def hunt(imgx,typ ,db):
global xtyp,tfile
imgy = stdSize(imgx,typ)
hcnt = 0
xtyp = typ
typex = {
# lim limh ms mx dz
'wt' : [130.0,150.0, 450, 4000, 60],
'h2o': [ 60.0, 80.0, 300, 1500, 35],
'fat': [ 9.0, 15.0, 500, 3000, 45] #300
}
exlist = {
'wt' : [8, 2, 11 ],
'h2o' : [2, 8 ],
'fat' : [7, 2 ]
} # 200 5 600 3000 works h20
txlist = {
'wt' : [ 160, 140 ],
'h2o' : [ 127 ,190, 160, 140 ], #160, 140, 205 ],
'fat' : [ 160, 190, 205 ]
}
lim = typex[typ][0] #limt[0]
limh = typex[typ][1]
ms = typex[typ][2]
mx = typex[typ][3]
dz = typex[typ][4]
#imgy.save(Gd)
for iex in exlist[typ]:
if db: print '-----rdNumber ms', ms, ' mx', mx #, imgy.area()
if db: print( ['erode image',iex])
for tx in txlist[typ]:
#img = imgy.binarize(tx).invert()
#img = imgy.erode(iex)
img = imgy.copy()
cvs(db,img)
hcnt = hcnt + 1
# n is the pattern, rmx the max size, rms min size for iHunt logs
n,rmx,rms = rdNumber(img,ex=iex, ms=ms ,dz=dz , mx = mx ,tval=tx )
if db: print ' Hunt 1 n ', hcnt, n # exit here
nn = 0; j = -1
n.reverse()
#nn = 100 * n[1] + 10 * n[2] + n[3] + n[4]/10.0
if db :
print 'n' ,n
#cpause(['nreverse',n],Gd)
for j, xin in enumerate(n):
nn = nn + xin * 10**(j-1)
if (nn > lim) and nn < limh:
iHunt.append((typ, hcnt, iex, tx, ms,rms, mx,rmx, nn ))
print 'hunt rtn' ,nn
cvs(db,img,typ)
return(nn)
print '>>>>>>>>>>>>>>>>>hunt ' ,typ, 'failed'
return (0) # eliminate hunt2 for now
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 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
