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CellProfiler/centrosome | centrosome/cpmorphology.py | find_neighbors | def find_neighbors(labels):
'''Find the set of objects that touch each object in a labels matrix
Construct a "list", per-object, of the objects 8-connected adjacent
to that object.
Returns three 1-d arrays:
* array of #'s of neighbors per object
* array of indexes per object to that object'... | python | def find_neighbors(labels):
'''Find the set of objects that touch each object in a labels matrix
Construct a "list", per-object, of the objects 8-connected adjacent
to that object.
Returns three 1-d arrays:
* array of #'s of neighbors per object
* array of indexes per object to that object'... | [
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CellProfiler/centrosome | centrosome/cpmorphology.py | distance_color_labels | def distance_color_labels(labels):
'''Recolor a labels matrix so that adjacent labels have distant numbers
'''
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'''Recolor a labels matrix so that adjacent labels have distant numbers
'''
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CellProfiler/centrosome | centrosome/cpmorphology.py | color_labels | def color_labels(labels, distance_transform = False):
'''Color a labels matrix so that no adjacent labels have the same color
distance_transform - if true, distance transform the labels to find out
which objects are closest to each other.
Create a label coloring matrix which assigns ... | python | def color_labels(labels, distance_transform = False):
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CellProfiler/centrosome | centrosome/cpmorphology.py | skeletonize | def skeletonize(image, mask=None, ordering = None):
'''Skeletonize the image
Take the distance transform.
Order the 1 points by the distance transform.
Remove a point if it has more than 1 neighbor and if removing it
does not change the Euler number.
image - the binary image to be skel... | python | def skeletonize(image, mask=None, ordering = None):
'''Skeletonize the image
Take the distance transform.
Order the 1 points by the distance transform.
Remove a point if it has more than 1 neighbor and if removing it
does not change the Euler number.
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CellProfiler/centrosome | centrosome/cpmorphology.py | skeletonize_labels | def skeletonize_labels(labels):
'''Skeletonize a labels matrix'''
#
# The trick here is to separate touching labels by coloring the
# labels matrix and then processing each color separately
#
colors = color_labels(labels)
max_color = np.max(colors)
if max_color == 0:
return label... | python | def skeletonize_labels(labels):
'''Skeletonize a labels matrix'''
#
# The trick here is to separate touching labels by coloring the
# labels matrix and then processing each color separately
#
colors = color_labels(labels)
max_color = np.max(colors)
if max_color == 0:
return label... | [
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CellProfiler/centrosome | centrosome/cpmorphology.py | label_skeleton | def label_skeleton(skeleton):
'''Label a skeleton so that each edge has a unique label
This operation produces a labels matrix where each edge between
two branchpoints has a different label. If the skeleton has been
properly eroded, there are three kinds of points:
1) point adjacent to 0 or 1 o... | python | def label_skeleton(skeleton):
'''Label a skeleton so that each edge has a unique label
This operation produces a labels matrix where each edge between
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CellProfiler/centrosome | centrosome/cpmorphology.py | skeleton_length | def skeleton_length(labels, indices=None):
'''Compute the length of all skeleton branches for labeled skeletons
labels - a labels matrix
indices - the indexes of the labels to be measured. Default is all
returns an array of one skeleton length per label.
'''
global __skel_length_table
... | python | def skeleton_length(labels, indices=None):
'''Compute the length of all skeleton branches for labeled skeletons
labels - a labels matrix
indices - the indexes of the labels to be measured. Default is all
returns an array of one skeleton length per label.
'''
global __skel_length_table
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CellProfiler/centrosome | centrosome/cpmorphology.py | distance_to_edge | def distance_to_edge(labels):
'''Compute the distance of a pixel to the edge of its object
labels - a labels matrix
returns a matrix of distances
'''
colors = color_labels(labels)
max_color = np.max(colors)
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'''Compute the distance of a pixel to the edge of its object
labels - a labels matrix
returns a matrix of distances
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CellProfiler/centrosome | centrosome/cpmorphology.py | regional_maximum | def regional_maximum(image, mask = None, structure=None, ties_are_ok=False):
'''Return a binary mask containing only points that are regional maxima
image - image to be transformed
mask - mask of relevant pixels
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image - image to be transformed
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CellProfiler/centrosome | centrosome/cpmorphology.py | all_connected_components | def all_connected_components(i,j):
'''Associate each label in i with a component #
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associations between labels i and j using a depth-first search.
i & j give the edges of the graph. The first step of the algorithm makes
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'''Associate each label in i with a component #
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CellProfiler/centrosome | centrosome/cpmorphology.py | pairwise_permutations | def pairwise_permutations(i, j):
'''Return all permutations of a set of groups
This routine takes two vectors:
i - the label of each group
j - the members of the group.
