def to8bitRange(values):
# Ensure the value is inside [minimum, maximum] range, then rezero by subtracting minimum, and divide by span (maximum - minimum)
return [
UnsignedByteType(
int(((min(max(val, minimum), maximum) - minimum) / span) *
range_max)) for val in values
]
def SpotDetectionRedFromHue(hue, data, ops, display):
# get the dimensions
dimensions2D=array( [hue.dimension(0), hue.dimension(1)], 'l')
shift=UnsignedByteType()
shift.setReal(-50)
hue_shift=ops.add(hue, shift)
display.createDisplay("shift", data.create(ImgPlus(hue_shift)))
#test1=ops.op("otsu", hue_shift)
manualthreshold=Manual()
# apply the threshold operation
thresholded=ops.run("threshold", ops.create( dimensions2D, BitType()), hue_shift, manualthreshold)
#thresholded=ops.run("threshold", ops.create( dimensions2D, BitType()), hue_shift, Otsu())
return thresholded
def createType(bytesPerPixel):
if 1:
return UnsignedByteType()
if 2:
return UnsignedShortType()
if 4:
return FloatType()
if 8:
return UnsignedLongType()
def SpotDetectionRedFromHue(hue, data, ops, display):
# get the dimensions
dimensions2D = array([hue.dimension(0), hue.dimension(1)], 'l')
shift = UnsignedByteType()
shift.setReal(-50)
hue_shift = ops.add(hue, shift)
display.createDisplay("shift", data.create(ImgPlus(hue_shift)))
#test1=ops.op("otsu", hue_shift)
manualthreshold = Manual()
# apply the threshold operation
thresholded = ops.run("threshold", ops.create(dimensions2D, BitType()),
hue_shift, manualthreshold)
#thresholded=ops.run("threshold", ops.create( dimensions2D, BitType()), hue_shift, Otsu())
return thresholded
def rescale_uint8(ops, img):
"""
Rescale input image (imglib2) to full uint8 range
"""
scale_op = NormalizeScaleRealTypes()
scale_op.setEnvironment(ops)
scale_op.initialize()
out_uint8 = ArrayImgFactory(UnsignedByteType()).create(img)
ops.convert().imageType(out_uint8, img, scale_op)
return out_uint8
class MandelbrotRealRandomAccess(RealRandomAccess):
def __init__(self):
self.t = UnsignedByteType()
self.pos = zeros(2, 'd')
def get(self):
self.t.set(M.mandelbrot(self.pos[0], self.pos[1], 255))
return self.t
def copyRealRandomAccess(self):
return self.copy()
def copy(self):
a = MandelbrotRealRandomAccess()
a.pos[0] = self.pos[0]
a.pos[1] = self.pos[1]
return a
def localize(self, position):
position[0] = self.pos[0]
position[1] = self.pos[1]
def getFloatPosition(self, d):
return Float(self.pos[d])
def getDoublePosition(self, d):
return self.pos[d]
def move(self, distance, d):
pass # many overlapping homonimous methods
def setPosition(self, position):
# ignore RealLozalizable instances of "position"
self.pos[0] = position[0]
self.pos[1] = position[1]
def setPosition(self, position, d):
self.pos[d] = position
def testRectangle(self):
# create a test image with a filled rectangle
testImg = ops.run("create.img", [256, 256], UnsignedByteType())
formula = "(p[0]>10 && p[0]<110 && p[1]>10 && p[1]<110)*255"
ops.image().equation(testImg, formula)
edgesImage = findEdges(testImg);
ra = edgesImage.randomAccess()
ra.setPosition([9, 9])
v1 = ra.get().get()
ra.setPosition([10, 10])
v2 = ra.get().get()
ra.setPosition([11,11])
v3 = ra.get().get()
ra.setPosition([12,12])
v4 = ra.get().get()
self.assertEquals(v1, 0)
self.assertEquals(v2, 64)
self.assertEquals(v3, 255)
self.assertEquals(v4, 0)
from net.imglib2.img.array import ArrayImgFactory
from net.imglib2.type.numeric.integer import UnsignedByteType, UnsignedShortType
from net.imglib2.util import Intervals
