def makeBeamBlockerMask(inputImg, maskImg):
img = cv2.imread(inputImg, 0)
gradImg = flt.sobel(img)
bImg = gradImg > numpy.percentile(gradImg, 99.5)
bImg = filters.areaFilter(bImg)
labelImg, numLabel, dictionary = imageProcess.regionProps(bImg)
line1 = [
dictionary['centroid'][0][0], dictionary['centroid'][0][1],
dictionary['orientation'][0]
]
line2 = [
dictionary['centroid'][1][0], dictionary['centroid'][1][1],
dictionary['orientation'][1]
]
bImg = imageDraw.fillBetweenLines([line1, line2], bImg)
bImg = imageProcess.binary_dilation(bImg, iterations=25)
bImg = numpy.logical_not(bImg)
cv2.imwrite(maskImg, bImg * 255)
plt.figure()
plt.subplot(121), plt.imshow(img)
plt.subplot(122), plt.imshow(bImg)
plt.show()
return None
def generateLabelImages(fp,
imgDir,
fontScale=1,
size=1,
rank=0,
structure=[[1, 1, 1], [1, 1, 1], [1, 1, 1]]):
[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
gImg = fp['/dataProcessing/gImgRawStack/' +
str(frame).zfill(zfillVal)].value
bImg = labelImg.astype('bool')
bImgBdry = imageProcess.normalize(imageProcess.boundary(bImg))
label, numLabel, dictionary = imageProcess.regionProps(
bImg, gImg, structure=structure, centroid=True)
bImg = imageProcess.normalize(bImg)
for j in range(len(dictionary['id'])):
bImgLabelN = label == dictionary['id'][j]
ID = numpy.max(bImgLabelN * labelImg)
bImg = imageProcess.textOnGrayImage(
bImg,
str(ID), (int(dictionary['centroid'][j][0]) + 3,
int(dictionary['centroid'][j][1]) - 3),
fontScale=fontScale,
color=127,
thickness=1)
finalImage = numpy.column_stack((bImg, numpy.maximum(bImgBdry, gImg)))
cv2.imwrite(imgDir + '/' + str(frame).zfill(zfillVal) + '.png',
finalImage)
return 0
def generateLabelImagesMatplotlib(fp,
imgDir,
fontScale=1,
size=1,
rank=0,
structure=[[1, 1, 1], [1, 1, 1], [1, 1, 1]]):
[row, col, numFrames, frameList] = misc.getVitals(fp)
particleList = fp.attrs['particleList']
zfillVal = fp.attrs['zfillVal']
procFrameList = numpy.array_split(frameList, size)
fig = plt.figure(figsize=(2, 1))
ax1 = fig.add_axes([0, 0, 0.5, 1])
ax2 = fig.add_axes([0.5, 0, 0.5, 1])
for frame in procFrameList[rank]:
labelImg = fp['/segmentation/labelStack/' +
str(frame).zfill(zfillVal)].value
gImg = fp['/dataProcessing/gImgRawStack/' +
str(frame).zfill(zfillVal)].value
gImgBlur = fp['/dataProcessing/filterStack/' +
str(frame).zfill(zfillVal)].value
id_center = []
for label in numpy.unique(labelImg)[1:]:
bImgLabelN = labelImg == label
temp1, temp2, dictionary = imageProcess.regionProps(
bImgLabelN, bImgLabelN, structure=structure, centroid=True)
id_center.append([
label, dictionary['centroid'][0][0],
dictionary['centroid'][0][1]
])
ax1.imshow(gImg)
ax1.imshow(labelImg, cmap='jet', alpha=0.5, vmax=100)
ax2.imshow(gImgBlur)
for label, r, c in id_center:
ax1.text(c, r, label, color='#FFFFFF', fontsize=1.5)
ax1.get_xaxis().set_visible(False)
ax1.get_yaxis().set_visible(False)
ax2.get_xaxis().set_visible(False)
ax2.get_yaxis().set_visible(False)
plt.savefig(imgDir + '/' + str(frame).zfill(zfillVal) + '.png')
ax1.cla(), ax2.cla()
plt.close()
return 0
if (flag == False):
bImgCrop, flag = fitFunc.fitting(gImgCrop, -theta)
bImgCropBdry = imageProcess.normalize(
imageProcess.boundary(bImgCrop))
bImgBdry[int(rCenter) - cropSize:int(rCenter) + cropSize + 1,
int(cCenter) - cropSize:int(cCenter) + cropSize +
1] = bImgCropBdry
#muR,muC,majorAxis,minorAxis,theta
if (flag == True):
bImg = imageProcess.fillHoles(bImgBdry)
label, numLabel, dictionary = imageProcess.regionProps(
bImg,
gImgRaw,
structure=[[1, 1, 1], [1, 1, 1], [1, 1, 1]],
centroid=True,
area=True,
effRadius=True)
outFile.write(
"%d 1 %f %f %f %f\n" %
(frame, dictionary['centroid'][0][0],
dictionary['centroid'][0][1], dictionary['effRadius'][0],
