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skimage.measure.approximate_polygon(coords, tolerance) [source]
Approximate a polygonal chain with the specified tolerance. It is based on the Douglas-Peucker algorithm. Note that the approximated polygon is always within the convex hull of the original polygon. Parameters
coords(N, 2) array
Coordinate array. ... | skimage.api.skimage.measure#skimage.measure.approximate_polygon |
skimage.measure.block_reduce(image, block_size, func=<function sum>, cval=0, func_kwargs=None) [source]
Downsample image by applying function func to local blocks. This function is useful for max and mean pooling, for example. Parameters
imagendarray
N-dimensional input image.
block_sizearray_like
Array con... | skimage.api.skimage.measure#skimage.measure.block_reduce |
class skimage.measure.CircleModel [source]
Bases: skimage.measure.fit.BaseModel Total least squares estimator for 2D circles. The functional model of the circle is: r**2 = (x - xc)**2 + (y - yc)**2
This estimator minimizes the squared distances from all points to the circle: min{ sum((r - sqrt((x_i - xc)**2 + (y_i -... | skimage.api.skimage.measure#skimage.measure.CircleModel |
estimate(data) [source]
Estimate circle model from data using total least squares. Parameters
data(N, 2) array
N points with (x, y) coordinates, respectively. Returns
successbool
True, if model estimation succeeds. | skimage.api.skimage.measure#skimage.measure.CircleModel.estimate |
predict_xy(t, params=None) [source]
Predict x- and y-coordinates using the estimated model. Parameters
tarray
Angles in circle in radians. Angles start to count from positive x-axis to positive y-axis in a right-handed system.
params(3, ) array, optional
Optional custom parameter set. Returns
xy(…, 2)... | skimage.api.skimage.measure#skimage.measure.CircleModel.predict_xy |
residuals(data) [source]
Determine residuals of data to model. For each point the shortest distance to the circle is returned. Parameters
data(N, 2) array
N points with (x, y) coordinates, respectively. Returns
residuals(N, ) array
Residual for each data point. | skimage.api.skimage.measure#skimage.measure.CircleModel.residuals |
__init__() [source]
Initialize self. See help(type(self)) for accurate signature. | skimage.api.skimage.measure#skimage.measure.CircleModel.__init__ |
class skimage.measure.EllipseModel [source]
Bases: skimage.measure.fit.BaseModel Total least squares estimator for 2D ellipses. The functional model of the ellipse is: xt = xc + a*cos(theta)*cos(t) - b*sin(theta)*sin(t)
yt = yc + a*sin(theta)*cos(t) + b*cos(theta)*sin(t)
d = sqrt((x - xt)**2 + (y - yt)**2)
where (xt... | skimage.api.skimage.measure#skimage.measure.EllipseModel |
estimate(data) [source]
Estimate circle model from data using total least squares. Parameters
data(N, 2) array
N points with (x, y) coordinates, respectively. Returns
successbool
True, if model estimation succeeds. References
1
Halir, R.; Flusser, J. “Numerically stable direct least squares fitt... | skimage.api.skimage.measure#skimage.measure.EllipseModel.estimate |
predict_xy(t, params=None) [source]
Predict x- and y-coordinates using the estimated model. Parameters
tarray
Angles in circle in radians. Angles start to count from positive x-axis to positive y-axis in a right-handed system.
params(5, ) array, optional
Optional custom parameter set. Returns
xy(…, 2)... | skimage.api.skimage.measure#skimage.measure.EllipseModel.predict_xy |
residuals(data) [source]
Determine residuals of data to model. For each point the shortest distance to the ellipse is returned. Parameters
data(N, 2) array
N points with (x, y) coordinates, respectively. Returns
residuals(N, ) array
Residual for each data point. | skimage.api.skimage.measure#skimage.measure.EllipseModel.residuals |
__init__() [source]
Initialize self. See help(type(self)) for accurate signature. | skimage.api.skimage.measure#skimage.measure.EllipseModel.__init__ |
skimage.measure.euler_number(image, connectivity=None) [source]
Calculate the Euler characteristic in binary image. For 2D objects, the Euler number is the number of objects minus the number of holes. For 3D objects, the Euler number is obtained as the number of objects plus the number of holes, minus the number of t... | skimage.api.skimage.measure#skimage.measure.euler_number |
skimage.measure.find_contours(image, level=None, fully_connected='low', positive_orientation='low', *, mask=None) [source]
Find iso-valued contours in a 2D array for a given level value. Uses the “marching squares” method to compute a the iso-valued contours of the input 2D array for a particular level value. Array v... | skimage.api.skimage.measure#skimage.measure.find_contours |
skimage.measure.grid_points_in_poly(shape, verts) [source]
Test whether points on a specified grid are inside a polygon. For each (r, c) coordinate on a grid, i.e. (0, 0), (0, 1) etc., test whether that point lies inside a polygon. Parameters
shapetuple (M, N)
Shape of the grid.
