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pressure_module.py
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pressure_module.py
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#pressure_module.py
from copy import copy,deepcopy
from math import ceil,floor,cos,sin
from scipy.interpolate import interp1d as scipy_interp1d
import cPickle
import numpy as np
from scipy import ndimage
from matplotlib import pyplot
from matplotlib.mlab import find
import re,tables
def interp1d(y, n=101, dtype=None, kind='linear', axis=-1, copy=True, bounds_error=True, fill_value=np.nan):
t0 = np.arange(y.shape[0])
t1 = np.linspace(0, y.shape[0]-1, n)
f = scipy_interp1d(t0, y, kind, axis, copy, bounds_error, fill_value)
y1 = f(t1)
if dtype != None:
y1 = np.asarray(y1, dtype=dtype)
return y1
#---------------------------------------------
# PRIVATE FUNCTIONS
#---------------------------------------------
def _centroid(I):
### get foot coordinates:
x,y = np.meshgrid(range(I.shape[1]),range(I.shape[0]))
x,y = x[I>0], y[I>0]
### compute weighted average:
w = I[I>0]
w = w/float(w.sum()) #sum can be an integer
return np.sum(w*x), np.sum(w*y)
def _centroidXYZ(I):
### get foot coordinates:
x,y,z = np.mgrid[0:I.shape[0], 0:I.shape[1], 0:I.shape[2]]
x,y,z = x[I>0], y[I>0], z[I>0]
### compute weighted average:
w = I[I>0]
w = w/float(w.sum()) #sum can be an integer
return np.sum(w*x), np.sum(w*y), np.sum(w*z)
def _crop_trial(I0,L):
uSteps = np.unique(L)[1:]
nSteps = uSteps.size
II = []
for step in range(nSteps):
I = deepcopy(I0)
I[L!=uSteps[step]] = 0
II.append( I )
return II
def _extract_pixelTS(I):
return I.flatten().reshape((-1,I.shape[2]))
def _find_heel_strikes(I,L):
uSteps = np.unique(L)[1:]
nSteps = uSteps.size
iSteps = []
for step in range(nSteps):
msk = L==uSteps[step]
for i in range(I.shape[2]):
if np.any(msk[:,:,i]):
break
iSteps.append(i)
return iSteps
def _find_steps(I0):
def hereRemoveSmallClusters(L,th=50):
### remove clusters:
nLabels = np.unique(L).size-1
for i in range(nLabels):
if (L==i+1).sum() < th:
L[L==i+1]=0
### renumber:
uLabels = np.unique(L)
for i,uLabel in enumerate(uLabels):
L[L==uLabel] = i
return L
def hereClusterDistances(L):
### extract centroids
xy = []
nLabels = np.unique(L).size-1
for i in range(nLabels):
xy.append( _centroidXYZ(np.array(L==i+1,dtype='int')) )
### compute distances
D = np.eye(nLabels)
D[D==0]=np.nan
for i in range(nLabels-1):
for ii in range(i+1,nLabels):
xy0,xy1 = np.array(xy[i]) , np.array(xy[ii])
D[i,ii] = ceil(np.sqrt(np.dot(xy0-xy1,xy0-xy1)))
return D
def hereRelabelClustersByDistance(labels, D, th=20):
nLabels = np.unique(labels).size-1
L = deepcopy(labels)
#### find close clusters:
D = D<th
for i in range(nLabels-1,0,-1):
ind = find(D[:i,i])
if len(ind)>0:
L[labels==(i+1)] = (ind[0]+1)
return L
L,nLabels = ndimage.measurements.label(I0)
L = hereRemoveSmallClusters(L,120)
D = hereClusterDistances(L)
L = hereRelabelClustersByDistance(L,D,50)
