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batchprocess_terminal.py
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batchprocess_terminal.py
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from PIL import Image,ImageDraw,ImageFont
#from PIL import ImageTk,ImageGrab
import cv2
from skimage import filters
import matplotlib.pyplot as pyplt
import numpy as np
from sklearn.cluster import KMeans
import tkintercorestat
import tkintercore
import cal_kernelsize
import os
import csv
import scipy.linalg as la
import multiprocessing
import time
import sys
batchfilepath=''
inputfolder=''
outputfolder=''
print('Number of arguments:', len(sys.argv), 'arguments.')
print('Argument List:')
args=list(sys.argv)
for i in range(1,len(args)):
print(args[i])
cwd=os.getcwd()
batchfilepath=cwd+'/'+args[1]
print('batchfilepath',batchfilepath)
inputfolder=cwd+'/'+args[2]
print('inputfolder',inputfolder)
outputfolder=cwd+'/'+args[3]
print('outputfolder',outputfolder[3])
existfiles=[]
localfiles=os.listdir(cwd)
for file in localfiles:
if file.find('.png')!=-1:
slash=file.find('.png')
existfilename=file[:slash+4]
print(existfilename)
if existfilename not in existfiles:
existfiles.append(existfilename)
batch_colorbandtable=np.array([[255,0,0],[255,127,0],[255,255,0],[127,255,0],[0,255,255],[0,127,255],[0,0,255],[127,0,255],[75,0,130],[255,0,255]],'uint8')
batch_filenames=[]
pcweight=0
pcs=0
kmeans=0
kmeans_sel=[]
maxthres=0
minthres=0
maxlw=0
minlw=0
std_nonzeroratio=0
def Open_batchfile():
global pcs,pcweight,kmeans,kmeans_sel,maxthres,minthres,maxlw,minlw,std_nonzeroratio
btfile=batchfilepath
if len(btfile)>0:
if '.txt' in btfile:
with open(btfile,mode='r') as f:
setting=f.readlines()
# print(setting)
pcweight=float(setting[0].split(',')[1])
pcs=int(setting[1].split(',')[1])+1
# print(pcs)
# for i in range(len(pcs)):
# pcs[i]=int(pcs[i])
kmeans=setting[2].split(',')[1]
kmeans=int(kmeans)
kmeans_sel=setting[3].split(',')[1:-1]
maxthres=setting[4].split(',')[1]
try:
maxthres=float(maxthres)
except:
print('Load Max area error','No Max area threshold value.')
return
minthres=setting[5].split(',')[1]
minthres=float(minthres)
maxlw=setting[6].split(',')[1]
maxlw=float(maxlw)
minlw=setting[7].split(',')[1]
minlw=float(minlw)
std_nonzeroratio=float(setting[8].split(',')[1])
for i in range(len(kmeans_sel)):
kmeans_sel[i]=int(kmeans_sel[i])
print('PCweight',pcweight,'PCsel',pcs,'KMeans',kmeans,'KMeans-Selection',kmeans_sel)
print('maxthres',maxthres,'minthres',minthres,'maxlw',maxlw,'minlw',minlw)
print('Batch settings','PCweight='+str(pcweight)+'\nPCsel='+str(pcs)+'\nKMeans='+str(kmeans)+
'\nCluster selection'+str(kmeans_sel)+'\nMax area='+str(maxthres)+
'\nMin area='+str(minthres)+'\nMax diagonal='+str(maxlw)+'\nMin diagonal='+
str(minlw))
def batch_findratio(originsize,objectsize):
oria=originsize[0]
orib=originsize[1]
obja=objectsize[0]
objb=objectsize[1]
if oria>obja or orib>objb:
ratio=round(max((oria/obja),(orib/objb)))
else:
ratio=round(min((obja/oria),(objb/orib)))
if oria*orib>850 * 850:
if ratio<2:
ratio=2
return ratio
def Open_batchfolder():
global batch_filenames
FOLDER=inputfolder
if len(FOLDER)>0:
print(FOLDER)
# for root, dirs,files in os.walk(FOLDER):
files=os.listdir(FOLDER)
for filename in files:
# print('root',root)
# print('dirs',dirs)
# print("filename",filename)
if filename not in existfiles:
batch_filenames.append(filename)
# batch_filenames.append(filename)
# Open_batchimage(FOLDER,filename)
# batch_singleband(filename)
# messagebox.showinfo('Finish loading','Loading Image finished')
# if len(batch_filenames)==0:
# messagebox.showerror('No file','No file under current folder')
# return
batch_filenames.sort()
print('total # files',len(batch_filenames))
class batch_img():
def __init__(self,size,bands):
self.size=size
self.bands=bands
class batch_ser_func():
def __init__(self,filename):
self.file=filename
self.folder=inputfolder
self.exportpath=outputfolder
self.batch_Multiimage={}
self.batch_Multigray={}
self.batch_Multitype={}
self.batch_Multiimagebands={}
self.batch_Multigraybands={}
self.batch_displaybandarray={}
self.batch_originbandarray={}
self.batch_originpcabands={}
self.batch_colordicesband={}
self.batch_results={}
self.kernersizes={}
self.reseglabels=None
self.displayfea_l=0
self.displayfea_w=0
self.RGB_vector=None
self.colorindex_vector=None
self.displaypclagels=None
def Open_batchimage(self):
try:
Filersc=cv2.imread(self.folder+'/'+self.file,flags=cv2.IMREAD_ANYCOLOR)
