'''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)

jk,cnt4d, hier = cv2.findContours(d,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)

m = len(cnt4d)

if m: msk.append(m)

else: msk.append(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'''

im0 = p.copy(); imt0 = 'input digit'

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)

imwk = p.copy()

t0 = np.sum(imwk) / 255

#cvs(db,d,'digit ' )

msk = [] # initialize mask

d = imwk.copy()

d[:, w/3:] = 0 # left 1/3 same as bitwise and

L = np.sum(d)/255 #

#cvs(db,d,'digit Left ' )

im1 = d.copy(); imt1 = 'digit Left'

msk = pxCount(d,msk)

d = imwk.copy() # middle vert third

d[ :, :2*w/5 ] = 0 # - left 1/3

d[ :, 3*w/6: ] = 0 # - right 1/3

Mv3 = np.sum(d)/255

#cvs(db,d,'digit v Mid 5th' )

im2 = d.copy(); imt2 = 'Middle 5th'

msk = pxCount(d ,msk ) #

# if typ == 'wt': msk[1] = 9

d = imwk.copy()

d[:, :2*w/3] = 0

R = np.sum(d)/255 # right 1/3

#cvs(db,d,'digit Right ' )

im3 = d.copy(); imt3 = 'digit Right'

msk = pxCount(d,msk)

d = imwk.copy() # upper 2nd Q

d[ :1*h/5, : ] = 0 # - upper 2/5

d[ 2*h/6:, : ] = 0 # - lower

T = np.sum(d)/255

#cvs(db,d,'digit T Q ' )

im4 = d.copy(); imt4 = 'Top quarter'

msk = pxCount(d,msk)

d = imwk.copy() # lower 1/4

d[ :3*h/5, : ] = 0 # - upper 2/5

d[ 4*h/5:, : ] = 0

B = np.sum(d)/255

#cvs(db,d,'digit Bottom Q' )

im5 = d.copy(); imt5 = 'Bottom quarter'

msk.insert(0,typ )

mm = pxCount(d,msk)

trb.write( '\telif mm == {}: n = {} \n'.format( mm , lb ) )

print 'digit mask elif mm == {}: n = {}'.format(mm, lb)

n = idRule( mm)

d = d - d

titles = [imt1,imt2,imt3,imt4,imt5,imt0]

images = [im1, im2, im3, im4, im5, im0]

if db : Show(titles,images)

for i in xrange(6):

plt.subplot(2,3,i+1),plt.imshow(images[i],'gray')

plt.title(titles[i])

plt.xticks([]),plt.yticks([])

if s == True:

line = '''

def idRule(mm):

\tn = -1

\tif 1 == 2: pass

'''

else:

print 'finishing idGen'

line = '''

\treturn n

if __name__ == '__main__':

db = 1

print idRule(['wt', 3,1,1])

'''