def test_eccentricity(self):
# Eccentricity (elongation) is measured with good accuracy and
# ~0.02 precision, as long as the mask is large enough to cover
# the whole object.
L = 101
dims = (L, L + 2) # avoid square images in tests
pos = np.array([50, 55])
cols = ['x', 'y']
ECC = 0
image = np.ones(dims, dtype='uint8')
draw_gaussian_spot(image, pos[::-1], 4, ecc=ECC)
actual = mr.locate(image, 21, 1, preprocess=False)['ecc']
expected = ECC
assert_allclose(actual, expected, atol=0.02)
ECC = 0.2
image = np.ones(dims, dtype='uint8')
draw_gaussian_spot(image, pos[::-1], 4, ecc=ECC)
actual = mr.locate(image, 21, 1, preprocess=False)['ecc']
expected = ECC
assert_allclose(actual, expected, atol=0.02)
ECC = 0.5
image = np.ones(dims, dtype='uint8')
draw_gaussian_spot(image, pos[::-1], 4, ecc=ECC)
actual = mr.locate(image, 21, 1, preprocess=False)['ecc']
expected = ECC
assert_allclose(actual, expected, atol=0.02)
def test_eccentricity(self):
# Eccentricity (elongation) is measured with good accuracy and
# ~0.02 precision, as long as the mask is large enough to cover
# the whole object.
L = 101
dims = (L, L + 2) # avoid square images in tests
pos = np.array([50, 55])
cols = ['x', 'y']
ECC = 0
image = np.ones(dims, dtype='uint8')
draw_gaussian_spot(image, pos[::-1], 4, ecc=ECC)
actual = mr.locate(image, 21, 1, preprocess=False)['ecc']
expected = ECC
assert_allclose(actual, expected, atol=0.02)
ECC = 0.2
image = np.ones(dims, dtype='uint8')
draw_gaussian_spot(image, pos[::-1], 4, ecc=ECC)
actual = mr.locate(image, 21, 1, preprocess=False)['ecc']
expected = ECC
assert_allclose(actual, expected, atol=0.02)
ECC = 0.5
image = np.ones(dims, dtype='uint8')
draw_gaussian_spot(image, pos[::-1], 4, ecc=ECC)
actual = mr.locate(image, 21, 1, preprocess=False)['ecc']
expected = ECC
assert_allclose(actual, expected, atol=0.02)
def compare(shape, count, radius, noise_level):
pos = gen_random_locations(shape, count)
image = draw_spots(shape, pos, radius, noise_level)
f = mr.locate(image, 2*radius + 1, minmass=1800)
actual = f[['x', 'y']].sort(['x', 'y'])
expected = DataFrame(pos, columns=['y', 'x'])[['x', 'y']].sort(['x', 'y'])
return actual, expected
def compare(shape, count, radius, noise_level):
pos = gen_random_locations(shape, count)
image = draw_spots(shape, pos, radius, noise_level)
f = mr.locate(image, 2 * radius + 1, minmass=1800)
actual = f[['x', 'y']].sort(['x', 'y'])
expected = DataFrame(pos, columns=['y', 'x'])[['x', 'y']].sort(['x', 'y'])
return actual, expected
def test_one_centered_gaussian(self):
L = 21
dims = (L, L + 2) # avoid square images in tests
pos = np.array([7, 13])
cols = ['x', 'y']
expected = DataFrame(pos.reshape(1, -1), columns=cols)
image = np.ones(dims, dtype='uint8')
draw_gaussian_spot(image, pos[::-1], 4)
actual = mr.locate(image, 9, 1, preprocess=False)[cols]
assert_allclose(actual, expected, atol=0.1)
def test_one_centered_gaussian(self):
L = 21
dims = (L, L + 2) # avoid square images in tests
pos = np.array([7, 13])
cols = ['x', 'y']
expected = DataFrame(pos.reshape(1, -1), columns=cols)
image = np.ones(dims, dtype='uint8')
draw_gaussian_spot(image, pos[::-1], 4)
actual = mr.locate(image, 9, 1, preprocess=False)[cols]
assert_allclose(actual, expected, atol=0.1)
def test_rg(self):
