]))
cvs(db,cxcopy,'cxcopy',3)
lx = [] ; ly = [] ; j = -1
for i, f in enumerate (Scon):
area = cv2.contourArea(f)
x,y,w,h = cv2.boundingRect(f)
cv2.drawContours(cxcopy,[f],0,(255,255,255),1)
if db: print ' x {} contour area {}'.format(x,area)
if (area > 18 and h >10
or area > 19 and w <9
): # 18 ok except red panel needs 16
j = j + 1
possible = cmask[y:y+h, x:x+w].copy()
if j == len(rn):
j = 1; lbx = -1
else:
lbx = rn[j]
if db: print ' x {} possible n {} w {} h {}'.format(x,lbx ,w,h)
parm.lst = identifyN(possible,lbx,db)
n = parm.lst[1]
cv2.drawContours(cxcopy,[f],0,(0,255,0),1) # draw after capture
if n <> -1:
lx.append((x,n))
ly.append(n) # approximate order
fd.write('{} \n'.format(parm.lst ))
if db: print '>>>match evaluate {} <<<'.format(ly)
cvs(db,cxcopy,'cxcopy',3)
lx = sorted(lx,key = lambda (x,n):x )
lx = [b for (a,b) in lx]
return lx # list of numbers in the panel
def __init__(self,fname,db,trb):
global tmpf
self.img = cv2.imread(fname)
if not fname == "input.png": cv2.imwrite("input.png",self.img)
self.cropped = Cropx(db,self.img) # crop image
[self.wt,
self.fat,
self.h2o] = Part(self,self.cropped,db) # partition cropped image
cv2.imwrite("fatTest.png", self.fat)
cv2.imwrite("h2oTest.png", self.h2o)
cv2.imwrite("wtTest.png", self.wt)
# hunt for the appropriate number in each partitioned image
if db: cvs(db,self.wt,'weight',0)
self.nwt = xrdTyp(self.wt,'wt',db,trb)
self.nfat = xrdTyp(self.fat,'fat',db,trb)
self.nh2o = xrdTyp(self.h2o,'h2o',db,trb)
#cvs(db,self.cropped,'input',0)
return(None)
def rdWeight(imgx,typ,db):
img = stdSize(imgx,typ)
imgray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret,thresh = cv2.threshold(imgray,127,255,0)
img = thresh.copy()
cvs(1,img,'x')
y1 = 45; y2 = y1 + 322; x1 = 152; x2=200
wtx00 = img[y1:y2, x1:x2].copy()
w100 = tMatch(wtx00,typ,db) # search for a number
# cvs(1,wtx00,'y')
y1 = 45; y2 = y1 + 322; x1 = 240; x2=360
wtx01 = img[y1:y2, x1:x2].copy()
# cvs(1,wtx01,'y')
w010 = tMatch(wtx01,typ,db) # search for a number
y1 = 45; y2 = y1 + 322; x1 = 405; x2=535
wtx02 = img[y1:y2, x1:x2].copy()
cvs(0,wtx02,'yy')
w001 = tMatch(wtx02,typ,db)
y1 = 45; y2 = y1 + 322; x1 = 580; x2=720
wtx03 = img[y1:y2, x1:x2].copy()
cvs(0,wtx03,'yy')
wp1 = tMatch(wtx03,typ,db)
print w100, w010, w001, wp1
n = 100 * w100 + 10 * w010 + w001 + float(wp1 / 10.0)
print n
def findBlobs(imx,ms,mx,erd,db,tval=127):
Erd = erd
Drd = int(Erd/2)
print 'This is find blobs 1.0 ms {} mx {} erd {} drd {}'.format( ms, mx , Erd ,Drd)
imgray = cv2.cvtColor(imx,cv2.COLOR_BGR2GRAY)
ret,thresh = cv2.threshold(imgray,tval,255,0)
thresh = erode(thresh,Erd)
thresh = dilate(thresh,Drd)
#print 'db findBlobs' , db
