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)
None, # Mirror enconding for reconstruction
'name':
'face_cvae_3'
}
model = CVAE(arch_def)
model.load_weights('models/face_cvae_3/face_cvae_3.weights')
test_root = '/home/martin/dataset/face_test'
test_imgs = [str(p) for p in Path(test_root).glob('*.jpg')]
batch_size = 8
epochs = 16
loss = []
tb = CVAEToolBox(model)
for i in range(len(test_imgs[:5])):
inp, outp = tb.load_and_reconstruct_image(test_imgs[i])
plt.subplot(2, 5, 1 + i)
plt.imshow(inp)
plt.title('Input')
plt.axis('off')
plt.subplot(2, 5, 6 + i)
plt.imshow(outp)
plt.title('Output')
plt.axis('off')
plt.show()
out = model.decode(tb.to_latent(tb.to_tensor(test_imgs[0])))
print(out)