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i | j
------
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1 | 2
1 | 3... | python | def pairwise_permutations(i, j):
'''Return all permutations of a set of groups
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i - the label of each group
j - the members of the group.
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i | j
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1 | 2
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CellProfiler/centrosome | centrosome/cpmorphology.py | is_local_maximum | def is_local_maximum(image, labels, footprint):
'''Return a boolean array of points that are local maxima
image - intensity image
labels - find maxima only within labels. Zero is reserved for background.
footprint - binary mask indicating the neighborhood to be examined
must be a ma... | python | def is_local_maximum(image, labels, footprint):
'''Return a boolean array of points that are local maxima
image - intensity image
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footprint - binary mask indicating the neighborhood to be examined
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CellProfiler/centrosome | centrosome/cpmorphology.py | angular_distribution | def angular_distribution(labels, resolution=100, weights=None):
'''For each object in labels, compute the angular distribution
around the centers of mass. Returns an i x j matrix, where i is
the number of objects in the label matrix, and j is the resolution
of the distribution (default 100), mapped fro... | python | def angular_distribution(labels, resolution=100, weights=None):
'''For each object in labels, compute the angular distribution
around the centers of mass. Returns an i x j matrix, where i is
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CellProfiler/centrosome | centrosome/cpmorphology.py | feret_diameter | def feret_diameter(chulls, counts, indexes):
'''Return the minimum and maximum Feret diameter for each object
This function takes the convex hull data, as generated by convex_hull
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CellProfiler/centrosome | centrosome/cpmorphology.py | is_obtuse | def is_obtuse(p1, v, p2):
'''Determine whether the angle, p1 - v - p2 is obtuse
p1 - N x 2 array of coordinates of first point on edge
v - N x 2 array of vertex coordinates
p2 - N x 2 array of coordinates of second point on edge
returns vector of booleans
'''
p1x = p1[:,1]
p1y ... | python | def is_obtuse(p1, v, p2):
'''Determine whether the angle, p1 - v - p2 is obtuse
p1 - N x 2 array of coordinates of first point on edge
v - N x 2 array of vertex coordinates
p2 - N x 2 array of coordinates of second point on edge
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CellProfiler/centrosome | centrosome/cpmorphology.py | get_outline_pts | def get_outline_pts(labels, idxs):
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CellProfiler/centrosome | centrosome/princomp.py | princomp | def princomp(x):
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Determine the principal components of a vector of measurements
x should be a M x N numpy array composed of M observations of n variables
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"""Determine the principal components of a vector of measurements
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... | python | def all_pairs(n):
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n - # of things
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CellProfiler/centrosome | centrosome/filter.py | stretch | def stretch(image, mask=None):
'''Normalize an image to make the minimum zero and maximum one
image - pixel data to be normalized
mask - optional mask of relevant pixels. None = don't mask
returns the stretched image
'''
image = np.array(image, float)
if np.product(image.shape) == 0:
... | python | def stretch(image, mask=None):
'''Normalize an image to make the minimum zero and maximum one
image - pixel data to be normalized
mask - optional mask of relevant pixels. None = don't mask
returns the stretched image
'''
image = np.array(image, float)
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mask - mask of significant pixels in data
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CellProfiler/centrosome | centrosome/filter.py | bilateral_filter | def bilateral_filter(image, mask, sigma_spatial, sigma_range,
sampling_spatial = None, sampling_range = None):
"""Bilateral filter of an image
image - image to be bilaterally filtered
mask - mask of significant points in image
sigma_spatial - standard deviation of the spatial Gaus... | python | def bilateral_filter(image, mask, sigma_spatial, sigma_range,
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CellProfiler/centrosome | centrosome/filter.py | laplacian_of_gaussian | def laplacian_of_gaussian(image, mask, size, sigma):
'''Perform the Laplacian of Gaussian transform on the image
image - 2-d image array
mask - binary mask of significant pixels
size - length of side of square kernel to use
sigma - standard deviation of the Gaussian
'''
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image - 2-d image array
mask - binary mask of significant pixels
size - length of side of square kernel to use
sigma - standard deviation of the Gaussian
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CellProfiler/centrosome | centrosome/filter.py | canny | def canny(image, mask, sigma, low_threshold, high_threshold):
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CellProfiler/centrosome | centrosome/filter.py | roberts | def roberts(image, mask=None):
'''Find edges using the Roberts algorithm