verts(V, 2) array
Specify th... | skimage.api.skimage.measure#skimage.measure.grid_points_in_poly |
skimage.measure.inertia_tensor(image, mu=None) [source]
Compute the inertia tensor of the input image. Parameters
imagearray
The input image.
muarray, optional
The pre-computed central moments of image. The inertia tensor computation requires the central moments of the image. If an application requires both... | skimage.api.skimage.measure#skimage.measure.inertia_tensor |
skimage.measure.inertia_tensor_eigvals(image, mu=None, T=None) [source]
Compute the eigenvalues of the inertia tensor of the image. The inertia tensor measures covariance of the image intensity along the image axes. (See inertia_tensor.) The relative magnitude of the eigenvalues of the tensor is thus a measure of the... | skimage.api.skimage.measure#skimage.measure.inertia_tensor_eigvals |
skimage.measure.label(input, background=None, return_num=False, connectivity=None) [source]
Label connected regions of an integer array. Two pixels are connected when they are neighbors and have the same value. In 2D, they can be neighbors either in a 1- or 2-connected sense. The value refers to the maximum number of... | skimage.api.skimage.measure#skimage.measure.label |
class skimage.measure.LineModelND [source]
Bases: skimage.measure.fit.BaseModel Total least squares estimator for N-dimensional lines. In contrast to ordinary least squares line estimation, this estimator minimizes the orthogonal distances of points to the estimated line. Lines are defined by a point (origin) and a u... | skimage.api.skimage.measure#skimage.measure.LineModelND |
estimate(data) [source]
Estimate line model from data. This minimizes the sum of shortest (orthogonal) distances from the given data points to the estimated line. Parameters
data(N, dim) array
N points in a space of dimensionality dim >= 2. Returns
successbool
True, if model estimation succeeds. | skimage.api.skimage.measure#skimage.measure.LineModelND.estimate |
predict(x, axis=0, params=None) [source]
Predict intersection of the estimated line model with a hyperplane orthogonal to a given axis. Parameters
x(n, 1) array
Coordinates along an axis.
axisint
Axis orthogonal to the hyperplane intersecting the line.
params(2, ) array, optional
Optional custom paramet... | skimage.api.skimage.measure#skimage.measure.LineModelND.predict |
predict_x(y, params=None) [source]
Predict x-coordinates for 2D lines using the estimated model. Alias for: predict(y, axis=1)[:, 0]
Parameters
yarray
y-coordinates.
params(2, ) array, optional
Optional custom parameter set in the form (origin, direction). Returns
xarray
Predicted x-coordinates. | skimage.api.skimage.measure#skimage.measure.LineModelND.predict_x |
predict_y(x, params=None) [source]
Predict y-coordinates for 2D lines using the estimated model. Alias for: predict(x, axis=0)[:, 1]
Parameters
xarray
x-coordinates.
params(2, ) array, optional
Optional custom parameter set in the form (origin, direction). Returns
yarray
Predicted y-coordinates. | skimage.api.skimage.measure#skimage.measure.LineModelND.predict_y |
residuals(data, params=None) [source]
Determine residuals of data to model. For each point, the shortest (orthogonal) distance to the line is returned. It is obtained by projecting the data onto the line. Parameters
data(N, dim) array
N points in a space of dimension dim.
