def lowrescv(limg, rimg, dmax=96):
shp = tf.shape(limg)[1:]
ht, wd = shp[0], shp[1]
ys, xs = tf.mod(ht, 2), tf.mod(wd, 2)
limg = tf.pad(
limg, tf.stack([[0, 0],
tf.stack([ys, 0]),
tf.stack([xs, 0]), [0, 0]]), 'SYMMETRIC')
limg = tf.nn.avg_pool(limg, [1, 2, 2, 1], [1, 2, 2, 1], 'VALID')
rimg = tf.pad(
rimg, tf.stack([[0, 0],
tf.stack([ys, 0]),
tf.stack([xs, 0]), [0, 0]]), 'SYMMETRIC')
rimg = tf.nn.avg_pool(rimg, [1, 2, 2, 1], [1, 2, 2, 1], 'VALID')
lrl = limg
limg, rimg = tf.image.rgb_to_yuv(limg), tf.image.rgb_to_yuv(rimg)
limg, rimg = slib.census(limg), slib.census(rimg)
cv = slib.hamming(limg, rimg, 0, 0, (shp[0] + 1) // 2, (shp[1] + 1) // 2,
dmax)
cv = (cv - [11.08282948, 0.02175535, 0.02679042
]) * [0.1949711, 35.91432953, 26.79782867]
shp = tf.shape(cv)
cv = tf.reshape(cv, tf.stack([shp[0], shp[1], shp[2], -1]))
return cv, lrl
def lowrescv(limg, rimg, imsz, dmax=96):
limg = tf.nn.avg_pool(limg, [1, 2, 2, 1], [1, 2, 2, 1], 'VALID')
rimg = tf.nn.avg_pool(rimg, [1, 2, 2, 1], [1, 2, 2, 1], 'VALID')
lim0 = limg
limg, rimg = tf.image.rgb_to_yuv(limg), tf.image.rgb_to_yuv(rimg)
limg, rimg = slib.census(limg), slib.census(rimg)
cv = slib.hamming(limg, rimg, 0, 0, imsz[0] // 2, imsz[1] // 2, dmax)
cv = (cv - [11.08282948, 0.02175535, 0.02679042
]) * [0.1949711, 35.91432953, 26.79782867]
shp = tf.shape(cv)
cv = tf.reshape(cv, tf.stack([shp[0], shp[1], shp[2], -1]))
return cv, lim0