def zero_ncc_test():
ncc_size = 5
image1 = np.zeros(
(2 * ncc_size - 1, 2 * ncc_size - 1, 3), dtype=np.float32)
image2 = image1
n1 = preprocess_ncc(image1, ncc_size)
n2 = preprocess_ncc(image2, ncc_size)
ncc = compute_ncc(n1, n2)
assert (np.abs(ncc) < 1e-6).all()
def offset_ncc_test():
ncc_size = 5
image1 = np.random.random((2 * ncc_size - 1, 2 * ncc_size - 1, 3))
image2 = image1 + 2
n1 = preprocess_ncc(image1, ncc_size)
n2 = preprocess_ncc(image2, ncc_size)
ncc = compute_ncc(n1, n2)
assert ncc.shape == (2 * ncc_size - 1, 2 * ncc_size - 1)
assert (np.abs(ncc[ncc_size, ncc_size] - 1) < 1e-6).all()
def offset_ncc_test():
ncc_size = 5
image1 = np.random.random((2 * ncc_size - 1, 2 * ncc_size - 1, 3))
image2 = image1 + 2
n1 = preprocess_ncc(image1, ncc_size)
n2 = preprocess_ncc(image2, ncc_size)
ncc = compute_ncc(n1, n2)
assert ncc.shape == (2 * ncc_size - 1, 2 * ncc_size - 1)
assertNear(ncc[ncc_size, ncc_size], 1, 1e-6)
def zero_ncc_test():
ncc_size = 5
image1 = np.zeros(
(2 * ncc_size - 1, 2 * ncc_size - 1, 3), dtype=np.float32)
image2 = image1
n1 = preprocess_ncc(image1, ncc_size)
n2 = preprocess_ncc(image2, ncc_size)
ncc = compute_ncc(n1, n2)
assertNear(ncc, 0, 1e-6)
def ncc_full_identity_test():
ncc_size = 5
ncc_half = ncc_size / 2
normalized = np.load('test_materials/fabrics_normalized.npy')
ncc = compute_ncc(normalized, normalized)
assert ncc.shape == normalized.shape[:2]
assertNear(ncc[:ncc_half, :], 0, 1e-5)
assertNear(ncc[-ncc_half:, :], 0, 1e-5)
assertNear(ncc[:, :ncc_half], 0, 1e-5)
assertNear(ncc[:, -ncc_half:], 0, 1e-5)
assertNear(ncc[ncc_half:-ncc_half, ncc_half:-ncc_half], 1, 1e-5)
def ncc_full_identity_test():
ncc_size = 5
ncc_half = ncc_size / 2
normalized = np.load('test_materials/fabrics_normalized.npy')
ncc = compute_ncc(normalized, normalized)
assert ncc.shape == normalized.shape[:2]
assertNear(ncc[:ncc_half, :], 0, 1e-5)
assertNear(ncc[-ncc_half:, :], 0, 1e-5)
assertNear(ncc[:, :ncc_half], 0, 1e-5)
assertNear(ncc[:, -ncc_half:], 0, 1e-5)
assertNear(ncc[ncc_half:-ncc_half, ncc_half:-ncc_half], 1, 1e-5)
def ncc_full_identity_test():
ncc_size = 5
ncc_half = int(ncc_size / 2)
normalized = np.load('test_materials/fabrics_normalized.npy')
ncc = compute_ncc(normalized, normalized)
assert ncc.shape == normalized.shape[:2]
assert (np.abs(ncc[:ncc_half, :]) < 1e-5).all()
assert (np.abs(ncc[-ncc_half:, :]) < 1e-5).all()
assert (np.abs(ncc[:, :ncc_half]) < 1e-5).all()
assert (np.abs(ncc[:, -ncc_half:]) < 1e-5).all()
assert (
np.abs(ncc[ncc_half:-ncc_half, ncc_half:-ncc_half] - 1) < 1e-5).all()
def ncc_full_shapes_test():
ncc_size = 5
image1 = imread('test_materials/ncc1.png')
image2 = imread('test_materials/ncc2.png')
n1 = preprocess_ncc(image1, ncc_size)
n2 = preprocess_ncc(image2, ncc_size)
result = compute_ncc(n1, n2)
correct = np.load('test_materials/ncc.npy')
assert result.shape == n1.shape[:2]
assert result.shape == n2.shape[:2]
assert (np.abs(result - correct) < 1e-5).all()
def correlated_ncc_test():
ncc_size = 5
ncc_half = ncc_size / 2