# An 8-bit 256x256x256 volume
img = ArrayImgFactory(UnsignedByteType()).create([256, 256, 256])
# Another image of the same type and dimensions, but empty
img2 = img.factory().create(
[img.dimension(d) for d in xrange(img.numDimensions())])
# Same, but easier reading of the image dimensions
img3 = img.factory().create(Intervals.dimensionsAsLongArray(img))
# Same, but use an existing img as an Interval from which to read out the dimensions
img4 = img.factory().create(img)
# Now we change the type: same kind of image and same dimensions,
# but crucially a different pixel type (16-bit) via a new ImgFactory
imgShorts = img.factory().imgFactory(UnsignedShortType()).create(img)
from net.imglib2.algorithm.math.ImgMath import compute, gen
from net.imglib2.img.display.imagej import ImageJFunctions as IL
from net.imglib2.type.numeric.integer import UnsignedByteType
from net.imglib2.img.array import ArrayImgs
from net.imglib2.util import Intervals
from net.imglib2 import KDTree, Point
locations = [(10, 15), (25, 40), (30, 75), (80, 60)]
points = [
Point.wrap([x, y]) for x, y in [(10, 15), (25, 40), (30, 75), (80, 60)]
]
values = [UnsignedByteType(v) for v in [128, 164, 200, 255]]
kt = KDTree(values, points)
dimensions = [100, 100]
op = gen(kt, 10)
target = ArrayImgs.unsignedBytes(
Intervals.dimensionsAsLongArray(op.getInterval()))
compute(op).into(target)
IL.wrap(target, "KDTree").show()
def __init__(self):
self.t = UnsignedByteType()
self.pos = zeros(2, 'd')
def intoImg(pixels):
img = ArrayImgFactory(UnsignedByteType()).create([4, 4])
System.arraycopy(pixels, 0,
img.update(None).getCurrentStorageArray(), 0, len(pixels))
return img
table.addValue("y", p[1])
table.addValue("sum", s)
table.show("Embryo intensities at peaks")
# Now show an image with white circles for every embryo found
# We'll use a sparse image: an image that doesn't have a data point for each pixel
# but instead has a function for generating pixel data from the x,y coordinates of each embryo
from net.imglib2 import KDTree, RealPoint, RealRandomAccess, RealRandomAccessible
from net.imglib2.neighborsearch import NearestNeighborSearchOnKDTree
from net.imglib2.img.display.imagej import ImageJFunctions as IL
from net.imglib2.type.numeric.integer import UnsignedByteType
from java.util import AbstractList
inside = UnsignedByteType(255)
outside = UnsignedByteType(0)
radius = 30 # = sigmaLarger, where sigmaLarger is the radius of the embryo
centers = dog.getSubpixelPeaks() # in floating-point coordinates
class ConstantValueList(AbstractList):
def __init__(self, constantvalue, count):
self.constantvalue = constantvalue
self.count = count
def get(self, index):
return self.constantvalue
def size(self):
return self.count
# Make any non-zero pixel be 1
for t in img1:
if 0 != t.getIntegerLong():
t.setOne()
for t in img2:
if 0 != t.getIntegerLong():
t.setOne()
# Make both fit within the same window, centered
dims1 = Intervals.dimensionsAsLongArray(img1)
dims2 = Intervals.dimensionsAsLongArray(img2)
dims3 = [max(a, b) for a, b in izip(dims1, dims2)]
zero = UnsignedByteType(0)
img1E = Views.extendValue(img1, zero)
img2E = Views.extendValue(img2, zero)
img1M = Views.interval(
img1E, [(dim1 - dim3) / 2 for dim1, dim3 in izip(dims1, dims3)],
[dim1 + (dim3 - dim1) / 2 - 1 for dim1, dim3 in izip(dims1, dims3)])
img2M = Views.interval(
img2E, [(dim2 - dim3) / 2 for dim2, dim3 in izip(dims2, dims3)],
[dim2 + (dim3 - dim2) / 2 - 1 for dim2, dim3 in izip(dims2, dims3)])
IL.show(img1M, "img1M")
IL.show(img2M, "img2M")
