def test_real_perfect_overlap(self):
"""
Test on decomposed image
"""
numTiles = [2, 3, 4]
overlap = [0.4]
for img in IMGS:
conv = find_fittest_converter(img)
data = conv.read_data(img)[0]
img = ensure2DImage(data)
for n in numTiles:
for o in overlap:
[tiles, _] = decompose_image(
img, o, n, "horizontalZigzag", False)
weaver = MeanWeaver()
for t in tiles:
weaver.addTile(t)
sz = len(weaver.getFullImage())
w = weaver.getFullImage()
numpy.testing.assert_allclose(w, img[:sz, :sz], rtol=1)
def test_gradient(self):
"""
Test if gradient appears in two images with different constant values
"""
img0 = numpy.ones((100, 100)) * 256
img1 = numpy.zeros((100, 100))
md0 = {
model.MD_SW_VERSION: "1.0-test",
model.MD_DESCRIPTION: u"test",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 2), # px, px
model.MD_PIXEL_SIZE: (1, 1), # m/px
model.MD_POS: (50, 50), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_IN_WL: (500e-9, 520e-9), # m
}
in0 = model.DataArray(img0, md0)
md1 = {
model.MD_SW_VERSION: "1.0-test",
model.MD_DESCRIPTION: u"test",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 2), # px, px
model.MD_PIXEL_SIZE: (1, 1), # m/px
model.MD_POS: (120, 50), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_IN_WL: (500e-9, 520e-9), # m
}
in1 = model.DataArray(img1, md1)
weaver = MeanWeaver()
weaver.addTile(in0)
weaver.addTile(in1)
outd = weaver.getFullImage()
# Visualize overlap mask
#from PIL import Image
# Image.fromarray(outd).show()
# Test that values in overlapping region decrease from left to right
o = outd[
10:-10, 70:
100] # the gradient is going to be small on the edges, so only check middle part
for row in o:
# the gradient can be small, so only test that the left pixel has a lower
# value than the value of the right pixel
self.assertLess(row[-1], row[0])
def test_gradient(self):
"""
Test if gradient appears in two images with different constant values
"""
img0 = numpy.ones((100, 100)) * 256
img1 = numpy.zeros((100, 100))
md0 = {
model.MD_SW_VERSION: "1.0-test",
model.MD_DESCRIPTION: u"test",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 2), # px, px
model.MD_PIXEL_SIZE: (1, 1), # m/px
model.MD_POS: (50, 50), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_IN_WL: (500e-9, 520e-9), # m
}
in0 = model.DataArray(img0, md0)
md1 = {
model.MD_SW_VERSION: "1.0-test",
model.MD_DESCRIPTION: u"test",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 2), # px, px
model.MD_PIXEL_SIZE: (1, 1), # m/px
model.MD_POS: (120, 50), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_IN_WL: (500e-9, 520e-9), # m
}
in1 = model.DataArray(img1, md1)
weaver = MeanWeaver()
weaver.addTile(in0)
weaver.addTile(in1)
outd = weaver.getFullImage()
# Visualize overlap mask
#from PIL import Image
# Image.fromarray(outd).show()
# Test that values in overlapping region decrease from left to right
o = outd[10:-10, 70:100] # the gradient is going to be small on the edges, so only check middle part
for row in o:
# the gradient can be small, so only test that the left pixel has a lower
# value than the value of the right pixel
self.assertLess(row[-1], row[0])
def test_synthetic_perfect_overlap(self):
"""
Test on synthetic image with exactly matching overlap, weaved image should be equal to original image
"""
img0 = numpy.zeros((100, 100))
img1 = numpy.zeros((100, 100))
img0[:, 80:90] = 1
img1[:, 10:20] = 1
exp_out = numpy.zeros((100, 170))
exp_out[:, 80:90] = 1
md0 = {
model.MD_SW_VERSION: "1.0-test",
model.MD_DESCRIPTION: u"test",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 2), # px, px
model.MD_PIXEL_SIZE: (1, 1), # m/px
model.MD_POS: (50, 50), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_IN_WL: (500e-9, 520e-9), # m
}
in0 = model.DataArray(img0, md0)
md1 = {
model.MD_SW_VERSION: "1.0-test",
model.MD_DESCRIPTION: u"test",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 2), # px, px
model.MD_PIXEL_SIZE: (1, 1), # m/px
model.MD_POS: (120, 50), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_IN_WL: (500e-9, 520e-9), # m
}
in1 = model.DataArray(img1, md1)
weaver = MeanWeaver()
weaver.addTile(in0)
weaver.addTile(in1)
outd = weaver.getFullImage()
