|
|
|
import numpy as np |
|
from numpy.testing import assert_allclose |
|
import thinplate as tps |
|
|
|
def test_numpy_fit(): |
|
c = np.array([ |
|
[0., 0, 0.0], |
|
[1., 0, 0.0], |
|
[1., 1, 0.0], |
|
[0, 1, 0.0], |
|
]) |
|
|
|
theta = tps.TPS.fit(c) |
|
assert_allclose(theta, 0) |
|
assert_allclose(tps.TPS.z(c, c, theta), c[:, 2]) |
|
|
|
c = np.array([ |
|
[0., 0, 1.0], |
|
[1., 0, 1.0], |
|
[1., 1, 1.0], |
|
[0, 1, 1.0], |
|
]) |
|
|
|
theta = tps.TPS.fit(c) |
|
assert_allclose(theta[:-3], 0) |
|
assert_allclose(theta[-3:], [1, 0, 0]) |
|
assert_allclose(tps.TPS.z(c, c, theta), c[:, 2], atol=1e-3) |
|
|
|
|
|
theta = tps.TPS.fit(c, reduced=True) |
|
assert len(theta) == c.shape[0] + 2 |
|
assert_allclose(theta[:-3], 0) |
|
assert_allclose(theta[-3:], [1, 0, 0]) |
|
assert_allclose(tps.TPS.z(c, c, theta), c[:, 2], atol=1e-3) |
|
|
|
c = np.array([ |
|
[0., 0, -.5], |
|
[1., 0, 0.5], |
|
[1., 1, 0.2], |
|
[0, 1, 0.8], |
|
]) |
|
|
|
theta = tps.TPS.fit(c) |
|
assert_allclose(tps.TPS.z(c, c, theta), c[:, 2], atol=1e-3) |
|
|
|
def test_numpy_densegrid(): |
|
|
|
|
|
|
|
import cv2 |
|
|
|
img = np.zeros((40, 40), dtype=np.uint8) |
|
img[10:21, 10:21] = 255 |
|
|
|
c_dst = np.array([ |
|
[0., 0], |
|
[1., 0], |
|
[1, 1], |
|
[0, 1], |
|
]) |
|
|
|
|
|
c_src = np.array([ |
|
[10., 10], |
|
[20., 10], |
|
[20, 20], |
|
[10, 20], |
|
]) / 40. |
|
|
|
theta = tps.tps_theta_from_points(c_src, c_dst) |
|
theta_r = tps.tps_theta_from_points(c_src, c_dst, reduced=True) |
|
|
|
grid = tps.tps_grid(theta, c_dst, (20,20)) |
|
grid_r = tps.tps_grid(theta_r, c_dst, (20,20)) |
|
|
|
mapx, mapy = tps.tps_grid_to_remap(grid, img.shape) |
|
warped = cv2.remap(img, mapx, mapy, cv2.INTER_CUBIC) |
|
|
|
assert img.min() == 0. |
|
assert img.max() == 255. |
|
assert warped.shape == (20,20) |
|
assert warped.min() == 255. |
|
assert warped.max() == 255. |
|
assert np.linalg.norm(grid.reshape(-1,2) - grid_r.reshape(-1,2)) < 1e-3 |
|
|
