def main(inp=""):
print("Colonyzer " + c2.__version__)
var = buildVars(inp=inp)
correction, fixedThresh, plots, initpos, fdict, fdir, nrow, ncol, cut, verbose, diffIms, updates, endpoint, edgemask, greenlab = (
var["lc"], var["fixedThresh"], var["plots"], var["initpos"],
var["fdict"], var["fdir"], var["nrow"], var["ncol"], var["cut"],
var["verbose"], var["diffims"], var["updates"], var["endpoint"],
var["edgemask"], var["greenlab"])
barcdict = checkImages(fdir, fdict, verbose=verbose)
if greenlab:
barcdict = {
x: barcdict[x]
for x in barcdict.keys()
if not c2.checkAnalysisStarted(barcdict[x][-1])
}
rept = c2.setupDirectories(barcdict, verbose=verbose)
if greenlab:
posfiles = [
resource_filename(Requirement.parse("colonyzer2"),
os.path.join("data", f)) for f in
["GreenLabLid.png", "CornerLid.png", "BottomRightLid.png"]
]
negfiles = [
resource_filename(Requirement.parse("colonyzer2"),
os.path.join("data", f)) for f in
["GreenLabNoLid.png", "CornerNoLid.png", "BottomRightNoLid.png"]
]
frects = [[0.15, 0.85, 0.5, 1.0], [0.0, 0.8, 0.125, 1.0],
[0.85, 0.8, 1.0, 1.0]]
pdistThresh, ndistThresh = 50.3, 53.333
checkLid = c2.makeLidTest(posfiles, negfiles, frects, pdistThresh,
ndistThresh, False)
else:
def checkLid(fname):
False
start = time.time()
while len(barcdict) > 0:
BARCODE, imdir, LATESTIMAGE, EARLIESTIMAGE, imRoot = prepareTimecourse(
barcdict, verbose=verbose, chklid=checkLid)
if plots:
pdf = PdfPages(
os.path.join(
os.path.dirname(EARLIESTIMAGE), "Output_Reports",
os.path.basename(EARLIESTIMAGE).split(".")[0] + ".pdf"))
else:
pdf = None
# Create empty file to indicate that barcode is currently being analysed, to allow parallel analysis (lock files)
tmp = open(
os.path.join(
os.path.dirname(EARLIESTIMAGE), "Output_Data",
os.path.basename(EARLIESTIMAGE).split(".")[0] + ".out"),
"w").close()
# Get latest image for thresholding and detecting culture locations
imN, arrN = c2.openImage(LATESTIMAGE)
# Get earliest image for lighting gradient correction
if (LATESTIMAGE == EARLIESTIMAGE) or endpoint:
im0, arr0 = imN, arrN
arrloc = arrN
else:
im0, arr0 = c2.openImage(EARLIESTIMAGE)
arrloc = arrN - arr0
#arrloc=np.maximum(0,arrloc)
if initpos:
InsData = c2.readInstructions(os.path.dirname(LATESTIMAGE),
searchUpStream=True)
# Load initial guesses from Colonyzer.txt file
(candx, candy, dx, dy) = loadLocationGuesses(LATESTIMAGE, InsData)
corner = [0, 0]
com = [0, 0]
guess = [0, 0]
# NOTE: assumes that grid returned by loadLocationGuesses is parallel to image edges
ny = nrow = len(np.unique(candy))
nx = ncol = len(np.unique(candx))
else:
# Automatically generate guesses for gridded array locations
(candx, candy, dx, dy, corner, com,
guess) = c2.estimateLocations(arrloc,
ncol,
nrow,
showPlt=plots,
pdf=pdf,
glob=False,
verbose=verbose,
nsol=updates)
# Update guesses and initialise locations data frame
locationsN = c2.locateCultures(
[int(round(cx - dx / 2.0)) for cx in candx],
[int(round(cy - dy / 2.0)) for cy in candy],
dx,
dy,
arrloc,
ncol,
nrow,
update=True)
mask = edgeFill2(arrN, 0.9)
c2.showIm(mask)
grd = mask.copy()
grd[:, :] = False
grd[min(locationsN.y - dy / 2):max(locationsN.y + dy / 2),
min(locationsN.x - dx / 2):max(locationsN.x + dx / 2)] = True
spots = np.logical_and(grd, mask)
agar = np.logical_and(grd, ~mask)
ave0 = np.mean(arr0[agar])
thresh = -99
if correction:
if cut:
pseudoempty = np.array(np.round(arr0.copy()), dtype=np.uint8)
bytemask = np.asarray(spots * 255, dtype=np.uint8)
filled = cv2.inpaint(pseudoempty, bytemask, 3, cv2.INPAINT_NS)
blurred = cv2.GaussianBlur(filled, (21, 21), 0)
pseudoempty[spots] = blurred[spots]
else:
pseudoempty = arr0
else:
pseudoempty = 0
for FILENAME in barcdict[BARCODE]:
