def find_matching_labels(self,goodness_threshold=0.6,rad=3):
modeldb = H5(ocfg.model_database)
print modeldb.h5.keys()
guess_dicts = [{}]
goodnesses = [-np.inf]
for key in modeldb.h5.keys():
guess_dict = {}
test = modeldb.get(key)['profile'][:]
if len(test)>len(self.profile):
test_profile = test[:len(self.profile)]
elif len(test)<len(self.profile):
test_profile = np.zeros(len(self.profile))
test_profile[:len(test)] = test
else:
test_profile = test
offset,goodness = translation1(test_profile,self.profile,debug=False)
self.logger.info('Comparing with model labels from %s: goodness %0.3f.'%(key,goodness))
if goodness>goodness_threshold:
test_labels = modeldb.get(key)['labels'].keys()
if len(test_labels)>0:
for test_label in test_labels:
search_position = modeldb.get(key)['labels'][test_label].value - offset
temp = np.zeros(self.profile.shape)
temp[search_position-rad:search_position+rad+1] = self.profile[search_position-rad:search_position+rad+1]
guess_dict[test_label] = np.argmax(temp)
guess_dicts.append(guess_dict)
goodnesses.append(goodness)
return guess_dicts[np.argmax(goodnesses)]
def plot_at(x):
global current_label,bscanindex
if x in label_dict.values():
existing_label = [key for key, value in label_dict.items() if value == x][0]
else:
existing_label = ''
plt.axes([l1,b2,hw,hh])
plt.cla()
bscan = np.abs(self.h5.h5[self.data_block][0,bscanindex,:,:])
#bscan = shear(bscan,1)
try:
test = np.mean(bscan[:,-20:],axis=1)
except:
test = np.mean(bscan,axis=1)
offset,goodness = translation1(test,working_profile,xlims=10,equalize=True)
cmin = np.median(bscan)
cmax = np.percentile(bscan,99.95) # saturate 0.05% of pixels
plt.imshow(bscan,interpolation='none',clim=(cmin,cmax),cmap='gray')
for label in label_dict.keys():
print label,label_dict[label]
label_z = z[label_dict[label]]
th = plt.text(bscan.shape[1],label_z-offset,label,ha='left',va='center',fontsize=8)
try:
plt.ylim((np.max(label_dict.values())+10,np.min(label_dict.values())-10))
except:
pass
plt.axes([l1,b1,fw,hh])
plt.cla()
plt.plot(z,working_profile)
plt.plot(z[x],working_profile[x],'ks')
valid = np.where(working_profile)[0]
plt.xlim((valid[0],valid[-1]))
plt.autoscale(False)
for label in label_dict.keys():
label_z = z[label_dict[label]]
th = plt.text(label_z,working_profile[label_z],label,ha='center',va='bottom')
plt.axes([l2,b2,hw,hh])
plt.cla()
plt.plot(z,working_profile)
plt.plot(z[x],working_profile[x],'ks')
plt.xlim((z[x]-10,z[x]+10))
z1 = max(0,x-10)
z2 = min(len(z),x+10)
ymin = np.min(working_profile[z1:z2])
ymax = np.max(working_profile[z1:z2])*1.25
plt.text(z[x],working_profile[x],existing_label,ha='center',va='bottom')
plt.ylim((ymin,ymax))
plt.title(current_label)
plt.draw()
def align_volume(self,vidx=0,rad=5):
self.logger.info('align_volume: Starting')
self.logger.info('align_volume: Getting volume from data store.')
