def test_uint8_to_float_and_back():
img = load_image(TEST_IMAGE_PATH)
img_float = uint8_to_float(img)
img_uint8 = float_to_uint8(img_float)
img_diff = img - img_uint8
assert img_diff.max() <= 1
def test_collapse_laplacian_pyramid():
img = load_image(TEST_IMAGE_PATH)
pyramid = create_laplacian_image_pyramid(img, 5)
display_image_pyramid(pyramid)
img_collapsed = collapse_laplacian_pyramid(pyramid)
display_image(img, "Original")
display_image(img_collapsed, "Recomposed", wait=True)
assert (img == img_collapsed).all()
def test_collapse_laplacian_pyramid():
img = load_image(TEST_IMAGE_PATH)
pyramid = create_laplacian_image_pyramid(img, 5)
display_image_pyramid(pyramid)
img_collapsed = collapse_laplacian_pyramid(pyramid)
display_image(img, "Original")
display_image(img_collapsed, "Recomposed", wait=True)
assert (img == img_collapsed).all()
def test_create_laplacian_image_pyramid():
pyramid_depth = 3
img = load_image(TEST_IMAGE_PATH)
pyramid = create_laplacian_image_pyramid(img, pyramid_depth)
display_image_pyramid(pyramid)
means = [np.average(f) for f in pyramid]
deviations = [np.std(f) for f in pyramid]
assert len(pyramid) == pyramid_depth
assert np.std(deviations) > 0.01 # one of the images is just the original sized down photo. there should be significantly more variance in that photo
assert pyramid[0].shape == img.shape
assert pyramid[1].shape[0] < pyramid[0].shape[0]
assert pyramid[2].shape[0] < pyramid[1].shape[0]
def test_create_gaussian_image_pyramid():
pyramid_depth = 3
img = load_image(TEST_IMAGE_PATH)
pyramid = create_gaussian_image_pyramid(img, pyramid_depth)
display_image_pyramid(pyramid)
means = [np.average(f) for f in pyramid]
deviations = [np.std(f) for f in pyramid]
assert len(pyramid) == pyramid_depth
assert np.std(means) < 1
assert np.std(deviations) < 1
assert pyramid[0].shape == img.shape
assert pyramid[1].shape[0] < pyramid[0].shape[0]
assert pyramid[2].shape[0] < pyramid[1].shape[0]
def test_create_gaussian_image_pyramid():
pyramid_depth = 3
img = load_image(TEST_IMAGE_PATH)
pyramid = create_gaussian_image_pyramid(img, pyramid_depth)
display_image_pyramid(pyramid)
means = [np.average(f) for f in pyramid]
deviations = [np.std(f) for f in pyramid]
assert len(pyramid) == pyramid_depth
assert np.std(means) < 1
assert np.std(deviations) < 1
assert pyramid[0].shape == img.shape
assert pyramid[1].shape[0] < pyramid[0].shape[0]
assert pyramid[2].shape[0] < pyramid[1].shape[0]
def test_create_laplacian_image_pyramid():
pyramid_depth = 3
img = load_image(TEST_IMAGE_PATH)
pyramid = create_laplacian_image_pyramid(img, pyramid_depth)
display_image_pyramid(pyramid)
means = [np.average(f) for f in pyramid]
deviations = [np.std(f) for f in pyramid]
assert len(pyramid) == pyramid_depth
assert np.std(
deviations
) > 0.01 # one of the images is just the original sized down photo. there should be significantly more variance in that photo
assert pyramid[0].shape == img.shape
assert pyramid[1].shape[0] < pyramid[0].shape[0]
assert pyramid[2].shape[0] < pyramid[1].shape[0]