# For Gaussians with radii 2, 3, 5, and 7 px, with proportionately
# chosen feature (mask) sizes, the 'size' comes out to be within 10%
# of the true Gaussian width.
# The IDL code has mistake in this area, documented here:
# http://www.physics.emory.edu/~weeks/idl/radius.html
L = 101
dims = (L, L + 2) # avoid square images in tests
pos = np.array([50, 55])
cols = ['x', 'y']
SIZE = 2
image = np.ones(dims, dtype='uint8')
draw_gaussian_spot(image, pos[::-1], SIZE)
actual = mr.locate(image, 7, 1, preprocess=False)['size']
expected = SIZE
assert_allclose(actual, expected, rtol=0.1)
SIZE = 3
image = np.ones(dims, dtype='uint8')
draw_gaussian_spot(image, pos[::-1], SIZE)
actual = mr.locate(image, 11, 1, preprocess=False)['size']
expected = SIZE
assert_allclose(actual, expected, rtol=0.1)
SIZE = 5
image = np.ones(dims, dtype='uint8')
draw_gaussian_spot(image, pos[::-1], SIZE)
actual = mr.locate(image, 17, 1, preprocess=False)['size']
expected = SIZE
assert_allclose(actual, expected, rtol=0.1)
SIZE = 7
image = np.ones(dims, dtype='uint8')
draw_gaussian_spot(image, pos[::-1], SIZE)
actual = mr.locate(image, 23, 1, preprocess=False)['size']
expected = SIZE
assert_allclose(actual, expected, rtol=0.1)
def test_rg(self):
# For Gaussians with radii 2, 3, 5, and 7 px, with proportionately
# chosen feature (mask) sizes, the 'size' comes out to be within 10%
# of the true Gaussian width.
# The IDL code has mistake in this area, documented here:
# http://www.physics.emory.edu/~weeks/idl/radius.html
L = 101
dims = (L, L + 2) # avoid square images in tests
pos = np.array([50, 55])
cols = ['x', 'y']
SIZE = 2
image = np.ones(dims, dtype='uint8')
draw_gaussian_spot(image, pos[::-1], SIZE)
actual = mr.locate(image, 7, 1, preprocess=False)['size']
expected = SIZE
assert_allclose(actual, expected, rtol=0.1)
SIZE = 3
image = np.ones(dims, dtype='uint8')
draw_gaussian_spot(image, pos[::-1], SIZE)
actual = mr.locate(image, 11, 1, preprocess=False)['size']
expected = SIZE
assert_allclose(actual, expected, rtol=0.1)
SIZE = 5
image = np.ones(dims, dtype='uint8')
draw_gaussian_spot(image, pos[::-1], SIZE)
actual = mr.locate(image, 17, 1, preprocess=False)['size']
expected = SIZE
assert_allclose(actual, expected, rtol=0.1)
SIZE = 7
image = np.ones(dims, dtype='uint8')
draw_gaussian_spot(image, pos[::-1], SIZE)
actual = mr.locate(image, 23, 1, preprocess=False)['size']
expected = SIZE
assert_allclose(actual, expected, rtol=0.1)
def test_topn(self):
L = 21
dims = (L, L + 2) # avoid square images in tests
cols = ['x', 'y']
PRECISION = 0.1
# top 2
pos1 = np.array([7, 7])
pos2 = np.array([14, 14])
pos3 = np.array([7, 14])
image = np.ones(dims, dtype='uint8')
image[tuple(pos1[::-1])] = 100
image[tuple(pos2[::-1])] = 80
image[tuple(pos3[::-1])] = 90
actual = mr.locate(image, 5, 1, topn=2, preprocess=False)[cols]
actual = actual.sort(['x', 'y']) # sort for reliable comparison
expected = DataFrame([[7, 7], [7, 14]], columns=cols).sort(['x', 'y'])
assert_allclose(actual, expected, atol=PRECISION)