image - the image to process
mask - mask of relevant points
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The following is the canonical citation for the algorithm:
L. Rob... | python | def roberts(image, mask=None):
'''Find edges using the Roberts algorithm
image - the image to process
mask - mask of relevant points
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mask - mask of relevant points
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mask - mask of relevant points
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CellProfiler/centrosome | centrosome/filter.py | prewitt | def prewitt(image, mask=None):
'''Find the edge magnitude using the Prewitt transform
image - image to process
mask - mask of relevant points
Return the square root of the sum of squares of the horizontal
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'''
return np.sqrt(hprewitt(image,mask)**2 + vprewi... | python | def prewitt(image, mask=None):
'''Find the edge magnitude using the Prewitt transform
image - image to process
mask - mask of relevant points
Return the square root of the sum of squares of the horizontal
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CellProfiler/centrosome | centrosome/filter.py | hprewitt | def hprewitt(image, mask=None):
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image - image to process
mask - mask of relevant points
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image - image to process
mask - mask of relevant points
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CellProfiler/centrosome | centrosome/filter.py | gabor | def gabor(image, labels, frequency, theta):
'''Gabor-filter the objects in an image
image - 2-d grayscale image to filter
labels - a similarly shaped labels matrix
frequency - cycles per trip around the circle
theta - angle of the filter. 0 to 2 pi
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'''Gabor-filter the objects in an image
image - 2-d grayscale image to filter
labels - a similarly shaped labels matrix
frequency - cycles per trip around the circle
theta - angle of the filter. 0 to 2 pi
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CellProfiler/centrosome | centrosome/filter.py | enhance_dark_holes | def enhance_dark_holes(image, min_radius, max_radius, mask=None):
'''Enhance dark holes using a rolling ball filter
image - grayscale 2-d image
radii - a vector of radii: we enhance holes at each given radius
'''
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... | python | def enhance_dark_holes(image, min_radius, max_radius, mask=None):
'''Enhance dark holes using a rolling ball filter
image - grayscale 2-d image
radii - a vector of radii: we enhance holes at each given radius
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CellProfiler/centrosome | centrosome/filter.py | granulometry_filter | def granulometry_filter(image, min_radius, max_radius, mask=None):
'''Enhances bright structures within a min and max radius using a rolling ball filter
image - grayscale 2-d image
radii - a vector of radii: we enhance holes at each given radius
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#
# Do 4-connected erosion
#
se = np.... | python | def granulometry_filter(image, min_radius, max_radius, mask=None):
'''Enhances bright structures within a min and max radius using a rolling ball filter
image - grayscale 2-d image
radii - a vector of radii: we enhance holes at each given radius
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image - grayscale 2-d image
radii - radius of filter in pixels
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'''Blur an image using a circular averaging filter (pillbox)
image - grayscale 2-d image
radii - radius of filter in pixels
The filter will be within a square matrix of side 2*radius+1
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CellProfiler/centrosome | centrosome/filter.py | velocity_kalman_model | def velocity_kalman_model():
'''Return a KalmanState set up to model objects with constant velocity
The observation and measurement vectors are i,j.
The state vector is i,j,vi,vj
'''
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... | python | def velocity_kalman_model():
'''Return a KalmanState set up to model objects with constant velocity
The observation and measurement vectors are i,j.
The state vector is i,j,vi,vj
'''
om = np.array([[1,0,0,0], [0, 1, 0, 0]])
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CellProfiler/centrosome | centrosome/filter.py | reverse_velocity_kalman_model | def reverse_velocity_kalman_model():
'''Return a KalmanState set up to model going backwards in time'''
om = np.array([[1,0,0,0], [0, 1, 0, 0]])
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return KalmanState(om, tm) | python | def reverse_velocity_kalman_model():
'''Return a KalmanState set up to model going backwards in time'''
om = np.array([[1,0,0,0], [0, 1, 0, 0]])
tm = np.array([[1,0,-1,0],
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CellProfiler/centrosome | centrosome/filter.py | kalman_filter | def kalman_filter(kalman_state, old_indices, coordinates, q, r):
'''Return the kalman filter for the features in the new frame
kalman_state - state from last frame
old_indices - the index per feature in the last frame or -1 for new
coordinates - Coordinates of the features in the new frame.
q - ... | python | def kalman_filter(kalman_state, old_indices, coordinates, q, r):
'''Return the kalman filter for the features in the new frame
kalman_state - state from last frame
old_indices - the index per feature in the last frame or -1 for new
coordinates - Coordinates of the features in the new frame.
