Generate Region of Interests (ROI) labeled arrays for simple shapes

This example notebook explain the use of analysis module "skbeam/core/roi" https://github.com/scikit-beam/scikit-beam/blob/master/skbeam/core/roi.py



In [1]:

    
import skbeam.core.roi as roi
import skbeam.core.correlation as corr

import numpy as np

import matplotlib.pyplot as plt
%matplotlib inline
from matplotlib.ticker import MaxNLocator
from matplotlib.colors import LogNorm

import xray_vision.mpl_plotting as mpl_plot

Easily switch between interactive and static matplotlib plots



In [2]:

    
interactive_mode = False

import matplotlib as mpl
if interactive_mode:
    %matplotlib notebook
else:
    %matplotlib inline

backend = mpl.get_backend()
cmap='viridis'

Draw annular (ring-shaped) regions of interest



In [3]:

    
center = (100., 100.)   # center of the rings

# Image shape which is used to determine the maximum extent of output pixel coordinates
img_shape = (200, 205)   

first_q = 10.0 # inner radius of the inner-most ring
delta_q = 5.0  #ring thickness

num_rings = 7  # number of Q rings

# step or spacing, spacing between rings 
one_step_q = 5.0  #  one spacing between rings

step_q = [2.5, 3.0, 5.8]   # differnt spacing between rings

Test when there is same spacing between rings



In [4]:

    
# inner and outer radius for each ring
edges = roi.ring_edges(first_q, width=delta_q, spacing=one_step_q,
                           num_rings=num_rings)
edges









    Out[4]:





array([[ 10.,  15.],
       [ 20.,  25.],
       [ 30.,  35.],
       [ 40.,  45.],
       [ 50.,  55.],
       [ 60.,  65.],
       [ 70.,  75.]])



In [5]:

    
#Elements not inside any ROI are zero; elements inside each
#ROI are 1, 2, 3, corresponding to the order they are specified in edges.
label_array = roi.rings(edges, center, img_shape)

# plot the figure
fig, axes = plt.subplots(figsize=(6, 5))
axes.set_title("Same spacing between rings")
im = mpl_plot.show_label_array(axes, label_array, cmap)
plt.show()

Test when there is different spacing between rings



In [6]:

    
# inner and outer radius for each ring

edges = roi.ring_edges(first_q, width=delta_q, spacing=step_q,
                           num_rings=4)
print("edges when there is different spacing between rings", edges)









    



edges when there is different spacing between rings [[ 10.   15. ]
 [ 17.5  22.5]
 [ 25.5  30.5]
 [ 36.3  41.3]]



In [7]:

    
#Elements not inside any ROI are zero; elements inside each
#ROI are 1, 2, 3, corresponding to the order they are specified in edges.
label_array = roi.rings(edges, center, img_shape)

# plot the figure
fig, axes = plt.subplots(figsize=(6, 5))
axes.set_title("Different spacing between rings")
axes.set_xlim(50, 150)
axes.set_ylim(50, 150)
im = mpl_plot.show_label_array(axes, label_array, cmap)
plt.show()

Test when there is no spacing between rings



In [8]:

    
# inner and outer radius for each ring
edges = roi.ring_edges(first_q, width=delta_q, num_rings=num_rings)
edges









    Out[8]:





array([[ 10.,  15.],
       [ 15.,  20.],
       [ 20.,  25.],
       [ 25.,  30.],
       [ 30.,  35.],
       [ 35.,  40.],
       [ 40.,  45.]])



In [9]:

    
#Elements not inside any ROI are zero; elements inside each
#ROI are 1, 2, 3, corresponding to the order they are specified in edges.
label_array = roi.rings(edges, center, img_shape)

# plot the figure
fig, axes = plt.subplots(figsize=(6, 5))
axes.set_title("There is no spacing between rings")
axes.set_xlim(50, 150)
axes.set_ylim(50, 150)
im = mpl_plot.show_label_array(axes, label_array, cmap)
plt.show()

Generate a ROI of Segmented Rings



In [10]:

    
center = (75, 75)   # center of the rings

#Image shape which is used to determine the maximum extent of output pixel coordinates
img_shape = (150, 140) 

first_q = 5.0  # inner radius of the inner-most ring
delta_q = 5.0  #ring thickness
num_rings = 4  # number of rings

slicing = 4 # number of pie slices or list of angles in radians
spacing = 4 # margin between rings, 0 by default

find the inner and outer radius of each ring



In [11]:

    
# inner and outer radius for each ring
edges = roi.ring_edges(first_q, width=delta_q, spacing=spacing,
                           num_rings=num_rings)

edges









    Out[11]:





array([[  5.,  10.],
       [ 14.,  19.],
       [ 23.,  28.],
       [ 32.,  37.]])



