tkintercore.py

import numpy, os
#import math
#import gdal  #geospatial data abstraction
#import osr
#import ogr  #simple features library, vector data access, pawrt of GDAL
#from pyproj import Proj, transform
#foldername='../drondata/alfalfa'
import csv
import time
from skimage.feature import corner_fast,corner_peaks,corner_harris,corner_shi_tomasi
#import cv2
#import matplotlib.pyplot as plt
global lastlinecount,misslabel
from sklearn.cluster import KMeans
from sklearn.mixture import GaussianMixture as GMM
from scipy.stats import shapiro
from scipy import ndimage as ndi
from skimage.morphology import watershed
from skimage.feature import peak_local_max




tinyareas=[]
colortable={}
colormatch={}
labellist=[]
elesize=[]
avgarea=None
greatareas=[]
exceptions=[]
class node:
    def __init__(self,i,j):
        self.i=i
        self.j=j
        self.label=0
        self.check=False

def renamelabels(area):
    res=area
    unique=numpy.unique(res)
    i=202501.0
    for uni in unique[1:]:
        res=numpy.where(res==uni,i,res)
        i+=1.0
    unique = numpy.unique(res)
    i=1.0
    for uni in unique[1:]:
        res=numpy.where(res==uni,i,res)
        i+=1.0
    return res


def combinecrops(area,subarea,i,ele,ulx,uly,rlx,rly):
    print('combinecrops: i='+str(i)+' ele='+str(ele))
    localarea=numpy.asarray(area)
    if i==ele:
        return localarea
    if i<ele:
        localarea=numpy.where(localarea==ele,i,localarea)
    else:
        subarea=numpy.where(subarea==i,ele,subarea)
        localarea[uly:rly+1,ulx:rlx+1]=subarea
    unique = numpy.unique(localarea)
    print(unique)
    return localarea

def sortline(linelist,midx,midy,linenum):
    localy={}
    localx={}
    for ele in linelist:
        localy.update({ele:midy[ele]})
        localx.update({ele:midx[ele]})
    i=0
    for ele in sorted(localx,key=localx.get):
        try:
            tempcolordict={ele:labellist[i+sum(elesize[:linenum])]}
            colortable.update(tempcolordict)
            print(tempcolordict)
            i+=1
        except IndexError:
            pass


def relabel(area):
    unique=numpy.unique(area).tolist()
    unique=unique[1:]
    midx={}
    midy={}
    colortable.clear()
    misslabel=0
    colorarea = numpy.zeros(area.shape)
    colorarea = colorarea.tolist()
    for ele in unique:
        eleloc=numpy.where(area==ele)
        ylist=eleloc[0].tolist()
        xlist=eleloc[1].tolist()
        y=ylist[int(len(ylist)/2)]
        x=xlist[int(len(xlist)/2)]
        txdict={ele:x}
        tydict={ele:y}
        midx.update(txdict)
        midy.update(tydict)
    linelist=[]
    linecount=0
    if len(elesize)!=0:
        for ele in sorted(midy,key=midy.get):
            linelist.append(ele)
            print(ele,midy[ele],midx[ele])
            #if len(linelist)%23==0:
            if linecount<len(elesize):
                if len(linelist)%elesize[linecount]==0:
                    #sortline(linelist,midx,midy,linecount-1)
                    sortline(linelist,midx,midy,linecount)
                    linelist[:]=[]
                    #colcount=1
                    linecount+=1
                    print(linecount)
                    misslabel=0
                    continue
                else:
                    misslabel=elesize[linecount]-len(linelist)
                    if misslabel==0:
                        misslabel=sum(elesize[:linecount])-sum(elesize)
            else:
                misslabel=0
                misslabel-=len(linelist)
        for i in range(len(area)):
            for j in range(len(area[0])):
                if area[i][j] != 0:
                    try:
                        colorarea[i][j] = colormatch[colortable[area[i][j]]]
                    except KeyError:
                        pass
        colorarea = numpy.asarray(colorarea)
    else:
        i=1
        colorarea=numpy.zeros(area.shape)
        for ele in sorted(midy,key=midy.get):
            tempdict={ele:i}
            colortable.update(tempdict)
            elelocs=numpy.where(area==ele)
            colorarea[elelocs]=i
            i+=1

    print(colortable)

    if misslabel==0 and len(labellist)>0:
        misslabel=len(labellist)-len(unique)
    return colorarea,misslabel,colortable

def labelgapnp(area):
    x=[0,-1,-1,-1,0,1,1,1]
    y=[1,1,0,-1,-1,-1,0,1]

    nodearea=numpy.where(area!=0)
    labelnum=2
    nodelist={}
    for k in range(len(nodearea[0])):
        tnode=node(nodearea[0][k],nodearea[1][k])
        tempdict={(nodearea[0][k],nodearea[1][k]):tnode}
        nodelist.update(tempdict)
    nodekeys=list(nodelist.keys())
    for key in nodekeys:
        if nodelist[key].check==False:
            nodelist[key].check=True
            nodelist[key].label=labelnum
            offspringlist=[]
            for m in range(len(y)):
                i=key[0]
                j=key[1]
                if (i+y[m],j+x[m]) in nodelist:
                    newkey=(i+y[m],j+x[m])
                    if nodelist[newkey].check==False:
                        nodelist[newkey].check=True
                        offspringlist.append(newkey)
            if len(offspringlist)==0:
                nodelist[key].label=0
            while(len(offspringlist)>0):
                currnodekey=offspringlist.pop(0)
                curri=currnodekey[0]
                currj=currnodekey[1]
                nodelist[currnodekey].label=labelnum
                for m in range(len(y)):
                    if (curri+y[m],currj+x[m]) in nodelist:
                        if nodelist[(curri+y[m],currj+x[m])].check==False:
                            nodelist[(curri+y[m],currj+x[m])].check=True
                            offspringlist.append((curri+y[m],currj+x[m]))
            labelnum+=1
    area=numpy.zeros(area.shape)
    for key in nodekeys:
        area[key[0],key[1]]=nodelist[key].label
    return area


def labelgap(area):
    x=[0,-1,-1,-1,0,1,1,1]
    y=[1,1,0,-1,-1,-1,0,1]
    nodearea=area.tolist()
    labelnum=2
    nodelist=[]
    for i in range(len(nodearea)):
        tempnodelist=[]
        for j in range(len(nodearea[0])):
            tnode=node(i,j)
            tempnodelist.append(tnode)
        nodelist.append(tempnodelist)
    for i in range(len(nodelist)):
        for j in range(len(nodelist[i])):
            if nodelist[i][j].check==False and nodearea[i][j]!=0:
                nodelist[i][j].check=True
                #print('nodelisti,j='+str(i)+','+str(j))
                nodelist[i][j].label=labelnum
                offspringlist=[]
                for k in range(len(y)):
                    if i+y[k]<len(nodelist) and i+y[k]>=0 and j+x[k]<len(nodelist[i]) and j+x[k]>=0:
                        if nodelist[i+y[k]][j+x[k]].check==False and nodearea[i+y[k]][j+x[k]]!=0:
                            nodelist[i+y[k]][j+x[k]].check=True
                            #print('nodelist i+ym,j+xn='+str(i+y[m])+','+str(j+x[n]))
                            offspringlist.append(nodelist[i+y[k]][j+x[k]])

                if len(offspringlist)==0:
                    nodelist[i][j].label=0
                while(len(offspringlist)>0):
                    currnode=offspringlist.pop(0)
                    curri=currnode.i
                    currj=currnode.j
                    nodelist[curri][currj].label=labelnum
                    for k in range(len(y)):
                        if curri+y[k]>=0 and curri+y[k]<len(nodelist) and currj+x[k]<len(nodelist[i]) and currj+x[k]>=0:
                            if nodelist[curri+y[k]][currj+x[k]].check==False and nodearea[curri+y[k]][currj+x[k]]!=0:
                                nodelist[curri+y[k]][currj+x[k]].check=True
                                #print('nodelist i+ym,j+xn='+str(i+y[m])+','+str(j+x[n]))
                                offspringlist.append(nodelist[curri+y[k]][currj+x[k]])
                labelnum+=1
    for i in range(len(nodearea)):
        for j in range(len(nodearea[0])):
            nodearea[i][j]=nodelist[i][j].label

    area=numpy.asarray(nodearea)
    return area

def get_boundary(area):

    contentlocs=numpy.where(area!=0)
    boundaryres=numpy.zeros(area.shape)
    x=[0,-1,-1,-1,0,1,1,1]
    y=[1,1,0,-1,-1,-1,0,1]
    for m in range(len(contentlocs[0])):
        for k in range(len(y)):
            i=contentlocs[0][m]+y[k]
            j=contentlocs[1][m]+x[k]
            if i>=0 and i<area.shape[0] and j>=0 and j<area.shape[1]:
                if area[i,j]==0:
                    boundaryres[contentlocs[0][m],contentlocs[1][m]]=1
                    break
    #localarea=area
    #distance=ndi.distance_transform_edt(localarea)
    #boundaryres=numpy.zeros(localarea.shape)
    #boundaryres=numpy.where(distance==1.0,1,boundaryres)
    return boundaryres


def get_boundaryloc(area,labelvalue):
    contentlocs=numpy.where(area==labelvalue)
    boundaryloc=([],[])

    x = [0, -1, -1, -1, 0, 1, 1, 1]
    y = [1, 1, 0, -1, -1, -1, 0, 1]
    for m in range(len(contentlocs[0])):
        for k in range(len(y)):
            i = contentlocs[0][m] + y[k]
            j = contentlocs[1][m] + x[k]
            if i >= 0 and i < area.shape[0] and j >= 0 and j < area.shape[1]:
                if area[i, j] == 0:
                    boundaryloc[0].append(contentlocs[0][m])
                    boundaryloc[1].append(contentlocs[1][m])
                    break
    return boundaryloc