startim = time.time()
im, arr = c2.openImage(FILENAME)
# Local updates on individual images to allow for slight movement of plate
#locationsN=c2.locateCultures([int(round(cx-dx/2.0)) for cx in candx],[int(round(cy-dy/2.0)) for cy in candy],dx,dy,arr,ncol,nrow,update=True)
ave = np.mean(arr[agar])
# Correct for minor lighting differences between images
arr = np.maximum(0, np.minimum(255, arr - (ave - ave0)))
# Subtract background (corrects for lighting differences within plate/image as well as making agar intensity correspond to zero signal)
arr = np.maximum(arr - pseudoempty, 0)
# Measure culture phenotypes
locations = c2.measureSizeAndColour(locationsN, arr, im, spots, 0,
BARCODE, FILENAME[0:-4])
# Write results to file
locations.to_csv(os.path.join(
os.path.dirname(FILENAME), "Output_Data",
os.path.basename(FILENAME).split(".")[0] + ".out"),
"\t",
index=False,
engine='python')
dataf = c2.saveColonyzer(
os.path.join(os.path.dirname(FILENAME), "Output_Data",
os.path.basename(FILENAME).split(".")[0] +
".dat"), locations, thresh, dx, dy)
# Visual check of culture locations
imthresh = c2.threshPreview(locations, mask, None)
r = 5
draw = ImageDraw.Draw(imthresh)
draw.ellipse((com[1] - r, com[0] - r, com[1] + r, com[0] + r),
fill=(255, 0, 0))
draw.ellipse(
(corner[1] - r, corner[0] - r, corner[1] + r, corner[0] + r),
fill=(255, 0, 0))
draw.ellipse(
(guess[1] - r, guess[0] - r, guess[1] + r, guess[0] + r),
fill=(255, 0, 0))
draw.ellipse(
(candx[0] - r, candy[0] - r, candx[0] + r, candy[0] + r),
fill=(255, 0, 0))
imthresh.save(
os.path.join(os.path.dirname(FILENAME), "Output_Images",
os.path.basename(FILENAME).split(".")[0] +
".png"))
# Get ready for next image
if verbose:
print("Finished {0} in {1:.2f}s".format(
os.path.basename(FILENAME),
time.time() - startim))
# Get ready for next image
if verbose:
print("Finished {0} in {1:.2f}s".format(os.path.basename(BARCODE),
time.time() - start))
barcdict = {
x: barcdict[x]
for x in barcdict.keys()
if not c2.checkAnalysisStarted(barcdict[x][-1])
}
if plots:
pdf.close()
print("No more barcodes to analyse... I'm done.")
def main(inp=""):
print("Colonyzer "+c2.__version__)
cythonFill=False
var=buildVars(inp=inp)
correction,fixedThresh,plots,initpos,fdict,fdir,nrow,ncol,cut,verbose,diffIms=(var["lc"],var["fixedThresh"],var["plots"],var["initpos"],var["fdict"],var["fdir"],var["nrow"],var["ncol"],var["cut"],var["verbose"],var["diffims"])
barcdict=checkImages(fdir,fdict,verbose=verbose)
rept=c2.setupDirectories(barcdict,verbose=verbose)
start=time.time()
while len(barcdict)>0:
BARCODE,imdir,InsData,LATESTIMAGE,EARLIESTIMAGE,imRoot=prepareTimecourse(barcdict,verbose=verbose)
if plots:
pdf=PdfPages(os.path.join(os.path.dirname(EARLIESTIMAGE),"Output_Reports",os.path.basename(EARLIESTIMAGE).split(".")[0]+".pdf"))
else:
pdf=None
# Create empty file to indicate that barcode is currently being analysed, to allow parallel analysis (lock files)
tmp=open(os.path.join(os.path.dirname(EARLIESTIMAGE),"Output_Data",os.path.basename(EARLIESTIMAGE).split(".")[0]+".out"),"w").close()
# Get latest image for thresholding and detecting culture locations
imN,arrN=c2.openImage(LATESTIMAGE)
# Get earliest image for lighting gradient correction
if(LATESTIMAGE==EARLIESTIMAGE):
im0,arr0=imN,arrN
else:
im0,arr0=c2.openImage(EARLIESTIMAGE)
if initpos:
# Load initial guesses from Colonyzer.txt file
(candx,candy,dx,dy)=loadLocationGuesses(LATESTIMAGE,InsData)
corner=[0,0]; com=[0,0]; guess=[0,0]
else:
# Automatically generate guesses for gridded array locations
(candx,candy,dx,dy,corner,com,guess)=c2.estimateLocations(arrN,ncol,nrow,showPlt=plots,pdf=pdf,glob=False,verbose=verbose)