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CellProfiler/centrosome | centrosome/filter.py | line_integration | def line_integration(image, angle, decay, sigma):
'''Integrate the image along the given angle
DIC images are the directional derivative of the underlying
image. This filter reconstructs the original image by integrating
along that direction.
image - a 2-dimensional array
angle - shear angle ... | python | def line_integration(image, angle, decay, sigma):
'''Integrate the image along the given angle
DIC images are the directional derivative of the underlying
image. This filter reconstructs the original image by integrating
along that direction.
image - a 2-dimensional array
angle - shear angle ... | [
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CellProfiler/centrosome | centrosome/filter.py | variance_transform | def variance_transform(img, sigma, mask=None):
'''Calculate a weighted variance of the image
This function caluclates the variance of an image, weighting the
local contributions by a Gaussian.
img - image to be transformed
sigma - standard deviation of the Gaussian
mask - mask of relevant pixe... | python | def variance_transform(img, sigma, mask=None):
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This function caluclates the variance of an image, weighting the
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sigma - standard deviation of the Gaussian
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CellProfiler/centrosome | centrosome/filter.py | inv_n | def inv_n(x):
'''given N matrices, return N inverses'''
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#
# 1
# ----- C(j,i)
# det(A)
#
# where C(j,i) is the cofactor of matrix A at position j,i
#
assert x.ndim == 3
assert x.shape[1] == x.shape[2]
c = np.array([ [... | python | def inv_n(x):
'''given N matrices, return N inverses'''
#
# The inverse of a small matrix (e.g. 3x3) is
#
# 1
# ----- C(j,i)
# det(A)
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CellProfiler/centrosome | centrosome/filter.py | det_n | def det_n(x):
'''given N matrices, return N determinants'''
assert x.ndim == 3
assert x.shape[1] == x.shape[2]
if x.shape[1] == 1:
return x[:,0,0]
result = np.zeros(x.shape[0])
for permutation in permutations(np.arange(x.shape[1])):
sign = parity(permutation)
result += np... | python | def det_n(x):
'''given N matrices, return N determinants'''
assert x.ndim == 3
assert x.shape[1] == x.shape[2]
if x.shape[1] == 1:
return x[:,0,0]
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CellProfiler/centrosome | centrosome/filter.py | parity | def parity(x):
'''The parity of a permutation
The parity of a permutation is even if the permutation can be
formed by an even number of transpositions and is odd otherwise.
The parity of a permutation is even if there are an even number of
compositions of even size and odd otherwise. A composition... | python | def parity(x):
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CellProfiler/centrosome | centrosome/filter.py | cofactor_n | def cofactor_n(x, i, j):
'''Return the cofactor of n matrices x[n,i,j] at position i,j
The cofactor is the determinant of the matrix formed by removing
row i and column j.
'''
m = x.shape[1]
mr = np.arange(m)
i_idx = mr[mr != i]
j_idx = mr[mr != j]
return det_n(x[:, i_idx[:, np.newa... | python | def cofactor_n(x, i, j):
'''Return the cofactor of n matrices x[n,i,j] at position i,j
The cofactor is the determinant of the matrix formed by removing
row i and column j.
'''
m = x.shape[1]
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j_idx = mr[mr != j]
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CellProfiler/centrosome | centrosome/filter.py | dot_n | def dot_n(x, y):
'''given two tensors N x I x K and N x K x J return N dot products
If either x or y is 2-dimensional, broadcast it over all N.
Dot products are size N x I x J.
Example:
x = np.array([[[1,2], [3,4], [5,6]],[[7,8], [9,10],[11,12]]])
y = np.array([[[1,2,3], [4,5,6]],[[7,8,9],[10,... | python | def dot_n(x, y):
'''given two tensors N x I x K and N x K x J return N dot products
If either x or y is 2-dimensional, broadcast it over all N.
Dot products are size N x I x J.
Example:
x = np.array([[[1,2], [3,4], [5,6]],[[7,8], [9,10],[11,12]]])
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CellProfiler/centrosome | centrosome/filter.py | permutations | def permutations(x):
'''Given a listlike, x, return all permutations of x
Returns the permutations of x in the lexical order of their indices:
e.g.
>>> x = [ 1, 2, 3, 4 ]
>>> for p in permutations(x):
>>> print p
[ 1, 2, 3, 4 ]
[ 1, 2, 4, 3 ]
[ 1, 3, 2, 4 ]
[ 1, 3, 4, 2 ]
... | python | def permutations(x):
'''Given a listlike, x, return all permutations of x
Returns the permutations of x in the lexical order of their indices:
e.g.
>>> x = [ 1, 2, 3, 4 ]
>>> for p in permutations(x):
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chunksize = CONVEX_HULL_CHUNKSIZE,
pass_cutoff = 16):
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chunksize = CONVEX_HULL_CHUNKSIZE,
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img - image to be transformed.
radius - radius of circle
nangles - # of angles to measure, e.g. nangles = 4 means accumulate at
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radius - radius of circle
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CellProfiler/centrosome | centrosome/filter.py | hessian | def hessian(image, return_hessian=True, return_eigenvalues=True, return_eigenvectors=True):
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at different scales.