def boundarywatershed(area,segbondtimes,boundarytype):   #area = 1's
    if avgarea is not None and numpy.count_nonzero(area)<avgarea/2:
        return area
    x=[0,-1,-1,-1,0,1,1,1]
    y=[1,1,0,-1,-1,-1,0,1]
    #x=[0,-1,0,1]
    #y=[1,0,-1,0]
    #
    #temptif=cv2.GaussianBlur(area,(3,3),0,0,cv2.BORDER_DEFAULT)
    #areaboundary=cv2.Laplacian(area,cv2.CV_8U,ksize=5)
    #plt.imshow(areaboundary)
    #areaboundary=find_boundaries(area,mode=boundarytype)
    areaboundary=get_boundary(area)
    #areaboundary=areaboundary*1   #boundary = 1's, nonboundary=0's
    temparea=area-areaboundary
    arealabels=labelgapnp(temparea)
    unique, counts = numpy.unique(arealabels, return_counts=True)
    if segbondtimes>=20:
        return area
    if(len(unique)>2):
        res=arealabels+areaboundary
        leftboundaryspots=numpy.where(areaboundary==1)

        leftboundary_y=leftboundaryspots[0].tolist()
        leftboundary_x=leftboundaryspots[1].tolist()
        for uni in unique[1:]:
            labelboundaryloc=get_boundaryloc(arealabels,uni)
            for m in range(len(labelboundaryloc[0])):
                for k in range(len(y)):
                    i = labelboundaryloc[0][m] + y[k]
                    j = labelboundaryloc[1][m] + x[k]
                    if i >= 0 and i < res.shape[0] and j >= 0 and j < res.shape[1]:
                        if res[i, j] == 1:
                            res[i,j]=uni
                            for n in range(len(leftboundary_y)):
                                if leftboundary_y[n]==i and leftboundary_x[n]==j:
                                    leftboundary_y.pop(n)
                                    leftboundary_x.pop(n)
                                    break
        '''
        res=res.tolist()
        for k in range(len(leftboundaryspots[0])):
            i=leftboundaryspots[0][k]
            j=leftboundaryspots[1][k]
            for m in range(len(y)):
                if i+y[m]>=0 and i+y[m]<len(res) and j+x[m]>=0 and j+x[m]<len(res[0]):
                    if areaboundary[i][j]>0:
                        diff=areaboundary[i][j]-res[i+y[m]][j+x[m]]
                        if diff<0:
                            res[i][j]=res[i+y[m]][j+x[m]]
                            break
        '''
        res=numpy.asarray(res)-1
        res=numpy.where(res<0,0,res)
        return res
    else:
        newarea=boundarywatershed(temparea,segbondtimes+1,boundarytype)*2
        res=newarea+areaboundary
        leftboundaryspots=numpy.where(res==1)

        leftboundary_y = leftboundaryspots[0].tolist()
        leftboundary_x = leftboundaryspots[1].tolist()
        unique=numpy.unique(newarea)
        for uni in unique[1:]:
            labelboundaryloc = get_boundaryloc(newarea, uni)
            for m in range(len(labelboundaryloc[0])):
                for k in range(len(y)):
                    i = labelboundaryloc[0][m] + y[k]
                    j = labelboundaryloc[1][m] + x[k]
                    if i >= 0 and i < res.shape[0] and j >= 0 and j < res.shape[1]:
                        if res[i, j] == 1:
                            res[i, j] = uni
                            for n in range(len(leftboundary_y)):
                                if leftboundary_y[n] == i and leftboundary_x[n] == j:
                                    leftboundary_y.pop(n)
                                    leftboundary_x.pop(n)
                                    break
        '''
        res=res.tolist()
        for k in range(len(leftboundaryspots[0])):
            i=leftboundaryspots[0][k]
            j=leftboundaryspots[1][k]
            for m in range(len(y)):
                if i+y[m]>=0 and i+y[m]<len(res) and j+x[m]>=0 and j+x[m]<len(res[0]):
                    if res[i][j]==1:
                        diff=res[i][j]-res[i+y[m]][j+x[m]]
                        if diff<0:
                            res[i][j]=res[i+y[m]][j+x[m]]
                            break
        '''
        res=numpy.asarray(res)/2
        res=numpy.where(res<1,0,res)
        return res

def manualboundarywatershed(area,avgarea):

    '''
    if numpy.count_nonzero(area)<avgarea/2:
        return area
    x=[0,-1,-1,-1,0,1,1,1]
    y=[1,1,0,-1,-1,-1,0,1]
    leftboundaryspots=numpy.where(area==1)
    pixelcount=1
    label=1

    for k in range(len(leftboundaryspots[0])):
        i=leftboundaryspots[0][k]
        j=leftboundaryspots[1][k]
        area[i][j]=label
        pixelcount+=1
        if pixelcount==int(avgarea):
            pixelcount=1
            label+=1
    unique,count=numpy.unique(area,return_counts=True)
    for i in range(1,len(count)):
        if count[i]<avgarea/2:
            area=numpy.where(area==unique[i],unique[i-1],area)
    '''
    maskpara=0.5
    possiblecount=int(numpy.count_nonzero(area)/avgarea)
    distance=ndi.distance_transform_edt(area)
    masklength=int((avgarea*maskpara)**0.5)-1
    local_maxi=peak_local_max(distance,indices=False,footprint=numpy.ones((masklength,masklength)),labels=area)
    markers=ndi.label(local_maxi)[0]
    unique=numpy.unique(markers)
    while(len(unique)-1>possiblecount):
        maskpara+=0.1
        masklength=int((avgarea*maskpara)**0.5)-1
        local_maxi=peak_local_max(distance,indices=False,footprint=numpy.ones((masklength,masklength)),labels=area)
        markers=ndi.label(local_maxi)[0]
        unique=numpy.unique(markers)
    while(len(unique)-1<possiblecount):
        maskpara-=0.1
        masklength=int((avgarea*maskpara)**0.5)-1
        try:
            local_maxi=peak_local_max(distance,indices=False,footprint=numpy.ones((masklength,masklength)),labels=area)
        except:
            maskpara+=0.1
            masklength=int((avgarea*maskpara)**0.5)-1
            local_maxi=peak_local_max(distance,indices=False,footprint=numpy.ones((masklength,masklength)),labels=area)
            markers=ndi.label(local_maxi)[0]
            break
        markers=ndi.label(local_maxi)[0]
        unique=numpy.unique(markers)
    localarea=watershed(-distance,markers,mask=area)

    return localarea






def cornerdivide(area,greatareas):
    global exceptions
    unique, counts = numpy.unique(area, return_counts=True)
    hist=dict(zip(unique,counts))
    del hist[0]
    meanpixel=sum(counts[1:])/len(counts[1:])
    while len(greatareas)>0:
        topkey=greatareas.pop(0)
        if topkey not in exceptions:
            locs=numpy.where(area==topkey)
            ulx,uly=min(locs[1]),min(locs[0])
            rlx,rly=max(locs[1]),max(locs[0])
            subarea=area[uly:rly+1,ulx:rlx+1]
            edges=get_boundary(subarea)
            tempsubarea=subarea/topkey
            newtempsubarea=numpy.where(tempsubarea!=1.,0,1)
            antitempsubarea=numpy.where((tempsubarea!=1.) & (tempsubarea!=0),tempsubarea,0)
            antitempsubarea=antitempsubarea*topkey.astype(int)
            corners=corner_peaks(corner_harris(edges),min_distance=1)
            times=len(locs[0])/meanpixel
            roundthreshold=0.6
            times=round(times-roundthreshold+0.5)
            cornerpoints=[]
            for item in corners:
                if tempsubarea[item[0],item[1]]==0:
                    cornerpoints.append(item)
            print(cornerpoints)




def manualdivide(area,greatareas):
    global exceptions
    unique, counts = numpy.unique(area, return_counts=True)
    hist=dict(zip(unique,counts))
    del hist[0]
    meanpixel=sum(counts[1:])/len(counts[1:])
    countseed=numpy.asarray(counts[1:])
    stdpixel=numpy.std(countseed)
    sortedkeys=list(sorted(hist,key=hist.get,reverse=True))
    #dimension=getdimension(area)
    #values=list(dimension.values())
    #meandimension=sum(values)/len(values)
    #stddimension=numpy.std(numpy.array(values))
    #updimention=min(meandimension+stddimension,meandimension*1.25)
    #lowdimention=meandimension-stddimension
    while len(greatareas)>0:
        topkey=greatareas.pop(0)
        #if topkey not in dimension:
        #    continue
        #topkeydimension=dimension[topkey]
        if topkey not in exceptions:
            locs=numpy.where(area==topkey)
            ulx,uly=min(locs[1]),min(locs[0])
            rlx,rly=max(locs[1]),max(locs[0])
            subarea=area[uly:rly+1,ulx:rlx+1]
            subarea=subarea.astype(float)
            tempsubarea=subarea/topkey
            newtempsubarea=numpy.where(tempsubarea!=1.,0,1).astype(int)
            antitempsubarea=numpy.where((tempsubarea!=1.) & (tempsubarea!=0),subarea,0)
            #antitempsubarea=antitempsubarea*topkey.astype(int)
            times=len(locs[0])/meanpixel
            #roundthreshold=0.6
            #times=round(times-roundthreshold+0.5)
            averagearea=len(locs[0])/times
            #newsubarea=manualboundarywatershed(newtempsubarea,meanpixel+stdpixel)#,windowsize)
            newsubarea=manualboundarywatershed(newtempsubarea,averagearea)
            labelunique,labcounts=numpy.unique(newsubarea,return_counts=True)
            labelunique=labelunique.tolist()
            labcounts=labcounts.tolist()
            #origin=labcounts[1]
            #if topkeydimension<updimention:
            #    for i in range(2,len(labcounts)):
            #        if labcounts[i]<origin/2:
            #            labelunique.pop(i)
            #            labcounts.pop(i)
            #            exceptions.append(topkey)
            #else:
            if len(labelunique)>2:
                keepdevide=True
                newlabelavgarea=sum(labcounts[1:])/len(labcounts[1:])
                #if newlabelavgarea<=lowrange:
                #if newlabelavgarea<=avgarea:
                #    keepdevide=False

