def removeParticles(fp, removeList, comm, size, rank):
[row, col, numFrames, frameList] = misc.getVitals(fp)
particleList = fp.attrs['particleList']
zfillVal = fp.attrs['zfillVal']
procFrameList = numpy.array_split(frameList, size)
for frame in procFrameList[rank]:
labelImg = fp['/segmentation/labelStack/' +
str(frame).zfill(zfillVal)].value
for r in removeList:
labelImg[labelImg == r] = 0
numpy.save(str(frame).zfill(zfillVal) + '.npy', labelImg)
comm.Barrier()
if (rank == 0):
for frame in frameList:
labelImg = numpy.load(str(frame).zfill(zfillVal) + '.npy')
fileIO.writeH5Dataset(
fp, '/segmentation/labelStack/' + str(frame).zfill(zfillVal),
labelImg)
fileIO.delete(str(frame).zfill(zfillVal) + '.npy')
for r in removeList:
fp.attrs['particleList'] = numpy.delete(
fp.attrs['particleList'],
numpy.where(fp.attrs['particleList'] == r))
comm.Barrier()
return 0
def globalRelabelParticles(fp, correctionList, comm, size, rank):
[row, col, numFrames, frameList] = misc.getVitals(fp)
particleList = fp.attrs['particleList']
zfillVal = fp.attrs['zfillVal']
procFrameList = numpy.array_split(frameList, size)
for frame in procFrameList[rank]:
labelImg = fp['/segmentation/labelStack/' +
str(frame).zfill(zfillVal)].value
for i in range(len(correctionList)):
for j in range(len(correctionList[i]) - 1):
labelImg[labelImg == correctionList[i]
[j]] = correctionList[i][-1]
numpy.save(str(frame).zfill(zfillVal) + '.npy', labelImg)
comm.Barrier()
if (rank == 0):
for frame in frameList:
labelImg = numpy.load(str(frame).zfill(zfillVal) + '.npy')
fileIO.writeH5Dataset(
fp, '/segmentation/labelStack/' + str(frame).zfill(zfillVal),
labelImg)
fileIO.delete(str(frame).zfill(zfillVal) + '.npy')
particleInFrame = numpy.unique(labelImg)[1:]
if (frame == frameList[0]):
particleList = particleInFrame.copy()
else:
particleList = numpy.unique(
numpy.append(particleList, particleInFrame))
fp.attrs['particleList'] = particleList
comm.Barrier()
return 0
def relabelParticles(fp, comm, size, rank):
[row, col, numFrames, frameList] = misc.getVitals(fp)
particleList = fp.attrs['particleList']
zfillVal = fp.attrs['zfillVal']
procFrameList = numpy.array_split(frameList, size)
maxLabel = numpy.max(particleList) + 1
counter = 1
newLabels = {}
for particle in particleList:
newLabels[particle] = []
for frame in frameList:
particlesInFrame = numpy.unique(
fp['/segmentation/labelStack/' +
str(frame).zfill(zfillVal)].value)[1:]
for p in particlesInFrame:
if not newLabels[p]:
newLabels[p] = [maxLabel, counter]
maxLabel += 1
counter += 1
for frame in procFrameList[rank]:
labelImg = fp['/segmentation/labelStack/' +
str(frame).zfill(zfillVal)].value
for key in newLabels.keys():
labelImg[labelImg == key] = newLabels[key][0]
for key in newLabels.keys():
labelImg[labelImg == newLabels[key][0]] = newLabels[key][1]
numpy.save(str(frame).zfill(zfillVal) + '.npy', labelImg)
comm.Barrier()
if (rank == 0):
for frame in frameList:
labelImg = numpy.load(str(frame).zfill(zfillVal) + '.npy')
if (counter < 256):
labelImg = labelImg.astype('uint8')
elif (counter < 65536):
labelImg = labelImg.astype('uint16')
else:
labelImg = labelImg.astype('uint32')
fileIO.writeH5Dataset(
fp, '/segmentation/labelStack/' + str(frame).zfill(zfillVal),
labelImg)
fileIO.delete(str(frame).zfill(zfillVal) + '.npy')
particleInFrame = numpy.unique(labelImg)[1:]
if (frame == frameList[0]):
particleList = particleInFrame.copy()
else:
particleList = numpy.unique(
numpy.append(particleList, particleInFrame))