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CellProfiler/centrosome | centrosome/filter.py | poisson_equation | def poisson_equation(image, gradient=1, max_iter=100, convergence=.01, percentile = 90.0):
'''Estimate the solution to the Poisson Equation
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context, we use a boundary condition where x is zero for background
pixels. Also, we set h... | python | def poisson_equation(image, gradient=1, max_iter=100, convergence=.01, percentile = 90.0):
'''Estimate the solution to the Poisson Equation
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CellProfiler/centrosome | centrosome/filter.py | KalmanState.predicted_state_vec | def predicted_state_vec(self):
'''The predicted state vector for the next time point
From Welch eqn 1.9
'''
if not self.has_cached_predicted_state_vec:
self.p_state_vec = dot_n(
self.translation_matrix,
self.state_vec[:, :, np.newaxis])[:,:,0]... | python | def predicted_state_vec(self):
'''The predicted state vector for the next time point
From Welch eqn 1.9
'''
if not self.has_cached_predicted_state_vec:
self.p_state_vec = dot_n(
self.translation_matrix,
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CellProfiler/centrosome | centrosome/filter.py | KalmanState.predicted_obs_vec | def predicted_obs_vec(self):
'''The predicted observation vector
The observation vector for the next step in the filter.
'''
if not self.has_cached_obs_vec:
self.obs_vec = dot_n(
self.observation_matrix,
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'''The predicted observation vector
The observation vector for the next step in the filter.
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CellProfiler/centrosome | centrosome/filter.py | KalmanState.map_frames | def map_frames(self, old_indices):
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old_indices - for each feature in the new frame, the index of the
old feature
'''
nfeatures = len(old_indices)
noldfeatures = len(self.state_vec)
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'''Rewrite the feature indexes based on the next frame's identities
old_indices - for each feature in the new frame, the index of the
old feature
'''
nfeatures = len(old_indices)
noldfeatures = len(self.state_vec)
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c.state_vec = self.state_vec.copy()
c.state_cov = self.state_cov.copy()
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'''Return a deep copy of the state'''
c = KalmanState(self.observation_matrix, self.translation_matrix)
c.state_vec = self.state_vec.copy()
c.state_cov = self.state_cov.copy()
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CellProfiler/centrosome | centrosome/bg_compensate.py | prcntiles | def prcntiles(x,percents):
'''Equivalent to matlab prctile(x,p), uses linear interpolation.'''
x=np.array(x).flatten()
listx = np.sort(x)
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CellProfiler/centrosome | centrosome/bg_compensate.py | automode | def automode(data):
'''Tries to guess if the image contains dark objects on a bright background (1)
or if the image contains bright objects on a dark background (-1),
or if it contains both dark and bright objects on a gray background (0).'''
pct=prcntiles(np.array(data),[1,20,80,99])
upper=... | python | def automode(data):
'''Tries to guess if the image contains dark objects on a bright background (1)
or if the image contains bright objects on a dark background (-1),
or if it contains both dark and bright objects on a gray background (0).'''
pct=prcntiles(np.array(data),[1,20,80,99])
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CellProfiler/centrosome | centrosome/bg_compensate.py | spline_factors | def spline_factors(u):
'''u is np.array'''
X = np.array([(1.-u)**3 , 4-(6.*(u**2))+(3.*(u**3)) , 1.+(3.*u)+(3.*(u**2))-(3.*(u**3)) , u**3]) * (1./6)
return X | python | def spline_factors(u):
'''u is np.array'''
X = np.array([(1.-u)**3 , 4-(6.*(u**2))+(3.*(u**3)) , 1.+(3.*u)+(3.*(u**2))-(3.*(u**3)) , u**3]) * (1./6)
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CellProfiler/centrosome | centrosome/bg_compensate.py | pick | def pick(picklist,val):
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CellProfiler/centrosome | centrosome/bg_compensate.py | confine | def confine(x,low,high):
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y[y < low] = low
y[y > high] = high
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y[y > high] = high
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CellProfiler/centrosome | centrosome/bg_compensate.py | gauss | def gauss(x,m_y,sigma):
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e_y = [np.exp((1.0/(2*float(sigma)**2)*-(n-m_y)**2)) for n in np.array(x)]
y = [1.0/(float(sigma) * np.sqrt(2 * np.pi)) * e for e in e_y]
return np.array(y) | python | def gauss(x,m_y,sigma):
'''returns the gaussian with mean m_y and std. dev. sigma,
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y = [1.0/(float(sigma) * np.sqrt(2 * np.pi)) * e for e in e_y]
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CellProfiler/centrosome | centrosome/bg_compensate.py | d2gauss | def d2gauss(x,m_y,sigma):
'''returns the second derivative of the gaussian with mean m_y,
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return gauss(x,m_y,sigma)*[-1/sigma**2 + (n-m_y)**2/sigma**4 for n in x] | python | def d2gauss(x,m_y,sigma):
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CellProfiler/centrosome | centrosome/bg_compensate.py | spline_matrix2d | def spline_matrix2d(x,y,px,py,mask=None):
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indices = np.nonzero(mask.T.fla... | python | def spline_matrix2d(x,y,px,py,mask=None):
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V = np.kron(spline_matrix(x,px),spline_matrix(y,py))
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CellProfiler/centrosome | centrosome/bg_compensate.py | splinefit2d | def splinefit2d(x, y, z, px, py, mask=None):
'''Make a least squares fit of the spline (px,py,pz) to the surface (x,y,z).
