02_TrackRoot.py 17.2 KB
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#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#
#Intialisation:

##Load libraries:
import numpy as np
import math as m
from scipy import misc
from scipy import signal as sg
import fnmatch
import os
import cv2
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.widgets import RectangleSelector

##Input parameter:
###Proximity threshold for root tip connection:
ThrProxConn=70
###Proximity threshold for root tip detection:
ThrProxDetec=15

##Count the number of pictures in the folder to get the number of keeped steps:
NbStp=len(fnmatch.filter(os.listdir('pictureWh'),'*.jpg'))


#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#
#Pick initial variable for the image post processing

##Load the last picture:
pict0=misc.imread('pictureWh/'+str(NbStp-1).zfill(4)+'.jpg')[:,:,0] 

##Pick an intensity threshold to make mask:
###Pick an initial value
ThrdPict=(np.amax(pict0)+np.amin(pict0))/2.
flag0=1
###Pick a value on a scale until it is suitable
while flag0: 
    try:
        pict1=pict0>ThrdPict
        plt.subplot(1,2,1)
        plt.imshow(pict1,cmap=plt.cm.Greys)
        plt.title('threshold value: '+'%0d'%ThrdPict)
        plt.subplot(1,2,2)
        plt.imshow(pict0)
        plt.colorbar()
        plt.title('pick a threshold value on the scale \n or close')
        a=plt.ginput(1,timeout=-1)
        plt.close()
        plt.show()
        ThrdPict=(np.amax(pict0)-np.amin(pict0))*a[0][1]+np.amin(pict0)
    except:
        flag0=0

##Save threshold:
os.system('mkdir pictureSkeleton > /dev/null')
np.savetxt('pictureSkeleton/image_threshold.txt',np.array([ThrdPict]))

##Select the areas not to consider to follow the root:
###Define selection functions:
def line_select_callback(eclick, erelease):
    global x1, y1, x2, y2
    x1,y1=eclick.xdata,eclick.ydata
    x2,y2=erelease.xdata,erelease.ydata

def toggle_selector(event):
    print(' Key pressed.')
    if event.key in ['Q', 'q'] and toggle_selector.RS.active:
        print(' RectangleSelector deactivated.')
        toggle_selector.RS.set_active(False)
    if event.key in ['A', 'a'] and not toggle_selector.RS.active:
        print(' RectangleSelector activated.')
        toggle_selector.RS.set_active(True)

###Stepwise selection of the ROI:
pict1_int=1-pict1.astype('int')
flag_tog=True
pict_mask=np.ones(pict1_int.shape)
while flag_tog:
    pict1_int=pict1_int*pict_mask
    x1=0; y1=0; x2=0; y2=0
    fig,current_ax=plt.subplots()
    plt.imshow(pict1_int,cmap=plt.cm.Greys)
    plt.title('select area not to consider, close to validate, close 2 times to quit')
    toggle_selector.RS=RectangleSelector(current_ax,line_select_callback,drawtype='box',useblit=True,button=[1, 3],minspanx=5,minspany=5,spancoords='pixels',interactive=True)
    plt.connect('key_press_event',toggle_selector)
    plt.show()
    plt.close()
    if (x1>0 or x2>0):
        pict_mask[int(y1):int(y2),int(x1):int(x2)]=0
    else:
        break

###Save mask:
misc.imsave('pictureSkeleton/ROI_mask.png',pict_mask)

##Select seed:
###Load the last picture:
pict0=misc.imread('pictureWh/'+'%04d'%(0)+'.jpg')[:,:,0] 
###Select:
pict1=pict0>ThrdPict
pict1=(pict1.astype(int)-pict_mask)<0
plt.imshow(pict1,cmap=plt.cm.Greys)
plt.title('select 2 points to zoom on the root top')
a=plt.ginput(2,timeout=-1)
plt.axis([min(a[0][0],a[1][0]),max(a[0][0],a[1][0]),max(a[0][1],a[1][1]),min(a[0][1],a[1][1])])
plt.title('zoom and clic on the head of the root and close')
JIseed=plt.ginput(1,timeout=-1)
plt.close()


#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#
#Extract the mask root network:

##Extraction for the last image:
###Compute the connected areas:
pict3=np.uint8(misc.imread('pictureWh/'+str(NbStp-1).zfill(4)+'.jpg')[:,:,0])
pict3=(((pict3>ThrdPict).astype(int)-pict_mask)<0).astype(int)
pict3=cv2.connectedComponents(np.uint8(pict3),4)[1]
###Extract the root area:
pict4=pict3==pict3[int(JIseed[0][1]),int(JIseed[0][0])]

##Compute the ROI limits:
I=np.where(pict4==1)
Imin=int(0.9*min(I[0])); Imax=int(min((1.1*max(I[0]),pict3.shape[0])))
Jmin=int(0.9*min(I[1])); Jmax=int(min((1.1*max(I[1]),pict3.shape[1])))

##Save limits and seed position:
np.savetxt('pictureSkeleton/IJ_edge.txt',np.array([Imin,Imax,Jmin,Jmax]),delimiter=' ')
np.savetxt('pictureSkeleton/IJ_seed.txt',np.array([JIseed[0][1],JIseed[0][0]]),delimiter=' ')

##Extract the mask of the root network for all the pictures:
print('Is extracting root mask...')
os.system('mkdir pictureSkeleton/mask')
###Loop over the pictures:
for iPct in range(NbStp):
    print(str(iPct)+' / '+str(NbStp))
    ####Load pictures:
    pictA=misc.imread('pictureWh/'+'%04d'%iPct+'.jpg')[:,:,0]
    ####Threshold and crop it:
    pictA=(((pictA>ThrdPict).astype(int)-pict_mask)<0).astype(int)
    pictA[0:int(JIseed[0][1]),:]=0.
    #~ pictA=(pictA<0.5
    
    #~ #!debug!
    #~ plt.imshow(pictA)
    #~ plt.show()
    #~ plt.close()
    
    ####Compute the connected areas:
    A=cv2.connectedComponentsWithStats(np.uint8(pictA),4)
    Nb=A[0]
    pictA=A[1]
    areaA=A[2][:,4]
    centA=A[3]
    ####Look for the correct area:
    if iPct==0:
        itArea=0; flag1=1
        while (flag1 & (itArea<=Nb)):
            itArea+=1
            #####Get current pixels
            I=np.where(pictA==itArea)
            #####If current area is big enough to be considered:
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            if (len(I[0])>500):
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                #####If the seed point is in the current area this is the root:
                if np.min(np.sqrt((I[0]-JIseed[0][1])**2.+(I[1]-JIseed[0][0])**2.))<2:
                    flag1=0
                    area0=len(I[0])
                    cent0=centA[np.where(area0==areaA)[0],:][0]
    else:
        #####Compute the difference of area:
        dArea=np.abs(areaA-area0)
        #####Compute the distance between centroids:
        dCent=np.sqrt((centA[:,0]-cent0[0])**2.+(centA[:,1]-cent0[1])**2.)
        #####Select the component with the closest area and centroid:
        I1=np.argsort(dArea)[0:5]
        I2=np.argsort(dCent)[0:5]
        I3=np.intersect1d(I1,I2)
        if len(I3)>0:
            itArea=I3[np.where(dArea[I3]==np.min(dArea[I3]))][0]
        else:
            print('root not properly detected')
        
        cent0=centA[itArea,:]
        area0=areaA[itArea]
    
    ###Extract the root area:
    pictA=pictA==itArea
    ###Crop the picture:
    pictA=pictA[Imin:Imax,Jmin:Jmax].astype(int)
    ###Save picture:
    misc.imsave('pictureSkeleton/mask/'+'%04d'%iPct+'.png',pictA)

print('...extraction done')


#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#
#Detection of the root tips:

##Prepare storage folder:
os.system('mkdir rootTip')
os.system('mkdir rootTip/byStep')

##Load time:
timePic=np.loadtxt('time.txt')

##Loop over the steps:
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for iPct in range(NbStp):  
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    ###Load the mask:
    pict4=misc.imread('pictureSkeleton/mask/'+'%04d'%iPct+'.png')
    ###Computation of the mask contour:
    image,contour,h=cv2.findContours(pict4,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
    contour=contour[0]
    ###Computation of the crossproduct direction:
    ####Load the contour coordinate:
    X=contour[:,0,0].astype('float')
    Y=contour[:,0,1].astype('float')
    ####If the root is long enough:
    if (len(X)>10):
        ####Smothen it:
        X0=np.convolve(X,np.ones(5)/5.,mode='same'); X0[0]=X[0]; X0[1]=X[1]; X0[-1]=X[-1]; X0[-2]=X[-2]
        Y0=np.convolve(Y,np.ones(5)/5.,mode='same'); Y0[0]=Y[0]; Y0[1]=Y[1]; Y0[-1]=Y[-1]; Y0[-2]=Y[-2]
        