cvs(db,thresh)
cmask = thresh.copy()
im3,cnt, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
# note lamda is expression for leftmost extreme of the contour
scon = sorted(cnt, key = lambda cnt: tuple(cnt[cnt[:,:,0].argmin()][0]))
rvl = []
for con in scon:
area = cv2.contourArea(con)
#if db: print area,
cnt_len = cv2.arcLength(con, True)
cntp = cv2.approxPolyDP(con, 0.02*cnt_len, True)
# print len(cntp) # look for suitable contours
x,y,w,h = cv2.boundingRect(con)
asp = abs( w - h )
#if db: print 'abs|w-h| {} w {} h {} '.format(asp,w,h)
if ( len(cntp) > 3 # at least 4
and asp > .1 # ie not round or square
and area > ms
and area < mx ): # note ( ... ) for implied continuation
rvl.append(con)
cv2.drawContours(imx,[con], 0, (0,255,255), 2) # yellow
else:
cv2.drawContours(imx,[con], 0, (0,0,255), 2) # red
# if db:cvs(db,imx,t=0)
return (rvl,cmask)
def identifyN(p, lb=0, db=0, typ="x"):
""" identify creates digit descriptor vectors by counting the contours under a set of
masks applied to the digit being examined. Masks are applied by setting the non mask
area of the image to zero, black. Other statistics collected are not used but could
be input to some future machine learning algorithm"""
d = np.zeros_like(p)
dx = d.copy() # nothing to see here
d = p.copy()
h, w = d.shape
# print 'id input w {} h {}'.format(w,h)
im2 = d.copy()
t0 = np.sum(im2) / 255
# cvs(db,d,'digit ' )
db = 1
d = im2.copy()
d[:, w / 3 :] = 0 # left 1/3 same as bitwise and
L = np.sum(d) / 255 #
cvs(db, d, "digit Left ")
msk = [] # initialize mask
msk = pxCount(d, msk)
d = im2.copy() # middle vert third
d[:, : w / 3] = 0 # - left 1/3
d[:, 2 * w / 3 :] = 0 # - right 1/3
Mv3 = np.sum(d) / 255
cvs(db, d, "digit v Mid3")
msk = pxCount(d, msk) #
if typ == "wt":
msk[1] = 9
d = im2.copy()
d[:, : 2 * w / 3] = 0
R = np.sum(d) / 255 # right 1/3
cvs(db, d, "digit Right ")
msk = pxCount(d, msk)
d = im2.copy() # upper 1/3
d[h / 3 :, :] = 0
T = np.sum(d) / 255
cvs(db, d, "digit Top ")
msk = pxCount(d, msk)
d = im2.copy() # middle horiz third
d[: h / 3, :] = 0 # - upper 1/3
d[2 * h / 3 :, :] = 0 # - lower 1/3
M3 = np.sum(d) / 255
cvs(db, d, " Mid 1/3")
msk = pxCount(d, msk)
d = im2.copy() # lower 1/3
d[: 2 * h / 3, :] = 0 # zero upper 2/3
B = np.sum(d) / 255
cvs(1, d, "digit Bottom ")
mm = pxCount(d, msk)
if 1:
print "elif mm == {}: n = {} # {} ".format(mm, lb, typ)
# statistics returned as descriptor
S = max(L, R, T, B) - min(L, R, T, B)
LR = abs(L - R)
TB = abs(T - B)
# identify the input
# L m R T m B # left right top bottom horiz vertical
if typ == "wt":
# if 1 == 2: pass
if mm == [2, 9, 2, 1, 2, 1]:
n = 1 # wt
elif mm == [1, 9, 1, 2, 1, 1]:
