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')