                #if keepdevide:
                newsubarea=newsubarea*topkey
                newlabel=labelunique.pop(-1)
                maxlabel=area.max()
                add=1
                while newlabel>1:
                    newsubarea=numpy.where(newsubarea==topkey*newlabel,maxlabel+add,newsubarea)
                    print('new label: '+str(maxlabel+add))
                    newlabelcount=len(numpy.where(newsubarea==maxlabel+add)[0].tolist())
                    #if newlabelcount>=meanpixel and newlabelcount<uprange:
                        #if (maxlabel+add) not in exceptions:    07102019
                            #exceptions.append(maxlabel+add)     07102019
                    print('add '+'label: '+str(maxlabel+add)+' count='+str(newlabelcount))
                    newlabel=labelunique.pop(-1)
                    add+=1

                newsubarea=newsubarea+antitempsubarea.astype(int)

                area[uly:rly+1,ulx:rlx+1]=newsubarea
                #labels=relabel(labels)
                unique, counts = numpy.unique(area, return_counts=True)
                hist=dict(zip(unique,counts))
                del hist[0]
                print('hist length='+str(len(counts)-1))
                print('max label='+str(area.max()))
                sortedkeys=list(sorted(hist,key=hist.get,reverse=True))
                meanpixel=sum(counts[1:])/len(counts[1:])
                countseed=numpy.asarray(counts[1:])
                stdpixel=numpy.std(countseed)

                #uprange=meanpixel+minisigma*stdpixel
                #lowrange=meanpixel-minisigma*stdpixel
                #zscore=(hist[topkey]-meanpixel)/stdpixel
                #dimension=getdimension(area)
                #values=list(dimension.values())
                #meandimension=sum(values)/len(values)
                #stddimension=numpy.std(numpy.array(values))
                #updimention=min(meandimension+stddimension,meandimension*1.25)

def divideloop(area,misslabel,avgarea,layers,par):
    global greatareas
    #while hist[topkey]>meanpixel*1.1 and topkey not in exceptions:
    unique, counts = numpy.unique(area, return_counts=True)
    hist=dict(zip(unique,counts))
    del hist[0]
    #print('hist length='+str(len(counts)-1))
    #print('max label='+str(labels.max()))
    meanpixel=sum(counts[1:])/len(counts[1:])
    countseed=numpy.asarray(counts[1:])
    stdpixel=numpy.std(countseed)
    leftsigma=(meanpixel-min(countseed))/stdpixel
    rightsigma=(max(countseed)-meanpixel)/stdpixel
    if leftsigma>rightsigma:
        minisigma=min(leftsigma,rightsigma)-0.5
    else:
        minisigma=min(leftsigma,rightsigma)
    uprange=meanpixel+minisigma*stdpixel
    lowrange=meanpixel-minisigma*stdpixel
    sortedkeys=list(sorted(hist,key=hist.get,reverse=True))
    topkey=sortedkeys.pop(0)
    #while len(sortedkeys)>0 and hist[topkey]>uprange and topkey in exceptions:
    halflength=0.5*len(sortedkeys)
    zscore=(hist[topkey]-meanpixel)/stdpixel
    #while len(sortedkeys)>halflength and hist[topkey]>uprange:
    maxareacount=misslabel
    greatareas=[]
    #while maxareacount>0: #or hist[topkey]>min(avgarea*1.25,uprange):
    #while len(sortedkeys)>halflength and zscore>=2.5:
    while len(sortedkeys)>0:
        print('topkey='+str(topkey),hist[topkey])
        #if topkey not in greatareas and topkey not in exceptions:
        if topkey!=0 and hist[topkey]>uprange and topkey not in exceptions:
        #topkey=sortedkeys.pop(0)
    #for i in sorted(hist,key=hist.get,reverse=True):
        #if j<firstfivepercent:
            #if hist[topkey]>meanpixel*1.5 and topkey not in exceptions:
            locs=numpy.where(area==topkey)
            ulx,uly=min(locs[1]),min(locs[0])
            rlx,rly=max(locs[1]),max(locs[0])
            width=rlx-ulx
            height=rly-uly
            #windowsize=min(width,height)
            #dividen=2
            subarea=area[uly:rly+1,ulx:rlx+1]
            tempsubarea=subarea/topkey
            newtempsubarea=numpy.where(tempsubarea!=1.,0,1)
            antitempsubarea=numpy.where((tempsubarea!=1.) & (tempsubarea!=0),subarea,0)
            #antitempsubarea=antitempsubarea*topkey.astype(int)

            newsubarea=boundarywatershed(newtempsubarea,1,'inner')#,windowsize)
            #newsubarea=manualboundarywatershed(newtempsubarea,meanpixel)
            labelunique,labcounts=numpy.unique(newsubarea,return_counts=True)
            labelunique=labelunique.tolist()
            if len(labelunique)>2:
                keepdevide=True
                newlabelavgarea=sum(labcounts[1:])/len(labcounts[1:])
                #if newlabelavgarea<=lowrange:
                #if newlabelavgarea<=avgarea:
                #    keepdevide=False

                #if keepdevide:
                newsubarea=newsubarea*topkey
                newlabel=labelunique.pop(-1)
                maxlabel=area.max()
                add=1
                while newlabel>1:
                    newsubarea=numpy.where(newsubarea==topkey*newlabel,maxlabel+add,newsubarea)
                    print('new label: '+str(maxlabel+add))
                    newlabelcount=len(numpy.where(newsubarea==maxlabel+add)[0].tolist())
                    #if newlabelcount>=meanpixel and newlabelcount<uprange:
                        #if (maxlabel+add) not in exceptions:    07102019
                        #if (maxlabel+add) not in exceptions:    07102019
                            #exceptions.append(maxlabel+add)     07102019
                    print('add '+'label: '+str(maxlabel+add)+' count='+str(newlabelcount))
                    newlabel=labelunique.pop(-1)
                    add+=1

                newsubarea=newsubarea+antitempsubarea.astype(int)
                area[uly:rly+1,ulx:rlx+1]=newsubarea
                #labels=relabel(labels)
                unique, counts = numpy.unique(area, return_counts=True)
                hist=dict(zip(unique,counts))
                del hist[0]
                print('hist length='+str(len(counts)-1))
                print('max label='+str(area.max()))
                sortedkeys=list(sorted(hist,key=hist.get,reverse=True))
                meanpixel=sum(counts[1:])/len(counts[1:])
                countseed=numpy.asarray(counts[1:])
                stdpixel=numpy.std(countseed)
                leftsigma=(meanpixel-min(countseed))/stdpixel
                rightsigma=(max(countseed)-meanpixel)/stdpixel
                minisigma=min(leftsigma,rightsigma)
                uprange=meanpixel+minisigma*stdpixel
                lowrange=meanpixel-minisigma*stdpixel
                #zscore=(hist[topkey]-meanpixel)/stdpixel
                topkey=sortedkeys.pop(0)
                maxareacount-=1
            else:
                if hist[topkey]>uprange:
                    if topkey not in greatareas:
                        greatareas.append(topkey)
                    topkey=sortedkeys.pop(0)
                else:
                    break

        else:
            topkey=sortedkeys.pop(0)
            #zscore=(hist[topkey]-meanpixel)/stdpixel
            #maxareacount-=1
    return area

def combineloop(area,misslabel,par):
    global tinyareas
    localarea=numpy.asarray(area)
    unique, counts = numpy.unique(localarea, return_counts=True)
    hist=dict(zip(unique,counts))
    del hist[0]
    #print('hist length='+str(len(counts)-1))
    #print('max label='+str(labels.max()))
    meanpixel=sum(counts[1:])/len(counts[1:])
    countseed=numpy.asarray(counts[1:])
    stdpixel=numpy.std(countseed)
    leftsigma=(meanpixel-min(countseed))/stdpixel
    rightsigma=(max(countseed)-meanpixel)/stdpixel
    minisigma=min(leftsigma,rightsigma)
    uprange=meanpixel+minisigma*stdpixel
    lowrange=meanpixel-minisigma*stdpixel
    sortedkeys=list(sorted(hist,key=hist.get))
    topkey=sortedkeys.pop(0)
    tinyareas=[]
    while misslabel<=0:# or gocombine==True:
    #while hist[topkey]<max(avgarea*0.75,lowrange):
        #topkey=sortedkeys.pop(0)
        print('uprange='+str(uprange))
        print('lowrange='+str(lowrange))
        print('combine part')
        i=topkey
        print(i,hist[i])
        if i not in tinyareas and hist[i]<lowrange:
        #if hist[i]<meanpixel:
            locs=numpy.where(localarea==i)
            ulx,uly=min(locs[1]),min(locs[0])
            rlx,rly=max(locs[1]),max(locs[0])
            width=rlx-ulx
            height=rly-uly
            #windowsize=min(width,height)
            #dividen=2
            subarea=localarea[uly:rly+1,ulx:rlx+1]
            tempsubarea=subarea/i
            #four direction searches
            stop=False
            poscombines=[]
            for j in range(1,11):
                up_unique=[]
                down_unique=[]
                left_unique=[]
                right_unique=[]
                maxlabel={}
                tempcombines=[]
                if uly-j>=0 and stop==False and len(up_unique)<2:
                    uparray=localarea[uly-j:uly,ulx:rlx+1]
                    up_unique=numpy.unique(uparray)
                    for x in range(len(up_unique)):
                        if up_unique[x]>0:
                            tempdict={up_unique[x]:hist[up_unique[x]]}
                            maxlabel.update(tempdict)
                if rly+j<localarea.shape[0] and stop==False and len(down_unique)<2:
                    downarray=localarea[rly+1:rly+j+1,ulx:rlx+1]
                    down_unique=numpy.unique(downarray)
                    for x in range(len(down_unique)):
                        if down_unique[x]>0:
                            tempdict={down_unique[x]:hist[down_unique[x]]}
                            maxlabel.update(tempdict)
                if ulx-j>=0 and stop==False and len(left_unique)<2:
                    leftarray=localarea[uly:rly+1,ulx-j:ulx]
                    left_unique=numpy.unique(leftarray)
                    for x in range(len(left_unique)):
                        if left_unique[x]>0:
                            tempdict={left_unique[x]:hist[left_unique[x]]}
                            maxlabel.update(tempdict)
                if ulx+j<localarea.shape[1] and stop==False and len(right_unique)<2:
                    rightarray=localarea[uly:rly+1,rlx+1:rlx+j+1]
                    right_unique=numpy.unique(rightarray)
                    for x in range(len(right_unique)):
                        if right_unique[x]>0:
                            tempdict={right_unique[x]:hist[right_unique[x]]}
                            maxlabel.update(tempdict)
                print(up_unique,down_unique,left_unique,right_unique)
                tempcombines.append(up_unique)
                tempcombines.append(down_unique)
                tempcombines.append(left_unique)
                tempcombines.append(right_unique)
                poscombines.append(tempcombines)
            tinylist=[]
            while(len(poscombines)>0 and stop==False):
                top=poscombines.pop(0)
                tinylist.append(top)
                toplist=[]
                for j in range(4):
                    toparray=top[j]
                    topunique=numpy.unique(toparray)
                    for ele in topunique:
                        toplist.append(ele)
                toplist=numpy.array(toplist)
                combunique,combcount=numpy.unique(toplist,return_counts=True)
                toplist=dict(zip(combunique,combcount))
                toplist=list(sorted(toplist,key=toplist.get,reverse=True))
                while(len(toplist)>0):
                    top=toplist.pop(0)
                    if top!=0:
                        topcount=hist[top]
                        if hist[i]+topcount>lowrange and hist[i]+topcount<uprange:
                            localarea=combinecrops(localarea,subarea,i,top,ulx,uly,rlx,rly)
                            stop=True
            if len(poscombines)==0 and stop==False:  #combine to the closest one
                tinyareas.append(topkey)
                #misslabel+=1

