image_path = Args.path # get the path of the images
image = mpimg.imread(image_path + image_name) # read the images
# change the axis
image_f = image.transpose(2, 0, 1)
image_f = image_f[np.newaxis, ...]
# show
plt.ion()
plt.figure(1) # show
plt.subplot(1, 2, 1)
fluency = score.fluency(image_f)
plt.imshow(image_f[0].transpose(1, 2, 0))
plt.axis('off')
plt.title('%s %d' % (image_name, fluency))
# %% Shuffle
shuffle_image, index0 = processing.shuffle(image_f, axis=2)
index0 = np.array(index0) # change to ndarray
plt.subplot(1, 2, 2) # show
plt.imshow(shuffle_image[0].transpose(1, 2, 0))
fluency = score.fluency(shuffle_image)
plt.title('shuffle %d' % fluency)
plt.axis('off')
plt.draw()
# %% SVD image sort
recover = model.ImageRecover(shuffle_image)
new_image1, index = recover.svd_imsort()
# show
plt.figure(2)
plt.imshow(new_image1[0].transpose(1, 2, 0))
fluency = score.fluency(new_image1)
plt.figure(1) # show
plt.subplot(1, 3, 1)
plt.imshow(image)
plt.axis('off')
plt.title(image_name)
# %% Gray
gray_image = processing.rgb2gray(image)
plt.subplot(1, 3, 2) # show
plt.imshow(gray_image, cmap='gray')
fluency = score.fluency(gray_image)
plt.axis('off')
plt.title('%s-Gray %d' % (image_name, fluency))
# %% Shuffle
shuffle_image, index0 = processing.shuffle(gray_image, axis=0)
index0 = np.array(index0) # change to ndarray
plt.subplot(1, 3, 3) # show
plt.imshow(shuffle_image, cmap='gray')
fluency = score.fluency(shuffle_image)
plt.title('shuffle %d' % fluency)
plt.axis('off')
plt.draw()
# %% SVD image sort
recover = model.ImageRecover(shuffle_image)
new_image1, index = recover.svd_imsort()
new_image1 = new_image1[0][0]
# show
plt.figure(2)
plt.imshow(new_image1, cmap='gray')
fluency = score.fluency(new_image1)
cnn2 = models.CNN_MNIST1().cuda() if Args.cuda else models.CNN_MNIST1()
elif datasets_name == 'CIFAR-10':
print('Using the CIFAR-10 dataset.')
data_train, data_test = datasets.CIFAR10(True)
num_index = [0, 1, 2, 3, 4] # save 0,1,2,3,4,5,6,7,8,9
axis = 2
data = data_train.data[:1000]
data = np.array(data).transpose(0, 3, 1, 2)
# networks
data_loader_train = torch.utils.data.DataLoader(dataset=data_train, batch_size=Args.batch_size, shuffle=True)
data_loader_test = torch.utils.data.DataLoader(dataset=data_test, batch_size=Args.batch_size, shuffle=True)
cnn0 = models.CNN_CIFAR10().cuda() if Args.cuda else models.CNN_CIFAR10()
cnn1 = models.CNN_CIFAR10().cuda() if Args.cuda else models.CNN_CIFAR10()
cnn2 = models.CNN_CIFAR10().cuda() if Args.cuda else models.CNN_CIFAR10()
#%% Shuffle and Recover
data_shuffled, index0 = processing.shuffle(data, axis=axis)
index0 = np.array(index0) # change to ndarray
best_index = [float('inf'), []]
# direct greed
recover = model.ImageRecover(data_shuffled)
data_recover, index = recover.direct_greed()
fluency = score.fluency(data_recover)
k_coeff, k_distance = score.Kendal(index0[index], range(len(index)))
if fluency < best_index[0]:
best_index[1] = index
best_index[0] = fluency
if Args.show:
plt.ion()
plt.figure(1)
plt.subplot(1, 3, 1) # show
datasets.demo_show(data[num_index], datasets_name)