def FindGridSpots(image, repetition):
"""
Find the coordinates of a grid of spots in an image. And find the
corresponding transformation to transform a grid centered around the origin
to the spots in an image.
Parameters
----------
image : array like
Data array containing the greyscale image.
repetition : tuple of ints
Number of expected spots in (X, Y).
Returns
-------
spot_coordinates : array like
A 2D array of shape (N, 2) containing the coordinates of the spots,
in respect to the top left of the image.
translation : tuple of two floats
Translation from the origin to the center of the grid in image space,
origin is top left of the image. Primary axis points right and the
secondary axis points down.
scaling : tuple of two floats
Scaling factors for primary and secondary axis.
rotation : float
Rotation in image space, positive rotation is clockwise.
shear : float
Horizontal shear factor. A positive shear factor transforms a coordinate
in the positive x direction parallel to the x axis.
"""
# Find the center coordinates of the spots in the image.
spot_positions = MaximaFind(image, repetition[0] * repetition[1])
if len(spot_positions) < repetition[0] * repetition[1]:
logging.warning(
'Not enough spots found, returning only the found spots.')
return spot_positions, None, None, None
# Estimate the two most common (orthogonal) directions in the grid of spots, defined in the image coordinate system.
lattice_constants = EstimateLatticeConstant(spot_positions)
# Each row in the lattice_constants array corresponds to one direction. By transposing the array the direction
# vectors are on the columns of the array. This allows us to directly use them as a transformation matrix.
transformation_matrix = numpy.transpose(lattice_constants)
# Translation is the mean of the spots, which is the distance from the origin to the center of the grid of spots.
translation = numpy.mean(spot_positions, axis=0)
transform_to_spot_positions = AffineTransform(matrix=transformation_matrix,
translation=translation)
# Iterative closest point algorithm - single iteration, to fit a grid to the found spot positions
grid = GridPoints(*repetition)
spot_grid = transform_to_spot_positions(grid)
tree = KDTree(spot_positions)
dd, ii = tree.query(spot_grid, k=1)
# Sort the original spot positions by mapping them to the order of the GridPoints.
pos_sorted = spot_positions[ii.ravel(), :]
# Find the transformation from a grid centered around the origin to the sorted positions.
transformation = AffineTransform.from_pointset(grid, pos_sorted)
spot_coordinates = transformation(grid)
return spot_coordinates, translation, transformation.scale, transformation.rotation, transformation.shear
def test_affine_transform_from_pointset_non_negative_scaling(self):
"""
AffineTransform.from_pointset should not return a negative value for
the scaling when there is a reflection in the point set for affine
transformations.
"""
src = numpy.array([(0., 0.), (1., 0.), (0., 2.)])
dst = numpy.array([(0., 0.), (-1., 0.), (0., 2.)])
tform = AffineTransform.from_pointset(src, dst)
self.assertTrue(numpy.all(tform.scale > 0.))
def test_affine_transform_from_pointset_known_values(self):
"""
AffineTransform.from_pointset should return known result with known
input for affine transformations.
"""
src = AFFINE_KNOWN_VALUES[0][-1]
for rotation, scale, shear, translation, dst in AFFINE_KNOWN_VALUES:
tform = AffineTransform.from_pointset(src, dst)
self.assertAlmostEqual(0., _angle_diff(rotation, tform.rotation))
numpy.testing.assert_array_almost_equal(scale, tform.scale)
self.assertAlmostEqual(shear, tform.shear)
numpy.testing.assert_array_almost_equal(translation, tform.translation)
def FindGridSpots(image, repetition):
"""
Find a grid of spots in an image.
Parameters
----------
image : array like
Data array containing the greyscale image.
repetition : tuple of ints
Number of expected spots in (X, Y).