return L
def _relabel_steps(L0,iSteps0):
iSteps = np.sort(iSteps0)
indSteps = np.argsort(iSteps0)
L = deepcopy(L0)
for ind,step in enumerate(indSteps):
L[L0==step+1] = ind+1
return L,iSteps
#---------------------------------------------
# PUBLIC FUNCTIONS
#---------------------------------------------
def check_DRO(II):
# ### check an RSscan dynamic roll-off
# pyplot.clf()
# for i,I in enumerate(II):
# pyplot.subplot(2,4,i+1); plot(peakP(I))
# for i,I in enumerate(II):
# pyplot.subplot(2,4,i+5); plot(extract_grf(I))
# ### plot entire trial:
# pyplot.axes([0.04,0.09,0.2,0.9])
# plot(peakP(I))
# pyplot.title('Entire trial')
# cmax = ceil(I.max()/10)*10
# cbh = pyplot.colorbar( cax=pyplot.axes([0.04,0.09,0.2,0.02]) , orientation='horizontal')
# pyplot.setp(cbh.ax, xticks=(0,0.5,1), xticklabels=np.linspace(0,cmax,3).astype(int), xlabel=r'Peak pressure $(Ncm^{-2})$' )
### plot extracted steps:
for i,Istep in enumerate(II):
pyplot.subplot(2,6,i+3); plot(peakP(Istep))
pyplot.title('Step %d' %(i+1))
### plot GRF for each step:
pyplot.axes([0.35,0.09,0.6,0.4])
leg_str = []
for i,Istep in enumerate(II):
X = extract_grf(Istep)
pyplot.plot(X)
leg_str.append('Step %d' %(i+1))
pyplot.xlabel('Time (frames)')
pyplot.ylabel('GRF (N)')
pyplot.legend(leg_str)
# ### plot full image:
# pyplot.axes([0.04,0.09,0.2,0.9])
# plot(peakP(I))
# cmax = ceil(I.max()/10)*10
# cbh = pyplot.colorbar( cax=pyplot.axes([0.04,0.09,0.2,0.02]) , orientation='horizontal')
# pyplot.setp(cbh.ax, xticks=(0,0.5,1), xticklabels=np.linspace(0,cmax,3).astype(int), xlabel=r'Peak pressure $(Ncm^{-2})$' )
# ### plot GRF:
# pyplot.axes([0.38,0.25,0.57,0.5])
# pyplot.plot(extract_grf(I))
# pyplot.xlabel('Time (frames)')
# pyplot.ylabel('GRF (N)')
def check_image(I):
pyplot.clf()
### plot full image:
pyplot.axes([0.04,0.09,0.2,0.9])
plot(peakP(I))
cmax = ceil(I.max()/10)*10
cbh = pyplot.colorbar( cax=pyplot.axes([0.04,0.09,0.2,0.02]) , orientation='horizontal')
pyplot.setp(cbh.ax, xticks=(0,0.5,1), xticklabels=np.linspace(0,cmax,3).astype(int), xlabel=r'Peak pressure $(Ncm^{-2})$' )
### plot GRF:
pyplot.axes([0.38,0.25,0.57,0.5])
pyplot.plot(extract_grf(I))
pyplot.xlabel('Time (frames)')
pyplot.ylabel('GRF (N)')
def check_registration(I0,I1):
if I0.ndim==3:
I0 = I0.max(axis=2)
if I1.ndim==3:
I1 = I1.max(axis=2)
pyplot.imshow(np.ma.masked_array(I0,I0==0), interpolation='nearest', origin='lower') ### source
pyplot.contour(I1>0, 1, colors=([0.1]*3,), linewidths=4) ### template
pyplot.setp(pyplot.gca(), xticks=[], yticks=[])
pyplot.axis('image')
def check_trial(I):
pyplot.clf()
### plot entire trial:
pyplot.axes([0.04,0.09,0.2,0.9])
plot(peakP(I))
pyplot.title('Entire trial')
cmax = ceil(I.max()/10)*10
cbh = pyplot.colorbar( cax=pyplot.axes([0.04,0.09,0.2,0.02]) , orientation='horizontal')
pyplot.setp(cbh.ax, xticks=(0,0.5,1), xticklabels=np.linspace(0,cmax,3).astype(int), xlabel=r'Peak pressure $(Ncm^{-2})$' )