params(2, ) array, optional
Optiona... | skimage.api.skimage.measure#skimage.measure.LineModelND.residuals |
__init__() [source]
Initialize self. See help(type(self)) for accurate signature. | skimage.api.skimage.measure#skimage.measure.LineModelND.__init__ |
skimage.measure.marching_cubes(volume, level=None, *, spacing=(1.0, 1.0, 1.0), gradient_direction='descent', step_size=1, allow_degenerate=True, method='lewiner', mask=None) [source]
Marching cubes algorithm to find surfaces in 3d volumetric data. In contrast with Lorensen et al. approach [2], Lewiner et al. algorith... | skimage.api.skimage.measure#skimage.measure.marching_cubes |
skimage.measure.marching_cubes_classic(volume, level=None, spacing=(1.0, 1.0, 1.0), gradient_direction='descent') [source]
Classic marching cubes algorithm to find surfaces in 3d volumetric data. Note that the marching_cubes() algorithm is recommended over this algorithm, because it’s faster and produces better resul... | skimage.api.skimage.measure#skimage.measure.marching_cubes_classic |
skimage.measure.marching_cubes_lewiner(volume, level=None, spacing=(1.0, 1.0, 1.0), gradient_direction='descent', step_size=1, allow_degenerate=True, use_classic=False, mask=None) [source]
Lewiner marching cubes algorithm to find surfaces in 3d volumetric data. In contrast to marching_cubes_classic(), this algorithm ... | skimage.api.skimage.measure#skimage.measure.marching_cubes_lewiner |
skimage.measure.mesh_surface_area(verts, faces) [source]
Compute surface area, given vertices & triangular faces Parameters
verts(V, 3) array of floats
Array containing (x, y, z) coordinates for V unique mesh vertices.
faces(F, 3) array of ints
List of length-3 lists of integers, referencing vertex coordina... | skimage.api.skimage.measure#skimage.measure.mesh_surface_area |
skimage.measure.moments(image, order=3) [source]
Calculate all raw image moments up to a certain order. The following properties can be calculated from raw image moments:
Area as: M[0, 0]. Centroid as: {M[1, 0] / M[0, 0], M[0, 1] / M[0, 0]}. Note that raw moments are neither translation, scale nor rotation inva... | skimage.api.skimage.measure#skimage.measure.moments |
skimage.measure.moments_central(image, center=None, order=3, **kwargs) [source]
Calculate all central image moments up to a certain order. The center coordinates (cr, cc) can be calculated from the raw moments as: {M[1, 0] / M[0, 0], M[0, 1] / M[0, 0]}. Note that central moments are translation invariant but not scal... | skimage.api.skimage.measure#skimage.measure.moments_central |
skimage.measure.moments_coords(coords, order=3) [source]
Calculate all raw image moments up to a certain order. The following properties can be calculated from raw image moments:
Area as: M[0, 0]. Centroid as: {M[1, 0] / M[0, 0], M[0, 1] / M[0, 0]}. Note that raw moments are neither translation, scale nor rotat... | skimage.api.skimage.measure#skimage.measure.moments_coords |
skimage.measure.moments_coords_central(coords, center=None, order=3) [source]
Calculate all central image moments up to a certain order. The following properties can be calculated from raw image moments:
Area as: M[0, 0]. Centroid as: {M[1, 0] / M[0, 0], M[0, 1] / M[0, 0]}. Note that raw moments are neither tra... | skimage.api.skimage.measure#skimage.measure.moments_coords_central |
skimage.measure.moments_hu(nu) [source]
Calculate Hu’s set of image moments (2D-only). Note that this set of moments is proofed to be translation, scale and rotation invariant. Parameters
nu(M, M) array
Normalized central image moments, where M must be >= 4. Returns
nu(7,) array
Hu’s set of image moment... | skimage.api.skimage.measure#skimage.measure.moments_hu |
skimage.measure.moments_normalized(mu, order=3) [source]
Calculate all normalized central image moments up to a certain order. Note that normalized central moments are translation and scale invariant but not rotation invariant. Parameters
mu(M,[ …,] M) array
Central image moments, where M must be greater than o... | skimage.api.skimage.measure#skimage.measure.moments_normalized |
skimage.measure.perimeter(image, neighbourhood=4) [source]
Calculate total perimeter of all objects in binary image. Parameters
image(N, M) ndarray
2D binary image.
neighbourhood4 or 8, optional
Neighborhood connectivity for border pixel determination. It is used to compute the contour. A higher neighbourho... | skimage.api.skimage.measure#skimage.measure.perimeter |
skimage.measure.perimeter_crofton(image, directions=4) [source]
Calculate total Crofton perimeter of all objects in binary image. Parameters
image(N, M) ndarray
2D image. If image is not binary, all values strictly greater than zero are considered as the object.
directions2 or 4, optional
Number of directio... | skimage.api.skimage.measure#skimage.measure.perimeter_crofton |
skimage.measure.points_in_poly(points, verts) [source]
Test whether points lie inside a polygon. Parameters
points(N, 2) array
Input points, (x, y).