fp.attrs['particleList'] = particleList
comm.Barrier()
return 0
def framewiseRelabelParticles(fp, frameWiseCorrectionList, comm, size, rank):
[row, col, numFrames, frameList] = misc.getVitals(fp)
particleList = fp.attrs['particleList']
zfillVal = fp.attrs['zfillVal']
procFrameList = numpy.array_split(frameList, size)
for frame in procFrameList[rank]:
labelImg = fp['/segmentation/labelStack/' +
str(frame).zfill(zfillVal)].value
for frameWiseCorrection in frameWiseCorrectionList:
subFrameList, subCorrectionList = frameWiseCorrection[
0], frameWiseCorrection[1]
for subFrame in subFrameList:
if (frame == subFrame):
newLabel = subCorrectionList[-1]
for oldLabel in subCorrectionList[:-1]:
labelImg[labelImg == oldLabel] = newLabel
numpy.save(str(frame).zfill(zfillVal) + '.npy', labelImg)
comm.Barrier()
if (rank == 0):
for frame in frameList:
labelImg = numpy.load(str(frame).zfill(zfillVal) + '.npy')
fileIO.writeH5Dataset(
fp, '/segmentation/labelStack/' + str(frame).zfill(zfillVal),
labelImg)
fileIO.delete(str(frame).zfill(zfillVal) + '.npy')
particleInFrame = numpy.unique(labelImg)[1:]
if (frame == frameList[0]):
particleList = particleInFrame.copy()
else:
particleList = numpy.unique(
numpy.append(particleList, particleInFrame))
fp.attrs['particleList'] = particleList
comm.Barrier()
return 0
#########
# PART 1
#########
if (rank == 0):
fp = fileIO.createH5(outputFile)
print "Reading raw images, writing to hdf5 file, and saving images"
fileIO.mkdirs(outputDir)
frameList = range(0, 23686)
numFrames = len(frameList) #frameList[-1]
for frame in tqdm(frameList):
gImg = cv2.imread(
inputDir + '/14-04-33.711_' + str(frame).zfill(0) + '.tif', 0)
gImg = gImg[200:200 + 1800, 70:70 + 1800]
fileIO.writeH5Dataset(
fp,
'/dataProcessing/gImgRawStack/' + str(frame + 1).zfill(zfillVal),
gImg)
cv2.imwrite(
outputDir + '/dataProcessing/gImgRawStack/' +
str(frame + 1).zfill(zfillVal) + '.png', gImg)
[row, col] = gImg.shape
fp.attrs['inputFile'] = inputFile
fp.attrs['outputFile'] = outputFile
fp.attrs['inputDir'] = inputDir
fp.attrs['outputDir'] = outputDir
fp.attrs['pixInNM'] = pixInNM
fp.attrs['pixInAngstrom'] = pixInNM * 10
fp.attrs['fps'] = fps
fp.attrs['microscope'] = microscope
fp.attrs['camera'] = camera
def labelParticles(fp,
centerDispRange=[5, 5],
perAreaChangeRange=[10, 20],
missFramesTh=10,
structure=[[0, 1, 0], [1, 1, 1], [0, 1, 0]]):
[row, col,
numFrames] = fp.attrs['row'], fp.attrs['col'], fp.attrs['numFrames']
frameList = fp.attrs['frameList']
zfillVal = fp.attrs['zfillVal']
labelStack = numpy.zeros([row, col, numFrames], dtype='uint32')
for frame in frameList:
str1 = str(frame) + '/' + str(frameList[-1])
str2 = '\r' + ' ' * len(str1) + '\r'
sys.stdout.write(str1)
bImg = fp['/segmentation/bImgStack/' +
str(frame).zfill(zfillVal)].value
gImg = fp['/dataProcessing/gImgRawStack/' +
str(frame).zfill(zfillVal)].value
if (frame == frameList[0]):
labelImg_0, numLabel_0, dictionary_0 = imageProcess.regionProps(
bImg, gImg, structure=structure, centroid=True, area=True)
maxID = numLabel_0
occurenceFrameList = [frame] * maxID
dictionary_0['frame'] = []
for i in range(len(dictionary_0['id'])):
dictionary_0['frame'].append(frame)
labelStack[:, :, frame - 1] = labelImg_0
else:
labelImg_1, numLabel_1, dictionary_1 = imageProcess.regionProps(
bImg, gImg, structure=structure, centroid=True, area=True)
if (numLabel_1 > 0):
areaMin = min(dictionary_1['area'])
areaMax = max(dictionary_1['area'])
for i in range(len(dictionary_1['id'])):