            unique, counts = numpy.unique(localarea, return_counts=True)
            hist=dict(zip(unique,counts))
            sortedkeys=list(sorted(hist,key=hist.get))
            meanpixel=sum(counts[1:])/len(counts[1:])
            countseed=numpy.asarray(counts[1:])
            stdpixel=numpy.std(countseed)
            leftsigma=(meanpixel-min(countseed))/stdpixel
            rightsigma=(max(countseed)-meanpixel)/stdpixel
            minisigma=min(leftsigma,rightsigma)
            uprange=meanpixel+minisigma*stdpixel
            lowrange=meanpixel-minisigma*stdpixel
            #if stop==False and leftsigma>rightsigma:
            #    localarea=numpy.where(localarea==topkey,0,localarea)
            topkey=sortedkeys.pop(0)
            print('hist leng='+str(len(unique[1:])))
        else:
            if len(sortedkeys)>0:
                topkey=sortedkeys.pop(0)
            else:
                misslabel+=1


    return localarea




'''
def combineloop(area,misslabel):
    localarea=numpy.asarray(area)
    unique, counts = numpy.unique(localarea, return_counts=True)
    hist=dict(zip(unique,counts))
    del hist[0]
    #print('hist length='+str(len(counts)-1))
    #print('max label='+str(labels.max()))
    meanpixel=sum(counts[1:])/len(counts)
    countseed=numpy.asarray(counts[1:])
    stdpixel=numpy.std(countseed)
    uprange=meanpixel+stdpixel
    lowrange=meanpixel-stdpixel
    sortedkeys=list(sorted(hist,key=hist.get))
    topkey=sortedkeys.pop(0)
    halflength=0.5*len(sortedkeys)
    lowpercentile=numpy.percentile(countseed,25)
    highpercentile=numpy.percentile(countseed,75)
    #if len(sortedkeys)>0 and hist[topkey]<meanpixel:
    #while len(sortedkeys)>0 and hist[topkey]<lowrange and topkey in exceptions:
    zscore=(hist[topkey]-meanpixel)/stdpixel
    #while len(sortedkeys)>halflength and hist[topkey]<=lowrange:
    #while len(sortedkeys)>halflength and zscore<=-2.5:
    while misslabel<=0:
        #topkey=sortedkeys.pop(0)
        print('uprange='+str(uprange))
        print('lowrange='+str(lowrange))
        print('combine part')
        i=topkey
        print(i,hist[i])
        if i not in exceptions:
        #if hist[i]<meanpixel:
            locs=numpy.where(localarea==i)
            ulx,uly=min(locs[1]),min(locs[0])
            rlx,rly=max(locs[1]),max(locs[0])
            width=rlx-ulx
            height=rly-uly
            #windowsize=min(width,height)
            #dividen=2
            subarea=localarea[uly:rly+1,ulx:rlx+1]
            tempsubarea=subarea/i
            #four direction searches
            j=1
            stop=False
            while(stop==False and j<=10):
                up_unique=[]
                down_unique=[]
                left_unique=[]
                right_unique=[]
                maxlabel={}
                if uly-j>=0 and stop==False and len(up_unique)<2:
                    uparray=localarea[uly-j:uly,ulx:rlx+1]
                    up_unique=numpy.unique(uparray)
                    for x in range(len(up_unique)):
                        if up_unique[x]>0:
                            tempdict={up_unique[x]:hist[up_unique[x]]}
                            maxlabel.update(tempdict)
                if rly+j<localarea.shape[0] and stop==False and len(down_unique)<2:
                    downarray=localarea[rly+1:rly+j+1,ulx:rlx+1]
                    down_unique=numpy.unique(downarray)
                    for x in range(len(down_unique)):
                        if down_unique[x]>0:
                            tempdict={down_unique[x]:hist[down_unique[x]]}
                            maxlabel.update(tempdict)
                if ulx-j>=0 and stop==False and len(left_unique)<2:
                    leftarray=localarea[uly:rly+1,ulx-j:ulx]
                    left_unique=numpy.unique(leftarray)
                    for x in range(len(left_unique)):
                        if left_unique[x]>0:
                            tempdict={left_unique[x]:hist[left_unique[x]]}
                            maxlabel.update(tempdict)
                if ulx+j<localarea.shape[1] and stop==False and len(right_unique)<2:
                    rightarray=localarea[uly:rly+1,rlx+1:rlx+j+1]
                    right_unique=numpy.unique(rightarray)
                    for x in range(len(right_unique)):
                        if right_unique[x]>0:
                            tempdict={right_unique[x]:hist[right_unique[x]]}
                            maxlabel.update(tempdict)
                print(up_unique,down_unique,left_unique,right_unique)
                labelpos={}
                for ele in maxlabel:
                    tempdict={ele:0}
                    try:
                        if ele in up_unique:
                            tempdict[ele]+=1
                    except TypeError:
                        pass
                    try:
                        if ele in down_unique:
                            tempdict[ele]+=1
                    except TypeError:
                        pass
                    try:
                        if ele in left_unique:
                            tempdict[ele]+=1
                    except TypeError:
                        pass
                    try:
                        if ele in right_unique:
                            tempdict[ele]+=1
                    except TypeError:
                        pass
                    labelpos.update(tempdict)
                if j==1:
                    combine=False
                    surhist={}
                    for ele in maxlabel:
                        surround=[]
                        if ele in subarea:
                            eleloc=numpy.where(subarea==ele)
                            for k in range(len(eleloc[0])):
                                try:
                                    surround.append(subarea[eleloc[0][k]-1][eleloc[1][k]])
                                except IndexError:
                                    pass
                                try:
                                    surround.append(subarea[eleloc[0][k]+1][eleloc[1][k]])
                                except IndexError:
                                    pass
                                try:
                                    surround.append(subarea[eleloc[0][k]][eleloc[1][k]-1])
                                except IndexError:
                                    pass
                                try:
                                    surround.append(subarea[eleloc[0][k]][eleloc[1][k]+1])
                                except IndexError:
                                    pass
                                if i in surround:
                                    combine=True
                        tempdict={ele:len(surround)}
                        surhist.update(tempdict)
                    if combine==True:
                        #uniq_label,count_label=numpy.unique(surround,return_counts=True)
                        #surhist=dict(zip(uniq_label,count_label))
                        for ele in sorted(surhist,key=surhist.get,reverse=True):
                            localarea=combinecrops(localarea,subarea,i,ele,ulx,uly,rlx,rly)
                            if maxlabel[ele]+hist[i]>=meanpixel and maxlabel[ele]+hist[i]<uprange:
                                if i<ele:
                                    if i not in exceptions:
                                        exceptions.append(i)
                                        print('add exceptions '+str(i))
                                else:
                                    if ele not in exceptions:
                                        exceptions.append(ele)
                                        print('add exceptions '+str(ele))
                            stop=True
                            break
                    for ele in sorted(labelpos,key=labelpos.get,reverse=True):
                        #if labelpos[ele]>1 and stop==False:
                        if stop==False:
                            if labelpos[ele]>1:
                                localarea=combinecrops(localarea,subarea,i,ele,ulx,uly,rlx,rly)
                                if maxlabel[ele]+hist[i]>=meanpixel and maxlabel[ele]+hist[i]<uprange:
                                    if i<ele:
                                        if i not in exceptions:
                                            exceptions.append(i)
                                            print('add exceptions '+str(i))
                                    else:
                                        if ele not in exceptions:
                                            exceptions.append(ele)
                                            print('add exceptions '+str(ele))
                                stop=True
                                break
                    if stop==False:
                        for ele in sorted(maxlabel,key=maxlabel.get):
                            freq=maxlabel[ele]
                            if ele in up_unique or ele in down_unique:
                                if freq+hist[i]<meanpixel:
                                    localarea=combinecrops(localarea,subarea,i,ele,ulx,uly,rlx,rly)
                                    stop=True
                                    break
                                else:
                                    if freq+hist[i]>=meanpixel and freq+hist[i]<=uprange:
                                        localarea=combinecrops(localarea,subarea,i,ele,ulx,uly,rlx,rly)
                                        if i<ele:
                                            if i not in exceptions:
                                                exceptions.append(i)
                                                print('add exceptions '+str(i))
                                        else:
                                            if ele not in exceptions:
                                                exceptions.append(ele)
                                                print('add exceptions '+str(ele))
                                        stop=True
                                        break
                else:
                    if stop==False:
                        for ele in sorted(maxlabel,key=maxlabel.get):
                            freq=maxlabel[ele]
                            if len(up_unique)>0 and ele in up_unique:
                                if freq+hist[i]<meanpixel:
                                    localarea=combinecrops(localarea,subarea,i,ele,ulx,uly,rlx,rly)
                                    stop=True
                                    break
                            if len(down_unique)>0 and ele in down_unique:
                                if freq+hist[i]<meanpixel:
                                    localarea=combinecrops(localarea,subarea,i,ele,ulx,uly,rlx,rly)
                                    stop=True
                                    break