# Update guesses and initialise locations data frame
locationsN=c2.locateCultures([int(round(cx-dx/2.0)) for cx in candx],[int(round(cy-dy/2.0)) for cy in candy],dx,dy,arrN,ncol,nrow,update=True)
if correction:
if cut:
mask=edgeFill(arr0,locationsN,0.8)
startFill=time.time()
if cythonFill:
pseudoempty=maskAndFillCython(arr0,maskN,0.005)
print("Inpainting using Cython & NumPy: "+str(time.time()-startFill)+" s")
else:
pseudoempty=maskAndFill(arr0,mask,0.005)
print("Inpainting using NumPy: "+str(time.time()-start)+" s")
else:
pseudoempty=arr0
# Smooth (pseudo-)empty image
(correction_map,average_back)=c2.makeCorrectionMap(pseudoempty,locationsN,verbose=verbose)
# Correct spatial gradient in final image
corrected_arrN=arrN*correction_map
else:
average_back=numpy.mean(arr0[numpy.min(locationsN.y):numpy.max(locationsN.y),numpy.min(locationsN.x):numpy.max(locationsN.x)])
corrected_arrN=arrN
# Trim outer part of image to remove plate walls
trimmed_arrN=corrected_arrN[max(0,int(round(min(locationsN.y)-dy/2.0))):min(arrN.shape[0],int(round((max(locationsN.y)+dy/2.0)))),max(0,int(round(min(locationsN.x)-dx/2.0))):min(arrN.shape[1],int(round((max(locationsN.x)+dx/2.0))))]
if fixedThresh>=0:
thresh=fixedThresh
else:
(thresh,bindat)=c2.automaticThreshold(trimmed_arrN,BARCODE,pdf=pdf)
if plots:
c2.plotModel(bindat,label=BARCODE,pdf=pdf)
# Mask for identifying culture areas
maskN=numpy.ones(arrN.shape,dtype=numpy.bool)
maskN[corrected_arrN<thresh]=False
for FILENAME in barcdict[BARCODE]:
startim=time.time()
im,arr=c2.openImage(FILENAME)
if correction:
arr=arr*correction_map
if diffIms:
# Correct for lighting differences between plates
arrsm=arr[max(0,int(round(min(locationsN.y)-dy/2.0))):min(arrN.shape[0],int(round((max(locationsN.y)+dy/2.0)))),max(0,int(round(min(locationsN.x)-dx/2.0))):min(arrN.shape[1],int(round((max(locationsN.x)+dx/2.0))))]
masksm=maskN[max(0,int(round(min(locationsN.y)-dy/2.0))):min(arrN.shape[0],int(round((max(locationsN.y)+dy/2.0)))),max(0,int(round(min(locationsN.x)-dx/2.0))):min(arrN.shape[1],int(round((max(locationsN.x)+dx/2.0))))]
meanPx=numpy.mean(arrsm[numpy.logical_not(masksm)])
arr=arr+(average_back-meanPx)
threshadj=thresh+(average_back-meanPx)
else:
threshadj=thresh
mask=numpy.ones(arr.shape,dtype=numpy.bool)
mask[corrected_arrN<threshadj]=False
# Measure culture phenotypes
locations=c2.measureSizeAndColour(locationsN,arr,im,mask,average_back,BARCODE,FILENAME[0:-4])
# Write results to file
locations.to_csv(os.path.join(os.path.dirname(FILENAME),"Output_Data",os.path.basename(FILENAME).split(".")[0]+".out"),"\t",index=False,engine='python')
dataf=c2.saveColonyzer(os.path.join(os.path.dirname(FILENAME),"Output_Data",os.path.basename(FILENAME).split(".")[0]+".dat"),locations,threshadj,dx,dy)
# Visual check of culture locations
imthresh=c2.threshPreview(arr,threshadj,locations)
r=5
draw=ImageDraw.Draw(imthresh)
draw.ellipse((com[1]-r,com[0]-r,com[1]+r,com[0]+r),fill=(255,0,0))
draw.ellipse((corner[1]-r,corner[0]-r,corner[1]+r,corner[0]+r),fill=(255,0,0))
draw.ellipse((guess[1]-r,guess[0]-r,guess[1]+r,guess[0]+r),fill=(255,0,0))
draw.ellipse((candx[0]-r,candy[0]-r,candx[0]+r,candy[0]+r),fill=(255,0,0))
imthresh.save(os.path.join(os.path.dirname(FILENAME),"Output_Images",os.path.basename(FILENAME).split(".")[0]+".png"))
# Get ready for next image
if verbose: print("Finished {0} in {1:.2f}s".format(os.path.basename(FILENAME),time.time()-startim))
# Get ready for next image
if verbose: print("Finished {0} in {1:.2f}s".format(os.path.basename(BARCODE),time.time()-start))
barcdict={x:barcdict[x] for x in barcdict.keys() if not c2.checkAnalysisStarted(barcdict[x][-1])}
if plots:
pdf.close()
print("No more barcodes to analyse... I'm done.")
