scimage 0.1.6

Algorithms for data analysis and image processing, including automation of feature extraction and characterization of images.

SciCharImage (scimage)

This opensource library provides algorithms for data analysis and image processing, including automation of feature extraction and characterization of image regions.

Example Outputs

github.com/amirchatraee/scimage#example-outputs

Shows the results of processing a synthesized image that represents peaks and valleys, as well as an image obtained from a plasma simlation. Cropping out and characterizing the detected regions.

Note

This library can be also beneficial to the astrophysics society. For example you can perform statistical analysis of thickness, length, and aspect ratio (length/half-thickness) of each current-sheet in a plasma. Examples of visualizations and results can be seen in a publication in the Journal of Physics of Plasmas: doi.org/10.1063/5.0040692

Contributing

https://github.com/amirchatraee/scimage

Pull requests are welcome!

Installation

pip install scimage

Using the library

See examples (simple-example.py, example.py) in the github repository, on how to use the library functions. Before running the examples, make sure to download the data files from the data folder of the repository.

Simple Example:

import sys
import numpy as np

import scimage.identification as ident # Identification of peaks and regions (image segmentation)
import scimage.characterization as char # Characterizing each detected region (e.g. thickness and length)
import scimage.plot_functions as scplt # Plotting functions
from scimage.file_functions import (load_simulation_image)

sys.setrecursionlimit(10000) # to avoid possible RecursionError


# Prepare a 2D plane image
values, nx, ny, lx, ly, coordinates_x, coordinates_y = load_simulation_image('data/data-512-512.npz')

noise_threshold = 0.1
ratio_of_boundary_to_max = 0.5
number_of_points_upto_local_boundary = 10

values_abs = np.abs(values)

# Detect local maximas:
good_indexes = ident.remove_noise(values_abs, noise_threshold)

local_maxima_values, indexes_of_local_maximas, maxima_values = \
	ident.find_local_maxima_at_selected_indexes(values_abs, good_indexes,
	                                            number_of_points_upto_local_boundary)
# Detect regions surrounding each maxima point (image segmentation)
indexes_of_points_of_all_regions, indexes_of_valid_local_maxima = \
	ident.detect_sheet_regions(values_abs, indexes_of_local_maximas, 
	                           ratio_of_boundary_to_max)

# Characterize one of the detected regions -------------------
selected_region = 0 # choose one region to process here as an example
indexes_of_points_of_one_region = indexes_of_points_of_all_regions[selected_region]

# first, cut out the selected region as a separate frame from the whole image
coordinates_x_in_frame, coordinates_y_in_frame, values_of_frame = \
	char.build_region_frame(indexes_of_points_of_one_region, 
	                        coordinates_x, coordinates_y, values)

# Now, estimate thickness of the region
min_val = np.max(values_of_frame) * 0.42
half_thickness_plus_side, half_thickness_minus_side = \
	char.characterize_region(values_of_frame, 
	         	            coordinates_x_in_frame, coordinates_y_in_frame, 
	         	            min_val)

# Also, find length with the pair-wise comparison method
length, p1, p2 = char.find_length_by_pariwise_distance(indexes_of_points_of_one_region, 
	                                                   coordinates_x, coordinates_y)


# Plot one region
scplt.plot_region(coordinates_x_in_frame, coordinates_y_in_frame, values_of_frame, p1, p2)
scplt.save_region_image(coordinates_x_in_frame, coordinates_y_in_frame, values_of_frame, p1, p2,
	                    selected_region, 'output/simple-example/detected_regions_with_length')

# Plot the whole image plane, together with the detected regions
scplt.plot_locations_of_local_maximas(coordinates_x, coordinates_y, values, 
	                                  noise_threshold, indexes_of_local_maximas)

scplt.plot_locations_of_region_points(coordinates_x, coordinates_y, values,
	                                  noise_threshold, indexes_of_points_of_all_regions)

print()
print ("Number of detected maximas:" , len(indexes_of_local_maximas))
print ("Number of detected regions:" , len(indexes_of_points_of_all_regions))
print("Region", selected_region,", with size", values_of_frame.shape, "characterized:")
print("Length:", length)
print("Thickness (half plus, half minus):", half_thickness_plus_side, half_thickness_minus_side)