height,width,channel=np.shape(Filersc)
Filesize=(height,width)
print('filesize:',height,width)
RGBfile=cv2.cvtColor(Filersc,cv2.COLOR_BGR2RGB)
Grayfile=cv2.cvtColor(Filersc,cv2.COLOR_BGR2Lab)
Grayfile=cv2.cvtColor(Grayfile,cv2.COLOR_BGR2GRAY)
Grayimg=batch_img(Filesize,Grayfile)
RGBbands=np.zeros((channel,height,width))
for j in range(channel):
band=RGBfile[:,:,j]
band=np.where(band==0,1e-6,band)
RGBbands[j,:,:]=band
RGBimg=batch_img(Filesize,RGBbands)
tempdict={self.file:RGBimg}
self.batch_Multiimagebands.update(tempdict)
tempdict={self.file:Grayfile}
self.batch_Multigray.update(tempdict)
tempdict={self.file:0}
self.batch_Multitype.update(tempdict)
tempdict={self.file:Grayimg}
self.batch_Multigraybands.update(tempdict)
except:
# messagebox.showerror('Invalid Image Format','Cannot open '+filename)
return False
return True
def fillbands(self,originbands,displaybands,vector,vectorindex,name,band):
tempdict={name:band}
if name not in originbands:
originbands.update(tempdict)
image=cv2.resize(band,(self.displayfea_w,self.displayfea_l),interpolation=cv2.INTER_LINEAR)
displaydict={name:image}
displaybands.update(displaydict)
fea_bands=image.reshape((self.displayfea_l*self.displayfea_w),1)[:,0]
vector[:,vectorindex]=vector[:,vectorindex]+fea_bands
return
def singleband(self):
try:
bands=self.batch_Multiimagebands[self.file].bands
except:
return
channel,fea_l,fea_w=bands.shape
print('bandsize',fea_l,fea_w)
if fea_l*fea_w>2000*2000:
ratio=batch_findratio([fea_l,fea_w],[2000,2000])
else:
ratio=1
print('ratio',ratio)
originbands={}
displays={}
displaybands=cv2.resize(bands[0,:,:],(int(fea_w/ratio),int(fea_l/ratio)),interpolation=cv2.INTER_LINEAR)
displayfea_l,displayfea_w=displaybands.shape
self.RGB_vector=np.zeros((displayfea_l*displayfea_w,3))
self.colorindex_vector=np.zeros((displayfea_l*displayfea_w,12))
self.displayfea_l,self.displayfea_w=displaybands.shape
self.RGB_vectorr=np.zeros((displayfea_l*displayfea_w,3))
self.colorindex_vector=np.zeros((displayfea_l*displayfea_w,12))
Red=bands[0,:,:]
Green=bands[1,:,:]
Blue=bands[2,:,:]
self.fillbands(originbands,displays,self.RGB_vector,0,'Band1',Red)
self.fillbands(originbands,displays,self.RGB_vector,1,'Band2',Green)
self.fillbands(originbands,displays,self.RGB_vector,2,'Band3',Blue)
#secondsmallest_R=np.partition(Red,1)[1][0]
#secondsmallest_G=np.partition(Green,1)[1][0]
#secondsmallest_B=np.partition(Blue,1)[1][0]
#Red=Red+secondsmallest_R
#Green=Green+secondsmallest_G
#Blue=Blue+secondsmallest_B
PAT_R=Red/(Red+Green)
PAT_G=Green/(Green+Blue)
PAT_B=Blue/(Blue+Red)
ROO_R=Red/Green
ROO_G=Green/Blue
ROO_B=Blue/Red
DIF_R=2*Red-Green-Blue
DIF_G=2*Green-Blue-Red
DIF_B=2*Blue-Red-Green
GLD_R=Red/(np.multiply(np.power(Blue,0.618),np.power(Green,0.382)))
GLD_G=Green/(np.multiply(np.power(Blue,0.618),np.power(Red,0.382)))
GLD_B=Blue/(np.multiply(np.power(Green,0.618),np.power(Red,0.382)))
self.fillbands(originbands,displays,self.colorindex_vector,0,'PAT_R',PAT_R)
self.fillbands(originbands,displays,self.colorindex_vector,1,'PAT_G',PAT_G)
self.fillbands(originbands,displays,self.colorindex_vector,2,'PAT_B',PAT_B)
self.fillbands(originbands,displays,self.colorindex_vector,3,'ROO_R',ROO_R)
self.fillbands(originbands,displays,self.colorindex_vector,4,'ROO_G',ROO_G)
self.fillbands(originbands,displays,self.colorindex_vector,5,'ROO_B',ROO_B)
self.fillbands(originbands,displays,self.colorindex_vector,6,'DIF_R',DIF_R)
self.fillbands(originbands,displays,self.colorindex_vector,7,'DIF_G',DIF_G)
self.fillbands(originbands,displays,self.colorindex_vector,8,'DIF_B',DIF_B)
self.fillbands(originbands,displays,self.colorindex_vector,9,'GLD_R',GLD_R)
self.fillbands(originbands,displays,self.colorindex_vector,10,'GLD_G',GLD_G)
self.fillbands(originbands,displays,self.colorindex_vector,11,'GLD_B',GLD_B)
NDI=128*((Green-Red)/(Green+Red)+1)
VEG=Green/(np.power(Red,0.667)*np.power(Blue,(1-0.667)))
Greenness=Green/(Green+Red+Blue)
CIVE=0.44*Red+0.811*Green+0.385*Blue+18.7845
MExG=1.262*Green-0.844*Red-0.311*Blue
NDRB=(Red-Blue)/(Red+Blue)
NGRDI=(Green-Red)/(Green+Red)
colorindex_vector=np.zeros((displayfea_l*displayfea_w,7))
self.fillbands(originbands,displays,colorindex_vector,0,'NDI',NDI)
self.fillbands(originbands,displays,colorindex_vector,1,'VEG',VEG)
self.fillbands(originbands,displays,colorindex_vector,2,'Greenness',Greenness)
self.fillbands(originbands,displays,colorindex_vector,3,'CIVE',CIVE)