### plot extracted steps:
II = extract_steps(I)
for i,Istep in enumerate(II):
pyplot.subplot(2,6,i+3); plot(peakP(Istep))
pyplot.title('Step %d' %(i+1))
### plot GRF for each step:
pyplot.axes([0.35,0.09,0.6,0.4])
leg_str = []
for i,Istep in enumerate(II):
X = extract_grf(Istep)
pyplot.plot(X)
leg_str.append('Step %d' %(i+1))
pyplot.xlabel('Time (frames)')
pyplot.ylabel('GRF (N)')
pyplot.legend(leg_str)
def contact_durn(I, unit='s', hz=1):
I = remove_empty_frames(I)
A = np.zeros(I.shape[:2])
for i in range(I.shape[0]):
for ii in range(I.shape[1]):
A[i,ii] = np.nonzero( (I[i,ii,])>0 )[0].size #number of nonzero elements
if unit=='s':
A = A/float(hz)
elif unit=='%':
A = A/float(I.shape[2])
return A
def extract_cop(I, interp=False, n=101):
r = []
for i in range(I.shape[2]):
r.append( _centroid(I[:,:,i]) )
r = np.array(r)
if interp:
r0 = interp1d(r[:,0], n=n)
r1 = interp1d(r[:,1], n=n)
r = np.vstack([r0,r1]).T
return r
def extract_grf(I):
X = _extract_pixelTS(I)
return X.sum(0)*0.5*0.5 #pressure: N/cm2; pixel size: 5 x 5 mm
def extract_steps(I):
L = _find_steps(I)
iSteps = _find_heel_strikes(I,L)
L,iSteps = _relabel_steps(L,iSteps)
II = _crop_trial(I,L)
return II
def find_max_grid(fnames):
GS = []
for fname in fnames:
I = load(fname)
GS.append( I.shape )
return tuple( np.max(GS,axis=0)[:2] )
def load(fname):
fid = tables.openFile(fname,mode='r')
I = fid.getNode('/I').read()
fid.close()
return I
def load_rois(fname):
return cPickle.load(open(fname,'rb'))['xy']
def peakP(I):
return I.max(axis=2)
def plot(I):
if I.ndim==3:
print 'Please compute a 2D image before plotting.'
return
else:
pyplot.imshow(np.ma.masked_array(I,I==0), interpolation='nearest', origin='lower')
pyplot.setp(pyplot.gca(), xticks=[], yticks=[])
def plot_rois(xy):
XY = np.array(xy)
pyplot.plot(XY[:,0], XY[:,1], 'wo', markersize=10)
def remove_empty_frames(I):
X = _extract_pixelTS(I)
i = np.any(X>0,axis=0)
return I[:,:,i]
def resize2D(I0,gs1):
gs0 = I0.shape
I1 = np.zeros(gs1)
nRows,nCols = gs1[0]-gs0[0] , gs1[1]-gs0[1]
xi0,yi0 = np.floor(0.5*nRows) , np.floor(0.5*nCols)
xi1,yi1 = xi0+gs0[0] , yi0+gs0[1]
I1[xi0:xi1,yi0:yi1] = I0
return I1
def resize(I0,gs1):
gs0 = I0.shape
nFrames0 = gs0[2]
I1 = np.zeros((gs1[0],gs1[1],nFrames0))
nRows,nCols = gs1[0]-gs0[0] , gs1[1]-gs0[1]
xi0,yi0 = np.floor(0.5*nRows) , np.floor(0.5*nCols)
xi1,yi1 = xi0+gs0[0] , yi0+gs0[1]
I1[xi0:xi1,yi0:yi1,:] = I0
return I1
def resize_all(fnames, pad=0):
print 'Finding maximum grid...'
gs = find_max_grid(fnames)
gs = [gs[0]+2*pad, gs[1]+2*pad]
print 'Max grid: = [%d %d]' %tuple(gs)
print 'Resizing and saving images...'
for fname in fnames:
I = load(fname)
I = resize(I,gs)
save(fname,I)
print 'Done.'
def save(fname,I):
fid = tables.openFile(fname,mode='w')
try:
atom = tables.UInt16Atom()
filter = tables.Filters(complevel=9, complib='zlib')
A = fid.createCArray(fid.root, 'I', atom, I.shape, filters=filter)
A[:] = I
fid.close()
except:
print 'Error saving file.'