verts(M, 2) array
Vertices of the polygon, sorted either clockwise or anti-clockwise. The first point may (but does not need to be) duplicated. Returns
m... | skimage.api.skimage.measure#skimage.measure.points_in_poly |
skimage.measure.profile_line(image, src, dst, linewidth=1, order=None, mode=None, cval=0.0, *, reduce_func=<function mean>) [source]
Return the intensity profile of an image measured along a scan line. Parameters
imagendarray, shape (M, N[, C])
The image, either grayscale (2D array) or multichannel (3D array, w... | skimage.api.skimage.measure#skimage.measure.profile_line |
skimage.measure.ransac(data, model_class, min_samples, residual_threshold, is_data_valid=None, is_model_valid=None, max_trials=100, stop_sample_num=inf, stop_residuals_sum=0, stop_probability=1, random_state=None, initial_inliers=None) [source]
Fit a model to data with the RANSAC (random sample consensus) algorithm. ... | skimage.api.skimage.measure#skimage.measure.ransac |
skimage.measure.regionprops(label_image, intensity_image=None, cache=True, coordinates=None, *, extra_properties=None) [source]
Measure properties of labeled image regions. Parameters
label_image(M, N[, P]) ndarray
Labeled input image. Labels with value 0 are ignored. Changed in version 0.14.1: Previously, lab... | skimage.api.skimage.measure#skimage.measure.regionprops |
skimage.measure.regionprops_table(label_image, intensity_image=None, properties=('label', 'bbox'), *, cache=True, separator='-', extra_properties=None) [source]
Compute image properties and return them as a pandas-compatible table. The table is a dictionary mapping column names to value arrays. See Notes section belo... | skimage.api.skimage.measure#skimage.measure.regionprops_table |
skimage.measure.shannon_entropy(image, base=2) [source]
Calculate the Shannon entropy of an image. The Shannon entropy is defined as S = -sum(pk * log(pk)), where pk are frequency/probability of pixels of value k. Parameters
image(N, M) ndarray
Grayscale input image.
basefloat, optional
The logarithmic base... | skimage.api.skimage.measure#skimage.measure.shannon_entropy |
skimage.measure.subdivide_polygon(coords, degree=2, preserve_ends=False) [source]
Subdivision of polygonal curves using B-Splines. Note that the resulting curve is always within the convex hull of the original polygon. Circular polygons stay closed after subdivision. Parameters
coords(N, 2) array
Coordinate arr... | skimage.api.skimage.measure#skimage.measure.subdivide_polygon |
Module: metrics
skimage.metrics.adapted_rand_error([…]) Compute Adapted Rand error as defined by the SNEMI3D contest.
skimage.metrics.contingency_table(im_true, …) Return the contingency table for all regions in matched segmentations.
skimage.metrics.hausdorff_distance(image0, …) Calculate the Hausdorff distance ... | skimage.api.skimage.metrics |
skimage.metrics.adapted_rand_error(image_true=None, image_test=None, *, table=None, ignore_labels=(0, )) [source]
Compute Adapted Rand error as defined by the SNEMI3D contest. [1] Parameters
image_truendarray of int
Ground-truth label image, same shape as im_test.
image_testndarray of int
Test image.
tabl... | skimage.api.skimage.metrics#skimage.metrics.adapted_rand_error |
skimage.metrics.contingency_table(im_true, im_test, *, ignore_labels=None, normalize=False) [source]
Return the contingency table for all regions in matched segmentations. Parameters
im_truendarray of int
Ground-truth label image, same shape as im_test.
im_testndarray of int
Test image.
ignore_labelsseque... | skimage.api.skimage.metrics#skimage.metrics.contingency_table |
skimage.metrics.hausdorff_distance(image0, image1) [source]
Calculate the Hausdorff distance between nonzero elements of given images. The Hausdorff distance [1] is the maximum distance between any point on image0 and its nearest point on image1, and vice-versa. Parameters
image0, image1ndarray
Arrays where Tru... | skimage.api.skimage.metrics#skimage.metrics.hausdorff_distance |
skimage.metrics.mean_squared_error(image0, image1) [source]
Compute the mean-squared error between two images. Parameters
image0, image1ndarray
Images. Any dimensionality, must have same shape. Returns
msefloat
The mean-squared error (MSE) metric. Notes Changed in version 0.16: This function was re... | skimage.api.skimage.metrics#skimage.metrics.mean_squared_error |
skimage.metrics.normalized_root_mse(image_true, image_test, *, normalization='euclidean') [source]
Compute the normalized root mean-squared error (NRMSE) between two images. Parameters
image_truendarray
Ground-truth image, same shape as im_test.
image_testndarray
Test image.