self.fillbands(originbands,displays,colorindex_vector,4,'MExG',MExG)
self.fillbands(originbands,displays,colorindex_vector,5,'NDRB',NDRB)
self.fillbands(originbands,displays,colorindex_vector,6,'NGRDI',NGRDI)
rgb_M=np.mean(self.RGB_vector.T,axis=1)
colorindex_M=np.mean(self.colorindex_vector.T,axis=1)
print('rgb_M',rgb_M,'colorindex_M',colorindex_M)
rgb_C=self.RGB_vector-rgb_M
colorindex_C=self.colorindex_vector-colorindex_M
rgb_V=np.corrcoef(rgb_C.T)
color_V=np.corrcoef(colorindex_C.T)
rgb_std=rgb_C/np.std(self.RGB_vector.T,axis=1)
color_std=colorindex_C/np.std(self.colorindex_vector.T,axis=1)
rgb_eigval,rgb_eigvec=np.linalg.eig(rgb_V)
color_eigval,color_eigvec=np.linalg.eig(color_V)
print('rgb_eigvec',rgb_eigvec)
print('color_eigvec',color_eigvec)
featurechannel=14
pcabands=np.zeros((self.colorindex_vector.shape[0],featurechannel))
for i in range(3):
pcn=rgb_eigvec[:,i]
pcnbands=np.dot(rgb_std,pcn)
pcvar=np.var(pcnbands)
print('rgb pc',i+1,'var=',pcvar)
pcabands[:,i]=pcabands[:,i]+pcnbands
pcabands[:,1]=np.copy(pcabands[:,2])
pcabands[:,2]=pcabands[:,2]*0
for i in range(2,featurechannel):
pcn=color_eigvec[:,i-2]
pcnbands=np.dot(color_std,pcn)
pcvar=np.var(pcnbands)
print('color index pc',i-1,'var=',pcvar)
pcabands[:,i]=pcabands[:,i]+pcnbands
displayfea_vector=np.concatenate((self.RGB_vector,self.colorindex_vector),axis=1)
self.batch_originpcabands.update({self.file:displayfea_vector})
pcabandsdisplay=pcabands.reshape(displayfea_l,displayfea_w,featurechannel)
tempdictdisplay={'LabOstu':pcabandsdisplay}
self.batch_displaybandarray.update({self.file:tempdictdisplay})
self.batch_originbandarray.update({self.file:originbands})
def singleband_oldversion(self):
try:
bands=self.batch_Multigraybands[self.file].bands
except:
return
bandsize=self.batch_Multigraybands[self.file].size
print('bandsize',bandsize)
try:
channel,height,width=bands.shape
except:
channel=0
if channel>1:
bands=bands[0,:,:]
ostu=filters.threshold_otsu(bands)
bands=bands.astype('float32')
bands=bands/ostu
if bandsize[0]*bandsize[1]>2000*2000:
ratio=batch_findratio([bandsize[0],bandsize[1]],[2000,2000])
else:
ratio=1
print('ratio',ratio)
originbands={}
displays={}
fea_l,fea_w=bands.shape
# fea_vector=np.zeros((fea_l*fea_w,10))
# pyplt.imsave('batch_bands.png',bands)
displaybands=cv2.resize(bands,(int(bandsize[1]/ratio),int(bandsize[0]/ratio)),interpolation=cv2.INTER_LINEAR)
# pyplt.imsave('batch_displaybands.png',displaybands)
displayfea_l,displayfea_w=displaybands.shape
self.displayfea_l,self.displayfea_w=displaybands.shape
fea_vector=np.zeros((displayfea_l*displayfea_w,3))
displayfea_vector=np.zeros((displayfea_l*displayfea_w,7))
colorfea_vector=np.zeros((displayfea_l*displayfea_w,7))
if 'LabOstu' not in originbands:
originbands.update({'LabOstu':bands})
fea_bands=bands.reshape(fea_l*fea_w,1)[:,0]
displayfea_bands=displaybands.reshape((displayfea_l*displayfea_w),1)[:,0]
# fea_vector[:,9]=fea_vector[:,0]+fea_bands
displayfea_vector[:,6]=displayfea_vector[:,6]+displayfea_bands
minv=displayfea_bands.min()
maxv=displayfea_bands.max()
fearange=maxv-minv
colorfeabands=displayfea_bands-minv
colorfeabands=colorfeabands/fearange*255
colorfea_vector[:,6]=colorfea_vector[:,6]+colorfeabands
displays.update({'LabOstu':displaybands})
bands=self.batch_Multiimagebands[self.file].bands
NDI=128*((bands[1,:,:]-bands[0,:,:])/(bands[1,:,:]+bands[0,:,:])+1)
tempdict={'NDI':NDI}
if 'NDI' not in originbands:
originbands.update(tempdict)
displaybands=cv2.resize(NDI,(int(bandsize[1]/ratio),int(bandsize[0]/ratio)),interpolation=cv2.INTER_LINEAR)
fea_bands=NDI.reshape(fea_l*fea_w,1)[:,0]
# originfea_vector[:,1]=originfea_vector[:,1]+fea_bands
displayfea_bands=displaybands.reshape((displayfea_l*displayfea_w),1)[:,0]
# fea_vector[:,1]=fea_vector[:,1]+fea_bands
displayfea_vector[:,1]=displayfea_vector[:,1]+displayfea_bands
minv=displayfea_bands.min()
maxv=displayfea_bands.max()
fearange=maxv-minv
colorfeabands=displayfea_bands-minv
colorfeabands=colorfeabands/fearange*255
colorfea_vector[:,6]=colorfea_vector[:,6]+colorfeabands
displaydict={'NDI':displaybands}
displays.update(displaydict)
Red=bands[0,:,:]
Green=bands[1,:,:]
Blue=bands[2,:,:]
tempdict={'Band1':Red}
if 'Band1' not in originbands:
originbands.update(tempdict)
image=cv2.resize(Red,(int(bandsize[1]/ratio),int(bandsize[0]/ratio)),interpolation=cv2.INTER_LINEAR)
displaydict={'Band1':image}
displays.update(displaydict)
fea_bands=Red.reshape(fea_l*fea_w,1)[:,0]