def main(inp=""):
#inp="-gixcp"
print("Colonyzer "+c2.__version__)
var=buildVars(inp=inp)
correction,fixedThresh,plots,initpos,fdict,fdir,nrow,ncol,cut,verbose,diffIms,updates,endpoint,edgemask,greenlab=(var["lc"],var["fixedThresh"],var["plots"],var["initpos"],var["fdict"],var["fdir"],var["nrow"],var["ncol"],var["cut"],var["verbose"],var["diffims"],var["updates"],var["endpoint"],var["edgemask"],var["greenlab"])
barcdict=checkImages(fdir,fdict,verbose=verbose)
rept=c2.setupDirectories(barcdict,verbose=verbose)
if greenlab:
posfiles=[resource_filename(Requirement.parse("colonyzer2"),os.path.join("data",f)) for f in ["GreenLabLid.png","CornerLid.png","BottomRightLid.png"]]
negfiles=[resource_filename(Requirement.parse("colonyzer2"),os.path.join("data",f)) for f in ["GreenLabNoLid.png","CornerNoLid.png","BottomRightNoLid.png"]]
frects=[[0.15,0.85,0.5,1.0],[0.0,0.8,0.125,1.0],[0.85,0.8,1.0,1.0]] # Correctly oriented plates...
pdistThresh,ndistThresh=50.3,53.333
checkLid=c2.detectlid.makeLidTest(posfiles,negfiles,frects,pdistThresh,ndistThresh,False)
else:
def checkLid(fname): False
start=time.time()
while len(barcdict)>0:
BARCs=sorted(barcdict)
BARCODE=BARCs[0]
# Create empty file to indicate that barcode is currently being analysed, to allow parallel analysis (lock files)
tmp=open(os.path.join(os.path.dirname(barcdict[BARCODE][0]),"Output_Data",os.path.basename(barcdict[BARCODE][0]).split(".")[0]+".out"),"w").close()
imdir=os.path.dirname(barcdict[BARCODE][0])
print("Analysing images labelled with the barcode "+BARCODE+" in "+imdir)
IMs=[b for b in barcdict[BARCODE] if not checkLid(b)]
if len(IMs)>0:
barcdict[BARCODE]=IMs
LATESTIMAGE=IMs[0]
EARLIESTIMAGE=IMs[-1]
imRoot=EARLIESTIMAGE.split(".")[0]
if verbose:
print("Earliest image: "+EARLIESTIMAGE)
print("Latest image: "+LATESTIMAGE)
if plots:
pdf=PdfPages(os.path.join(os.path.dirname(EARLIESTIMAGE),"Output_Reports",os.path.basename(EARLIESTIMAGE).split(".")[0]+".pdf"))
else:
pdf=None
# Get latest image for thresholding and detecting culture locations
imN,arrN=c2.openImage(LATESTIMAGE)
# Get earliest image for lighting gradient correction
if (LATESTIMAGE==EARLIESTIMAGE) or endpoint:
im0,arr0=imN,arrN
arrloc=arrN
else:
im0,arr0=c2.openImage(EARLIESTIMAGE)
arrloc=arrN-arr0
#arrloc=np.maximum(0,arrloc)
if initpos:
InsData=c2.readInstructions(os.path.dirname(LATESTIMAGE),searchUpStream=True)
# Load initial guesses from Colonyzer.txt file
(candx,candy,dx,dy)=loadLocationGuesses(LATESTIMAGE,InsData)
corner=[0,0]; com=[0,0]; guess=[0,0]
# NOTE: assumes that grid returned by loadLocationGuesses is parallel to image edges
ny=nrow=len(np.unique(candy))
nx=ncol=len(np.unique(candx))
else:
# Automatically generate guesses for gridded array locations
(candx,candy,dx,dy,corner,com,guess)=c2.estimateLocations(arrloc,ncol,nrow,showPlt=plots,pdf=pdf,glob=False,verbose=verbose,nsol=updates)
# Update guesses and initialise locations data frame
locationsN=c2.locateCultures([int(round(cx-dx/2.0)) for cx in candx],[int(round(cy-dy/2.0)) for cy in candy],dx,dy,arrloc,ncol,nrow,update=True)
mask=edgeFill2(arrN,0.9)
grd=mask.copy()
grd[:,:]=False
grd[min(locationsN.y-dy/2):max(locationsN.y+dy/2),min(locationsN.x-dx/2):max(locationsN.x+dx/2)]=True
spots=np.logical_and(grd,mask)
agar=np.logical_and(grd,~mask)
ave0=np.mean(arr0[agar])
thresh=-99
if correction:
if cut:
pseudoempty = np.array(np.round(arr0.copy()),dtype=np.uint8)
bytemask = np.asarray(spots*255, dtype=np.uint8)