        #~ # ! Debug !:
        #~ plt.imshow(pict4)
        #~ plt.plot(X,Y,'-r')
        #~ plt.plot(X0,Y0,'-g')
        #~ plt.show()
        
        ####Computation of the cross-product:
        Curv=np.zeros(len(contour))
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        for itCurv in range(1,len(contour)-1):
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            x0=X0[itCurv-1]; y0=Y0[itCurv-1]
            x1=X0[itCurv]; y1=Y0[itCurv]
            x2=X0[itCurv+1]; y2=Y0[itCurv+1]
            abs1=m.sqrt((x1-x0)**2+(y1-y0)**2)
            n1x=-(y1-y0)/abs1
            n1y=(x1-x0)/abs1
            abs2=m.sqrt((x2-x1)**2+(y2-y1)**2)
            n2x=-(y2-y1)/abs2
            n2y=(x2-x1)/abs2
            Curv[itCurv]=n1x*n2y-n1y*n2x
        
        ####Smoothen it:
        Curv0=np.convolve(Curv,np.ones(10)/10.,mode='same')
        
        # ! Debug !:
        #~ plt.plot(Curv,'-r')
        #~ plt.plot(Curv0,'-g')
        #~ plt.show()
        
        ###Detection of the root tips:  
        ####Extract cross-product extremum and save tip positions:
        I=np.where((Curv0<-0.12) & (Y0>10))[0]
        if (len(I)>0):
            #####Detect continuous low curvature areas:
            J=np.unique(np.append([0],np.where(I[0:len(I)-1]-I[1:len(I)]<-2)[0]))
            if (len(J)>1):
                IXtr=np.zeros(len(J))
                for itXtr in range(len(J)-1):
                    IXtr[itXtr]=I[J[itXtr]+np.where(Curv0[I[J[itXtr]+1:J[itXtr+1]+1]]==np.amin(Curv0[I[J[itXtr]+1:J[itXtr+1]+1]]))[0][0]]
                
                IXtr[len(J)-1]=I[J[itXtr+1]+np.where(Curv0[I[J[itXtr+1]+1:len(I)]]==np.amin(Curv0[I[J[itXtr+1]+1:len(I)]]))[0][0]]
                IXtr=IXtr.astype(int)
            else:
                IXtr=np.array([I[np.where(Curv0[I]==np.amin(Curv0[I]))[0][0]]])
            
            #####Extract root tip coordinates:
            vecTip=np.zeros([1,2])
            for itXtr in range(len(IXtr)):
                vecTip=np.append(vecTip,[np.array([X0[IXtr[itXtr]],Y0[IXtr[itXtr]]])],axis=0)
            
            vecTip=np.delete(vecTip,0,0)
            #####Remove multi detection:
            if vecTip.shape[0]>1:
                vecD=np.sqrt((vecTip[0:-1,0]-vecTip[1:,0])**2.+(vecTip[0:-1,1]-vecTip[1:,1])**2.)
                I=np.where(vecD<ThrProxDetec)
                for itI in I:
                    vecTip=np.delete(vecTip,itI,axis=0)
            
            #####Save data:
            np.savetxt('rootTip/byStep/'+'%04d'%iPct+'.txt',vecTip,delimiter=' ')
            
        else:
            vecTip=np.zeros(2)
            np.savetxt('rootTip/byStep/'+'%04d'%iPct+'.txt',vecTip,delimiter=' ')

##Plot and save root tip pathes in color time evolution:
###Loop over the steps:
X=[]; Y=[]; T=[]
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for iPct in range(NbStp):
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    if os.path.isfile('rootTip/byStep/'+'%04d'%iPct+'.txt'): 
        XY_tmp=np.loadtxt('rootTip/byStep/'+'%04d'%iPct+'.txt')
        if XY_tmp.ndim>1: 
            X.append(XY_tmp[:,0])
            Y.append(XY_tmp[:,1])
            T.append(timePic[iPct]*np.ones(XY_tmp.shape[0]))
        else:
            if XY_tmp[0]!=0:
                X.append(np.array(XY_tmp[0]))
                Y.append(np.array(XY_tmp[1]))
                T.append(np.array(timePic[iPct]))