# top 1
actual = mr.locate(image, 5, 1, topn=1, preprocess=False)[cols]
actual = actual.sort(['x', 'y']) # sort for reliable comparison
expected = DataFrame([[7, 7]], columns=cols).sort(['x', 'y'])
assert_allclose(actual, expected, atol=PRECISION)
def test_topn(self):
L = 21
dims = (L, L + 2) # avoid square images in tests
cols = ['x', 'y']
PRECISION = 0.1
# top 2
pos1 = np.array([7, 7])
pos2 = np.array([14, 14])
pos3 = np.array([7, 14])
image = np.ones(dims, dtype='uint8')
image[tuple(pos1[::-1])] = 100
image[tuple(pos2[::-1])] = 80
image[tuple(pos3[::-1])] = 90
actual = mr.locate(image, 5, 1, topn=2, preprocess=False)[cols]
actual = actual.sort(['x', 'y']) # sort for reliable comparison
expected = DataFrame([[7, 7], [7, 14]], columns=cols).sort(['x', 'y'])
assert_allclose(actual, expected, atol=PRECISION)
# top 1
actual = mr.locate(image, 5, 1, topn=1, preprocess=False)[cols]
actual = actual.sort(['x', 'y']) # sort for reliable comparison
expected = DataFrame([[7, 7]], columns=cols).sort(['x', 'y'])
assert_allclose(actual, expected, atol=PRECISION)
def test_subpx_precision(self):
L = 21
dims = (L, L + 2) # avoid square images in tests
cols = ['x', 'y']
PRECISION = 0.1
# one bright pixel
pos = np.array([7, 13])
image = np.ones(dims, dtype='uint8')
image[tuple(pos[::-1])] = 100
actual = mr.locate(image, 3, 1, preprocess=False)[cols]
expected = DataFrame(pos.reshape(1, -1), columns=cols)
assert_allclose(actual, expected, atol=0.001) # especially precise
# two neighboring pixels of equal brightness
pos1 = np.array([7, 13])
pos2 = np.array([8, 13])
pos = np.array([7.5, 13]) # center is between pixels
image = np.ones(dims, dtype='uint8')
image[tuple(pos1[::-1])] = 100
image[tuple(pos2[::-1])] = 100
actual = mr.locate(image, 5, 1, preprocess=False)[cols]
expected = DataFrame(pos.reshape(1, -1), columns=cols)
assert_allclose(actual, expected, atol=PRECISION)
# two neighboring pixels of unequal brightness
pos1 = np.array([7, 13])
pos2 = np.array([8, 13])
pos = np.array([7.25, 13]) # center is between pixels, biased left
image = np.ones(dims, dtype='uint8')
image[tuple(pos1[::-1])] = 100
image[tuple(pos2[::-1])] = 50
actual = mr.locate(image, 5, 1, preprocess=False)[cols]
expected = DataFrame(pos.reshape(1, -1), columns=cols)
assert_allclose(actual, expected, atol=PRECISION)
pos = np.array([7.75, 13]) # center is between pixels, biased right
image = np.ones(dims, dtype='uint8')
image[tuple(pos1[::-1])] = 50
image[tuple(pos2[::-1])] = 100
actual = mr.locate(image, 5, 1, preprocess=False)[cols]
expected = DataFrame(pos.reshape(1, -1), columns=cols)
assert_allclose(actual, expected, atol=PRECISION)
pos1 = np.array([7, 12])
pos2 = np.array([7, 13])
pos = np.array([7, 12.25]) # center is between pixels, biased down
image = np.ones(dims, dtype='uint8')
image[tuple(pos1[::-1])] = 100
image[tuple(pos2[::-1])] = 50
actual = mr.locate(image, 5, 1, preprocess=False)[cols]
expected = DataFrame(pos.reshape(1, -1), columns=cols)
