def test_ijv():
numpy.random.seed(0)
image = numpy.random.uniform(size=(50, 50, 3)).astype(numpy.float32)
image[0, 0] = 1
labels0 = numpy.zeros(image.shape[:2], int)
labels0[20:30, 20:30] = 1
labels1 = numpy.zeros(image.shape[:2], int)
labels1[25:35, 25:35] = 2
labels = [labels0, labels1]
expected = image.copy()
mask = numpy.zeros(image.shape[:2], bool)
mask[20:30, 20] = True
mask[20:30, 29] = True
mask[20, 20:30] = True
mask[29, 20:30] = True
mask[25:35, 25] = True
mask[25:35, 34] = True
mask[25, 25:35] = True
mask[34, 25:35] = True
expected[mask, 0] = 1
expected[mask, 1:] = 0
workspace, module = make_workspace(image, labels=labels)
module.wants_color.value = cellprofiler.modules.overlayoutlines.WANTS_COLOR
module.outlines[0].color.value = "Red"
module.line_mode.value = "Inner"
module.run(workspace)
output_image = workspace.image_set.get_image(OUTPUT_IMAGE_NAME)
numpy.testing.assert_array_equal(output_image.pixel_data, expected)
def test_04_01_ijv(self):
numpy.random.seed(0)
image = numpy.random.uniform(size=(50, 50, 3)).astype(numpy.float32)
image[0, 0] = 1
labels0 = numpy.zeros(image.shape[:2], int)
labels0[20:30, 20:30] = 1
labels1 = numpy.zeros(image.shape[:2], int)
labels1[25:35, 25:35] = 2
labels = [labels0, labels1]
expected = image.copy()
mask = numpy.zeros(image.shape[:2], bool)
mask[20:30, 20] = True
mask[20:30, 29] = True
mask[20, 20:30] = True
mask[29, 20:30] = True
mask[25:35, 25] = True
mask[25:35, 34] = True
mask[25, 25:35] = True
mask[34, 25:35] = True
expected[mask, 0] = 1
expected[mask, 1:] = 0
workspace, module = self.make_workspace(image, labels=labels)
module.wants_color.value = cellprofiler.modules.overlayoutlines.WANTS_COLOR
module.outlines[0].color.value = "Red"
module.line_mode.value = "Inner"
module.run(workspace)
output_image = workspace.image_set.get_image(OUTPUT_IMAGE_NAME)
numpy.testing.assert_array_equal(output_image.pixel_data, expected)
def test_power():
image = numpy.ones((20, 10))
images_and_masks = [(image.copy(), None) for i in range(9)]
for i, (img, _) in enumerate(images_and_masks):
img[5, 5] *= numpy.sin(2 * numpy.pi * float(i) / 9.0)
image_out = run_image_set(
cellprofiler.modules.makeprojection.P_POWER, images_and_masks, frequency=9
)
i, j = numpy.mgrid[: image.shape[0], : image.shape[1]]
numpy.testing.assert_almost_equal(image_out.pixel_data[(i != 5) & (j != 5)], 0)
assert image_out.pixel_data[5, 5] > 1
def test_brightfield():
image = numpy.ones((20, 10))
images_and_masks = [(image.copy(), None) for i in range(9)]
for i, (img, _) in enumerate(images_and_masks):
if i < 5:
img[:5, :5] = 0
else:
img[:5, 5:] = 0
image_out = run_image_set(
cellprofiler.modules.makeprojection.P_BRIGHTFIELD, images_and_masks
)
i, j = numpy.mgrid[: image.shape[0], : image.shape[1]]
numpy.testing.assert_almost_equal(image_out.pixel_data[(i > 5) | (j < 5)], 0)
numpy.testing.assert_almost_equal(image_out.pixel_data[(i < 5) & (j >= 5)], 1)
def test_gray_max_possible():
numpy.random.seed(0)
image = numpy.random.uniform(size=(50, 50)).astype(numpy.float32) * 0.5
outline = numpy.zeros((50, 50), bool)
outline[20:31, 20:31] = 1
outline[21:30, 21:30] = 0
expected = image.copy()
expected[outline.astype(bool)] = 1
workspace, module = make_workspace(image, labels=[outline.astype(int)])
module.blank_image.value = False
module.wants_color.value = cellprofiler.modules.overlayoutlines.WANTS_GRAYSCALE
module.max_type.value = cellprofiler.modules.overlayoutlines.MAX_POSSIBLE
module.line_mode.value = "Inner"
module.run(workspace)
output_image = workspace.image_set.get_image(OUTPUT_IMAGE_NAME)
numpy.testing.assert_almost_equal(output_image.pixel_data, expected)