avol = np.abs(self.h5.get(self.data_block)[vidx,:,:,:])
avol = np.swapaxes(avol,0,1)
self.logger.info('align_volume: Smoothing volume with smoothing kernel of size %d pixels.'%rad)
if rad:
print time.time()
avol = lateral_smooth_3d(avol,rad)
print time.time()
sys.exit()
ndepth,nslow,nfast = avol.shape
offset_submatrix = np.zeros((nslow,nfast))
goodness_submatrix = np.zeros((nslow,nfast))
profile = self.profile
if len(profile)>ndepth:
profile = profile[:ndepth]
if ndepth>len(profile):
avol = avol[:len(profile),:,:]
ndepth,nslow,nfast = avol.shape
x = []
y = []
z = []
w = []
for islow in range(nslow):
pct_done = int(round(100*float(islow)/float(nslow)))
if islow%10==0:
self.logger.info('align_volume: Aligning A-scans, volume %d is %d percent done.'%(vidx,pct_done))
for ifast in range(nfast):
test = avol[:,islow,ifast]
offset,goodness = translation1(profile,test,debug=False)
x.append(ifast)
y.append(islow)
z.append(offset)
w.append(goodness)
offset_submatrix[islow,ifast] = offset
goodness_submatrix[islow,ifast] = goodness
fitting = True
if fitting: # revisit this later; may be of use
ptile = 75
goodness_threshold=np.percentile(w,ptile)
self.logger.info('align_volume: Goodness %dth percentile %0.3f used as threshold.'%(ptile,goodness_threshold))
valid = np.where(w>goodness_threshold)[0]
x0 = x
y0 = y
self.logger.info('align_volume: Using %d of %d points (%d percent) for fit.'%(len(valid),len(w),float(len(valid))/float(len(w))*100))
x = np.array(x)
y = np.array(y)
z = np.array(z)
w = np.array(w)
x = x[valid]
y = y[valid]
z = z[valid]
w = w[valid]
mode='median_filter'
if mode=='spline':
self.logger.info('Spline fitting surface to A-line axial positions.')
tck = bisplrep(x,y,z,w=w,xb=0,xe=nfast-1,yb=0,ye=nslow-1)
self.logger.info('Evaluating spline function at A-line coordinates.')
fit_surface = bisplev(np.arange(nslow),np.arange(nfast),tck)
if mode=='polyfit2d':
self.logger.info('Polynomial fitting surface to A-line axial positions.')
p = polyfit2d(x,y,z,order=2)
self.logger.info('Evaluating polynomial function at A-line coordinates.')
xx,yy = np.meshgrid(np.arange(nfast),np.arange(nslow))
fit_surface = polyval2d(xx,yy,p)
if mode=='median_filter':
# This is a dumb way to fit. Use the goodness matrix, dummy, perhaps with 2D splines!
self.logger.info('Median filtering to create a smoothed offset surface.')
slow_height = np.median(offset_submatrix,axis=1)
plt.figure()
plt.plot(slow_height)
plt.figure()
debias = (offset_submatrix.T-slow_height).T
plt.figure()
plt.imshow(debias)
plt.colorbar()
fit_surface_1 = median_filter(offset_submatrix,(3,3))
plt.figure()
plt.imshow(fit_surface_1)
plt.title('straight fit')
plt.colorbar()
fit_surface_2 = (median_filter(debias,(3,3)).T+slow_height).T
plt.figure()
plt.imshow(fit_surface_2)
plt.title('debiased fit')
plt.colorbar()
plt.show()
sys.exit()
if mode=='interp2d':
self.logger.info('Using interp2d to create a smoothed offset surface.')
interpolation_function = interp2d(x,y,z)
fit_surface = interpolation_function(x0,y0)
print fit_surface
print fit_surface.shape
# print fit_surface
# print fit_surface.shape
if True:
clim = np.min(offset_submatrix),np.max(offset_submatrix)
plt.figure()
plt.imshow(offset_submatrix,interpolation='none',clim=clim)
plt.colorbar()
plt.figure()
plt.imshow(fit_surface,interpolation='none',clim=clim)
plt.colorbar()
plt.figure()
plt.imshow(offset_submatrix-fit_surface,interpolation='none')
plt.colorbar()
plt.show()
#goodness_used = np.zeros(goodness_submatrix.shape)
#goodness_used[np.where(goodness_submatrix>goodness_threshold)] = 1.0
else:
fit_surface = offset_submatrix
return offset_submatrix,goodness_submatrix,fit_surface
def make_bscan(self,factor):
# select region for B-projection
x,y = self.click_collector(self.cones,'Click top and bottom edges of region for B-scan')
if len(x)<2:
self.acdb.close()
sys.exit('Done!')