normalization{‘euclidean’, ‘mi... | skimage.api.skimage.metrics#skimage.metrics.normalized_root_mse |
skimage.metrics.peak_signal_noise_ratio(image_true, image_test, *, data_range=None) [source]
Compute the peak signal to noise ratio (PSNR) for an image. Parameters
image_truendarray
Ground-truth image, same shape as im_test.
image_testndarray
Test image.
data_rangeint, optional
The data range of the inp... | skimage.api.skimage.metrics#skimage.metrics.peak_signal_noise_ratio |
skimage.metrics.structural_similarity(im1, im2, *, win_size=None, gradient=False, data_range=None, multichannel=False, gaussian_weights=False, full=False, **kwargs) [source]
Compute the mean structural similarity index between two images. Parameters
im1, im2ndarray
Images. Any dimensionality with same shape.
... | skimage.api.skimage.metrics#skimage.metrics.structural_similarity |
skimage.metrics.variation_of_information(image0=None, image1=None, *, table=None, ignore_labels=()) [source]
Return symmetric conditional entropies associated with the VI. [1] The variation of information is defined as VI(X,Y) = H(X|Y) + H(Y|X). If X is the ground-truth segmentation, then H(X|Y) can be interpreted as... | skimage.api.skimage.metrics#skimage.metrics.variation_of_information |
Module: morphology
skimage.morphology.area_closing(image[, …]) Perform an area closing of the image.
skimage.morphology.area_opening(image[, …]) Perform an area opening of the image.
skimage.morphology.ball(radius[, dtype]) Generates a ball-shaped structuring element.
skimage.morphology.binary_closing(image[, …... | skimage.api.skimage.morphology |
skimage.morphology.area_closing(image, area_threshold=64, connectivity=1, parent=None, tree_traverser=None) [source]
Perform an area closing of the image. Area closing removes all dark structures of an image with a surface smaller than area_threshold. The output image is larger than or equal to the input image for ev... | skimage.api.skimage.morphology#skimage.morphology.area_closing |
skimage.morphology.area_opening(image, area_threshold=64, connectivity=1, parent=None, tree_traverser=None) [source]
Perform an area opening of the image. Area opening removes all bright structures of an image with a surface smaller than area_threshold. The output image is thus the largest image smaller than the inpu... | skimage.api.skimage.morphology#skimage.morphology.area_opening |
skimage.morphology.ball(radius, dtype=<class 'numpy.uint8'>) [source]
Generates a ball-shaped structuring element. This is the 3D equivalent of a disk. A pixel is within the neighborhood if the Euclidean distance between it and the origin is no greater than radius. Parameters
radiusint
The radius of the ball-sh... | skimage.api.skimage.morphology#skimage.morphology.ball |
skimage.morphology.binary_closing(image, selem=None, out=None) [source]
Return fast binary morphological closing of an image. This function returns the same result as greyscale closing but performs faster for binary images. The morphological closing on an image is defined as a dilation followed by an erosion. Closing... | skimage.api.skimage.morphology#skimage.morphology.binary_closing |
skimage.morphology.binary_dilation(image, selem=None, out=None) [source]
Return fast binary morphological dilation of an image. This function returns the same result as greyscale dilation but performs faster for binary images. Morphological dilation sets a pixel at (i,j) to the maximum over all pixels in the neighbor... | skimage.api.skimage.morphology#skimage.morphology.binary_dilation |
skimage.morphology.binary_erosion(image, selem=None, out=None) [source]
Return fast binary morphological erosion of an image. This function returns the same result as greyscale erosion but performs faster for binary images. Morphological erosion sets a pixel at (i,j) to the minimum over all pixels in the neighborhood... | skimage.api.skimage.morphology#skimage.morphology.binary_erosion |
skimage.morphology.binary_opening(image, selem=None, out=None) [source]
Return fast binary morphological opening of an image. This function returns the same result as greyscale opening but performs faster for binary images. The morphological opening on an image is defined as an erosion followed by a dilation. Opening... | skimage.api.skimage.morphology#skimage.morphology.binary_opening |
skimage.morphology.black_tophat(image, selem=None, out=None) [source]