# originfea_vector[:,2]=originfea_vector[:,2]+fea_bands
displayfea_bands=image.reshape((displayfea_l*displayfea_w),1)[:,0]
fea_vector[:,0]=fea_vector[:,0]+displayfea_bands
# displayfea_vector[:,2]=displayfea_vector[:,2]+displayfea_bands
tempdict={'Band2':Green}
if 'Band2' not in originbands:
originbands.update(tempdict)
image=cv2.resize(Green,(int(bandsize[1]/ratio),int(bandsize[0]/ratio)),interpolation=cv2.INTER_LINEAR)
displaydict={'Band2':image}
displays.update(displaydict)
fea_bands=Green.reshape(fea_l*fea_w,1)[:,0]
# originfea_vector[:,3]=originfea_vector[:,3]+fea_bands
displayfea_bands=image.reshape((displayfea_l*displayfea_w),1)[:,0]
fea_vector[:,1]=fea_vector[:,1]+displayfea_bands
# displayfea_vector[:,3]=displayfea_vector[:,3]+displayfea_bands
tempdict={'Band3':Blue}
if 'Band3' not in originbands:
originbands.update(tempdict)
# originfea_vector[:,4]=originfea_vector[:,4]+Blue
image=cv2.resize(Blue,(int(bandsize[1]/ratio),int(bandsize[0]/ratio)),interpolation=cv2.INTER_LINEAR)
displaydict={'Band3':image}
displays.update(displaydict)
fea_bands=Blue.reshape(fea_l*fea_w,1)[:,0]
displayfea_bands=image.reshape((displayfea_l*displayfea_w),1)[:,0]
fea_vector[:,2]=fea_vector[:,2]+displayfea_bands
# displayfea_vector[:,4]=displayfea_vector[:,4]+displayfea_bands
Greenness = bands[1, :, :] / (bands[0, :, :] + bands[1, :, :] + bands[2, :, :])
tempdict = {'Greenness': Greenness}
if 'Greenness' not in originbands:
originbands.update(tempdict)
# originfea_vector[:,5]=originfea_vector[:,5]+Greenness
image=cv2.resize(Greenness,(int(bandsize[1]/ratio),int(bandsize[0]/ratio)),interpolation=cv2.INTER_LINEAR)
#image=image.reshape((int(bandsize[1]/ratio),int(bandsize[0]/ratio),3))
displaydict={'Greenness':image}
#displaybandarray.update(worktempdict)
displays.update(displaydict)
fea_bands=Greenness.reshape(fea_l*fea_w,1)[:,0]
displayfea_bands=image.reshape((displayfea_l*displayfea_w),1)[:,0]
# fea_vector[:,5]=fea_vector[:,5]+fea_bands
minv=displayfea_bands.min()
maxv=displayfea_bands.max()
fearange=maxv-minv
colorfeabands=displayfea_bands-minv
colorfeabands=colorfeabands/fearange*255
colorfea_vector[:,2]=colorfea_vector[:,2]+colorfeabands
displayfea_vector[:,2]=displayfea_vector[:,2]+displayfea_bands
VEG=bands[1,:,:]/(np.power(bands[0,:,:],0.667)*np.power(bands[2,:,:],(1-0.667)))
tempdict={'VEG':VEG}
if 'VEG' not in originbands:
originbands.update(tempdict)
# originfea_vector[:,6]=originfea_vector[:,6]+VEG
image=cv2.resize(VEG,(int(bandsize[1]/ratio),int(bandsize[0]/ratio)),interpolation=cv2.INTER_LINEAR)
kernel=np.ones((4,4),np.float32)/16
#displaybandarray.update({'LabOstu':})
#image=image.reshape((int(bandsize[1]/ratio),int(bandsize[0]/ratio),3))
worktempdict={'VEG':cv2.filter2D(image,-1,kernel)}
displays.update(worktempdict)
fea_bands=VEG.reshape(fea_l*fea_w,1)[:,0]
displayfea_bands=image.reshape((displayfea_l*displayfea_w),1)[:,0]
# fea_vector[:,6]=fea_vector[:,6]+fea_bands
minv=displayfea_bands.min()
maxv=displayfea_bands.max()
fearange=maxv-minv
colorfeabands=displayfea_bands-minv
colorfeabands=colorfeabands/fearange*255
colorfea_vector[:,3]=colorfea_vector[:,3]+colorfeabands
displayfea_vector[:,3]=displayfea_vector[:,3]+displayfea_bands
CIVE=0.441*bands[0,:,:]-0.811*bands[1,:,:]+0.385*bands[2,:,:]+18.78745
tempdict={'CIVE':CIVE}
if 'CIVE' not in originbands:
originbands.update(tempdict)
# originfea_vector[:,7]=originfea_vector[:,7]+CIVE
image=cv2.resize(CIVE,(int(bandsize[1]/ratio),int(bandsize[0]/ratio)),interpolation=cv2.INTER_LINEAR)
#image=image.reshape((int(bandsize[1]/ratio),int(bandsize[0]/ratio),3))
worktempdict={'CIVE':image}
displays.update(worktempdict)
fea_bands=CIVE.reshape(fea_l*fea_w,1)[:,0]
displayfea_bands=image.reshape((displayfea_l*displayfea_w),1)[:,0]
# fea_vector[:,7]=fea_vector[:,7]+fea_bands
displayfea_vector[:,4]=displayfea_vector[:,4]+displayfea_bands
minv=displayfea_bands.min()
maxv=displayfea_bands.max()
fearange=maxv-minv
colorfeabands=displayfea_bands-minv
colorfeabands=colorfeabands/fearange*255
colorfea_vector[:,4]=colorfea_vector[:,4]+colorfeabands
MExG=1.262*bands[1,:,:]-0.884*bands[0,:,:]-0.311*bands[2,:,:]
tempdict={'MExG':MExG}
if 'MExG' not in originbands:
originbands.update(tempdict)
# originfea_vector[:,8]=originfea_vector[:,8]+MExG
image=cv2.resize(MExG,(int(bandsize[1]/ratio),int(bandsize[0]/ratio)),interpolation=cv2.INTER_LINEAR)
#image=image.reshape((int(bandsize[1]/ratio),int(bandsize[0]/ratio),3))
worktempdict={'MExG':image}
