def showImage(roiLbl, imNum, imageDir, dataDir):
# image = odp.stitchImage(imageFileName, pixSize, magnification, background)
expPath = pathlib.Path(imageDir)
# Generate image file Path by combining the region of interest lable with the experiment path
roiFolder = pathlib.Path('./' + roiLbl)
imageFileName = pathlib.Path('./' + roiLbl + '_' + f'{imNum:00d}' + '.mat')
imageFilePath = expPath / roiFolder / imageFileName
# Load Region of Interest Data. This HDF5 file should containt location of image stitch coordinates
dataPath = pathlib.Path(dataDir)
initPath = list(dataPath.glob('*Index_ODELAYData.hdf5'))
initData = fio.loadData(initPath[0])
roiPath = dataPath / 'ODELAY Roi Data' / f'{roiLbl}.hdf5'
roiData = fio.loadData(roiPath)
background = initData['backgroundImage']
# This data should be extracted from the Experiment Index file or stage data file.
pixSize = initData['pixSize']
magnification = initData['magnification']
stInd = f'{imNum-1:03d}'
stitchCorners = roiData['stitchMeta'][stInd]['imPix']
# breakpoint()
anImage = opl.assembleImage(imageFilePath, pixSize, magnification,
background, stitchCorners)
im = anImage['Bf']
# im = opl.SobelGradient(im)
imSize = im.shape
# This data should be recorded from image display to make sure the image is visible.
imageHist = histogram1d(im.ravel(), 2**16, [0, 2**16],
weights=None).astype('float')
# Calculate the cumulative probability ignoring zero values
cumHist = np.cumsum(imageHist)
cumProb = (cumHist - cumHist[0]) / (cumHist[2**16 - 1] - cumHist[0])
# set low and high values ot normalize image contrast.
loval = np.argmax(cumProb > 0.00001)
hival = np.argmax(cumProb >= 0.9995)
adjIm = np.array((im.astype('float') - loval.astype('float')) /
(hival.astype('float') - loval.astype('float')) * 254,
dtype='uint8')
rsIm = cv2.resize(adjIm, (round(imSize[1] / 5), round(imSize[0] / 5)))
cv2.imshow('Display Image', rsIm)
k = cv2.waitKey(0)
if k == 107 or k == -1:
cv2.destroyWindow('Display Image')
return k
def export_tiffs(fileloc, roi):
''' Export CSV file tables of Growth Curves and fit data. '''
with open(fileloc.configfile, 'r') as fileIn:
odelayConfig = json.load(fileIn)
dataPath = pathlib.Path(odelayConfig['LocalDataDir'])
expIndexPath = [*dataPath.glob('*Index_ODELAYData.*')]
if len(expIndexPath) == 1:
expData = fio.loadData(expIndexPath[0])
roiList = [*expData['roiFiles']]
roiList.sort()
if roi == 'all':
renderTiffs(odelayConfig, roiList)
elif roi in roiList:
# click.echo('This should not block')
renderTiffs(odelayConfig, [roi])
else:
click.echo('Roi ID was not in the experiment Roi List')
# df.to_csv(r'Path where you want to store the exported CSV file\File Name.csv')
return None
def export_csv(fileloc, roiind):
''' Export CSV file tables of Growth Curves and fit data. '''
with open(fileloc.configfile, 'r') as fileIn:
odelayConfig = json.load(fileIn)
dataPath = pathlib.Path(odelayConfig['LocalDataDir'])
indexList = [k for k in dataPath.glob('*Index_ODELAYData.*')]
if len(indexList) == 1:
expIndexPath = dataPath / indexList[0]
expData = fio.loadData(expIndexPath)
expKeys = [*expData]
if 'roiFiles' in expKeys:
roiList = [*expData['roiFiles']]
roiList.sort()
elif 'ImageVars' in expKeys:
roiList = [*expData['ImageVars']['StrainID']]
roiList.sort()
if roiind == 'all':
fio.exportcsv(dataPath, roiList)
elif roiind in roiList:
fio.exportcsv(dataPath, [roiind])
else:
click.echo('Roi ID was not in the experiment Roi List')
return None
def process_mac(fileloc, roi):
'''Process region of interest or whole experiment'''
with open(fileloc.configfile, 'r') as fileIn:
odelayConfig = json.load(fileIn)
imagePath = pathlib.Path(odelayConfig['LocalImageDir'])
dataPath = pathlib.Path(odelayConfig['LocalDataDir'])
indexList = [*dataPath.glob('*Index_ODELAYData.*')]
if len(indexList) == 1:
expIndexPath = dataPath / indexList[0]
expData = fio.loadData(expIndexPath)
roiList = [*expData['roiFiles']]
roiList.sort()
if roi == 'all':
processMacAll(odelayConfig, roiList)
elif roi in roiList:
# click.echo('This should not block')
processMacAll(odelayConfig, [roi])
# click.echo('This is after the function that should not block')
else:
click.echo(
'Could not find the correct index file or there were more than one in the diretory'
)
return None
def readImage(self, lowcut=0.0005, highcut=0.99995):
roiLbl = self.roiLbl
imNum = self.imageNumber
imagePath = pathlib.Path(self.odelayConfig['LocalImageDir'])
dataPath = pathlib.Path(self.odelayConfig['LocalDataDir'])
# Generate image file Path by combining the region of interest lable with the experiment path
roiFolder = pathlib.Path('./' + roiLbl)
imageFileName = pathlib.Path('./' + roiLbl + '_' + f'{imNum:00d}' +
'.mat')
imageFilePath = imagePath / roiFolder / imageFileName
# Load Region of Interest Data. This HDF5 file should containt location of image stitch coordinates
roiPath = dataPath / 'ODELAY Roi Data' / f'{roiLbl}.hdf5'
roiData = fio.loadData(roiPath)
background = self.experimentData['backgroundImage']
