core.roi Module¶
This module contain convenience methods to generate ROI labeled arrays for simple shapes such as rectangles and concentric circles.
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skbeam.core.roi.auto_find_center_rings(avg_img, sigma=1, no_rings=4, min_samples=3, residual_threshold=1, max_trials=1000)¶ This will find the center of the speckle pattern and the radii of the most intense rings.
- Parameters
- avg_img2D array
shape of the image
- sigmafloat, optional
Standard deviation of the Gaussian filter.
- no_ringsint, optional
number of rings
- min_sampleint, optional
The minimum number of data points to fit a model to.
- residual_thresholdfloat, optional
Maximum distance for a data point to be classified as an inlier.
- max_trialsint, optional
Maximum number of iterations for random sample selection.
- Returns
- centertuple
center co-ordinates of the speckle pattern
- image2D array
Indices of pixels that belong to the rings, directly index into an array
- radiilist
values of the radii of the rings
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skbeam.core.roi.bar(edges, shape, horizontal=True, values=None)¶ Draw bars defined by edges from one edge to the other of image_shape
Bars will be horizontal or vertical depending on the value of horizontal
- Parameters
- edgeslist
List of tuples of inner (left or top) and outer (right or bottom) edges of each bar. e.g., edges=[(1, 2), (11, 12), (21, 22)]
- shapetuple
Shape of the image in which to create the ROIs e.g., shape=(512, 512)
- horizontalbool, optional
True: Make horizontal bars False: Make vertical bars Defaults to True
- valuesarray, optional
image pixels co-ordinates
- Returns
- label_arrayarray
Elements not inside any ROI are zero; elements inside each ROI are 1, 2, 3, corresponding to the order they are specified in edges. Has shape=`image shape`
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skbeam.core.roi.box(shape, v_edges, h_edges=None, h_values=None, v_values=None)¶ - Draw box shaped rois when the horizontal and vertical edges
are provided.
- Parameters
- shapetuple
Shape of the image in which to create the ROIs e.g., shape=(512, 512)
- v_edgeslist
giving the inner and outer edges of each vertical bar e.g., [(1, 2), (11, 12), (21, 22)]
- h_edgeslist, optional
giving the inner and outer edges of each horizontal bar e.g., [(1, 2), (11, 12), (21, 22)]
- h_valuesarray, optional
image pixels co-ordinates in horizontal direction shape has to be image shape
- v_valuesarray, optional
image pixels co-ordinates in vertical direction shape has to be image shape
- Returns
- label_arrayarray
Elements not inside any ROI are zero; elements inside each ROI are 1, 2, 3, corresponding to the order they are specified in edges.
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skbeam.core.roi.circular_average(image, calibrated_center, threshold=0, nx=100, pixel_size=(1, 1), min_x=None, max_x=None, mask=None)¶ Circular average of the the image data The circular average is also known as the radial integration
- Parameters
- imagearray
Image to compute the average as a function of radius
- calibrated_centertuple
The center of the image in pixel units argument order should be (row, col)
- thresholdint, optional
Ignore counts below threshold default is zero
- nxint, optional
number of bins in x defaults is 100 bins
- pixel_sizetuple, optional
The size of a pixel (in a real unit, like mm). argument order should be (pixel_height, pixel_width) default is (1, 1)
- min_xfloat, optional number of pixels
Left edge of first bin defaults to minimum value of x
- max_xfloat, optional number of pixels
Right edge of last bin defaults to maximum value of x
- maskmask for 2D data. Assumes 1 is non masked and 0 masked.
None defaults to no mask.
- Returns
- bin_centersarray
The center of each bin in R. shape is (nx, )
- ring_averagesarray
Radial average of the image. shape is (nx, ).
See also
bad_to_nan_genCreate a mask with np.nan entries
bin_gridBin and integrate an image, given the radial array of pixels Useful for nonlinear spacing (Ewald curvature)
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skbeam.core.roi.extract_label_indices(labels)¶ This will find the label’s required region of interests (roi’s), number of roi’s count the number of pixels in each roi’s and pixels list for the required roi’s.
- Parameters
- labelsarray
labeled array; 0 is background. Each ROI is represented by a distinct label (i.e., integer).
- Returns
- label_maskarray
1D array labeling each foreground pixel e.g., [1, 1, 1, 1, 2, 2, 1, 1]
- indicesarray
1D array of indices into the raveled image for all foreground pixels (labeled nonzero) e.g., [5, 6, 7, 8, 14, 15, 21, 22]
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skbeam.core.roi.kymograph(images, labels, num)¶ This function will provide data for graphical representation of pixels variation over time for required ROI.
- Parameters
- imagesarray
Image stack. dimensions are: (num_img, num_rows, num_cols)
- labelsarray
labeled array; 0 is background. Each ROI is represented by an integer
- numint
The ROI to turn into a kymograph
- Returns
- kymographarray
data for graphical representation of pixels variation over time for required ROI
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skbeam.core.roi.lines(end_points, shape)¶ - Parameters
- end_pointsiterable
coordinates of the starting point and the ending point of each line: e.g., [(start_x, start_y, end_x, end_y), (x1, y1, x2, y2)]
- shapetuple
Image shape which is used to determine the maximum extent of output pixel coordinates. Order is (rr, cc).