###Make position vector:
X0=np.hstack(X)
Y0=np.hstack(Y)
T0=np.hstack(T)

pict4=misc.imread('pictureSkeleton/mask/'+'%04d'%(NbStp-1)+'.png')
plt.imshow(pict4,plt.cm.Greys)
sc=plt.scatter(X0,Y0,c=T0,s=5,cmap=plt.cm.get_cmap('jet'))
plt.colorbar(sc)
plt.title('root tip position in time')
plt.savefig('rootTip/rootTipTime.png',dpi=400)
plt.savefig('rootTip/rootTipTime.svg')
plt.close()


#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#
#Tracking of the root tips:

##Prepare storage folder:
os.system('mkdir rootTip/byTip')

###Loop over the steps
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for iPct in range(NbStp): 
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    ###Load current tips position vector:
    A=np.loadtxt('rootTip/byStep/'+'%04d'%iPct+'.txt')
    if (A.ndim>0):
        ####Load the positions:
        if (A.ndim==1):
            Xc=np.array([A[0]]); Yc=np.array([A[1]])
        else:
            Xc=A[:,0]; Yc=A[:,1]
        
        ####Check that root tip have been properly detected:
        if Xc[0]!=0:
            ####Load the time:
            tc=timePic[iPct]
            if (iPct==0):
                ###Initialize storage matrix:
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                for iPt in range(len(Xc)):
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                    if (iPt==0):
                        MatRoot=[np.array([Xc[iPt],Yc[iPt],tc])]
                    else:
                        MatRoot=MatRoot+[np.array([Xc[iPt],Yc[iPt],tc])]
            else:
                ###Track roots:
                while (len(Xc)>0):
                    ####Get current coordinates:
                    xc=Xc[0]; yc=Yc[0]
                    ####Remove them from storage vector:
                    Xc=np.delete(Xc,0); Yc=np.delete(Yc,0)
                    ####Measure the distance to the previously detected tips:
                    Dc=np.zeros(len(MatRoot))
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                    for iPt in range(len(MatRoot)):
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                        #####Load the last position:
                        if (MatRoot[iPt].ndim==1):
                            xcc=MatRoot[iPt][0]; ycc=MatRoot[iPt][1]
                        else:
                            xcc=MatRoot[iPt][-1][0]; ycc=MatRoot[iPt][-1][1]
                        
                        #####Measure the distance:
                        Dc[iPt]=m.sqrt((xc-xcc)**2.+(yc-ycc)**2.)
                        
                    ####Connect to the closest if close enough or add a new root:
                    iPt0=np.where(Dc==np.min(Dc))[0][0]
                    if Dc[iPt0]<ThrProxConn:
                        MatRoot[iPt0]=np.vstack((MatRoot[iPt0],np.array([xc,yc,tc])))
                    else:
                        MatRoot=MatRoot+[np.array([xc,yc,tc])]

##Remove artefact (root too short):
###Prepare storage matrices:
MatRoot0=MatRoot
MatRoot=[]
###Loop over detected roots:
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for iMrt in range(len(MatRoot0)): 
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    if (MatRoot0[iMrt].ndim>1):
        ####Load the current root:
        curRoot=MatRoot0[iMrt]
        ####Compute the root amplitude:
        rtAmp=np.max(np.array([np.max(curRoot[:,0])-np.min(curRoot[:,0]),np.max(curRoot[:,1])-np.min(curRoot[:,1])]))
        ####Keep if the root is long enough:
        if rtAmp>ThrProxConn/2.:
            if (len(MatRoot)==0):
                MatRoot=[curRoot]
            else:
                MatRoot=MatRoot+[MatRoot0[iMrt]]

# ! Debug !:
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#~ for iMrt in range(len(MatRoot)):
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     #~ curRoot=MatRoot0[iMrt]
     #~ plt.plot(curRoot[:,0],curRoot[:,1],'-')

#~ plt.show()
#~ plt.close()