image1 = np.random.random((2 * ncc_size - 1, 2 * ncc_size - 1, 3))
image2 = image1
n1 = preprocess_ncc(image1, ncc_size)
n2 = preprocess_ncc(image2, ncc_size)
ncc = compute_ncc(n1, n2)
assertNear(ncc[:ncc_half, :], 0, 1e-5)
assertNear(ncc[-ncc_half:, :], 0, 1e-5)
assertNear(ncc[:, :ncc_half], 0, 1e-5)
assertNear(ncc[:, -ncc_half:], 0, 1e-5)
assertNear(ncc[ncc_half:-ncc_half, ncc_half:-ncc_half], 1, 1e-5)
def correlated_ncc_test():
ncc_size = 5
ncc_half = ncc_size / 2
image1 = np.random.random((2 * ncc_size - 1, 2 * ncc_size - 1, 3))
image2 = image1
n1 = preprocess_ncc(image1, ncc_size)
n2 = preprocess_ncc(image2, ncc_size)
ncc = compute_ncc(n1, n2)
assertNear(ncc[:ncc_half, :], 0, 1e-5)
assertNear(ncc[-ncc_half:, :], 0, 1e-5)
assertNear(ncc[:, :ncc_half], 0, 1e-5)
assertNear(ncc[:, -ncc_half:], 0, 1e-5)
assertNear(ncc[ncc_half:-ncc_half, ncc_half:-ncc_half], 1, 1e-5)
def offset_and_scale_ncc_test():
ncc_size = 5
ncc_half = ncc_size / 2
image1 = np.random.random((2 * ncc_size - 1, 2 * ncc_size - 1, 3))
image2 = image1 * 2 + 3
n1 = preprocess_ncc(image1, ncc_size)
n2 = preprocess_ncc(image2, ncc_size)
ncc = compute_ncc(n1, n2)
assert ncc.shape == (2 * ncc_size - 1, 2 * ncc_size - 1)
assertNear(ncc[:ncc_half, :], 0, 1e-6)
assertNear(ncc[-ncc_half:, :], 0, 1e-6)
assertNear(ncc[:, :ncc_half], 0, 1e-6)
assertNear(ncc[:, -ncc_half:], 0, 1e-6)
assertNear(ncc[ncc_half:-ncc_half, ncc_half:-ncc_half], 1, 1e-6)
def offset_and_scale_ncc_test():
ncc_size = 5
ncc_half = ncc_size / 2
image1 = np.random.random((2 * ncc_size - 1, 2 * ncc_size - 1, 3))
image2 = image1 * 2 + 3
n1 = preprocess_ncc(image1, ncc_size)
n2 = preprocess_ncc(image2, ncc_size)
ncc = compute_ncc(n1, n2)
assert ncc.shape == (2 * ncc_size - 1, 2 * ncc_size - 1)
assertNear(ncc[:ncc_half, :], 0, 1e-6)
assertNear(ncc[-ncc_half:, :], 0, 1e-6)
assertNear(ncc[:, :ncc_half], 0, 1e-6)
assertNear(ncc[:, -ncc_half:], 0, 1e-6)
assertNear(ncc[ncc_half:-ncc_half, ncc_half:-ncc_half], 1, 1e-6)
def anticorrelated_ncc_test():
ncc_size = 5
ncc_half = ncc_size / 2
image1 = np.random.random((2 * ncc_size - 1, 2 * ncc_size - 1, 3))
image2 = -image1
n1 = preprocess_ncc(image1, ncc_size)
n2 = preprocess_ncc(image2, ncc_size)
ncc = compute_ncc(n1, n2)
assert (np.abs(ncc[:ncc_half, :]) < 1e-5).all()
assert (np.abs(ncc[-ncc_half:, :]) < 1e-5).all()
assert (np.abs(ncc[:, :ncc_half]) < 1e-5).all()
assert (np.abs(ncc[:, -ncc_half:]) < 1e-5).all()
assert (
np.abs(ncc[ncc_half:-ncc_half, ncc_half:-ncc_half] - -1) < 1e-5).all()
def ncc_full_offset_test():
ncc_size = 5
image = imread('test_materials/justinpic_c.png')
split = image.shape[1] / 2
left = image[:, :split, :]
right = image[:, split:, :]
n1 = preprocess_ncc(left, ncc_size)
n2 = preprocess_ncc(right, ncc_size)
result = compute_ncc(n1, n2)
correct = np.load('test_materials/justin_ncc.npy')
assert result.shape == n1.shape[:2]
assert result.shape == n2.shape[:2]
assert (np.abs(result - correct) < 1e-5).all()
def scale_ncc_test():
ncc_size = 5
ncc_half = ncc_size / 2