flag = 0
bImg_1_LabelN = labelImg_1 == dictionary_1['id'][i]
center_1 = dictionary_1['centroid'][i]
area_1 = dictionary_1['area'][i]
frame_1 = frame
if (areaMax - areaMin > 0):
factor = 1.0 * (area_1 - areaMin) / (areaMax - areaMin)
perAreaChangeTh = perAreaChangeRange[1] - factor * (
perAreaChangeRange[1] - perAreaChangeRange[0])
centerDispTh = centerDispRange[1] - factor * (
centerDispRange[1] - centerDispRange[0])
else:
perAreaChangeTh = perAreaChangeRange[1]
centerDispTh = centerDispRange[1]
closeness, J = 1e10, 0
for j in range(len(dictionary_0['id']) - 1, -1, -1):
center_0 = dictionary_0['centroid'][j]
area_0 = dictionary_0['area'][j]
frame_0 = dictionary_0['frame'][j]
centerDisp = numpy.sqrt((center_1[0] - center_0[0])**2 +
(center_1[1] - center_0[1])**2)
perAreaChange = 100.0 * numpy.abs(area_1 -
area_0) / numpy.maximum(
area_1, area_0)
missFrames = frame_1 - frame_0
if (centerDisp <= centerDispTh):
if (perAreaChange <= perAreaChangeTh):
if (missFrames <= missFramesTh):
if (centerDisp < closeness):
closeness = centerDisp
J = j
flag = 1
if (flag == 1):
labelStack[:, :, frame -
1] += (bImg_1_LabelN *
dictionary_0['id'][J]).astype('uint32')
dictionary_0['centroid'][J] = center_1
dictionary_0['area'][J] = area_1
dictionary_0['frame'][J] = frame
if (flag == 0):
maxID += 1
occurenceFrameList.append(frame)
labelN_1 = bImg_1_LabelN * maxID
labelStack[:, :, frame - 1] += labelN_1.astype('uint32')
dictionary_0['id'].append(maxID)
dictionary_0['centroid'].append(center_1)
dictionary_0['area'].append(area_1)
dictionary_0['frame'].append(frame)
sys.stdout.flush()
sys.stdout.write(str2)
sys.stdout.flush()
#if (labelStack.max() < 256):
#labelStack = labelStack.astype('uint8')
#elif (labelStack.max()<65536):
#labelStack = labelStack.astype('uint16')
print "Checking for multiple particles in a single frame"
for frame in frameList:
labelImg = labelStack[:, :, frame - 1]
numLabel = imageProcess.regionProps(labelImg.astype('bool'),
gImg,
structure=structure)[1]
if (numLabel != numpy.size(numpy.unique(labelImg)[1:])):
for N in numpy.unique(labelImg)[1:]:
labelImgN = labelImg == N
numLabel = imageProcess.regionProps(labelImgN,
gImg,
structure=structure)[1]
if (numLabel > 1):
labelImg[labelImg == N] = 0
labelStack[:, :, frame - 1] = labelImg
for frame in frameList:
fileIO.writeH5Dataset(
fp, '/segmentation/labelStack/' + str(frame).zfill(zfillVal),
labelStack[:, :, frame - 1])
del labelStack
return maxID, occurenceFrameList
def connectIndividualLabelStacks(fp, frameList, size, centerDispRange,
perAreaChangeRange, missFramesTh):
print "Connecting each labelled substacks using the primary processor"
dictionary_0 = pickle.load(open('dictionary_' + str(size - 1), 'rb'))
newID = max(dictionary_0['id']) + 1
dictionary_0 = pickle.load(open('dictionary_' + str(0), 'rb'))
for rank in range(1, size):
dictionary_1 = pickle.load(open('dictionary_' + str(rank), 'rb'))
labelStack = numpy.load('labelStack_' + str(rank) + '.npy')
areaMin, areaMax = min(dictionary_1['area']), max(dictionary_1['area'])
for i in range(len(dictionary_1['id'])):
flag = 0
label_1 = dictionary_1['id'][i]
center_1_init = dictionary_1['initialCentroid'][i]
area_1_init = dictionary_1['initialArea'][i]
frame_1_init = dictionary_1['initialFrame'][i]
center_1_fin = dictionary_1['centroid'][i]
area_1_fin = dictionary_1['area'][i]
frame_1_fin = dictionary_1['frame'][i]
if (areaMax - areaMin > 0):
factor = 1.0 * (area_1 - areaMin) / (areaMax - areaMin)