filled = cv2.inpaint(pseudoempty,bytemask,10,cv2.INPAINT_TELEA)
blurred = cv2.GaussianBlur(filled,(21,21),0)
pseudoempty[spots] = blurred[spots]
else:
pseudoempty=arr0
else:
pseudoempty=0
for FILENAME in barcdict[BARCODE]:
startim=time.time()
im,arr=c2.openImage(FILENAME)
# Local updates on individual images to allow for slight movement of plate
#locationsN=c2.locateCultures([int(round(cx-dx/2.0)) for cx in candx],[int(round(cy-dy/2.0)) for cy in candy],dx,dy,arr,ncol,nrow,update=True)
ave=np.mean(arr[agar])
# Correct for minor lighting differences between images
arr=np.maximum(0,np.minimum(255,arr-(ave-ave0)))
# Subtract background (corrects for lighting differences within plate/image as well as making agar intensity correspond to zero signal)
arr=np.maximum(arr-pseudoempty,0)
# Measure culture phenotypes
locations=c2.measureSizeAndColour(locationsN,arr,im,spots,0,BARCODE,FILENAME[0:-4])
# Write results to file
locations.to_csv(os.path.join(os.path.dirname(FILENAME),"Output_Data",os.path.basename(FILENAME).split(".")[0]+".out"),"\t",index=False,engine='python')
dataf=c2.saveColonyzer(os.path.join(os.path.dirname(FILENAME),"Output_Data",os.path.basename(FILENAME).split(".")[0]+".dat"),locations,thresh,dx,dy)
# Visual check of culture locations
useMask=False
if useMask:
impreview=c2.threshPreview(locations,mask,None)
else:
arr_stretch=255*arr/np.max(arr)
impreview=c2.threshPreview(locations,arr_stretch,None)
r=5
draw=ImageDraw.Draw(impreview)
draw.ellipse((com[1]-r,com[0]-r,com[1]+r,com[0]+r),fill=(255,0,0))
draw.ellipse((corner[1]-r,corner[0]-r,corner[1]+r,corner[0]+r),fill=(255,0,0))
draw.ellipse((guess[1]-r,guess[0]-r,guess[1]+r,guess[0]+r),fill=(255,0,0))
draw.ellipse((candx[0]-r,candy[0]-r,candx[0]+r,candy[0]+r),fill=(255,0,0))
impreview.save(os.path.join(os.path.dirname(FILENAME),"Output_Images",os.path.basename(FILENAME).split(".")[0]+".png"))
# Get ready for next image
if verbose: print("Finished {0} in {1:.2f}s".format(os.path.basename(FILENAME),time.time()-startim))
# Get ready for next image
if verbose: print("Finished {0} in {1:.2f}s".format(os.path.basename(BARCODE),time.time()-start))
barcdict={x:barcdict[x] for x in barcdict.keys() if not (c2.checkAnalysisStarted(barcdict[x][-1]) or x==BARCODE)}
if plots and len(IMs)>0:
pdf.close()
print("No more barcodes to analyse... I'm done.")
def main(inp=""):
#inp="-gixcp"
print("Colonyzer " + c2.__version__)
var = buildVars(inp=inp)
correction, fixedThresh, plots, initpos, fdict, fdir, nrow, ncol, cut, verbose, diffIms, updates, endpoint, edgemask, greenlab = (
var["lc"], var["fixedThresh"], var["plots"], var["initpos"],
var["fdict"], var["fdir"], var["nrow"], var["ncol"], var["cut"],
var["verbose"], var["diffims"], var["updates"], var["endpoint"],
var["edgemask"], var["greenlab"])
if (var['remove']):
barcdict = checkImages(fdir, fdict, verbose=verbose, checkDone=False)
rept = c2.setupDirectories(barcdict, verbose=verbose, remove=True)
barcdict = checkImages(fdir, fdict, verbose=verbose)
rept = c2.setupDirectories(barcdict, verbose=verbose)
if greenlab:
posfiles = [
resource_filename(Requirement.parse("colonyzer2"),
os.path.join("data", f)) for f in
["GreenLabLid.png", "CornerLid.png", "BottomRightLid.png"]
]
negfiles = [
resource_filename(Requirement.parse("colonyzer2"),
os.path.join("data", f)) for f in
["GreenLabNoLid.png", "CornerNoLid.png", "BottomRightNoLid.png"]
]
frects = [[0.15, 0.85, 0.5, 1.0], [0.0, 0.8, 0.125, 1.0],
[0.85, 0.8, 1.0, 1.0]] # Correctly oriented plates...
