def run_script():
# We can use import inside of code blocks to limit the scope.
import math
from ij import IJ, ImagePlus
from ij.process import FloatProcessor
blank = IJ.createImage("Blank", "32-bit black", img_size, img_size, 1)
# This create a list of lists. Each inner list represents a line.
# pixel_matrix[0] is the first line where y=0.
pixel_matrix = split_list(blank.getProcessor().getPixels(),
wanted_parts=img_size)
# This swaps x and y coordinates.
# http://stackoverflow.com/questions/8421337/rotating-a-two-dimensional-array-in-python
# As zip() creates tuples, we have to convert each one by using list().
pixel_matrix = [list(x) for x in zip(*pixel_matrix)]
for y in range(img_size):
for x in range(img_size):
# This function oszillates between 0 and 1.
# The distance of 2 maxima in a row/column is given by spacing.
val = (0.5 * (math.cos(2 * math.pi / spacing * x) +
math.sin(2 * math.pi / spacing * y)))**2
# When assigning, we multiply the value by the amplitude.
pixel_matrix[x][y] = amplitude * val
# The constructor of FloatProcessor works fine with a 2D Python list.
crystal = ImagePlus("Crystal", FloatProcessor(pixel_matrix))
# Crop without selection is used to duplicate an image.
crystal_with_noise = crystal.crop()
crystal_with_noise.setTitle("Crystal with noise")
IJ.run(crystal_with_noise, "Add Specified Noise...",
"standard=%d" % int(amplitude / math.sqrt(2)))
# As this is a demo, we don't want to be ask to save an image on closing it.
# In Python True and False start with capital letters.
crystal_with_noise.changes = False
crystal.show()
crystal_with_noise.show()
filtered = fft_filter(crystal_with_noise)
# We create a lambda function to be used as a parameter of img_calc().
subtract = lambda values: values[0] - values[1]
# This is a short form for:
# def subtract(values):
# return values[0] - values[1]
# The first time we call img_calc with 2 images.
difference = img_calc(subtract, crystal, filtered, title="Difference of 2")
difference.show()
# The first time we call img_calc with 3 images.
minimum = img_calc(min,
crystal,
filtered,
crystal_with_noise,
title="Minimum of 3")
minimum.show()
for imp in (crystal, crystal_with_noise, filtered, difference, minimum):
IJ.run(imp, "Measure", "")
def run_script():
# We can use import inside of code blocks to limit the scope.
import math
from ij import IJ, ImagePlus
from ij.process import FloatProcessor
blank = IJ.createImage("Blank", "32-bit black", img_size, img_size, 1)
# This create a list of lists. Each inner list represents a line.
# pixel_matrix[0] is the first line where y=0.
pixel_matrix = split_list(blank.getProcessor().getPixels(), wanted_parts=img_size)
# This swaps x and y coordinates.
# http://stackoverflow.com/questions/8421337/rotating-a-two-dimensional-array-in-python
# As zip() creates tuples, we have to convert each one by using list().
pixel_matrix = [list(x) for x in zip(*pixel_matrix)]
for y in range(img_size):
for x in range(img_size):
# This function oszillates between 0 and 1.
# The distance of 2 maxima in a row/column is given by spacing.
val = (0.5 * (math.cos(2*math.pi/spacing*x) + math.sin(2*math.pi/spacing*y)))**2
# When assigning, we multiply the value by the amplitude.
pixel_matrix[x][y] = amplitude * val
# The constructor of FloatProcessor works fine with a 2D Python list.
crystal = ImagePlus("Crystal", FloatProcessor(pixel_matrix))
# Crop without selection is used to duplicate an image.
crystal_with_noise = crystal.crop()
crystal_with_noise.setTitle("Crystal with noise")
IJ.run(crystal_with_noise, "Add Specified Noise...", "standard=%d" % int(amplitude/math.sqrt(2)))