perAreaChangeTh = perAreaChangeRange[1] - factor * (
perAreaChangeRange[1] - perAreaChangeRange[0])
centerDispTh = centerDispRange[1] - factor * (
centerDispRange[1] - centerDispRange[0])
else:
perAreaChangeTh = perAreaChangeRange[1]
centerDispTh = centerDispRange[1]
closeness, J = 1e10, 0
for j in range(len(dictionary_0['id'])):
label_0 = dictionary_0['id'][j]
center_0_fin = dictionary_0['centroid'][j]
area_0_fin = dictionary_0['area'][j]
frame_0_fin = dictionary_0['frame'][j]
centerDisp = numpy.sqrt(
(center_1_init[0] - center_0_fin[0])**2 +
(center_1_init[1] - center_0_fin[1])**2)
perAreaChange = 100.0 * numpy.abs(area_1_init -
area_0_fin) / numpy.maximum(
area_1_init, area_0_fin)
missFrames = frame_1_init - frame_0_fin
if (centerDisp <= centerDispTh):
if (perAreaChange <= perAreaChangeTh):
if (missFrames <= missFramesTh):
if (centerDisp < closeness):
closeness = centerDisp
label_0 = dictionary_0['id'][j]
flag = 1
if (flag == 1):
dictionary_0['centroid'][i] = center_1_fin
dictionary_0['area'][i] = area_1_fin
dictionary_0['frame'][i] = frame1_fin
labelStack[labelStack == label_1] = label_0
else:
dictionary_0['id'].append(label_1)
dictionary_0['centroid'].append(center_1_fin)
dictionary_0['area'].append(area_1_fin)
dictionary_0['frame'].append(frame_fin)
dictionary_0['initialCentroid'].append(center_1_init)
dictionary_0['initialArea'].append(area_1_init)
dictionary_0['initialFrame'].append(frame_1_init)
numpy.save('labelStack_' + str(rank) + '.npy', labelStack)
for rank in range(size):
if (rank == 0):
labelStack = numpy.load('labelStack_' + str(rank) + '.npy')
else:
labelStack = numpy.concatenate(
(labelStack, numpy.load('labelStack_' + str(rank) + '.npy')),
axis=2)
occurenceFrameList = []
particleList = dictionary_0['id']
newParticleList = range(1, len(dictionary_0['id']) + 1)
dictionary_0['id'] = []
for particle, newParticle in zip(particleList, newParticleList):
labelStack[labelStack == particle] = newParticle
dictionary_0['id'].append(newParticle)
occurenceFrameList.append(
[newParticle, dictionary_0['initialFrame'][newParticle - 1]])
for frame in frameList:
fileIO.writeH5Dataset(
fp, '/segmentation/labelStack/' + str(frame).zfill(zfillVal),
labelStack[:, :, frame - 1])
return newParticleList[-1], occurenceFrameList
def labelParticlesFromText(fp,
centerDispRange=[5, 5],
perAreaChangeRange=[20, 20],
missFramesTh=10,
scale=1):
[row, col,
numFrames] = fp.attrs['row'], fp.attrs['col'], fp.attrs['numFrames']
frameList = range(1, numFrames + 1)
zfillVal = fp.attrs['zfillVal']
segmentation = numpy.loadtxt(fp.attrs['outputDir'] + '/segmentation.dat')
dictionary_0, dictionary_1, particleLabel = {}, {}, []
#labelStack = numpy.zeros([row*scale,col*scale,numFrames], dtype='uint32')
for frame in tqdm(frameList):
data = segmentation[segmentation[:, 0] == frame]
if (frame == frameList[0]):
maxID = int(data[-1, 1])
occurenceFrameList = [frame] * maxID
dictionary_0['id'] = []
dictionary_0['frame'] = []
dictionary_0['centroid'] = []
dictionary_0['area'] = []
dictionary_0['radius'] = []
for particle, r, c, rad, area in data[:, 1:]:
dictionary_0['id'].append(particle)
dictionary_0['frame'].append(frame)
dictionary_0['centroid'].append([r, c])
dictionary_0['radius'].append(rad)
dictionary_0['area'].append(area)
particleLabel.append(particle)
else:
if (data.size > 0):
areaMin, areaMax = data[:, 5].min(), data[:, 5].max()
for particle, r, c, rad, area in data[:, 1:]:
flag = 0
center_1 = [r, c]
area_1 = area