                            else:
                                if freq+hist[i]>=meanpixel and freq+hist[i]<=uprange:
                                    localarea=combinecrops(localarea,subarea,i,ele,ulx,uly,rlx,rly)
                                    if i<ele:
                                        if i not in exceptions:
                                            exceptions.append(i)
                                            print('add exceptions '+str(i))
                                    else:
                                        if ele not in exceptions:
                                            exceptions.append(ele)
                                            print('add exceptions '+str(ele))
                                    stop=True
                                    break


                j+=1
            if j>=10 and stop==False:
                if topkey not in exceptions and hist[topkey]<math.floor(lowrange-minisigma*stdpixel):
                    for ele in sorted(maxlabel,key=maxlabel.get):
                        if len(up_unique)>1 and ele in up_unique:
                            if hist[topkey]+maxlabel[ele]<uprange+stdpixel:
                                localarea=combinecrops(localarea,subarea,i,ele,ulx,uly,rlx,rly)
                                if i<ele:
                                    if i not in exceptions:
                                        exceptions.append(i)
                                        print('add exceptions '+str(i))
                                else:
                                    if ele not in exceptions:
                                        exceptions.append(ele)
                                        print('add exceptions '+str(ele))
                                break
                        if len(down_unique)>1 and ele in down_unique:
                            if hist[topkey]+maxlabel[ele]<uprange+stdpixel:
                                localarea=combinecrops(localarea,subarea,i,ele,ulx,uly,rlx,rly)
                                if i<ele:
                                    if i not in exceptions:
                                        exceptions.append(i)
                                        print('add exceptions '+str(i))
                                else:
                                    if ele not in exceptions:
                                        exceptions.append(ele)
                                        print('add exceptions '+str(ele))
                                break

                        if len(up_unique)>1 and len(down_unique)<=1 and ele in up_unique and hist[topkey]+maxlabel[ele]<uprange+minisigma*stdpixel:
                            localarea=combinecrops(localarea,subarea,i,ele,ulx,uly,rlx,rly)
                            if i<ele:
                                if i not in exceptions:
                                    exceptions.append(i)
                                    print('add exceptions '+str(i))
                            else:
                                if ele not in exceptions:
                                    exceptions.append(ele)
                                    print('add exceptions '+str(ele))
                            break
                        if len(down_unique)>1 and len(up_unique)<=1 and ele in down_unique and hist[topkey]+maxlabel[ele]<uprange+minisigma*stdpixel:
                            localarea=combinecrops(localarea,subarea,i,ele,ulx,uly,rlx,rly)
                            if i<ele:
                                if i not in exceptions:
                                    exceptions.append(i)
                                    print('add exceptions '+str(i))
                            else:
                                if ele not in exceptions:
                                    exceptions.append(ele)
                                    print('add exceptions '+str(ele))
                            break

                if topkey in localarea and topkey not in exceptions:
                    exceptions.append(topkey)
                    print('add exceptions '+str(topkey))
            unique, counts = numpy.unique(localarea, return_counts=True)
            hist=dict(zip(unique,counts))
            sortedkeys=list(sorted(hist,key=hist.get))
            meanpixel=sum(counts[1:])/len(counts)
            countseed=numpy.asarray(counts[1:])
            stdpixel=numpy.std(countseed)
            uprange=meanpixel+stdpixel
            lowrange=meanpixel-stdpixel
            topkey=sortedkeys.pop(0)
            #i=topkey
            print('hist leng='+str(len(unique[1:])))
            misslabel+=1
        else:
            topkey=sortedkeys.pop(0)
            misslabel+=1
    #topkey=sortedkeys.pop(0)
    #meanpixel=sum(counts[1:])/len(counts)
    #countseed=numpy.asarray(counts[1:])
    #stdpixel=numpy.std(countseed)
    #uprange=meanpixel+stdpixel
    #lowrange=meanpixel-stdpixel
    #if len(unique)-1==len(labellist):
    #    break
    return localarea
'''

#def checkvalid(mislabel,hist,sortedkeys,uprange,lowrange,avgarea):
def checkvalid(pvalue,leftsigma,rightsigma):
    allpass=True
    godivide=False
    gocombine=False
    '''
    if mislabel>0:
        allpass=False
        godivide=True
        return allpass,godivide,gocombine
    if mislabel<0:
        allpass=False
        gocombine=True
        #if hist[sortedkeys[1]]>avgarea*1.25:
        #    godivide=True
        return allpass,godivide,gocombine
    for key in sortedkeys:
        if hist[key]>uprange and key not in exceptions and key!=0:
            allpass=False
            godivide=True
            break
    for key in sortedkeys:
        if hist[key]<lowrange and key not in exceptions and key!=0:
            allpass=False
            gocombine=True
            break
    return allpass,godivide,gocombine
    '''
    if pvalue<0.05:
        allpass=False
        if rightsigma>leftsigma:
            godivide=True
        else:
            #if round(leftsigma)>3:
            #    godivide=True
            #else:
            gocombine=True

    return allpass,godivide,gocombine

def getdimension(area):
    unique=numpy.unique(area)
    unique=unique.tolist()
    dimensiondict={}
    for ele in unique:
        if ele!=0 and ele!=1:
            eleloc=numpy.where(area==ele)
            xdist=max(eleloc[1])-min(eleloc[1])
            ydist=max(eleloc[0])-min(eleloc[0])
            dimension=xdist*ydist
            tempdict={ele:dimension}
            if ele not in dimensiondict:
                dimensiondict.update(tempdict)
    return dimensiondict

def exploraround(originimage,area,itertime):
    res=area
    x = [0, -1, -1, -1, 0, 1, 1, 1]
    y = [1, 1, 0, -1, -1, -1, 0, 1]
    #x = [0, -1, -1, -1, 0, 1, 1, 1, 0, -1, -2, -2, -2, -2, -2, -1, 0, 1, 2, 2, 2, 2, 2, 1]
    #y = [1, 1, 0, -1, -1, -1, 0, 1, -2, -2, -2, -1, 0, 1, 2, 2, 2, 2, 2, 1, 0, -1, -2, -2]
    for u in range(itertime):
        unique=numpy.unique(res)
        for uni in unique[1:]:
            uniboundaryloc=get_boundaryloc(res,uni)
            for m in range(len(uniboundaryloc[0])):
                for k in range(len(y)):
                    i = uniboundaryloc[0][m] + y[k]
                    j = uniboundaryloc[1][m] + x[k]
                    if i >= 0 and i < res.shape[0] and j >= 0 and j < res.shape[1]:
                        if originimage[i, j] == 1 and res[i,j]==0:
                            res[i, j] = uni


    return res


def findmissitem(originimage,area,coinparts):
    res=area
    tempband=numpy.zeros(area.shape)
    temparea=numpy.where(res==0)
    tempband[temparea]=1
    if len(coinparts)>0:
        coinkeys=coinparts.keys()
        for coin in coinkeys:
            tempband[coinparts[coin]]=0
    tempband=tempband*originimage
    unique=numpy.unique(tempband,return_counts=True)
    labelunique,labelcounts=numpy.unique(area,return_counts=True)
    meanpixel=sum(labelcounts[1:])/len(labelcounts[1:])
    std=numpy.std(labelcounts[1:])
    if len(unique)>2:
        boundaryarea=boundarywatershed(tempband,1,'inner')
        boundaryarea=boundaryarea.astype(int)
        maxlabel=numpy.amax(area)
        boundaryarea=boundaryarea*(maxlabel*2)
        tempunique,tempcount=numpy.unique(boundaryarea,return_counts=True)
        tempdict=dict(zip(tempunique,tempcount))
        for uni in tempunique[1:]:
            currcount=tempdict[uni]
            if currcount>(meanpixel) or currcount<(meanpixel-3*std):
                boundaryarea=numpy.where(boundaryarea==uni,0,boundaryarea)
        res=area+boundaryarea
    return res



def findcoin(area):

    unique, counts = numpy.unique(area, return_counts=True)
    '''
    hist=dict(zip(unique[1:],counts[1:]))
    rankhist=sorted(hist,key=hist.get,reverse=True)
    lenwidth=1e6
    occupy=0.0
    coincandi=0
    for i in range(min(5,len(rankhist))):
        item=rankhist[i]
        itemloc=numpy.where(area==item)
        coinulx=min(itemloc[1])
        coinuly=min(itemloc[0])
        coinrlx=max(itemloc[1])
        coinrly=max(itemloc[0])
        square=abs((coinrly-coinuly)-(coinrlx-coinulx))
        tempoccupy=len(itemloc[0])/((coinrly-coinuly)*(coinrlx-coinulx))
        if square<lenwidth and tempoccupy>occupy:
            coincandi=i
            occupy=tempoccupy
            lenwidth=square

    maxpixellabel=rankhist[coincandi]

    '''
    maxpixel=max(counts[1:])
    maxpixelind=counts.tolist().index(maxpixel)
    maxpixellabel=unique[maxpixelind]
    '''
    countarray=numpy.asarray([unique[1:],counts[1:]]).transpose()
    clf=KMeans(n_clusters=2,init='k-means++',n_init=10,random_state=0)
    clscounts=clf.fit_predict(countarray)
    classuniq,classcount=numpy.unique(clscounts,return_counts=True)
    if classcount[0]<classcount[1]:
        refind=0
    else:
        refind=1
    refind=classuniq[refind]
    refind=numpy.where(clscounts==refind)
    refind=unique[1:][refind]
    '''
    coinparts={}

    #for i in range(len(refind)):
    coinlocs=numpy.where(area==maxpixellabel)
    coinparts.update({maxpixellabel:coinlocs})

    '''
    coinulx=min(coinlocs[1])
    coinuly=min(coinlocs[0])
    coinrlx=max(coinlocs[1])
    coinrly=max(coinlocs[0])
    #width=max(coinlocs[1])-min(coinlocs[1])
    #height=max(coinlocs[0])-min(coinlocs[1])
    temparea=area[coinuly:coinrly+1,coinulx:coinrlx+1]
    tempsubarea=temparea/maxpixellabel
    newtempsubarea=numpy.where(tempsubarea!=1.,0,1)
    #antitempsubarea=numpy.where((tempsubarea!=1.)&(tempsubarea!=0),temparea,0)
    newarea=boundarywatershed(newtempsubarea,1,'inner')
    newuni,newcount=numpy.unique(newarea,return_counts=True)
    print(newuni,newcount)
    maxarea=max(newcount[1:])
    maxarealabel=newcount[1:].tolist().index(maxarea)
    maxarealabel=newuni[1:][maxarealabel]
    temparea=numpy.where(newarea==maxarealabel,65535,temparea)
    area[coinuly:coinrly+1,coinulx:coinrlx+1]=temparea
    coinlocs=numpy.where(area==65535)

    coinparts.update({maxpixellabel:coinlocs})
    '''

