def printcoords():
File = filedialog.askopenfilename(parent=root, title='Choose an image.')
print('changing data...')
data_img = []
img = imread(File)
img = Image.fromarray(img)
img = fit(img, size=(64, 64))
img = transpose(img, (2, 0, 1))
data_img.append(img)
np.save(opts.path, np.asarray(data_img))
test_dataset = TestData(label=opts.label,
path=opts.path,
transform=transforms.ToTensor())
dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=1)
cvae = CVAE(200).to(device)
cvae.load_params(opts.CVAE_PATH)
evaluation_dir = opts.CVAE_PATH + 'evalFolder'
try:
os.mkdir(evaluation_dir)
except:
print('file already created')
cvae.eval()
test_x, test_y = iter(dataloader).next()
for i in range(3):
test_rec, test_mean, test_log_var, test_predict = cvae(test_x, test_y)
save_image(test_x.data, join(evaluation_dir, 'input.png'))
save_image(test_rec.data, join(evaluation_dir, 'output_test.png'))
x = test_x.data
y = test_y
mu, logVar, y = cvae.encode(x)
z = cvae.reparameterization(mu, logVar)
sample1 = cvae.decode(
torch.LongTensor(np.ones(y.size(), dtype=int)).type_as(z), z)
sample2 = cvae.decode(
torch.LongTensor(np.zeros(y.size(), dtype=int)).type_as(z), z)
save_image(sample1.cpu().data, join(evaluation_dir, 'sample1.png'))
save_image(sample2.cpu().data, join(evaluation_dir, 'sample2.png'))
arr = ['input.png', 'sample1.png', 'sample2.png']
toImage = Image.new('RGBA', (584, 128))
for j in range(3):
fromImge = Image.open(join(evaluation_dir, arr[j]))
fromImge = fromImge.resize((128, 128), Image.ANTIALIAS)
loc = (128 * j + 80, 0)
toImage.paste(fromImge, loc)
toImage.save('merged' + str(i) + '.png')
arr = ['merged0.png', 'merged1.png', 'merged2.png']
toImage = Image.new('RGBA', (584, 384))
for j in range(3):
fromImge = Image.open(arr[j])
loc = (0, 128 * j)
toImage.paste(fromImge, loc)
toImage.save('merged.png')
filename = ImageTk.PhotoImage(Image.open('merged.png'))
canvas.image = filename
canvas.create_image(124, 10, anchor='nw', image=filename)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Dataset
datasets, dataloaders, dataset_sizes = get_data(num_quadrant_inputs=1,
batch_size=128)
baseline_net = BaselineNet(500, 500)
baseline_net.load_state_dict(
torch.load('/Users/carlossouza/Downloads/baseline_net_q1.pth',
map_location='cpu'))
baseline_net.eval()
cvae_net = CVAE(200, 500, 500, baseline_net)
cvae_net.load_state_dict(
torch.load('/Users/carlossouza/Downloads/cvae_net_q1.pth',
map_location='cpu'))
cvae_net.eval()
visualize(device=device,
num_quadrant_inputs=1,
pre_trained_baseline=baseline_net,
pre_trained_cvae=cvae_net,
num_images=10,
num_samples=10)
# df = generate_table(
# device=device,
# num_quadrant_inputs=1,
# pre_trained_baseline=baseline_net,
# pre_trained_cvae=cvae_net,
# num_particles=10,
# col_name='{} quadrant'.format(1)