n = 3 # wt
elif mm == [2, 9, 3, 1, 4, 2]:
n = 0 # wt
elif mm == [2, 9, 2, 1, 4, 1]:
n = 0 # wt
elif mm == [2, 9, 2, 1, 2, 1]:
n = 1 # wt
elif mm == [1, 9, 1, 2, 1, 1]:
n = 4 # wt
elif mm == [2, 9, 3, 1, 4, 2]:
n = 0 # wt
elif mm == [2, 9, 2, 1, 4, 1]:
n = 0 # wt
elif mm == [1, 9, 2, 1, 2, 1]:
n = 1 # wt
elif mm == [1, 9, 1, 2, 1, 1]:
n = 4 # wt
elif mm == [2, 9, 3, 1, 4, 2]:
n = 0 # wt
elif mm == [1, 9, 1, 1, 1, 1]:
n = 8 # wt
elif mm == [1, 9, 2, 1, 2, 1]:
n = 1 # wt
elif mm == [1, 9, 2, 2, 2, 1]:
n = 4 # wt
elif mm == [3, 9, 3, 1, 4, 3]:
n = 0 # wt
elif mm == [1, 9, 1, 1, 1, 1]:
n = 8 # wt
elif mm == [1, 9, 2, 1, 2, 1]:
n = 1 # wt
elif mm == [1, 9, 1, 2, 1, 1]:
n = 4 # wt
elif mm == [2, 9, 3, 1, 4, 2]:
n = 0 # wt
elif mm == [1, 9, 2, 1, 1, 1]:
n = 2 # wt
elif mm == [1, 9, 2, 1, 2, 1]:
n = 1 # wt
elif mm == [1, 9, 1, 2, 1, 1]:
n = 4 # wt
elif mm == [2, 9, 3, 1, 4, 2]:
n = 0 # wt
elif mm == [1, 9, 1, 2, 1, 1]:
n = 4 # wt
elif mm == [2, 9, 2, 1, 2, 1]:
n = 1 # wt
elif mm == [1, 9, 2, 2, 2, 1]:
n = 4 # wt
elif mm == [2, 9, 3, 1, 4, 2]:
n = 0 # wt
elif mm == [1, 9, 1, 1, 1, 1]:
n = 2 # wt
elif mm == [1, 9, 2, 1, 2, 1]:
n = 1 # wt
elif mm == [2, 9, 1, 1, 1, 1]:
n = 3 # wt
elif mm == [1, 9, 1, 2, 1, 1]:
n = 4 # wt
elif mm == [1, 9, 2, 1, 1, 2]:
n = 8 # wt
elif mm == [1, 9, 1, 1, 1, 1]:
n = 8 # wt
else:
n = -1
elif typ in ["fat", "h2o"]:
if 1 == 2:
pass
elif mm == [2, 2, 3, 1, 4, 2]:
n = 0
elif mm == [2, 3, 3, 1, 4, 2]:
n = 0
elif mm == [2, 2, 2, 1, 2, 1]:
n = 0
elif mm == [2, 2, 3, 1, 4, 2]:
n = 0
elif mm == [3, 2, 2, 1, 4, 2]:
n = 0
elif mm == [2, 2, 3, 1, 4, 2]:
n = 0
elif mm == [2, 3, 3, 1, 4, 2]:
n = 0
elif mm == [3, 2, 2, 1, 4, 2]:
n = 0
elif mm == [2, 3, 3, 1, 4, 2]:
n = 0
elif mm == [2, 2, 2, 1, 2, 1]:
n = 1
elif mm == [2, 2, 1, 1, 2, 1]:
n = 1
elif mm == [1, 2, 2, 1, 2, 1]:
n = 1
elif mm == [2, 2, 2, 1, 2, 1]:
n = 1
elif mm == [1, 0, 2, 1, 2, 2]:
n = 1
elif mm == [2, 2, 2, 1, 2, 1]:
n = 1
elif mm == [1, 2, 2, 1, 2, 1]:
n = 1
elif mm == [0, 1, 3, 1, 2, 2]:
n = 1
elif mm == [0, 1, 2, 1, 2, 1]:
n = 1
elif mm == [1, 4, 1, 1, 1, 1]:
n = 2
elif mm == [2, 3, 2, 1, 1, 2]:
n = 2
elif mm == [1, 3, 3, 1, 1, 2]:
n = 2
elif mm == [2, 3, 3, 1, 1, 2]:
n = 2
elif mm == [1, 3, 3, 1, 1, 2]:
n = 2
elif mm == [3, 2, 1, 1, 1, 1]:
n = 3
elif mm == [3, 2, 2, 1, 1, 2]:
n = 3
elif mm == [2, 2, 1, 1, 1, 1]:
n = 3
elif mm == [2, 3, 1, 1, 1, 2]:
n = 3
elif mm == [1, 1, 2, 2, 2, 1]:
n = 4
elif mm == [2, 1, 2, 1, 1, 1]:
n = 5
elif mm == [1, 1, 2, 1, 1, 1]:
n = 6
elif mm == [0, 3, 2, 1, 2, 1]:
n = 7
elif mm == [0, 2, 2, 1, 2, 1]:
n = 7
elif mm == [1, 1, 2, 1, 2, 2]:
n = 7
elif mm == [1, 3, 2, 1, 2, 1]:
n = 7
