Vehicle-Anti-Theft-Face-Rec.../venv/Lib/site-packages/skimage/transform/tests/test_integral.py

47 lines
1.5 KiB
Python

import numpy as np
from skimage.transform import integral_image, integrate
from skimage._shared.testing import assert_equal
np.random.seed(0)
x = (np.random.rand(50, 50) * 255).astype(np.uint8)
s = integral_image(x)
def test_validity():
y = np.arange(12).reshape((4, 3))
y = (np.random.rand(50, 50) * 255).astype(np.uint8)
assert_equal(integral_image(y)[-1, -1],
y.sum())
def test_basic():
assert_equal(x[12:24, 10:20].sum(), integrate(s, (12, 10), (23, 19)))
assert_equal(x[:20, :20].sum(), integrate(s, (0, 0), (19, 19)))
assert_equal(x[:20, 10:20].sum(), integrate(s, (0, 10), (19, 19)))
assert_equal(x[10:20, :20].sum(), integrate(s, (10, 0), (19, 19)))
def test_single():
assert_equal(x[0, 0], integrate(s, (0, 0), (0, 0)))
assert_equal(x[10, 10], integrate(s, (10, 10), (10, 10)))
def test_vectorized_integrate():
r0 = np.array([12, 0, 0, 10, 0, 10, 30])
c0 = np.array([10, 0, 10, 0, 0, 10, 31])
r1 = np.array([23, 19, 19, 19, 0, 10, 49])
c1 = np.array([19, 19, 19, 19, 0, 10, 49])
expected = np.array([x[12:24, 10:20].sum(),
x[:20, :20].sum(),
x[:20, 10:20].sum(),
x[10:20, :20].sum(),
x[0, 0],
x[10, 10],
x[30:, 31:].sum()])
start_pts = [(r0[i], c0[i]) for i in range(len(r0))]
end_pts = [(r1[i], c1[i]) for i in range(len(r0))]
assert_equal(expected, integrate(s, start_pts, end_pts))