def test_rand_weighted_crop_large_roi(self):
for p in TEST_NDARRAYS:
for q in TEST_NDARRAYS:
img = self.segn[0]
n_samples = 3
crop = RandWeightedCropd(("img", "seg"), "weight",
(10000, 400), n_samples, "location")
weight = np.zeros_like(img)
weight[0, 30, 17] = 1.1
weight[0, 10, 1] = 1
crop.set_random_state(10)
data = {
"img": p(img),
"seg": p(self.imt[0]),
"weight": q(weight)
}
result = crop(data)
self.assertTrue(len(result) == n_samples)
np.testing.assert_allclose(result[0]["img"].shape,
(1, 128, 64))
np.testing.assert_allclose(result[0]["seg"].shape,
(1, 128, 64))
for c, e in zip(crop.centers, [[64, 32], [64, 32], [64, 32]]):
assert_allclose(c, e, type_test=False)
assert_allclose(result[1]["location"], [64, 32],
type_test=False)
def test_rand_weighted_crop_patch_index(self):
for p in TEST_NDARRAYS:
for q in TEST_NDARRAYS:
img = self.imt[0]
n_samples = 3
crop = RandWeightedCropd(("img", "seg"), "w", (10, -1, -1),
n_samples)
weight = np.zeros_like(img)
weight[0, 7, 17] = 1.1
weight[0, 13, 31] = 1.1
weight[0, 24, 21] = 1
crop.set_random_state(10)
result = crop({
"img": p(img),
"seg": p(self.segn[0]),
"w": q(weight),
PostFix.meta("img"): {
"affine": None
}
})
self.assertTrue(len(result) == n_samples)
for c, e in zip(crop.centers,
[[14, 32, 40], [41, 32, 40], [20, 32, 40]]):
assert_allclose(c, e, type_test=False)
for i in range(n_samples):
np.testing.assert_allclose(result[i]["img"].shape,
(1, 10, 64, 80))
np.testing.assert_allclose(result[i]["seg"].shape,
(1, 10, 64, 80))
np.testing.assert_allclose(
result[i][PostFix.meta("img")]["patch_index"], i)
np.testing.assert_allclose(
result[i][PostFix.meta("seg")]["patch_index"], i)
def test_rand_weighted_crop_bad_w(self):
for p in TEST_NDARRAYS:
for q in TEST_NDARRAYS:
img = self.imt[0]
n_samples = 3
crop = RandWeightedCropd(("img", "seg"), "w", (48, 64, 80),
n_samples)
weight = np.zeros_like(img)
weight[0, 30, 17] = np.inf
weight[0, 10, 1] = -np.inf
weight[0, 10, 20] = -np.nan
crop.set_random_state(10)
result = crop({
"img": p(img),
"seg": p(self.segn[0]),
"w": q(weight)
})
self.assertTrue(len(result) == n_samples)
np.testing.assert_allclose(result[0]["img"].shape,
(1, 48, 64, 80))
np.testing.assert_allclose(result[0]["seg"].shape,
(1, 48, 64, 80))
for c, e in zip(crop.centers,
[[24, 32, 40], [24, 32, 40], [24, 32, 40]]):
assert_allclose(c, e, type_test=False)
def test_rand_weighted_crop_default_roi(self):
for p in TEST_NDARRAYS:
for q in TEST_NDARRAYS:
img = self.imt[0]
n_samples = 3
crop = RandWeightedCropd(("img", "seg"), "w", (10, -1, -1),
n_samples)
weight = np.zeros_like(img)
weight[0, 7, 17] = 1.1
weight[0, 13, 31] = 1.1
weight[0, 24, 21] = 1
crop.set_random_state(10)
result = crop({
"img": p(img),
"seg": p(self.segn[0]),
"w": q(weight)
})
self.assertTrue(len(result) == n_samples)
np.testing.assert_allclose(result[0]["img"].shape,
(1, 10, 64, 80))
np.testing.assert_allclose(result[0]["seg"].shape,
(1, 10, 64, 80))
for c, e in zip(crop.centers,
[[14, 32, 40], [41, 32, 40], [20, 32, 40]]):
assert_allclose(c, e, type_test=False)
def test_rand_weighted_crop_patch_index(self):