elif mm == [0, 3, 2, 1, 2, 1]:
n = 7
elif mm == [0, 2, 2, 1, 2, 1]:
n = 7
elif mm == [0, 3, 2, 1, 2, 1]:
n = 7
elif mm == [1, 3, 2, 1, 2, 1]:
n = 7
elif mm == [0, 3, 2, 1, 2, 1]:
n = 7
elif mm == [1, 1, 1, 1, 1, 1]:
n = 8
elif mm == [3, 2, 1, 1, 1, 2]:
n = 9
else:
n = -1
else:
n = -1
lb = int(lb)
descriptor.lst = [lb, n, t0, L, R, T, B, S, LR, TB, M3, Mv3]
d = d - d
return n
lb = int(lb)
descriptor.lst = [lb, n, t0, L, R, T, B, S, LR, TB, M3, Mv3]
d = d - d
return n
def prtTable(digits, labels):
db = 1
dts = np.zeros((10, descriptor.lstN), dtype="int32")
for d, lb in zip(digits, labels):
# print ' ',descriptor.head
## if lb in range(10):
n = identifyN(d, lb, db)
# print 'input {} id n {} '.format(lb,n)
if __name__ == "__main__":
db = 1
for typ in ["wt", "h2o", "fat"]:
fwt = typ + "Test.png"
imgx = cv2.imread(fwt)
cvs(db, imgx, "DigitStat")
print xrdTyp(imgx, typ, 0)
# identifyN(imgx,0,1)
cvd()
## print __doc__
## digits, labels = cwload_digits_lst("c:\\Train\\*.png" )
## prtTable(digits,labels)
## cvd()
cv2.imwrite("wtTest.png", self.wt)
# hunt for the appropriate number in each partitioned image
if db: cvs(db,self.wt,'weight',0)
self.nwt = xrdTyp(self.wt,'wt',db,trb)
self.nfat = xrdTyp(self.fat,'fat',db,trb)
self.nh2o = xrdTyp(self.h2o,'h2o',db,trb)
#cvs(db,self.cropped,'input',0)
return(None)
if __name__ == '__main__':
global db,trb
db = 0
filename = 'ScTest/T2015Doris/20150130_093919.jpg'
filename = "input.png"
print 'weight.py', filename
s = Scale(filename,db,trb)
print s.name, 'wt is' ,s.nwt,'iHunt global'
for i in iHunt:
print i
cvs(db,s.cropped ,'test image display',0) # test image display
cvd()
idGen(0,trb) # close file and cleanup
trb.close()
print 'finishing idGen'
line = '''
\treturn n
if __name__ == '__main__':
db = 1
print idRule(['wt', 3,1,1])
'''
trb.write(line)
if __name__ == '__main__':
db = 1
trb = open('0idGRule.py','w') ## open file fo r write
idGen(1,trb) # open file
for typ in ['fat']:#,'wt' , 'fat']:
fwt = typ + 'Test.png'
imgx = cv2.imread(fwt )
print xrdTyp(imgx,typ,db,trb )
cvs(db,imgx,'DigitStat')
#identifyN(imgx,0,1)
#cvs(db,imgx,'Digitstat')
idGen(0,trb) # close file and cleanup
trb.close()
cvd()
h,w = img.shape[:2]
print 'image h {} w {}'.format(h,w)
y1 = 0 ; y2 = int(.1 * h)
x1 = int (.75 * w) ; x2 = int(w)
a = img[ y1:y2, x1:x2 ].copy()
return(h,w,a)
if __name__ == '__main__':
global db
db = 1
manifest = 'testFile.txt'
mdata = open(manifest,'w')
path = 'pics/sc*' #os.getcwd() #get the current directory
files = glob.glob( path)
for fx in files:
img = cv2.imread(fx)
h,w,ROI = Part(img,db)
cvs(1, ROI, 'input') #cv2.imshow('input',ROI) # cvs(db,ROI)
usr = raw_input('what numbers?')