assert_allclose(actual, expected, atol=PRECISION)
pos = np.array([7, 12.75]) # center is between pixels, biased up
image = np.ones(dims, dtype='uint8')
image[tuple(pos1[::-1])] = 50
image[tuple(pos2[::-1])] = 100
actual = mr.locate(image, 5, 1, preprocess=False)[cols]
expected = DataFrame(pos.reshape(1, -1), columns=cols)
assert_allclose(actual, expected, atol=PRECISION)
# four neighboring pixels of unequal brightness
pos1 = np.array([7, 13])
pos2 = np.array([8, 13])
pos3 = np.array([7, 13])
pos4 = np.array([8, 13])
pos = np.array([7.25, 13]) # center is between pixels, biased left
image = np.ones(dims, dtype='uint8')
image[tuple(pos1[::-1])] = 100
image[tuple(pos2[::-1])] = 50
image[tuple(pos3[::-1])] = 100
image[tuple(pos4[::-1])] = 50
actual = mr.locate(image, 5, 1, preprocess=False)[cols]
expected = DataFrame(pos.reshape(1, -1), columns=cols)
assert_allclose(actual, expected, atol=PRECISION)
pos = np.array([7.75, 13]) # center is between pixels, biased right
image = np.ones(dims, dtype='uint8')
image[tuple(pos1[::-1])] = 50
image[tuple(pos2[::-1])] = 100
image[tuple(pos3[::-1])] = 50
image[tuple(pos4[::-1])] = 100
actual = mr.locate(image, 5, 1, preprocess=False)[cols]
expected = DataFrame(pos.reshape(1, -1), columns=cols)
assert_allclose(actual, expected, atol=PRECISION)
pos1 = np.array([7, 12])
pos2 = np.array([7, 13])
pos3 = np.array([7, 12])
pos4 = np.array([7, 13])
pos = np.array([7, 12.25]) # center is between pixels, biased down
image = np.ones(dims, dtype='uint8')
image[tuple(pos1[::-1])] = 100
image[tuple(pos2[::-1])] = 50
actual = mr.locate(image, 5, 1, preprocess=False)[cols]
expected = DataFrame(pos.reshape(1, -1), columns=cols)
assert_allclose(actual, expected, atol=PRECISION)
pos = np.array([7, 12.75]) # center is between pixels, biased up
image = np.ones(dims, dtype='uint8')
image[tuple(pos1[::-1])] = 50
image[tuple(pos2[::-1])] = 100
actual = mr.locate(image, 5, 1, preprocess=False)[cols]
expected = DataFrame(pos.reshape(1, -1), columns=cols)
assert_allclose(actual, expected, atol=PRECISION)
def test_subpx_precision(self):
L = 21
dims = (L, L + 2) # avoid square images in tests
cols = ['x', 'y']
PRECISION = 0.1
# one bright pixel
pos = np.array([7, 13])
image = np.ones(dims, dtype='uint8')
image[tuple(pos[::-1])] = 100
actual = mr.locate(image, 3, 1, preprocess=False)[cols]
expected = DataFrame(pos.reshape(1, -1), columns=cols)
assert_allclose(actual, expected, atol=0.001) # especially precise
# two neighboring pixels of equal brightness
pos1 = np.array([7, 13])
pos2 = np.array([8, 13])
pos = np.array([7.5, 13]) # center is between pixels
image = np.ones(dims, dtype='uint8')
image[tuple(pos1[::-1])] = 100
image[tuple(pos2[::-1])] = 100
actual = mr.locate(image, 5, 1, preprocess=False)[cols]
expected = DataFrame(pos.reshape(1, -1), columns=cols)
assert_allclose(actual, expected, atol=PRECISION)
# two neighboring pixels of unequal brightness
pos1 = np.array([7, 13])
pos2 = np.array([8, 13])
pos = np.array([7.25, 13]) # center is between pixels, biased left