Return black top hat of an image. The black top hat of an image is defined as its morphological closing minus the original image. This operation returns the dark spots of the image that are smaller than the structuring element. Note that dark spots... | skimage.api.skimage.morphology#skimage.morphology.black_tophat |
skimage.morphology.closing(image, selem=None, out=None) [source]
Return greyscale morphological closing of an image. The morphological closing on an image is defined as a dilation followed by an erosion. Closing can remove small dark spots (i.e. “pepper”) and connect small bright cracks. This tends to “close” up (dar... | skimage.api.skimage.morphology#skimage.morphology.closing |
skimage.morphology.convex_hull_image(image, offset_coordinates=True, tolerance=1e-10) [source]
Compute the convex hull image of a binary image. The convex hull is the set of pixels included in the smallest convex polygon that surround all white pixels in the input image. Parameters
imagearray
Binary input image... | skimage.api.skimage.morphology#skimage.morphology.convex_hull_image |
skimage.morphology.convex_hull_object(image, *, connectivity=2) [source]
Compute the convex hull image of individual objects in a binary image. The convex hull is the set of pixels included in the smallest convex polygon that surround all white pixels in the input image. Parameters
image(M, N) ndarray
Binary in... | skimage.api.skimage.morphology#skimage.morphology.convex_hull_object |
skimage.morphology.cube(width, dtype=<class 'numpy.uint8'>) [source]
Generates a cube-shaped structuring element. This is the 3D equivalent of a square. Every pixel along the perimeter has a chessboard distance no greater than radius (radius=floor(width/2)) pixels. Parameters
widthint
The width, height and dept... | skimage.api.skimage.morphology#skimage.morphology.cube |
skimage.morphology.diameter_closing(image, diameter_threshold=8, connectivity=1, parent=None, tree_traverser=None) [source]
Perform a diameter closing of the image. Diameter closing removes all dark structures of an image with maximal extension smaller than diameter_threshold. The maximal extension is defined as the ... | skimage.api.skimage.morphology#skimage.morphology.diameter_closing |
skimage.morphology.diameter_opening(image, diameter_threshold=8, connectivity=1, parent=None, tree_traverser=None) [source]
Perform a diameter opening of the image. Diameter opening removes all bright structures of an image with maximal extension smaller than diameter_threshold. The maximal extension is defined as th... | skimage.api.skimage.morphology#skimage.morphology.diameter_opening |
skimage.morphology.diamond(radius, dtype=<class 'numpy.uint8'>) [source]
Generates a flat, diamond-shaped structuring element. A pixel is part of the neighborhood (i.e. labeled 1) if the city block/Manhattan distance between it and the center of the neighborhood is no greater than radius. Parameters
radiusint
T... | skimage.api.skimage.morphology#skimage.morphology.diamond |
skimage.morphology.dilation(image, selem=None, out=None, shift_x=False, shift_y=False) [source]
Return greyscale morphological dilation of an image. Morphological dilation sets a pixel at (i,j) to the maximum over all pixels in the neighborhood centered at (i,j). Dilation enlarges bright regions and shrinks dark regi... | skimage.api.skimage.morphology#skimage.morphology.dilation |
skimage.morphology.disk(radius, dtype=<class 'numpy.uint8'>) [source]
Generates a flat, disk-shaped structuring element. A pixel is within the neighborhood if the Euclidean distance between it and the origin is no greater than radius. Parameters
radiusint
The radius of the disk-shaped structuring element. Re... | skimage.api.skimage.morphology#skimage.morphology.disk |
skimage.morphology.erosion(image, selem=None, out=None, shift_x=False, shift_y=False) [source]
Return greyscale morphological erosion of an image. Morphological erosion sets a pixel at (i,j) to the minimum over all pixels in the neighborhood centered at (i,j). Erosion shrinks bright regions and enlarges dark regions.... | skimage.api.skimage.morphology#skimage.morphology.erosion |
skimage.morphology.flood(image, seed_point, *, selem=None, connectivity=None, tolerance=None) [source]
Mask corresponding to a flood fill. Starting at a specific seed_point, connected points equal or within tolerance of the seed value are found. Parameters
imagendarray
An n-dimensional array.
