def test_plt_keypoints():
num_monkeys = 1
bm = bad_monkey(num_monkeys)
# height, width = bm.size(2), bm.size(3)
# heatmap = torch.rand(num_monkeys, 10, height, width, requires_grad=False)
# k = MF.spacial_softmax(heatmap)
k = torch.empty(1, 5, 2).uniform_(0.0, 1.0)
image = plot_keypoints_on_image(k[0], bm[0], radius=7, thickness=3)
plt.imshow(image)
plt.show()
def test_flowfield():
u = UniImageViewer()
x = bad_monkey()
theta = torch.tensor([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]]).expand(1, -1, -1)
grid = F.affine_grid(theta, x.shape)
out = F.grid_sample(x, grid)
u.render(out[0], block=True)
def test_tps_random():
images = []
u = UniImageViewer(screen_resolution=(2400, 1200))
x = bad_monkey()
for i in range(5, 10):
set = []
for _ in range(8):
pass
# set.append(tps_random(x, num_control_points=20, var=1 / i))
st = torch.cat(set, dim=2)
images.append(st)
img = torch.cat(images, dim=3)
u.render(img, block=True)
def test_tps():
u = UniImageViewer()
x = bad_monkey()
theta = torch.tensor([[[0.0, 0.0], [0., 0.], [0., 0.], [0., 0.], [0., 0.],
[0., 0.], [0.0, 0.0]]])
c = torch.tensor([
[0., 0],
[1., 0],
[1., 1],
[0, 1],
]).unsqueeze(0)
grid = tps_grid(theta, c, x.shape)
out = F.grid_sample(x, grid)
u.render(out[0], block=True)
def test_display_keypoints():
x = com.bad_monkey()
d = u.ResultsLogger('model_name', 'run_id')
k = com.keypoints()
d.display(x, blocking=True)
def test_resize():
x = com.bad_monkey()
x = u.resize2D(x[0], (512, 512))
d = u.ResultsLogger('model_name', 'run_id')
d.display(x, blocking=True)
def test_dual_tps_random_batched():
u = UniImageViewer(screen_resolution=(2400, 1200))
x = bad_monkey(2)