##Computation of the speed:
###Prepare to store the speed amplitude:
Vmin=float("inf"); Vmax=0.
###Loop over root trajectories:
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for iMrt in range(len(MatRoot)):    
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    ####Get the current trajectory:
    Xc=MatRoot[iMrt][:,0]; Yc=MatRoot[iMrt][:,1]; Tc=MatRoot[iMrt][:,2]
    ####Compute the speed:
    Vc=np.sqrt((Xc[1:len(Xc)]-Xc[0:len(Xc)-1])**2+(Yc[1:len(Yc)]-Yc[0:len(Yc)-1])**2)/(Tc[1:len(Tc)]-Tc[0:len(Tc)-1])
    Vc=np.append(Vc,0.)
    ####Get the speed amplitude:
    if (max(Vc[np.where(Vc<float("inf"))])>Vmax):
        Vmax=max(Vc[np.where(Vc<float("inf"))])
    
    if (min(Vc[np.where(Vc>0)])<Vmin):
        Vmin=min(Vc[np.where(Vc>0)])
    
    ####Smooth data:
    if (len(Vc)>10):
        Vc=np.convolve(Vc,np.ones(4)/4.,mode='same')
    
    ####Store data:
    MatRoot[iMrt]=np.transpose(np.vstack((Xc,Yc,Tc,Vc)))


##Storage of the data:
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for iMrt in range(len(MatRoot)): 
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    np.savetxt('rootTip/byTip/'+'%04d'%iMrt+'.txt',MatRoot[iMrt],delimiter=' ')

##Plot by speed:
###Prepare for graphical output:
pict4=misc.imread('pictureSkeleton/mask/'+'%04d'%(NbStp-1)+'.png')
plt.imshow(pict4,plt.cm.Greys)
###Loop over roots:
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for iMrt in range(len(MatRoot)):    
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    Xc=MatRoot[iMrt][:,0]; Yc=MatRoot[iMrt][:,1]; Tc=MatRoot[iMrt][:,2]; Vc=MatRoot[iMrt][:,3] 
    if iMrt==0:
        sc=plt.scatter(Xc,Yc,c=Vc,s=5,cmap=plt.cm.get_cmap('jet'))
        plt.colorbar(sc)
    else:
        plt.scatter(Xc,Yc,c=Vc,s=5,cmap=plt.cm.get_cmap('jet'))

plt.title('root tip speed')
plt.savefig('rootTip/rootTipSpeed.png',dpi=400)
plt.savefig('rootTip/rootTipSpeed.svg')
plt.close()


#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#~/"\~#
#Other graphical outputs:

##function to plot the root skeleton with the time: 
timePic=np.loadtxt('time.txt')
os.system('mkdir tmp')

def PlotSkeleton(iPct):
    global timePic 
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    print(iPct)
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    ###Load the time:
    t0=timePic[iPct]
    h0,m0=divmod(t0,60)
    j0,h0=divmod(h0,24)
    ###Load pictures:
    pictA=misc.imread('pictureSkeleton/mask/'+'%04d'%iPct+'.png')
    ###Plot picture:
    plt.imshow(pictA,cmap=plt.cm.Greys)
    plt.axis('off')
    plt.title('%02d'%j0+'days   '+'%02d'%h0+'hours  -  '+str(iPct))
    plt.savefig('tmp/'+'%04d'%iPct+'.png',dpi=150)
    plt.close()

##Cropping:
print('... is plotting root skeleton')
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for itStp in range(NbStp):
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    PlotSkeleton(itStp)

##Make movie: 
print('... is making the movie')
null=os.system('ffmpeg -y -r 15 -f image2 -i tmp/%04d.png -qscale 1 RtSkl.avi > /dev/null')

##Remove folders:
null=os.system('rm -f -R tmp')


##space time root network:
os.system('mkdir tmp')
fig = plt.figure()
ax = fig.gca(projection='3d')
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for iMrt in range(len(MatRoot)):    
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    Xc=MatRoot[iMrt][:,0]; Yc=MatRoot[iMrt][:,1]; Tc=MatRoot[iMrt][:,2];
    ax.plot(Tc/(24*60),Xc,-Yc,'k-',linewidth=3)

plt.title('Root tip positions')
ax.set_xlabel('time (d)')
ax.set_ylabel('x (px)')
ax.set_zlabel('y (px)')
ang=np.linspace(70,70+360,360)
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for iPic in range(len(ang)):
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    ax.view_init(5,ang[iPic])
    plt.savefig('tmp/'+'%04d'%iPic+'.png',dpi=150)

plt.close()
null=os.system('ffmpeg -y -r 15 -f image2 -i tmp/%04d.png -qscale 1 TimeSpaceRoot.avi')
null=os.system('rm -f -R tmp')