def test_03_02_gray_max_possible(self):
numpy.random.seed(0)
image = numpy.random.uniform(size=(50, 50)).astype(numpy.float32) * .5
outline = numpy.zeros((50, 50), bool)
outline[20:31, 20:31] = 1
outline[21:30, 21:30] = 0
expected = image.copy()
expected[outline.astype(bool)] = 1
workspace, module = self.make_workspace(image, labels=[outline.astype(int)])
module.blank_image.value = False
module.wants_color.value = cellprofiler.modules.overlayoutlines.WANTS_GRAYSCALE
module.max_type.value = cellprofiler.modules.overlayoutlines.MAX_POSSIBLE
module.line_mode.value = "Inner"
module.run(workspace)
output_image = workspace.image_set.get_image(OUTPUT_IMAGE_NAME)
numpy.testing.assert_array_equal(output_image.pixel_data, expected)
def test_wrong_size_gray():
"""Regression test of IMG-961 - image and outline size differ"""
numpy.random.seed(41)
image = numpy.random.uniform(size=(50, 50)).astype(numpy.float32) * 0.5
outline = numpy.zeros((60, 40), bool)
outline[20:31, 20:31] = True
outline[21:30, 21:30] = False
expected = image.copy()
expected[:50, :40][outline[:50, :40]] = 1
workspace, module = make_workspace(image, labels=[outline.astype(int)])
module.blank_image.value = False
module.wants_color.value = cellprofiler.modules.overlayoutlines.WANTS_GRAYSCALE
module.max_type.value = cellprofiler.modules.overlayoutlines.MAX_POSSIBLE
module.line_mode.value = "Inner"
module.run(workspace)
output_image = workspace.image_set.get_image(OUTPUT_IMAGE_NAME)
numpy.testing.assert_almost_equal(output_image.pixel_data, expected)
def test_03_03_wrong_size_gray(self):
'''Regression test of IMG-961 - image and outline size differ'''
numpy.random.seed(41)
image = numpy.random.uniform(size=(50, 50)).astype(numpy.float32) * .5
outline = numpy.zeros((60, 40), bool)
outline[20:31, 20:31] = True
outline[21:30, 21:30] = False
expected = image.copy()
expected[:50, :40][outline[:50, :40]] = 1
workspace, module = self.make_workspace(image, labels=[outline.astype(int)])
module.blank_image.value = False
module.wants_color.value = cellprofiler.modules.overlayoutlines.WANTS_GRAYSCALE
module.max_type.value = cellprofiler.modules.overlayoutlines.MAX_POSSIBLE
module.line_mode.value = "Inner"
module.run(workspace)
output_image = workspace.image_set.get_image(OUTPUT_IMAGE_NAME)
self.assertTrue(numpy.all(output_image.pixel_data == expected))
def test_color_to_color_outlines():
numpy.random.seed(0)
image = numpy.random.uniform(size=(50, 50, 3)).astype(numpy.float32)
image[0, 0] = 1
outline = numpy.zeros((50, 50), bool)
outline[20:31, 20:31] = 1
outline[21:30, 21:30] = 0
expected = image.copy()
expected[:, :, 0][outline.astype(bool)] = 1
expected[:, :, 1][outline.astype(bool)] = 0
expected[:, :, 2][outline.astype(bool)] = 0
workspace, module = make_workspace(image, labels=[outline.astype(int)])
module.wants_color.value = cellprofiler.modules.overlayoutlines.WANTS_COLOR
module.outlines[0].color.value = "Red"
module.line_mode.value = "Inner"
module.run(workspace)
output_image = workspace.image_set.get_image(OUTPUT_IMAGE_NAME)
numpy.testing.assert_array_equal(output_image.pixel_data, expected)
def test_02_02_color_to_color_outlines(self):
numpy.random.seed(0)
image = numpy.random.uniform(size=(50, 50, 3)).astype(numpy.float32)
image[0, 0] = 1
outline = numpy.zeros((50, 50), bool)
outline[20:31, 20:31] = 1
outline[21:30, 21:30] = 0
expected = image.copy()
expected[:, :, 0][outline.astype(bool)] = 1
expected[:, :, 1][outline.astype(bool)] = 0
expected[:, :, 2][outline.astype(bool)] = 0
workspace, module = self.make_workspace(image, labels=[outline.astype(int)])