radius_1 = rad
frame_1 = frame
if (areaMax - areaMin > 0):
factor = 1.0 * (area_1 - areaMin) / (areaMax - areaMin)
perAreaChangeTh = perAreaChangeRange[1] - factor * (
perAreaChangeRange[1] - perAreaChangeRange[0])
centerDispTh = centerDispRange[1] - factor * (
centerDispRange[1] - centerDispRange[0])
else:
perAreaChangeTh = perAreaChangeRange[1]
centerDispTh = centerDispRange[1]
closeness, J = 1e10, 0
for j in range(len(dictionary_0['id']) - 1, -1, -1):
frame_0 = dictionary_0['frame'][j]
center_0 = dictionary_0['centroid'][j]
radius_0 = dictionary_0['radius'][j]
area_0 = dictionary_0['area'][j]
centerDisp = numpy.sqrt((center_1[0] - center_0[0])**2 +
(center_1[1] - center_0[1])**2)
perAreaChange = 100.0 * numpy.abs(area_1 -
area_0) / numpy.maximum(
area_1, area_0)
missFrames = frame_1 - frame_0
if (centerDisp <= centerDispTh):
if (perAreaChange <= perAreaChangeTh):
if (missFrames <= missFramesTh):
if (centerDisp < closeness):
closeness = centerDisp
J = j
flag = 1
if (flag == 1):
dictionary_0['frame'][J] = frame
dictionary_0['centroid'][J] = center_1
dictionary_0['radius'][J] = radius_1
dictionary_0['area'][J] = area_1
particleLabel.append(dictionary_0['id'][J])
if (flag == 0):
maxID += 1
occurenceFrameList.append(frame)
dictionary_0['id'].append(maxID)
dictionary_0['frame'].append(frame)
dictionary_0['centroid'].append(center_1)
dictionary_0['radius'].append(radius_1)
dictionary_0['area'].append(area_1)
particleLabel.append(maxID)
labels = numpy.column_stack((segmentation, particleLabel))
numRows, numCols = labels.shape
flagArr = numpy.zeros(numRows, dtype='bool')
print "Checking for multiple particles in a single frame"
for frame, particle, r, c, rad, area, label, nRow in tqdm(
numpy.column_stack((labels, range(numRows)))):
for n in range(int(nRow) + 1, numRows):
if (frame != labels[n, 0]):
break
elif (label == labels[n, 6]):
flagArr[[n, int(nRow)]] = True
outFile = open(fp.attrs['outputDir'] + '/tracking.dat', 'wb')
print "Writing the labeled imaged to text file"
for frame, particle, r, c, rad, area, label, nRow in tqdm(
numpy.column_stack((labels, range(numRows)))):
if (flagArr[int(nRow)] == True):
labels[int(nRow), 6] = 0
outFile.write("%d %d %d %d %f %f 0\n" %
(frame, particle, r, c, rad, area))
else:
#rr,cc = circle(int(r*scale),int(c*scale),int(rad*scale))
#if ((rr<0).any()==True or (cc<0).any()==True):
#pass
#elif ((rr>row*scale-1).any()==True or (cc>col*scale-1).any()==True):
#pass
#else:
#labelStack[rr,cc,int(frame)-1] = label
outFile.write("%d %d %d %d %f %f %d\n" %
(frame, particle, r, c, rad, area, label))
outFile.close()
#for frame in tqdm(frameList):
#data =
#fileIO.writeH5Dataset(fp,'/segmentation/labelStack/'+str(frame).zfill(zfillVal),labelStack[:,:,frame-1])
#del labelStack
#return maxID, occurenceFrameList
print "Saving the labelled particles in h5 file"
DATA = numpy.loadtxt(fp.attrs['outputDir'] + '/tracking.dat')
labelStack = numpy.zeros([row * scale, col * scale], dtype='uint32')
for frame in tqdm(frameList):
data = DATA[DATA[:, 0] == frame]
bImg = fp['/segmentation/bImgStack/' +
str(frame).zfill(zfillVal)].value
[labelImg, numLabel] = ndimage.label(bImg,
structure=[[1, 1, 1], [1, 1, 1],
[1, 1, 1]])
for f, particle, r, c, rad, area, label in data:
bImgLabelN = labelImg == particle
labelStack = labelStack + bImgLabelN * label
fileIO.writeH5Dataset(
fp, '/segmentation/labelStack/' + str(frame).zfill(zfillVal),
labelStack)
labelStack[:] = 0
return maxID, occurenceFrameList