image1 = np.random.random((2 * ncc_size - 1, 2 * ncc_size - 1, 3))
image2 = image1 * 2
n1 = preprocess_ncc(image1, ncc_size)
n2 = preprocess_ncc(image2, ncc_size)
ncc = compute_ncc(n1, n2)
assert ncc.shape == (2 * ncc_size - 1, 2 * ncc_size - 1)
assert (np.abs(ncc[:ncc_half, :]) < 1e-5).all()
assert (np.abs(ncc[-ncc_half:, :]) < 1e-5).all()
assert (np.abs(ncc[:, :ncc_half]) < 1e-5).all()
assert (np.abs(ncc[:, -ncc_half:]) < 1e-5).all()
assert (
np.abs(ncc[ncc_half:-ncc_half, ncc_half:-ncc_half] - 1) < 1e-5).all()
def ncc_full_shapes_test():
ncc_size = 5
image1 = imread('test_materials/ncc1.png')
image2 = imread('test_materials/ncc2.png')
image1 = image1.astype(np.float32)
image2 = image2.astype(np.float32)
n1 = preprocess_ncc(image1, ncc_size)
n2 = preprocess_ncc(image2, ncc_size)
result = compute_ncc(n1, n2)
correct = np.load('test_materials/ncc.npy')
assert result.shape == n1.shape[:2]
assert result.shape == n2.shape[:2]
assertNear(result, correct, 1e-5)
def ncc_full_offset_test():
ncc_size = 5
image = imread('test_materials/justinpic_c.png')
image = image.astype(np.float32)
split = image.shape[1] / 2
left = image[:, :split, :]
right = image[:, split:, :]
n1 = preprocess_ncc(left, ncc_size)
n2 = preprocess_ncc(right, ncc_size)
result = compute_ncc(n1, n2)
correct = np.load('test_materials/justin_ncc.npy')
assert result.shape == n1.shape[:2]
assert result.shape == n2.shape[:2]
assertNear(result, correct, 1e-5)
points_left = np.float32(points_left.reshape(-1, 2))
points_right = np.float32(points_right.reshape(-1, 2))
H, mask = cv2.findHomography(points_left, points_right)
assert (mask == 1).all()
projected_left = cv2.warpPerspective(left, H, (width, height))
"""
Normalize this projected left image.
"""
left_normalized = preprocess_ncc(projected_left, ncc_size)
"""
Compute the NCC score between the right and left images.
"""
ncc = compute_ncc(right_normalized, left_normalized)
volume.append(ncc)
projected_gif_writer.append(np.uint8(projected_left))
ncc_gif_writer.append(np.uint8(255 * np.clip(ncc / 2 + 0.5, 0, 1)))
sys.stdout.write('Progress: {0}\r'.format(int(100 * pos / len(depths))))
sys.stdout.flush()
print ''
toc = time.time()
print 'Plane sweep took {0} seconds'.format(toc - tic)
ncc_gif_writer.close()
points_right = np.float32(points_right.reshape(-1, 2))
H, mask = cv2.findHomography(points_left, points_right)
assert (mask == 1).all()
projected_left = cv2.warpPerspective(left, H, (width, height))
"""
Normalize this projected left image.
"""
left_normalized = preprocess_ncc(projected_left, ncc_size)
"""
Compute the NCC score between the right and left images.
"""
ncc = compute_ncc(right_normalized, left_normalized)
volume.append(ncc)
projected_gif_writer.append(np.uint8(projected_left))
ncc_gif_writer.append(np.uint8(255 * np.clip(ncc / 2 + 0.5, 0, 1)))
sys.stdout.write('Progress: {0}\r'.format(int(100 * pos / len(depths))))
sys.stdout.flush()
print ''
toc = time.time()
print 'Plane sweep took {0} seconds'.format(toc - tic)
ncc_gif_writer.close()
