def test_grey():
# very weak tests.
img = np.zeros((20, 21))
img[:10, 10:] = 0.2
img[10:, :10] = 0.4
img[10:, 10:] = 0.6
seg = felzenszwalb(img, sigma=0)
# we expect 4 segments:
assert_equal(len(np.unique(seg)), 4)
# that mostly respect the 4 regions:
for i in range(4):
hist = np.histogram(img[seg == i], bins=[0, 0.1, 0.3, 0.5, 1])[0]
assert_greater(hist[i], 40)
def test_minsize():
# single-channel:
img = data.coins()[20:168, 0:128]
for min_size in np.arange(10, 100, 10):
segments = felzenszwalb(img, min_size=min_size, sigma=3)
counts = np.bincount(segments.ravel())
# actually want to test greater or equal.
assert_greater(counts.min() + 1, min_size)
# multi-channel:
coffee = data.coffee()[::4, ::4]
for min_size in np.arange(10, 100, 10):
segments = felzenszwalb(coffee, min_size=min_size, sigma=3)
counts = np.bincount(segments.ravel())
# actually want to test greater or equal.
assert_greater(counts.min() + 1, min_size)
def test_grey():
rnd = np.random.RandomState(0)
img = np.zeros((20, 21))
img[:10, 10:] = 0.2
img[10:, :10] = 0.4
img[10:, 10:] = 0.6
img += 0.1 * rnd.normal(size=img.shape)
seg = quickshift(img, kernel_size=2, max_dist=3, random_seed=0,
convert2lab=False, sigma=0)
# we expect 4 segments:
assert_equal(len(np.unique(seg)), 4)
# that mostly respect the 4 regions:
for i in range(4):
hist = np.histogram(img[seg == i], bins=[0, 0.1, 0.3, 0.5, 1])[0]
assert_greater(hist[i], 20)
def test_grey():
rnd = np.random.RandomState(0)
img = np.zeros((20, 21))
img[:10, 10:] = 0.2
img[10:, :10] = 0.4
img[10:, 10:] = 0.6
img += 0.1 * rnd.normal(size=img.shape)
seg = quickshift(img, kernel_size=2, max_dist=3, random_seed=0,
convert2lab=False, sigma=0)
# we expect 4 segments:
assert_equal(len(np.unique(seg)), 4)
# that mostly respect the 4 regions:
for i in range(4):
hist = np.histogram(img[seg == i], bins=[0, 0.1, 0.3, 0.5, 1])[0]
assert_greater(hist[i], 20)
def test_minsize():
# single-channel:
img = data.coins()[20:168, 0:128]
for min_size in np.arange(10, 100, 10):
segments = felzenszwalb(img, min_size=min_size, sigma=3)
counts = np.bincount(segments.ravel())
# actually want to test greater or equal.
assert_greater(counts.min() + 1, min_size)
# multi-channel:
coffee = data.coffee()[::4, ::4]
for min_size in np.arange(10, 100, 10):
segments = felzenszwalb(coffee, min_size=min_size, sigma=3)
counts = np.bincount(segments.ravel())
# actually want to test greater or equal.
# the construction doesn't guarantee min_size is respected
# after intersecting the sementations for the colors
assert_greater(np.mean(counts) + 1, min_size)
def test_minsize():
# single-channel:
img = data.coins()[20:168,0:128]
for min_size in np.arange(10, 100, 10):
segments = felzenszwalb(img, min_size=min_size, sigma=3)
counts = np.bincount(segments.ravel())
# actually want to test greater or equal.
assert_greater(counts.min() + 1, min_size)
# multi-channel:
coffee = data.coffee()[::4, ::4]
for min_size in np.arange(10, 100, 10):
segments = felzenszwalb(coffee, min_size=min_size, sigma=3)
counts = np.bincount(segments.ravel())
# actually want to test greater or equal.
# the construction doesn't guarantee min_size is respected
# after intersecting the sementations for the colors
assert_greater(np.mean(counts) + 1, min_size)