# This data should be extracted from the Experiment Index file or stage data file.
pixSize = self.experimentData['pixSize']
magnification = self.experimentData['magnification']
stInd = f'{imNum-1:03d}'
stitchCorners = roiData['stitchMeta'][stInd]['imPix']
anImage = opl.assembleImage(imageFilePath, pixSize, magnification,
background, stitchCorners)
im = anImage['Bf']
# make a histogram of the image in the bitdept that the image was recorded.
imageHist = histogram1d(im.ravel(), 2**16, [0, 2**16],
weights=None).astype('float')
# Calculate the cumulative probability ignoring zero values
cumHist = np.zeros(imageHist.shape, dtype='float')
cumHist[1:] = np.cumsum(imageHist[1:])
# if you expect a lot of zero set
cumProb = (cumHist - cumHist[0]) / (cumHist[2**16 - 1] - cumHist[0])
# set low and high values ot normalize image contrast.
loval = np.argmax(cumProb >= lowcut)
hival = np.argmax(cumProb >= highcut)
scIm = (im.astype('float') - loval.astype('float')) / (
hival.astype('float') - loval.astype('float')) * 254
lim = np.iinfo('uint8')
scIm = np.clip(scIm, lim.min, lim.max)
# Set image data type and make sure the array is contiguous in memory.
imageData = np.require(scIm, dtype='uint8', requirements='C')
# Set data as a QImage. This is a greyscale image
Qim = QImage(imageData.data, imageData.shape[1], imageData.shape[0],
imageData.shape[1], QImage.Format_Grayscale8)
Qim.data = imageData
return Qim
def loadExperimentData(self):
imagePath = pathlib.Path(self.odelayConfig['LocalImageDir'])
dataPath = pathlib.Path(self.odelayConfig['LocalDataDir'])
indexList = [k for k in dataPath.glob('*Index_ODELAYData.*')]
if len(indexList) == 1:
expIndexPath = dataPath / indexList[0]
expData = fio.loadData(expIndexPath)
return expData
def plot_summary(fileloc, organism):
'''plot summary figure of entire experiment'''
with open(fileloc.configfile, 'r') as fileIn:
odelayConfig = json.load(fileIn)
dataPath = pathlib.Path(odelayConfig['LocalDataDir'])
summaryList = list(dataPath.glob('*summary.hdf5'))
if len(summaryList) == 1 and summaryList[0].exists():
expDict = fio.loadData(summaryList[0])
click.echo('summary loaded')
odp.figExpSummary(expDict, organism)
return None
def plot_gc(fileloc, roiid):
'''Plot a growth curve figure with the Region of Intrest'''
with open(fileloc.configfile, 'r') as fileIn:
odelayConfig = json.load(fileIn)
dataPath = pathlib.Path(odelayConfig['LocalDataDir'])
roi = roiid[0]
organism = roiid[1]
if roi == 'all':
summaryList = list(dataPath.glob('*summary.hdf5'))
roiFolder = list(dataPath.glob('ODELAY Roi Data'))
if len(summaryList) == 1 and summaryList[0].exists():
expDict = fio.loadData(summaryList[0])
click.echo('summary loaded')
saveFolder = dataPath / 'ODELAY Growth Curves'
if not saveFolder.exists():
saveFolder.mkdir()
for roi in expDict.keys():
try:
savePath = saveFolder / f'{roi}.png'
odp.figPlotGCs(expDict[roi],
organism=organism,
saveAll=True,
savePath=savePath)
except:
click.echo(f"{roi} didn't print")
else:
click.echo('Summary file does not exist please make one')
else:
summaryList = list(dataPath.glob('*summary.hdf5'))
roiFolder = list(dataPath.glob('ODELAY Roi Data'))
# roiPath = roiFolder[0] / f'{roi}.hdf5'
if len(summaryList) == 1 and summaryList[0].exists():
expDict = fio.loadData(summaryList[0])
click.echo('summary loaded')
if roi in expDict.keys():
odp.figPlotGCs(expDict[roi], organism=organism)
elif len(roiFolder) == 1 and roiFolder[0].joinPath(
f'{roi}.hdf5').exists():
roiData = fio.loadData(roiPath)
rc = roiData['fitData'].shape
idVec = np.arange(rc[0], dtype='uint32')
inds = roiData['fitData'][:, 0] > 0
roiDict = {}
roiDict['fitData'] = roiData['fitData'][inds, :]
roiDict['objectArea'] = roiData['objectArea'][inds, :]
roiDict['timePoints'] = roiData['timePoints']
roiDict['objID'] = idVec[inds]
roiDict['roi'] = roi
roiDict['roiInfo'] = roiData['roiInfo']
odp.figPlotGCs(roiDict, organism=organism)
else:
click.echo('Path to Roi file or to summary file broken.')
click.echo('Please check local data path to experiment folder')
return None