x1 = np.min(x)
x2 = np.max(x)
y1 = np.min(y)
y2 = np.max(y)
sy,sx = self.cones.shape
y1 = max(0,y1)
y2 = min(sy,y2)
subvol = self.volume[y1:y2,:,:]
subvol_scaled = []
profs = []
newsy = factor[0]*subvol.shape[1]
newsx = factor[1]*subvol.shape[2]
for iy in range(subvol.shape[0]):
#scaled = self.fftinterp(subvol[iy,:,:],factor)
#scaled = self.fftinterp2(subvol[iy,:,:],factor)
scaled = sp.misc.imresize(subvol[iy,:,:],(newsy,newsx))
subvol_scaled.append(scaled)
profs.append(np.mean(scaled,axis=1))
sy = len(profs[0])
shifts = [0]
for prof in profs[1:]:
tx,g = translation1(profs[0],prof)
shifts.append(tx)
shifts = -np.array(shifts)
shifts = shifts - np.min(shifts)
cropped_sy = sy - np.max(shifts)
aligned = []
for im,x in zip(subvol_scaled,shifts):
im = im[x:x+cropped_sy,:]
aligned.append(im)
aligned = np.array(aligned)
bscan_linear = np.mean(aligned,axis=0)
bscan_log = np.log(bscan_linear)
cclims = np.percentile(self.cones[20:-20,20:-20],(1,99))
pclims = np.percentile(bscan_linear,(2,99.5))
plclims = np.percentile(bscan_log,(5,99.5))
bscan_tag = '%s_%03d_%03d'%(self.tag,y1,y2)
cones_fn = os.path.join(self.outdir,'%s_cones.png'%(self.tag))
self.savefig(cones_fn,self.cones,percentiles=(.5,99.9),hspan=None,scale_factor=3.0)
isos_fn = os.path.join(self.outdir,'%s_isos.png'%(self.tag))
self.savefig(isos_fn,self.isos,percentiles=(.5,99.9),hspan=None,scale_factor=3.0)
cost_fn = os.path.join(self.outdir,'%s_cost.png'%(self.tag))
self.savefig(cost_fn,self.cost,percentiles=(.5,99.9),hspan=None,scale_factor=3.0)
cones_fn = os.path.join(self.outdir,'%s_cones.png'%bscan_tag)
self.savefig(cones_fn,self.cones,percentiles=(.5,99.9),hspan=[y1,y2],scale_factor=3.0)
linear_fn = os.path.join(self.outdir,'%s_bscan_linear.png'%bscan_tag)
self.savefig(linear_fn,bscan_linear,percentiles=(2,99.9),scale_factor=3.0)
log_fn = os.path.join(self.outdir,'%s_bscan_log.png'%bscan_tag)
self.savefig(log_fn,bscan_log,percentiles=(3,99.75),scale_factor=3.0)
plt.show()
plt.close('all')
def align_volume_multiscale(self,vidx=0,rad=2,debug=True):
self.logger.info('align_volume_multiscale: Starting')
self.logger.info('align_volume_multiscale: Getting volume from data store.')