pdistThresh, ndistThresh = 50.3, 53.333
checkLid = c2.detectlid.makeLidTest(posfiles, negfiles, frects,
pdistThresh, ndistThresh, False)
else:
def checkLid(fname):
False
start = time.time()
while len(barcdict) > 0:
BARCs = sorted(barcdict)
BARCODE = BARCs[0]
# Create empty file to indicate that barcode is currently being analysed, to allow parallel analysis (lock files)
tmp = open(
os.path.join(
os.path.dirname(barcdict[BARCODE][0]), "Output_Data",
os.path.basename(barcdict[BARCODE][0]).split(".")[0] + ".out"),
"w").close()
imdir = os.path.dirname(barcdict[BARCODE][0])
print("Analysing images labelled with the barcode " + BARCODE +
" in " + imdir)
IMs = [b for b in barcdict[BARCODE] if not checkLid(b)]
if len(IMs) > 0:
barcdict[BARCODE] = IMs
LATESTIMAGE = IMs[0]
EARLIESTIMAGE = IMs[-1]
imRoot = EARLIESTIMAGE.split(".")[0]
if verbose:
print("Earliest image: " + EARLIESTIMAGE)
print("Latest image: " + LATESTIMAGE)
if plots:
pdf = PdfPages(
os.path.join(
os.path.dirname(EARLIESTIMAGE), "Output_Reports",
os.path.basename(EARLIESTIMAGE).split(".")[0] +
".pdf"))
else:
pdf = None
# Get latest image for thresholding and detecting culture locations
imN, arrN = c2.openImage(LATESTIMAGE)
# Get earliest image for lighting gradient correction
if (LATESTIMAGE == EARLIESTIMAGE) or endpoint:
im0, arr0 = imN, arrN
arrloc = arrN
else:
im0, arr0 = c2.openImage(EARLIESTIMAGE)
arrloc = arrN - arr0
#arrloc=np.maximum(0,arrloc)
if initpos:
InsData = c2.readInstructions(os.path.dirname(LATESTIMAGE),
searchUpStream=True)
# Load initial guesses from Colonyzer.txt file
(candx, candy, dx,
dy) = loadLocationGuesses(LATESTIMAGE, InsData)
corner = [0, 0]
com = [0, 0]
guess = [0, 0]
# NOTE: assumes that grid returned by loadLocationGuesses is parallel to image edges
ny = nrow = len(np.unique(candy))
nx = ncol = len(np.unique(candx))
else:
if verbose:
print("Initial estimate for location of spots on plate...")
# Automatically generate guesses for gridded array locations
(candx, candy, dx, dy, corner, com,
guess) = c2.estimateLocations(arrloc,
ncol,
nrow,
showPlt=plots,
pdf=pdf,
glob=False,
verbose=verbose,
nsol=updates)
# Update guesses and initialise locations data frame
if verbose:
print("Updating estimate for location of spots on plate...")
locationsN = c2.locateCultures(
[int(round(cx - dx / 2.0)) for cx in candx],
[int(round(cy - dy / 2.0)) for cy in candy],
dx,
dy,
arrloc,
ncol,
nrow,
update=True)
mask = edgeFill2(arrN, var['slopefill'])
grd = mask.copy()
grd[:, :] = False
grd[int(round(min(locationsN.y -
dy / 2))):int(round(max(locationsN.y + dy / 2))),
int(round(min(locationsN.x -
dx / 2))):int(round(max(locationsN.x +
dx / 2)))] = True
spots = np.logical_and(grd, mask)
agar = np.logical_and(grd, ~mask)
ave0 = np.mean(arr0[agar])
thresh = -99
mask = None
if correction:
if cut:
if verbose: print("Constructing pseudoempty plate...")
pseudoempty = np.array(np.round(arr0.copy()),
dtype=np.uint8)
bytemask = np.asarray(spots * 255, dtype=np.uint8)
filled = cv2.inpaint(pseudoempty, bytemask, 10,
cv2.INPAINT_TELEA)
blurred = cv2.GaussianBlur(filled, (21, 21), 0)
pseudoempty[spots] = blurred[spots]
filled = None
blurred = None
else:
pseudoempty = arr0
else:
pseudoempty = 0
# Segment final image
#thresh,bindat=c2.automaticThreshold(arrN-pseudoempty)
thresh, tmask = cv2.threshold(
np.array(np.round(np.maximum((arrN - pseudoempty)[grd], 0)),
dtype=np.uint8), 0, 255,
cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
thresh = thresh - 15
tmask = None
for FILENAME in barcdict[BARCODE]:
startim = time.time()
im, arr = c2.openImage(FILENAME)
# Local updates on individual images to allow for slight movement of plate
#locationsN=c2.locateCultures([int(round(cx-dx/2.0)) for cx in candx],[int(round(cy-dy/2.0)) for cy in candy],dx,dy,arr,ncol,nrow,update=True)
ave = np.mean(arr[agar])
# Correct for minor lighting differences between images
arr = np.maximum(0, np.minimum(255, arr - (ave - ave0)))
# Subtract background (corrects for lighting differences within plate/image as well as making agar intensity correspond to zero signal)
arr = np.maximum(arr - pseudoempty, 0)
