def upsample_and_correlate(im, filt, stride=(2,2), apron=(0,0)):
#print 'u&c shapes', im.shape, filt.shape
if im.ndim is 2:
tmp = zeros( array(im.shape) * array(stride) + (array(apron)*2+1))
tmp[apron[0]:-apron[0]:stride[0], apron[0]:-apron[0]:stride[0]] = im
elif im.ndim is 3:
target_shape = concatenate(((array(im.shape[0:2]) * array(stride)
+ array(apron)), [im.shape[2]]))
tmp = zeros(target_shape)
tmp[apron[0]-1:-apron[0]+2:stride[0],
apron[1]-1:-apron[1]+2:stride[1],:] = im
f = filt.copy()
f.shape = (1, f.shape[0], f.shape[1], 1)
return fbcorr(tmp, f, mode='same')
def correlate_and_downsample(im, filt, stride=1):
#print 'c&d shapes', im.shape, filt.shape
f = filt.copy()
if f.ndim is not 4 and f.ndim - im.ndim != 1:
if im.ndim is 2:
f.shape = (1, filt.shape[0], filt.shape[1])
elif im.ndim is 3:
f.shape = (1, filt.shape[0], filt.shape[1], 1)
use_cthor = True
if use_cthor:
p='cthor'
pkw = {'variant':'simple:debug'}
else:
p = 'scipy_naive'
pkw = {}
result = fbcorr(im, f, mode='same', stride=stride,
plugin=p, plugin_kwargs=pkw)
return result