If mask is given, only masked points are used for the regression.'''
if mask is None:
V = np.array(spline_matrix2d(x, y, px, py))
a = np.array(z.T.flatten())
pz ... | python | def splinefit2d(x, y, z, px, py, mask=None):
'''Make a least squares fit of the spline (px,py,pz) to the surface (x,y,z).
If mask is given, only masked points are used for the regression.'''
if mask is None:
V = np.array(spline_matrix2d(x, y, px, py))
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CellProfiler/centrosome | centrosome/bg_compensate.py | backgr | def backgr(img, mask = None, mode=MODE_AUTO, thresh=2, splinepoints=5, scale=1,
maxiter=40, convergence = .001):
'''Iterative spline-based background correction.
mode - one of MODE_AUTO, MODE_DARK, MODE_BRIGHT or MODE_GRAY
thresh - thresh is threshold to cut at, in units of sigma.
spli... | python | def backgr(img, mask = None, mode=MODE_AUTO, thresh=2, splinepoints=5, scale=1,
maxiter=40, convergence = .001):
'''Iterative spline-based background correction.
mode - one of MODE_AUTO, MODE_DARK, MODE_BRIGHT or MODE_GRAY
thresh - thresh is threshold to cut at, in units of sigma.
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CellProfiler/centrosome | centrosome/bg_compensate.py | bg_compensate | def bg_compensate(img, sigma, splinepoints, scale):
'''Reads file, subtracts background. Returns [compensated image, background].'''
from PIL import Image
import pylab
from matplotlib.image import pil_to_array
from centrosome.filter import canny
import matplotlib
img = Image.open(img)
... | python | def bg_compensate(img, sigma, splinepoints, scale):
'''Reads file, subtracts background. Returns [compensated image, background].'''
from PIL import Image
import pylab
from matplotlib.image import pil_to_array
from centrosome.filter import canny
import matplotlib
img = Image.open(img)
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CellProfiler/centrosome | centrosome/mode.py | mode | def mode(a):
'''Compute the mode of an array
a: an array
returns a vector of values which are the most frequent (more than one
if there is a tie).
'''
a = np.asanyarray(a)
if a.size == 0:
return np.zeros(0, a.dtype)
aa = a.flatten()
aa.sort()
indices = np.hstack... | python | def mode(a):
'''Compute the mode of an array
a: an array
returns a vector of values which are the most frequent (more than one
if there is a tie).
'''
a = np.asanyarray(a)
if a.size == 0:
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aa.sort()
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CellProfiler/centrosome | centrosome/otsu.py | otsu | def otsu(data, min_threshold=None, max_threshold=None,bins=256):
"""Compute a threshold using Otsu's method
data - an array of intensity values between zero and one
min_threshold - only consider thresholds above this minimum value
max_threshold - only consider thresholds below this maxi... | python | def otsu(data, min_threshold=None, max_threshold=None,bins=256):
"""Compute a threshold using Otsu's method
data - an array of intensity values between zero and one
min_threshold - only consider thresholds above this minimum value
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CellProfiler/centrosome | centrosome/otsu.py | entropy | def entropy(data, bins=256):
"""Compute a threshold using Ray's entropy measurement
data - an array of intensity values between zero and one
bins - we bin the data into this many equally-spaced bins, then pick
the bin index that optimizes the metric
"""
... | python | def entropy(data, bins=256):
"""Compute a threshold using Ray's entropy measurement
data - an array of intensity values between zero and one
bins - we bin the data into this many equally-spaced bins, then pick
the bin index that optimizes the metric
"""
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CellProfiler/centrosome | centrosome/otsu.py | otsu3 | def otsu3(data, min_threshold=None, max_threshold=None,bins=128):
"""Compute a threshold using a 3-category Otsu-like method
data - an array of intensity values between zero and one
min_threshold - only consider thresholds above this minimum value
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CellProfiler/centrosome | centrosome/otsu.py | entropy_score | def entropy_score(var,bins, w=None, decimate=True):
'''Compute entropy scores, given a variance and # of bins
'''
if w is None:
n = len(var)
w = np.arange(0,n,n//bins) / float(n)
if decimate:
n = len(var)
var = var[0:n:n//bins]
score = w * np.log(var * w * np.sqr... | python | def entropy_score(var,bins, w=None, decimate=True):
'''Compute entropy scores, given a variance and # of bins
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if w is None:
n = len(var)
w = np.arange(0,n,n//bins) / float(n)
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CellProfiler/centrosome | centrosome/otsu.py | running_variance | def running_variance(x):
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S[i] = S[i-1]+(x[i]-mean[i-1])*(x[i]-mean[i])
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'''
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# x[... | python | def running_variance(x):
'''Given a vector x, compute the variance for x[0:i]
Thank you http://www.johndcook.com/standard_deviation.html
S[i] = S[i-1]+(x[i]-mean[i-1])*(x[i]-mean[i])
var(i) = S[i] / (i-1)
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CellProfiler/centrosome | centrosome/outline.py | outline | def outline(labels):
"""Given a label matrix, return a matrix of the outlines of the labeled objects
If a pixel is not zero and has at least one neighbor with a different
value, then it is part of the outline.