#mask=edgeFill2(arr,var['slopefill'])
mask = np.ones(arr.shape, dtype=np.bool)
mask[arr < thresh] = False
# Measure culture phenotypes
locations = c2.measureSizeAndColour(locationsN, arr, im,
np.logical_and(grd, mask),
0, BARCODE, FILENAME[0:-4])
# Write results to file
locations.to_csv(os.path.join(
os.path.dirname(FILENAME), "Output_Data",
os.path.basename(FILENAME).split(".")[0] + ".out"),
"\t",
index=False)
dataf = c2.saveColonyzer(
os.path.join(
os.path.dirname(FILENAME), "Output_Data",
os.path.basename(FILENAME).split(".")[0] + ".dat"),
locations, thresh, dx, dy)
# Visual check of culture locations
linewidth = max(1, int(round(min(im.size) / 500.0)))
impreview_mask = c2.threshPreview(locations,
mask,
None,
linethick=linewidth,
circlerad=linewidth)
impreview = c2.threshPreview(locations,
255 * arr / np.max(arr),
None,
linethick=linewidth,
circlerad=linewidth)
r = max(1, int(round(linewidth / 2.0)))
draw = ImageDraw.Draw(impreview)
draw.ellipse((com[1] - r, com[0] - r, com[1] + r, com[0] + r),
fill=(255, 0, 0))
draw.ellipse((corner[1] - r, corner[0] - r, corner[1] + r,
corner[0] + r),
fill=(255, 0, 0))
draw.ellipse(
(guess[1] - r, guess[0] - r, guess[1] + r, guess[0] + r),
fill=(255, 0, 0))
draw.ellipse(
(candx[0] - r, candy[0] - r, candx[0] + r, candy[0] + r),
fill=(255, 0, 0))
impreview.save(
os.path.join(
os.path.dirname(FILENAME), "Output_Images",
os.path.basename(FILENAME).split(".")[0] + ".png"))
impreview_mask.save(
os.path.join(
os.path.dirname(FILENAME), "Output_Images",
os.path.basename(FILENAME).split(".")[0] +
"_AREA.png"))
# Get ready for next image
if verbose:
print("Finished {0} in {1:.2f}s".format(
os.path.basename(FILENAME),
time.time() - startim))
im = None
arr = None
# Get ready for next image
if verbose:
print("Finished {0} in {1:.2f}s".format(os.path.basename(BARCODE),
time.time() - start))
barcdict = {
x: barcdict[x]
for x in barcdict.keys()
if not (c2.checkAnalysisStarted(barcdict[x][-1]) or x == BARCODE)
}
if plots and len(IMs) > 0:
pdf.close()
print("No more barcodes to analyse... I'm done.")
def main(inp=""):
print("Colonyzer "+c2.__version__)
cythonFill=False
var=buildVars(inp=inp)
correction,fixedThresh,plots,initpos,fdict,fdir,nrow,ncol,cut,verbose,diffIms,updates,endpoint,edgemask=(var["lc"],var["fixedThresh"],var["plots"],var["initpos"],var["fdict"],var["fdir"],var["nrow"],var["ncol"],var["cut"],var["verbose"],var["diffims"],var["updates"],var["endpoint"],var["edgemask"])
barcdict=checkImages(fdir,fdict,verbose=verbose)
rept=c2.setupDirectories(barcdict,verbose=verbose)
start=time.time()
while len(barcdict)>0:
BARCODE,imdir,LATESTIMAGE,EARLIESTIMAGE,imRoot=prepareTimecourse(barcdict,verbose=verbose)
if plots:
pdf=PdfPages(os.path.join(os.path.dirname(EARLIESTIMAGE),"Output_Reports",os.path.basename(EARLIESTIMAGE).split(".")[0]+".pdf"))
else:
pdf=None
# Create empty file to indicate that barcode is currently being analysed, to allow parallel analysis (lock files)
tmp=open(os.path.join(os.path.dirname(EARLIESTIMAGE),"Output_Data",os.path.basename(EARLIESTIMAGE).split(".")[0]+".out"),"w").close()
# Get latest image for thresholding and detecting culture locations
imN,arrN=c2.openImage(LATESTIMAGE)
# Get earliest image for lighting gradient correction
if (LATESTIMAGE==EARLIESTIMAGE) or endpoint:
im0,arr0=imN,arrN
arrloc=arrN
else:
im0,arr0=c2.openImage(EARLIESTIMAGE)
arrloc=arrN-arr0
#arrloc=np.maximum(0,arrloc)
if initpos:
InsData=c2.readInstructions(os.path.dirname(LATESTIMAGE),searchUpStream=True)
# Load initial guesses from Colonyzer.txt file
(candx,candy,dx,dy)=loadLocationGuesses(LATESTIMAGE,InsData)
corner=[0,0]; com=[0,0]; guess=[0,0]
# NOTE: assumes that grid returned by loadLocationGuesses is parallel to image edges
ny=nrow=len(np.unique(candy))
nx=ncol=len(np.unique(candx))
else:
# Automatically generate guesses for gridded array locations
(candx,candy,dx,dy,corner,com,guess)=c2.estimateLocations(arrloc,ncol,nrow,showPlt=plots,pdf=pdf,glob=False,verbose=verbose,nsol=updates)
