def test_disparity_random_stereogram():
"""
Checks the disparity map for random stereogram
"""
H = 51
W = 51
disparity = 4
im_left, im_right = generate_random_stereogram(
im_size=(H, W, 3), disparity=4)
block_size = 5
disp_map = calculate_disparity_map(
im_left, im_right, block_size, ssd_similarity_measure)
# define the limits where we will encounter non-zero disparity maps
x_lims = torch.tensor([H//2 - H//4 - block_size//2-1, H//2 + H//4 + block_size//2-1],
dtype=torch.long)
y_lims = torch.tensor([W//2 - W//4 - block_size//2 - disparity-1, W//2 + W//4 + block_size//2-1],
dtype=torch.long)
# get the points where disparity map is greater than zero
nonzero_idx = torch.nonzero(disp_map)
# we will see the effect of stereo outside this region too, depending on the block size
falsevals = torch.nonzero(~(
(nonzero_idx[:, 0] >= x_lims[0]) &
(nonzero_idx[:, 0] <= x_lims[1]) &
(nonzero_idx[:, 1] >= y_lims[0]) &
(nonzero_idx[:, 1] <= y_lims[1])
))
assert ~falsevals.shape[0]
def test_generate_random_stereogram():
"""
Tests the generation of random dot stereogram
"""
H, W = (51, 51)
disparity_val = 4
left_img, right_img = generate_random_stereogram(im_size=(H, W, 2),
disparity=disparity_val)
print(left_img)
print(right_img)
print("left_img.shape {}".format(left_img.shape))
print("right_img.shape {}".format(right_img.shape))
# assert that they are same in all the channels
for ch_idx in range(1, left_img.shape[2]):
assert torch.nonzero(
left_img[:, :, 0] != left_img[:, :, ch_idx]).shape[0] == 0
assert torch.nonzero(
right_img[:, :, 0] != right_img[:, :, ch_idx]).shape[0] == 0
diff_img = torch.abs(left_img - right_img)
# get the region where left and right images are different
nonzero_idx = torch.nonzero(diff_img)
falsevals = torch.nonzero(~(
(nonzero_idx[:, 0] >= 13) & (nonzero_idx[:, 0] <= 37)
& (nonzero_idx[:, 1] >= 9) & (nonzero_idx[:, 1] <= 37)))
assert falsevals.shape[0] == 0