def test_output_flow():
"""calc flow"""
net = Net(2)
imga = Image.open(IMG1)
imgb = Image.open(IMG2)
imga = np.asarray(imga).astype('float32') / 255
imgb = np.asarray(imgb).astype('float32') / 255
imga = imga[..., [2, 1, 0]]
imgb = imgb[..., [2, 1, 0]]
with tf.Session() as sess:
net.restore(sess, checkpoint)
imga = np.expand_dims(imga, 0)
imgb = np.expand_dims(imgb, 0)
print(imga.shape, imgb.shape)
flow = net.flow_results.eval(feed_dict={
'flow/inputa:0': imga,
'flow/inputb:0': imgb,
})
x = flow[0]
x = _viz_flow(x[..., 0], x[..., 1])
Image.fromarray(x, 'RGB').save('flow.png')
def test_read_flow():
from VSR.Framework.Callbacks import _viz_flow
dut = DATASETS['MINICHAIRS']
config = Config(batch=8, scale=1, depth=2, patch_size=96, steps_per_epoch=100, convert_to='RGB', crop='random')
loader = QuickLoader(dut, 'train', config, True, n_threads=8)
r = loader.make_one_shot_iterator('1GB', shuffle=True)
loader.prefetch('1GB')
list(r)
r = loader.make_one_shot_iterator('8GB', shuffle=True)
img, flow, name = list(r)[0]
ref0 = img[0, 0, ...]
ref1 = img[0, 1, ...]
u = flow[0, 0, ..., 0]
v = flow[0, 0, ..., 1]
ImageProcess.array_to_img(ref0, 'RGB').show()
ImageProcess.array_to_img(ref1, 'RGB').show()
ImageProcess.array_to_img(_viz_flow(u, v), 'RGB').show()