def whiteninggray(images,dc=True):
"""
This function automatically whites gray images (Image only, data range 0-255, precalculated filter).
Images is a 3d-ndarray with dtype=float32, with dimension ordering:
Number_Of_Data, YSize, XSize
Return 3d-ndarray with dtype=float32, with dimension ordering:
Number_Of_Data, YSize-6, XSize-6
"""
global _WHITE_COE
_l.ASSERTTYPE(images)
if len(images.shape)!=3: raise Exception, "3d-ndarray needed."
#Remove DC Coefficient
if dc:
if images.shape[0]==1:
tmp=images
else:
tmp=_l.empty(images.shape)
_l.removeDC(images.reshape(images.shape[0],images.shape[1]*images.shape[2]),tmp.reshape(images.shape[0],images.shape[1]*images.shape[2]))
else:
tmp=images
#Convolution to get result
out=_l.empty((images.shape[0],1,images.shape[1]-6,images.shape[2]-6))
_l.convolution4D(tmp.reshape(images.shape[0],images.shape[1],images.shape[2],1),_WHITE_COE,out)
return out.reshape(images.shape[0],images.shape[1]-6,images.shape[2]-6)
def rgb2cielab(image):
"""
This function translates a RGB image (0~255 for each pixel) to a CIEL*A*B* image (0~100 for L*, -127~127 for A*, -127~127 for B*)
Image is a 3d-ndarray with dtype=uint8, with dimension ordering:
YSize, XSize, [R,G,B]
Returns 3d-ndarray with dtype=float32, with dimension ordering:
YSize, XSize, [L,A,B]
"""
global _xyz,_lfunc,_ffunc
ishape=image.shape
dmat=numpy.array(image.reshape((-1,3)),'f')
dmat/=255
dmat=dmat.dot(_xyz)
x=numpy.array(dmat[:,0],'f')
y=numpy.array(dmat[:,1],'f')
z=numpy.array(dmat[:,2],'f')
x/=0.950456
z/=1.088754
l=_l.empty((dmat.shape[0],))
_l.transform(_lfunc,l,y)
fx=_l.empty((dmat.shape[0],))
fy=_l.empty((dmat.shape[0],))
fz=_l.empty((dmat.shape[0],))
_l.transform(_ffunc,fx,x)
_l.transform(_ffunc,fy,y)
_l.transform(_ffunc,fz,z)
a=500*(fx-fy)
b=500*(fy-fz)
dmat[:,0]=l
dmat[:,1]=a
dmat[:,2]=b
return dmat.reshape(ishape)
def whiteningcielab(image):
"""
This function does whitening on a CIEL*A*B* colorspace color image.
It uses predefined filter to do convolution on image.
Image is a 3d-ndarray with dtype=float32, with dimension ordering:
YSize, XSize, [L,A,B]
Returns 3d-ndarray with dtype=float32, with dimension ordering:
YSize-2, XSize-2, [w_L,w_A,w_B]
"""
global _WHITE_CIELAB_COE
_l.ASSERTTYPE(image)
out=_l.empty((1,3,image.shape[0]-2,image.shape[1]-2))
ireshape=image.reshape((1,image.shape[0],image.shape[1],image.shape[2]))
_l.convolution4D(ireshape,_WHITE_CIELAB_COE,out)
return numpy.array(out.reshape((3,image.shape[0]-2,image.shape[1]-2)).transpose((1,2,0)),'f')
