the_image = ImDict[image_name]
image_size = np.shape(the_image)
NRows = image_size[0]
NCols = image_size[1]
NBands = image_size[2]
print("Lokalizacja obrazu: \t", filename)
print("Nazwa obrazu: \t\t\t", image_name)
print("Rozmiar: \t\t\t\t", "wiersze: ", NRows, " kolumny: ", NCols,
" zakresy: ", NBands)
print("Ilośc pikseli (ilość kolumn * ilość wierszy): ", NRows * NCols)
print()
print("*** Converting image to uint8 ***")
print("---------------------------------")
# converted_image = mo.numpy_to_uint8(the_image)
the_image = mo.numpy_to_uint8(the_image)
print()
print("*** Loading labels ***")
print("---------------------------------")
# To juz jest w uint8
filename_labels = 'C:/TestingCatalog/AI_data/Indian Pines/Indian_pines_gt.mat'
ImDict_labels = io.loadmat(filename_labels)
image_name_labels = 'indian_pines_gt'
the_image_labels = ImDict_labels[image_name_labels]
image_size_labels = np.shape(the_image_labels)
NRows_labels = image_size_labels[0]
NCols_labels = image_size_labels[1]
'''
import matplotlib.pyplot as plt
plt.imshow(the_image_labels)
if __name__ == '__main__':
print("START")
filename = 'C:/TestingCatalog/AI_data/Indian Pines/Indian_pines_corrected.mat'
ImDict = io.loadmat(filename)
the_image = ImDict['indian_pines_corrected']
image_size = np.shape(the_image)
NRows = image_size[0]
NCols = image_size[1]
NBands = image_size[2]
# przykład
# img = open_image('92AV3C.lan')
print(type(the_image))
converted_image = mo.numpy_to_uint8(the_image)
# Wyświetlanie obrazka
view = imshow(converted_image, (29, 20, 11))
view = imshow(the_image, (29, 20, 11))
print(view)
# Wyświtlanie kostki - nie działa
# import spectral
# spectral.settings.WX_GL_DEPTH_SIZE = 16
# print(np.size(the_image))
# print(np.shape(the_image))
# smaller_img = the_image[0:30, 0:30, 0:30]
# print(np.size(smaller_img))
# print(np.shape(smaller_img))
# view_cube(smaller_img, bands=[29, 20, 11])
def run_machine(input_net, first=True):
to_file = False
# Przekierowanie wyjścia do pliku
if to_file:
import sys
orig_stdout = sys.stdout
output_file = open('output_file.txt', 'w')
sys.stdout = output_file
print("START")
start_time = time.time()
print("Start time: ", time.ctime(start_time))
print()
print("*** Loading data ***")
print("---------------------------------")
filename = 'Indian_pines_corrected.mat'
ImDict = io.loadmat(data_dir + filename)
image_name = 'indian_pines_corrected'
the_image = ImDict[image_name]
image_size = np.shape(the_image)
NRows = image_size[0]
NCols = image_size[1]
NBands = image_size[2]
print("Lokalizacja obrazu: \t", data_dir + filename)
print("Nazwa obrazu: \t\t\t", image_name)
print("Rozmiar: \t\t\t\t", "wiersze: ", NRows, " kolumny: ", NCols,
" zakresy: ", NBands)
print("Ilośc pikseli (ilość kolumn * ilość wierszy): ", NRows * NCols)
print()
print("*** Converting image to uint8 ***")
print("---------------------------------")
# converted_image = mo.numpy_to_uint8(the_image)
the_image = mo.numpy_to_uint8(the_image)
print()
print("*** Loading labels ***")
print("---------------------------------")
# To juz jest w uint8
filename_labels = 'Indian_pines_gt.mat'
ImDict_labels = io.loadmat(data_dir + filename_labels)
image_name_labels = 'indian_pines_gt'
the_image_labels = ImDict_labels[image_name_labels]
image_size_labels = np.shape(the_image_labels)
NRows_labels = image_size_labels[0]
NCols_labels = image_size_labels[1]
'''
import matplotlib.pyplot as plt
plt.imshow(the_image_labels)
plt.show()
'''
labels = set()
for row in the_image_labels:
for element in row:
labels.add(element)
num_labels = len(labels)
print("Lokalizacja obrazu: \t", filename_labels)
print("Nazwa obrazu: \t\t\t", image_name_labels)
print("Rozmiar: \t\t\t\t", "wiersze: ", NRows_labels, " kolumny: ",
NCols_labels)
print("Ilośc etykiet: ", num_labels, " Etykiety: ", labels)
print()
print("*** Creating dataset and dataloader ***")
print("---------------------------------")
import torch.utils.data as utils
list_of_tensors = []
for row in the_image:
for element in row:
list_of_tensors.append(torch.Tensor(element))
list_of_tensors_labels = []
for row in the_image_labels:
for element in row:
list_of_tensors_labels.append(torch.Tensor([element]))
my_tensor = torch.stack(list_of_tensors)
my_tensor_labels = torch.stack(list_of_tensors_labels)
my_dataset = utils.TensorDataset(my_tensor, my_tensor_labels)
my_dataloader = utils.DataLoader(my_dataset)
print("Number of elements in dataset: ", my_dataset.__len__())
print()
print("*** Creating autoencoder ***")
print("---------------------------------")
my_net = input_net
print(my_net)
params = list(my_net.parameters())
print("Params size: ", params.__len__())
for parameter in params:
print(len(parameter))
if my_net.getName() != 'none':
print()
print("*** Creating optimizer ***")
print("---------------------------------")
optimizer = torch.optim.Adam(my_net.parameters(), weight_decay=1e-5)
criterion = nn.MSELoss()
if first and my_net.getName() != 'none':
print()
print("*** Learning ***")
print("---------------------------------")
from torch.autograd import Variable
num_epochs = 3
batch_size = 128
learning_rate = 1e-3
for epoch in range(num_epochs):
for i, data in enumerate(my_dataloader):
img, _ = data
img = Variable(img).cpu()
# ===================forward=====================
output = my_net(img)
loss = criterion(output, img)
# ===================backward====================
optimizer.zero_grad()
loss.backward()
optimizer.step()
# ===================log========================
print(type(loss))
#print(len(loss))
print(type(loss.item()))
print('epoch [', epoch + 1, '/', num_epochs, '], loss:',
loss.item())
# if epoch % 10 == 0:
# pic = to_img(output.cpu().data)
print()
print("*** Saving model to file ***")
print("---------------------------------")
autoencoder_learned_file = './autoencoder_' + my_net.getType(
) + '_' + my_net.getName() + '.pth'
torch.save(my_net.state_dict(), autoencoder_learned_file)
if not first and my_net.getName() != 'none':
print()
print("*** Loading model from file ***")
print("---------------------------------")
autoencoder_learned_file = './autoencoder_' + my_net.getType(
) + ' ' + my_net.getName() + '.pth'
my_net.load_state_dict(torch.load(autoencoder_learned_file))
my_net.eval()
print()
print("*** Checking code for one element ***")
print("---------------------------------")
print(my_net.getCode(list_of_tensors[0]))
print()
print("*** Clustering ***")
print("---------------------------------")
print()
img_name = 'clustering_kmeans_' + my_net.getType(
) + '_autoencoder_' + my_net.getName() + '.png'
clustering_kmeans(the_image, my_net, np.shape(the_image_labels), img_dir,
img_name)
print("\nEND")
end_time = time.time()
print("End time: ", time.ctime(end_time))
print("Duration: ", int(end_time - start_time), " seconds")
# Closing file
if to_file:
sys.stdout = orig_stdout
output_file.close()