avol = np.abs(self.h5.get(self.data_block)[vidx,:,:,:])
avol = np.swapaxes(avol,0,1)
avol = (avol-np.mean(avol,axis=0))/np.std(avol,axis=0)
# NOTE TO SELF: DON'T F*****G FORGET TO REMOVE THIS, YOU F*****G IDIOT
# IF YOU DO, IT WILL RUIN YOUR LIFE FOR A MONTH WHILE YOU DEBUG IT
# NOTE TO SELF: DON'T F*****G FORGET TO REMOVE THIS, YOU F*****G IDIOT
# IF YOU DO, IT WILL RUIN YOUR LIFE FOR A MONTH WHILE YOU DEBUG IT
# NOTE TO SELF: DON'T F*****G FORGET TO REMOVE THIS, YOU F*****G IDIOT
# IF YOU DO, IT WILL RUIN YOUR LIFE FOR A MONTH WHILE YOU DEBUG IT
# NOTE TO SELF: DON'T F*****G FORGET TO REMOVE THIS, YOU F*****G IDIOT
# IF YOU DO, IT WILL RUIN YOUR LIFE FOR A MONTH WHILE YOU DEBUG IT
# NOTE TO SELF: DON'T F*****G FORGET TO REMOVE THIS, YOU F*****G IDIOT
# IF YOU DO, IT WILL RUIN YOUR LIFE FOR A MONTH WHILE YOU DEBUG IT
# NOTE TO SELF: DON'T F*****G FORGET TO REMOVE THIS, YOU F*****G IDIOT
# IF YOU DO, IT WILL RUIN YOUR LIFE FOR A MONTH WHILE YOU DEBUG IT
avol = avol[:,:-2,:]
ndepth,nslow,nfast = avol.shape
profile = np.zeros(self.profile.shape)
profile[...] = self.profile[...]
profile = (profile-np.mean(profile))/np.std(profile)
if len(profile)>ndepth:
profile = profile[:ndepth]
if ndepth>len(profile):
avol = avol[:len(profile),:,:]
ndepth,nslow,nfast = avol.shape
gross_profile = np.mean(np.mean(avol,axis=2),axis=1)
gross_offset,gross_goodness = translation1(gross_profile,profile,debug=False)
offset_submatrix = np.ones((nslow,nfast))*gross_offset
goodness_submatrix = np.ones((nslow,nfast))*gross_goodness
# def smooth(v,k):
# # an interface to lateral_smooth_3d that handles the
# # transposing to minimize confusion
# return np.transpose(lateral_smooth_3d(np.transpose(avol_bc,(2,0,1)),k),(1,2,0))
def show_brightest_layer(v):
# assumes depth is first dimension
p = np.mean(np.mean(v,axis=2),axis=1)
mdepth = np.argmax(p)
layer = v[mdepth,:,:]
plt.figure()
plt.imshow(layer)
plt.colorbar()
# fft the model, for cross-correlation by broadcasting
f0 = np.fft.fft(profile,axis=0)
self.logger.info('align_volume_multiscale: model length %d, FFT length %d.'%(len(profile),len(f0)))
started = False
sz,sy,sx = avol.shape
initial_step = float(max(sy,sx))
final_exp = -np.log(1.49999/initial_step)
steps = np.round(200*np.exp(-np.linspace(0.0,final_exp,10)))
for k in steps:
self.logger.info('align_volume_multiscale: smoothing layers in the volume')
smoothed_avol = lateral_smooth_3d(avol,k)
# depth is still first dimension
f1 = np.fft.fft(smoothed_avol,axis=0)
f1c = f1.conjugate()
# transpose depth to the final dimension:
f1_transposed = np.transpose(f1,(1,2,0))
f1c_transposed = np.transpose(f1c,(1,2,0))
num_transposed = f0*f1c_transposed
denom_transposed = np.abs(f0)*np.abs(f1_transposed)
frac_transposed = num_transposed/denom_transposed
# now transpose back:
frac = np.transpose(frac_transposed,(2,0,1))
ir = np.abs(np.fft.ifft(frac,axis=0))
print np.mean(ir),np.max(ir),np.min(ir)
goodness = np.max(ir,axis=0)
tx = np.argmax(ir,axis=0)
if not started:
#goodness_final = goodness.copy()
#tx_final = tx.copy()
goodness_final = np.zeros(goodness.shape)
tx_final = np.zeros(tx.shape)
goodness_final[...] = goodness[...]
tx_final[...] = tx[...]
