A demo Colonyzer script: looking for cultures in a wider search space

Search.py

from colonyzer2.functions import *
import time, sys, os, numpy, PIL

print("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
print("Note that this script requires a Colonyzer.txt file (as generated by ColonyzerParametryzer) describing initial guess for culture array")
print("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")

# Lydall lab file naming convention
# First 15 characters in filename identify unique plates
# Remaining charaters can be used to store date, time etc.
barcRange=(0,15)

# Disabling lighting correction
correction=False
fixedThresh=0
threshplots=False

start=time.time()

# Find image files which have yet to be analysed
barcdict=getBarcodes(os.getcwd(),barcRange)
# Setup output directories if not already present
rept=setupDirectories(barcdict)
if len(rept)>0:
    print ("Newly created directories:")
    for line in rept:
        print rept

InsData=readInstructions(os.getcwd())

while len(barcdict)>0:
    BARCODE=barcdict.keys()[0]
    print(BARCODE)
    # Set this to zero to use true last image
    # Note that image names already in reverse order
    # Stepping back from final image avoids images with cracked agar
    latestindex=int(round(0.1*len(barcdict[BARCODE]))) 
    LATESTIMAGE=barcdict[BARCODE][latestindex]
    EARLIESTIMAGE=barcdict[BARCODE][-1]
    print("Earliest: "+EARLIESTIMAGE)
    print("Latest: "+LATESTIMAGE)
    imRoot=EARLIESTIMAGE.split(".")[0]

    # Generate pdf report about distributions
    if threshplots:
        pdf=PdfPages(BARCODE+'_HistogramReport.pdf')
    
    # Indicate that barcode is currently being analysed, to allow parallel analysis
    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=openImage(LATESTIMAGE)
    # Get earliest image for lighting gradient correction
    im0,arr0=openImage(EARLIESTIMAGE)

    # If we have ColonyzerParametryzer output for this filename, use it for initial culture location estimates
    if os.path.basename(LATESTIMAGE) in InsData:
        (candx,candy,dx,dy)=SetUp(InsData[os.path.basename(LATESTIMAGE)])
    # If there are multiple calibrations available, choose the best one based on date of image capture
    elif any(isinstance(el, list) for el in InsData['default']):
        imname=os.path.basename(LATESTIMAGE).split(".")[0]
        imdate=imname[-19:-9]
        (candx,candy,dx,dy)=SetUp(InsData['default'],imdate)
    else:
        (candx,candy,dx,dy)=SetUp(InsData['default'])

    # Update guesses and initialise locations data frame
    locationsN=locateCultures(candx,candy,dx,dy,arrN,search=0.5)

    # Trim outer part of image to remove plate walls
    trimmed_arr=arrN[max(0,min(locationsN.y)-dy):min(arr0.shape[0],(max(locationsN.y)+dy)),max(0,(min(locationsN.x)-dx)):min(arr0.shape[1],(max(locationsN.x)+dx))]
    #showIm(trimmed_arr)
    if fixedThresh!=0:
        thresh=fixedThresh
    else:
        if threshplots:
            (thresh,bindat)=automaticThreshold(trimmed_arr,BARCODE,pdf)
            plotModel(bindat,label=BARCODE,pdf=pdf)
        else:
            (thresh,bindat)=automaticThreshold(trimmed_arr)
    if threshplots:
        pdf.close()   

    # Mask for identifying culture areas
    maskN=numpy.ones(arrN.shape,dtype=numpy.bool)
    maskN[arrN<thresh]=False

    # Cut out pixels from EARLIESTIMAGE based on mask from LATESTIMAGE and fill in
    pseudoempty=maskAndFill(arr0,maskN,5)

    # Smooth (pseudo-)empty image 
    (correction_map,average_back)=makeCorrectionMap(pseudoempty,locationsN,printMess=correction)

    for FILENAME in barcdict[BARCODE]:
        im,arr=openImage(FILENAME)
        if correction:
            arr=arr*correction_map
        
        # Correct for lighting differences between plates
        arrsm=arr[numpy.min(locationsN.y):numpy.max(locationsN.y),numpy.min(locationsN.x):numpy.max(locationsN.x)]
        masksm=maskN[numpy.min(locationsN.y):numpy.max(locationsN.y),numpy.min(locationsN.x):numpy.max(locationsN.x)]
        meanPx=numpy.mean(arrsm[numpy.logical_not(masksm)])

        arr=arr+(average_back-meanPx)
        threshadj=thresh+(average_back-meanPx)

        mask=numpy.ones(arr.shape,dtype=numpy.bool)
        mask[arrN<threshadj]=False

        # Measure culture phenotypes
        locations=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=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=threshPreview(arr,threshadj,locations)
        imthresh.save(os.path.join(os.path.dirname(FILENAME),"Output_Images",os.path.basename(FILENAME).split(".")[0]+".png"))

    # Get ready for next image
    print("Finished: "+FILENAME+" "+str(time.time()-start)+" s")
    barcdict=getBarcodes(os.getcwd(),barcRange)
    
    # Setup output directories if not already present
    rept=setupDirectories(barcdict)

print("No more barcodes to analyse... I'm done.")