# Scale by half (too slow otherwise) -- ERROR: the smaller one (img1) doesn't remain centered.
class Loader(ClassLoader):
def defineClass(self, name, bytes):
# Inheritance of protected methods is complicated in jython
m = super(ClassLoader, self).__thisclass__.getDeclaredMethod("defineClass", String, Class.forName("[B"), Integer.TYPE, Integer.TYPE)
m.setAccessible(True)
return m.invoke(self, name.replace("/", "."), bytes, 0, len(bytes))
clazz = Loader().defineClass(class_name, cw.toByteArray())
"""
loader = ClassLoader.getSystemClassLoader()
defineClass = loader.getClass().getSuperclass().getSuperclass().getSuperclass().getDeclaredMethod("defineClass", String, Class.forName("[B"), Integer.TYPE, Integer.TYPE)
defineClass.setAccessible(True)
bytes = cw.toByteArray()
clazz = defineClass.invoke(loader, class_name.replace("/", "."), bytes, 0, len(bytes))
"""
bt = UnsignedByteType(120)
ft = FloatType()
conv = clazz.newInstance()
conv.convert(bt, ft)
print ft
width = img.dimension(0)
min_width = 32
imgE = Views.extendBorder(integralImg)
blockSide = 1
# Corners for level 1: a box of 2x2
corners = [[0, 0], [1, 0], [0, 1], [1, 1]]
pyramid = [] # level 0 is the image itself, not added
while width > min_width:
blockSide *= 2
width /= 2
# Scale the corner coordinates to make the block larger
cs = [[c * blockSide for c in corner] for corner in corners]
blockRead = div(block(imgE, cs), pow(blockSide, 2)) # the op
# a RandomAccessibleInterval view of the op, computed with shorts but seen as bytes
view = blockRead.view(UnsignedShortType(), UnsignedByteType())
# Views.subsample by 2 will turn a 512-pixel width to a 257 width,
# so crop to proper interval 256
level = Views.interval(
Views.subsample(view, blockSide),
[0] * img.numDimensions(), # min
[
img.dimension(d) / blockSide - 1
for d in xrange(img.numDimensions())
]) # max
pyramid.append(level)
"""
for i, level in enumerate(pyramid):
imp_level = IL.wrap(level, str(i+1))
imp_level.show()
win = imp.getWindow().getCanvas().
from net.imglib2.img.display.imagej import ImageJFunctions as IL
from net.imglib2.img.array import ArrayImg
from net.imglib2.img.basictypeaccess import ByteAccess
from net.imglib2.type.numeric.integer import UnsignedByteType
from math import sin, cos, pi
# Example 1: a virtual blank image. No pixel array backing.
t = UnsignedByteType()
img = ArrayImg(
type('ConstantValue', (ByteAccess, ), {
'getValue': lambda self, index: 255
})(), [512, 512], t.getEntitiesPerPixel())
img.setLinkedType(t.getNativeTypeFactory().createLinkedType(img))
#IL.wrap(img, "white").show()
# Example 1b: a virtual blank image, with the ByteAccess unpacked into a class
class ConstantValue(ByteAccess):
def __init__(self, value):
self.value = value
def getValue(self, index):
return self.value
def setValue(self, index, value):
pass
t = UnsignedByteType()
data = ConstantValue(255)
from net.imglib2.view import Views
from net.imglib2.util import Intervals
from net.imglib2.view import IterableRandomAccessibleInterval
from net.imagej.ops.stats import IterableMax
from net.imglib2.algorithm.neighborhood import RectangleShape
try:
os.unlink(output_path)
except OSError:
pass
input = scifio.datasetIO().open(input_path)
ui.show(input)
# add constant
input = ops.math().add(input, UnsignedByteType(100))
ui.show(input)
# normalization
normalized = ops.create().img(input)
ops.image().normalize(normalized, input)
ui.show(normalized)
# convolution
kernel = ops.run("create.img", [3, 3], FloatType())
for p in kernel:
p.set(1.0 / kernel.size())
convoluted = ops.create().img(normalized)
ops.filter().convolve(convoluted, Views.extendMirrorSingle(normalized), kernel)
ui.show(convoluted)
#@ DatasetService ds
"""
ops threshold types here:
https://github.com/imagej/imagej-ops/tree/master/src/main/java/net/imagej/ops/threshold
"""
# Apply an automatic threshold from a given method. The threshold value 'threshold_value'
# can be modulated by a relative parameter called 'relative_threshold' (if equal to 1 it does
# not modify 'threshold_value')
from net.imglib2.type.numeric.integer import UnsignedByteType
# Get the histogram
histo = ops.run("image.histogram", data)
# Get the threshold
threshold_value = ops.run("threshold.%s" % method_threshold, histo)
# Modulate 'threshold_value' by 'relative_threshold'
threshold_value = int(round(threshold_value.get() * relative_threshold))
# We should not have to do that...
threshold_value = UnsignedByteType(threshold_value)
# Apply the threshold
thresholded = ops.run("threshold.apply", data, threshold_value)
# Create output Dataset
output = ds.create(thresholded)
#@ OpService ops
#@ SCIFIO scifio
#@ String input_path
#@ String output_path
#@ UIService ui
#@ DatasetService datasets
import os
from net.imglib2.type.numeric.integer import UnsignedByteType
from net.imglib2.type.logic import BitType
from net.imglib2.view import Views
try:
os.unlink(output_path)
except OSError:
pass
input = scifio.datasetIO().open(input_path)
output = ops.create().img(input, BitType())
threshold = UnsignedByteType(128)
ops.threshold().apply(output, input, threshold)
output8bit = ops.convert().uint8(output)
output8bit = ops.math().multiply(output8bit, UnsignedByteType(255))
scifio.datasetIO().save(datasets.create(output8bit), output_path)
ui.show(output8bit)
print("OK")