started = True
better_goodness_mask = np.zeros(goodness.shape)
small_shifts_mask = np.zeros(tx.shape)
else:
better_goodness = (goodness-goodness_final)>-.2
shift_limit = 10
small_shifts = np.abs(tx-tx_final)<shift_limit
better_goodness_mask = np.zeros(goodness.shape)
better_goodness_mask[np.where(better_goodness)] = 1.0
small_shifts_mask = np.zeros(tx.shape)
small_shifts_mask[np.where(small_shifts)] = 1.0
refinements = better_goodness_mask*small_shifts_mask
refined_idx = np.where(refinements)
print refined_idx
goodness_final[refined_idx] = goodness[refined_idx]
tx_final[refined_idx] = tx[refined_idx]
if debug:
plt.clf()
plt.subplot(2,4,1)
plt.cla()
plt.imshow(goodness-goodness_final,cmap='gray')
plt.colorbar()
plt.title('$\Delta$ current goodness k=%d'%k)
plt.subplot(2,4,2)
plt.cla()
plt.imshow(tx-tx_final,cmap='gray')
plt.colorbar()
plt.title('$\Delta$ current tx k=%d'%k)
plt.subplot(2,4,3)
plt.cla()
plt.imshow(better_goodness_mask,cmap='gray')
plt.title('better_goodness_mask')
plt.colorbar()
plt.subplot(2,4,4)
plt.cla()
plt.imshow(refinements,cmap='gray')
plt.title('refinements')
plt.colorbar()
plt.subplot(2,4,5)
plt.cla()
plt.imshow(goodness_final,cmap='gray')
plt.colorbar()
plt.title('overall goodness k=%d'%k)
plt.subplot(2,4,6)
plt.cla()
plt.imshow(tx_final,cmap='gray')
plt.colorbar()
plt.title('overall tx k=%d'%k)
plt.subplot(2,4,7)
plt.cla()
plt.imshow(small_shifts_mask,cmap='gray')
plt.colorbar()
plt.title('small_shifts_mask')
plt.subplot(2,4,8)
plt.cla()
plt.imshow(np.mean(smoothed_avol,axis=2),cmap='gray')
plt.colorbar()
plt.show()
def align_volume_to_profile_multiscale(avol,profile,debug=False):
# put depth first
avol = np.swapaxes(avol,0,1)
# for testing use a recognizable image:
#avol = np.load('/home/rjonnal/code/octopod/kid_with_grenade_volume.npy')
ndepth,nslow,nfast = avol.shape
profile = (profile-np.mean(profile))/np.std(profile)
if len(profile)>ndepth:
print 'growing volume to to %d pixels'%len(profile)
new_avol = np.ones((len(profile),nslow,nfast))*np.mean(avol)
new_avol[:ndepth,:,:] = avol
avol = new_avol
elif ndepth>len(profile):
print 'growing profile ot %d pixels'%ndepth
new_profile = np.ones(ndepth)*np.mean(profile)
new_profile[:len(profile)] = profile
profile = new_profile
else:
print 'profile and volume match in depth'
plen = len(profile)
gross_profile = np.mean(np.mean(avol,axis=2),axis=1)
gross_offset,gross_goodness = translation1(gross_profile,profile,debug=False)
offset_submatrix = np.ones((nslow,nfast))*gross_offset
goodness_submatrix = np.ones((nslow,nfast))*gross_goodness
def show_brightest_layer(v,mdepth=None,tstring=''):
if mdepth is None:
# assumes depth is first dimension
p = np.mean(np.mean(v,axis=2),axis=1)
mdepth = np.argmax(p)
print 'showing layer %d'%mdepth
layer = v[mdepth,:,:]
plt.figure()
plt.imshow(layer)
plt.title(tstring)
plt.colorbar()
return mdepth
def show_four(v,tstring='',other=None):
plt.figure()
p = np.mean(np.mean(v,axis=2),axis=1)
idx = np.argsort(p)[::-1]
if other is not None:
extrapanel = 1
else:
extrapanel = 0
for k in range(4):
depth = idx[k]
plt.subplot(1,4+extrapanel,k+1)