displays.update(worktempdict)
fea_bands=MExG.reshape(fea_l*fea_w,1)[:,0]
displayfea_bands=image.reshape((displayfea_l*displayfea_w),1)[:,0]
# fea_vector[:,8]=fea_vector[:,8]+fea_bands
displayfea_vector[:,5]=displayfea_vector[:,5]+displayfea_bands
minv=displayfea_bands.min()
maxv=displayfea_bands.max()
fearange=maxv-minv
colorfeabands=displayfea_bands-minv
colorfeabands=colorfeabands/fearange*255
colorfea_vector[:,5]=colorfea_vector[:,5]+colorfeabands
NDVI=(bands[0,:,:]-bands[2,:,:])/(bands[0,:,:]+bands[2,:,:])
tempdict={'NDVI':NDVI}
if 'NDVI' not in originbands:
originbands.update(tempdict)
# originfea_vector[:,0]=originfea_vector[:,9]+NDVI
image=cv2.resize(NDVI,(int(bandsize[1]/ratio),int(bandsize[0]/ratio)),interpolation=cv2.INTER_LINEAR)
#image=image.reshape((int(bandsize[1]/ratio),int(bandsize[0]/ratio),3))
worktempdict={'NDVI':image}
displays.update(worktempdict)
fea_bands=NDVI.reshape(fea_l*fea_w,1)[:,0]
displayfea_bands=image.reshape((displayfea_l*displayfea_w),1)[:,0]
# fea_vector[:,0]=fea_vector[:,9]+fea_bands
displayfea_vector[:,0]=displayfea_vector[:,0]+displayfea_bands
minv=displayfea_bands.min()
maxv=displayfea_bands.max()
fearange=maxv-minv
colorfeabands=displayfea_bands-minv
colorfeabands=colorfeabands/fearange*255
colorfea_vector[:,0]=colorfea_vector[:,0]+colorfeabands
NGRDI=(bands[1,:,:]-bands[0,:,:])/(bands[1,:,:]+bands[0,:,:])
tempdict={'NGRDI':NGRDI}
if 'NGRDI' not in originbands:
originbands.update(tempdict)
image=cv2.resize(NGRDI,(int(bandsize[1]/ratio),int(bandsize[0]/ratio)),interpolation=cv2.INTER_LINEAR)
#image=image.reshape((int(bandsize[1]/ratio),int(bandsize[0]/ratio),3))
worktempdict={'NGRDI':image}
displays.update(worktempdict)
if channel>=1:
nirbands=self.batch_Multigraybands[self.file].bands
NDVI=(nirbands[0,:,:]-bands[1,:,:])/(nirbands[0,:,:]+bands[1,:,:])
tempdict={'NDVI':NDVI}
#if 'NDVI' not in originbandarray:
originbands.update(tempdict)
image=cv2.resize(NDVI,(int(bandsize[1]/ratio),int(bandsize[0]/ratio)),interpolation=cv2.INTER_LINEAR)
#image=image.reshape((int(bandsize[1]/ratio),int(bandsize[0]/ratio),3))
worktempdict={'NDVI':image}
displays.update(worktempdict)
'''PCA part'''
displayfea_vector=np.concatenate((fea_vector,displayfea_vector),axis=1)
M=np.mean(displayfea_vector.T,axis=1)
OM=np.mean(fea_vector.T,axis=1)
print('M',M,'M shape',M.shape, 'OM',OM,'OM Shape',OM.shape)
C=displayfea_vector-M
OC=fea_vector-OM
#max=np.max(C.T,axis=1)
#print('MAX',max)
#C=C/max
print('C',C,'OC',OC)
#V=np.cov(C.T)
V=np.corrcoef(C.T)
OV=np.corrcoef(OC.T)
std=np.std(displayfea_vector.T,axis=1)
O_std=np.std(fea_vector.T,axis=1)
print(std,O_std)
std_displayfea=C/std
O_stddisplayfea=OC/O_std
print(std_displayfea,O_stddisplayfea)
#eigvalues,eigvectors=np.linalg.eig(V)
#n,m=displayfea_vector.shape
#C=np.dot(displayfea_vector.T,displayfea_vector)/(n-1)
V_var=np.cov(std_displayfea.T)
print('COV',V_var)
print('COR',V)
eigvalues=la.eigvals(V_var)
#eigvalues=np.linalg.eigvals(C)
print('eigvalue',eigvalues)
idx=np.argsort(eigvalues)
print('idx',idx)
eigvalues,eigvectors=np.linalg.eig(V)
print('eigvalue',eigvalues)
print('eigvectors',eigvectors)
eigvalueperc={}
featurechannel=10
# for i in range(len(eigvalues)):
# print('percentage',i,eigvalues[i]/sum(eigvalues))
# eigvalueperc.update({i:eigvalues[i]/sum(eigvalues)})
# #if eigvalues[i]>0:
# featurechannel+=1
# o_eigenvalue,o_eigenvector=np.linalg.eig(OV)
pcabands=np.zeros((displayfea_vector.shape[0],featurechannel))
# o_pcabands=np.zeros((fea_vector.shape[0],featurechannel))
pcavar={}
#separate PCA
# for i in range(3):
# pcn=o_eigenvector[:,i]
# pcnbands=np.dot(O_stddisplayfea,pcn)
# pcvar=np.var(pcnbands)
# print('pc',i+1,' var=',pcvar)
# pcabands[:,i]=pcabands[:,i]+pcnbands
# for i in range(7):
# pcn=eigvectors[:,i]
# # opcn=o_eigenvector[:,i]
# #pcnbands=np.dot(displayfea_vector,pcn)
# pcnbands=np.dot(std_displayfea,pcn)
# # opcnbands=np.dot(O_stddisplayfea,opcn)
# pcvar=np.var(pcnbands)
# print('pc',i+1,' var=',pcvar)
# temppcavar={i:pcvar}
# pcavar.update(temppcavar)
# # pcnbands=np.dot(C,pcn)
# # opcnbands=np.dot(OC,opcn)
# pcabands[:,i+3]=pcabands[:,i+3]+pcnbands
#combined PCa
for i in range(featurechannel):
pcn=eigvectors[:,i]
# pcnbands=np.dot(std_displayfea,pcn)
pcnbands=np.dot(C,pcn)
pcvar=np.var(pcnbands)
print('pc',i+1,' var=',pcvar)
temppcavar={i:pcvar}
pcavar.update(temppcavar)
pcabands[:,i]=pcabands[:,i]+pcnbands
# o_pcabands[:,i]=o_pcabands[:,i]+opcnbands
# sortvar=sorted(pcavar,key=pcavar.get)
# print(sortvar)
# for i in range(len(sortvar)):
# pcn=eigvectors[:,sortvar[i]]
# pcnbands=np.dot(displayfea_vector,pcn)
# pcabands[:,i]=pcabands[:,i]+pcnbands
#np.savetxt('pcs.csv',pcabands,delimiter=',',fmt='%s')
#np.savetxt('color-index.csv',displayfea_vector,delimiter=',',fmt='%s')
#high,width=pcabands.shape
#fp=open('pcs.csv',w)
#fc=open('color-index.csv',w)
#head=['Otsu','NDI','R','G','B','Greenness','VEG','CIVE','MExG','NDVI']
#for i in range(high):
# '''No PCA'''
# colorfea_vector=np.concatenate((fea_vector,colorfea_vector),axis=1)
# displayfea_vector=np.concatenate((fea_vector,displayfea_vector),axis=1)
# M=np.mean(colorfea_vector.T,axis=1)
# print('colorfea_vector M',M)
# pcabands=np.copy(colorfea_vector)
# featurechannel=10
#threedplot(pcabands)
# self.batch_originpcabands.update({self.file:o_pcabands})
self.batch_originpcabands.update({self.file:displayfea_vector})
pcabandsdisplay=pcabands.reshape(displayfea_l,displayfea_w,featurechannel)
#originbands={'LabOstu':pcabandsdisplay}
tempdictdisplay={'LabOstu':pcabandsdisplay}
#displaybandarray.update({file:displays})
self.batch_displaybandarray.update({self.file:tempdictdisplay})
self.batch_originbandarray.update({self.file:originbands})
def kmeansclassify(self):
if kmeans==0:
print('Kmeans error','Kmeans should greater than 0')
return None
file=self.file
originpcabands=self.batch_displaybandarray[file]['LabOstu']
pcah,pcaw,pcac=originpcabands.shape
tempband=np.zeros((pcah,pcaw,1))
if pcweight==0.0:
tempband[:,:,0]=tempband[:,:,0]+originpcabands[:,:,pcs]
else:
if pcweight<0.0:
rgbpc=originpcabands[:,:,0]
else:
rgbpc=originpcabands[:,:,1]
rgbpc=(rgbpc-rgbpc.min())*255/(rgbpc.max()-rgbpc.min())
firstterm=abs(pcweight)*2*rgbpc
colorpc=originpcabands[:,:,pcs]
colorpc=(colorpc-colorpc.min())*255/(colorpc.max()-colorpc.min())
secondterm=(1-abs(pcweight)*2)*colorpc
tempband[:,:,0]=tempband[:,:,0]+firstterm+secondterm
self.displaypclagels=np.copy(tempband[:,:,0])
if kmeans==1:
print('kmeans=1')
displaylabels=np.mean(tempband,axis=2)
pyplt.imsave(file+'_k=1.png',displaylabels)
else:
if kmeans>1:
h,w,c=tempband.shape
print('shape',tempband.shape)
reshapedtif=tempband.reshape(tempband.shape[0]*tempband.shape[1],c)
print('reshape',reshapedtif.shape)
clf=KMeans(n_clusters=kmeans,init='k-means++',n_init=10,random_state=0)
tempdisplayimg=clf.fit(reshapedtif)
# print('label=0',np.any(tempdisplayimg==0))
displaylabels=tempdisplayimg.labels_.reshape((self.batch_displaybandarray[self.file]['LabOstu'].shape[0],
self.batch_displaybandarray[self.file]['LabOstu'].shape[1]))
clusterdict={}
displaylabels=displaylabels+10
for i in range(kmeans):
locs=np.where(tempdisplayimg.labels_==i)
maxval=reshapedtif[locs].max()
print(maxval)
clusterdict.update({maxval:i+10})
print(clusterdict)
sortcluster=list(sorted(clusterdict))
print(sortcluster)
for i in range(len(sortcluster)):
cluster_num=clusterdict[sortcluster[i]]
displaylabels=np.where(displaylabels==cluster_num,i,displaylabels)
return displaylabels
def kmeansclassify_oldversion(self):
if kmeans==0:
print('Kmeans error','Kmeans should greater than 0')
return None
file=self.file
originpcabands=self.batch_displaybandarray[self.file]['LabOstu']
pcah,pcaw,pcac=originpcabands.shape
print(self.file,'originpcabands',pcah,pcaw,pcac)
pcakeys=pcs
tempband=np.zeros((pcah,pcaw,len(pcakeys)))
for i in range(len(pcakeys)):
channel=int(pcakeys[i])-1
tempband[:,:,i]=tempband[:,:,i]+originpcabands[:,:,channel]
if kmeans==1:
print('kmeans=1')
displaylabels=np.mean(tempband,axis=2)
pyplt.imsave(file+'_k=1.png',displaylabels)
else:
#tempband=displaybandarray[currentfilename]['LabOstu']
if kmeans>1:
h,w,c=tempband.shape
print('shape',tempband.shape)
reshapedtif=tempband.reshape(tempband.shape[0]*tempband.shape[1],c)
print('reshape',reshapedtif.shape)
clf=KMeans(n_clusters=kmeans,init='k-means++',n_init=10,random_state=0)
tempdisplayimg=clf.fit(reshapedtif)
# print('label=0',np.any(tempdisplayimg==0))
displaylabels=tempdisplayimg.labels_.reshape((self.batch_displaybandarray[self.file]['LabOstu'].shape[0],
self.batch_displaybandarray[self.file]['LabOstu'].shape[1]))
clusterdict={}
displaylabels=displaylabels+10
for i in range(kmeans):
locs=np.where(tempdisplayimg.labels_==i)
maxval=reshapedtif[locs].max()
print(maxval)
clusterdict.update({maxval:i+10})
print(clusterdict)
sortcluster=list(sorted(clusterdict))
print(sortcluster)
for i in range(len(sortcluster)):
cluster_num=clusterdict[sortcluster[i]]
displaylabels=np.where(displaylabels==cluster_num,i,displaylabels)
return displaylabels
def generateimgplant(self,displaylabels):
colordicesband=np.copy(displaylabels)
tempdisplayimg=np.zeros((self.batch_displaybandarray[self.file]['LabOstu'].shape[0],
self.batch_displaybandarray[self.file]['LabOstu'].shape[1]))
colordivimg=np.zeros((self.batch_displaybandarray[self.file]['LabOstu'].shape[0],
self.batch_displaybandarray[self.file]['LabOstu'].shape[1]))
for i in range(len(kmeans_sel)):
sk=kmeans_sel[i]-1
tempdisplayimg=np.where(displaylabels==sk,1,tempdisplayimg)
currentlabels=np.copy(tempdisplayimg)
originbinaryimg=np.copy(tempdisplayimg)
tempcolorimg=np.copy(displaylabels).astype('float32')
ratio=batch_findratio([tempdisplayimg.shape[0],tempdisplayimg.shape[1]],[850,850])
if tempdisplayimg.shape[0]*tempdisplayimg.shape[1]<850*850:
tempdisplayimg=cv2.resize(tempdisplayimg,(int(tempdisplayimg.shape[1]*ratio),int(tempdisplayimg.shape[0]*ratio)))
colordivimg=cv2.resize(tempcolorimg,(int(colordivimg.shape[1]*ratio),int(colordivimg.shape[0]*ratio)))
else:
tempdisplayimg=cv2.resize(tempdisplayimg,(int(tempdisplayimg.shape[1]/ratio),int(tempdisplayimg.shape[0]/ratio)))
colordivimg=cv2.resize(tempcolorimg,(int(colordivimg.shape[1]/ratio),int(colordivimg.shape[0]/ratio)))
binaryimg=np.zeros((tempdisplayimg.shape[0],tempdisplayimg.shape[1],3))
colordeimg=np.zeros((colordivimg.shape[0],colordivimg.shape[1],3))
locs=np.where(tempdisplayimg==1)
binaryimg[locs]=[240,228,66]
for i in range(kmeans):
locs=np.where(colordivimg==i)
colordeimg[locs]=batch_colorbandtable[i]
Image.fromarray(colordeimg.astype('uint8')).save(self.file+'-allcolorindex.png',"PNG")
Image.fromarray((binaryimg.astype('uint8'))).save(self.file+'-binaryimg.png',"PNG")
return currentlabels,originbinaryimg
def resegment(self):
if type(self.reseglabels) == type(None):
return False
labels=np.copy(self.reseglabels)
reseglabels,border,colortable,labeldict=tkintercorestat.resegmentinput(labels,minthres,maxthres,minlw,maxlw)
self.batch_results.update({self.file:(labeldict,{})})
return True
def extraction(self,currentlabels):
if kmeans==1:
print('Invalid Class #','#Class = 1, try change it to 2 or more, and refresh Color-Index.')
return False
nonzeros=np.count_nonzero(currentlabels)
print('nonzero counts',nonzeros)
nonzeroloc=np.where(currentlabels!=0)
try:
ulx,uly=min(nonzeroloc[1]),min(nonzeroloc[0])
except:
print('Invalid Colorindices','Need to process colorindicies')
return False
rlx,rly=max(nonzeroloc[1]),max(nonzeroloc[0])
nonzeroratio=float(nonzeros)/((rlx-ulx)*(rly-uly))
print(nonzeroratio)
if nonzeroratio>std_nonzeroratio*2:
return False
dealpixel=nonzeroratio*currentlabels.shape[0]*currentlabels.shape[1]
ratio=1
if nonzeroratio<=0.2:# and nonzeroratio>=0.1:
ratio=batch_findratio([currentlabels.shape[0],currentlabels.shape[1]],[1600,1600])
if currentlabels.shape[0]*currentlabels.shape[1]>1600*1600:
workingimg=cv2.resize(currentlabels,(int(currentlabels.shape[1]/ratio),int(currentlabels.shape[0]/ratio)),interpolation=cv2.INTER_LINEAR)
else:
#ratio=1
#print('nonzeroratio',ratio)
workingimg=np.copy(currentlabels)
segmentratio=0
else:
print('deal pixel',dealpixel)
if dealpixel>512000:
if currentlabels.shape[0]*currentlabels.shape[1]>850*850:
segmentratio=batch_findratio([currentlabels.shape[0],currentlabels.shape[1]],[850,850])
if segmentratio<2:
segmentratio=2
workingimg=cv2.resize(currentlabels,(int(currentlabels.shape[1]/segmentratio),int(currentlabels.shape[0]/segmentratio)),interpolation=cv2.INTER_LINEAR)
else:
segmentratio=1
#print('ratio',ratio)
workingimg=np.copy(currentlabels)
pixelmmratio=1.0
coin=False
print('nonzeroratio:',ratio,'segmentation ratio',segmentratio)
print('workingimgsize:',workingimg.shape)
pyplt.imsave('workingimg.png',workingimg)
originlabels=None
if originlabels is None:
originlabels,border,colortable,originlabeldict=tkintercorestat.init(workingimg,workingimg,'',workingimg,10,coin)
self.reseglabels=originlabels
self.batch_results.update({self.file:(originlabeldict,{})})
return True
def savePCAimg(self,originfile):
file=self.file
path=self.exportpath
originpcabands=self.batch_displaybandarray[file]['LabOstu']
pcah,pcaw,pcac=originpcabands.shape
tempband=np.zeros((pcah,pcaw))
if pcweight==0.0:
tempband=tempband+originpcabands[:,:,pcs]
else:
if pcweight<0.0:
rgbpc=originpcabands[:,:,0]
else:
rgbpc=originpcabands[:,:,1]
rgbpc=(rgbpc-rgbpc.min())*255/(rgbpc.max()-rgbpc.min())
