torch.save(state, './checkpoint/autoencoder.t7')
test_A_ = target_A[0:14]
test_B_ = target_B[0:14]
test_A = var_to_np(target_A[0:14])
test_B = var_to_np(target_B[0:14])
figure_A = np.stack([
test_A,
var_to_np(model(test_A_, 'A')),
var_to_np(model(test_A_, 'B')),
], axis=1)
figure_B = np.stack([
test_B,
var_to_np(model(test_B_, 'B')),
var_to_np(model(test_B_, 'A')),
], axis=1)
figure = np.concatenate([figure_A, figure_B], axis=0)
figure = figure.transpose((0, 1, 3, 4, 2))
figure = figure.reshape((4, 7) + figure.shape[1:])
figure = stack_images(figure)
figure = np.clip(figure * 255, 0, 255).astype('uint8')
# cv2.imshow("", figure)
cv2.imwrite("./data/"+str(nums)+'.jpg', figure)
# key = cv2.waitKey(1)
# if key == ord('q'):
# exit()
from pylsci import Lsci
from util import stack_images, show_image, read_image
lsci = Lsci()
print('temporal LSCI ...')
speckle_imgs = read_image('img/temporal.png')
speckle_img_stack = stack_images(speckle_imgs)
print(speckle_img_stack.shape)
t_lsci = lsci.temporal_contrast(speckle_img_stack)
show_image(t_lsci)
print(t_lsci.shape)
print('spatio-temporal LSCI ...')
speckle_imgs = read_image('img/temporal.png')
speckle_img_stack = stack_images(speckle_imgs)
print(speckle_img_stack.shape)
st_lsci = lsci.spatio_temporal_contrast(speckle_img_stack)
show_image(st_lsci)
print(st_lsci.shape)
print('spatial LSCI ...')
speckle_img = read_image('img/spatial.tif')
print(speckle_img.shape)
s_lsci = lsci.spatial_contrast(speckle_img)
show_image(s_lsci)
print(s_lsci.shape)
print('success')