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 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)
