import os
import ClearMap.Settings as settings
filename = os.path.join(settings.ClearMapPath,
'Test/Data/ImageAnalysis/cfos-substack.tif')
import ClearMap.Visualization.Plot as plt
import ClearMap.IO as io
data = io.readData(filename, z=(0, 26))
import ClearMap.ImageProcessing.BackgroundRemoval as bgr
dataBGR = bgr.removeBackground(data.astype('float'), size=(3, 3), verbose=True)
from ClearMap.ImageProcessing.Filter.DoGFilter import filterDoG
dataDoG = filterDoG(dataBGR, size=(8, 8, 4), verbose=True)
from ClearMap.ImageProcessing.MaximaDetection import findExtendedMaxima
dataMax = findExtendedMaxima(dataDoG, hMax=None, verbose=True, threshold=10)
from ClearMap.ImageProcessing.MaximaDetection import findCenterOfMaxima
cells = findCenterOfMaxima(data, dataMax)
from ClearMap.ImageProcessing.CellSizeDetection import detectCellShape
dataShape = detectCellShape(dataDoG, cells, threshold=15)
plt.plotOverlayLabel(dataDoG / dataDoG.max(), dataShape, z=(10, 16))
def detectSpots(img,
detectSpotsParameter=None,
correctIlluminationParameter=None,
removeBackgroundParameter=None,
filterDoGParameter=None,
findExtendedMaximaParameter=None,
detectCellShapeParameter=None,
verbose=False,
out=sys.stdout,
**parameter):
"""Detect Cells in 3d grayscale image using DoG filtering and maxima detection
Effectively this function performs the following steps:
* illumination correction via :func:`~ClearMap.ImageProcessing.IlluminationCorrection.correctIllumination`
* background removal via :func:`~ClearMap.ImageProcessing.BackgroundRemoval.removeBackground`
* difference of Gaussians (DoG) filter via :func:`~ClearMap.ImageProcessing.Filter.filterDoG`
* maxima detection via :func:`~ClearMap.ImageProcessing.MaximaDetection.findExtendedMaxima`
* cell shape detection via :func:`~ClearMap.ImageProcessing.CellSizeDetection.detectCellShape`
* cell intensity and size measurements via: :func:`~ClearMap.ImageProcessing.CellSizeDetection.findCellIntensity`,
:func:`~ClearMap.ImageProcessing.CellSizeDetection.findCellSize`.
Note:
Processing steps are done in place to save memory.
Arguments:
img (array): image data
detectSpotParameter: image processing parameter as described in the individual sub-routines
verbose (bool): print progress information
out (object): object to print progress information to
Returns:
tuple: tuple of arrays (cell coordinates, raw intensity, fully filtered intensty, illumination and background corrected intensity [, cell size])
"""
timer = Timer()
# correct illumination
correctIlluminationParameter = getParameter(
detectSpotsParameter, "correctIlluminationParameter",
correctIlluminationParameter)
img1 = img.copy()
img1 = correctIllumination(
img1,
correctIlluminationParameter=correctIlluminationParameter,
verbose=verbose,
out=out,
**parameter)
# background subtraction in each slice
#img2 = img.copy();
removeBackgroundParameter = getParameter(detectSpotsParameter,
"removeBackgroundParameter",
removeBackgroundParameter)
img2 = removeBackground(
img1,
removeBackgroundParameter=removeBackgroundParameter,
verbose=verbose,
out=out,
**parameter)
#DoG filter
filterDoGParameter = getParameter(detectSpotsParameter,
"filterDoGParameter", filterDoGParameter)
dogSize = getParameter(filterDoGParameter, "size", None)
#img3 = img2.copy();
img3 = filterDoG(img2,
filterDoGParameter=filterDoGParameter,
verbose=verbose,
out=out,
**parameter)
# extended maxima
findExtendedMaximaParameter = getParameter(detectSpotsParameter,