- Returns
- label_arrayarray
Elements not inside any ROI are zero; elements inside each ROI are 1, 2, 3, corresponding to the order they are specified in coords. Order is (rr, cc).
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skbeam.core.roi.mean_intensity(images, labeled_array, index=None)¶ Compute the mean intensity for each ROI in the image list
- Parameters
- imageslist
List of images
- labeled_arrayarray
labeled array; 0 is background. Each ROI is represented by a nonzero integer. It is not required that the ROI labels are contiguous
- indexint, list, optional
The ROI’s to use. If None, this function will extract averages for all ROIs
- Returns
- mean_intensityarray
The mean intensity of each ROI for all images Dimensions:
len(mean_intensity) == len(index)
len(mean_intensity[0]) == len(images)
- indexlist
The labels for each element of the mean_intensity list
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skbeam.core.roi.rectangles(coords, shape)¶ This function wil provide the indices array for rectangle region of interests.
- Parameters
- coordsiterable
coordinates of the upper-left corner and width and height of each rectangle: e.g., [(x, y, w, h), (x, y, w, h)]
- shapetuple
Image shape which is used to determine the maximum extent of output pixel coordinates. Order is (rr, cc).
- Returns
- label_arrayarray
Elements not inside any ROI are zero; elements inside each ROI are 1, 2, 3, corresponding to the order they are specified in coords. Order is (rr, cc).
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skbeam.core.roi.ring_edges(inner_radius, width, spacing=0, num_rings=None)¶ Calculate the inner and outer radius of a set of rings.
The number of rings, their widths, and any spacing between rings can be specified. They can be uniform or varied.
- Parameters
- inner_radiusfloat
inner radius of the inner-most ring
- widthfloat or list of floats
ring thickness If a float, all rings will have the same thickness.
- spacingfloat or list of floats, optional
margin between rings, 0 by default If a float, all rings will have the same spacing. If a list, the length of the list must be one less than the number of rings.
- num_ringsint, optional
number of rings Required if width and spacing are not lists and number cannot thereby be inferred. If it is given and can also be inferred, input is checked for consistency.
- Returns
- edgesarray
inner and outer radius for each ring
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skbeam.core.roi.rings(edges, center, shape)¶ Draw annual (ring-shaped) shaped regions of interest.
Each ring will be labeled with an integer. Regions outside any ring will be filled with zeros.
- Parameters
- edges: list
giving the inner and outer radius of each ring e.g., [(1, 2), (11, 12), (21, 22)]
- center: tuple
point in image where r=0; may be a float giving subpixel precision. Order is (rr, cc).
- shape: tuple
Image shape which is used to determine the maximum extent of output pixel coordinates. Order is (rr, cc).
- Returns
- label_arrayarray
Elements not inside any ROI are zero; elements inside each ROI are 1, 2, 3, corresponding to the order they are specified in edges.
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skbeam.core.roi.roi_max_counts(images_sets, label_array)¶ Return the brightest pixel in any ROI in any image in the image set.
- Parameters
- images_setsarray
iterable of 4D arrays shapes is: (len(images_sets), )
- label_arrayarray
labeled array; 0 is background. Each ROI is represented by a distinct label (i.e., integer).
- Returns
- max_countsint
maximum pixel counts
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skbeam.core.roi.roi_pixel_values(image, labels, index=None)¶ This will provide intensities of the ROI’s of the labeled array according to the pixel list eg: intensities of the rings of the labeled array
- Parameters
- imagearray
image data dimensions are: (rr, cc)
- labelsarray
labeled array; 0 is background. Each ROI is represented by a distinct label (i.e., integer).
- index_listlist, optional
labels list eg: 5 ROI’s index = [1, 2, 3, 4, 5]
- Returns
- roi_pixlist
intensities of the ROI’s of the labeled array according to the pixel list
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skbeam.core.roi.segmented_rings(edges, segments, center, shape, offset_angle=0)¶ - Parameters
- edgesarray
inner and outer radius for each ring
- segmentsint or list
number of pie slices or list of angles in radians That is, 8 produces eight equal-sized angular segments, whereas a list can be used to produce segments of unequal size.
- centertuple
point in image where r=0; may be a float giving subpixel precision. Order is (rr, cc).
- shape: tuple
Image shape which is used to determine the maximum extent of output pixel coordinates. Order is (rr, cc).
- angle_offsetfloat or array, optional
offset in radians from offset_angle=0 along the positive X axis
- Returns
- label_arrayarray
Elements not inside any ROI are zero; elements inside each ROI are 1, 2, 3, corresponding to the order they are specified in edges and segments
See also
ring_edgesCalculate the inner and outer radius of a set of rings.