    '''
    coinlocs=numpy.where(area==rankhist[coincandi])
    '''





    #coinparts.update({rankhist[coincandi]:coinlocs})

    '''
    for uni in unique[1:]:
        #if uni!=rankhist[coincandi]:
        if uni!=refind:
            templocs=numpy.where(area==uni)
            tempulx=min(templocs[1])
            tempuly=min(templocs[0])
            temprlx=max(templocs[1])
            temprly=max(templocs[0])
            #inside coin boundingbox
            if tempulx>=coinulx and tempulx<=coinrlx and temprlx>=coinulx and temprlx<=coinrlx:
                if tempuly>=coinuly and tempuly<=coinrly and temprly>=coinuly and temprly<=coinrly:
                    if uni not in coinparts:
                        coinparts.update({uni:templocs})
                        continue
            if (tempulx>coinulx and tempulx<coinrlx) or (temprlx>coinulx and temprlx<coinrlx):
                if (tempuly>coinuly and tempuly<coinrly) or (temprly>coinuly and temprly<coinrly):
                    if uni not in coinparts:
                        coinparts.update({uni:templocs})
                        continue
    '''
    return coinparts


            #intersect with coin boundingbox

def intervaldivideloop(processintervals,area,sortedlist):
    for i in reversed (range(len(processintervals))):
        proceslabel=sortedlist[processintervals[i]]
        locs=numpy.where(area==proceslabel)
        ulx,uly=min(locs[1]),min(locs[0])
        rlx,rly=max(locs[1]),max(locs[0])
        subarea=area[uly:rly+1,ulx:rlx+1]
        tempsubarea=subarea/proceslabel
        newtempsubarea=numpy.where(tempsubarea!=1.,0,1)
        antitempsubarea=numpy.where((tempsubarea!=1.) & (tempsubarea!=0),subarea,0)
        newsubarea=boundarywatershed(newtempsubarea,1,'inner')
        labelunique,labcounts=numpy.unique(newsubarea,return_counts=True)
        labelunique=labelunique.tolist()
        if len(labelunique)>2:
            newsubarea=newsubarea*proceslabel
            newlabel=labelunique.pop(-1)
            maxlabel=area.max()
            add=1
            while newlabel>1:
                newsubarea=numpy.where(newsubarea==proceslabel*newlabel,maxlabel+add,newsubarea)
                print('new label: '+str(maxlabel+add))
                newlabelcount=len(numpy.where(newsubarea==maxlabel+add)[0].tolist())
                print('add '+'label: '+str(maxlabel+add)+' count='+str(newlabelcount))
                newlabel=labelunique.pop(-1)
                add+=1

            newsubarea=newsubarea+antitempsubarea.astype(int)
            area[uly:rly+1,ulx:rlx+1]=newsubarea
            unique, counts = numpy.unique(area, return_counts=True)
            hist=dict(zip(unique,counts))
            del hist[0]
            print('hist length='+str(len(counts)-1))
            print('max label='+str(area.max()))
    return area

def intervalcombineloop(processintervals,area,sortedlist):
    for i in range(len(processintervals)):
        unique,counts=numpy.unique(area,return_counts=True)
        hist=dict(zip(unique,counts))
        processlabel=sortedlist[processintervals[i]]
        locs=numpy.where(area==processlabel)
        ulx,uly=min(locs[1]),min(locs[0])
        rlx,rly=max(locs[1]),max(locs[0])
        subarea=area[uly:rly+1,ulx:rlx+1]
        stop=False
        poscombines=[]
        for j in range(1,11):
            up_unique=[]
            down_unique=[]
            left_unique=[]
            right_unique=[]
            maxlabel={}
            tempcombines=[]
            if uly-j>=0 and stop==False and len(up_unique)<2:
                uparray=area[uly-j:uly,ulx:rlx+1]
                up_unique=numpy.unique(uparray)
                for x in range(len(up_unique)):
                    if up_unique[x]>0:
                        tempdict={up_unique[x]:hist[up_unique[x]]}
                        maxlabel.update(tempdict)
            if rly+j<area.shape[0] and stop==False and len(down_unique)<2:
                downarray=area[rly+1:rly+j+1,ulx:rlx+1]
                down_unique=numpy.unique(downarray)
                for x in range(len(down_unique)):
                    if down_unique[x]>0:
                        tempdict={down_unique[x]:hist[down_unique[x]]}
                        maxlabel.update(tempdict)
            if ulx-j>=0 and stop==False and len(left_unique)<2:
                leftarray=area[uly:rly+1,ulx-j:ulx]
                left_unique=numpy.unique(leftarray)
                for x in range(len(left_unique)):
                    if left_unique[x]>0:
                        tempdict={left_unique[x]:hist[left_unique[x]]}
                        maxlabel.update(tempdict)
            if ulx+j<area.shape[1] and stop==False and len(right_unique)<2:
                rightarray=area[uly:rly+1,rlx+1:rlx+j+1]
                right_unique=numpy.unique(rightarray)
                for x in range(len(right_unique)):
                    if right_unique[x]>0:
                        tempdict={right_unique[x]:hist[right_unique[x]]}
                        maxlabel.update(tempdict)
            print(up_unique,down_unique,left_unique,right_unique)
            tempcombines.append(up_unique)
            tempcombines.append(down_unique)
            tempcombines.append(left_unique)
            tempcombines.append(right_unique)
            poscombines.append(tempcombines)
        tinylist=[]
        while(len(poscombines)>0 and stop==False):
            top=poscombines.pop(0)
            tinylist.append(top)
            toplist=[]
            for j in range(4):
                toparray=top[j]
                topunique=numpy.unique(toparray)
                for ele in topunique:
                    toplist.append(ele)
            toplist=numpy.array(toplist)
            combunique,combcount=numpy.unique(toplist,return_counts=True)
            toplist=dict(zip(combunique,combcount))
            toplist=list(sorted(toplist,key=toplist.get,reverse=True))
            #for k in range(len(toplist)):
            #    top=toplist[k]
            while(len(toplist)>0):
            #if(len(toplist)>0):
                top=toplist.pop(0)
                if top!=0:
                    topcount=hist[top]
                    #if hist[i]+topcount>lowrange and hist[i]+topcount<uprange:
                    area=combinecrops(area,subarea,processlabel,top,ulx,uly,rlx,rly)
                    stop=True
                    break
    return area

def touchingcointmethod(unique,counts):
    normalhist=dict(zip(unique[1:],counts[1:]))
    sortedlist=dict(sorted((value,key) for (key,value) in normalhist.items()))
    sortedarray=list(sortedlist.keys())
    stat,p=shapiro(numpy.asarray(sortedarray))
    alpha=0.05
    if p<alpha:
        end=-1
        begin=1
        while len(sortedarray)>0:
            sortedarray=numpy.asarray(list(sortedlist.keys())[:end])
            stat,p=shapiro(sortedarray)
            if p<alpha:
                sortedarray=numpy.asarray(list(sortedlist.keys()))[begin:]
                stat,p=shapiro(sortedarray)
                if p<alpha:
                    sortedarray=numpy.asarray(list(sortedlist.keys()))[begin:end]
                    stat,p=shapiro(sortedarray)
                    if p<alpha:
                        begin+=1
                        end-=1
                    else:
                        break
                else:
                    end+=1
                    break
            else:
                begin-=1
                break
        singlekernels=list(sortedlist.keys())[:end]
        print(singlekernels)
        '''
        maxinterval=0
        for i in range(len(singlekernels)):
            if i+1<len(singlekernels):
                inteval=singlekernels[i+1]-singlekernels[i]
                if inteval>maxinterval:
                    maxinterval=inteval
        '''
        multikernels=list(sortedlist.keys())[end:]

        #to do: find kernels from multikernels that less than maxinterval filter out kernels greater than maxinterval
        '''
        for i in range(len(multikernels)):
            if i==0:
                interval=multikernels[i]-singlekernels[-1]
            else:
                interval=multikernels[i]-multikernels[i-1]
            if interval<maxinterval:
                    singlekernels.append(multikernels[i])
            else:
                break
        multikernels=multikernels[i:]
        '''
        print(multikernels)
        while(len(multikernels)>0):
            '''
            processkernels=[]
            for i in range(len(multikernels)):
                if i==0:
                    processkernels.append(multikernels[i])
                if i+1<len(multikernels):
                    interval=multikernels[i+1]-multikernels[i]
                    if interval>maxinterval:
                        break
                    else:
                        processkernels.append(multikernels[i+1])
            multikernels=multikernels[i+1:]
            '''
            labels=intervaldivideloop(multikernels,labels,sortedlist)
            unique, counts = numpy.unique(labels, return_counts=True)
            normalhist=dict(zip(unique[1:],counts[1:]))
            sortedlist=dict(sorted((value,key) for (key,value) in normalhist.items()))
            kernellist=list(sortedlist.keys())
            stat,p=shapiro(numpy.asarray(kernellist))
            alpha=0.05
            if p<alpha:
                end=-1
                begin=1
                while len(kernellist)>0:
                    kernellist=numpy.asarray(list(sortedlist.keys())[:end])
                    stat,p=shapiro(kernellist)
                    if p<alpha:
                        kernellist=numpy.asarray(list(sortedlist.keys()))[begin:]
                        stat,p=shapiro(kernellist)
                        if p<alpha:
                            kernellist=numpy.asarray(list(sortedlist.keys()))[begin:end]
                            stat,p=shapiro(kernellist)
                            if p<alpha:
                                begin+=1
                                end-=1
                            else:
                                break
                        else:
                            end+=1
                            break
                    else:
                        begin-=1
                        break
                singlekernels=list(sortedlist.keys())[:end]  #need to do when end = -1, begin = 0 condition
                #while p<alpha:
                #    sortedarray=numpy.asarray(list(sortedlist.keys())[:end])
                #    stat,p=shapiro(sortedarray)
                #    end-=1
                #singlekernels=list(sortedlist.keys())[:end]
                '''
                for i in range(len(kernellist)):
                    if i==0:
                        singlekernels.append(kernellist[i])
                    else:
                        interval=kernellist[i+1]-kernellist[i]
                        if interval>maxinterval:
                            break
                        else:
                            singlekernels.append(kernellist[i])
                '''
                multikernels=list(sortedlist.keys())[end:]
                if end==-1 and begin>0:
                    labels=intervalcombineloop(list(sortedlist.keys())[:begin],labels,sortedlist)
                    unique, counts = numpy.unique(labels, return_counts=True)
                    normalhist=dict(zip(unique[1:],counts[1:]))
                    sortedlist=dict(sorted((value,key) for (key,value) in normalhist.items()))
                    kernellist=list(sortedlist.keys())
                    stat,cp=shapiro(numpy.asarray(kernellist))
                    alpha=0.05
                    if cp<alpha:
                        end=-1
                        begin=1
                        while len(kernellist)>0:
                            kernellist=numpy.asarray(list(sortedlist.keys())[:end])
                            stat,cp=shapiro(kernellist)
                            if cp<alpha:
                                kernellist=numpy.asarray(list(sortedlist.keys()))[begin:]
                                stat,cp=shapiro(kernellist)
                                if cp<alpha:
                                    kernellist=numpy.asarray(list(sortedlist.keys()))[begin:end]
                                    stat,cp=shapiro(kernellist)
                                    if cp<alpha:
                                        begin+=1
                                        end-=1
                                    else:
                                        break
                                else:
                                    end+=1
                                    break
                            else:
                                begin-=1
                                break
                        singlekernels=list(sortedlist.keys())[:end]
                        #while p<alpha:
                        #    sortedarray=numpy.asarray(list(sortedlist.keys())[:end])
                        #    stat,p=shapiro(sortedarray)
                        #    end-=1
                        #singlekernels=list(sortedlist.keys())[:end]
                        '''
                        for i in range(len(kernellist)):
                            if i==0:
                                singlekernels.append(kernellist[i])
                            else:
                                interval=kernellist[i+1]-kernellist[i]
                                if interval>maxinterval:
                                    break
                                else:
                                    singlekernels.append(kernellist[i])
                        '''
                        multikernels=list(sortedlist.keys())[end:]
                # to do: if end==-1 and begin==0:


