with tf.Session() as session:
limage = tf.placeholder(tf.float32, [None, None, None, num_channels],
name='limage')
rimage = tf.placeholder(tf.float32, [None, None, None, num_channels],
name='rimage')
targets = tf.placeholder(tf.float32, [None, FLAGS.disp_range],
name='targets')
snet = nf.create(limage, rimage, targets, FLAGS.net_type)
lmap = tf.placeholder(tf.float32, [None, None, None, 64], name='lmap')
rmap = tf.placeholder(tf.float32, [None, None, None, 64], name='rmap')
map_prod = nf.map_inner_product(lmap, rmap)
saver = tf.train.Saver()
saver.restore(session, tf.train.latest_checkpoint(FLAGS.model_dir))
for i in range(FLAGS.start_id, FLAGS.start_id + FLAGS.num_imgs):
file_id = file_ids[i]
if FLAGS.data_version == 'kitti2015':
linput = misc.imread(
('%s/image_2/%06d_10.png') % (FLAGS.data_root, file_id))
rinput = misc.imread(
('%s/image_3/%06d_10.png') % (FLAGS.data_root, file_id))
elif FLAGS.data_version == 'kitti2012':
linput = misc.imread(
def main(_):
print(FLAGS.util_root)
np.random.seed(123)
file_ids = np.fromfile(os.path.join(FLAGS.util_root, 'myPerm.bin'), '<f4')
if FLAGS.data_version == 'kitti2015':
num_channels = 3
elif FLAGS.data_version == 'kitti2012':
num_channels = 1
scale_factor = 255 / (FLAGS.disp_range - 1)
if not os.path.exists(FLAGS.out_dir):
os.makedirs(FLAGS.out_dir)
with tf.Session() as session:
limage = tf.placeholder(tf.float32, [None, None, None, num_channels],
name='limage')
rimage = tf.placeholder(tf.float32, [None, None, None, num_channels],
name='rimage')
targets = tf.placeholder(tf.float32, [None, FLAGS.disp_range],
name='targets')
snet = nf.create(limage, rimage, targets, FLAGS.net_type)
lmap = tf.placeholder(tf.float32, [None, None, None, 64], name='lmap')
rmap = tf.placeholder(tf.float32, [None, None, None, 64], name='rmap')
map_prod = nf.map_inner_product(lmap, rmap)
saver = tf.train.Saver()
saver.restore(session, tf.train.latest_checkpoint(FLAGS.model_dir))
for i in range(FLAGS.start_id, FLAGS.start_id + FLAGS.num_imgs):
file_id = file_ids[i]
if FLAGS.data_version == 'kitti2015':
linput = misc.imread(
('%s/image_2/%06d_10.png') % (FLAGS.data_root, file_id))
rinput = misc.imread(
('%s/image_3/%06d_10.png') % (FLAGS.data_root, file_id))
elif FLAGS.data_version == 'kitti2012':
linput = misc.imread(
('%s/image_0/%06d_10.png') % (FLAGS.data_root, file_id))
rinput = misc.imread(
('%s/image_1/%06d_10.png') % (FLAGS.data_root, file_id))
linput = (linput - linput.mean()) / linput.std()
rinput = (rinput - rinput.mean()) / rinput.std()
linput = linput.reshape(1, linput.shape[0], linput.shape[1],
num_channels)
rinput = rinput.reshape(1, rinput.shape[0], rinput.shape[1],
num_channels)
test_dict = {
limage: linput,
rimage: rinput,
snet['is_training']: False
}
limage_map, rimage_map = session.run(
[snet['lbranch'], snet['rbranch']], feed_dict=test_dict)
map_width = limage_map.shape[2]
unary_vol = np.zeros(
(limage_map.shape[1], limage_map.shape[2], FLAGS.disp_range))
for loc in range(FLAGS.disp_range):
x_off = -loc
l = limage_map[:, :, max(0, -x_off):map_width, :]
r = rimage_map[:, :, 0:min(map_width, map_width + x_off), :]
res = session.run(map_prod, feed_dict={lmap: l, rmap: r})
unary_vol[:, max(0, -x_off):map_width, loc] = res[0, :, :]
print('Image %s processed.' % (i + 1))
pred = np.argmax(unary_vol, axis=2) * scale_factor
misc.imsave('%s/disp_map_%06d_10.png' % (FLAGS.out_dir, file_id),
pred)