"findExtendedMaximaParameter",
findExtendedMaximaParameter)
hMax = getParameter(findExtendedMaximaParameter, "hMax", None)
imgmax = findExtendedMaxima(
img3,
findExtendedMaximaParameter=findExtendedMaximaParameter,
verbose=verbose,
out=out,
**parameter)
#center of maxima
if not hMax is None:
centers = findCenterOfMaxima(img,
imgmax,
verbose=verbose,
out=out,
**parameter)
else:
centers = findPixelCoordinates(imgmax,
verbose=verbose,
out=out,
**parameter)
#cell size detection
detectCellShapeParameter = getParameter(detectSpotsParameter,
"detectCellShapeParameter",
detectCellShapeParameter)
cellShapeThreshold = getParameter(detectCellShapeParameter, "threshold",
None)
if not cellShapeThreshold is None:
# cell shape via watershed
imgshape = detectCellShape(
img2,
centers,
detectCellShapeParameter=detectCellShapeParameter,
verbose=verbose,
out=out,
**parameter)
#size of cells
csize = findCellSize(imgshape,
maxLabel=centers.shape[0],
out=out,
**parameter)
#intensity of cells
cintensity = findCellIntensity(img,
imgshape,
maxLabel=centers.shape[0],
verbose=verbose,
out=out,
**parameter)
#intensity of cells in background image
cintensity2 = findCellIntensity(img2,
imgshape,
maxLabel=centers.shape[0],
verbose=verbose,
out=out,
**parameter)
#intensity of cells in dog filtered image
if dogSize is None:
cintensity3 = cintensity2
else:
cintensity3 = findCellIntensity(img3,
imgshape,
maxLabel=centers.shape[0],
verbose=verbose,
out=out,
**parameter)
if verbose:
out.write(timer.elapsedTime(head="Spot Detection") + "\n")
#remove cell;s of size 0
idz = csize > 0
return (centers[idz],
numpy.vstack((cintensity[idz], cintensity3[idz],
cintensity2[idz], csize[idz])).transpose())
else:
#intensity of cells
cintensity = findIntensity(img,
centers,
verbose=verbose,
out=out,
**parameter)
#intensity of cells in background image
cintensity2 = findIntensity(img2,
centers,
verbose=verbose,
out=out,
**parameter)
#intensity of cells in dog filtered image
if dogSize is None:
cintensity3 = cintensity2
else:
cintensity3 = findIntensity(img3,
centers,
verbose=verbose,
out=out,
**parameter)
if verbose:
out.write(timer.elapsedTime(head="Spot Detection") + "\n")
return (centers, numpy.vstack(
(cintensity, cintensity3, cintensity2)).transpose())
def detectSpots(img,
detectSpotsParameter=None,
correctIlluminationParameter=None,
removeBackgroundParameter=None,
filterDoGParameter=None,
findExtendedMaximaParameter=None,
detectCellShapeParameter=None,
verbose=False,
out=sys.stdout,
**parameter):
"""Detect Cells in 3d grayscale image using DoG filtering and maxima detection
Effectively this function performs the following steps:
* illumination correction via :func:`~ClearMap.ImageProcessing.IlluminationCorrection.correctIllumination`
* background removal via :func:`~ClearMap.ImageProcessing.BackgroundRemoval.removeBackground`
* difference of Gaussians (DoG) filter via :func:`~ClearMap.ImageProcessing.Filter.filterDoG`
* maxima detection via :func:`~ClearMap.ImageProcessing.MaximaDetection.findExtendedMaxima`
* cell shape detection via :func:`~ClearMap.ImageProcessing.CellSizeDetection.detectCellShape`
* cell intensity and size measurements via: :func:`~ClearMap.ImageProcessing.CellSizeDetection.findCellIntensity`,
:func:`~ClearMap.ImageProcessing.CellSizeDetection.findCellSize`.
detectCells
Note:
Processing steps are done in place to save memory.