seed_pointtuple ... | skimage.api.skimage.morphology#skimage.morphology.flood |
skimage.morphology.flood_fill(image, seed_point, new_value, *, selem=None, connectivity=None, tolerance=None, in_place=False, inplace=None) [source]
Perform flood filling on an image. Starting at a specific seed_point, connected points equal or within tolerance of the seed value are found, then set to new_value. Par... | skimage.api.skimage.morphology#skimage.morphology.flood_fill |
skimage.morphology.h_maxima(image, h, selem=None) [source]
Determine all maxima of the image with height >= h. The local maxima are defined as connected sets of pixels with equal grey level strictly greater than the grey level of all pixels in direct neighborhood of the set. A local maximum M of height h is a local m... | skimage.api.skimage.morphology#skimage.morphology.h_maxima |
skimage.morphology.h_minima(image, h, selem=None) [source]
Determine all minima of the image with depth >= h. The local minima are defined as connected sets of pixels with equal grey level strictly smaller than the grey levels of all pixels in direct neighborhood of the set. A local minimum M of depth h is a local mi... | skimage.api.skimage.morphology#skimage.morphology.h_minima |
skimage.morphology.label(input, background=None, return_num=False, connectivity=None) [source]
Label connected regions of an integer array. Two pixels are connected when they are neighbors and have the same value. In 2D, they can be neighbors either in a 1- or 2-connected sense. The value refers to the maximum number... | skimage.api.skimage.morphology#skimage.morphology.label |
skimage.morphology.local_maxima(image, selem=None, connectivity=None, indices=False, allow_borders=True) [source]
Find local maxima of n-dimensional array. The local maxima are defined as connected sets of pixels with equal gray level (plateaus) strictly greater than the gray levels of all pixels in the neighborhood.... | skimage.api.skimage.morphology#skimage.morphology.local_maxima |
skimage.morphology.local_minima(image, selem=None, connectivity=None, indices=False, allow_borders=True) [source]
Find local minima of n-dimensional array. The local minima are defined as connected sets of pixels with equal gray level (plateaus) strictly smaller than the gray levels of all pixels in the neighborhood.... | skimage.api.skimage.morphology#skimage.morphology.local_minima |
skimage.morphology.max_tree(image, connectivity=1) [source]
Build the max tree from an image. Component trees represent the hierarchical structure of the connected components resulting from sequential thresholding operations applied to an image. A connected component at one level is parent of a component at a higher ... | skimage.api.skimage.morphology#skimage.morphology.max_tree |
skimage.morphology.max_tree_local_maxima(image, connectivity=1, parent=None, tree_traverser=None) [source]
Determine all local maxima of the image. The local maxima are defined as connected sets of pixels with equal gray level strictly greater than the gray levels of all pixels in direct neighborhood of the set. The ... | skimage.api.skimage.morphology#skimage.morphology.max_tree_local_maxima |
skimage.morphology.medial_axis(image, mask=None, return_distance=False) [source]
Compute the medial axis transform of a binary image Parameters
imagebinary ndarray, shape (M, N)
The image of the shape to be skeletonized.
maskbinary ndarray, shape (M, N), optional
If a mask is given, only those elements in i... | skimage.api.skimage.morphology#skimage.morphology.medial_axis |
skimage.morphology.octagon(m, n, dtype=<class 'numpy.uint8'>) [source]
Generates an octagon shaped structuring element. For a given size of (m) horizontal and vertical sides and a given (n) height or width of slanted sides octagon is generated. The slanted sides are 45 or 135 degrees to the horizontal axis and hence ... | skimage.api.skimage.morphology#skimage.morphology.octagon |
skimage.morphology.octahedron(radius, dtype=<class 'numpy.uint8'>) [source]
Generates a octahedron-shaped structuring element. This is the 3D equivalent of a diamond. A pixel is part of the neighborhood (i.e. labeled 1) if the city block/Manhattan distance between it and the center of the neighborhood is no greater t... | skimage.api.skimage.morphology#skimage.morphology.octahedron |
skimage.morphology.opening(image, selem=None, out=None) [source]
Return greyscale morphological opening of an image. The morphological opening on an image is defined as an erosion followed by a dilation. Opening can remove small bright spots (i.e. “salt”) and connect small dark cracks. This tends to “open” up (dark) ... | skimage.api.skimage.morphology#skimage.morphology.opening |
skimage.morphology.reconstruction(seed, mask, method='dilation', selem=None, offset=None) [source]
Perform a morphological reconstruction of an image. Morphological reconstruction by dilation is similar to basic morphological dilation: high-intensity values will replace nearby low-intensity values. The basic dilation... | skimage.api.skimage.morphology#skimage.morphology.reconstruction |
skimage.morphology.rectangle(nrows, ncols, dtype=<class 'numpy.uint8'>) [source]
Generates a flat, rectangular-shaped structuring element. Every pixel in the rectangle generated for a given width and given height belongs to the neighborhood. Parameters
nrowsint
The number of rows of the rectangle.
ncolsint
... | skimage.api.skimage.morphology#skimage.morphology.rectangle |
skimage.morphology.remove_small_holes(ar, area_threshold=64, connectivity=1, in_place=False) [source]
Remove contiguous holes smaller than the specified size. Parameters
arndarray (arbitrary shape, int or bool type)
The array containing the connected components of interest.