print fx , 'has', usr
if usr == 'q':
break
print >> mdata, usr , fx # create a list of user anotated filenames
cvd()
mdata.close()
cvd()
def identifyN(p,lb=0,db=0):
''' identify creates digit descriptor vectors by counting the contours under a set of
masks applied to the digit being examined. Masks are applied by setting the non mask
area of the image to zero, black. Other statistics collected are not used but could
be input to some future machine learning algorithm'''
d = np.zeros_like(p)
d = p.copy()
h,w = d.shape
#qqprint 'id input w {} h {}'.format(w,h)
im2 = d.copy()
t0 = np.sum(im2) / 255
#cvs(db,d,'digit ' )
d = im2.copy()
d[:, w/3:] = 0 # left 1/3 same as bitwise and
L = np.sum(d)/255 #
cvs(db,d,'digit Left ' )
msk = [] # initialize mask
msk = pxCount(d,msk)
d = im2.copy() # middle vert third
d[ :, :w/3 ] = 0 # - left 1/3
d[ :, 2*w/3: ] = 0 # - right 1/3
Mv3 = np.sum(d)/255
cvs(db,d,'digit v Mid3' )
msk = pxCount(d,msk )
d = im2.copy()
d[:, :2*w/3] = 0
R = np.sum(d)/255 # right 1/3
cvs(db,d,'digit Right ' )
msk = pxCount(d,msk)
d = im2.copy() # upper 1/3
d[ h/3:, : ] = 0
T = np.sum(d)/255
cvs(db,d,'digit Top ' )
msk = pxCount(d,msk)
d = im2.copy() # middle horiz third
d[ :h/3, : ] = 0 # - upper 1/3
d[ 2*h/3:, : ] = 0 # - lower 1/3
M3 = np.sum(d)/255
cvs(db,d,' Mid 1/3' )
msk = pxCount(d,msk)
d = im2.copy() # lower 1/3
d[ :2*h/3, : ] = 0 # zero upper 2/3
B = np.sum(d)/255
cvs(1,d,'digit Bottom ' )
msk = pxCount(d,msk)
if db: print 'elif mm == {}: n = {}'.format( mm, lb)
# statistics returned as descriptor
S = ( max(L,R,T,B) - min(L,R,T,B))
LR = abs(L-R)
TB = abs(T-B)
# identify the input
# L m R T m B # left right top bottom horiz vertical
if mm == [9,9,9,9,9,9,9,9]: n = 0
elif mm == [0, 2, 2, 1, 2, 1]: n = 1
elif mm == [3, 2, 2, 1, 1, 2]: n = 3
elif mm == [2, 1, 2, 1, 1, 2]: n = 8
elif mm == [0, 2, 2, 1, 2, 1]: n = 1
elif mm == [1, 3, 4, 2, 7, 1]: n = 4
elif mm == [3, 4, 6, 3, 4, 3]: n = 6
elif mm == [2, 4, 2, 2, 4, 1]: n = 0
elif mm == [1, 2, 2, 1, 2, 1]: n = 7
elif mm == [2, 3, 2, 1, 4, 1]: n = 0
elif mm == [2, 3, 2, 1, 4, 1]: n = 0
else :n = -1
lb = int(lb)
descriptor.lst = [lb,n,t0,L,R,T,B,S,LR,TB,M3,Mv3 ]
d = d - d
return n
if __name__ == '__main__':
db = False
tst = 'wt'
imgx = cv2.imread(tst +'Test.png')
#imgx = cv2.imread(fn)
img = stdSize(imgx,tst) #cv2.resize(imgx, (1040,410))
# srt = img.copy()
h,w = img.shape[:2]
#cvs(db,img)
ms = 325; mx = 10000; erd =12; tx=160
(cnt,cmask) = findBlobs( img,ms,mx,erd,db,tx) # will modify img to show cnt
cnt = sorted(cnt, key = lambda cnt: tuple(cnt[cnt[:,:,0].argmin()][0]))
print ' input is {} width {} height {} ms {} mx {} tval {} '\
.format(tst,w, h,ms,mx,tx)
x,y,w,h = boundsBlob(cnt)
print (x,y,w,h)
