h,w = img.shape

scale = target_height*1.0/h

target_width = int(scale*w)

output = interpolation.affine_transform( 1.0*img,eye(2)/scale,

order=order,

output_shape=(target_height,target_width),

mode='constant',cval=cval )

output = array(output,dtype=dtype)

def __init__(self,target_height=48,params=(4,1.0,0.3)):

self.target_height = target_height

self.range,self.smoothness,self.extra = params

def setHeight(self,target_height):

self.target_height = target_height

def measure(self,line):

h,w = line.shape

smoothed = filters.gaussian_filter(line,(h*0.5,h*self.smoothness),mode='constant')

smoothed += 0.001*filters.uniform_filter(smoothed,(h*0.5,w),mode='constant')

self.shape = (h,w)

a = argmax(smoothed,axis=0)

a = filters.gaussian_filter(a,h*self.extra)

self.center = array(a,'i')

deltas = abs(arange(h)[:,newaxis]-self.center[newaxis,:])

self.mad = mean(deltas[line!=0])

self.r = int(1+self.range*self.mad)

def dewarp(self,img,cval=0,dtype=dtype('f')):

assert img.shape==self.shape

h,w = img.shape

hpadding = self.r

padded = vstack([cval*ones((hpadding,w)),img,cval*ones((hpadding,w))])

center = self.center + hpadding

dewarped = [padded[center[i]-self.r:center[i]+self.r,i] for i in range(w)]

dewarped = array(dewarped,dtype=dtype).T

return dewarped

def normalize(self,img,order=1,dtype=dtype('f'),cval=0):

dewarped = self.dewarp(img,cval=cval,dtype=dtype)

h,w = dewarped.shape

scaled = scale_to_h(dewarped,self.target_height,order=order,dtype=dtype,cval=cval)

return scaled