"""
output = numpy.zeros(labels.shape, labels.dtype)
lr_different = labels[1... | python | def outline(labels):
"""Given a label matrix, return a matrix of the outlines of the labeled objects
If a pixel is not zero and has at least one neighbor with a different
value, then it is part of the outline.
"""
output = numpy.zeros(labels.shape, labels.dtype)
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CellProfiler/centrosome | centrosome/neighmovetrack.py | euclidean_dist | def euclidean_dist(point1, point2):
"""Compute the Euclidean distance between two points.
Parameters
----------
point1, point2 : 2-tuples of float
The input points.
Returns
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d : float
The distance between the input points.
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>>> point1 = ... | python | def euclidean_dist(point1, point2):
"""Compute the Euclidean distance between two points.
Parameters
----------
point1, point2 : 2-tuples of float
The input points.
Returns
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d : float
The distance between the input points.
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CellProfiler/centrosome | centrosome/neighmovetrack.py | CellFeatures.from_labels | def from_labels(labels):
"""
Creates list of cell features based on label image (1-oo pixel values)
@return: list of cell features in the same order as labels
"""
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"""
Creates list of cell features based on label image (1-oo pixel values)
@return: list of cell features in the same order as labels
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CellProfiler/centrosome | centrosome/neighmovetrack.py | Trace.from_detections_assignment | def from_detections_assignment(detections_1, detections_2, assignments):
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Creates traces out of given assignment and cell data.
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# check if the match is between existing cells
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CellProfiler/centrosome | centrosome/neighmovetrack.py | NeighbourMovementTracking.run_tracking | def run_tracking(self, label_image_1, label_image_2):
"""
Tracks cells between input label images.
@returns: injective function from old objects to new objects (pairs of [old, new]). Number are compatible with labels.
"""
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CellProfiler/centrosome | centrosome/neighmovetrack.py | NeighbourMovementTracking.is_cell_big | def is_cell_big(self, cell_detection):
"""
Check if the cell is considered big.
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@return:
"""
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CellProfiler/centrosome | centrosome/neighmovetrack.py | NeighbourMovementTracking.find_closest_neighbours | def find_closest_neighbours(cell, all_cells, k, max_dist):
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Find k closest neighbours of the given cell.
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CellProfiler/centrosome | centrosome/neighmovetrack.py | NeighbourMovementTracking.calculate_basic_cost | def calculate_basic_cost(self, d1, d2):
"""
Calculates assignment cost between two cells.
"""
distance = euclidean_dist(d1.center, d2.center) / self.scale
area_change = 1 - min(d1.area, d2.area) / max(d1.area, d2.area)
return distance + self.parameters_cost_initial["are... | python | def calculate_basic_cost(self, d1, d2):
"""
Calculates assignment cost between two cells.
"""
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CellProfiler/centrosome | centrosome/neighmovetrack.py | NeighbourMovementTracking.calculate_localised_cost | def calculate_localised_cost(self, d1, d2, neighbours, motions):
"""
Calculates assignment cost between two cells taking into account the movement of cells neighbours.
:param CellFeatures d1: detection in first frame
:param CellFeatures d2: detection in second frame
"""
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CellProfiler/centrosome | centrosome/neighmovetrack.py | NeighbourMovementTracking.calculate_costs | def calculate_costs(self, detections_1, detections_2, calculate_match_cost, params):
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CellProfiler/centrosome | centrosome/neighmovetrack.py | NeighbourMovementTracking.solve_assignement | def solve_assignement(self, costs):
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Solves assignment problem using Hungarian implementation by Brian M. Clapper.
