#!/usr/bin/env anduril
//$OPT --wrapper slurm-prefix
//$OPT --pipe "tee $ANDURIL_EXECUTION_DIR/_log"
//$OPT --pipe "anduril-pager --ls -t --title $ANDURIL_EXECUTION_DIR"
//$OPT --threads 4
//$POST anduril-result-browser --unused "$ANDURIL_EXECUTION_DIR"

import anduril.builtin._
import anduril.tools._
import anduril.anima._
import org.anduril.runtime._

object Analysis {
// Download the data set
var filedownload=URLInput(url="https://www.broadinstitute.org/bbbc/BBBC010/BBBC010_v1_images.zip")

// Custom script to extract the archive
var fileunarchive=QuickBash(in=filedownload, script="mkdir $out; cd $out; unzip -j $in")

//Define the folder of input images
var filedir=fileunarchive.out
// create a list of files matching a pattern    
var transmitted=Folder2Array(folder1=filedir,
                        filePattern="(.*w2.*tif)")

//----------------------------------------------------------------------
// Convert the images to grayscale PNG
var signal=ImageMagick(ina1=transmitted,command="@ina1@ -fx R -auto-level @out@")
// Preprocess images, inverting color,equalizing intensity and background removal.
val sigprocess=MatlabOp(in1=signal,
           script="foo=imclose(adapthisteq(mat2gray(imcomplement(in1))),strel('disk',2,0));"+
                  "out=(foo-medfilt2(foo,[21 21]));")
// Segment images with Otsu method, and constant correction, removing small clutter
val wormask=SegmentImage(in=sigprocess.out,
                        method="otsu",minArea=200,
                        corr=0.6,fill=false,
                        maxArea=0,minInt=0.001)
// Thin the mask to skeletons
val skeleton=MatlabOp(in1=wormask.mask,
           script="out=bwmorph(imclose(in1,strel('disk',2,0)),'thin',Inf);")
// Measure the features from skeletons
val skfeatures=SkeletonFeatures(skeleton=skeleton.out,
                      images=sigprocess.out,
                      maxDist=150)
// Create a visualization of the segmentation and skeletonization
val seg_visu=MergeImage(in=makeArray(sigprocess.out,wormask.perimeter,skeleton.out), colors="G,R,C")

val seg_visu_foss=ImageMagick(in=makeArray(sigprocess.out,wormask.perimeter,skeleton.out),
                command=" -compose Plus \\( @a1@ -normalize +level-colors Black,Green \\) "+
                        " \\( @a2@ -auto-level +level-colors ,'rgb(220,0,0)' \\) -composite "+
                        " \\( @a3@ -auto-level +level-colors ,Cyan \\) -composite "+
                        "@out@")
// Remove short skeletons from the data
val measurements_filt=CSVFilter(skfeatures.out, lowBound="Length=20")
// Calculate an additional feature from the measured
val measurements_calc=CSVTransformer(csv1=measurements_filt,
        transform1="csv1[,c('File','Distance','Alpha')]",
        transform2="csv1[,'Distance']/csv1[,'Length']",
        columnNames="c('File','D','Alpha','DLRatio')")
// Calculate per-image summaries
val measurements_median=CSVSummary(measurements_calc, 
                    summaryType="median", clusterCol="File", counts=false)
// Parse the filename for ground truth
val measurements_grouped=CSVTransformer(measurements_median,
        transform1="paste('G',as.integer(gsub('(.*)(..)(_w2).*','\\\\2',csv1[,'File'])>12),sep='')",
        transform2="csv1",
        columnNames="c('Group',colnames(csv1))")
// Split the data to training and validation sets
val images_split=CSVSplit(measurements_grouped, labelCol="File")

val sets_split=ArraySplit(images_split, N=2)
val sets_visu=FolderSplit(seg_visu, N=2)

val training_set=CSVListJoin(in=sets_split.out1, fileCol="")
val validation_set=CSVListJoin(in=sets_split.out2, fileCol="")
// Train and classify with random forest classifier
val rf_classifier=WekaClassifier(data=training_set,
               testdata=validation_set,
               classifydata=validation_set,
               classColumn="Group",
               columnsToRemove="File",
               crossValidation=500,
               methodClass="weka.classifiers.trees.RandomForest")
// Train and classify with naive bayes classifier
val nbc_classifier=WekaClassifier(data=training_set,
               testdata=validation_set,
               classifydata=validation_set,
               classColumn="Group",
               columnsToRemove="File",
               crossValidation=500,
               methodClass="weka.classifiers.bayes.NaiveBayes")
// Create annotations for the classified images
val annotations=CSVTransformer(csv1=validation_set,
                           csv2=rf_classifier.predictedClasses,
            transform1="csv1[,'File']",
            transform2="paste('Class:',csv2[,'Group'],'Prediction:',"+
            "csv2[,'PredictedClass'],'<br>',csv1[,'File'])",
            columnNames="c('File','Annotation')")
// Create a result HTML image gallery with annotations
val gallery=ImageGallery(inRoot=sets_visu.out("2"),
                     csvRoot=annotations,
                     annotationCol="Annotation")

// collect results from different classifiers:
val compare_classifers=CSVListJoin(in=makeArray("RF"->rf_classifier.confusion, 
                                      "NBC"->nbc_classifier.confusion), 
                                      fileCol="Method")


val highlight_self=SyntaxHighlight(in = INPUT("worms2.scala"),
                    language        = "Anduril",
                    rowNumbers      = true,
                    title           = "Code listing",
                    PNGFont         = "Andale Mono",
                    PNGSize         = 14,
                    PNGPad          = 5)


}