module.wants_color.value = cellprofiler.modules.overlayoutlines.WANTS_COLOR
module.outlines[0].color.value = "Red"
module.line_mode.value = "Inner"
module.run(workspace)
output_image = workspace.image_set.get_image(OUTPUT_IMAGE_NAME)
numpy.testing.assert_array_equal(output_image.pixel_data, expected)
def test_wrong_size_color_to_color():
numpy.random.seed(25)
image = numpy.random.uniform(size=(50, 50, 3)).astype(numpy.float32)
image[0, 0] = 1
outline = numpy.zeros((60, 40), bool)
outline[20:31, 20:31] = 1
outline[21:30, 21:30] = 0
expected = image.copy()
sub_expected = expected[:50, :40]
sub_expected[:, :, 0][outline[:50, :40].astype(bool)] = 1
sub_expected[:, :, 1][outline[:50, :40].astype(bool)] = 0
sub_expected[:, :, 2][outline[:50, :40].astype(bool)] = 0
workspace, module = make_workspace(image, labels=[outline.astype(int)])
module.wants_color.value = cellprofiler.modules.overlayoutlines.WANTS_COLOR
module.outlines[0].color.value = "Red"
module.line_mode.value = "Inner"
module.run(workspace)
output_image = workspace.image_set.get_image(OUTPUT_IMAGE_NAME)
assert numpy.all(output_image.pixel_data == expected)
def test_02_05_wrong_size_color_to_color(self):
numpy.random.seed(25)
image = numpy.random.uniform(size=(50, 50, 3)).astype(numpy.float32)
image[0, 0] = 1
outline = numpy.zeros((60, 40), bool)
outline[20:31, 20:31] = 1
outline[21:30, 21:30] = 0
expected = image.copy()
sub_expected = expected[:50, :40]
sub_expected[:, :, 0][outline[:50, :40].astype(bool)] = 1
sub_expected[:, :, 1][outline[:50, :40].astype(bool)] = 0
sub_expected[:, :, 2][outline[:50, :40].astype(bool)] = 0
workspace, module = self.make_workspace(image, labels=[outline.astype(int)])
module.wants_color.value = cellprofiler.modules.overlayoutlines.WANTS_COLOR
module.outlines[0].color.value = "Red"
module.line_mode.value = "Inner"
module.run(workspace)
output_image = workspace.image_set.get_image(OUTPUT_IMAGE_NAME)
self.assertTrue(numpy.all(output_image.pixel_data == expected))
def test_flip_top_to_bottom():
numpy.random.seed(0)
image = numpy.random.uniform(size=(3, 3)).astype(numpy.float32)
mask = numpy.array([[True, True, True], [False, True, True],
[True, False, True]])
expected_mask = numpy.array([[True, False, True], [False, True, True],
[True, True, True]])
expected = image.copy()
expected[2, :] = image[0, :]
expected[0, :] = image[2, :]
def fn(module):
assert isinstance(module,
cellprofiler.modules.flipandrotate.FlipAndRotate)
module.flip_choice.value = cellprofiler.modules.flipandrotate.FLIP_TOP_TO_BOTTOM
module.rotate_choice.value = cellprofiler.modules.flipandrotate.ROTATE_NONE
output_image, angle = run_module(image, mask=mask, fn=fn)
assert angle == 0
assert numpy.all(output_image.mask == expected_mask)
assert numpy.all(
numpy.abs(output_image.pixel_data -
expected) <= numpy.finfo(float).eps)
def test_flip_left_to_right():
numpy.random.seed(0)
image = numpy.random.uniform(size=(3, 3))
mask = numpy.array([[True, True, True], [False, True, True],
[True, False, True]])
expected_mask = numpy.array([[True, True, True], [True, True, False],
[True, False, True]])
expected = image.copy()
expected[:, 2] = image[:, 0]
expected[:, 0] = image[:, 2]
def fn(module):
assert isinstance(module,
cellprofiler.modules.flipandrotate.FlipAndRotate)
module.flip_choice.value = cellprofiler.modules.flipandrotate.FLIP_LEFT_TO_RIGHT
module.rotate_choice.value = cellprofiler.modules.flipandrotate.ROTATE_NONE
output_image, angle = run_module(image, mask=mask, fn=fn)
assert angle == 0
assert numpy.all(output_image.mask == expected_mask)
assert numpy.all(
numpy.abs(output_image.pixel_data -
expected) <= numpy.finfo(numpy.float32).eps)