firstterm=abs(pcweight)*2*rgbpc
colorpc=originpcabands[:,:,pcs]
colorpc=(colorpc-colorpc.min())*255/(colorpc.max()-colorpc.min())
secondterm=(1-abs(pcweight)*2)*colorpc
tempband=tempband+firstterm+secondterm
displaylabels=np.copy(tempband)
if displaylabels.min()<0:
displaylabels=displaylabels-displaylabels.min()
colorrange=displaylabels.max()-displaylabels.min()
displaylabels=displaylabels*255/colorrange
grayimg=Image.fromarray(displaylabels.astype('uint8'),'L')
originheight,originwidth=self.batch_Multigraybands[file].size
origingray=grayimg.resize([originwidth,originheight],resample=Image.BILINEAR)
origingray.save(path+'/'+originfile+'-PCAimg.png',"PNG")
# addcolorstrip()
return
def savePCAimg_oldversion(self,originfile):
file=self.file
path=self.exportpath
originpcabands=self.batch_displaybandarray[file]['LabOstu']
pcah,pcaw,pcac=originpcabands.shape
pcakeys=pcs
tempband=np.zeros((pcah,pcaw,len(pcakeys)))
for i in range(len(pcakeys)):
channel=int(pcakeys[i])-1
tempband[:,:,i]=tempband[:,:,i]+originpcabands[:,:,channel]
displaylabels=np.mean(tempband,axis=2)
# generateimgplant(displaylabels)
# grayimg=(((displaylabels-displaylabels.min())/(displaylabels.max()-displaylabels.min()))*255.9).astype(np.uint8)
# pyplt.imsave('k=1.png',displaylabels.astype('uint8'))
# pyplt.imsave('k=1.png',grayimg)
if displaylabels.min()<0:
displaylabels=displaylabels-displaylabels.min()
colorrange=displaylabels.max()-displaylabels.min()
displaylabels=displaylabels*255/colorrange
grayimg=Image.fromarray(displaylabels.astype('uint8'),'L')
originheight,originwidth=self.batch_Multigraybands[file].size
origingray=grayimg.resize([originwidth,originheight],resample=Image.BILINEAR)
origingray.save(path+'/'+originfile+'-PCAimg.png',"PNG")
# addcolorstrip()
return
def showcounting(self,tup,number=True,frame=True,header=True,whext=False,blkext=False):
labels=tup[0]
colortable=tup[2]
coinparts=tup[3]
filename=tup[4]
uniquelabels=list(colortable.keys())
imgrsc=cv2.imread(inputfolder+'/'+filename,flags=cv2.IMREAD_ANYCOLOR)
imgrsc=cv2.cvtColor(imgrsc,cv2.COLOR_BGR2RGB)
imgrsc=cv2.resize(imgrsc,(labels.shape[1],labels.shape[0]),interpolation=cv2.INTER_LINEAR)
image=Image.fromarray(imgrsc)
if whext==True:
# blkbkg=np.zeros((labels.shape[0],labels.shape[1],3),dtype='float')
whbkg=np.zeros((labels.shape[0],labels.shape[1],3),dtype='float')
whbkg[:,:,:]=[255,255,255]
itemlocs=np.where(labels!=0)
# blkbkg[itemlocs]=imgrsc[itemlocs]
whbkg[itemlocs]=imgrsc[itemlocs]
image=Image.fromarray(whbkg.astype('uint8'))
if blkext==True:
blkbkg=np.zeros((labels.shape[0],labels.shape[1],3),dtype='float')
itemlocs=np.where(labels!=0)
blkbkg[itemlocs]=imgrsc[itemlocs]
blkbkg[itemlocs]=imgrsc[itemlocs]
image=Image.fromarray(blkbkg.astype('uint8'))
print('showcounting_resize',image.size)
image.save('beforlabel.gif',append_images=[image])
draw=ImageDraw.Draw(image)
sizeuniq,sizecounts=np.unique(labels,return_counts=True)
minsize=min(sizecounts)
suggsize=int(minsize**0.5)
if suggsize>22:
suggsize=22
if suggsize<14:
suggsize=14
font=ImageFont.truetype('cmb10.ttf',size=suggsize)
for uni in uniquelabels:
if uni!=0:
pixelloc = np.where(labels == uni)
try:
ulx = min(pixelloc[1])
except:
continue
uly = min(pixelloc[0])
rlx = max(pixelloc[1])
rly = max(pixelloc[0])
midx = ulx + int((rlx - ulx) / 2)
midy = uly + int((rly - uly) / 2)
print(ulx, uly, rlx, rly)
if frame==True:
draw.polygon([(ulx,uly),(rlx,uly),(rlx,rly),(ulx,rly)],outline='red')
if number==True:
if uni in colortable:
canvastext = str(colortable[uni])
else:
canvastext = 'No label'
# if imgtypevar.get()=='0':
draw.text((midx-1, midy+1), text=canvastext, font=font, fill='white')
draw.text((midx+1, midy+1), text=canvastext, font=font, fill='white')
draw.text((midx-1, midy-1), text=canvastext, font=font, fill='white')
draw.text((midx+1, midy-1), text=canvastext, font=font, fill='white')
#draw.text((midx,midy),text=canvastext,font=font,fill=(141,2,31,0))
draw.text((midx,midy),text=canvastext,font=font,fill='black')
if header==True:
content='item count:'+str(len(uniquelabels))+'\n File: '+filename
contentlength=len(content)+50
#rectext=canvas.create_text(10,10,fill='black',font='Times 16',text=content,anchor=NW)
draw.text((10-1, 10+1), text=content, font=font, fill='white')
draw.text((10+1, 10+1), text=content, font=font, fill='white')
draw.text((10-1, 10-1), text=content, font=font, fill='white')
draw.text((10+1, 10-1), text=content, font=font, fill='white')