fid.close()
def save_rois(fname,xy):
cPickle.dump( {'xy':xy}, open(fname,'wb'))
def select_rois(I, n=5):
plot(I)
return pyplot.ginput(n=n, timeout=-1, show_clicks=True)
def subsample(I, xy):
if type(I)==list:
z = [subsample(II,xy) for II in I]
if type(I)==np.ndarray:
z = []
for i in range(len(xy)):
x,y = xy[i][1], xy[i][0]
x0,y0 = floor(x), floor(y)
x1,y1 = x0+1, y0+1
z.append( (x1-x)*(y1-y)*I[x0,y0] + (x1-x)*(y-y0)*I[x0,y1] + (x-x0)*(y1-y)*I[x1,y0] + (x-x0)*(y-y0)*I[x1,y1] )
return np.array(z)
# I[x0,y0]*(x1-x)*(y1-y) + I[x1,y0]*(x-x0)*(y1-y) + I[x0,y1]*(x1-x)*(y-y0) + I[x1,y1]*(x-x0)*(y-y0)
def tight(I):
B = I>0
if I.ndim==2:
ix = B.any(1); ix0,ix1 = min(find(ix)) , max(find(ix))+1
iy = B.any(0); iy0,iy1 = min(find(iy)) , max(find(iy))+1
return I[ix0:ix1,iy0:iy1]
elif I.ndim==3:
ix = B.max(2).any(1); ix0,ix1 = min(find(ix)) , max(find(ix))+1
iy = B.max(2).any(0); iy0,iy1 = min(find(iy)) , max(find(iy))+1
iz = B.max(0).any(0); iz0,iz1 = min(find(iz)) , max(find(iz))+1
return I[ix0:ix1,iy0:iy1,iz0:iz1]
def time2first(I, unit='s', hz=1):
I = remove_empty_frames(I)
A = np.argmax(I>0, axis=2)
if unit=='s':
A = A/float(hz)
elif unit=='%':
A = A/float(I.shape[2])
return A
def time2peak(I, unit='s', hz=1):
I = remove_empty_frames(I)
A = np.argmax(I, axis=2)
if unit=='s':
A = A/float(hz)
elif unit=='%':
A = A/float(I.shape[2])
return A
def transformXYRS(I,q): #q is a four-tuple: (x,y,theta,scale)
def shiftFn(x,c0,q0): #( original pixel coordinates , image centroid , rigid parameters )
c = (c0[1],c0[0])
q = (-q0[1],-q0[0],q0[2])
s = 1.0/q0[3]
cq3,sq3 = cos(q[2]) , sin(q[2])
xp0 = c[0] + q[0]*cq3 - q[1]*sq3 +s*x[0]*cq3 -x[1]*sq3 -s*c[0]*cq3 +c[1]*sq3
xp1 = c[1] + q[0]*sq3 + q[1]*cq3 +x[0]*sq3 +s*x[1]*cq3 -c[0]*sq3 -s*c[1]*cq3
return ( xp0, xp1, x[2] )
c = (0.5*I.shape[1],0.5*I.shape[0])
I = ndimage.geometric_transform(I, shiftFn, order=1, extra_arguments=(c,q))
return I
def transform2D(I, q): #q is a four-tuple: (x,y,theta,scale)
def shiftFn(x, c0, q0): #( original pixel coordinates , image centroid , rigid parameters )
c = c0[1],c0[0]
q = -q0[1], -q0[0], q0[2]
s = 1.0/q0[3]
cq3,sq3 = cos(q[2]) , sin(q[2])
xp0 = c[0] + q[0]*cq3 - q[1]*sq3 +s*x[0]*cq3 -x[1]*sq3 -s*c[0]*cq3 +c[1]*sq3
xp1 = c[1] + q[0]*sq3 + q[1]*cq3 +x[0]*sq3 +s*x[1]*cq3 -c[0]*sq3 -s*c[1]*cq3
return xp0, xp1
c = 0.5*I.shape[1], 0.5*I.shape[0]
I = ndimage.geometric_transform(I, shiftFn, order=1, extra_arguments=(c,q))
return I