            else:
                break

        #kernelindex=list(sortedlist.keys()).index(multikernels[0])
        #sortedarray=numpy.asarray(list(sortedlist.keys())[:kernelindex])




    minsinglesize=min(singlekernels)


def processinput(input,validmap,avgarea,layers,ittimes=30,coin=True,shrink=0):
    band=input
    row=band.shape[0]
    col=band.shape[1]
    boundaryarea=boundarywatershed(band,1,'inner')
    ''' watershed method
    distance=ndi.distance_transform_edt(band)
    local_maxi=peak_local_max(distance,indices=False,footprint=numpy.ones((3,3)),labels=band)
    markers=ndi.label(local_maxi)[0]
    watershedlabels=watershed(-distance,markers,mask=band)
    watershedlabels=watershedlabels.astype(int)
    watershedlabels,misslabel=relabel(watershedlabels)
    unilabel=numpy.unique(watershedlabels)
    print('watershed labels= '+str(len(unilabel)-1))
    '''

    #tempband=numpy.where(boundaryarea==1,0,band)
    #boundaryarea=boundarywatershed(tempband,1,'inner')
    #unique=numpy.unique(boundaryarea).tolist()
    #for i in range(len(unique)):
    #    boundaryarea=numpy.where(boundaryarea==unique[i],i,boundaryarea)
    boundaryarea=boundaryarea.astype(int)
    originmethod,misslabel,localcolortable=relabel(boundaryarea)


    band1=numpy.where(originmethod<0,0,originmethod)
    band2=255-band1
    band3=255-band1
    '''
    out_fn='originmethod.tif'
    gtiffdriver=gdal.GetDriverByName('GTiff')
    out_ds=gtiffdriver.Create(out_fn,col,row,3,3)
    out_band=out_ds.GetRasterBand(1)
    out_band.WriteArray(band1)
    out_band=out_ds.GetRasterBand(2)
    out_band.WriteArray(band2)
    out_band=out_ds.GetRasterBand(3)
    out_band.WriteArray(band3)
    out_ds.FlushCache()
    '''
    labels=numpy.where(boundaryarea<1,0,boundaryarea)
    #labels=boundaryarea
    unique, counts = numpy.unique(labels, return_counts=True)
    '''
    maxpixel=max(counts[1:])
    maxpixelind=counts.index(maxpixel)
    maxpixellabel=unique[maxpixelind]
    coinlocs=numpy.where(labels==maxpixellabel)

    countarray=numpy.asarray([unique[1:],counts[1:]]).transpose()
    clf=KMeans(n_clusters=2,init='k-means++',n_init=10,random_state=0)
    clscounts=clf.fit_predict(countarray)
    classuniq,classcount=numpy.unique(clscounts,return_counts=True)
    if classcount[0]<classcount[1]:
        refind=0
    else:
        refind=1
    refind=classuniq[refind]
    refind=clscounts.tolist().index(refind)
    refind=unique[1:][refind]
    coinloc=numpy.where(labels==refind)
    print(clscounts)
    labels=numpy.where(labels==refind,0,labels)
    '''
    if coin:
        coinparts=findcoin(labels)
        coinkeys=coinparts.keys()
        for part in coinkeys:
            locs=coinparts[part]
            labels[locs]=0#=numpy.where(labels==part,0,labels)
    else:
        coinparts={}

    unique, counts = numpy.unique(labels, return_counts=True)

    hist=dict(zip(unique,counts))
    dimention=getdimension(labels)
    #labels=labels.tolist()
    divide=0
    docombine=0
    #mixedarray=numpy.column_stack((counts[1:],counts[1:]))
    with open('countlist.csv','w') as f:
        writer=csv.writer(f)
        templist=counts[1:].tolist()
        for item in templist:
            tempitem=str(item)
            writer.writerow([tempitem])
    f.close()
    #print(numpy.column_stack(counts[1:]))
    meanpixel=sum(counts[1:])/len(counts[1:])
    countseed=numpy.asarray(counts[1:])











    stat,p=shapiro(countseed)
    alpha=0.05
    if p>alpha:
        print('like gaussian')
    else:
        print('does not like gaussian')
    #models=[GMM(n).fit(numpy.asarray(mixedarray)) for n in range(1,21)]
    #biclist=[]
    #for m in models:
    #    biclist.append(m.bic(mixedarray))
    #n_component=biclist.index(min(biclist))+1
    #models=GMM(n_component,covariance_type='full',random_state=0).fit(mixedarray)
    #gmmlabels=models.predict(mixedarray)
    stdpixel=numpy.std(countseed)
    leftsigma=(meanpixel-min(countseed))/stdpixel
    rightsigma=(max(countseed)-meanpixel)/stdpixel
    sortedkeys=list(sorted(hist,key=hist.get,reverse=True))

    #allinexceptsions,godivide,gocombine=checkvalid(misslabel,hist,sortedkeys,uprange,lowrange,avgarea)
    allinexceptsions,godivide,gocombine=checkvalid(p,leftsigma,rightsigma)
    par=0.0
    #while allinexceptsions is False:
    lastgreatarea=[]
    lasttinyarea=[]
    for it in range(ittimes):
        if godivide==False and gocombine==False:
            break
    #while godivide==True or gocombine==True:
        try:
            del hist[0]
        except KeyError:
            #continue
            pass
        print('hist length='+str(len(counts)-1))
        print('max label='+str(labels.max()))
        meanpixel=sum(counts[1:])/len(counts[1:])
        countseed=numpy.asarray(counts[1:])
        with open('countseed'+str(it)+'.csv','w') as f:
            csvwriter=csv.writer(f)
            content=['index','pixels']
            csvwriter.writerow(content)
            for i in range(len(counts[1:])):
                content=[str(i+1),str(counts[1:][i])]
                csvwriter.writerow(content)
            f.close()


        stdpixel=numpy.std(countseed)
        leftsigma=(meanpixel-min(countseed))/stdpixel
        rightsigma=(max(countseed)-meanpixel)/stdpixel
        minisigma=min(leftsigma,rightsigma)
        uprange=meanpixel+minisigma*stdpixel
        lowrange=meanpixel-minisigma*stdpixel
        sortedkeys=list(sorted(hist,key=hist.get,reverse=True))
        #j=0

        if godivide is True:
            labels=divideloop(labels,misslabel,avgarea,layers,par)
            #unique=numpy.unique(labels).tolist()
            #for i in range(len(unique)):
            #    labels=numpy.where(labels==unique[i],i,labels)
            unique, counts = numpy.unique(labels, return_counts=True)
            meanpixel=sum(counts[1:])/len(counts[1:])
            countseed=numpy.asarray(counts[1:])
            stdpixel=numpy.std(countseed)
            leftsigma=(meanpixel-min(countseed))/stdpixel
            rightsigma=(max(countseed)-meanpixel)/stdpixel
            minisigma=min(leftsigma,rightsigma)
            uprange=meanpixel+minisigma*stdpixel
            lowrange=meanpixel-minisigma*stdpixel
            divide+=1
            outputlabel,misslabel,localcolortable=relabel(labels)
            band1=numpy.where(outputlabel<0,0,outputlabel)
            band2=255-band1
            band3=255-band1
            '''
            out_fn='labeled_divide'+str(divide)+'.tif'
            gtiffdriver=gdal.GetDriverByName('GTiff')
            out_ds=gtiffdriver.Create(out_fn,col,row,3,3)
            out_band=out_ds.GetRasterBand(1)
            out_band.WriteArray(band1)
            out_band=out_ds.GetRasterBand(2)
            out_band.WriteArray(band2)
            out_band=out_ds.GetRasterBand(3)
            out_band.WriteArray(band3)
            out_ds.FlushCache()
            '''
            if lastgreatarea==greatareas and len(lastgreatarea)!=0:
                manualdivide(labels,greatareas)
                #cornerdivide(labels,greatareas)
            lastgreatarea[:]=greatareas[:]
        stat,p=shapiro(countseed)
        #allinexceptsions,godivide,gocombine=checkvalid(misslabel,hist,sortedkeys,uprange,lowrange,avgarea)
        allinexceptsions,godivide,gocombine=checkvalid(p,leftsigma,rightsigma)
        dimention=getdimension(labels)
        if gocombine is True:
            labels=combineloop(labels,misslabel,par)
            #unique=numpy.unique(labels).tolist()
            #for i in range(len(unique)):
            #    labels=numpy.where(labels==unique[i],i,labels)
            unique, counts = numpy.unique(labels, return_counts=True)
            meanpixel=sum(counts[1:])/len(counts[1:])
            countseed=numpy.asarray(counts[1:])
            stdpixel=numpy.std(countseed)
            leftsigma=(meanpixel-min(countseed))/stdpixel
            rightsigma=(max(countseed)-meanpixel)/stdpixel
            minisigma=min(leftsigma,rightsigma)
            uprange=meanpixel+minisigma*stdpixel
            lowrange=meanpixel-minisigma*stdpixel
            docombine+=1
            outputlabel,misslabel,localcolortable=relabel(labels)
            band1=numpy.where(outputlabel<0,0,outputlabel)
            band2=255-band1
            band3=255-band1
            '''
            out_fn='labeled_combine'+str(docombine)+'.tif'
            gtiffdriver=gdal.GetDriverByName('GTiff')
            out_ds=gtiffdriver.Create(out_fn,col,row,3,3)
            out_band=out_ds.GetRasterBand(1)
            out_band.WriteArray(band1)
            out_band=out_ds.GetRasterBand(2)
            out_band.WriteArray(band2)
            out_band=out_ds.GetRasterBand(3)
            out_band.WriteArray(band3)
            out_ds.FlushCache()
            '''
        par+=0.05
        unique, counts = numpy.unique(labels, return_counts=True)
        meanpixel=sum(counts[1:])/len(counts[1:])
        countseed=numpy.asarray(counts[1:])
        stdpixel=numpy.std(countseed)
        hist=dict(zip(unique,counts))
        sortedkeys=list(sorted(hist,key=hist.get,reverse=True))
        for ele in sorted(hist,key=hist.get):
            if ele not in tinyareas:
                print(ele,hist[ele])