In [12]:

    
#Elements not inside any ROI are zero; elements inside each
#ROI are 1, 2, 3, corresponding to the order they are specified in edges.
label_array = roi.segmented_rings(edges, slicing, center,
                                      img_shape, offset_angle=0)

# plot the figure
fig, axes = plt.subplots(figsize=(6, 5))
axes.set_title("Segmented Rings")
axes.set_xlim(38, 120)
axes.set_ylim(38, 120)
im = mpl_plot.show_label_array(axes, label_array, cmap)
plt.show()

Segmented rings using list of angles in radians



In [13]:

    
slicing = np.radians([0, 60, 120, 240, 300])
slicing









    Out[13]:





array([ 0.        ,  1.04719755,  2.0943951 ,  4.1887902 ,  5.23598776])



In [14]:

    
#Elements not inside any ROI are zero; elements inside each
#ROI are 1, 2, 3, corresponding to the order they are specified in edges.
label_array = roi.segmented_rings(edges, slicing, center,
                                      img_shape, offset_angle=0)

# plot the figure
fig, axes = plt.subplots(figsize=(6, 5))
axes.set_title("Segmented Rings")
axes.set_xlim(38, 120)
axes.set_ylim(38, 120)
im = mpl_plot.show_label_array(axes, label_array, cmap="gray")
plt.show()

Generate a ROI of Pies



In [15]:

    
first_q = 0  

# inner and outer radius for each ring
edges = roi.ring_edges(first_q, width=50, num_rings=1)

edges









    Out[15]:





array([[  0.,  50.]])



In [16]:

    
slicing = 10 # number of pie slices or list of angles in radians

#Elements not inside any ROI are zero; elements inside each
#ROI are 1, 2, 3, corresponding to the order they are specified in edges.
label_array = roi.segmented_rings(edges, slicing, center,
                                      img_shape, offset_angle=0)

# plot the figure
fig, axes = plt.subplots(figsize=(6, 5))
axes.set_title("Pies")
axes.set_xlim(20, 140)
axes.set_ylim(20, 140)
im = mpl_plot.show_label_array(axes, label_array, cmap)
plt.show()

Rectangle region of interests.



In [17]:

    
# Image shape which is used to determine the maximum extent of output pixel coordinates
shape = (15, 26)

# coordinates of the upper-left corner and width and height of each rectangle
roi_data = np.array(([2, 2, 6, 3], [6, 7, 8, 5], [8, 18, 5, 10]),
                        dtype=np.int64)

#Elements not inside any ROI are zero; elements inside each ROI are 1, 2, 3, corresponding
# to the order they are specified in coords.
label_array = roi.rectangles(roi_data, shape)
roi_inds, pixel_list = roi.extract_label_indices(label_array)

Generate Bar ROI's



In [18]:

    
edges = [[3, 4], [5, 7], [12, 15]]
edges









    Out[18]:





[[3, 4], [5, 7], [12, 15]]

Create Horizontal bars and Vertical bars



In [19]:

    
h_label_array = roi.bar(edges, (20, 25))   # Horizontal Bars



In [20]:

    
v_label_array = roi.bar(edges, (20, 25), horizontal=False)   # Vertical Bars

Create Box ROI's



In [21]:

    
b_label_array = roi.box((20, 25), edges)

Plot bar rois, box rois and rectangle rois



In [22]:

    
fig, axes = plt.subplots(2, 2, figsize=(12, 10))
axes[1, 0].set_title("Horizontal Bars")
im = mpl_plot.show_label_array(axes[1, 0], h_label_array, cmap)
axes[0, 1].set_title("Vertical Bars")
im = mpl_plot.show_label_array(axes[0, 1], v_label_array, cmap)
axes[1, 1].set_title("Box Rois")
im = mpl_plot.show_label_array(axes[1, 1], b_label_array, cmap)
axes[0, 0].set_title("Rectangle Rois")
im = mpl_plot.show_label_array(axes[0, 0], label_array, cmap)
plt.show()

Create line ROI's



In [23]:

    
label_lines= roi.lines(([0, 45, 50, 256], [56, 60, 80, 150]), (150, 250))
# plot the figure
fig, axes = plt.subplots(figsize=(6, 5))
axes.set_title("Lines")
im = mpl_plot.show_label_array(axes, label_lines, cmap)
plt.show()



In [24]:

    
import skbeam
print(skbeam.__version__)









    



0.0.8+42.g679637f



In [ ]: