diff options
Diffstat (limited to 'silx/image/test/test_bilinear.py')
| -rw-r--r-- | silx/image/test/test_bilinear.py | 52 |
1 files changed, 44 insertions, 8 deletions
diff --git a/silx/image/test/test_bilinear.py b/silx/image/test/test_bilinear.py index 12d0067..55eaccb 100644 --- a/silx/image/test/test_bilinear.py +++ b/silx/image/test/test_bilinear.py @@ -24,7 +24,7 @@ __authors__ = ["J. Kieffer"] __license__ = "MIT" -__date__ = "02/08/2016" +__date__ = "25/11/2020" import unittest import numpy @@ -45,6 +45,10 @@ class TestBilinear(unittest.TestCase): gb = numpy.exp(-b * b / 6000) gg = numpy.outer(ga, gb) b = BilinearImage(gg) + + self.assertAlmostEqual(b.maxi, 1, 2, "maxi is almost 1") + self.assertLess(b.mini, 0.3, "mini should be around 0.23") + ok = 0 for s in range(self.N): i, j = numpy.random.randint(100), numpy.random.randint(100) @@ -78,8 +82,8 @@ class TestBilinear(unittest.TestCase): self.assertEqual(ok, self.N, "Maximum is always found") def test_map(self): - N = 100 - y, x = numpy.ogrid[:N, :N + 10] + N = 6 + y, x = numpy.ogrid[:N,:N + 10] img = x + y b = BilinearImage(img) x2d = numpy.zeros_like(y) + x @@ -87,16 +91,47 @@ class TestBilinear(unittest.TestCase): res1 = b.map_coordinates((y2d, x2d)) self.assertEqual(abs(res1 - img).max(), 0, "images are the same (corners)") - x2d = numpy.zeros_like(y) + (x[:, :-1] + 0.5) - y2d = numpy.zeros_like(x[:, :-1]) + y + x2d = numpy.zeros_like(y) + (x[:,:-1] + 0.5) + y2d = numpy.zeros_like(x[:,:-1]) + y res1 = b.map_coordinates((y2d, x2d)) - self.assertEqual(abs(res1 - img[:, :-1] - 0.5).max(), 0, "images are the same (middle)") + self.assertEqual(abs(res1 - img[:,:-1] - 0.5).max(), 0, "images are the same (middle)") - x2d = numpy.zeros_like(y[:-1, :]) + (x[:, :-1] + 0.5) - y2d = numpy.zeros_like(x[:, :-1]) + (y[:-1, :] + 0.5) + x2d = numpy.zeros_like(y[:-1,:]) + (x[:,:-1] + 0.5) + y2d = numpy.zeros_like(x[:,:-1]) + (y[:-1,:] + 0.5) res1 = b.map_coordinates((y2d, x2d)) self.assertEqual(abs(res1 - img[:-1, 1:]).max(), 0, "images are the same (center)") + def test_mask_grad(self): + N = 100 + img = numpy.arange(N * N).reshape(N, N) + # No mask on the boundaries, makes the test complicated, pixel always separated + masked = 2 * numpy.random.randint(0, int((N - 1) / 2), size=(2, N)) + 1 + mask = numpy.zeros((N, N), dtype=numpy.uint8) + mask[(masked[0], masked[1])] = 1 + self.assertLessEqual(mask.sum(), N, "At most N pixels are masked") + + b = BilinearImage(img, mask=mask) + self.assertEqual(b.has_mask, True, "interpolator has mask") + self.assertEqual(b.maxi, N * N - 1, "maxi is N²-1") + self.assertEqual(b.mini, 0, "mini is 0") + + y, x = numpy.ogrid[:N,:N] + x2d = numpy.zeros_like(y) + x + y2d = numpy.zeros_like(x) + y + res1 = b.map_coordinates((y2d, x2d)) + self.assertEqual(numpy.nanmax(abs(res1 - img)), 0, "images are the same (corners), or Nan ") + + x2d = numpy.zeros_like(y) + (x[:,:-1] + 0.5) + y2d = numpy.zeros_like(x[:,:-1]) + y + res1 = b.map_coordinates((y2d, x2d)) + self.assertLessEqual(numpy.max(abs(res1 - img[:, 1:] + 1 / 2.)), 0.5, "images are the same (middle) +/- 0.5") + + x2d = numpy.zeros_like(y[:-1]) + (x[:,:-1] + 0.5) + y2d = numpy.zeros_like(x[:,:-1]) + (y[:-1] + 0.5) + res1 = b.map_coordinates((y2d, x2d)) + exp = 0.25 * (img[:-1,:-1] + img[:-1, 1:] + img[1:,:-1] + img[1:, 1:]) + self.assertLessEqual(abs(res1 - exp).max(), N / 4, "images are almost the same (center)") + def test_profile_grad(self): N = 100 img = numpy.arange(N * N).reshape(N, N) @@ -139,4 +174,5 @@ def suite(): testsuite.addTest(TestBilinear("test_map")) testsuite.addTest(TestBilinear("test_profile_grad")) testsuite.addTest(TestBilinear("test_profile_gaus")) + testsuite.addTest(TestBilinear("test_mask_grad")) return testsuite |