Returns
-------
spot_coordinates : array like
A 2D array of shape (N, 2) containing the coordinates of the spots.
translation : tuple of two floats
scaling : tuple of two floats
rotation : float
shear : float
"""
spot_positions = MaximaFind(image, repetition[0] * repetition[1])
if len(spot_positions) < repetition[0] * repetition[1]:
logging.warning(
'Not enough spots found, returning only the found spots.')
return spot_positions, None, None, None
# Estimate transformation
lattice_constants = EstimateLatticeConstant(spot_positions)
transformation_matrix = numpy.transpose(lattice_constants)
if numpy.linalg.det(lattice_constants) < 0.:
transformation_matrix = numpy.fliplr(transformation_matrix)
translation = numpy.mean(spot_positions, axis=0)
transform_to_spot_positions = AffineTransform(matrix=transformation_matrix,
translation=translation)
# Iterative closest point algorithm - single iteration, to fit a grid to the found spot positions
grid = GridPoints(*repetition)
spot_grid = transform_to_spot_positions(grid)
tree = KDTree(spot_positions)
dd, ii = tree.query(spot_grid, k=1)
pos_sorted = spot_positions[ii.ravel(), :]
transformation = AffineTransform.from_pointset(grid, pos_sorted)
spot_coordinates = transformation(grid)
return spot_coordinates, translation, transformation.scale, transformation.rotation, transformation.shear
def FindGridSpots(image, repetition, spot_size=18, method=GRID_AFFINE):
"""
Find the coordinates of a grid of spots in an image. And find the
corresponding transformation to transform a grid centered around the origin
to the spots in an image.
Parameters
----------
image : array like
Data array containing the greyscale image.
repetition : tuple of ints
Number of expected spots in (X, Y). Where the total number of expected spots must be at least 6.
spot_size : int
A length in pixels somewhat larger than a typical spot.
method : GRID_AFFINE or GRID_SIMILARITY
The transformation method used to get the returned grid of spots.
If the similarity method is used the returned grid has 90 degree angles with equal scaling in x and y.
It the affine method is used the returned grid contains a shear component, therefore the angles in the grid
do not have to be 90 degrees. The grid can also have different scaling in x and y.
Returns
-------
spot_coordinates : array like
A 2D array of shape (N, 2) containing the coordinates of the spots,
in respect to the top left of the image.
translation : tuple of two floats
Translation from the origin to the center of the grid in image space,
origin is top left of the image. Primary axis points right and the
secondary axis points down.
scaling : tuple of two floats or float
Scaling factors for primary and secondary axis when the affine method is used.
Single scaling factor when the similarity method is used.
rotation : float
Rotation in image space, positive rotation is clockwise.
shear : float
Horizontal shear factor. A positive shear factor transforms a coordinate
in the positive x direction parallel to the x axis. The shear is None
when similarity method is used.
"""
if repetition[0] * repetition[1] < 6:
raise ValueError(
"Need at least 6 expected points to properly find the grid.")
# Find the center coordinates of the spots in the image.
spot_positions = MaximaFind(image,
repetition[0] * repetition[1],
len_object=spot_size)
if len(spot_positions) < repetition[0] * repetition[1]:
logging.warning(
'Not enough spots found, returning only the found spots.')
return spot_positions, None, None, None, None
# Estimate the two most common (orthogonal) directions in the grid of spots, defined in the image coordinate system.
lattice_constants = EstimateLatticeConstant(spot_positions)
# Each row in the lattice_constants array corresponds to one direction. By transposing the array the direction
# vectors are on the columns of the array. This allows us to directly use them as a transformation matrix.
transformation_matrix = numpy.transpose(lattice_constants)
# Translation is the mean of the spots, which is the distance from the origin to the center of the grid of spots.
translation = numpy.mean(spot_positions, axis=0)
transform_to_spot_positions = AffineTransform(transformation_matrix,
translation)
# Iterative closest point algorithm - single iteration, to fit a grid to the found spot positions
grid = GridPoints(*repetition)
spot_grid = transform_to_spot_positions.apply(grid)
tree = KDTree(spot_positions)
dd, ii = tree.query(spot_grid, k=1)
# Sort the original spot positions by mapping them to the order of the GridPoints.
pos_sorted = spot_positions[ii.ravel(), :]
# Find the transformation from a grid centered around the origin to the sorted positions.
if method == GRID_AFFINE:
transformation = AffineTransform.from_pointset(grid, pos_sorted)
scale, rotation, shear = alt_transformation_matrix_to_implicit(
transformation.matrix, "RSU")
elif method == GRID_SIMILARITY:
transformation = SimilarityTransform.from_pointset(grid, pos_sorted)
scale, rotation, _ = alt_transformation_matrix_to_implicit(
transformation.matrix, "RSU")
shear = None # The similarity transform does not have a shear component.
else:
raise ValueError(
"Method: %s is unknown, should be 'affine' or 'similarity'." %
method)
spot_coordinates = transformation.apply(grid)
return spot_coordinates, translation, scale, rotation, shear