        leftsigma=(meanpixel-min(countseed))/stdpixel
        rightsigma=(max(countseed)-meanpixel)/stdpixel
        stat,p=shapiro(countseed)
        #allinexceptsions,godivide,gocombine=checkvalid(misslabel,hist,sortedkeys,uprange,lowrange,avgarea)
        allinexceptsions,godivide,gocombine=checkvalid(p,leftsigma,rightsigma)
        dimention=getdimension(labels)
    #if len(greatareas)>0:
    #    manualdivide(labels,misslabel,avgarea)


    print('DONE!!!  counts='+str(len(counts)))
    labels=exploraround(validmap,labels,shrink)
    #if shrink:
    #    labels=findmissitem(validmap,labels,coinparts)
    #coinkeys=coinparts.keys()
    #for part in coinkeys:
    #    temploc=coinparts[part]
    #    labels[temploc]=65535


    labels=renamelabels(labels)


    colorlabels,misslabel,localcolortable=relabel(labels)
    band1=numpy.where(colorlabels<0,0,colorlabels)
    band2=255-band1
    band3=255-band1

    #NDVIbounary=find_boundaries(labels,mode='inner')
    #NDVIbounary=get_boundary(labels)
    #NDVIbounary=NDVIbounary*1
    NDVIbounary=get_boundary(labels)
    NDVIbounary=NDVIbounary*255
    '''
    out_fn='modifiedsegmethod.tif'
    gtiffdriver=gdal.GetDriverByName('GTiff')
    out_ds=gtiffdriver.Create(out_fn,col,row,3,3)
    out_band=out_ds.GetRasterBand(1)
    out_band.WriteArray(band1)
    out_band=out_ds.GetRasterBand(2)
    out_band.WriteArray(band2)
    out_band=out_ds.GetRasterBand(3)
    out_band.WriteArray(band3)
    out_ds.FlushCache()
    '''

    restoredband=numpy.multiply(input,labels)
    res=NDVIbounary
    return labels,res,colortable,greatareas,tinyareas,coinparts

def kmeansprocess(pixellocs,input,counts):
    global greatareas,tinyareas
    greatareas=[]
    tinyareas=[]
    pixelarray=[]
    for i in range(len(pixellocs[0])):
        pixelarray.append([pixellocs[0][i],pixellocs[1][i]])
    pixelarray=numpy.array(pixelarray)
    clf=KMeans(n_clusters=counts,init='k-means++',n_init=10,random_state=0)
    arraylabels=clf.fit_predict(pixelarray)
    labels=numpy.zeros((450,450))
    for i in range(len(pixellocs[0])):
        labels[pixellocs[0][i],pixellocs[1][i]]=arraylabels[i]
    colorlabels,misslabel,localcolortable=relabel(labels)
    band1=numpy.where(colorlabels<0,0,colorlabels)
    band2=255-band1
    band3=255-band1
    #NDVIbounary=find_boundaries(labels,mode='inner')
    #NDVIbounary=NDVIbounary*1
    NDVIboundary=get_boundary(labels)
    NDVIbounary=NDVIboundary*255
    '''
    out_fn='modifiedsegmethod.tif'
    gtiffdriver=gdal.GetDriverByName('GTiff')
    out_ds=gtiffdriver.Create(out_fn,450,450,3,3)
    out_band=out_ds.GetRasterBand(1)
    out_band.WriteArray(band1)
    out_band=out_ds.GetRasterBand(2)
    out_band.WriteArray(band2)
    out_band=out_ds.GetRasterBand(3)
    out_band.WriteArray(band3)
    out_ds.FlushCache()
    '''
    restoredband=numpy.multiply(input,labels)
    res=NDVIbounary

    return labels,res,colortable,greatareas,tinyareas

def init(input,validmap,map,layers,ittimes,coin):
    global avgarea,elesize,labellist
    '''load plantting map'''
    fields=[]
    rows=[]
    counts=0
    elesize=[]
    labellist=[]
    input=input.astype(int)
    validmap=validmap.astype(int)
    if map!='':
        print('open map at: '+map)
        with open(map,mode='r',encoding='utf-8-sig') as f:
            csvreader=csv.reader(f)
            for row in csvreader:
                rows.append(row)
                temprow=[]
                for ele in row:
                    if ele is not '':
                        temprow.append(ele)
                elesize.append(len(temprow))
        rowlen=len(rows[0])
        for i in range(len(rows)):
            for j in range(len(rows[i])):
                if rows[i][j]!='':
                    labellist.append(rows[i][j])
                    counts+=1
        avgarea=numpy.count_nonzero(input)/counts
        plantcount = len(labellist)
        print(plantcount)
        tempcount = 3
        for i in range(len(labellist)):  # add color code for each labels
            tempdict = {}
            if labellist[i].find('C') == -1:
                if tempcount <= 255:
                    tempdict = {labellist[i]: tempcount}
                else:
                    tempdict = {labellist[i]: 255 - (tempcount)}
                tempcount += 1
            else:
                if labellist[i].find('C') != -1 and labellist[i].find('U') != -1:
                    if tempcount <= 255:
                        tempdict = {labellist[i]: tempcount}
                    else:
                        tempdict = {labellist[i]: 255 - (tempcount)}
                    tempcount += 1
                else:
                    tempdict = {labellist[i]: 255}
            if labellist[i] not in colormatch:
                colormatch.update(tempdict)
        print(colormatch)
    pixellocs=numpy.where(input!=0)
    ulx,uly=min(pixellocs[1]),min(pixellocs[0])
    rlx,rly=max(pixellocs[1]),max(pixellocs[0])
    squarearea=(rlx-ulx)*(rly-uly)
    occupiedratio=len(pixellocs[0])/squarearea
    print(avgarea,occupiedratio)
    shrink=0
    '''
    if coin==True:
        inputboundary=get_boundary(input)
        input=numpy.where(inputboundary==1,0,input)
        pixellocs=numpy.where(input!=0)
        ulx,uly=min(pixellocs[1]),min(pixellocs[0])
        rlx,rly=max(pixellocs[1]),max(pixellocs[0])
        squarearea=(rlx-ulx)*(rly-uly)
        occupiedratio=len(pixellocs[0])/squarearea
        print(avgarea,occupiedratio)
        shrink+=1

    if occupiedratio>0.1:
        while(occupiedratio>0.15):
            #distance=ndi.distance_transform_edt(input)
            inputboundary=get_boundary(input)
            input=numpy.where(inputboundary==1,0,input)
            #input=numpy.where(distance==1.0,0,input)
            pixellocs=numpy.where(input!=0)
            ulx,uly=min(pixellocs[1]),min(pixellocs[0])
            rlx,rly=max(pixellocs[1]),max(pixellocs[0])
            squarearea=(rlx-ulx)*(rly-uly)
            occupiedratio=len(pixellocs[0])/squarearea
            print(avgarea,occupiedratio)
            shrink+=1
        #for i in range(int(round(occupiedratio/0.1))):
        #    distance=ndi.distance_transform_edt(input)
        #    input=numpy.where(distance==1.0,0,input)
    '''
    uniquelabel,labelcounts=numpy.unique(labellist,return_counts=True)
    distance=ndi.distance_transform_edt(input)
    localmax=peak_local_max(distance,labels=input,footprint=numpy.ones((3,3)),indices=False)
    makers=ndi.label(localmax)[0]
    labels=watershed(-distance,mask=input)
    print(labels)

    #lastlinecount=lastline

    #if occupiedratio>=0.5:
    labels,res,colortable,greatareas,tinyareas,coinparts=processinput(input,validmap,avgarea,layers,ittimes,coin,shrink)
    #else:
    #    labels,res,colortable,greatareas,tinyareas=kmeansprocess(pixellocs,input,counts)

    return labels,res,colortable,greatareas,tinyareas,coinparts

def manual(labels,map,boundary,colortable,greatareas,tinyareas):
    labels=manualdivide(labels,greatareas)


    return labels,boundary,colortable,greatareas,tinyareas

def makeboundary(labels):
    #NDVIbounary = find_boundaries(labels, mode='inner')
    #NDVIbounary = NDVIbounary * 1
    NDVIbounary = get_boundary(labels)
    NDVIbounary = NDVIbounary * 255
    return NDVIbounary

#inputimg=gdal.Open('tempNDVI400x400.tif')
#band=inputimg.GetRasterBand(1)
#band=band.ReadAsArray()
#mapfile='wheatmap.csv'

#layers=[]
#for i in range(2):
#    layer=gdal.Open('layersclass'+str(i)+'.tif')
#    lband=layer.GetRasterBand(1)
#    lband=lband.ReadAsArray()
#    layers.append(lband)

#ittimes=30

#init(band,mapfile,layers,ittimes)