def test_dsift_channels(): try: from menpo.feature import dsift except ImportError: skip("Cyvlfeat must be installed to run this unit test") n_cases = 3 num_bins_horizontal = np.random.randint(1, 3, [n_cases, 1]) num_bins_vertical = np.random.randint(1, 3, [n_cases, 1]) num_or_bins = np.random.randint(7, 9, [n_cases, 1]) cell_size_horizontal = np.random.randint(1, 10, [n_cases, 1]) cell_size_vertical = np.random.randint(1, 10, [n_cases, 1]) channels = np.random.randint(1, 4, [n_cases]) for i in range(n_cases): image = MaskedImage(np.random.randn(channels[i], 40, 40)) dsift_img = dsift( image, window_step_horizontal=1, window_step_vertical=1, num_bins_horizontal=num_bins_horizontal[i, 0], num_bins_vertical=num_bins_vertical[i, 0], num_or_bins=num_or_bins[i, 0], cell_size_horizontal=cell_size_horizontal[i, 0], cell_size_vertical=cell_size_vertical[i, 0], ) n_channels = (num_bins_horizontal[i, 0] * num_bins_vertical[i, 0] * num_or_bins[i, 0]) assert_allclose(dsift_img.n_channels, n_channels)
def test_dsift_values(): from menpo.feature import dsift image = Image([[1, 2, 3, 4], [2, 1, 3, 4], [1, 2, 3, 4], [2, 1, 3, 4]]) sift_img = dsift(image, cell_size_horizontal=2, cell_size_vertical=2) assert_allclose(np.around(sift_img.pixels[0, 0, 0], 6), 76.002098000000004, rtol=1e-04) assert_allclose(np.around(sift_img.pixels[1, 0, 1], 6), 139.76733400000001, rtol=1e-04) assert_allclose(np.around(sift_img.pixels[0, 1, 0], 6), 155.95297199999999, rtol=1e-04) assert_allclose(np.around(sift_img.pixels[5, 1, 1], 6), 18.307358000000001, rtol=1e-04)
def test_dsift_values(): try: from menpo.feature import dsift except ImportError: skip("Cyvlfeat must be installed to run this unit test") # Equivalent to the transpose of image in Matlab image = Image([[1, 2, 3, 4], [2, 1, 3, 4], [1, 2, 3, 4], [2, 1, 3, 4]]) sift_img = dsift(image, cell_size_horizontal=2, cell_size_vertical=2) assert_allclose(np.around(sift_img.pixels[0, 0, 0], 6), 19.719786, rtol=1e-04) assert_allclose(np.around(sift_img.pixels[1, 0, 1], 6), 141.535736, rtol=1e-04) assert_allclose(np.around(sift_img.pixels[0, 1, 0], 6), 184.377472, rtol=1e-04) assert_allclose(np.around(sift_img.pixels[5, 1, 1], 6), 39.04007, rtol=1e-04)
def test_dsift_values(): from menpo.feature import dsift # Equivalent to the transpose of image in Matlab image = Image([[1, 2, 3, 4], [2, 1, 3, 4], [1, 2, 3, 4], [2, 1, 3, 4]]) sift_img = dsift(image, cell_size_horizontal=2, cell_size_vertical=2) assert_allclose(np.around(sift_img.pixels[0, 0, 0], 6), 19.719786, rtol=1e-04) assert_allclose(np.around(sift_img.pixels[1, 0, 1], 6), 141.535736, rtol=1e-04) assert_allclose(np.around(sift_img.pixels[0, 1, 0], 6), 184.377472, rtol=1e-04) assert_allclose(np.around(sift_img.pixels[5, 1, 1], 6), 39.04007, rtol=1e-04)
def test_dsift_values(): from menpo.feature import dsift # Equivalent to the transpose of image in Matlab image = Image([[1, 2, 3, 4], [2, 1, 3, 4], [1, 2, 3, 4], [2, 1, 3, 4]]) sift_img = dsift(image, cell_size_horizontal=2, cell_size_vertical=2) assert_allclose(np.around(sift_img.pixels[0, 0, 0], 6), 19.719786, rtol=1e-04) assert_allclose(np.around(sift_img.pixels[1, 0, 1], 6), 141.535736, rtol=1e-04) assert_allclose(np.around(sift_img.pixels[0, 1, 0], 6), 184.377472, rtol=1e-04) assert_allclose(np.around(sift_img.pixels[5, 1, 1], 6), 39.04007, rtol=1e-04) assert 1
def test_dsift_channels(): from menpo.feature import dsift n_cases = 3 num_bins_horizontal = np.random.randint(1, 3, [n_cases, 1]) num_bins_vertical = np.random.randint(1, 3, [n_cases, 1]) num_or_bins = np.random.randint(7, 9, [n_cases, 1]) cell_size_horizontal = np.random.randint(1, 10, [n_cases, 1]) cell_size_vertical = np.random.randint(1, 10, [n_cases, 1]) channels = np.random.randint(1, 4, [n_cases]) for i in range(n_cases): image = MaskedImage(np.random.randn(channels[i], 40, 40)) dsift_img = dsift(image, window_step_horizontal=1, window_step_vertical=1, num_bins_horizontal=num_bins_horizontal[i, 0], num_bins_vertical=num_bins_vertical[i, 0], num_or_bins=num_or_bins[i, 0], cell_size_horizontal=cell_size_horizontal[i, 0], cell_size_vertical=cell_size_vertical[i, 0]) n_channels = (num_bins_horizontal[i, 0] * num_bins_vertical[i, 0] * num_or_bins[i, 0]) assert_allclose(dsift_img.n_channels, n_channels)
def sift_svs_shape(pc, xr, yr, groups=None): store_image = dsift(svs_shape(pc, xr, yr, groups)) return store_image
def float32_dsift(x): return dsift(x).astype(np.float32)