def lab(filename,extra):
I = Image.open(filename)
L = ct.rgb_to_cielab_i_X(I,0)
L = singularityCL.spec(L,extra)
if(extra[3]):
a = ct.rgb_to_cielab_i_X(I,1)
b = ct.rgb_to_cielab_i_X(I,2)
a = singularityCL.spec(a,extra)
b = singularityCL.spec(b,extra)
return np.vstack((L,a,b))
return np.array(L)
def inner_localMF(I, N, extra):
if(N==0):
return sg.spec(I,extra)
w,h = I.shape
# crop?
return np.vstack(( inner_localMF(I[0:w/2,0:h/2], N-1, extra), \
inner_localMF(I[0:w/2,h/2:h], N-1, extra), \
inner_localMF(I[w/2:w,0:h/2], N-1, extra), \
inner_localMF(I[w/2:w,h/2:w], N-1, extra) ))
def extractMF(filename):
features_fname = filename + '.sift'
sift.process_image(filename, features_fname)
locs, descriptors = sift.read_features_from_file(features_fname)
sh = locs.shape[0]
res = np.zeros((sh,SIZE_LOCAL_FEATURE)).astype(np.float32)
extra = [20,False,True,False,0,0,0]
WIN = 5
for i in range(sh):
x = np.int32(round(locs[i][0]))
y = np.int32(round(locs[i][1]))
I = Image.open(filename)
Nx,Ny = I.size
a = sg.spec(I.crop((max(x-WIN,0),max(y-WIN,0),min(x+WIN,Nx-1),min(y+WIN,Ny-1))),extra)
res[i] = a
print res.shape
return res