img = self.imt[0]
n_samples = 3
crop = RandWeightedCropd(("img", "seg"), "w", (10, -1, -1), n_samples)
weight = np.zeros_like(img)
weight[0, 7, 17] = 1.1
weight[0, 13, 31] = 1.1
weight[0, 24, 21] = 1
crop.set_random_state(10)
result = crop({
"img": img,
"seg": self.segn[0],
"w": weight,
"img_meta_dict": {
"affine": None
}
})
self.assertTrue(len(result) == n_samples)
np.testing.assert_allclose(np.asarray(crop.centers),
[[14, 32, 40], [41, 32, 40], [20, 32, 40]])
for i in range(n_samples):
np.testing.assert_allclose(result[i]["img"].shape, (1, 10, 64, 80))
np.testing.assert_allclose(result[i]["seg"].shape, (1, 10, 64, 80))
np.testing.assert_allclose(
result[i]["img_meta_dict"]["patch_index"], i)
np.testing.assert_allclose(
result[i]["seg_meta_dict"]["patch_index"], i)
def test_rand_weighted_cropd(self, _, init_params, input_data,
expected_shape, expected_centers):
crop = RandWeightedCropd(**init_params)
crop.set_random_state(10)
result = crop(input_data)
self.assertTrue(len(result) == init_params["num_samples"])
_len = len(tuple(input_data.keys()))
self.assertTupleEqual(
tuple(result[0].keys())[:_len], tuple(input_data.keys()))
def test_rand_weighted_crop_large_roi(self):
img = self.segn[0]
n_samples = 3
crop = RandWeightedCropd("img", "w", (10000, 400, 80), n_samples)
weight = np.zeros_like(img)
weight[0, 30, 17, 20] = 1.1
weight[0, 10, 1, 17] = 1
crop.set_random_state(10)
result = crop({"img": img, "w": weight})
self.assertTrue(len(result) == n_samples)
np.testing.assert_allclose(result[0]["img"].shape, (1, 48, 64, 80))
np.testing.assert_allclose(np.asarray(crop.centers),
[[24, 32, 40], [24, 32, 40], [24, 32, 40]])
def test_rand_weighted_crop_small_roi(self):
img = self.seg1[0]
n_samples = 3
crop = RandWeightedCropd("img", "w", (8, 10, 12), n_samples)
weight = np.zeros_like(img)
weight[0, 5, 30, 17] = 1.1
weight[0, 8, 40, 31] = 1
weight[0, 11, 23, 21] = 1
crop.set_random_state(10)
result = crop({"img": img, "w": weight})
self.assertTrue(len(result) == n_samples)
np.testing.assert_allclose(result[0]["img"].shape, (1, 8, 10, 12))
np.testing.assert_allclose(np.asarray(crop.centers),
[[11, 23, 21], [5, 30, 17], [8, 40, 31]])
def test_rand_weighted_crop_bad_w(self):
img = self.imt[0]
n_samples = 3
crop = RandWeightedCropd(("img", "seg"), "w", (20, 40), n_samples)
weight = np.zeros_like(img)
weight[0, 30, 17] = np.inf
weight[0, 10, 1] = -np.inf
weight[0, 10, 20] = -np.nan
crop.set_random_state(10)
result = crop({"img": img, "seg": self.segn[0], "w": weight})
self.assertTrue(len(result) == n_samples)
np.testing.assert_allclose(result[0]["img"].shape, (1, 20, 40))
np.testing.assert_allclose(result[0]["seg"].shape, (1, 20, 40))
np.testing.assert_allclose(np.asarray(crop.centers),
[[63, 37], [31, 43], [66, 20]])
def test_rand_weighted_crop_default_roi(self):
img = self.imt[0]
n_samples = 3
crop = RandWeightedCropd(("img", "seg"), "w", (10, -1, -1), n_samples)
weight = np.zeros_like(img)
weight[0, 7, 17] = 1.1
weight[0, 13, 31] = 1.1
weight[0, 24, 21] = 1
crop.set_random_state(10)
result = crop({"img": img, "seg": self.segn[0], "w": weight})