image = np.ones(dims, dtype='uint8')
image[tuple(pos1[::-1])] = 100
image[tuple(pos2[::-1])] = 50
actual = mr.locate(image, 5, 1, preprocess=False)[cols]
expected = DataFrame(pos.reshape(1, -1), columns=cols)
assert_allclose(actual, expected, atol=PRECISION)
pos = np.array([7.75, 13]) # center is between pixels, biased right
image = np.ones(dims, dtype='uint8')
image[tuple(pos1[::-1])] = 50
image[tuple(pos2[::-1])] = 100
actual = mr.locate(image, 5, 1, preprocess=False)[cols]
expected = DataFrame(pos.reshape(1, -1), columns=cols)
assert_allclose(actual, expected, atol=PRECISION)
pos1 = np.array([7, 12])
pos2 = np.array([7, 13])
pos = np.array([7, 12.25]) # center is between pixels, biased down
image = np.ones(dims, dtype='uint8')
image[tuple(pos1[::-1])] = 100
image[tuple(pos2[::-1])] = 50
actual = mr.locate(image, 5, 1, preprocess=False)[cols]
expected = DataFrame(pos.reshape(1, -1), columns=cols)
assert_allclose(actual, expected, atol=PRECISION)
pos = np.array([7, 12.75]) # center is between pixels, biased up
image = np.ones(dims, dtype='uint8')
image[tuple(pos1[::-1])] = 50
image[tuple(pos2[::-1])] = 100
actual = mr.locate(image, 5, 1, preprocess=False)[cols]
expected = DataFrame(pos.reshape(1, -1), columns=cols)
assert_allclose(actual, expected, atol=PRECISION)
# four neighboring pixels of unequal brightness
pos1 = np.array([7, 13])
pos2 = np.array([8, 13])
pos3 = np.array([7, 13])
pos4 = np.array([8, 13])
pos = np.array([7.25, 13]) # center is between pixels, biased left
image = np.ones(dims, dtype='uint8')
image[tuple(pos1[::-1])] = 100
image[tuple(pos2[::-1])] = 50
image[tuple(pos3[::-1])] = 100
image[tuple(pos4[::-1])] = 50
actual = mr.locate(image, 5, 1, preprocess=False)[cols]
expected = DataFrame(pos.reshape(1, -1), columns=cols)
assert_allclose(actual, expected, atol=PRECISION)
pos = np.array([7.75, 13]) # center is between pixels, biased right
image = np.ones(dims, dtype='uint8')
image[tuple(pos1[::-1])] = 50
image[tuple(pos2[::-1])] = 100
image[tuple(pos3[::-1])] = 50
image[tuple(pos4[::-1])] = 100
actual = mr.locate(image, 5, 1, preprocess=False)[cols]
expected = DataFrame(pos.reshape(1, -1), columns=cols)
assert_allclose(actual, expected, atol=PRECISION)
pos1 = np.array([7, 12])
pos2 = np.array([7, 13])
pos3 = np.array([7, 12])
pos4 = np.array([7, 13])
pos = np.array([7, 12.25]) # center is between pixels, biased down
image = np.ones(dims, dtype='uint8')
image[tuple(pos1[::-1])] = 100
image[tuple(pos2[::-1])] = 50
actual = mr.locate(image, 5, 1, preprocess=False)[cols]
expected = DataFrame(pos.reshape(1, -1), columns=cols)
assert_allclose(actual, expected, atol=PRECISION)
pos = np.array([7, 12.75]) # center is between pixels, biased up
image = np.ones(dims, dtype='uint8')
image[tuple(pos1[::-1])] = 50
image[tuple(pos2[::-1])] = 100
actual = mr.locate(image, 5, 1, preprocess=False)[cols]
expected = DataFrame(pos.reshape(1, -1), columns=cols)
assert_allclose(actual, expected, atol=PRECISION)