Arguments:
img (array): image data
detectSpotParameter: image processing parameter as described in the individual sub-routines
verbose (bool): print progress information
out (object): object to print progress information to
Returns:
tuple: tuple of arrays (cell coordinates, raw intensity, fully filtered intensty, illumination and background corrected intensity [, cell size])
"""
timer = Timer()
# normalize data -> to check
#img = img.astype('float');
#dmax = 0.075 * 65535;
#ids = img > dmax;
#img[ids] = dmax;
#img /= dmax;
#out.write(timer.elapsedTime(head = 'Normalization'));
#img = dataset[600:1000,1600:1800,800:830];
#img = dataset[600:1000,:,800:830];
# correct illumination
correctIlluminationParameter = getParameter(
detectSpotsParameter, "correctIlluminationParameter",
correctIlluminationParameter)
img1 = img.copy()
img1 = correctIllumination(
img1,
correctIlluminationParameter=correctIlluminationParameter,
verbose=verbose,
out=out,
**parameter)
# background subtraction in each slice
#img2 = img.copy();
removeBackgroundParameter = getParameter(detectSpotsParameter,
"removeBackgroundParameter",
removeBackgroundParameter)
img2 = removeBackground(
img1,
removeBackgroundParameter=removeBackgroundParameter,
verbose=verbose,
out=out,
**parameter)
# mask
#timer.reset();
#if mask == None: #explicit mask
# mask = img > 0.01;
# mask = binary_opening(mask, self.structureELement('Disk', (3,3,3)));
#img[img < 0.01] = 0; # masking in place # extended maxima
#out.write(timer.elapsedTime(head = 'Mask'));
#DoG filter
filterDoGParameter = getParameter(detectSpotsParameter,
"filterDoGParameter", filterDoGParameter)
dogSize = getParameter(filterDoGParameter, "size", None)
#img3 = img2.copy();
img3 = filterDoG(img2,
filterDoGParameter=filterDoGParameter,
verbose=verbose,
out=out,
**parameter)
# normalize
# imax = img.max();
# if imax == 0:
# imax = 1;
# img /= imax;
# extended maxima
findExtendedMaximaParameter = getParameter(detectSpotsParameter,
"findExtendedMaximaParameter",
findExtendedMaximaParameter)
hMax = getParameter(findExtendedMaximaParameter, "hMax", None)
imgmax = findExtendedMaxima(
img3,
findExtendedMaximaParameter=findExtendedMaximaParameter,
verbose=verbose,
out=out,
**parameter)
#center of maxima
if not hMax is None:
centers = findCenterOfMaxima(img,
imgmax,
verbose=verbose,
out=out,
**parameter)
else:
centers = findPixelCoordinates(imgmax,
verbose=verbose,
out=out,
**parameter)
#cell size detection
detectCellShapeParameter = getParameter(detectSpotsParameter,
"detectCellShapeParameter",
detectCellShapeParameter)
cellShapeThreshold = getParameter(detectCellShapeParameter, "threshold",
None)
if not cellShapeThreshold is None:
# cell shape via watershed
imgshape = detectCellShape(
img2,
centers,
detectCellShapeParameter=detectCellShapeParameter,
verbose=verbose,
out=out,
**parameter)
#size of cells
csize = findCellSize(imgshape,
maxLabel=centers.shape[0],
out=out,
**parameter)
#intensity of cells
cintensity = findCellIntensity(img,
imgshape,
maxLabel=centers.shape[0],
verbose=verbose,
out=out,
**parameter)
#intensity of cells in background image
cintensity2 = findCellIntensity(img2,
imgshape,
maxLabel=centers.shape[0],
verbose=verbose,
out=out,
**parameter)
#intensity of cells in dog filtered image
if dogSize is None:
cintensity3 = cintensity2
else:
cintensity3 = findCellIntensity(img3,
imgshape,
maxLabel=centers.shape[0],
verbose=verbose,
out=out,
**parameter)
if verbose:
out.write(timer.elapsedTime(head='Spot Detection') + '\n')
#remove cell;s of size 0
idz = csize > 0
return (centers[idz],
numpy.vstack((cintensity[idz], cintensity3[idz],
cintensity2[idz], csize[idz])).transpose())
else:
#intensity of cells
cintensity = findIntensity(img,
centers,
verbose=verbose,
out=out,
**parameter)
#intensity of cells in background image
cintensity2 = findIntensity(img2,
centers,
verbose=verbose,
out=out,
**parameter)
#intensity of cells in dog filtered image
if dogSize is None:
cintensity3 = cintensity2
else:
cintensity3 = findIntensity(img3,
centers,
verbose=verbose,
out=out,
**parameter)
if verbose:
out.write(timer.elapsedTime(head='Spot Detection') + '\n')
return (centers, numpy.vstack(
(cintensity, cintensity3, cintensity2)).transpose())
img = io.readData(cfos_fn)
img = img[..., numpy.newaxis]
img = img.astype('int16')