# As this is a demo, we don't want to be ask to save an image on closing it.
# In Python True and False start with capital letters.
crystal_with_noise.changes = False
crystal.show()
crystal_with_noise.show()
filtered = fft_filter(crystal_with_noise)
# We create a lambda function to be used as a parameter of img_calc().
subtract = lambda values: values[0] - values[1]
""" This is a short form for:
def subtract(values):
return values[0] - values[1]
"""
# The first time we call img_calc with 2 images.
difference = img_calc(subtract, crystal, filtered, title="Difference of 2")
difference.show()
# The first time we call img_calc with 3 images.
minimum = img_calc(min, crystal, filtered, crystal_with_noise, title="Minimum of 3")
minimum.show()
for imp in (crystal, crystal_with_noise, filtered, difference, minimum):
IJ.run(imp, "Measure", "")
def snapshot(imp, c, z, f, x, y, L):
'''Take a L x L snapshot centered at (x,y) at channel c, slice z and frame f
Parameters:
imp: an ImagePlus object for processing
c, z, f: specificed channel, slice (z-dimension) and frame coordinate
x, y: targeted center position to take the snapshot
L: desired edge lengths of the snapshot in pixels
Returns:
cropped: an ImagePlus object of the snapshot image
'''
# Import inside function to limit scope
from ij import ImagePlus
# Get the dimensions of the images
cN = imp.getNChannels()
zN = imp.getNSlices()
# fN = imp.getNFrames()
# Convert specified position (c,z,f) to index assuming hyperstack order is czt (default)
sliceIndex = int(cN * zN * (f-1) + cN * (z-1) + c)
imp.setSlice(sliceIndex)
ipTemp = imp.getProcessor()
impTemp = ImagePlus('temp', ipTemp)
# Expand the image by half of L to make sure final duplicate is the same size
# when requested (x,y) could be within L pixels of the edge
wOld = ipTemp.getWidth()
hOld = ipTemp.getHeight()
wNew = wOld + int(L)
hNew = hOld + int(L)
ipNew = ipTemp.createProcessor(wNew, hNew)
ipNew.setColor(0) # 0 = black color
ipNew.insert(ipTemp, int(L/2), int(L/2))
imgNew = ImagePlus('new', ipNew)
# Use the specified center coordinate x, y and the target side length to create ROI
imgNew.setRoi(int(x), int(y), int(L), int(L))
# cropped = ImagePlus('cropped', ipNew.crop())
cropped = imgNew.crop()
impTemp.close()
imgNew.close()
return cropped
def generate_kymograph(data_to_plot, colormap_string, title_string, trim=True):
"""Display one-channel kymograph with point furthest from the edges along the middle of the kymograph """
kym_height = 2 * max([len(data) for data in data_to_plot]) + 1
ip = FloatProcessor(len(data_to_plot), kym_height)
# normalise such that point furthest from the anchors is in the middle of the kymograph
maxy = 0
miny = kym_height
for idx, data in enumerate(data_to_plot):
dist = [
mb.vector_length(data[0][0], p) * mb.vector_length(data[-1][0], p)
for p in [d[0] for d in data]
]
distal_idx = dist.index(max(dist))
pix = ip.getPixels()
for kidx, didx in zip(
range(((kym_height - 1) / 2 + 1),
((kym_height - 1) / 2 + 1) + len(data) - distal_idx),
range(distal_idx, len(data))):
pix[kidx * len(data_to_plot) + idx] = data[didx][1]
for kidx, didx in zip(
range(((kym_height - 1) / 2 + 1) - distal_idx,
((kym_height - 1) / 2 + 1)), range(0, distal_idx)):
pix[kidx * len(data_to_plot) + idx] = data[didx][1]
maxy = ((kym_height - 1) / 2 + 1) + len(data) - distal_idx if (
((kym_height - 1) / 2 + 1) + len(data) -
distal_idx) > maxy else maxy
miny = ((kym_height - 1) / 2 + 1) - distal_idx if ((
(kym_height - 1) / 2 + 1) - distal_idx) < miny else miny
imp = ImagePlus(title_string, ip)
IJ.run(imp, colormap_string, "")
if trim:
maxtomiddle = maxy - (kym_height - 1) / 2 + 1
mintomiddle = (kym_height - 1) / 2 + 1 - miny
if maxtomiddle > mintomiddle:
miny = (kym_height - 1) / 2 + 1 - maxtomiddle
else:
maxy = (kym_height - 1) / 2 + 1 + mintomiddle
if (maxy - miny) / 2 == round((maxy - miny) / 2):
maxy = maxy - 1
imp.setRoi(Roi(0, miny, imp.getWidth(), (maxy - miny)))
imp = imp.crop()
imp.show()
return imp