# Update guesses and initialise locations data frame
locationsN=c2.locateCultures([int(round(cx-dx/2.0)) for cx in candx],[int(round(cy-dy/2.0)) for cy in candy],dx,dy,arrloc,ncol,nrow,update=True)
mask=edgeFill(arr0,locationsN,dx,dy,0.8)
if correction:
if cut:
startFill=time.time()
if cythonFill:
pseudoempty=c2.maskAndFillCython(arr0,maskN,0.005)
print("Inpainting using Cython & np: "+str(time.time()-startFill)+" s")
else:
pseudoempty=c2.maskAndFill(arr0,mask,0.005)
print("Inpainting using np: "+str(time.time()-start)+" s")
else:
pseudoempty=arr0
# Smooth (pseudo-)empty image
(correction_map,average_back)=c2.makeCorrectionMap(pseudoempty,locationsN,verbose=verbose)
# Correct spatial gradient in final image
corrected_arrN=arrN*correction_map
else:
average_back=np.mean(arr0[np.min(locationsN.y):np.max(locationsN.y),np.min(locationsN.x):np.max(locationsN.x)])
corrected_arrN=arrN
# Trim outer part of image to remove plate walls
trimmed_arrN=corrected_arrN[max(0,int(round(min(locationsN.y)-dy/2.0))):min(arrN.shape[0],int(round((max(locationsN.y)+dy/2.0)))),max(0,int(round(min(locationsN.x)-dx/2.0))):min(arrN.shape[1],int(round((max(locationsN.x)+dx/2.0))))]
if edgemask:
thresh=-99
else:
if fixedThresh>=0:
thresh=fixedThresh
else:
(thresh,bindat)=c2.automaticThreshold(trimmed_arrN,BARCODE,pdf=pdf)
if plots:
c2.plotModel(bindat,label=BARCODE,pdf=pdf)
# Mask for identifying culture areas
maskN=np.ones(arrN.shape,dtype=np.bool)
maskN[corrected_arrN<thresh]=False
for FILENAME in barcdict[BARCODE]:
startim=time.time()
im,arr=c2.openImage(FILENAME)
# Local updates on individual images to allow for slight movement of plate
#locationsN=c2.locateCultures([int(round(cx-dx/2.0)) for cx in candx],[int(round(cy-dy/2.0)) for cy in candy],dx,dy,arr,ncol,nrow,update=True)
if correction:
arr=arr*correction_map
if diffIms:
# Correct for lighting differences between plates
arrsm=arr[max(0,int(round(min(locationsN.y)-dy/2.0))):min(arrN.shape[0],int(round((max(locationsN.y)+dy/2.0)))),max(0,int(round(min(locationsN.x)-dx/2.0))):min(arrN.shape[1],int(round((max(locationsN.x)+dx/2.0))))]
masksm=maskN[max(0,int(round(min(locationsN.y)-dy/2.0))):min(arrN.shape[0],int(round((max(locationsN.y)+dy/2.0)))),max(0,int(round(min(locationsN.x)-dx/2.0))):min(arrN.shape[1],int(round((max(locationsN.x)+dx/2.0))))]
meanPx=np.mean(arrsm[np.logical_not(masksm)])
arr=arr+(average_back-meanPx)
#arr=np.maximum(0,arr)
threshadj=thresh+(average_back-meanPx)
aveB=average_back
else:
threshadj=thresh
aveB=0
if not edgemask:
mask=np.ones(arr.shape,dtype=np.bool)
mask[corrected_arrN<threshadj]=False
# Measure culture phenotypes
locations=c2.measureSizeAndColour(locationsN,arr,im,mask,average_back,BARCODE,FILENAME[0:-4])
# Write results to file
locations.to_csv(os.path.join(os.path.dirname(FILENAME),"Output_Data",os.path.basename(FILENAME).split(".")[0]+".out"),"\t",index=False,engine='python')
dataf=c2.saveColonyzer(os.path.join(os.path.dirname(FILENAME),"Output_Data",os.path.basename(FILENAME).split(".")[0]+".dat"),locations,threshadj,dx,dy)
# Visual check of culture locations
if edgemask:
imthresh=c2.threshPreview(locations,mask,None)
else:
imthresh=c2.threshPreview(locations,arr,threshadj)
r=5
draw=ImageDraw.Draw(imthresh)
draw.ellipse((com[1]-r,com[0]-r,com[1]+r,com[0]+r),fill=(255,0,0))
draw.ellipse((corner[1]-r,corner[0]-r,corner[1]+r,corner[0]+r),fill=(255,0,0))
draw.ellipse((guess[1]-r,guess[0]-r,guess[1]+r,guess[0]+r),fill=(255,0,0))
draw.ellipse((candx[0]-r,candy[0]-r,candx[0]+r,candy[0]+r),fill=(255,0,0))
imthresh.save(os.path.join(os.path.dirname(FILENAME),"Output_Images",os.path.basename(FILENAME).split(".")[0]+".png"))
# Get ready for next image
if verbose: print("Finished {0} in {1:.2f}s".format(os.path.basename(FILENAME),time.time()-startim))
# Get ready for next image
if verbose: print("Finished {0} in {1:.2f}s".format(os.path.basename(BARCODE),time.time()-start))
barcdict={x:barcdict[x] for x in barcdict.keys() if not c2.checkAnalysisStarted(barcdict[x][-1])}
if plots:
pdf.close()
print("No more barcodes to analyse... I'm done.")