# converting data to smaller integer types can be more memory efficient!
imgB = bgr.removeBackground(img, size=backgroundSize, verbose=True)
io.writeData(os.path.join(resultDir, fName + '_BackgroundRemoval.tif'),
imgB)
##image filter
imgD = filterDoG(img, size=DoGSize, verbose=True)
io.writeData(os.path.join(resultDir, fName + '_FilterDoG.tif'), imgD)
#Detect Maxima
imgMaxima = findExtendedMaxima(img,
hMax=None,
verbose=True,
threshold=maximaThresh)
points = findCenterOfMaxima(img, imgMaxima, verbose=True)
points = points.astype('int16')
#threshold intensities
dataShape = detectCellShape(imgD,
points,
threshold=cellShapeThresh,
verbose=True)
cellSizesPre = findCellSize(dataShape, maxlabel=points.shape[0])
io.writeData(os.path.join(resultDir, fName + '_CellShapes.tif'),
255 * dataShape.astype('int32') / dataShape.max())
#cintensity = findIntensity(imgD, points);
import os
import ClearMap.Settings as settings
filename = os.path.join(settings.ClearMapPath, "Test/Data/ImageAnalysis/cfos-substack.tif")
import ClearMap.Visualization.Plot as plt
import ClearMap.IO as io
data = io.readData(filename, z=(0, 26))
import ClearMap.ImageProcessing.BackgroundRemoval as bgr
dataBGR = bgr.removeBackground(data.astype("float"), size=(3, 3), verbose=True)
from ClearMap.ImageProcessing.Filter.DoGFilter import filterDoG
dataDoG = filterDoG(dataBGR, size=(8, 8, 4), verbose=True)
from ClearMap.ImageProcessing.MaximaDetection import findExtendedMaxima
dataMax = findExtendedMaxima(dataDoG, hMax=None, verbose=True, threshold=10)
from ClearMap.ImageProcessing.MaximaDetection import findCenterOfMaxima
cells = findCenterOfMaxima(data, dataMax)
plt.plotOverlayPoints(data, cells, z=(10, 16))
def detectSpots(img, detectSpotsParameter = None, correctIlluminationParameter = None, removeBackgroundParameter = None,
filterDoGParameter = None, findExtendedMaximaParameter = None, detectCellShapeParameter = None,
verbose = False, out = sys.stdout, **parameter):
"""Detect Cells in 3d grayscale image using DoG filtering and maxima detection
Effectively this function performs the following steps:
* illumination correction via :func:`~ClearMap.ImageProcessing.IlluminationCorrection.correctIllumination`
* background removal via :func:`~ClearMap.ImageProcessing.BackgroundRemoval.removeBackground`
* difference of Gaussians (DoG) filter via :func:`~ClearMap.ImageProcessing.Filter.filterDoG`
* maxima detection via :func:`~ClearMap.ImageProcessing.MaximaDetection.findExtendedMaxima`
* cell shape detection via :func:`~ClearMap.ImageProcessing.CellSizeDetection.detectCellShape`
* cell intensity and size measurements via: :func:`~ClearMap.ImageProcessing.CellSizeDetection.findCellIntensity`,
:func:`~ClearMap.ImageProcessing.CellSizeDetection.findCellSize`.
Note:
Processing steps are done in place to save memory.