plt.imshow(v[depth,:,:],cmap='gray')
plt.colorbar()
plt.title(depth)
if extrapanel:
plt.subplot(1,5,5)
plt.imshow(other)
plt.colorbar()
# fft the model, for cross-correlation by broadcasting
f0 = np.fft.fft(profile,axis=0)
started = False
sz,sy,sx = avol.shape
initial_step = float(max(sy,sx))/4.0
#final_exp = -np.log(1.49999/initial_step)
#steps = np.round(100*np.exp(-np.linspace(0.0,final_exp,8)))
#steps = np.array([50,40,30,25,20,15,10,8,6,5,4,3,2,1])
n_steps = 3
step_steepness = 5.0
steps = np.linspace(initial_step**(1.0/step_steepness),0.8,n_steps)**step_steepness/2.0
ellipticities = np.linspace(1.0,0.5**.25,n_steps)**4
#print steps
# show_volume = True
# if show_volume:
# for s in range(avol.shape[1]):
# plt.cla()
# plt.imshow(avol[:,s,:],clim=clim,cmap='gray')
# plt.pause(.001)
for rad,ellip in zip(steps,ellipticities):
smoothed_avol,kernel = lateral_smooth_3d(avol,rad,ellipticity=0.8)
# depth is still first dimension
#show_four(avol,other=kernel)
#show_four(smoothed_avol,other=kernel)
#plt.show()
smoothed_avol = (smoothed_avol-np.mean(smoothed_avol,axis=0))/np.std(smoothed_avol,axis=0)
#show_brightest_layer(smoothed_avol)
f1 = np.fft.fft(smoothed_avol,axis=0)
f1c = f1.conjugate()
# transpose depth to the final dimension:
f1_transposed = np.transpose(f1,(1,2,0))
f1c_transposed = np.transpose(f1c,(1,2,0))
num_transposed = f0*f1c_transposed
denom_transposed = np.abs(f0)*np.abs(f1_transposed)
denom_transposed[np.where(denom_transposed==0)]=1.0
frac_transposed = num_transposed/denom_transposed
# now transpose back:
frac = np.transpose(frac_transposed,(2,0,1))
ir = np.abs(np.fft.ifft(frac,axis=0))
goodness = np.max(ir,axis=0)
tx = np.argmax(ir,axis=0)
tx[np.where(tx>plen//2)] = tx[np.where(tx>plen//2)] - plen
#if tx > shape // 2:
# tx -= shape
if not started:
#goodness_final = goodness.copy()
#tx_final = tx.copy()
goodness_final = np.zeros(goodness.shape)
tx_final = np.zeros(tx.shape)
goodness_final[...] = goodness[...]
tx_final[...] = tx[...]
started = True
better_goodness_mask = np.zeros(goodness.shape)
small_shifts_mask = np.zeros(tx.shape)
else:
better_goodness = (goodness-goodness_final)>0.0
shift_limit = 10
small_shifts = np.abs(tx-tx_final)<shift_limit
better_goodness_mask = np.zeros(goodness.shape)
better_goodness_mask[np.where(better_goodness)] = 1.0
small_shifts_mask = np.zeros(tx.shape)
small_shifts_mask[np.where(small_shifts)] = 1.0
refinements = better_goodness_mask*small_shifts_mask
refined_idx = np.where(refinements)
goodness_final[refined_idx] = goodness[refined_idx]
tx_final[refined_idx] = tx[refined_idx]
if debug:
plt.subplot(1,2,1)
plt.cla()
plt.plot(np.mean(np.mean(smoothed_avol,axis=2),axis=1))
plt.plot(profile)
plt.title(rad)
plt.subplot(1,2,2)
plt.cla()
plt.imshow(tx_final)
plt.title('%d,%d'%(tx_final.min(),tx_final.max()))
plt.pause(1)
if debug:
plt.subplot(1,2,1)
plt.cla()
plt.plot(np.mean(np.mean(smoothed_avol,axis=2),axis=1))
plt.plot(profile)
plt.subplot(1,2,2)
plt.cla()
plt.imshow(tx_final)
plt.title('final %d,%d'%(tx_final.min(),tx_final.max()))
plt.pause(1)
return tx_final,goodness_final