area_thresholdint, optional (defaul... | skimage.api.skimage.morphology#skimage.morphology.remove_small_holes |
skimage.morphology.remove_small_objects(ar, min_size=64, connectivity=1, in_place=False) [source]
Remove objects smaller than the specified size. Expects ar to be an array with labeled objects, and removes objects smaller than min_size. If ar is bool, the image is first labeled. This leads to potentially different be... | skimage.api.skimage.morphology#skimage.morphology.remove_small_objects |
skimage.morphology.skeletonize(image, *, method=None) [source]
Compute the skeleton of a binary image. Thinning is used to reduce each connected component in a binary image to a single-pixel wide skeleton. Parameters
imagendarray, 2D or 3D
A binary image containing the objects to be skeletonized. Zeros represen... | skimage.api.skimage.morphology#skimage.morphology.skeletonize |
skimage.morphology.skeletonize_3d(image) [source]
Compute the skeleton of a binary image. Thinning is used to reduce each connected component in a binary image to a single-pixel wide skeleton. Parameters
imagendarray, 2D or 3D
A binary image containing the objects to be skeletonized. Zeros represent background,... | skimage.api.skimage.morphology#skimage.morphology.skeletonize_3d |
skimage.morphology.square(width, dtype=<class 'numpy.uint8'>) [source]
Generates a flat, square-shaped structuring element. Every pixel along the perimeter has a chessboard distance no greater than radius (radius=floor(width/2)) pixels. Parameters
widthint
The width and height of the square. Returns
selem... | skimage.api.skimage.morphology#skimage.morphology.square |
skimage.morphology.star(a, dtype=<class 'numpy.uint8'>) [source]
Generates a star shaped structuring element. Start has 8 vertices and is an overlap of square of size 2*a + 1 with its 45 degree rotated version. The slanted sides are 45 or 135 degrees to the horizontal axis. Parameters
aint
Parameter deciding th... | skimage.api.skimage.morphology#skimage.morphology.star |
skimage.morphology.thin(image, max_iter=None) [source]
Perform morphological thinning of a binary image. Parameters
imagebinary (M, N) ndarray
The image to be thinned.
max_iterint, number of iterations, optional
Regardless of the value of this parameter, the thinned image is returned immediately if an itera... | skimage.api.skimage.morphology#skimage.morphology.thin |
skimage.morphology.watershed(image, markers=None, connectivity=1, offset=None, mask=None, compactness=0, watershed_line=False) [source]
Deprecated function. Use skimage.segmentation.watershed instead. Find watershed basins in image flooded from given markers. Parameters
imagendarray (2-D, 3-D, …) of integers
Da... | skimage.api.skimage.morphology#skimage.morphology.watershed |
skimage.morphology.white_tophat(image, selem=None, out=None) [source]
Return white top hat of an image. The white top hat of an image is defined as the image minus its morphological opening. This operation returns the bright spots of the image that are smaller than the structuring element. Parameters
imagendarray... | skimage.api.skimage.morphology#skimage.morphology.white_tophat |
Module: registration
skimage.registration.optical_flow_ilk(…[, …]) Coarse to fine optical flow estimator.
skimage.registration.optical_flow_tvl1(…) Coarse to fine optical flow estimator.
skimage.registration.phase_cross_correlation(…) Efficient subpixel image translation registration by cross-correlation. optic... | skimage.api.skimage.registration |
skimage.registration.optical_flow_ilk(reference_image, moving_image, *, radius=7, num_warp=10, gaussian=False, prefilter=False, dtype=<class 'numpy.float32'>) [source]
Coarse to fine optical flow estimator. The iterative Lucas-Kanade (iLK) solver is applied at each level of the image pyramid. iLK [1] is a fast and ro... | skimage.api.skimage.registration#skimage.registration.optical_flow_ilk |
skimage.registration.optical_flow_tvl1(reference_image, moving_image, *, attachment=15, tightness=0.3, num_warp=5, num_iter=10, tol=0.0001, prefilter=False, dtype=<class 'numpy.float32'>) [source]
Coarse to fine optical flow estimator. The TV-L1 solver is applied at each level of the image pyramid. TV-L1 is a popular... | skimage.api.skimage.registration#skimage.registration.optical_flow_tvl1 |
skimage.registration.phase_cross_correlation(reference_image, moving_image, *, upsample_factor=1, space='real', return_error=True, reference_mask=None, moving_mask=None, overlap_ratio=0.3) [source]
Efficient subpixel image translation registration by cross-correlation. This code gives the same precision as the FFT up... | skimage.api.skimage.registration#skimage.registration.phase_cross_correlation |
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