dictionary['area'][0] * pixInNM * pixInNM))
#outFile.write("%d 1 %f %f %f %f %f %f\n" %(frame,int(rCenter)-cropSize+muR,int(cCenter)-cropSize+muC,theta,numpy.pi*majorAxis*minorAxis,majorAxis,minorAxis))
#frame,particle,r,c,rad,area,a,b,theta
finalImg = numpy.column_stack((numpy.maximum(gImgNorm,
bImgBdry), gImgNorm))
cv2.imwrite(
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
labelImg = fp['/segmentation/labelStack/' +
str(frame).zfill(zfillVal)].value
gImgRaw = fp['/dataProcessing/gImgRawStack/' +
str(frame).zfill(zfillVal)].value
outFile.write("%f " % (1.0 * frame / fps))
for particle in particleList:
bImg = labelImg == particle
if (bImg.max() == True):
label, numLabel, dictionary = imageProcess.regionProps(
bImg,
gImgRaw,
structure=structure,
centroid=centroid,
area=area,
perimeter=perimeter,
circularity=circularity,
pixelList=pixelList,
bdryPixelList=bdryPixelList,
effRadius=effRadius,
radius=radius,
circumRadius=circumRadius,
inRadius=inRadius,
radiusOFgyration=radiusOFgyration)
outFile.write("%f %f %f %f %f %f " %
(dictionary['centroid'][0][1] * pixInNM,
(row - dictionary['centroid'][0][0]) * pixInNM,
dictionary['area'][0] * pixInNM * pixInNM,
dictionary['perimeter'][0] * pixInNM,
dictionary['circularity'][0],
dictionary['effRadius'][0] * pixInNM))
else:
def labelSubstacks(fp, frameList, comm, size, rank, centerDispRange,
perAreaChangeRange, missFramesTh, structure):
if (rank == 0):
print "Labelling image stack"
labelStack = numpy.zeros([row, col, len(frameList)], dtype='uint32')
for frame in frameList:
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
dictionary_0['frame'] = []
dictionary_0['initialCentroid'], dictionary_0[
'initialArea'], dictionary_0['initialFrame'] = [], [], []
for i in range(len(dictionary_0['id'])):
dictionary_0['frame'].append(frame)
dictionary_0['initialCentroid'].append(
dictionary_1['centroid'][i])
dictionary_0['initialArea'].append(dictionary_1['area'][i])
dictionary_0['initialFrame'].append(frame)
labelStack[:, :, frame - frameList[0]] = 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 - frameList[0]] += (
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
labelN_1 = bImg_1_LabelN * maxID
labelStack[:, :, frame -
frameList[0]] += 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)
dictionary_0['initialCentroid'].append(center_1)
dictionary_0['initialArea'].append(area_1)
dictionary_0['initialFrame'].append(frame)
if (rank == 0):
print "Checking for multiple particles in a single frame"
for frame in frameList:
labelImg = labelStack[:, :, frame - frameList[0]]
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 - frameList[0]] = labelImg
numpy.save('labelStack_' + str(rank) + '.npy', labelStack)
pickle.dump(dictionary_0, open('dictionary_' + str(rank), 'wb'))
return 0
totalCount += 1
gImg = numpy.reshape(x, (row, col))
gImgNorm = imageProcess.normalize(gImg)
gImg = imageProcess.invert(gImgNorm)
# THRESHOLD METHOD 1 (OTSU)
bImg = gImg >= imageProcess.otsuThreshold(gImg)
# THRESHOLD METHOD 2 (KAPUR)
# bImg = gImg >= imageProcess.threshold_kapur(gImg)
# THRESHOLD METHOD 2 (MEAN)
# bImg = gImg >= numpy.mean(gImg)
bImg = imageProcess.binary_opening(bImg, iterations=4)
labelImg, numLabel, dictionary = imageProcess.regionProps(bImg)
distanceFromCentre = 1e10
for i in range(numLabel):
rCenter, cCenter = dictionary['centroid'][i]
distance = numpy.sqrt((rCenter - row / 2.0)**2 +
(cCenter - col / 2.0)**2)
if (distance < distanceFromCentre):
distanceFromCentre = distance
centerLabel = i + 1
nearestNeighborDistance = 1e10
for i in range(numLabel):
if (i != centerLabel - 1):
if (dictionary['minDist'][centerLabel - 1][i] <
nearestNeighborDistance):
nearestNeighborDistance = dictionary['minDist'][centerLabel -