tl = (x-10,y-10) ; br = ( x + w+10, y+ h+10) # 10 for more room
cv2.rectangle(img,(tl),(br),(255,255,255),5) # white rectangle
for i, f in enumerate (cnt):
cv2.drawContours(img,[f],0,(255,0,0),2)
## x,y,w,h = cv2.boundingRect(f)
## a = cv2.contourArea(f)
## asp = round(abs( 1.00 - float(w)/float(h) ),2)
## print 'x {} y {}\tw {}\th {}\ta {}\tasp {} '.format( x, y , w, h,a,asp)
cvs(1,cmask)
cvs(1,img,t=0)
cvd()
(cnt,cmask) = findBlobs( img,ms,mx,erd,db,tx) # will modify img to show cnt
cnt = sorted(cnt, key = lambda cnt: tuple(cnt[cnt[:,:,0].argmin()][0]))
print ' input is {} width {} height {} ms {} mx {} tval {} '\
.format(tst,w, h,ms,mx,tx)
print len(cnt) ,'blobs were found shape is list'
## x,y,w,h = cv2.boundingRect(cnt) # needs single image or array
## print 'boundingRec {} {} {} {}'.format( x,y,w,h)
a,b = analBlob2(cnt)
print a,b, 'analBlob2'
x,y, w,h = analBlob(cnt)
tl = (x-10,y-10) ; br = ( x + w+10, y+ h+10) # 10 for more room
cv2.rectangle(cmask,(tl),(br),(255,255,255),5) # white rectangle
## print 'min x {}'.format(minx)
for i, f in enumerate (cnt):
#print '{} cnt {}'.format(i,f)
cv2.drawContours(img,[f],0,(255,0,0),1)
#print 'contour array f[0] ', f[0] ,
x,y,w,h = cv2.boundingRect(f)
a = cv2.contourArea(f)
asp = round(abs( 1.00 - float(w)/float(h) ),2)
print 'x {} y {}\tw {}\th {}\ta {}\tasp {} '.format( x, y , w, h,a,asp)
cv2.rectangle(img,(tl),(br),(255,255,255),5)
cvs(1,img,'img 3',3)
cvd()
def tMatch(im1,typ,db):
if db: cvs(db,im1)
path = ('Train/pxl/{}P*.png'.format( typ ) ) #os.getcwd()
#print path
files = glob.glob(path)
rtn = ['0','0','0','0','0','0','0']
for n,f in enumerate(sorted(files)): # read templates from Train
if db: print 'Tmatch', f[9:-4],
im2 = cv2.imread(f,0)
w1, h1 = im1.shape[::-1] # image
w2, h2 = im2.shape[::-1] # panel
if db: print 'image w {} h {} panel w {} h {}'.format(w1,h1,w2,h2)
if w1-w2 < -30:
yt1 =int(.5*(h2-h1)); yt2 = yt1 + h1 ; xt1 = 0 ; xt2= w1
if db: print 'crop ',yt1, yt2, xt1, xt2
om2 = im2[yt1:yt2, xt1:xt2].copy() # panel crop
else:
om2 = cv2.resize(im2, ( w1, h1 ) ) # panel resize
w1, h1 = im1.shape[::-1] # image
w2, h2 = om2.shape[::-1] # panel
if db: print 'om1 w {}, h {} im2 w {} h {}'.format(w1, h1,w2, h2)
res = cv2.bitwise_and(im1,om2)
pxlcnt = np.sum(res) / 255 # count of pixels
if db:
print pxlcnt, 'count of pixels'
cvs(db,res,'z')
PL= {
# pxl limit
'wt' : [ 300 ],
'fat': [ 300 ],
'h2o': [ 200 ]
}
if pxlcnt > PL[typ][0] :
rtn[n] = '1'
drtn = ''.join(rtn)
''' ptrn translates the results of the pattern analysis into numbers '''
ptrn = {
'1110111' : 0,
'1100000' : 1, # leading 1 in weight
'0000011' : 1, # internal 1
'1011101' : 2,
'0011111' : 3,
'0101011' : 4,
'0111110' : 5,
'1111110' : 6,
'0000111' : 7,
'1111111' : 8,
'0111111' : 9
}