numpy.testing.assert_equal(outd, exp_out)
def test_one_tile(self):
"""
Test that when there is only one tile, it's returned as-is
"""
img12 = numpy.zeros((2048, 1937), dtype=numpy.uint16) + 4000
md = {
model.MD_SW_VERSION: "1.0-test",
model.MD_DESCRIPTION: u"test",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 2), # px, px
model.MD_PIXEL_SIZE: (1e-6, 2e-5), # m/px
model.MD_POS: (1e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_IN_WL: (500e-9, 520e-9), # m
model.MD_DIMS: "YX",
}
intile = model.DataArray(img12, md)
weaver = MeanWeaver()
weaver.addTile(intile)
outd = weaver.getFullImage()
self.assertEqual(outd.shape, intile.shape)
numpy.testing.assert_array_equal(outd, intile)
self.assertEqual(outd.metadata, intile.metadata)
# Same thing but with a typical SEM data
img8 = numpy.zeros((256, 356), dtype=numpy.uint8) + 40
md8 = {
model.MD_DESCRIPTION: u"test sem",
model.MD_ACQ_DATE: time.time(),
model.MD_PIXEL_SIZE: (1.3e-6, 1.3e-6), # m/px
model.MD_POS: (10e-3, 30e-3), # m
model.MD_DWELL_TIME: 1.2e-6, # s
model.MD_DIMS: "YX",
}
intile = model.DataArray(img8, md8)
weaver = MeanWeaver()
weaver.addTile(intile)
outd = weaver.getFullImage()
self.assertEqual(outd.shape, intile.shape)
numpy.testing.assert_array_equal(outd, intile)
self.assertEqual(outd.metadata, intile.metadata)
def test_synthetic_perfect_overlap(self):
"""
Test on synthetic image with exactly matching overlap, weaved image should be equal to original image
"""
img0 = numpy.zeros((100, 100))
img1 = numpy.zeros((100, 100))
img0[:, 80:90] = 1
img1[:, 10:20] = 1
exp_out = numpy.zeros((100, 170))
exp_out[:, 80:90] = 1
md0 = {
model.MD_SW_VERSION: "1.0-test",
model.MD_DESCRIPTION: u"test",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 2), # px, px
model.MD_PIXEL_SIZE: (1, 1), # m/px
model.MD_POS: (50, 50), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_IN_WL: (500e-9, 520e-9), # m
}
in0 = model.DataArray(img0, md0)
md1 = {
model.MD_SW_VERSION: "1.0-test",
model.MD_DESCRIPTION: u"test",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 2), # px, px
model.MD_PIXEL_SIZE: (1, 1), # m/px
model.MD_POS: (120, 50), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_IN_WL: (500e-9, 520e-9), # m
}
in1 = model.DataArray(img1, md1)
weaver = MeanWeaver()
weaver.addTile(in0)
weaver.addTile(in1)
outd = weaver.getFullImage()
numpy.testing.assert_equal(outd, exp_out)
def test_real_perfect_overlap(self):
"""
Test on decomposed image
"""
numTiles = [2, 3, 4]
overlap = [0.4]
for img in IMGS:
conv = find_fittest_converter(img)
data = conv.read_data(img)[0]
img = ensure2DImage(data)
for n in numTiles:
for o in overlap:
[tiles, _] = decompose_image(img, o, n, "horizontalZigzag",
False)
weaver = MeanWeaver()
for t in tiles:
weaver.addTile(t)
sz = len(weaver.getFullImage())
w = weaver.getFullImage()
numpy.testing.assert_allclose(w, img[:sz, :sz], rtol=1)
def test_one_tile(self):
"""
Test that when there is only one tile, it's returned as-is
"""
img12 = numpy.zeros((2048, 1937), dtype=numpy.uint16) + 4000
md = {
model.MD_SW_VERSION: "1.0-test",
model.MD_DESCRIPTION: u"test",
model.MD_ACQ_DATE: time.time(),
model.MD_BPP: 12,
model.MD_BINNING: (1, 2), # px, px
model.MD_PIXEL_SIZE: (1e-6, 2e-5), # m/px
model.MD_POS: (1e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_IN_WL: (500e-9, 520e-9), # m
}
intile = model.DataArray(img12, md)
weaver = MeanWeaver()
weaver.addTile(intile)
outd = weaver.getFullImage()
self.assertEqual(outd.shape, intile.shape)
numpy.testing.assert_array_equal(outd, intile)
self.assertEqual(outd.metadata, intile.metadata)
# Same thing but with a typical SEM data
img8 = numpy.zeros((256, 356), dtype=numpy.uint8) + 40
md8 = {
model.MD_DESCRIPTION: u"test sem",
model.MD_ACQ_DATE: time.time(),
model.MD_PIXEL_SIZE: (1.3e-6, 1.3e-6), # m/px
model.MD_POS: (10e-3, 30e-3), # m
model.MD_DWELL_TIME: 1.2e-6, # s
}
intile = model.DataArray(img8, md8)
weaver = CollageWeaverReverse()
weaver.addTile(intile)
outd = weaver.getFullImage()
self.assertEqual(outd.shape, intile.shape)
numpy.testing.assert_array_equal(outd, intile)
self.assertEqual(outd.metadata, intile.metadata)