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CellProfiler/centrosome | centrosome/smooth.py | circular_gaussian_kernel | def circular_gaussian_kernel(sd,radius):
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CellProfiler/centrosome | centrosome/threshold.py | get_threshold | def get_threshold(threshold_method, threshold_modifier, image,
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threshold_range_min = None, threshold_range_max = None,
threshold_correction_factor = 1.0,
adaptive_window_size = 10, **kwargs):
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mask=None, labels = None,
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CellProfiler/centrosome | centrosome/threshold.py | get_global_threshold | def get_global_threshold(threshold_method, image, mask = None, **kwargs):
"""Compute a single threshold over the whole image"""
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CellProfiler/centrosome | centrosome/threshold.py | get_adaptive_threshold | def get_adaptive_threshold(threshold_method, image, threshold,
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CellProfiler/centrosome | centrosome/threshold.py | get_per_object_threshold | def get_per_object_threshold(method, image, threshold, mask=None, labels=None,
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CellProfiler/centrosome | centrosome/threshold.py | get_mog_threshold | def get_mog_threshold(image, mask=None, object_fraction = 0.2):
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CellProfiler/centrosome | centrosome/threshold.py | get_background_threshold | def get_background_threshold(image, mask = None):
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CellProfiler/centrosome | centrosome/threshold.py | get_robust_background_threshold | def get_robust_background_threshold(image,
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CellProfiler/centrosome | centrosome/threshold.py | mad | def mad(a):
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CellProfiler/centrosome | centrosome/threshold.py | get_ridler_calvard_threshold | def get_ridler_calvard_threshold(image, mask = None):
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CellProfiler/centrosome | centrosome/threshold.py | get_kapur_threshold | def get_kapur_threshold(image, mask=None):
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CellProfiler/centrosome | centrosome/threshold.py | get_maximum_correlation_threshold | def get_maximum_correlation_threshold(image, mask = None, bins = 256):
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CellProfiler/centrosome | centrosome/threshold.py | weighted_variance | def weighted_variance(image, mask, binary_image):
"""Compute the log-transformed variance of foreground and background
image - intensity image used for thresholding
mask - mask of ignored pixels
binary_image - binary image marking foreground and background
"""
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"""Compute the log-transformed variance of foreground and background
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mask - mask of ignored pixels
binary_image - binary image marking foreground and background
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"marking"... | train | https://github.com/CellProfiler/centrosome/blob/7bd9350a2d4ae1b215b81eabcecfe560bbb1f32a/centrosome/threshold.py#L691-L718 |
CellProfiler/centrosome | centrosome/threshold.py | sum_of_entropies | def sum_of_entropies(image, mask, binary_image):
"""Bin the foreground and background pixels and compute the entropy
of the distribution of points among the bins
"""
mask=mask.copy()
mask[np.isnan(image)] = False
if not np.any(mask):
return 0
#
# Clamp the dynamic range of the f... | python | def sum_of_entropies(image, mask, binary_image):
"""Bin the foreground and background pixels and compute the entropy
of the distribution of points among the bins
"""
mask=mask.copy()
mask[np.isnan(image)] = False
if not np.any(mask):
return 0
#
# Clamp the dynamic range of the f... | [
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CellProfiler/centrosome | centrosome/threshold.py | log_transform | def log_transform(image):
'''Renormalize image intensities to log space
Returns a tuple of transformed image and a dictionary to be passed into
inverse_log_transform. The minimum and maximum from the dictionary
can be applied to an image by the inverse_log_transform to
convert it back to its f... | python | def log_transform(image):
'''Renormalize image intensities to log space
Returns a tuple of transformed image and a dictionary to be passed into
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can be applied to an image by the inverse_log_transform to
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CellProfiler/centrosome | centrosome/threshold.py | numpy_histogram | def numpy_histogram(a, bins=10, range=None, normed=False, weights=None):
'''A version of numpy.histogram that accounts for numpy's version'''
args = inspect.getargs(np.histogram.__code__)[0]
if args[-1] == "new":
return np.histogram(a, bins, range, normed, weights, new=True)
return np.histogram(... | python | def numpy_histogram(a, bins=10, range=None, normed=False, weights=None):
'''A version of numpy.histogram that accounts for numpy's version'''
args = inspect.getargs(np.histogram.__code__)[0]
if args[-1] == "new":
return np.histogram(a, bins, range, normed, weights, new=True)
return np.histogram(... | [
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CellProfiler/centrosome | centrosome/rankorder.py | rank_order | def rank_order(image, nbins=None):
"""Return an image of the same shape where each pixel has the
rank-order value of the corresponding pixel in the image.
The returned image's elements are of type np.uint32 which
simplifies processing in C code.
"""
flat_image = image.ravel()
sort_order = fl... | python | def rank_order(image, nbins=None):
"""Return an image of the same shape where each pixel has the
rank-order value of the corresponding pixel in the image.
The returned image's elements are of type np.uint32 which
simplifies processing in C code.
"""
flat_image = image.ravel()
sort_order = fl... | [
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