try:
n = ptrn[drtn]
print 'number {} ptrn {} '.format(n , drtn)
return(n)
except KeyError:
print 'bad pattern here ' , drtn
return 0
def identifyN(p,lb=0,db=0):
msk = []
def pxCount(d,lb = 0,s=0 ):
global msk
jk,cnt4d, hier = cv2.findContours(d,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
m = len(cnt4d)
## if m > 0:
## for cn in cnt4d:
## print cv2.contourArea(cn)
if s == 1:
msk = []
msk.append(m)
return msk
d = np.zeros_like(p)
d = p.copy()
h,w = d.shape
im2 = d.copy()
t0 = np.sum(im2) / 255
cvs(db,d,'digit ',5)
d[:, w/2:] = 0 # same as bitwise and
L = np.sum(d)/255
cvs(db,d,'digit ',5)
pxCount(d,lb,1)
d = im2.copy()
d[:, :w/2] = 0
R = np.sum(d)/255
cvs(db,d,'digit ',5)
pxCount(d)
d = im2.copy() # upper half
d[ h/2:, : ] = 0
T = np.sum(d)/255
cvs(db,d,'digit ',5)
pxCount(d)
d = im2.copy() # lower half
d[ :h/2, : ] = 0 # zero upper half
B = np.sum(d)/255
cvs(db,d,'digit ',5)
pxCount(d)
d = im2.copy() # middle horiz third
d[ :h/3, : ] = 0 # - upper 1/3
d[ 2*h/3:, : ] = 0 # - lower 1/3
M3 = np.sum(d)/255
cvs(db,d,'digit h Mid 1/3',5)
pxCount(d )
d = im2.copy() # middle vert third
d[ :, :w/3 ] = 0 # - left 1/3
d[ :, 2*w/3: ] = 0 # - right 1/3
Mv3 = np.sum(d)/255
cvs(db,d,'digit v Mid3',5)
mm = pxCount(d ,lb,2)
if db: print 'final mask for {} {}'.format(lb,mm)
# statistics
S = ( max(L,R,T,B) - min(L,R,T,B))
LR = abs(L-R)
TB = abs(T-B)
# identify the input
if (t0 > 75
# L R T B h v # left right top bottom horiz vertical
and mm == [1, 1, 1, 1, 2, 2]): n = 0
elif mm == [1, 1, 1, 1, 2, 1]: n = 1
elif mm == [0, 1, 1, 1, 2, 1]: n = 1 # hack for bad threshold
elif mm == [1, 1, 1, 1, 1, 1] and h == 11 : n = 1
elif mm == [2, 2, 1, 1, 2, 3]: n = 2
elif mm == [1, 2, 1, 1, 1, 3] :n = 2 # noisy 2
elif mm == [3, 1, 1, 1, 1, 3]: n = 3
elif mm == [3, 1, 1, 1, 2, 3]: n = 3 # noisy 3
elif mm == [1, 1, 1, 1, 2, 2]: n = 4
elif mm == [1, 1, 1, 1, 1, 2]: n = 4 # noisy 4
elif mm == [2, 2, 1, 2, 1, 3]: n = 5
elif mm == [1, 2, 1, 2, 1, 3]: n = 6
elif mm == [2, 1, 1, 1, 1, 2]: n = 7
elif mm == [1, 1, 2, 2, 1, 3]: n = 8
elif mm == [2, 1, 1, 1, 1, 3]: n = 9
elif mm == [1, 1, 1, 2, 1, 3]: n = 9 # noisy 9
else :n = -1
lb = int(lb)
parm.lst = [lb,n,t0,L,R,T,B,S,LR,TB,M3,Mv3 ]
d = d - d
return parm.lst
rn1 = '145283595148'
rn2 = '3021312168188'
rn3 = '177438101129'
rn4 = '10872676794'
rn5 = '99201918'
rn6 = '3242604254'
rn7 = '14753612200200'
rn8 = '691482738'
dfile = 'digits.txt'
fd = open(dfile,'w')
for f,rn in zip( [fx5 ],[rn5 ]): #,fx2,fx3,fx4] : #,fx2,fx3,fx4]:
h,w,ROI = Part(f,db)
cvs(db,ROI,'roi',3)
listx = evalGame(ROI,fd,rn,db) # ::-1 is string reverse
print 'eval game Harr ',listx
print ' rn value was ',map(int,list(rn))
#print 'eval game MatchShape',listy
print f
fd.close()
printsort()
cvd()