Arguments:
img (array): image data
detectSpotParameter: image processing parameter as described in the individual sub-routines
verbose (bool): print progress information
out (object): object to print progress information to
Returns:
tuple: tuple of arrays (cell coordinates, raw intensity, fully filtered intensty, illumination and background corrected intensity [, cell size])
"""
timer = Timer();
# normalize data -> to check
#img = img.astype('float');
#dmax = 0.075 * 65535;
#ids = img > dmax;
#img[ids] = dmax;
#img /= dmax;
#out.write(timer.elapsedTime(head = 'Normalization'));
#img = dataset[600:1000,1600:1800,800:830];
#img = dataset[600:1000,:,800:830];
# correct illumination
correctIlluminationParameter = getParameter(detectSpotsParameter, "correctIlluminationParameter", correctIlluminationParameter);
img1 = img.copy();
img1 = correctIllumination(img1, correctIlluminationParameter = correctIlluminationParameter, verbose = verbose, out = out, **parameter)
# background subtraction in each slice
#img2 = img.copy();
removeBackgroundParameter = getParameter(detectSpotsParameter, "removeBackgroundParameter", removeBackgroundParameter);
img2 = removeBackground(img1, removeBackgroundParameter = removeBackgroundParameter, verbose = verbose, out = out, **parameter)
# mask
#timer.reset();
#if mask == None: #explicit mask
# mask = img > 0.01;
# mask = binary_opening(mask, self.structureELement('Disk', (3,3,3)));
#img[img < 0.01] = 0; # masking in place # extended maxima
#out.write(timer.elapsedTime(head = 'Mask'));
#DoG filter
filterDoGParameter = getParameter(detectSpotsParameter, "filterDoGParameter", filterDoGParameter);
dogSize = getParameter(filterDoGParameter, "size", None);
#img3 = img2.copy();
img3 = filterDoG(img2, filterDoGParameter = filterDoGParameter, verbose = verbose, out = out, **parameter);
# normalize
# imax = img.max();
# if imax == 0:
# imax = 1;
# img /= imax;
# extended maxima
findExtendedMaximaParameter = getParameter(detectSpotsParameter, "findExtendedMaximaParameter", findExtendedMaximaParameter);
hMax = getParameter(findExtendedMaximaParameter, "hMax", None);
imgmax = findExtendedMaxima(img3, findExtendedMaximaParameter = findExtendedMaximaParameter, verbose = verbose, out = out, **parameter);
#center of maxima
if not hMax is None:
centers = findCenterOfMaxima(img, imgmax, verbose = verbose, out = out, **parameter);
else:
centers = findPixelCoordinates(imgmax, verbose = verbose, out = out, **parameter);
#cell size detection
detectCellShapeParameter = getParameter(detectSpotsParameter, "detectCellShapeParameter", detectCellShapeParameter);
cellShapeThreshold = getParameter(detectCellShapeParameter, "threshold", None);
if not cellShapeThreshold is None:
# cell shape via watershed
imgshape = detectCellShape(img2, centers, detectCellShapeParameter = detectCellShapeParameter, verbose = verbose, out = out, **parameter);
#size of cells
csize = findCellSize(imgshape, maxLabel = centers.shape[0], out = out, **parameter);
#intensity of cells
cintensity = findCellIntensity(img, imgshape, maxLabel = centers.shape[0], verbose = verbose, out = out, **parameter);
#intensity of cells in background image
cintensity2 = findCellIntensity(img2, imgshape, maxLabel = centers.shape[0], verbose = verbose, out = out, **parameter);
#intensity of cells in dog filtered image
if dogSize is None:
cintensity3 = cintensity2;
else:
cintensity3 = findCellIntensity(img3, imgshape, maxLabel = centers.shape[0], verbose = verbose, out = out, **parameter);
if verbose:
out.write(timer.elapsedTime(head = 'Spot Detection') + '\n');
#remove cell;s of size 0
idz = csize > 0;
return ( centers[idz], numpy.vstack((cintensity[idz], cintensity3[idz], cintensity2[idz], csize[idz])).transpose());
else:
#intensity of cells
cintensity = findIntensity(img, centers, verbose = verbose, out = out, **parameter);
#intensity of cells in background image
cintensity2 = findIntensity(img2, centers, verbose = verbose, out = out, **parameter);
#intensity of cells in dog filtered image
if dogSize is None:
cintensity3 = cintensity2;
else:
cintensity3 = findIntensity(img3, centers, verbose = verbose, out = out, **parameter);
if verbose:
out.write(timer.elapsedTime(head = 'Spot Detection') + '\n');
return ( centers, numpy.vstack((cintensity, cintensity3, cintensity2)).transpose());