self.assertTrue(len(result) == n_samples)
np.testing.assert_allclose(result[0]["img"].shape, (1, 10, 64, 80))
np.testing.assert_allclose(result[0]["seg"].shape, (1, 10, 64, 80))
np.testing.assert_allclose(np.asarray(crop.centers),
[[14, 32, 40], [41, 32, 40], [20, 32, 40]])
def test_rand_weighted_crop_default_roi(self):
img = self.imt[0]
n_samples = 3
crop = RandWeightedCropd("im", "weight", (10, -1), n_samples, "coords")
weight = np.zeros_like(img)
weight[0, 30, 17] = 1.1
weight[0, 40, 31] = 1
weight[0, 80, 21] = 1
crop.set_random_state(10)
data = {"im": img, "weight": weight, "others": np.nan}
result = crop(data)
self.assertTrue(len(result) == n_samples)
np.testing.assert_allclose(result[0]["im"].shape, (1, 10, 64))
np.testing.assert_allclose(np.asarray(crop.centers),
[[14, 32], [105, 32], [20, 32]])
np.testing.assert_allclose(result[1]["coords"], [105, 32])
def test_rand_weighted_crop_small_roi(self):
for p in TEST_NDARRAYS:
for q in TEST_NDARRAYS:
img = self.seg1[0]
n_samples = 3
crop = RandWeightedCropd("img", "w", (8, 10, 12), n_samples)
weight = np.zeros_like(img)
weight[0, 5, 30, 17] = 1.1
weight[0, 8, 40, 31] = 1
weight[0, 11, 23, 21] = 1
crop.set_random_state(10)
result = crop({"img": p(img), "w": q(weight)})
self.assertTrue(len(result) == n_samples)
np.testing.assert_allclose(result[0]["img"].shape, (1, 8, 10, 12))
for c, e in zip(crop.centers, [[11, 23, 21], [5, 30, 17], [8, 40, 31]]):
assert_allclose(c, e, type_test=False)
def test_rand_weighted_crop_large_roi(self):
img = self.segn[0]
n_samples = 3
crop = RandWeightedCropd(("img", "seg"), "weight", (10000, 400),
n_samples, "location")
weight = np.zeros_like(img)
weight[0, 30, 17] = 1.1
weight[0, 10, 1] = 1
crop.set_random_state(10)
data = {"img": img, "seg": self.imt[0], "weight": weight}
result = crop(data)
self.assertTrue(len(result) == n_samples)
np.testing.assert_allclose(result[0]["img"].shape, (1, 128, 64))
np.testing.assert_allclose(result[0]["seg"].shape, (1, 128, 64))
np.testing.assert_allclose(np.asarray(crop.centers),
[[64, 32], [64, 32], [64, 32]])
np.testing.assert_allclose(result[1]["location"], [64, 32])
def test_rand_weighted_crop_default_roi(self):
for p in TEST_NDARRAYS:
for q in TEST_NDARRAYS:
img = self.imt[0]
n_samples = 3
crop = RandWeightedCropd("im", "weight", (10, -1), n_samples, "coords")
weight = np.zeros_like(img)
weight[0, 30, 17] = 1.1
weight[0, 40, 31] = 1
weight[0, 80, 21] = 1
crop.set_random_state(10)
data = {"im": p(img), "weight": q(weight), "others": np.nan}
result = crop(data)
self.assertTrue(len(result) == n_samples)
np.testing.assert_allclose(result[0]["im"].shape, (1, 10, 64))
for c, e in zip(crop.centers, [[14, 32], [105, 32], [20, 32]]):
assert_allclose(c, e, type_test=False)
assert_allclose(result[1]["coords"], [105, 32], type_test=False)
def test_rand_weighted_cropd(self, _, init_params, input_data, expected_shape, expected_centers):
crop = RandWeightedCropd(**init_params)
crop.set_random_state(10)
result = crop(input_data)
self.assertTrue(len(result) == init_params["num_samples"])